[python] Bump version: 0.6.4 → 0.6.5

changed the error msg for query with wrong vector dim thus need this
change to pass the nodejs tests.

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.4.4
+current_version = 0.4.13
 commit = True
 message = Bump version: {current_version} → {new_version}
 tag = True
@@ -9,4 +9,4 @@ tag_name = v{new_version}
 
 [bumpversion:file:rust/ffi/node/Cargo.toml]
 
-[bumpversion:file:rust/vectordb/Cargo.toml]
+[bumpversion:file:rust/lancedb/Cargo.toml]

.cargo/config.toml

@@ -0,0 +1,40 @@
+[profile.release]
+lto = "fat"
+codegen-units = 1
+
+[profile.release-with-debug]
+inherits = "release"
+debug = true
+# Prioritize compile time over runtime performance
+codegen-units = 16
+lto = "thin"
+
+[target.'cfg(all())']
+rustflags = [
+    "-Wclippy::all",
+    "-Wclippy::style",
+    "-Wclippy::fallible_impl_from",
+    "-Wclippy::manual_let_else",
+    "-Wclippy::redundant_pub_crate",
+    "-Wclippy::string_add_assign",
+    "-Wclippy::string_add",
+    "-Wclippy::string_lit_as_bytes",
+    "-Wclippy::string_to_string",
+    "-Wclippy::use_self",
+    "-Dclippy::cargo",
+    "-Dclippy::dbg_macro",
+    # not too much we can do to avoid multiple crate versions
+    "-Aclippy::multiple-crate-versions",
+    "-Aclippy::wildcard_dependencies",
+]
+
+[target.x86_64-unknown-linux-gnu]
+rustflags = ["-C", "target-cpu=haswell", "-C", "target-feature=+avx2,+fma,+f16c"]
+
+[target.aarch64-apple-darwin]
+rustflags = ["-C", "target-cpu=apple-m1", "-C", "target-feature=+neon,+fp16,+fhm,+dotprod"]
+
+# Not all Windows systems have the C runtime installed, so this avoids library
+# not found errors on systems that are missing it.
+[target.x86_64-pc-windows-msvc]
+rustflags = ["-Ctarget-feature=+crt-static"]

.github/workflows/build_linux_wheel/action.yml

@@ -0,0 +1,58 @@
+# We create a composite action to be re-used both for testing and for releasing
+name: build-linux-wheel
+description: "Build a manylinux wheel for lance"
+inputs:
+  python-minor-version:
+    description: "8, 9, 10, 11, 12"
+    required: true
+  args:
+    description: "--release"
+    required: false
+    default: ""
+  arm-build:
+    description: "Build for arm64 instead of x86_64"
+    # Note: this does *not* mean the host is arm64, since we might be cross-compiling.
+    required: false
+    default: "false"
+runs:
+  using: "composite"
+  steps:
+    - name: CONFIRM ARM BUILD
+      shell: bash
+      run: |
+        echo "ARM BUILD: ${{ inputs.arm-build }}"
+    - name: Build x86_64 Manylinux wheel
+      if: ${{ inputs.arm-build == 'false' }}
+      uses: PyO3/maturin-action@v1
+      with:
+        command: build
+        working-directory: python
+        target: x86_64-unknown-linux-gnu
+        manylinux: "2_17"
+        args: ${{ inputs.args }}
+        before-script-linux: |
+          set -e
+          yum install -y openssl-devel \
+            && curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-$(uname -m).zip > /tmp/protoc.zip \
+            && unzip /tmp/protoc.zip -d /usr/local \
+            && rm /tmp/protoc.zip
+    - name: Build Arm Manylinux Wheel
+      if: ${{ inputs.arm-build == 'true' }}
+      uses: PyO3/maturin-action@v1
+      with:
+        command: build
+        working-directory: python
+        target: aarch64-unknown-linux-gnu
+        manylinux: "2_24"
+        args: ${{ inputs.args }}
+        before-script-linux: |
+          set -e
+          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

.github/workflows/build_mac_wheel/action.yml

@@ -0,0 +1,25 @@
+# We create a composite action to be re-used both for testing and for releasing
+name: build_wheel
+description: "Build a lance wheel"
+inputs:
+  python-minor-version:
+    description: "8, 9, 10, 11"
+    required: true
+  args:
+    description: "--release"
+    required: false
+    default: ""
+runs:
+  using: "composite"
+  steps:
+    - name: Install macos dependency
+      shell: bash
+      run: |
+        brew install protobuf
+    - name: Build wheel
+      uses: PyO3/maturin-action@v1
+      with:
+        command: build
+        args: ${{ inputs.args }}
+        working-directory: python
+        interpreter: 3.${{ inputs.python-minor-version }}

.github/workflows/build_windows_wheel/action.yml

@@ -0,0 +1,33 @@
+# We create a composite action to be re-used both for testing and for releasing
+name: build_wheel
+description: "Build a lance wheel"
+inputs:
+  python-minor-version:
+    description: "8, 9, 10, 11"
+    required: true
+  args:
+    description: "--release"
+    required: false
+    default: ""
+runs:
+  using: "composite"
+  steps:
+    - name: Install Protoc v21.12
+      working-directory: C:\
+      run: |
+        New-Item -Path 'C:\protoc' -ItemType Directory
+        Set-Location C:\protoc
+        Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+        7z x protoc.zip
+        Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+      shell: powershell
+    - name: Build wheel
+      uses: PyO3/maturin-action@v1
+      with:
+        command: build
+        args: ${{ inputs.args }}
+        working-directory: python
+    - uses: actions/upload-artifact@v3
+      with:
+        name: windows-wheels
+        path: python\target\wheels

.github/workflows/cargo-publish.yml

@@ -16,7 +16,7 @@ jobs:
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - uses: Swatinem/rust-cache@v2
         with:
           workspaces: rust
@@ -26,4 +26,4 @@ jobs:
           sudo apt install -y protobuf-compiler libssl-dev
       - name: Publish the package
         run: |
-          cargo publish -p vectordb --all-features --token ${{ secrets.CARGO_REGISTRY_TOKEN }}
+          cargo publish -p lancedb --all-features --token ${{ secrets.CARGO_REGISTRY_TOKEN }}

.github/workflows/docs.yml

@@ -24,12 +24,16 @@ jobs:
     environment:
       name: github-pages
       url: ${{ steps.deployment.outputs.page_url }}
-    runs-on: ubuntu-22.04
+    runs-on: buildjet-8vcpu-ubuntu-2204
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
+      - name: Install dependecies needed for ubuntu
+        run: |
+          sudo apt install -y protobuf-compiler libssl-dev
+          rustup update && rustup default        
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: "pip"
@@ -42,7 +46,7 @@ jobs:
       - name: Set up node
         uses: actions/setup-node@v3
         with:
-          node-version: ${{ matrix.node-version }}
+          node-version: 20
           cache: 'npm'
           cache-dependency-path: node/package-lock.json
       - uses: Swatinem/rust-cache@v2
@@ -61,10 +65,10 @@ jobs:
         working-directory: node
         run: |
           npx typedoc --plugin typedoc-plugin-markdown --out ../docs/src/javascript src/index.ts
-          cp ../docs/src/javascript.md ../docs/src/javascript/javascript.md
       - name: Build docs
+        working-directory: docs
         run: |
-          PYTHONPATH=. mkdocs build -f docs/mkdocs.yml
+          PYTHONPATH=. mkdocs build
       - name: Setup Pages
         uses: actions/configure-pages@v2
       - name: Upload artifact

.github/workflows/docs_test.yml

@@ -18,26 +18,28 @@ on:
 env:
   # 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.
-  RUSTFLAGS: "-C debuginfo=1"
+  RUSTFLAGS: "-C debuginfo=1 -C target-cpu=native -C target-feature=+f16c,+avx2,+fma"
   RUST_BACKTRACE: "1"
 
 jobs:
   test-python:
     name: Test doc python code
-    runs-on: ${{ matrix.os }}
+    runs-on: "buildjet-8vcpu-ubuntu-2204"
-    strategy:
-      matrix:
-        python-minor-version: [ "11" ]
-        os: ["ubuntu-22.04"]
     steps:
     - name: Checkout
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
+    - name: Install dependecies needed for ubuntu
+      run: |
+        sudo apt install -y protobuf-compiler libssl-dev
+        rustup update && rustup default
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
-        python-version: 3.${{ matrix.python-minor-version }}
+        python-version: 3.11
         cache: "pip"
         cache-dependency-path: "docs/test/requirements.txt"
+    - name: Rust cache
+      uses: swatinem/rust-cache@v2        
     - name: Build Python
       working-directory: docs/test
       run:
@@ -52,45 +54,43 @@ jobs:
         for d in *; do cd "$d"; echo "$d".py; python "$d".py; cd ..; done
   test-node:
     name: Test doc nodejs code
-    runs-on: ${{ matrix.os }}
+    runs-on: "buildjet-8vcpu-ubuntu-2204"
+    timeout-minutes: 60
     strategy:
-      matrix:
+      fail-fast: false
-        node-version: [ "18" ]
-        os: ["ubuntu-22.04"]
     steps:
     - name: Checkout
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
       with:
         fetch-depth: 0
         lfs: true
     - name: Set up Node
-      uses: actions/setup-node@v3
+      uses: actions/setup-node@v4
       with:
-        node-version: ${{ matrix.node-version }}
+        node-version: 20
     - name: Install dependecies needed for ubuntu
-      if: ${{ matrix.os == 'ubuntu-22.04' }}
       run: |
         sudo apt install -y protobuf-compiler libssl-dev
-    - name: Install node dependencies
+        rustup update && rustup default
-      run: |
-        cd docs/test
-        npm install 
     - name: Rust cache
       uses: swatinem/rust-cache@v2
-    - name: Install LanceDB
+    - name: Install node dependencies
       run: |
-        cd docs/test/node_modules/vectordb 
+        sudo swapoff -a
+        sudo fallocate -l 8G /swapfile
+        sudo chmod 600 /swapfile
+        sudo mkswap /swapfile
+        sudo swapon /swapfile
+        sudo swapon --show
+        cd node
         npm ci
         npm run build-release
-        npm run tsc
+        cd ../docs
-    - name: Create test files
+        npm install
-      run: |
-        cd docs/test
-        node md_testing.js
     - name: Test
       env:
         LANCEDB_URI: ${{ secrets.LANCEDB_URI }}
         LANCEDB_DEV_API_KEY: ${{ secrets.LANCEDB_DEV_API_KEY }}
       run: |
-        cd docs/test/node
+        cd docs
-        for d in *; do cd "$d"; echo "$d".js; node "$d".js; cd ..; done
+        npm t

.github/workflows/make-release-commit.yml

@@ -26,7 +26,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out main
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           ref: main
           persist-credentials: false
@@ -37,10 +37,10 @@ jobs:
         run: |
           git config user.name 'Lance Release'
           git config user.email 'lance-dev@lancedb.com'
-      - name: Set up Python 3.10
+      - name: Set up Python 3.11
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - name: Bump version, create tag and commit
         run: |
           pip install bump2version

.github/workflows/node.yml

@@ -24,27 +24,6 @@ env:
   RUST_BACKTRACE: "1"
 
 jobs:
-  lint:
-    name: Lint
-    runs-on: ubuntu-22.04
-    defaults:
-      run:
-        shell: bash
-        working-directory: node
-    steps:
-    - uses: actions/checkout@v3
-      with:
-        fetch-depth: 0
-        lfs: true
-    - uses: actions/setup-node@v3
-      with:
-        node-version: 20
-        cache: 'npm'
-        cache-dependency-path: node/package-lock.json
-    - name: Lint
-      run: |
-        npm ci
-        npm run lint
   linux:
     name: Linux (Node ${{ matrix.node-version }})
     timeout-minutes: 30
@@ -57,7 +36,7 @@ jobs:
         shell: bash
         working-directory: node
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         fetch-depth: 0
         lfs: true
@@ -89,7 +68,7 @@ jobs:
         shell: bash
         working-directory: node
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         fetch-depth: 0
         lfs: true
@@ -128,7 +107,7 @@ jobs:
       # this one is for dynamodb
       DYNAMODB_ENDPOINT: http://localhost:4566
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         fetch-depth: 0
         lfs: true

.github/workflows/nodejs.yml

@@ -29,7 +29,7 @@ jobs:
         shell: bash
         working-directory: nodejs
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         fetch-depth: 0
         lfs: true
@@ -49,6 +49,7 @@ jobs:
         cargo clippy --all --all-features -- -D warnings
         npm ci
         npm run lint
+        npm run chkformat
   linux:
     name: Linux (NodeJS ${{ matrix.node-version }})
     timeout-minutes: 30
@@ -61,7 +62,7 @@ jobs:
         shell: bash
         working-directory: nodejs
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         fetch-depth: 0
         lfs: true
@@ -84,13 +85,13 @@ jobs:
       run: npm run test
   macos:
     timeout-minutes: 30
-    runs-on: "macos-13"
+    runs-on: "macos-14"
     defaults:
       run:
         shell: bash
         working-directory: nodejs
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         fetch-depth: 0
         lfs: true
@@ -111,4 +112,3 @@ jobs:
     - name: Test
       run: |
         npm run test
-

.github/workflows/npm-publish.yml

@@ -15,7 +15,7 @@ jobs:
         working-directory: node
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - uses: actions/setup-node@v3
         with:
           node-version: 20
@@ -45,13 +45,13 @@ jobs:
             runner: macos-13
           - arch: aarch64-apple-darwin
             # xlarge is implicitly arm64.
-            runner: macos-13-xlarge
+            runner: macos-14
     runs-on: ${{ matrix.config.runner }}
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Install system dependencies
         run: brew install protobuf
       - name: Install npm dependencies
@@ -80,10 +80,25 @@ jobs:
           - arch: x86_64
             runner: ubuntu-latest
           - arch: aarch64
-            runner: buildjet-4vcpu-ubuntu-2204-arm
+            # For successful fat LTO builds, we need a large runner to avoid OOM errors.
+            runner: buildjet-16vcpu-ubuntu-2204-arm
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
+      # Buildjet aarch64 runners have only 1.5 GB RAM per core, vs 3.5 GB per core for
+      # x86_64 runners. To avoid OOM errors on ARM, we create a swap file.
+      - name: Configure aarch64 build
+        if: ${{ matrix.config.arch == 'aarch64' }}
+        run: |
+          free -h
+          sudo fallocate -l 16G /swapfile
+          sudo chmod 600 /swapfile
+          sudo mkswap /swapfile
+          sudo swapon /swapfile
+          echo "/swapfile swap swap defaults 0 0" >> sudo /etc/fstab
+          # print info
+          swapon --show
+          free -h
       - name: Build Linux Artifacts
         run: |
           bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }}
@@ -104,7 +119,7 @@ jobs:
         target: [x86_64-pc-windows-msvc]
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Install Protoc v21.12
         working-directory: C:\
         run: |
@@ -154,7 +169,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           ref: main
           persist-credentials: false

.github/workflows/pypi-publish.yml

@@ -5,27 +5,88 @@ on:
     types: [published]
 
 jobs:
-  publish:
+  linux:
-    runs-on: ubuntu-latest
+    timeout-minutes: 60
-    # Only runs on tags that matches the python-make-release action
+    strategy:
-    if: startsWith(github.ref, 'refs/tags/python-v')
+      matrix:
-    defaults:
+        python-minor-version: ["8"]
-      run:
+        platform:
-        shell: bash
+          - x86_64
-        working-directory: python
+          - aarch64
+    runs-on: "ubuntu-22.04"
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          lfs: true
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: 3.${{ matrix.python-minor-version }}
-      - name: Build distribution
+      - uses: ./.github/workflows/build_linux_wheel
-        run: |
-          ls -la
-          pip install wheel setuptools --upgrade
-          python setup.py sdist bdist_wheel
-      - name: Publish
-        uses: pypa/gh-action-pypi-publish@v1.8.5
         with:
-          password: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          python-minor-version: ${{ matrix.python-minor-version }}
-          packages-dir: python/dist
+          args: "--release --strip"
+          arm-build: ${{ matrix.platform == 'aarch64' }}
+      - uses: ./.github/workflows/upload_wheel
+        with:
+          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          repo: "pypi"
+  mac:
+    timeout-minutes: 60
+    runs-on: ${{ matrix.config.runner }}
+    strategy:
+      matrix:
+        python-minor-version: ["8"]
+        config:
+          - target: x86_64-apple-darwin
+            runner: macos-13
+          - target: aarch64-apple-darwin
+            runner: macos-14
+    env:
+      MACOSX_DEPLOYMENT_TARGET: 10.15
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.ref }}
+          fetch-depth: 0
+          lfs: true
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.12
+      - uses: ./.github/workflows/build_mac_wheel
+        with:
+          python-minor-version: ${{ matrix.python-minor-version }}
+          args: "--release --strip --target ${{ matrix.config.target }}"
+      - uses: ./.github/workflows/upload_wheel
+        with:
+          python-minor-version: ${{ matrix.python-minor-version }}
+          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          repo: "pypi"
+  windows:
+    timeout-minutes: 60
+    runs-on: windows-latest
+    strategy:
+      matrix:
+        python-minor-version: ["8"]
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.ref }}
+          fetch-depth: 0
+          lfs: true
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.${{ matrix.python-minor-version }}
+      - uses: ./.github/workflows/build_windows_wheel
+        with:
+          python-minor-version: ${{ matrix.python-minor-version }}
+          args: "--release --strip"
+          vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
+      - uses: ./.github/workflows/upload_wheel
+        with:
+          python-minor-version: ${{ matrix.python-minor-version }}
+          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          repo: "pypi"

.github/workflows/python-make-release-commit.yml

@@ -26,7 +26,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Check out main
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
       with:
         ref: main
         persist-credentials: false
@@ -37,10 +37,10 @@ jobs:
       run: |
         git config user.name 'Lance Release'
         git config user.email 'lance-dev@lancedb.com'
-    - name: Set up Python 3.10
+    - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
-        python-version: "3.10"
+        python-version: "3.11"
     - name: Bump version, create tag and commit
       working-directory: python
       run: |

.github/workflows/python.yml

@@ -14,49 +14,133 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  linux:
+  lint:
+    name: "Lint"
     timeout-minutes: 30
-    strategy:
-      matrix:
-        python-minor-version: [ "8", "9", "10", "11" ]
     runs-on: "ubuntu-22.04"
     defaults:
       run:
         shell: bash
         working-directory: python
     steps:
-    - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
           lfs: true
       - name: Set up Python
-      uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
-        python-version: 3.${{ matrix.python-minor-version }}
+          python-version: "3.11"
-    - name: Install lancedb
+      - name: Install ruff
         run: |
-        pip install -e .[tests]
+          pip install ruff==0.2.2
-        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
-        pip install pytest pytest-mock ruff
       - name: Format check
         run: ruff format --check .
       - name: Lint
         run: ruff .
-    - name: Run tests
+  doctest:
-      run: pytest -m "not slow" -x -v --durations=30 tests
+    name: "Doctest"
-    - name: doctest
+    timeout-minutes: 30
-      run: pytest --doctest-modules lancedb
+    runs-on: "ubuntu-22.04"
+    defaults:
+      run:
+        shell: bash
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+          cache: "pip"
+      - name: Install protobuf
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: python
+      - name: Install
+        run: |
+          pip install -e .[tests,dev,embeddings]
+          pip install tantivy
+          pip install mlx
+      - name: Doctest
+        run: pytest --doctest-modules python/lancedb
+  linux:
+    name: "Linux: python-3.${{ matrix.python-minor-version }}"
+    timeout-minutes: 30
+    strategy:
+      matrix:
+        python-minor-version: ["8", "11"]
+    runs-on: "ubuntu-22.04"
+    defaults:
+      run:
+        shell: bash
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Install protobuf
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.${{ matrix.python-minor-version }}
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: python
+      - uses: ./.github/workflows/build_linux_wheel
+      - uses: ./.github/workflows/run_tests
+      # Make sure wheels are not included in the Rust cache
+      - name: Delete wheels
+        run: rm -rf target/wheels
   platform:
-    name: "Platform: ${{ matrix.config.name }}"
+    name: "Mac: ${{ matrix.config.name }}"
     timeout-minutes: 30
     strategy:
       matrix:
         config:
-          - name: x86 Mac
+          - name: x86
             runner: macos-13
-          - name: Arm Mac
+          - name: Arm
-            runner: macos-13-xlarge
+            runner: macos-14
-          - name: x86 Windows
+    runs-on: "${{ matrix.config.runner }}"
+    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: "3.11"
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: python
+      - uses: ./.github/workflows/build_mac_wheel
+      - uses: ./.github/workflows/run_tests
+      # Make sure wheels are not included in the Rust cache
+      - name: Delete wheels
+        run: rm -rf target/wheels
+  windows:
+    name: "Windows: ${{ matrix.config.name }}"
+    timeout-minutes: 30
+    strategy:
+      matrix:
+        config:
+          - name: x86
             runner: windows-latest
     runs-on: "${{ matrix.config.runner }}"
     defaults:
@@ -64,21 +148,22 @@ jobs:
         shell: bash
         working-directory: python
     steps:
-    - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
           lfs: true
       - name: Set up Python
-      uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.11"
-    - name: Install lancedb
+      - uses: Swatinem/rust-cache@v2
-      run: |
+        with:
-        pip install -e .[tests]
+          workspaces: python
-        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+      - uses: ./.github/workflows/build_windows_wheel
-        pip install pytest pytest-mock
+      - uses: ./.github/workflows/run_tests
-    - name: Run tests
+      # Make sure wheels are not included in the Rust cache
-      run: pytest -m "not slow" -x -v --durations=30 tests
+      - name: Delete wheels
+        run: rm -rf target/wheels
   pydantic1x:
     timeout-minutes: 30
     runs-on: "ubuntu-22.04"
@@ -87,21 +172,22 @@ jobs:
         shell: bash
         working-directory: python
     steps:
-    - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
           lfs: true
+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler
       - name: Set up Python
-      uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.9
       - name: Install lancedb
         run: |
           pip install "pydantic<2"
           pip install -e .[tests]
-        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+          pip install tantivy
-        pip install pytest pytest-mock
       - name: Run tests
-      run: pytest -m "not slow" -x -v --durations=30 tests
+        run: pytest -m "not slow" -x -v --durations=30 python/tests
-    - name: doctest
-      run: pytest --doctest-modules lancedb

.github/workflows/run_tests/action.yml

@@ -0,0 +1,17 @@
+name: run-tests
+
+description: "Install lance wheel and run unit tests"
+inputs:
+  python-minor-version:
+    required: true
+    description: "8 9 10 11 12"
+runs:
+  using: "composite"
+  steps:
+    - name: Install lancedb
+      shell: bash
+      run: |
+        pip3 install $(ls target/wheels/lancedb-*.whl)[tests,dev]
+    - name: pytest
+      shell: bash
+      run: pytest -m "not slow" -x -v --durations=30 python/python/tests

.github/workflows/rust.yml

@@ -32,7 +32,7 @@ jobs:
         shell: bash
         working-directory: rust
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
           fetch-depth: 0
           lfs: true
@@ -55,7 +55,7 @@ jobs:
         shell: bash
         working-directory: rust
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
           fetch-depth: 0
           lfs: true
@@ -70,18 +70,20 @@ jobs:
       run: cargo build --all-features
     - name: Run tests
       run: cargo test --all-features
+    - name: Run examples
+      run: cargo run --example simple
   macos:
     timeout-minutes: 30
     strategy:
       matrix:
-        mac-runner: [ "macos-13", "macos-13-xlarge" ]
+        mac-runner: [ "macos-13", "macos-14" ]
     runs-on: "${{ matrix.mac-runner }}"
     defaults:
       run:
         shell: bash
         working-directory: rust
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
             fetch-depth: 0
             lfs: true
@@ -99,7 +101,7 @@ jobs:
   windows:
     runs-on: windows-2022
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - uses: Swatinem/rust-cache@v2
         with:
           workspaces: rust
@@ -117,3 +119,4 @@ jobs:
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
           cargo build
           cargo test
+  

.github/workflows/update_package_lock_run.yml

@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           ref: main
           persist-credentials: false

.github/workflows/upload_wheel/action.yml

@@ -0,0 +1,29 @@
+name: upload-wheel
+
+description: "Upload wheels to Pypi"
+inputs:
+  os:
+    required: true
+    description: "ubuntu-22.04 or macos-13"
+  repo:
+    required: false
+    description: "pypi or testpypi"
+    default: "pypi"
+  token:
+    required: true
+    description: "release token for the repo"
+
+runs:
+  using: "composite"
+  steps:
+    - name: Install dependencies
+      shell: bash
+      run: |
+        python -m pip install --upgrade pip
+        pip install twine
+    - name: Publish wheel
+      env:
+        TWINE_USERNAME: __token__
+        TWINE_PASSWORD: ${{ inputs.token }}
+      shell: bash
+      run: twine upload --repository ${{ inputs.repo }} target/wheels/lancedb-*.whl

.gitignore

@@ -22,6 +22,11 @@ python/dist
 
 **/.hypothesis
 
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.dll
+
 ## Javascript
 *.node
 **/node_modules
@@ -34,4 +39,6 @@ dist
 ## Rust
 target
 
+**/sccache.log
+
 Cargo.lock

.pre-commit-config.yaml

@@ -5,17 +5,14 @@ repos:
     -   id: check-yaml
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
--   repo: https://github.com/psf/black
-    rev: 22.12.0
-    hooks:
-    -   id: black
 -   repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.0.277
+    rev: v0.2.2
     hooks:
     - id: ruff
--   repo: https://github.com/pycqa/isort
+- repo: https://github.com/pre-commit/mirrors-prettier
-    rev: 5.12.0
+  rev: v3.1.0
   hooks:
-    - id: isort
+    - id: prettier
-      name: isort (python)
+      files: "nodejs/.*"
+      exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*

Cargo.toml

@@ -1,37 +1,41 @@
 [workspace]
-members = ["rust/ffi/node", "rust/vectordb", "nodejs"]
+members = ["rust/ffi/node", "rust/lancedb", "nodejs", "python"]
 # Python package needs to be built by maturin.
 exclude = ["python"]
 resolver = "2"
 
 [workspace.package]
 edition = "2021"
-authors = ["Lance Devs <dev@lancedb.com>"]
+authors = ["LanceDB Devs <dev@lancedb.com>"]
 license = "Apache-2.0"
 repository = "https://github.com/lancedb/lancedb"
+description = "Serverless, low-latency vector database for AI applications"
+keywords = ["lancedb", "lance", "database", "vector", "search"]
+categories = ["database-implementations"]
 
 [workspace.dependencies]
-lance = { "version" = "=0.9.9", "features" = ["dynamodb"] }
+lance = { "version" = "=0.10.5", "features" = ["dynamodb"] }
-lance-index = { "version" = "=0.9.9" }
+lance-index = { "version" = "=0.10.5" }
-lance-linalg = { "version" = "=0.9.9" }
+lance-linalg = { "version" = "=0.10.5" }
-lance-testing = { "version" = "=0.9.9" }
+lance-testing = { "version" = "=0.10.5" }
 # Note that this one does not include pyarrow
-arrow = { version = "49.0.0", optional = false }
+arrow = { version = "50.0", optional = false }
-arrow-array = "49.0"
+arrow-array = "50.0"
-arrow-data = "49.0"
+arrow-data = "50.0"
-arrow-ipc = "49.0"
+arrow-ipc = "50.0"
-arrow-ord = "49.0"
+arrow-ord = "50.0"
-arrow-schema = "49.0"
+arrow-schema = "50.0"
-arrow-arith = "49.0"
+arrow-arith = "50.0"
-arrow-cast = "49.0"
+arrow-cast = "50.0"
 async-trait = "0"
-chrono = "0.4.23"
+chrono = "0.4.35"
 half = { "version" = "=2.3.1", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"
 log = "0.4"
 object_store = "0.9.0"
+pin-project = "1.0.7"
 snafu = "0.7.4"
 url = "2"
 num-traits = "0.2"

README.md

@@ -51,12 +51,19 @@ npm install vectordb
 const lancedb = require('vectordb');
 const db = await lancedb.connect('data/sample-lancedb');
 
-const table = await db.createTable('vectors',
+const table = await db.createTable({
-      [{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
+  name: 'vectors',
-       { id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }])
+  data:  [
+    { id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
+    { id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }
+  ]
+})
 
 const query = table.search([0.1, 0.3]).limit(2);
 const results = await query.execute();
+
+// You can also search for rows by specific criteria without involving a vector search.
+const rowsByCriteria = await table.search(undefined).where("price >= 10").execute();
 ```
 
 **Python**

ci/build_linux_artifacts.sh

@@ -13,7 +13,9 @@ docker build \
     .
 popd
 
+# We turn on memory swap to avoid OOM killer
 docker run \
     -v $(pwd):/io -w /io \
+    --memory-swap=-1 \
     lancedb-node-manylinux \
     bash ci/manylinux_node/build.sh $ARCH

dockerfiles/Dockerfile

@@ -0,0 +1,27 @@
+#Simple base dockerfile that supports basic dependencies required to run lance with FTS and Hybrid Search
+#Usage docker build -t lancedb:latest -f Dockerfile .
+FROM python:3.10-slim-buster
+
+# Install Rust
+RUN apt-get update && apt-get install -y curl build-essential && \
+  curl https://sh.rustup.rs -sSf | sh -s -- -y
+
+# Set the environment variable for Rust
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+# Install protobuf compiler
+RUN apt-get install -y protobuf-compiler && \
+  apt-get clean && \
+  rm -rf /var/lib/apt/lists/*
+
+RUN apt-get -y update &&\
+  apt-get -y upgrade && \
+  apt-get -y install git
+
+
+# Verify installations
+RUN python --version && \
+  rustc --version && \
+  protoc --version
+
+RUN pip install tantivy lancedb

docs/README.md

@@ -33,3 +33,12 @@ You can run a local server to test the docs prior to deployment by navigating to
 cd docs
 mkdocs serve
 ```
+
+### Run doctest for typescript example
+
+```bash
+cd lancedb/docs
+npm i
+npm run build
+npm run all
+```

docs/mkdocs.yml

@@ -27,7 +27,6 @@ theme:
     - content.tabs.link
     - content.action.edit
     - toc.follow
-    # - toc.integrate
     - navigation.top
     - navigation.tabs
     - navigation.tabs.sticky
@@ -57,6 +56,16 @@ plugins:
           - https://arrow.apache.org/docs/objects.inv
           - https://pandas.pydata.org/docs/objects.inv
 - mkdocs-jupyter
+- ultralytics:
+    verbose: True
+    enabled: True
+    default_image: "assets/lancedb_and_lance.png" # Default image for all pages
+    add_image: True # Automatically add meta image
+    add_keywords: True # Add page keywords in the header tag
+    add_share_buttons: True # Add social share buttons
+    add_authors: False # Display page authors
+    add_desc: False
+    add_dates: False
 
 markdown_extensions:
 - admonition
@@ -67,7 +76,9 @@ markdown_extensions:
     line_spans: __span
     pygments_lang_class: true
 - pymdownx.inlinehilite
-- pymdownx.snippets
+- pymdownx.snippets:
+    base_path: ..
+    dedent_subsections: true
 - pymdownx.superfences
 - pymdownx.tabbed:
     alternate_style: true
@@ -88,15 +99,18 @@ nav:
     - Building an ANN index: ann_indexes.md
     - Vector Search: search.md
     - Full-text search: fts.md
+    - Hybrid search:
+      - Overview: hybrid_search/hybrid_search.md
+      - Comparing Rerankers: hybrid_search/eval.md
+      - Airbnb financial data example: notebooks/hybrid_search.ipynb
     - Filtering: sql.md
     - Versioning & Reproducibility: notebooks/reproducibility.ipynb
     - Configuring Storage: guides/storage.md
   - 🧬 Managing embeddings:
     - Overview: embeddings/index.md
-    - Explicit management: embeddings/embedding_explicit.md
+    - Embedding functions: embeddings/embedding_functions.md
-    - Implicit management: embeddings/embedding_functions.md
+    - Available models: embeddings/default_embedding_functions.md
-    - Available Functions: embeddings/default_embedding_functions.md
+    - User-defined embedding functions: embeddings/custom_embedding_function.md
-    - Custom Embedding Functions: embeddings/api.md
     - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
     - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
   - 🔌 Integrations:
@@ -125,13 +139,19 @@ nav:
       - Serverless Website Chatbot: examples/serverless_website_chatbot.md
       - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
       - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
+    - 🦀 Rust:
+      - Overview: examples/examples_rust.md
   - 🔧 CLI & Config: cli_config.md
   - 💭 FAQs: faq.md
   - ⚙️ API reference:
     - 🐍 Python: python/python.md
-    - 👾 JavaScript: javascript/javascript.md
+    - 👾 JavaScript: javascript/modules.md
+    - 🦀 Rust: https://docs.rs/lancedb/latest/lancedb/
   - ☁️ LanceDB Cloud:
     - Overview: cloud/index.md
+    - API reference:
+      - 🐍 Python: python/saas-python.md
+      - 👾 JavaScript: javascript/saas-modules.md
 
 
 - Quick start: basic.md
@@ -145,15 +165,18 @@ nav:
     - Building an ANN index: ann_indexes.md
     - Vector Search: search.md
     - Full-text search: fts.md
+    - Hybrid search:
+      - Overview: hybrid_search/hybrid_search.md
+      - Comparing Rerankers: hybrid_search/eval.md
+      - Airbnb financial data example: notebooks/hybrid_search.ipynb
     - Filtering: sql.md
     - Versioning & Reproducibility: notebooks/reproducibility.ipynb
     - Configuring Storage: guides/storage.md
 - Managing Embeddings:
   - Overview: embeddings/index.md
-  - Explicit management: embeddings/embedding_explicit.md
+  - Embedding functions: embeddings/embedding_functions.md
-  - Implicit management: embeddings/embedding_functions.md
+  - Available models: embeddings/default_embedding_functions.md
-  - Available Functions: embeddings/default_embedding_functions.md
+  - User-defined embedding functions: embeddings/custom_embedding_function.md
-  - Custom Embedding Functions: embeddings/api.md
   - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
   - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
 - Integrations:
@@ -167,28 +190,34 @@ nav:
   - Pydantic: python/pydantic.md
   - Voxel51: integrations/voxel51.md
   - PromptTools: integrations/prompttools.md
-- Python examples:
+- Examples:
   - examples/index.md
   - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
   - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
   - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
   - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
   - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
-- Javascript examples:
+  - YouTube Transcript Search (JS): examples/youtube_transcript_bot_with_nodejs.md
-  - Overview: examples/examples_js.md
-  - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
   - Serverless Chatbot from any website: examples/serverless_website_chatbot.md
   - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
 - API reference:
+  - Overview: api_reference.md
   - Python: python/python.md
-  - Javascript: javascript/javascript.md
+  - Javascript: javascript/modules.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/saas-modules.md
 
 extra_css:
   - styles/global.css
   - styles/extra.css
 
+extra_javascript:
+  - "extra_js/init_ask_ai_widget.js"
+
 extra:
   analytics:
     provider: google

docs/package-lock.json

@@ -0,0 +1,132 @@
+{
+  "name": "lancedb-docs-test",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "lancedb-docs-test",
+      "version": "1.0.0",
+      "license": "Apache 2",
+      "dependencies": {
+        "apache-arrow": "file:../node/node_modules/apache-arrow",
+        "vectordb": "file:../node"
+      },
+      "devDependencies": {
+        "@types/node": "^20.11.8",
+        "typescript": "^5.3.3"
+      }
+    },
+    "../node": {
+      "name": "vectordb",
+      "version": "0.4.6",
+      "cpu": [
+        "x64",
+        "arm64"
+      ],
+      "license": "Apache-2.0",
+      "os": [
+        "darwin",
+        "linux",
+        "win32"
+      ],
+      "dependencies": {
+        "@apache-arrow/ts": "^14.0.2",
+        "@neon-rs/load": "^0.0.74",
+        "apache-arrow": "^14.0.2",
+        "axios": "^1.4.0"
+      },
+      "devDependencies": {
+        "@neon-rs/cli": "^0.0.160",
+        "@types/chai": "^4.3.4",
+        "@types/chai-as-promised": "^7.1.5",
+        "@types/mocha": "^10.0.1",
+        "@types/node": "^18.16.2",
+        "@types/sinon": "^10.0.15",
+        "@types/temp": "^0.9.1",
+        "@types/uuid": "^9.0.3",
+        "@typescript-eslint/eslint-plugin": "^5.59.1",
+        "cargo-cp-artifact": "^0.1",
+        "chai": "^4.3.7",
+        "chai-as-promised": "^7.1.1",
+        "eslint": "^8.39.0",
+        "eslint-config-standard-with-typescript": "^34.0.1",
+        "eslint-plugin-import": "^2.26.0",
+        "eslint-plugin-n": "^15.7.0",
+        "eslint-plugin-promise": "^6.1.1",
+        "mocha": "^10.2.0",
+        "openai": "^4.24.1",
+        "sinon": "^15.1.0",
+        "temp": "^0.9.4",
+        "ts-node": "^10.9.1",
+        "ts-node-dev": "^2.0.0",
+        "typedoc": "^0.24.7",
+        "typedoc-plugin-markdown": "^3.15.3",
+        "typescript": "*",
+        "uuid": "^9.0.0"
+      },
+      "optionalDependencies": {
+        "@lancedb/vectordb-darwin-arm64": "0.4.6",
+        "@lancedb/vectordb-darwin-x64": "0.4.6",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.4.6",
+        "@lancedb/vectordb-linux-x64-gnu": "0.4.6",
+        "@lancedb/vectordb-win32-x64-msvc": "0.4.6"
+      }
+    },
+    "../node/node_modules/apache-arrow": {
+      "version": "14.0.2",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@types/command-line-args": "5.2.0",
+        "@types/command-line-usage": "5.0.2",
+        "@types/node": "20.3.0",
+        "@types/pad-left": "2.1.1",
+        "command-line-args": "5.2.1",
+        "command-line-usage": "7.0.1",
+        "flatbuffers": "23.5.26",
+        "json-bignum": "^0.0.3",
+        "pad-left": "^2.1.0",
+        "tslib": "^2.5.3"
+      },
+      "bin": {
+        "arrow2csv": "bin/arrow2csv.js"
+      }
+    },
+    "node_modules/@types/node": {
+      "version": "20.11.8",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.11.8.tgz",
+      "integrity": "sha512-i7omyekpPTNdv4Jb/Rgqg0RU8YqLcNsI12quKSDkRXNfx7Wxdm6HhK1awT3xTgEkgxPn3bvnSpiEAc7a7Lpyow==",
+      "dev": true,
+      "dependencies": {
+        "undici-types": "~5.26.4"
+      }
+    },
+    "node_modules/apache-arrow": {
+      "resolved": "../node/node_modules/apache-arrow",
+      "link": true
+    },
+    "node_modules/typescript": {
+      "version": "5.3.3",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.3.3.tgz",
+      "integrity": "sha512-pXWcraxM0uxAS+tN0AG/BF2TyqmHO014Z070UsJ+pFvYuRSq8KH8DmWpnbXe0pEPDHXZV3FcAbJkijJ5oNEnWw==",
+      "dev": true,
+      "bin": {
+        "tsc": "bin/tsc",
+        "tsserver": "bin/tsserver"
+      },
+      "engines": {
+        "node": ">=14.17"
+      }
+    },
+    "node_modules/undici-types": {
+      "version": "5.26.5",
+      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
+      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
+      "dev": true
+    },
+    "node_modules/vectordb": {
+      "resolved": "../node",
+      "link": true
+    }
+  }
+}

docs/package.json

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

docs/requirements.txt

@@ -3,3 +3,4 @@ mkdocs-jupyter==0.24.1
 mkdocs-material==9.5.3
 mkdocstrings[python]==0.20.0
 pydantic
+mkdocs-ultralytics-plugin==0.0.44

docs/src/ann_indexes.md

@@ -7,26 +7,18 @@ for brute-force scanning of the entire vector space.
 A vector index is faster but less accurate than exhaustive search (kNN or flat search).
 LanceDB provides many parameters to fine-tune the index's size, the speed of queries, and the accuracy of results.
 
-Currently, LanceDB does *not* automatically create the ANN index.
+## Disk-based Index
-LanceDB has optimized code for kNN as well. For many use-cases, datasets under 100K vectors won't require index creation at all.
-If you can live with <100ms latency, skipping index creation is a simpler workflow while guaranteeing 100% recall.
 
-In the future we will look to automatically create and configure the ANN index as data comes in.
+Lance provides an `IVF_PQ` disk-based index. It uses **Inverted File Index (IVF)** to first divide
-
+the dataset into `N` partitions, and then applies **Product Quantization** to compress vectors in each partition.
-## Types of Index
+See the [indexing](concepts/index_ivfpq.md) concepts guide for more information on how this works.
-
-Lance can support multiple index types, the most widely used one is `IVF_PQ`.
-
-* `IVF_PQ`: use **Inverted File Index (IVF)** to first divide the dataset into `N` partitions,
-   and then use **Product Quantization** to compress vectors in each partition.
-* `DiskANN` (**Experimental**): organize the vector as a on-disk graph, where the vertices approximately
-   represent the nearest neighbors of each vector.
 
 ## Creating an IVF_PQ Index
 
 Lance supports `IVF_PQ` index type by default.
 
 === "Python"
+
     Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
 
     ```python
@@ -46,25 +38,42 @@ Lance supports `IVF_PQ` index type by default.
     tbl.create_index(num_partitions=256, num_sub_vectors=96)
     ```
 
-=== "Javascript"
+=== "Typescript"
-     ```javascript
-     const vectordb = require('vectordb')
-     const db = await vectordb.connect('data/sample-lancedb')
 
-     let data = []
+    ```typescript
-     for (let i = 0; i < 10_000; i++) {
+    --8<--- "docs/src/ann_indexes.ts:import"
-         data.push({vector: Array(1536).fill(i), id: `${i}`, content: "", longId: `${i}`},)
+
-     }
+    --8<-- "docs/src/ann_indexes.ts:ingest"
-     const table = await db.createTable('my_vectors', data)
-     await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 256, num_sub_vectors: 96 })
     ```
 
-- **metric** (default: "L2"): The distance metric to use. By default it uses euclidean distance "`L2`".
+=== "Rust"
+
+    ```rust
+    --8<-- "rust/lancedb/examples/ivf_pq.rs:create_index"
+    ```
+
+    IVF_PQ index parameters are more fully defined in the [crate docs](https://docs.rs/lancedb/latest/lancedb/index/vector/struct.IvfPqIndexBuilder.html).
+
+The following IVF_PQ paramters can be specified:
+
+- **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** (default: 256): The number of partitions of the index.
+- **num_partitions**: The number of partitions in the index. The default is the square root
-- **num_sub_vectors** (default: 96): The number of sub-vectors (M) that will be created during Product Quantization (PQ).
+  of the number of rows.
-For D dimensional vector, it will be divided into `M` of `D/M` sub-vectors, each of which is presented by
+
-a single PQ code.
+!!! note
+
+    In the synchronous python SDK and node's `vectordb` the default is 256. This default has
+    changed in the asynchronous python SDK and node's `lancedb`.
+
+- **num_sub_vectors**: The number of sub-vectors (M) that will be created during Product Quantization (PQ).
+  For D dimensional vector, it will be divided into `M` subvectors with dimension `D/M`, each of which is replaced by
+  a single PQ code. The default is the dimension of the vector divided by 16.
+
+!!! note
+
+    In the synchronous python SDK and node's `vectordb` the default is currently 96. This default has
+    changed in the asynchronous python SDK and node's `lancedb`.
 
 <figure markdown>
   ![IVF PQ](./assets/ivf_pq.png)
@@ -78,7 +87,7 @@ Using GPU for index creation requires [PyTorch>2.0](https://pytorch.org/) being
 
 You can specify the GPU device to train IVF partitions via
 
-- **accelerator**: Specify to ``cuda`` or ``mps`` (on Apple Silicon) to enable GPU training.
+- **accelerator**: Specify to `cuda` or `mps` (on Apple Silicon) to enable GPU training.
 
 === "Linux"
 
@@ -92,7 +101,7 @@ You can specify the GPU device to train IVF partitions via
      )
      ```
 
-=== "Macos"
+=== "MacOS"
 
      <!-- skip-test -->
      ```python
@@ -104,12 +113,11 @@ You can specify the GPU device to train IVF partitions via
      )
      ```
 
-Trouble shootings:
+Troubleshooting:
 
-If you see ``AssertionError: Torch not compiled with CUDA enabled``, you need to [install
+If you see `AssertionError: Torch not compiled with CUDA enabled`, you need to [install
 PyTorch with CUDA support](https://pytorch.org/get-started/locally/).
 
-
 ## Querying an ANN Index
 
 Querying vector indexes is done via the [search](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.search) function.
@@ -127,6 +135,7 @@ There are a couple of parameters that can be used to fine-tune the search:
   Note: refine_factor is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
 
 === "Python"
+
     ```python
     tbl.search(np.random.random((1536))) \
         .limit(2) \
@@ -134,41 +143,43 @@ There are a couple of parameters that can be used to fine-tune the search:
         .refine_factor(10) \
         .to_pandas()
     ```
-     ```
+
+    ```text
                                               vector       item       _distance
     0  [0.44949695, 0.8444449, 0.06281311, 0.23338133...  item 1141  103.575333
     1  [0.48587373, 0.269207, 0.15095535, 0.65531915,...  item 3953  108.393867
     ```
 
-=== "Javascript"
+=== "Typescript"
-     ```javascript
+
-     const results_1 = await table
+    ```typescript
-         .search(Array(1536).fill(1.2))
+    --8<-- "docs/src/ann_indexes.ts:search1"
-         .limit(2)
-         .nprobes(20)
-         .refineFactor(10)
-         .execute()
     ```
 
-The search will return the data requested in addition to the distance of each item.
+=== "Rust"
 
+    ```rust
+    --8<-- "rust/lancedb/examples/ivf_pq.rs:search1"
+    ```
+
+    Vector search options are more fully defined in the [crate docs](https://docs.rs/lancedb/latest/lancedb/query/struct.Query.html#method.nearest_to).
+
+The search will return the data requested in addition to the distance of each item.
 
 ### Filtering (where clause)
 
 You can further filter the elements returned by a search using a where clause.
 
 === "Python"
+
      ```python
      tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
      ```
 
-=== "Javascript"
+=== "Typescript"
+
      ```javascript
-     const results_2 = await table
+     --8<-- "docs/src/ann_indexes.ts:search2"
-         .search(Array(1536).fill(1.2))
-         .where("id != '1141'")
-         .limit(2)
-         .execute()
      ```
 
 ### Projections (select clause)
@@ -176,34 +187,42 @@ You can further filter the elements returned by a search using a where clause.
 You can select the columns returned by the query using a select clause.
 
 === "Python"
+
      ```python
      tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
      ```
-     ```
+
+
+     ```text
                                                    vector _distance
      0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
      1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
      ...
      ```
 
-=== "Javascript"
+=== "Typescript"
-     ```javascript
+
-     const results_3 = await table
+     ```typescript
-         .search(Array(1536).fill(1.2))
+     --8<-- "docs/src/ann_indexes.ts:search3"
-         .select(["id"])
-         .limit(2)
-         .execute()
      ```
 
 ## FAQ
 
+### Why do I need to manually create an index?
+
+Currently, LanceDB does _not_ automatically create the ANN index.
+LanceDB is well-optimized for kNN (exhaustive search) via a disk-based index. For many use-cases,
+datasets of the order of ~100K vectors don't require index creation. If you can live with up to
+100ms latency, skipping index creation is a simpler workflow while guaranteeing 100% recall.
+
 ### When is it necessary to create an ANN vector index?
 
-`LanceDB` has manually-tuned SIMD code for computing vector distances.
+`LanceDB` comes out-of-the-box with highly optimized SIMD code for computing vector similarity.
-In our benchmarks, computing 100K pairs of 1K dimension vectors takes **less than 20ms**.
+In our benchmarks, computing distances for 100K pairs of 1K dimension vectors takes **less than 20ms**.
-For small datasets (< 100K rows) or applications that can accept 100ms latency, vector indices are usually not necessary.
+We observe that for small datasets (~100K rows) or for applications that can accept 100ms latency,
+vector indices are usually not necessary.
 
-For large-scale or higher dimension vectors, it is beneficial to create vector index.
+For large-scale or higher dimension vectors, it can beneficial to create vector index for performance.
 
 ### How big is my index, and how many memory will it take?
 

docs/src/ann_indexes.ts

@@ -0,0 +1,53 @@
+// --8<-- [start:import]
+import * as vectordb from "vectordb";
+// --8<-- [end:import]
+
+(async () => {
+  // --8<-- [start:ingest]
+  const db = await vectordb.connect("data/sample-lancedb");
+
+  let data = [];
+  for (let i = 0; i < 10_000; i++) {
+    data.push({
+      vector: Array(1536).fill(i),
+      id: `${i}`,
+      content: "",
+      longId: `${i}`,
+    });
+  }
+  const table = await db.createTable("my_vectors", data);
+  await table.createIndex({
+    type: "ivf_pq",
+    column: "vector",
+    num_partitions: 16,
+    num_sub_vectors: 48,
+  });
+  // --8<-- [end:ingest]
+
+  // --8<-- [start:search1]
+  const results_1 = await table
+    .search(Array(1536).fill(1.2))
+    .limit(2)
+    .nprobes(20)
+    .refineFactor(10)
+    .execute();
+  // --8<-- [end:search1]
+
+  // --8<-- [start:search2]
+  const results_2 = await table
+    .search(Array(1536).fill(1.2))
+    .where("id != '1141'")
+    .limit(2)
+    .execute();
+  // --8<-- [end:search2]
+
+  // --8<-- [start:search3]
+  const results_3 = await table
+    .search(Array(1536).fill(1.2))
+    .select(["id"])
+    .limit(2)
+    .execute();
+  // --8<-- [end:search3]
+
+  console.log("Ann indexes: done");
+})();

docs/src/api_reference.md

@@ -0,0 +1,7 @@
+# API Reference
+
+The API reference for the LanceDB client SDKs are available at the following locations:
+
+- [Python](python/python.md)
+- [JavaScript](javascript/modules.md)
+- [Rust](https://docs.rs/lancedb/latest/lancedb/index.html)

docs/src/basic.md

@@ -3,7 +3,7 @@
 !!! info "LanceDB can be run in a number of ways:"
 
     * Embedded within an existing backend (like your Django, Flask, Node.js or FastAPI application)
-    * Connected to directly from a client application like a Jupyter notebook for analytical workloads
+    * Directly from a client application like a Jupyter notebook for analytical workloads
     * Deployed as a remote serverless database
 
 ![](assets/lancedb_embedded_explanation.png)
@@ -11,43 +11,82 @@
 ## Installation
 
 === "Python"
+
       ```shell
       pip install lancedb
       ```
 
-=== "Javascript"
+=== "Typescript"
+
       ```shell
       npm install vectordb
       ```
 
-## How to connect to a database
+=== "Rust"
+
+    ```shell
+    cargo add lancedb
+    ```
+
+    !!! info "To use the lancedb create, you first need to install protobuf."
+
+    === "macOS"
+
+        ```shell
+        brew install protobuf
+        ```
+
+    === "Ubuntu/Debian"
+
+        ```shell
+        sudo apt install -y protobuf-compiler libssl-dev
+        ```
+
+    !!! info "Please also make sure you're using the same version of Arrow as in the [lancedb crate](https://github.com/lancedb/lancedb/blob/main/Cargo.toml)"
+
+## Connect to a database
 
 === "Python"
+
       ```python
       import lancedb
       uri = "data/sample-lancedb"
       db = lancedb.connect(uri)
       ```
 
-      LanceDB will create the directory if it doesn't exist (including parent directories).
+=== "Typescript"
 
-      If you need a reminder of the uri, use the `db.uri` property.
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:import"
 
-=== "Javascript"
+    --8<-- "docs/src/basic_legacy.ts:open_db"
-      ```javascript
-      const lancedb = require("vectordb");
-
-      const uri = "data/sample-lancedb";
-      const db = await lancedb.connect(uri);
     ```
 
+=== "Rust"
+
+    ```rust
+    #[tokio::main]
+    async fn main() -> Result<()> {
+        --8<-- "rust/lancedb/examples/simple.rs:connect"
+    }
+    ```
+
+    !!! info "See [examples/simple.rs](https://github.com/lancedb/lancedb/tree/main/rust/lancedb/examples/simple.rs) for a full working example."
+
 LanceDB will create the directory if it doesn't exist (including parent directories).
 
 If you need a reminder of the uri, you can call `db.uri()`.
 
-## How to create a table
+## Create a table
+
+### Create a table from initial data
+
+If you have data to insert into the table at creation time, you can simultaneously create a
+table and insert the data into it.  The schema of the data will be used as the schema of the
+table.
 
 === "Python"
+
     ```python
     tbl = db.create_table("my_table",
                     data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
@@ -59,6 +98,7 @@
     to the `create_table` method.
 
     You can also pass in a pandas DataFrame directly:
+
     ```python
     import pandas as pd
     df = pd.DataFrame([{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
@@ -66,65 +106,109 @@
     tbl = db.create_table("table_from_df", data=df)
     ```
 
-=== "Javascript"
+=== "Typescript"
-    ```javascript
+
-    const tb = await db.createTable(
+    ```typescript
-        "myTable",
+    --8<-- "docs/src/basic_legacy.ts:create_table"
-        [{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-         {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}]
-    )
     ```
 
     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 `createTable` function.
 
+=== "Rust"
 
-!!! info "Under the hood, LanceDB is converting the input data into an Apache Arrow table and persisting it to disk in [Lance format](https://www.github.com/lancedb/lance)."
+    ```rust
+    --8<-- "rust/lancedb/examples/simple.rs:create_table"
+    ```
 
-### Creating an empty table
+    If the table already exists, LanceDB will raise an error by default.  See
+    [the mode option](https://docs.rs/lancedb/latest/lancedb/connection/struct.CreateTableBuilder.html#method.mode)
+    for details on how to overwrite (or open) existing tables instead.
+
+    !!! Providing table records in Rust
+
+        The Rust SDK currently expects data to be provided as an Arrow
+        [RecordBatchReader](https://docs.rs/arrow-array/latest/arrow_array/trait.RecordBatchReader.html)
+        Support for additional formats (such as serde or polars) is on the roadmap.
+
+!!! info "Under the hood, LanceDB reads in the Apache Arrow data and persists it to disk using the [Lance format](https://www.github.com/lancedb/lance)."
+
+### Create an empty table
 
 Sometimes you may not have the data to insert into the table at creation time.
-In this case, you can create an empty table and specify the schema.
+In this case, you can create an empty table and specify the schema, so that you can add
+data to the table at a later time (as long as it conforms to the schema).  This is
+similar to a `CREATE TABLE` statement in SQL.
 
 === "Python"
+
       ```python
       import pyarrow as pa
       schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
       tbl = db.create_table("empty_table", schema=schema)
       ```
 
-## How to open an existing table
+=== "Typescript"
 
-Once created, you can open a table using the following code:
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:create_empty_table"
+    ```
+
+=== "Rust"
+
+    ```rust
+    --8<-- "rust/lancedb/examples/simple.rs:create_empty_table"
+    ```
+
+## Open an existing table
+
+Once created, you can open a table as follows:
 
 === "Python"
+
     ```python
     tbl = db.open_table("my_table")
     ```
 
+=== "Typescript"
+
+    ```typescript
+    const tbl = await db.openTable("myTable");
+    ```
+
+=== "Rust"
+
+    ```rust
+    --8<-- "rust/lancedb/examples/simple.rs:open_existing_tbl"
+    ```
+
 If you forget the name of your table, you can always get a listing of all table names:
 
+=== "Python"
+
     ```python
     print(db.table_names())
     ```
 
 === "Javascript"
-      ```javascript
-      const tbl = await db.openTable("myTable");
-      ```
-
-      If you forget the name of your table, you can always get a listing of all table names:
 
     ```javascript
     console.log(await db.tableNames());
     ```
 
-## How to add data to a table
+=== "Rust"
 
-After a table has been created, you can always add more data to it using
+    ```rust
+    --8<-- "rust/lancedb/examples/simple.rs:list_names"
+    ```
+
+## Add data to a table
+
+After a table has been created, you can always add more data to it as follows:
 
 === "Python"
+
     ```python
 
     # Option 1: Add a list of dicts to a table
@@ -137,48 +221,109 @@ After a table has been created, you can always add more data to it using
     tbl.add(data)
     ```
 
-=== "Javascript"
+=== "Typescript"
-      ```javascript
+
-      await tbl.add([{vector: [1.3, 1.4], item: "fizz", price: 100.0},
+    ```typescript
-                     {vector: [9.5, 56.2], item: "buzz", price: 200.0}])
+    --8<-- "docs/src/basic_legacy.ts:add"
     ```
 
-## How to search for (approximate) nearest neighbors
+=== "Rust"
 
-Once you've embedded the query, you can find its nearest neighbors using the following code:
+    ```rust
+    --8<-- "rust/lancedb/examples/simple.rs:add"
+    ```
+
+## Search for nearest neighbors
+
+Once you've embedded the query, you can find its nearest neighbors as follows:
 
 === "Python"
+
     ```python
     tbl.search([100, 100]).limit(2).to_pandas()
     ```
 
     This returns a pandas DataFrame with the results.
 
-=== "Javascript"
+=== "Typescript"
-      ```javascript
+
-      const query = await tbl.search([100, 100]).limit(2).execute();
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:search"
     ```
 
-## How to delete rows from a table
+=== "Rust"
+
+    ```rust
+    use futures::TryStreamExt;
+
+    --8<-- "rust/lancedb/examples/simple.rs:search"
+    ```
+
+    !!! Query vectors in Rust
+        Rust does not yet support automatic execution of embedding functions.  You will need to
+        calculate embeddings yourself.  Support for this is on the roadmap and can be tracked at
+        https://github.com/lancedb/lancedb/issues/994
+
+        Query vectors can be provided as Arrow arrays or a Vec/slice of Rust floats.
+        Support for additional formats (e.g. `polars::series::Series`) is on the roadmap.
+
+By default, LanceDB runs a brute-force scan over dataset to find the K nearest neighbours (KNN).
+For tables with more than 50K vectors, creating an ANN index is recommended to speed up search performance.
+LanceDB allows you to create an ANN index on a table as follows:
+
+=== "Python"
+
+    ```py
+    tbl.create_index()
+    ```
+
+=== "Typescript"
+
+    ```{.typescript .ignore}
+    --8<-- "docs/src/basic_legacy.ts:create_index"
+    ```
+
+=== "Rust"
+
+    ```rust
+     --8<-- "rust/lancedb/examples/simple.rs:create_index"
+    ```
+
+!!! note "Why do I need to create an index manually?"
+    LanceDB does not automatically create the ANN index for two reasons. The first is that it's optimized
+    for really fast retrievals via a disk-based index, and the second is that data and query workloads can
+    be very diverse, so there's no one-size-fits-all index configuration. LanceDB provides many parameters
+    to fine-tune index size, query latency and accuracy. See the section on
+    [ANN indexes](ann_indexes.md) for more details.
+
+## Delete rows 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"
+
     ```python
     tbl.delete('item = "fizz"')
     ```
 
-=== "Javascript"
+=== "Typescript"
-      ```javascript
+
-      await tbl.delete('item = "fizz"')
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:delete"
+    ```
+
+=== "Rust"
+
+    ```rust
+    --8<-- "rust/lancedb/examples/simple.rs:delete"
     ```
 
 The deletion predicate is a SQL expression that supports the same expressions
-as the `where()` clause on a search. They can be as simple or complex as needed.
+as the `where()` clause (`only_if()` in Rust) on a search. They can be as
-To see what expressions are supported, see the [SQL filters](sql.md) section.
+simple or complex as needed.  To see what expressions are supported, see the
-
+[SQL filters](sql.md) section.
 
 === "Python"
 
@@ -188,11 +333,16 @@ To see what expressions are supported, see the [SQL filters](sql.md) section.
 
       Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)
 
-## How to remove a table
+=== "Rust"
+
+      Read more: [lancedb::Table::delete](https://docs.rs/lancedb/latest/lancedb/table/struct.Table.html#method.delete)
+
+## Drop a table
 
 Use the `drop_table()` method on the database to remove a table.
 
 === "Python"
+
       ```python
       db.drop_table("my_table")
       ```
@@ -201,14 +351,21 @@ Use the `drop_table()` method on the database to remove a table.
       By default, if the table does not exist an exception is raised. To suppress this,
       you can pass in `ignore_missing=True`.
 
-=== "JavaScript"
+=== "Typescript"
-      ```javascript
+
-      await db.dropTable('myTable')
+      ```typescript
+      --8<-- "docs/src/basic_legacy.ts:drop_table"
       ```
 
       This permanently removes the table and is not recoverable, unlike deleting rows.
       If the table does not exist an exception is raised.
 
+=== "Rust"
+
+    ```rust
+    --8<-- "rust/lancedb/examples/simple.rs:drop_table"
+    ```
+
 !!! note "Bundling `vectordb` apps with Webpack"
 
     If you're using the `vectordb` module in JavaScript, since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.

docs/src/basic_legacy.ts

@@ -0,0 +1,92 @@
+// --8<-- [start:import]
+import * as lancedb from "vectordb";
+import { Schema, Field, Float32, FixedSizeList, Int32, Float16 } from "apache-arrow";
+// --8<-- [end:import]
+import * as fs from "fs";
+import { Table as ArrowTable, Utf8 } from "apache-arrow";
+
+const example = async () => {
+  fs.rmSync("data/sample-lancedb", { recursive: true, force: true });
+  // --8<-- [start:open_db]
+  const lancedb = require("vectordb");
+  const uri = "data/sample-lancedb";
+  const db = await lancedb.connect(uri);
+  // --8<-- [end:open_db]
+
+  // --8<-- [start:create_table]
+  const tbl = await db.createTable(
+    "myTable",
+    [
+      { vector: [3.1, 4.1], item: "foo", price: 10.0 },
+      { vector: [5.9, 26.5], item: "bar", price: 20.0 },
+    ],
+    { writeMode: lancedb.WriteMode.Overwrite }
+  );
+  // --8<-- [end:create_table]
+
+  // --8<-- [start:add]
+  const newData = Array.from({ length: 500 }, (_, i) => ({
+    vector: [i, i + 1],
+    item: "fizz",
+    price: i * 0.1,
+  }));
+  await tbl.add(newData);
+  // --8<-- [end:add]
+
+  // --8<-- [start:create_index]
+  await tbl.createIndex({
+    type: "ivf_pq",
+    num_partitions: 2,
+    num_sub_vectors: 2,
+  });
+  // --8<-- [end:create_index]
+
+  // --8<-- [start:create_empty_table]
+  const schema = new Schema([
+    new Field("id", new Int32()),
+    new Field("name", new Utf8()),
+  ]);
+  const empty_tbl = await db.createTable({ name: "empty_table", schema });
+  // --8<-- [end:create_empty_table]
+
+  // --8<-- [start:create_f16_table]
+  const dim = 16
+  const total = 10
+  const f16_schema = new Schema([
+      new Field('id', new Int32()),
+      new Field(
+        'vector',
+        new FixedSizeList(dim, new Field('item', new Float16(), true)),
+        false
+      )
+    ])
+  const data = lancedb.makeArrowTable(
+      Array.from(Array(total), (_, i) => ({
+        id: i,
+        vector: Array.from(Array(dim), Math.random)
+      })),
+      { f16_schema }
+    )
+  const table = await db.createTable('f16_tbl', data)
+  // --8<-- [end:create_f16_table]
+
+  // --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"');
+  // --8<-- [end:delete]
+
+  // --8<-- [start:drop_table]
+  await db.dropTable("myTable");
+  // --8<-- [end:drop_table]
+};
+
+async function main() {
+  await example();
+  console.log("Basic example: done");
+}
+
+main();

docs/src/concepts/index_ivfpq.md

@@ -31,7 +31,7 @@ As an example, consider starting with 128-dimensional vector consisting of 32-bi
 
 While PQ helps with reducing the size of the index, IVF primarily addresses search performance. The primary purpose of an inverted file index is to facilitate rapid and effective nearest neighbor search by narrowing down the search space.
 
-In IVF, the PQ vector space is divided into *Voronoi cells*, which are essentially partitions that consist of all the points in the space that are within a threshold distance of the given region's seed point. These seed points are used to create an inverted index that correlates each centroid with a list of vectors in the space, allowing a search to be restricted to just a subset of vectors in the index.
+In IVF, the PQ vector space is divided into *Voronoi cells*, which are essentially partitions that consist of all the points in the space that are within a threshold distance of the given region's seed point. These seed points are initialized by running K-means over the stored vectors. The centroids of K-means turn into the seed points which then each define a region. These regions are then are used to create an inverted index that correlates each centroid with a list of vectors in the space, allowing a search to be restricted to just a subset of vectors in the index.
 
 ![](../assets/ivfpq_ivf_desc.webp)
 
@@ -81,24 +81,4 @@ The above query will perform a search on the table `tbl` using the given query v
 * `to_pandas()`: Convert the results to a pandas DataFrame
 
 And there you have it! You now understand what an IVF-PQ index is, and how to create and query it in LanceDB.
-
+To see how to create an IVF-PQ index in LanceDB, take a look at the [ANN indexes](../ann_indexes.md) section.
-
-## FAQ
-
-### When is it necessary to create a vector index?
-
-LanceDB has manually-tuned SIMD code for computing vector distances. In our benchmarks, computing 100K pairs of 1K dimension vectors takes **<20ms**. For small datasets (<100K rows) or applications that can accept up to 100ms latency, vector indices are usually not necessary.
-
-For large-scale or higher dimension vectors, it is beneficial to create vector index.
-
-### How big is my index, and how much memory will it take?
-
-In LanceDB, all vector indices are disk-based, meaning that when responding to a vector query, only the relevant pages from the index file are loaded from disk and cached in memory. Additionally, each sub-vector is usually encoded into 1 byte PQ code.
-
-For example, with 1024-dimension vectors, if we choose `num_sub_vectors = 64`, each sub-vector has `1024 / 64 = 16` float32 numbers. Product quantization can lead to approximately `16 * sizeof(float32) / 1 = 64` times of space reduction.
-
-### How to choose `num_partitions` and `num_sub_vectors` for IVF_PQ index?
-
-`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 1K-4K rows lead to a good latency/recall.
-
-`num_sub_vectors` specifies how many PQ short codes to generate on each vector. Because PQ is a lossy compression of the original vector, a higher `num_sub_vectors` usually results in less space distortion, and thus yields better accuracy. However, a higher `num_sub_vectors` also causes heavier I/O and more PQ computation, and thus, higher latency. `dimension / num_sub_vectors` should be a multiple of 8 for optimum SIMD efficiency.

docs/src/embeddings/custom_embedding_function.md

@@ -17,6 +17,7 @@ Let's implement `SentenceTransformerEmbeddings` class. All you need to do is imp
 
 ```python
 from lancedb.embeddings import register
+from lancedb.util import attempt_import_or_raise
 
 @register("sentence-transformers")
 class SentenceTransformerEmbeddings(TextEmbeddingFunction):
@@ -81,7 +82,7 @@ class OpenClipEmbeddings(EmbeddingFunction):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        open_clip = self.safe_import("open_clip", "open-clip") # EmbeddingFunction util to import external libs and raise if not found
+        open_clip = attempt_import_or_raise("open_clip", "open-clip") # EmbeddingFunction util to import external libs and raise if not found
         model, _, preprocess = open_clip.create_model_and_transforms(
             self.name, pretrained=self.pretrained
         )
@@ -109,14 +110,14 @@ class OpenClipEmbeddings(EmbeddingFunction):
         if isinstance(query, str):
             return [self.generate_text_embeddings(query)]
         else:
-            PIL = self.safe_import("PIL", "pillow")
+            PIL = attempt_import_or_raise("PIL", "pillow")
             if isinstance(query, PIL.Image.Image):
                 return [self.generate_image_embedding(query)]
             else:
                 raise TypeError("OpenClip supports str or PIL Image as query")
 
     def generate_text_embeddings(self, text: str) -> np.ndarray:
-        torch = self.safe_import("torch")
+        torch = attempt_import_or_raise("torch")
         text = self.sanitize_input(text)
         text = self._tokenizer(text)
         text.to(self.device)
@@ -175,7 +176,7 @@ class OpenClipEmbeddings(EmbeddingFunction):
             The image to embed. If the image is a str, it is treated as a uri.
             If the image is bytes, it is treated as the raw image bytes.
         """
-        torch = self.safe_import("torch")
+        torch = attempt_import_or_raise("torch")
         # TODO handle retry and errors for https
         image = self._to_pil(image)
         image = self._preprocess(image).unsqueeze(0)
@@ -183,7 +184,7 @@ class OpenClipEmbeddings(EmbeddingFunction):
             return self._encode_and_normalize_image(image)
 
     def _to_pil(self, image: Union[str, bytes]):
-        PIL = self.safe_import("PIL", "pillow")
+        PIL = attempt_import_or_raise("PIL", "pillow")
         if isinstance(image, bytes):
             return PIL.Image.open(io.BytesIO(image))
         if isinstance(image, PIL.Image.Image):

docs/src/embeddings/default_embedding_functions.md

@@ -9,6 +9,9 @@ Contains the text embedding functions registered by default.
 ### Sentence transformers
 Allows you to set parameters when registering a `sentence-transformers` object.
 
+!!! info
+    Sentence transformer embeddings are normalized by default. It is recommended to use normalized embeddings for similarity search.
+
 | Parameter | Type | Default Value | Description |
 |---|---|---|---|
 | `name` | `str` | `all-MiniLM-L6-v2` | The name of the model |
@@ -44,6 +47,7 @@ LanceDB registers the OpenAI embeddings function in the registry by default, as
 | Parameter | Type | Default Value | Description |
 |---|---|---|---|
 | `name` | `str` | `"text-embedding-ada-002"` | The name of the model. |
+| `dim` | `int` |  Model default   | For OpenAI's newer text-embedding-3 model, we can specify a dimensionality that is smaller than the 1536 size. This feature supports it |
 
 
 ```python
@@ -119,7 +123,7 @@ texts = [{"text": "Capitalism has been dominant in the Western world since the e
 tbl.add(texts)
 ```
 
-## Gemini Embedding Function
+### Gemini Embeddings
 With Google's Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity. For more on how and why you should use embeddings, refer to the Embeddings guide.
 The Gemini Embedding Model API supports various task types:
 
@@ -155,6 +159,52 @@ tbl.add(df)
 rs = tbl.search("hello").limit(1).to_pandas()
 ```
 
+### AWS Bedrock Text Embedding Functions
+AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function.
+You can do so by using `awscli` and also add your session_token:
+```shell
+aws configure
+aws configure set aws_session_token "<your_session_token>"
+```
+to ensure that the credentials are set up correctly, you can run the following command:
+```shell
+aws sts get-caller-identity
+```
+
+Supported Embedding modelIDs are:
+* `amazon.titan-embed-text-v1`
+* `cohere.embed-english-v3`
+* `cohere.embed-multilingual-v3`
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| **name** | str | "amazon.titan-embed-text-v1" | The model ID of the bedrock model to use. Supported base models for Text Embeddings: amazon.titan-embed-text-v1, cohere.embed-english-v3, cohere.embed-multilingual-v3 |
+| **region** | str | "us-east-1" | Optional name of the AWS Region in which the service should be called (e.g., "us-east-1"). |
+| **profile_name** | str | None | Optional name of the AWS profile to use for calling the Bedrock service. If not specified, the default profile will be used. |
+| **assumed_role** | str | None | Optional ARN of an AWS IAM role to assume for calling the Bedrock service. If not specified, the current active credentials will be used. |
+| **role_session_name** | str | "lancedb-embeddings" | Optional name of the AWS IAM role session to use for calling the Bedrock service. If not specified, a "lancedb-embeddings" name will be used. |
+| **runtime** | bool | True | Optional choice of getting different client to perform operations with the Amazon Bedrock service. |
+| **max_retries** | int | 7 | Optional number of retries to perform when a request fails. |
+
+Usage Example:
+
+```python
+model = get_registry().get("bedrock-text").create()
+
+class TextModel(LanceModel):
+    text: str = model.SourceField()
+    vector: Vector(model.ndims()) = model.VectorField()
+
+df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
+db = lancedb.connect("tmp_path")
+tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+tbl.add(df)
+rs = tbl.search("hello").limit(1).to_pandas()
+```
+
 ## Multi-modal embedding functions
 Multi-modal embedding functions allow you to query your table using both images and text.
 
@@ -174,7 +224,6 @@ This embedding function supports ingesting images as both bytes and urls. You ca
 !!! info
     LanceDB supports ingesting images directly from accessible links.
 
-
 ```python
 
 db = lancedb.connect(tmp_path)
@@ -240,4 +289,67 @@ print(actual.label)
 
 ```
 
+### Imagebind embeddings
+We have support for [imagebind](https://github.com/facebookresearch/ImageBind) model embeddings. You can download our version of the packaged model via - `pip install imagebind-packaged==0.1.2`.
+
+This function is registered as `imagebind` and supports Audio, Video and Text modalities(extending to Thermal,Depth,IMU data):
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"imagebind_huge"` | Name of the model. |
+| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
+| `normalize` | `bool` | `False` | set to `True` to normalize your inputs before model ingestion. |
+
+Below is an example demonstrating how the API works:
+
+```python
+db = lancedb.connect(tmp_path)
+registry = EmbeddingFunctionRegistry.get_instance()
+func = registry.get("imagebind").create()
+
+class ImageBindModel(LanceModel):
+    text: str
+    image_uri: str = func.SourceField()
+    audio_path: str
+    vector: Vector(func.ndims()) = func.VectorField()
+
+# add locally accessible image paths
+text_list=["A dog.", "A car", "A bird"]
+image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
+audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]
+
+# Load data
+inputs = [
+    {"text": a, "audio_path": b, "image_uri": c}
+    for a, b, c in zip(text_list, audio_paths, image_paths)
+]
+
+#create table and add data
+table = db.create_table("img_bind", schema=ImageBindModel)
+table.add(inputs)
+```
+
+Now, we can search using any modality:
+
+#### image search
+```python
+query_image = "./assets/dog_image2.jpg" #download an image and enter that path here
+actual = table.search(query_image).limit(1).to_pydantic(ImageBindModel)[0]
+print(actual.text == "dog")
+```
+#### audio search
+
+```python
+query_audio = "./assets/car_audio2.wav" #download an audio clip and enter path here
+actual = table.search(query_audio).limit(1).to_pydantic(ImageBindModel)[0]
+print(actual.text == "car")
+```
+#### Text search
+You can add any input query and fetch the result as follows:
+```python
+query = "an animal which flies and tweets" 
+actual = table.search(query).limit(1).to_pydantic(ImageBindModel)[0]
+print(actual.text == "bird")
+```
+
 If you have any questions about the embeddings API, supported models, or see a relevant model missing, please raise an issue [on GitHub](https://github.com/lancedb/lancedb/issues).

docs/src/embeddings/embedding_explicit.md

@@ -1,141 +0,0 @@
-In this workflow, you define your own embedding function and pass it as a callable to LanceDB, invoking it in your code to generate the embeddings. Let's look at some examples.
-
-### Hugging Face
-
-!!! note
-    Currently, the Hugging Face method is only supported in the Python SDK.
-
-=== "Python"
-    The most popular open source option is to use the [sentence-transformers](https://www.sbert.net/) 
-    library, which can be installed via pip.
-
-    ```bash
-    pip install sentence-transformers
-    ```
-
-    The example below shows how to use the `paraphrase-albert-small-v2` model to generate embeddings 
-    for a given document.
-
-    ```python
-    from sentence_transformers import SentenceTransformer
-
-    name="paraphrase-albert-small-v2"
-    model = SentenceTransformer(name)
-
-    # used for both training and querying
-    def embed_func(batch):
-        return [model.encode(sentence) for sentence in batch]
-    ```
-
-### OpenAI
-
-Another popular alternative is to use an external API like OpenAI's [embeddings API](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
-
-=== "Python"
-      ```python
-        import openai
-        import os
-
-        # 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-..."
-
-        # verify that the API key is working
-        assert len(openai.Model.list()["data"]) > 0
-
-        def embed_func(c):
-            rs = openai.Embedding.create(input=c, engine="text-embedding-ada-002")
-            return [record["embedding"] for record in rs["data"]]
-      ```
-
-=== "JavaScript"
-      ```javascript
-        const lancedb = require("vectordb");
-
-        // You need to provide an OpenAI API key
-        const apiKey = "sk-..."
-        // The embedding function will create embeddings for the 'text' column
-        const embedding = new lancedb.OpenAIEmbeddingFunction('text', apiKey)
-      ```
-
-## Applying an embedding function to data
-
-=== "Python"
-    Using an embedding function, you can apply it to raw data
-    to generate embeddings for each record.
-
-    Say you have a pandas DataFrame with a `text` column that you want embedded,
-    you can use the `with_embeddings` function to generate embeddings and add them to 
-    an existing table.
-
-    ```python
-     import pandas as pd
-     from lancedb.embeddings import with_embeddings
-
-     df = pd.DataFrame(
-        [
-            {"text": "pepperoni"},
-            {"text": "pineapple"}
-        ]
-    )
-     data = with_embeddings(embed_func, df)
-
-     # The output is used to create / append to a table
-     # db.create_table("my_table", data=data)
-    ```
-
-    If your data is in a different column, you can specify the `column` kwarg to `with_embeddings`.
-
-    By default, LanceDB calls the function with batches of 1000 rows. This can be configured
-    using the `batch_size` parameter to `with_embeddings`.
-
-    LanceDB automatically wraps the function with retry and rate-limit logic to ensure the OpenAI
-    API call is reliable.
-
-=== "JavaScript"
-    Using an embedding function, you can apply it to raw data
-    to generate embeddings for each record.
-
-    Simply pass the embedding function created above and LanceDB will use it to generate
-    embeddings for your data.
-
-    ```javascript
-    const db = await lancedb.connect("data/sample-lancedb");
-    const data = [
-    { text: "pepperoni"},
-    { text: "pineapple"}
-    ]
-
-    const table = await db.createTable("vectors", data, embedding)
-    ```
-
-## Querying using an embedding function
-
-!!! warning
-    At query time, you **must** use the same embedding function you used to vectorize your data.
-    If you use a different embedding function, the embeddings will not reside in the same vector
-    space and the results will be nonsensical.
-
-=== "Python"
-     ```python
-     query = "What's the best pizza topping?"
-     query_vector = embed_func([query])[0]
-     results = (
-        tbl.search(query_vector)
-        .limit(10)
-        .to_pandas()
-     )
-     ```
-
-     The above snippet returns a pandas DataFrame with the 10 closest vectors to the query.
-
-=== "JavaScript"
-     ```javascript
-      const results = await table
-        .search("What's the best pizza topping?")
-        .limit(10)
-        .execute()
-     ```
-
-     The above snippet returns an array of records with the top 10 nearest neighbors to the query.

docs/src/embeddings/embedding_functions.md

@@ -3,20 +3,45 @@ Representing multi-modal data as vector embeddings is becoming a standard practi
 For this purpose, LanceDB introduces an **embedding functions API**, that allow you simply set up once, during the configuration stage of your project. After this, the table remembers it, effectively making the embedding functions *disappear in the background* so you don't have to worry about manually passing callables, and instead, simply focus on the rest of your data engineering pipeline.
 
 !!! warning
-    Using the implicit embeddings management approach means that you can forget about the manually passing around embedding
+    Using the embedding function registry means that you don't have to explicitly generate the embeddings yourself. 
-    functions in your code, as long as you don't intend to change it at a later time. If your embedding function changes,
+    However, if your embedding function changes, you'll have to re-configure your table with the new embedding function 
-    you'll have to re-configure your table with the new embedding function and regenerate the embeddings.
+    and regenerate the embeddings. In the future, we plan to support the ability to change the embedding function via
+    table metadata and have LanceDB automatically take care of regenerating the embeddings.
+
 
 ## 1. Define the embedding function
-We have some pre-defined embedding functions in the global registry, with more coming soon. Here's let's an implementation of CLIP as example.
-```
-registry = EmbeddingFunctionRegistry.get_instance()
-clip = registry.get("open-clip").create()
 
+=== "Python"
+    In the LanceDB python SDK, we define a global embedding function registry with
+    many different embedding models and even more coming soon. 
+    Here's let's an implementation of CLIP as example.
+
+    ```python
+    from lancedb.embeddings import get_registry
+
+    registry = get_registry()
+    clip = registry.get("open-clip").create()
+    ```
+
+    You can also define your own embedding function by implementing the `EmbeddingFunction` 
+    abstract base interface. It subclasses Pydantic Model which can be utilized to write complex schemas simply as we'll see next!
+
+=== "JavaScript""
+    In the TypeScript SDK, the choices are more limited. For now, only the OpenAI
+    embedding function is available.
+
+    ```javascript
+    const lancedb = require("vectordb");
+
+    // You need to provide an OpenAI API key
+    const apiKey = "sk-..."
+    // The embedding function will create embeddings for the 'text' column
+    const embedding = new lancedb.OpenAIEmbeddingFunction('text', apiKey)
     ```
-You can also define your own embedding function by implementing the `EmbeddingFunction` abstract base interface. It subclasses Pydantic Model which can be utilized to write complex schemas simply as we'll see next!
 
 ## 2. Define the data model or schema
+
+=== "Python"
     The embedding function defined above abstracts away all the details about the models and dimensions required to define the schema. You can simply set a field as **source** or **vector** column. Here's how:
 
     ```python
@@ -27,38 +52,78 @@ class Pets(LanceModel):
 
     `VectorField` tells LanceDB to use the clip embedding function to generate query embeddings for the `vector` column and `SourceField` ensures that when adding data, we automatically use the specified embedding function to encode `image_uri`.
 
-## 3. Create LanceDB table
+=== "JavaScript"
-Now that we have chosen/defined our embedding function and the schema, we can create the table:
 
+    For the TypeScript SDK, a schema can be inferred from input data, or an explicit
+    Arrow schema can be provided.
+
+## 3. Create table and add data
+
+Now that we have chosen/defined our embedding function and the schema, 
+we can create the table and ingest data without needing to explicitly generate
+the embeddings at all:
+
+=== "Python"
     ```python
     db = lancedb.connect("~/lancedb")
     table = db.create_table("pets", schema=Pets)
 
-```
-
-That's it! We've provided all the information needed to embed the source and query inputs. We can now forget about the model and dimension details and start to build our VectorDB pipeline.
-
-## 4. Ingest lots of data and query your table
-Any new or incoming data can just be added and it'll be vectorized automatically.
-
-```python
     table.add([{"image_uri": u} for u in uris])
     ```
 
+=== "JavaScript"
+
+    ```javascript
+    const db = await lancedb.connect("data/sample-lancedb");
+    const data = [
+    { text: "pepperoni"},
+    { text: "pineapple"}
+    ]
+
+    const table = await db.createTable("vectors", data, embedding)
+    ```
+
+## 4. Querying your table
+Not only can you forget about the embeddings during ingestion, you also don't
+need to worry about it when you query the table:
+
+=== "Python"
+
     Our OpenCLIP query embedding function supports querying via both text and images:
 
     ```python
-result = table.search("dog")
+    results = (
+        table.search("dog")
+        .limit(10)
+        .to_pandas()
+    )
     ```
 
-Let's query an image:
+    Or we can search using an image:
 
     ```python
     p = Path("path/to/images/samoyed_100.jpg")
     query_image = Image.open(p)
+    results = (
         table.search(query_image)
+        .limit(10)
+        .to_pandas()
+    )
     ```
 
+    Both of the above snippet returns a pandas DataFrame with the 10 closest vectors to the query.
+
+=== "JavaScript"
+
+    ```javascript
+    const results = await table
+    .search("What's the best pizza topping?")
+    .limit(10)
+    .execute()
+    ```    
+    
+    The above snippet returns an array of records with the top 10 nearest neighbors to the query.
+
 ---
 
 ## Rate limit Handling
@@ -100,4 +165,5 @@ rs[2].image
 
 ![](../assets/dog_clip_output.png)
 
-Now that you have the basic idea about implicit management via embedding functions, let's dive deeper into a [custom API](./api.md) that you can use to implement your own embedding functions.
+Now that you have the basic idea about LanceDB embedding functions and the embedding function registry,
+let's dive deeper into defining your own [custom functions](./custom_embedding_function.md).

docs/src/embeddings/index.md

@@ -1,8 +1,14 @@
-Due to the nature of vector embeddings, they can be used to represent any kind of data, from text to images to audio. This makes them a very powerful tool for machine learning practitioners. However, there's no one-size-fits-all solution for generating embeddings - there are many different libraries and APIs (both commercial and open source) that can be used to generate embeddings from structured/unstructured data.
+Due to the nature of vector embeddings, they can be used to represent any kind of data, from text to images to audio. 
+This makes them a very powerful tool for machine learning practitioners. 
+However, there's no one-size-fits-all solution for generating embeddings - there are many different libraries and APIs 
+(both commercial and open source) that can be used to generate embeddings from structured/unstructured data.
 
-LanceDB supports 2 methods of vectorizing your raw data into embeddings.
+LanceDB supports 3 methods of working with embeddings.
 
-1. **Explicit**: By manually calling LanceDB's `with_embedding` function to vectorize your data via an `embed_func` of your choice
+1. You can manually generate embeddings for the data and queries. This is done outside of LanceDB.
-2. **Implicit**: Allow LanceDB to embed the data and queries in the background as they come in, by using the table's `EmbeddingRegistry` information
+2. You can use the built-in [embedding functions](./embedding_functions.md) to embed the data and queries in the background.
+3. For python users, you can define your own [custom embedding function](./custom_embedding_function.md)
+   that extends the default embedding functions.
 
-See the [explicit](embedding_explicit.md) and [implicit](embedding_functions.md) embedding sections for more details.
+For python users, there is also a legacy [with_embeddings API](./legacy.md).
+It is retained for compatibility and will be removed in a future version.

docs/src/embeddings/legacy.md

@@ -0,0 +1,99 @@
+The legacy `with_embeddings` API is for Python only and is deprecated.
+
+### Hugging Face
+
+The most popular open source option is to use the [sentence-transformers](https://www.sbert.net/) 
+library, which can be installed via pip.
+
+```bash
+pip install sentence-transformers
+```
+
+The example below shows how to use the `paraphrase-albert-small-v2` model to generate embeddings 
+for a given document.
+
+```python
+from sentence_transformers import SentenceTransformer
+
+name="paraphrase-albert-small-v2"
+model = SentenceTransformer(name)
+
+# used for both training and querying
+def embed_func(batch):
+    return [model.encode(sentence) for sentence in batch]
+```
+
+
+### OpenAI
+
+Another popular alternative is to use an external API like OpenAI's [embeddings API](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
+
+```python
+import openai
+import os
+
+# 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-..."
+
+client = openai.OpenAI()
+
+def embed_func(c):    
+    rs = client.embeddings.create(input=c, model="text-embedding-ada-002")
+    return [record.embedding for record in rs["data"]]
+```
+
+
+## Applying an embedding function to data
+
+Using an embedding function, you can apply it to raw data
+to generate embeddings for each record.
+
+Say you have a pandas DataFrame with a `text` column that you want embedded,
+you can use the `with_embeddings` function to generate embeddings and add them to 
+an existing table.
+
+```python
+    import pandas as pd
+    from lancedb.embeddings import with_embeddings
+
+    df = pd.DataFrame(
+        [
+            {"text": "pepperoni"},
+            {"text": "pineapple"}
+        ]
+    )
+    data = with_embeddings(embed_func, df)
+
+    # The output is used to create / append to a table
+    tbl = db.create_table("my_table", data=data)
+```
+
+If your data is in a different column, you can specify the `column` kwarg to `with_embeddings`.
+
+By default, LanceDB calls the function with batches of 1000 rows. This can be configured
+using the `batch_size` parameter to `with_embeddings`.
+
+LanceDB automatically wraps the function with retry and rate-limit logic to ensure the OpenAI
+API call is reliable.
+
+## Querying using an embedding function
+
+!!! warning
+    At query time, you **must** use the same embedding function you used to vectorize your data.
+    If you use a different embedding function, the embeddings will not reside in the same vector
+    space and the results will be nonsensical.
+
+=== "Python"
+     ```python
+     query = "What's the best pizza topping?"
+     query_vector = embed_func([query])[0]
+     results = (
+        tbl.search(query_vector)
+        .limit(10)
+        .to_pandas()
+     )
+     ```
+
+     The above snippet returns a pandas DataFrame with the 10 closest vectors to the query.

docs/src/examples/examples_rust.md

@@ -0,0 +1,3 @@
+# Examples: Rust
+
+Our Rust SDK is now stable. Examples are coming soon.

docs/src/examples/image_embeddings_roboflow.md

@@ -43,7 +43,7 @@ pip install lancedb
 We also need to install a specific commit of `tantivy`, a dependency of the LanceDB full text search engine we will use later in this guide:
 
 ```
-pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+pip install tantivy
 ```
 
 Create a new Python file and add the following code:

docs/src/examples/index.md

@@ -2,10 +2,11 @@
 
 ## Recipes and example code
 
-LanceDB provides language APIs, allowing you to embed a database in your language of choice. We currently provide Python and Javascript APIs, with the Rust API and examples actively being worked on and will be available soon.
+LanceDB provides language APIs, allowing you to embed a database in your language of choice.
 
 * 🐍 [Python](examples_python.md) examples
-* 👾 [JavaScript](exampled_js.md) examples
+* 👾 [JavaScript](examples_js.md) examples
+* 🦀 Rust examples (coming soon)
 
 ## Applications powered by LanceDB
 

docs/src/examples/modal_langchain.py

@@ -1,6 +1,5 @@
 import pickle
 import re
-import sys
 import zipfile
 from pathlib import Path
 
@@ -79,7 +78,10 @@ def qanda_langchain(query):
     download_docs()
     docs = store_docs()
 
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200,)
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+    )
     documents = text_splitter.split_documents(docs)
     embeddings = OpenAIEmbeddings()
 

docs/src/extra_js/init_ask_ai_widget.js

@@ -0,0 +1,11 @@
+document.addEventListener("DOMContentLoaded", function () {
+    var script = document.createElement("script");
+    script.src = "https://widget.kapa.ai/kapa-widget.bundle.js";
+    script.setAttribute("data-website-id", "c5881fae-cec0-490b-b45e-d83d131d4f25");
+    script.setAttribute("data-project-name", "LanceDB");
+    script.setAttribute("data-project-color", "#000000");
+    script.setAttribute("data-project-logo", "https://avatars.githubusercontent.com/u/108903835?s=200&v=4");
+  script.setAttribute("data-modal-example-questions","Help me create an IVF_PQ index,How do I do an exhaustive search?,How do I create a LanceDB table?,Can I use my own embedding function?");
+    script.async = true;
+    document.head.appendChild(script);
+  });

docs/src/faq.md

@@ -16,7 +16,7 @@ As we mention in our talk titled “[Lance, a modern columnar data format](https
 
 ### Why build in Rust? 🦀
 
-We believe that the Rust ecosystem has attained mainstream maturity and that Rust will form the underpinnings of large parts of the data and ML landscape in a few years. Performance, latency and reliability are paramount to a vector DB, and building in Rust allows us to iterate and release updates more rapidly due to Rust’s safety guarantees. Both Lance (the data format) and LanceDB (the database) are written entirely in Rust. We also provide Python and JavaScript client libraries to interact with the database. Our Rust API is a little rough around the edges right now, but is fast becoming on par with the Python and JS APIs.
+We believe that the Rust ecosystem has attained mainstream maturity and that Rust will form the underpinnings of large parts of the data and ML landscape in a few years. Performance, latency and reliability are paramount to a vector DB, and building in Rust allows us to iterate and release updates more rapidly due to Rust’s safety guarantees. Both Lance (the data format) and LanceDB (the database) are written entirely in Rust. We also provide Python, JavaScript, and Rust client libraries to interact with the database.
 
 ### What is the difference between LanceDB OSS and LanceDB Cloud?
 
@@ -40,11 +40,11 @@ LanceDB and its underlying data format, Lance, are built to scale to really larg
 
 No. LanceDB is blazing fast (due to its disk-based index) for even brute force kNN search, within reason. In our benchmarks, computing 100K pairs of 1000-dimension vectors takes less than 20ms. For small datasets of ~100K records or applications that can accept ~100ms latency, an ANN index is usually not necessary.
 
-For large-scale (>1M) or higher dimension vectors, it is beneficial to create an ANN index.
+For large-scale (>1M) or higher dimension vectors, it is beneficial to create an ANN index. See the [ANN indexes](ann_indexes.md) section for more details.
 
 ### Does LanceDB support full-text search?
 
-Yes, LanceDB supports full-text search (FTS) via [Tantivy](https://github.com/quickwit-oss/tantivy). Our current FTS integration is Python-only, and our goal is to push it down to the Rust level in future versions to enable much more powerful search capabilities available to our Python, JavaScript and Rust clients.
+Yes, LanceDB supports full-text search (FTS) via [Tantivy](https://github.com/quickwit-oss/tantivy). Our current FTS integration is Python-only, and our goal is to push it down to the Rust level in future versions to enable much more powerful search capabilities available to our Python, JavaScript and Rust clients.  Follow along in the [Github issue](https://github.com/lancedb/lance/issues/1195)
 
 ### How can I speed up data inserts?
 
@@ -69,3 +69,19 @@ MinIO supports an S3 compatible API. In order to connect to a MinIO instance, yo
 - Set the envvar `AWS_ENDPOINT` to the URL of your MinIO API
 - Set the envvars `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` with your MinIO credential
 - Call `lancedb.connect("s3://minio_bucket_name")`
+
+### Where can I find benchmarks for LanceDB?
+
+Refer to this [post](https://blog.lancedb.com/benchmarking-lancedb-92b01032874a) for recent benchmarks.
+
+### How much data can LanceDB practically manage without effecting performance?
+
+We target good performance on ~10-50 billion rows and ~10-30 TB of data.
+
+### Does LanceDB support concurrent operations?
+
+LanceDB can handle concurrent reads very well, and can scale horizontally. The main constraint is how well the [storage layer](https://lancedb.github.io/lancedb/concepts/storage/) you've chosen scales. For writes, we support concurrent writing, though too many concurrent writers can lead to failing writes as there is a limited number of times a writer retries a commit
+
+!!! info "Multiprocessing with LanceDB"
+
+    For multiprocessing you should probably not use ```fork``` as lance is multi-threaded internally and ```fork``` and multi-thread do not work well.[Refer to this discussion](https://discuss.python.org/t/concerns-regarding-deprecation-of-fork-with-alive-threads/33555)

docs/src/fts.md

@@ -1,6 +1,6 @@
 # Full-text search
 
-LanceDB provides support for full-text search via [Tantivy](https://github.com/quickwit-oss/tantivy) (currently Python only), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions. Our goal is to push the FTS integration down to the Rust level in the future, so that it's available for JavaScript users as well.
+LanceDB provides support for full-text search via [Tantivy](https://github.com/quickwit-oss/tantivy) (currently Python only), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions. Our goal is to push the FTS integration down to the Rust level in the future, so that it's available for Rust and JavaScript users as well.  Follow along at [this Github issue](https://github.com/lancedb/lance/issues/1195)
 
 A hybrid search solution combining vector and full-text search is also on the way.
 
@@ -75,21 +75,70 @@ applied on top of the full text search results. This can be invoked via the fami
 table.search("puppy").limit(10).where("meta='foo'").to_list()
 ```
 
-## Syntax
+## Sorting
 
-For full-text search you can perform either a phrase query like "the old man and the sea", 
+You can pre-sort the documents by specifying `ordering_field_names` when
-or a structured search query like "(Old AND Man) AND Sea".
+creating the full-text search index. Once pre-sorted, you can then specify
-Double quotes are used to disambiguate.
+`ordering_field_name` while searching to return results sorted by the given
+field. For example, 
 
-For example: 
+```
+table.create_fts_index(["text_field"], ordering_field_names=["sort_by_field"])
 
-If you intended "they could have been dogs OR cats" as a phrase query, this actually
+(table.search("terms", ordering_field_name="sort_by_field")
-raises a syntax error since `OR` is a recognized operator. If you make `or` lower case,
+ .limit(20)
-this avoids the syntax error. However, it is cumbersome to have to remember what will
+ .to_list())
-conflict with the query syntax. Instead, if you search using 
+```
-`table.search('"they could have been dogs OR cats"')`, then the syntax checker avoids
-checking inside the quotes.
 
+!!! note
+    If you wish to specify an ordering field at query time, you must also
+    have specified it during indexing time. Otherwise at query time, an
+    error will be raised that looks like `ValueError: The field does not exist: xxx`
+
+!!! note
+    The fields to sort on must be of typed unsigned integer, or else you will see 
+    an error during indexing that looks like 
+    `TypeError: argument 'value': 'float' object cannot be interpreted as an integer`.
+
+!!! note
+    You can specify multiple fields for ordering at indexing time.
+    But at query time only one ordering field is supported.
+
+
+## Phrase queries vs. terms queries
+
+For full-text search you can specify either a **phrase** query like `"the old man and the sea"`,
+or a **terms** search query like `"(Old AND Man) AND 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).
+
+!!! tip "Note"
+    The query parser will raise an exception on queries that are ambiguous. For example, in the query `they could have been dogs OR cats`, `OR` is capitalized so it's considered a keyword query operator. But it's ambiguous how the left part should be treated. So if you submit this search query as is, you'll get `Syntax Error: they could have been dogs OR cats`.
+
+    ```py
+    # This raises a syntax error
+    table.search("they could have been dogs OR cats")
+    ```
+
+    On the other hand, lowercasing `OR` to `or` will work, because there are no capitalized logical operators and
+    the query is treated as a phrase query.
+
+    ```py
+    # This works!
+    table.search("they could have been dogs or cats")
+    ```
+
+It can be cumbersome to have to remember what will cause a syntax error depending on the type of
+query you want to perform. To make this simpler, when you want to perform a phrase query, you can
+enforce it in one of two ways:
+
+1. Place the double-quoted query inside single quotes. For example, `table.search('"they could have been dogs OR cats"')` is treated as
+a phrase query.
+2. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
+itself contains double quotes. For example, `table.search('the cats OR dogs were not really "pets" at all').phrase_query()`
+is treated as a phrase query.
+
+In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested
+double quotes replaced by single quotes.
 
 ## Configurations
 
@@ -112,4 +161,3 @@ table.create_fts_index(["text1", "text2"], writer_heap_size=heap, replace=True)
 2. We currently only support local filesystem paths for the FTS index.
    This is a tantivy limitation. We've implemented an object store plugin
    but there's no way in tantivy-py to specify to use it.
-

docs/src/guides/storage.md

@@ -68,6 +68,82 @@ Alternatively, if you are using AWS SSO, you can use the `AWS_PROFILE` and `AWS_
 
 You can see a full list of environment variables [here](https://docs.rs/object_store/latest/object_store/aws/struct.AmazonS3Builder.html#method.from_env).
 
+!!! tip "Automatic cleanup for failed writes"
+
+    LanceDB uses [multi-part uploads](https://docs.aws.amazon.com/AmazonS3/latest/userguide/mpuoverview.html) when writing data to S3 in order to maximize write speed. LanceDB will abort these uploads when it shuts down gracefully, such as when cancelled by keyboard interrupt. However, in the rare case that LanceDB crashes, it is possible that some data will be left lingering in your account. To cleanup this data, we recommend (as AWS themselves do) that you setup a lifecycle rule to delete in-progress uploads after 7 days. See the AWS guide:
+
+    **[Configuring a bucket lifecycle configuration to delete incomplete multipart uploads](https://docs.aws.amazon.com/AmazonS3/latest/userguide/mpu-abort-incomplete-mpu-lifecycle-config.html)**
+
+#### AWS IAM Permissions
+
+If a bucket is private, then an IAM policy must be specified to allow access to it. For many development scenarios, using broad permissions such as a PowerUser account is more than sufficient for working with LanceDB. However, in many production scenarios, you may wish to have as narrow as possible permissions.
+
+For **read and write access**, LanceDB will need a policy such as:
+
+```json
+{
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Effect": "Allow",
+            "Action": [
+              "s3:PutObject",
+              "s3:GetObject",
+              "s3:DeleteObject",
+            ],
+            "Resource": "arn:aws:s3:::<bucket>/<prefix>/*"
+        },
+        {
+            "Effect": "Allow",
+            "Action": [
+                "s3:ListBucket",
+                "s3:GetBucketLocation"
+            ],
+            "Resource": "arn:aws:s3:::<bucket>",
+            "Condition": {
+                "StringLike": {
+                    "s3:prefix": [
+                        "<prefix>/*"
+                    ]
+                }
+            }
+        }
+    ]
+}
+```
+
+For **read-only access**, LanceDB will need a policy such as:
+
+```json
+{
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Effect": "Allow",
+            "Action": [
+              "s3:GetObject",
+            ],
+            "Resource": "arn:aws:s3:::<bucket>/<prefix>/*"
+        },
+        {
+            "Effect": "Allow",
+            "Action": [
+                "s3:ListBucket",
+                "s3:GetBucketLocation"
+            ],
+            "Resource": "arn:aws:s3:::<bucket>",
+            "Condition": {
+                "StringLike": {
+                    "s3:prefix": [
+                        "<prefix>/*"
+                    ]
+                }
+            }
+        }
+    ]
+}
+```
+
 #### S3-compatible stores
 
 LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you must specify two environment variables: `AWS_ENDPOINT` and `AWS_DEFAULT_REGION`. `AWS_ENDPOINT` should be the URL of the S3-compatible store, and `AWS_DEFAULT_REGION` should be the region to use.

docs/src/guides/tables.md

@@ -16,10 +16,23 @@ This guide will show how to create tables, insert data into them, and update the
     db = lancedb.connect("./.lancedb")
     ```
 
+=== "Javascript"
+
+    Initialize a VectorDB connection and create a table using one of the many methods listed below.
+
+    ```javascript
+    const lancedb = require("vectordb");
+
+    const uri = "data/sample-lancedb";
+    const db = await lancedb.connect(uri);
+    ```
+
     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.
 
 ### From list of tuples or dictionaries
 
+=== "Python"
+
     ```python
     import lancedb
 
@@ -32,7 +45,6 @@ This guide will show how to create tables, insert data into them, and update the
 
     db["my_table"].head()
     ```
-
     !!! info "Note"
         If the table already exists, LanceDB will raise an error by default. 
 
@@ -51,6 +63,27 @@ This guide will show how to create tables, insert data into them, and update the
         db.create_table("name", data, mode="overwrite")
         ```
 
+=== "Javascript"
+    You can create a LanceDB table in JavaScript using an array of JSON records as follows.
+
+    ```javascript
+    const tb = await db.createTable("my_table", [{
+        "vector": [3.1, 4.1],
+        "item": "foo",
+        "price": 10.0
+    }, {
+        "vector": [5.9, 26.5],
+        "item": "bar",
+        "price": 20.0
+    }]);
+    ```
+    !!! info "Note"
+    If the table already exists, LanceDB will raise an error by default. If you want to overwrite the table, you need to specify the `WriteMode` in the createTable function.
+
+    ```javascript
+    const table = await con.createTable(tableName, data, { writeMode: WriteMode.Overwrite })
+    ```
+
     ### From a Pandas DataFrame
 
     ```python
@@ -69,6 +102,8 @@ This guide will show how to create tables, insert data into them, and update the
     !!! 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. 
+
     ```python
     custom_schema = pa.schema([
     pa.field("vector", pa.list_(pa.float32(), 4)),
@@ -97,26 +132,44 @@ This guide will show how to create tables, insert data into them, and update the
     table = db.create_table("pl_table", data=data)
     ```
 
-    ### From PyArrow Tables
+### From an Arrow Table
-    You can also create LanceDB tables directly from PyArrow tables
+=== "Python"
+    You can also create LanceDB tables directly from Arrow tables. 
+    LanceDB supports float16 data type!
 
     ```python
-    table = pa.Table.from_arrays(
+    import pyarrows as pa
+    import numpy as np
+    
+    dim = 16
+    total = 2
+    schema = pa.schema(
         [
-                pa.array([[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
+            pa.field("vector", pa.list_(pa.float16(), dim)),
-                        pa.list_(pa.float32(), 4)),
+            pa.field("text", pa.string())
-                pa.array(["foo", "bar"]),
+        ]
-                pa.array([10.0, 20.0]),
-            ],
-            ["vector", "item", "price"],
     )
+    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)
+    ```
 
-    db = lancedb.connect("db")
+=== "Javascript"
+    You can also create LanceDB tables directly from Arrow tables. 
+    LanceDB supports Float16 data type!
 
-    tbl = db.create_table("my_table", table)
+    ```javascript
+    --8<-- "docs/src/basic_legacy.ts:create_f16_table"
     ```
 
 ### From Pydantic Models
+
 When you create an empty table without data, you must specify the table schema.
 LanceDB supports creating tables by specifying a PyArrow schema or a specialized
 Pydantic model called `LanceModel`.
@@ -261,37 +314,6 @@ This guide will show how to create tables, insert data into them, and update the
 
 You can also use iterators of other types like Pandas DataFrame or Pylists directly in the above example.
 
-=== "JavaScript"
-    Initialize a VectorDB connection and create a table using one of the many methods listed below.
-
-    ```javascript
-    const lancedb = require("vectordb");
-
-    const uri = "data/sample-lancedb";
-    const db = await lancedb.connect(uri);
-    ```
-
-    You can create a LanceDB table in JavaScript using an array of JSON records as follows.
-
-    ```javascript
-    const tb = await db.createTable("my_table", [{
-        "vector": [3.1, 4.1],
-        "item": "foo",
-        "price": 10.0
-    }, {
-        "vector": [5.9, 26.5],
-        "item": "bar",
-        "price": 20.0
-    }]);
-    ```
-
-    !!! info "Note"
-    If the table already exists, LanceDB will raise an error by default. If you want to overwrite the table, you need to specify the `WriteMode` in the createTable function.
-
-    ```javascript
-    const table = await con.createTable(tableName, data, { writeMode: WriteMode.Overwrite })
-    ```
-
 ## Open existing tables
 
 === "Python"
@@ -614,6 +636,70 @@ The `values` parameter is used to provide the new values for the columns as lite
 
     When rows are updated, they are moved out of the index. The row will still show up in ANN queries, but the query will not be as fast as it would be if the row was in the index. If you update a large proportion of rows, consider rebuilding the index afterwards.
 
+## Consistency
+
+In LanceDB OSS, users can set the `read_consistency_interval` parameter on connections to achieve different levels of read consistency. This parameter determines how frequently the database synchronizes with the underlying storage system to check for updates made by other processes. If another process updates a table, the database will not see the changes until the next synchronization.
+
+There are three possible settings for `read_consistency_interval`:
+
+1. **Unset (default)**: The database does not check for updates to tables made by other processes. This provides the best query performance, but means that clients may not see the most up-to-date data. This setting is suitable for applications where the data does not change during the lifetime of the table reference.
+2. **Zero seconds (Strong consistency)**: The database checks for updates on every read. This provides the strongest consistency guarantees, ensuring that all clients see the latest committed data. However, it has the most overhead. This setting is suitable when consistency matters more than having high QPS.
+3. **Custom interval (Eventual consistency)**: The database checks for updates at a custom interval, such as every 5 seconds. This provides eventual consistency, allowing for some lag between write and read operations. Performance wise, this is a middle ground between strong consistency and no consistency check. This setting is suitable for applications where immediate consistency is not critical, but clients should see updated data eventually.
+
+!!! tip "Consistency in LanceDB Cloud"
+
+    This is only tune-able in LanceDB OSS. In LanceDB Cloud, readers are always eventually consistent.
+
+=== "Python"
+    
+    To set strong consistency, use `timedelta(0)`:
+
+    ```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`:
+
+    ```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`:
+
+    ```python
+    db = lancedb.connect("./.lancedb")
+    table = db.open_table("my_table")
+    
+    # (Other writes happen to my_table from another process)
+
+    # Check for updates
+    table.checkout_latest()
+    ```
+
+=== "JavaScript/Typescript"
+
+    To set strong consistency, use `0`:
+
+    ```javascript
+    const db = await lancedb.connect({ uri: "./.lancedb", readConsistencyInterval: 0 });
+    const table = await db.openTable("my_table");
+    ```
+
+    For eventual consistency, specify the update interval as seconds:
+
+    ```javascript
+    const db = await lancedb.connect({ uri: "./.lancedb", readConsistencyInterval: 5 });
+    const table = await db.openTable("my_table");
+    ```
+
+<!-- Node doesn't yet support the version time travel: https://github.com/lancedb/lancedb/issues/1007 
+    Once it does, we can show manual consistency check for Node as well.
+-->
+
 ## What's next?
 
 Learn the best practices on creating an ANN index and getting the most out of it.

docs/src/hybrid_search/eval.md

@@ -0,0 +1,49 @@
+# Hybrid Search
+
+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.
+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>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, application specific so it's hard to generalize.
+
+### Example evaluation of hybrid search with Reranking
+
+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`
+
+| Reranker | Top-3 | Top-5 | Top-10 |
+| --- | --- | --- | --- |
+| Linear Combination | `0.73` | `0.74` | `0.85` |
+| Cross Encoder | `0.71` | `0.70` | `0.77` |
+| Cohere | `0.81` | `0.81` | `0.85` |
+| ColBERT | `0.68` | `0.68` | `0.73` |
+
+<p>
+<img src="https://github.com/AyushExel/assets/assets/15766192/d57b1780-ef27-414c-a5c3-73bee7808a45">
+</p>
+
+<b> With OpenAI embedding-v3-small </b>
+
+Vector Search baseline - `0.59`
+
+| Reranker | Top-3 | Top-5 | Top-10 |
+| --- | --- | --- | --- |
+| Linear Combination | `0.68` | `0.70` | `0.84` |
+| Cross Encoder | `0.72` | `0.72` | `0.79` |
+| Cohere | `0.79` | `0.79` | `0.84` |
+| ColBERT | `0.70` | `0.70` | `0.76` |
+
+<p>
+<img src="https://github.com/AyushExel/assets/assets/15766192/259adfd2-6ec6-4df6-a77d-1456598970dd">
+</p>
+
+### Conclusion
+
+The results show that the reranking methods are able to improve the search results. However, the improvement is not consistent across all rerankers. The choice of reranker depends on the dataset and the application. It is also important to note that the reranking methods are not a replacement for the search methods. They are complementary and should be used together to get the best results. The speed to recall tradeoff is also an important factor to consider when choosing the reranker.

docs/src/hybrid_search/hybrid_search.md

@@ -0,0 +1,242 @@
+# Hybrid Search
+
+LanceDB supports both semantic and keyword-based search (also termed full-text search, or FTS). In real world applications, it is often useful to combine these two approaches to get the best best results. For example, you may want to search for a document that is semantically similar to a query document, but also contains a specific keyword. This is an example of *hybrid search*, a search algorithm that combines multiple search techniques.
+
+## 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 .
+
+```python
+import os
+
+import lancedb
+import openai
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+
+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()
+```
+
+By default, LanceDB uses `LinearCombinationReranker(weight=0.7)` 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:
+
+
+### `rerank()` arguments
+* `normalize`: `str`, default `"score"`:
+    The method to normalize the scores. Can be "rank" or "score". If "rank", the scores are converted to ranks and then normalized. If "score", the scores are normalized directly.
+* `reranker`: `Reranker`, default `LinearCombinationReranker(weight=0.7)`.
+    The reranker to use. If not specified, the default reranker is used.
+
+
+## Available Rerankers
+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. Here's a list of available re-rankers:
+
+### Linear Combination Reranker
+This is the default re-ranker used by LanceDB. It combines the results of semantic and full-text search using a linear combination of the scores. The weights for the linear combination can be specified. It defaults to 0.7, i.e, 70% weight for semantic search and 30% weight for full-text search.
+
+
+```python
+from lancedb.rerankers import LinearCombinationReranker
+
+reranker = LinearCombinationReranker(weight=0.3) # Use 0.3 as the weight for vector search
+
+results = table.search("rebel", query_type="hybrid").rerank(reranker=reranker).to_pandas()
+```
+
+### Arguments
+----------------
+* `weight`: `float`, default `0.7`:
+    The weight to use for the semantic search score. The weight for the full-text search score is `1 - weights`.
+* `fill`: `float`, default `1.0`:
+        The score to give to results that are only in one of the two result sets.This is treated as penalty, so a higher value means a lower score.
+        TODO: We should just hardcode this-- its pretty confusing as we invert scores to calculate final score
+* `return_score` : str, default `"relevance"`
+        options are "relevance" or "all"
+        The type of score to return. If "relevance", will return only the `_relevance_score. If "all", will return all scores from the vector and FTS search along with the relevance score.
+
+### Cohere Reranker
+This re-ranker uses the [Cohere](https://cohere.ai/) API to combine the results of semantic and full-text search. You can use this re-ranker by passing `CohereReranker()` to the `rerank()` method. Note that you'll need to set the `COHERE_API_KEY` environment variable to use this re-ranker.
+
+```python
+from lancedb.rerankers import CohereReranker
+
+reranker = CohereReranker()
+
+results = table.search("vampire weekend", query_type="hybrid").rerank(reranker=reranker).to_pandas()
+```
+
+### Arguments
+----------------
+* `model_name` : str, default `"rerank-english-v2.0"`
+        The name of the cross encoder model to use. Available cohere models are:
+        - rerank-english-v2.0
+        - rerank-multilingual-v2.0
+* `column` : str, default `"text"`
+        The name of the column to use as input to the cross encoder model.
+* `top_n` : str, default `None`
+        The number of results to return. If None, will return all results.
+
+!!! Note
+    Only returns `_relevance_score`. Does not support `return_score = "all"`.
+
+### Cross Encoder Reranker
+This reranker uses the [Sentence Transformers](https://www.sbert.net/) library to combine the results of semantic and full-text search. You can use it by passing `CrossEncoderReranker()` to the `rerank()` method.
+
+```python
+from lancedb.rerankers import CrossEncoderReranker
+
+reranker = CrossEncoderReranker()
+
+results = table.search("harmony hall", query_type="hybrid").rerank(reranker=reranker).to_pandas()
+```
+
+
+### Arguments
+----------------
+* `model` : str, default `"cross-encoder/ms-marco-TinyBERT-L-6"`
+        The name of the cross encoder model to use. Available cross encoder models can be found [here](https://www.sbert.net/docs/pretrained_cross-encoders.html)
+* `column` : str, default `"text"`
+        The name of the column to use as input to the cross encoder model.
+* `device` : str, default `None`
+        The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu".
+
+!!! Note
+    Only returns `_relevance_score`. Does not support `return_score = "all"`.
+
+
+### ColBERT Reranker
+This reranker uses the ColBERT model to combine the results of semantic and full-text search. You can use it by passing `ColbertrReranker()` to the `rerank()` method. 
+
+ColBERT reranker model calculates relevance of given docs against the query and don't take existing fts and vector search scores into account, so it currently only supports `return_score="relevance"`. By default, it looks for `text` column to rerank the results. But you can specify the column name to use as input to the cross encoder model as described below.
+
+```python
+from lancedb.rerankers import ColbertReranker
+
+reranker = ColbertReranker()
+
+results = table.search("harmony hall", query_type="hybrid").rerank(reranker=reranker).to_pandas()
+```
+
+### Arguments
+----------------
+* `model_name` : `str`, default `"colbert-ir/colbertv2.0"`
+        The name of the cross encoder model to use.
+* `column` : `str`, default `"text"`
+        The name of the column to use as input to the cross encoder model.
+* `return_score` : `str`, default `"relevance"`
+        options are `"relevance"` or `"all"`. Only `"relevance"` is supported for now.
+
+!!! Note
+    Only returns `_relevance_score`. Does not support `return_score = "all"`.
+
+### OpenAI Reranker
+This reranker uses the OpenAI API to combine the results of semantic and full-text search. You can use it by passing `OpenaiReranker()` to the `rerank()` method.
+
+!!! Note
+    This prompts chat model to rerank results which is not a dedicated reranker model. This should be treated as experimental.
+
+!!! Tip
+    - You might run out of token limit so set the search `limits` based on your token limit.
+    - It is recommended to use gpt-4-turbo-preview, the default model, older models might lead to undesired behaviour
+
+```python
+from lancedb.rerankers import OpenaiReranker
+
+reranker = OpenaiReranker()
+
+results = table.search("harmony hall", query_type="hybrid").rerank(reranker=reranker).to_pandas()
+```
+
+### Arguments
+----------------
+* `model_name` : `str`, default `"gpt-4-turbo-preview"`
+    The name of the cross encoder model to use.
+* `column` : `str`, default `"text"`
+    The name of the column to use as input to the cross encoder model.
+* `return_score` : `str`, default `"relevance"`
+    options are "relevance" or "all". Only "relevance" is supported for now.
+* `api_key` : `str`, default `None`
+    The API key to use. If None, will use the OPENAI_API_KEY environment variable.
+
+
+## Building Custom Rerankers
+You can build your own custom reranker by subclassing the `Reranker` class and implementing the `rerank_hybrid()` method. Here's an example of a custom reranker that combines the results of semantic and full-text search using a linear combination of the scores.
+
+The `Reranker` base interface comes with a `merge_results()` method that can be used to combine the results of semantic and full-text search. This is a vanilla merging algorithm that simply concatenates the results and removes the duplicates without taking the scores into consideration. It only keeps the first copy of the row encountered. This works well in cases that don't require the scores of semantic and full-text search to combine the results. If you want to use the scores or want to support `return_score="all"`, you'll need to implement your own merging algorithm.
+
+```python
+
+from lancedb.rerankers import Reranker
+import pyarrow as pa
+
+class MyReranker(Reranker):
+    def __init__(self, param1, param2, ..., return_score="relevance"):
+        super().__init__(return_score)
+        self.param1 = param1
+        self.param2 = param2
+    
+    def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table):
+        # Use the built-in merging function
+        combined_result = self.merge_results(vector_results, fts_results)
+        
+        # Do something with the combined results
+        # ...
+
+        # Return the combined results
+        return combined_result
+
+```
+
+### Example of a Custom Reranker
+For the sake of simplicity let's build custom reranker that just enchances the Cohere Reranker by accepting a filter query, and accept other CohereReranker params as kwags.
+
+```python
+
+from typing import List, Union
+import pandas as pd
+from lancedb.rerankers import CohereReranker
+
+class MofidifiedCohereReranker(CohereReranker):
+    def __init__(self, filters: Union[str, List[str]], **kwargs):
+        super().__init__(**kwargs)
+        filters = filters if isinstance(filters, list) else [filters]
+        self.filters = filters
+
+    def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table)-> pa.Table:
+        combined_result = super().rerank_hybrid(query, vector_results, fts_results)
+        df = combined_result.to_pandas()
+        for filter in self.filters:
+            df = df.query("not text.str.contains(@filter)")
+
+        return pa.Table.from_pandas(df)
+
+```
+
+!!! tip
+    The `vector_results` and `fts_results` are pyarrow tables. You can convert them to pandas dataframes using `to_pandas()` method and perform any operations you want. After you are done, you can convert the dataframe back to pyarrow table using `pa.Table.from_pandas()` method and return it.

docs/src/index.md

@@ -28,7 +28,7 @@ LanceDB **Cloud** is a SaaS (software-as-a-service) solution that runs serverles
 
 * Fast production-scale vector similarity, full-text & hybrid search and a SQL query interface (via [DataFusion](https://github.com/apache/arrow-datafusion))
 
-* Native Python and Javascript/Typescript support
+* Python, Javascript/Typescript, and Rust support
 
 * Store, query & manage multi-modal data (text, images, videos, point clouds, etc.), not just the embeddings and metadata
 
@@ -54,3 +54,4 @@ The following pages go deeper into the internal of LanceDB and how to use it.
 * [Ecosystem Integrations](integrations/index.md): Integrate LanceDB with other tools in the data ecosystem
 * [Python API Reference](python/python.md): Python OSS and Cloud API references
 * [JavaScript API Reference](javascript/modules.md): JavaScript OSS and Cloud API references
+* [Rust API Reference](https://docs.rs/lancedb/latest/lancedb/index.html): Rust API reference

docs/src/integrations/index.md

@@ -13,7 +13,7 @@ Get started using these examples and quick links.
 | Integrations | |
 |---|---:|
 | <h3> LlamaIndex </h3>LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. Llama index integrates with LanceDB as the serverless VectorDB. <h3>[Lean More](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html) </h3> |<img src="../assets/llama-index.jpg" alt="image" width="150" height="auto">|
-| <h3>Langchain</h3>Langchain allows building applications with LLMs through composability <h3>[Lean More](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
+| <h3>Langchain</h3>Langchain allows building applications with LLMs through composability <h3>[Lean More](https://python.langchain.com/docs/integrations/vectorstores/lancedb) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
 | <h3>Langchain TS</h3> Javascript bindings for Langchain. It integrates with LanceDB's serverless vectordb allowing you to build powerful AI applications through composibility using only serverless functions. <h3>[Learn More]( https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
 | <h3>Voxel51</h3>  It is an open source toolkit that enables you to build better computer vision workflows by improving the quality of your datasets and delivering insights about your models.<h3>[Learn More](./voxel51.md) | <img src="../assets/voxel.gif" alt="image" width="150" height="auto">|
 | <h3>PromptTools</h3>  Offers a set of free, open-source tools for testing and experimenting with models, prompts, and configurations. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks. You can use it to experiment with different configurations of LanceDB, and test how LanceDB integrates with the LLM of your choice.<h3>[Learn More](./prompttools.md) | <img src="../assets/prompttools.jpeg" alt="image" width="150" height="auto">|

docs/src/javascript.md

@@ -1,62 +0,0 @@
-# Javascript API Reference
-
-This section contains the API reference for LanceDB Javascript API.
-
-## Installation
-
-```bash
-npm install vectordb
-```
-
-This will download the appropriate native library for your platform. We currently
-support:
-
-* Linux (x86_64 and aarch64)
-* MacOS (Intel and ARM/M1/M2)
-* Windows (x86_64 only)
-
-We do not yet support musl-based Linux (such as Alpine Linux) or arch64 Windows.
-
-## Usage
-
-### Basic Example
-Connect to a local directory
-```javascript
-const lancedb = require('vectordb');
-//connect to a local database
-const db = await lancedb.connect('data/sample-lancedb');
-```
-Connect to LancdDB cloud
-```javascript
-connect to LanceDB Cloud
-const db = await lancedb.connect({
-    uri: "db://my-database",
-    apiKey: "sk_...",
-    region: "us-east-1"
-});
-```
-Create a table followed by a search
-```javascript
-const table = await db.createTable("my_table",
-      [{ id: 1, vector: [0.1, 1.0], item: "foo", price: 10.0 },
-      { id: 2, vector: [3.9, 0.5], item: "bar", price: 20.0 }])
-const results = await table.search([0.1, 0.3]).limit(20).execute();
-console.log(results);
-```
-
-The [examples](./examples) folder contains complete examples.
-
-## Table of contents
-### Connection
-Connect to a LanceDB database.
-
-- [Connection](interfaces/Connection.md)
-### Table
-A Table is a collection of Records in a LanceDB Database.
-
-- [Table](interfaces/Table.md)
-### Query
-The LanceDB Query
-
-- [Query](classes/Query.md)
-

docs/src/javascript/classes/DefaultWriteOptions.md

@@ -38,4 +38,4 @@ A [WriteMode](../enums/WriteMode.md) to use on this operation
 
 #### Defined in
 
-[index.ts:1070](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1070)
+[index.ts:1019](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1019)

docs/src/javascript/classes/LocalConnection.md

@@ -46,7 +46,7 @@ A connection to a LanceDB database.
 
 #### Defined in
 
-[index.ts:496](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L496)
+[index.ts:489](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L489)
 
 ## Properties
 
@@ -56,7 +56,7 @@ A connection to a LanceDB database.
 
 #### Defined in
 
-[index.ts:494](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L494)
+[index.ts:487](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L487)
 
 ___
 
@@ -74,7 +74,7 @@ ___
 
 #### Defined in
 
-[index.ts:493](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L493)
+[index.ts:486](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L486)
 
 ## Accessors
 
@@ -92,7 +92,7 @@ ___
 
 #### Defined in
 
-[index.ts:501](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L501)
+[index.ts:494](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L494)
 
 ## Methods
 
@@ -113,7 +113,7 @@ Creates a new Table, optionally initializing it with new data.
 | Name | Type |
 | :------ | :------ |
 | `name` | `string` \| [`CreateTableOptions`](../interfaces/CreateTableOptions.md)\<`T`\> |
-| `data?` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] |
+| `data?` | `Record`\<`string`, `unknown`\>[] |
 | `optsOrEmbedding?` | [`WriteOptions`](../interfaces/WriteOptions.md) \| [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)\<`T`\> |
 | `opt?` | [`WriteOptions`](../interfaces/WriteOptions.md) |
 
@@ -127,7 +127,7 @@ Creates a new Table, optionally initializing it with new data.
 
 #### Defined in
 
-[index.ts:549](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L549)
+[index.ts:542](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L542)
 
 ___
 
@@ -158,7 +158,7 @@ ___
 
 #### Defined in
 
-[index.ts:583](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L583)
+[index.ts:576](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L576)
 
 ___
 
@@ -184,7 +184,7 @@ Drop an existing table.
 
 #### Defined in
 
-[index.ts:637](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L637)
+[index.ts:630](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L630)
 
 ___
 
@@ -210,7 +210,7 @@ Open a table in the database.
 
 #### Defined in
 
-[index.ts:517](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L517)
+[index.ts:510](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L510)
 
 ▸ **openTable**\<`T`\>(`name`, `embeddings`): `Promise`\<[`Table`](../interfaces/Table.md)\<`T`\>\>
 
@@ -239,7 +239,7 @@ Connection.openTable
 
 #### Defined in
 
-[index.ts:525](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L525)
+[index.ts:518](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L518)
 
 ▸ **openTable**\<`T`\>(`name`, `embeddings?`): `Promise`\<[`Table`](../interfaces/Table.md)\<`T`\>\>
 
@@ -266,7 +266,7 @@ Connection.openTable
 
 #### Defined in
 
-[index.ts:529](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L529)
+[index.ts:522](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L522)
 
 ___
 
@@ -286,4 +286,4 @@ Get the names of all tables in the database.
 
 #### Defined in
 
-[index.ts:508](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L508)
+[index.ts:501](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L501)

docs/src/javascript/classes/LocalTable.md

@@ -74,7 +74,7 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 
 #### Defined in
 
-[index.ts:649](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L649)
+[index.ts:642](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L642)
 
 • **new LocalTable**\<`T`\>(`tbl`, `name`, `options`, `embeddings`)
 
@@ -95,7 +95,7 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 
 #### Defined in
 
-[index.ts:656](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L656)
+[index.ts:649](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L649)
 
 ## Properties
 
@@ -105,7 +105,7 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 
 #### Defined in
 
-[index.ts:646](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L646)
+[index.ts:639](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L639)
 
 ___
 
@@ -115,7 +115,7 @@ ___
 
 #### Defined in
 
-[index.ts:645](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L645)
+[index.ts:638](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L638)
 
 ___
 
@@ -125,7 +125,7 @@ ___
 
 #### Defined in
 
-[index.ts:644](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L644)
+[index.ts:637](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L637)
 
 ___
 
@@ -143,7 +143,7 @@ ___
 
 #### Defined in
 
-[index.ts:647](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L647)
+[index.ts:640](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L640)
 
 ___
 
@@ -153,7 +153,7 @@ ___
 
 #### Defined in
 
-[index.ts:643](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L643)
+[index.ts:636](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L636)
 
 ___
 
@@ -179,7 +179,7 @@ Creates a filter query to find all rows matching the specified criteria
 
 #### Defined in
 
-[index.ts:695](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L695)
+[index.ts:688](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L688)
 
 ## Accessors
 
@@ -197,7 +197,7 @@ Creates a filter query to find all rows matching the specified criteria
 
 #### Defined in
 
-[index.ts:675](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L675)
+[index.ts:668](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L668)
 
 ___
 
@@ -215,7 +215,7 @@ ___
 
 #### Defined in
 
-[index.ts:875](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L875)
+[index.ts:849](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L849)
 
 ## Methods
 
@@ -229,7 +229,7 @@ Insert records into this Table.
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
 
 #### Returns
 
@@ -243,7 +243,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:703](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L703)
+[index.ts:696](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L696)
 
 ___
 
@@ -257,7 +257,7 @@ ___
 
 #### Defined in
 
-[index.ts:887](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L887)
+[index.ts:861](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L861)
 
 ___
 
@@ -267,8 +267,6 @@ ___
 
 Clean up old versions of the table, freeing disk space.
 
-Note: this API is not yet available on LanceDB Cloud
-
 #### Parameters
 
 | Name | Type | Description |
@@ -282,7 +280,7 @@ Note: this API is not yet available on LanceDB Cloud
 
 #### Defined in
 
-[index.ts:833](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L833)
+[index.ts:808](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L808)
 
 ___
 
@@ -295,8 +293,6 @@ Run the compaction process on the table.
 This can be run after making several small appends to optimize the table
 for faster reads.
 
-Note: this API is not yet available on LanceDB Cloud
-
 #### Parameters
 
 | Name | Type | Description |
@@ -311,7 +307,7 @@ Metrics about the compaction operation.
 
 #### Defined in
 
-[index.ts:857](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L857)
+[index.ts:831](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L831)
 
 ___
 
@@ -331,7 +327,7 @@ Returns the number of rows in this table.
 
 #### Defined in
 
-[index.ts:773](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L773)
+[index.ts:749](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L749)
 
 ___
 
@@ -361,7 +357,7 @@ VectorIndexParams.
 
 #### Defined in
 
-[index.ts:758](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L758)
+[index.ts:734](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L734)
 
 ___
 
@@ -396,7 +392,7 @@ await table.createScalarIndex('my_col')
 
 #### Defined in
 
-[index.ts:766](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L766)
+[index.ts:742](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L742)
 
 ___
 
@@ -422,7 +418,7 @@ Delete rows from this table.
 
 #### Defined in
 
-[index.ts:782](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L782)
+[index.ts:758](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L758)
 
 ___
 
@@ -444,7 +440,7 @@ Creates a filter query to find all rows matching the specified criteria
 
 #### Defined in
 
-[index.ts:691](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L691)
+[index.ts:684](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L684)
 
 ___
 
@@ -458,7 +454,7 @@ ___
 
 #### Defined in
 
-[index.ts:880](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L880)
+[index.ts:854](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L854)
 
 ___
 
@@ -484,7 +480,7 @@ Get statistics about an index.
 
 #### Defined in
 
-[index.ts:871](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L871)
+[index.ts:845](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L845)
 
 ___
 
@@ -504,7 +500,7 @@ List the indicies on this table.
 
 #### Defined in
 
-[index.ts:867](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L867)
+[index.ts:841](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L841)
 
 ___
 
@@ -518,7 +514,7 @@ Insert records into this Table, replacing its contents.
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table Type Table is ArrowTable |
+| `data` | `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
 
 #### Returns
 
@@ -532,7 +528,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:732](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L732)
+[index.ts:716](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L716)
 
 ___
 
@@ -558,7 +554,7 @@ Creates a search query to find the nearest neighbors of the given search term
 
 #### Defined in
 
-[index.ts:683](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L683)
+[index.ts:676](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L676)
 
 ___
 
@@ -584,4 +580,4 @@ Update rows in this table.
 
 #### Defined in
 
-[index.ts:795](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L795)
+[index.ts:771](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L771)

docs/src/javascript/classes/MakeArrowTableOptions.md

@@ -1,56 +0,0 @@
-[vectordb](../README.md) / [Exports](../modules.md) / MakeArrowTableOptions
-
-# Class: MakeArrowTableOptions
-
-Options to control the makeArrowTable call.
-
-## Table of contents
-
-### Constructors
-
-- [constructor](MakeArrowTableOptions.md#constructor)
-
-### Properties
-
-- [schema](MakeArrowTableOptions.md#schema)
-- [vectorColumns](MakeArrowTableOptions.md#vectorcolumns)
-
-## Constructors
-
-### constructor
-
-• **new MakeArrowTableOptions**(`values?`)
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `values?` | `Partial`\<[`MakeArrowTableOptions`](MakeArrowTableOptions.md)\> |
-
-#### Defined in
-
-[arrow.ts:56](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/arrow.ts#L56)
-
-## Properties
-
-### schema
-
-• `Optional` **schema**: `Schema`\<`any`\>
-
-Provided schema.
-
-#### Defined in
-
-[arrow.ts:49](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/arrow.ts#L49)
-
-___
-
-### vectorColumns
-
-• **vectorColumns**: `Record`\<`string`, `VectorColumnOptions`\>
-
-Vector columns
-
-#### Defined in
-
-[arrow.ts:52](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/arrow.ts#L52)

docs/src/javascript/classes/OpenAIEmbeddingFunction.md

@@ -40,7 +40,7 @@ An embedding function that automatically creates vector representation for a giv
 
 #### Defined in
 
-[embedding/openai.ts:22](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/embedding/openai.ts#L22)
+[embedding/openai.ts:21](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/openai.ts#L21)
 
 ## Properties
 
@@ -50,17 +50,17 @@ An embedding function that automatically creates vector representation for a giv
 
 #### Defined in
 
-[embedding/openai.ts:20](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/embedding/openai.ts#L20)
+[embedding/openai.ts:19](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/openai.ts#L19)
 
 ___
 
 ### \_openai
 
-• `Private` `Readonly` **\_openai**: `OpenAI`
+• `Private` `Readonly` **\_openai**: `any`
 
 #### Defined in
 
-[embedding/openai.ts:19](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/embedding/openai.ts#L19)
+[embedding/openai.ts:18](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/openai.ts#L18)
 
 ___
 
@@ -76,7 +76,7 @@ The name of the column that will be used as input for the Embedding Function.
 
 #### Defined in
 
-[embedding/openai.ts:56](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/embedding/openai.ts#L56)
+[embedding/openai.ts:50](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/openai.ts#L50)
 
 ## Methods
 
@@ -102,4 +102,4 @@ Creates a vector representation for the given values.
 
 #### Defined in
 
-[embedding/openai.ts:43](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/embedding/openai.ts#L43)
+[embedding/openai.ts:38](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/openai.ts#L38)

docs/src/javascript/classes/Query.md

@@ -65,7 +65,7 @@ A builder for nearest neighbor queries for LanceDB.
 
 #### Defined in
 
-[query.ts:38](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L38)
+[query.ts:38](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L38)
 
 ## Properties
 
@@ -75,7 +75,7 @@ A builder for nearest neighbor queries for LanceDB.
 
 #### Defined in
 
-[query.ts:36](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L36)
+[query.ts:36](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L36)
 
 ___
 
@@ -85,7 +85,7 @@ ___
 
 #### Defined in
 
-[query.ts:33](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L33)
+[query.ts:33](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L33)
 
 ___
 
@@ -95,7 +95,7 @@ ___
 
 #### Defined in
 
-[query.ts:29](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L29)
+[query.ts:29](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L29)
 
 ___
 
@@ -105,7 +105,7 @@ ___
 
 #### Defined in
 
-[query.ts:34](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L34)
+[query.ts:34](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L34)
 
 ___
 
@@ -115,7 +115,7 @@ ___
 
 #### Defined in
 
-[query.ts:31](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L31)
+[query.ts:31](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L31)
 
 ___
 
@@ -125,7 +125,7 @@ ___
 
 #### Defined in
 
-[query.ts:35](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L35)
+[query.ts:35](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L35)
 
 ___
 
@@ -135,7 +135,7 @@ ___
 
 #### Defined in
 
-[query.ts:26](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L26)
+[query.ts:26](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L26)
 
 ___
 
@@ -145,7 +145,7 @@ ___
 
 #### Defined in
 
-[query.ts:28](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L28)
+[query.ts:28](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L28)
 
 ___
 
@@ -155,7 +155,7 @@ ___
 
 #### Defined in
 
-[query.ts:30](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L30)
+[query.ts:30](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L30)
 
 ___
 
@@ -165,7 +165,7 @@ ___
 
 #### Defined in
 
-[query.ts:32](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L32)
+[query.ts:32](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L32)
 
 ___
 
@@ -175,7 +175,7 @@ ___
 
 #### Defined in
 
-[query.ts:27](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L27)
+[query.ts:27](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L27)
 
 ___
 
@@ -201,7 +201,7 @@ A filter statement to be applied to this query.
 
 #### Defined in
 
-[query.ts:87](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L87)
+[query.ts:87](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L87)
 
 ## Methods
 
@@ -223,7 +223,7 @@ Execute the query and return the results as an Array of Objects
 
 #### Defined in
 
-[query.ts:115](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L115)
+[query.ts:115](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L115)
 
 ___
 
@@ -245,7 +245,7 @@ A filter statement to be applied to this query.
 
 #### Defined in
 
-[query.ts:82](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L82)
+[query.ts:82](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L82)
 
 ___
 
@@ -259,7 +259,7 @@ ___
 
 #### Defined in
 
-[query.ts:143](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L143)
+[query.ts:142](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L142)
 
 ___
 
@@ -281,7 +281,7 @@ Sets the number of results that will be returned
 
 #### Defined in
 
-[query.ts:55](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L55)
+[query.ts:55](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L55)
 
 ___
 
@@ -307,7 +307,7 @@ MetricType for the different options
 
 #### Defined in
 
-[query.ts:102](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L102)
+[query.ts:102](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L102)
 
 ___
 
@@ -329,7 +329,7 @@ The number of probes used. A higher number makes search more accurate but also s
 
 #### Defined in
 
-[query.ts:73](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L73)
+[query.ts:73](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L73)
 
 ___
 
@@ -349,7 +349,7 @@ ___
 
 #### Defined in
 
-[query.ts:107](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L107)
+[query.ts:107](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L107)
 
 ___
 
@@ -371,7 +371,7 @@ Refine the results by reading extra elements and re-ranking them in memory.
 
 #### Defined in
 
-[query.ts:64](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L64)
+[query.ts:64](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L64)
 
 ___
 
@@ -393,4 +393,4 @@ Return only the specified columns.
 
 #### Defined in
 
-[query.ts:93](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/query.ts#L93)
+[query.ts:93](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L93)

docs/src/javascript/classes/RemoteConnection.md

@@ -1,224 +0,0 @@
-[vectordb](../README.md) / [Exports](../modules.md) / RemoteConnection
-
-# Class: RemoteConnection
-
-Remote connection.
-
-## Implements
-
-- [`Connection`](../interfaces/Connection.md)
-
-## Table of contents
-
-### Constructors
-
-- [constructor](RemoteConnection.md#constructor)
-
-### Properties
-
-- [\_client](RemoteConnection.md#_client)
-- [\_dbName](RemoteConnection.md#_dbname)
-
-### Accessors
-
-- [uri](RemoteConnection.md#uri)
-
-### Methods
-
-- [createTable](RemoteConnection.md#createtable)
-- [dropTable](RemoteConnection.md#droptable)
-- [openTable](RemoteConnection.md#opentable)
-- [tableNames](RemoteConnection.md#tablenames)
-
-## Constructors
-
-### constructor
-
-• **new RemoteConnection**(`opts`)
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `opts` | [`ConnectionOptions`](../interfaces/ConnectionOptions.md) |
-
-#### Defined in
-
-[remote/index.ts:48](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L48)
-
-## Properties
-
-### \_client
-
-• `Private` `Readonly` **\_client**: `HttpLancedbClient`
-
-#### Defined in
-
-[remote/index.ts:45](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L45)
-
-___
-
-### \_dbName
-
-• `Private` `Readonly` **\_dbName**: `string`
-
-#### Defined in
-
-[remote/index.ts:46](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L46)
-
-## Accessors
-
-### uri
-
-• `get` **uri**(): `string`
-
-#### Returns
-
-`string`
-
-#### Implementation of
-
-[Connection](../interfaces/Connection.md).[uri](../interfaces/Connection.md#uri)
-
-#### Defined in
-
-[remote/index.ts:75](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L75)
-
-## Methods
-
-### createTable
-
-▸ **createTable**\<`T`\>(`nameOrOpts`, `data?`, `optsOrEmbedding?`, `opt?`): `Promise`\<[`Table`](../interfaces/Table.md)\<`T`\>\>
-
-Creates a new Table, optionally initializing it with new data.
-
-#### Type parameters
-
-| Name |
-| :------ |
-| `T` |
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `nameOrOpts` | `string` \| [`CreateTableOptions`](../interfaces/CreateTableOptions.md)\<`T`\> |
-| `data?` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] |
-| `optsOrEmbedding?` | [`WriteOptions`](../interfaces/WriteOptions.md) \| [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)\<`T`\> |
-| `opt?` | [`WriteOptions`](../interfaces/WriteOptions.md) |
-
-#### Returns
-
-`Promise`\<[`Table`](../interfaces/Table.md)\<`T`\>\>
-
-#### Implementation of
-
-[Connection](../interfaces/Connection.md).[createTable](../interfaces/Connection.md#createtable)
-
-#### Defined in
-
-[remote/index.ts:107](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L107)
-
-___
-
-### dropTable
-
-▸ **dropTable**(`name`): `Promise`\<`void`\>
-
-Drop an existing table.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `name` | `string` | The name of the table to drop. |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-#### Implementation of
-
-[Connection](../interfaces/Connection.md).[dropTable](../interfaces/Connection.md#droptable)
-
-#### Defined in
-
-[remote/index.ts:175](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L175)
-
-___
-
-### openTable
-
-▸ **openTable**(`name`): `Promise`\<[`Table`](../interfaces/Table.md)\<`number`[]\>\>
-
-Open a table in the database.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `name` | `string` | The name of the table. |
-
-#### Returns
-
-`Promise`\<[`Table`](../interfaces/Table.md)\<`number`[]\>\>
-
-#### Implementation of
-
-[Connection](../interfaces/Connection.md).[openTable](../interfaces/Connection.md#opentable)
-
-#### Defined in
-
-[remote/index.ts:91](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L91)
-
-▸ **openTable**\<`T`\>(`name`, `embeddings`): `Promise`\<[`Table`](../interfaces/Table.md)\<`T`\>\>
-
-#### Type parameters
-
-| Name |
-| :------ |
-| `T` |
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `name` | `string` |
-| `embeddings` | [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)\<`T`\> |
-
-#### Returns
-
-`Promise`\<[`Table`](../interfaces/Table.md)\<`T`\>\>
-
-#### Implementation of
-
-Connection.openTable
-
-#### Defined in
-
-[remote/index.ts:92](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L92)
-
-___
-
-### tableNames
-
-▸ **tableNames**(`pageToken?`, `limit?`): `Promise`\<`string`[]\>
-
-#### Parameters
-
-| Name | Type | Default value |
-| :------ | :------ | :------ |
-| `pageToken` | `string` | `''` |
-| `limit` | `number` | `10` |
-
-#### Returns
-
-`Promise`\<`string`[]\>
-
-#### Implementation of
-
-[Connection](../interfaces/Connection.md).[tableNames](../interfaces/Connection.md#tablenames)
-
-#### Defined in
-
-[remote/index.ts:80](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L80)

docs/src/javascript/classes/RemoteTable.md

@@ -1,470 +0,0 @@
-[vectordb](../README.md) / [Exports](../modules.md) / RemoteTable
-
-# Class: RemoteTable\<T\>
-
-A LanceDB Table is the collection of Records. Each Record has one or more vector fields.
-
-## Type parameters
-
-| Name | Type |
-| :------ | :------ |
-| `T` | `number`[] |
-
-## Implements
-
-- [`Table`](../interfaces/Table.md)\<`T`\>
-
-## Table of contents
-
-### Constructors
-
-- [constructor](RemoteTable.md#constructor)
-
-### Properties
-
-- [\_client](RemoteTable.md#_client)
-- [\_embeddings](RemoteTable.md#_embeddings)
-- [\_name](RemoteTable.md#_name)
-
-### Accessors
-
-- [name](RemoteTable.md#name)
-- [schema](RemoteTable.md#schema)
-
-### Methods
-
-- [add](RemoteTable.md#add)
-- [countRows](RemoteTable.md#countrows)
-- [createIndex](RemoteTable.md#createindex)
-- [createScalarIndex](RemoteTable.md#createscalarindex)
-- [delete](RemoteTable.md#delete)
-- [indexStats](RemoteTable.md#indexstats)
-- [listIndices](RemoteTable.md#listindices)
-- [overwrite](RemoteTable.md#overwrite)
-- [search](RemoteTable.md#search)
-- [update](RemoteTable.md#update)
-
-## Constructors
-
-### constructor
-
-• **new RemoteTable**\<`T`\>(`client`, `name`)
-
-#### Type parameters
-
-| Name | Type |
-| :------ | :------ |
-| `T` | `number`[] |
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `client` | `HttpLancedbClient` |
-| `name` | `string` |
-
-#### Defined in
-
-[remote/index.ts:234](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L234)
-
-• **new RemoteTable**\<`T`\>(`client`, `name`, `embeddings`)
-
-#### Type parameters
-
-| Name | Type |
-| :------ | :------ |
-| `T` | `number`[] |
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `client` | `HttpLancedbClient` |
-| `name` | `string` |
-| `embeddings` | [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)\<`T`\> |
-
-#### Defined in
-
-[remote/index.ts:235](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L235)
-
-## Properties
-
-### \_client
-
-• `Private` `Readonly` **\_client**: `HttpLancedbClient`
-
-#### Defined in
-
-[remote/index.ts:230](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L230)
-
-___
-
-### \_embeddings
-
-• `Private` `Optional` `Readonly` **\_embeddings**: [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)\<`T`\>
-
-#### Defined in
-
-[remote/index.ts:231](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L231)
-
-___
-
-### \_name
-
-• `Private` `Readonly` **\_name**: `string`
-
-#### Defined in
-
-[remote/index.ts:232](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L232)
-
-## Accessors
-
-### name
-
-• `get` **name**(): `string`
-
-#### Returns
-
-`string`
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[name](../interfaces/Table.md#name)
-
-#### Defined in
-
-[remote/index.ts:250](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L250)
-
-___
-
-### schema
-
-• `get` **schema**(): `Promise`\<`any`\>
-
-#### Returns
-
-`Promise`\<`any`\>
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[schema](../interfaces/Table.md#schema)
-
-#### Defined in
-
-[remote/index.ts:254](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L254)
-
-## Methods
-
-### add
-
-▸ **add**(`data`): `Promise`\<`number`\>
-
-Insert records into this Table.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
-
-#### Returns
-
-`Promise`\<`number`\>
-
-The number of rows added to the table
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[add](../interfaces/Table.md#add)
-
-#### Defined in
-
-[remote/index.ts:273](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L273)
-
-___
-
-### countRows
-
-▸ **countRows**(): `Promise`\<`number`\>
-
-Returns the number of rows in this table.
-
-#### Returns
-
-`Promise`\<`number`\>
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[countRows](../interfaces/Table.md#countrows)
-
-#### Defined in
-
-[remote/index.ts:372](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L372)
-
-___
-
-### createIndex
-
-▸ **createIndex**(`indexParams`): `Promise`\<`void`\>
-
-Create an ANN index on this Table vector index.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `indexParams` | [`IvfPQIndexConfig`](../interfaces/IvfPQIndexConfig.md) | The parameters of this Index, |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-**`See`**
-
-VectorIndexParams.
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[createIndex](../interfaces/Table.md#createindex)
-
-#### Defined in
-
-[remote/index.ts:326](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L326)
-
-___
-
-### createScalarIndex
-
-▸ **createScalarIndex**(`column`, `replace`): `Promise`\<`void`\>
-
-Create a scalar index on this Table for the given column
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `column` | `string` | The column to index |
-| `replace` | `boolean` | If false, fail if an index already exists on the column Scalar indices, like vector indices, can be used to speed up scans. A scalar index can speed up scans that contain filter expressions on the indexed column. For example, the following scan will be faster if the column `my_col` has a scalar index: ```ts const con = await lancedb.connect('./.lancedb'); const table = await con.openTable('images'); const results = await table.where('my_col = 7').execute(); ``` Scalar indices can also speed up scans containing a vector search and a prefilter: ```ts const con = await lancedb.connect('././lancedb'); const table = await con.openTable('images'); const results = await table.search([1.0, 2.0]).where('my_col != 7').prefilter(true); ``` Scalar indices can only speed up scans for basic filters using equality, comparison, range (e.g. `my_col BETWEEN 0 AND 100`), and set membership (e.g. `my_col IN (0, 1, 2)`) Scalar indices can be used if the filter contains multiple indexed columns and the filter criteria are AND'd or OR'd together (e.g. `my_col < 0 AND other_col> 100`) Scalar indices may be used if the filter contains non-indexed columns but, depending on the structure of the filter, they may not be usable. For example, if the column `not_indexed` does not have a scalar index then the filter `my_col = 0 OR not_indexed = 1` will not be able to use any scalar index on `my_col`. |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-**`Examples`**
-
-```ts
-const con = await lancedb.connect('././lancedb')
-const table = await con.openTable('images')
-await table.createScalarIndex('my_col')
-```
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[createScalarIndex](../interfaces/Table.md#createscalarindex)
-
-#### Defined in
-
-[remote/index.ts:368](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L368)
-
-___
-
-### delete
-
-▸ **delete**(`filter`): `Promise`\<`void`\>
-
-Delete rows from this table.
-
-This can be used to delete a single row, many rows, all rows, or
-sometimes no rows (if your predicate matches nothing).
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `filter` | `string` | A filter in the same format used by a sql WHERE clause. The filter must not be empty. |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-**`Examples`**
-
-```ts
-const con = await lancedb.connect("./.lancedb")
-const data = [
-   {id: 1, vector: [1, 2]},
-   {id: 2, vector: [3, 4]},
-   {id: 3, vector: [5, 6]},
-];
-const tbl = await con.createTable("my_table", data)
-await tbl.delete("id = 2")
-await tbl.countRows() // Returns 2
-```
-
-If you have a list of values to delete, you can combine them into a
-stringified list and use the `IN` operator:
-
-```ts
-const to_remove = [1, 5];
-await tbl.delete(`id IN (${to_remove.join(",")})`)
-await tbl.countRows() // Returns 1
-```
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[delete](../interfaces/Table.md#delete)
-
-#### Defined in
-
-[remote/index.ts:377](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L377)
-
-___
-
-### indexStats
-
-▸ **indexStats**(`indexUuid`): `Promise`\<[`IndexStats`](../interfaces/IndexStats.md)\>
-
-Get statistics about an index.
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `indexUuid` | `string` |
-
-#### Returns
-
-`Promise`\<[`IndexStats`](../interfaces/IndexStats.md)\>
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[indexStats](../interfaces/Table.md#indexstats)
-
-#### Defined in
-
-[remote/index.ts:414](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L414)
-
-___
-
-### listIndices
-
-▸ **listIndices**(): `Promise`\<[`VectorIndex`](../interfaces/VectorIndex.md)[]\>
-
-List the indicies on this table.
-
-#### Returns
-
-`Promise`\<[`VectorIndex`](../interfaces/VectorIndex.md)[]\>
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[listIndices](../interfaces/Table.md#listindices)
-
-#### Defined in
-
-[remote/index.ts:403](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L403)
-
-___
-
-### overwrite
-
-▸ **overwrite**(`data`): `Promise`\<`number`\>
-
-Insert records into this Table, replacing its contents.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
-
-#### Returns
-
-`Promise`\<`number`\>
-
-The number of rows added to the table
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[overwrite](../interfaces/Table.md#overwrite)
-
-#### Defined in
-
-[remote/index.ts:300](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L300)
-
-___
-
-### search
-
-▸ **search**(`query`): [`Query`](Query.md)\<`T`\>
-
-Creates a search query to find the nearest neighbors of the given search term
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `query` | `T` | The query search term |
-
-#### Returns
-
-[`Query`](Query.md)\<`T`\>
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[search](../interfaces/Table.md#search)
-
-#### Defined in
-
-[remote/index.ts:269](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L269)
-
-___
-
-### update
-
-▸ **update**(`args`): `Promise`\<`void`\>
-
-Update rows in this table.
-
-This can be used to update a single row, many rows, all rows, or
-sometimes no rows (if your predicate matches nothing).
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `args` | [`UpdateArgs`](../interfaces/UpdateArgs.md) \| [`UpdateSqlArgs`](../interfaces/UpdateSqlArgs.md) | see [UpdateArgs](../interfaces/UpdateArgs.md) and [UpdateSqlArgs](../interfaces/UpdateSqlArgs.md) for more details |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-**`Examples`**
-
-```ts
-const con = await lancedb.connect("./.lancedb")
-const data = [
-   {id: 1, vector: [3, 3], name: 'Ye'},
-   {id: 2, vector: [4, 4], name: 'Mike'},
-];
-const tbl = await con.createTable("my_table", data)
-
-await tbl.update({
-  where: "id = 2",
-  values: { vector: [2, 2], name: "Michael" },
-})
-
-let results = await tbl.search([1, 1]).execute();
-// Returns [
-//   {id: 2, vector: [2, 2], name: 'Michael'}
-//   {id: 1, vector: [3, 3], name: 'Ye'}
-// ]
-```
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[update](../interfaces/Table.md#update)
-
-#### Defined in
-
-[remote/index.ts:383](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/remote/index.ts#L383)

docs/src/javascript/enums/MetricType.md

@@ -22,7 +22,7 @@ Cosine distance
 
 #### Defined in
 
-[index.ts:1092](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1092)
+[index.ts:1041](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1041)
 
 ___
 
@@ -34,7 +34,7 @@ Dot product
 
 #### Defined in
 
-[index.ts:1097](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1097)
+[index.ts:1046](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1046)
 
 ___
 
@@ -46,4 +46,4 @@ Euclidean distance
 
 #### Defined in
 
-[index.ts:1087](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1087)
+[index.ts:1036](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1036)

docs/src/javascript/enums/WriteMode.md

@@ -22,7 +22,7 @@ Append new data to the table.
 
 #### Defined in
 
-[index.ts:1058](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1058)
+[index.ts:1007](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1007)
 
 ___
 
@@ -34,7 +34,7 @@ Create a new [Table](../interfaces/Table.md).
 
 #### Defined in
 
-[index.ts:1054](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1054)
+[index.ts:1003](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1003)
 
 ___
 
@@ -46,4 +46,4 @@ Overwrite the existing [Table](../interfaces/Table.md) if presented.
 
 #### Defined in
 
-[index.ts:1056](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1056)
+[index.ts:1005](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1005)

docs/src/javascript/interfaces/AwsCredentials.md

@@ -18,7 +18,7 @@
 
 #### Defined in
 
-[index.ts:57](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L57)
+[index.ts:54](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L54)
 
 ___
 
@@ -28,7 +28,7 @@ ___
 
 #### Defined in
 
-[index.ts:59](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L59)
+[index.ts:56](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L56)
 
 ___
 
@@ -38,4 +38,4 @@ ___
 
 #### Defined in
 
-[index.ts:61](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L61)
+[index.ts:58](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L58)

docs/src/javascript/interfaces/CleanupStats.md

@@ -19,7 +19,7 @@ The number of bytes removed from disk.
 
 #### Defined in
 
-[index.ts:904](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L904)
+[index.ts:878](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L878)
 
 ___
 
@@ -31,4 +31,4 @@ The number of old table versions removed.
 
 #### Defined in
 
-[index.ts:908](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L908)
+[index.ts:882](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L882)

docs/src/javascript/interfaces/CompactionMetrics.md

@@ -22,7 +22,7 @@ fragments added.
 
 #### Defined in
 
-[index.ts:959](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L959)
+[index.ts:933](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L933)
 
 ___
 
@@ -35,7 +35,7 @@ file.
 
 #### Defined in
 
-[index.ts:954](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L954)
+[index.ts:928](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L928)
 
 ___
 
@@ -47,7 +47,7 @@ The number of new fragments that were created.
 
 #### Defined in
 
-[index.ts:949](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L949)
+[index.ts:923](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L923)
 
 ___
 
@@ -59,4 +59,4 @@ The number of fragments that were removed.
 
 #### Defined in
 
-[index.ts:945](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L945)
+[index.ts:919](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L919)

docs/src/javascript/interfaces/CompactionOptions.md

@@ -24,7 +24,7 @@ Default is true.
 
 #### Defined in
 
-[index.ts:927](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L927)
+[index.ts:901](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L901)
 
 ___
 
@@ -38,7 +38,7 @@ the deleted rows. Default is 10%.
 
 #### Defined in
 
-[index.ts:933](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L933)
+[index.ts:907](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L907)
 
 ___
 
@@ -50,7 +50,7 @@ The maximum number of rows per group. Defaults to 1024.
 
 #### Defined in
 
-[index.ts:921](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L921)
+[index.ts:895](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L895)
 
 ___
 
@@ -63,7 +63,7 @@ the number of cores on the machine.
 
 #### Defined in
 
-[index.ts:938](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L938)
+[index.ts:912](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L912)
 
 ___
 
@@ -77,4 +77,4 @@ Defaults to 1024 * 1024.
 
 #### Defined in
 
-[index.ts:917](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L917)
+[index.ts:891](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L891)

docs/src/javascript/interfaces/Connection.md

@@ -9,7 +9,6 @@ Connection could be local against filesystem or remote against a server.
 ## Implemented by
 
 - [`LocalConnection`](../classes/LocalConnection.md)
-- [`RemoteConnection`](../classes/RemoteConnection.md)
 
 ## Table of contents
 
@@ -32,7 +31,7 @@ Connection could be local against filesystem or remote against a server.
 
 #### Defined in
 
-[index.ts:188](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L188)
+[index.ts:183](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L183)
 
 ## Methods
 
@@ -60,7 +59,7 @@ Creates a new Table, optionally initializing it with new data.
 
 #### Defined in
 
-[index.ts:212](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L212)
+[index.ts:207](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L207)
 
 ▸ **createTable**(`name`, `data`): `Promise`\<[`Table`](Table.md)\<`number`[]\>\>
 
@@ -71,7 +70,7 @@ Creates a new Table and initialize it with new data.
 | Name | Type | Description |
 | :------ | :------ | :------ |
 | `name` | `string` | The name of the table. |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
 
 #### Returns
 
@@ -79,7 +78,7 @@ Creates a new Table and initialize it with new data.
 
 #### Defined in
 
-[index.ts:226](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L226)
+[index.ts:221](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L221)
 
 ▸ **createTable**(`name`, `data`, `options`): `Promise`\<[`Table`](Table.md)\<`number`[]\>\>
 
@@ -90,7 +89,7 @@ Creates a new Table and initialize it with new data.
 | Name | Type | Description |
 | :------ | :------ | :------ |
 | `name` | `string` | The name of the table. |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
 | `options` | [`WriteOptions`](WriteOptions.md) | The write options to use when creating the table. |
 
 #### Returns
@@ -99,7 +98,7 @@ Creates a new Table and initialize it with new data.
 
 #### Defined in
 
-[index.ts:238](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L238)
+[index.ts:233](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L233)
 
 ▸ **createTable**\<`T`\>(`name`, `data`, `embeddings`): `Promise`\<[`Table`](Table.md)\<`T`\>\>
 
@@ -116,7 +115,7 @@ Creates a new Table and initialize it with new data.
 | Name | Type | Description |
 | :------ | :------ | :------ |
 | `name` | `string` | The name of the table. |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
 | `embeddings` | [`EmbeddingFunction`](EmbeddingFunction.md)\<`T`\> | An embedding function to use on this table |
 
 #### Returns
@@ -125,7 +124,7 @@ Creates a new Table and initialize it with new data.
 
 #### Defined in
 
-[index.ts:251](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L251)
+[index.ts:246](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L246)
 
 ▸ **createTable**\<`T`\>(`name`, `data`, `embeddings`, `options`): `Promise`\<[`Table`](Table.md)\<`T`\>\>
 
@@ -142,7 +141,7 @@ Creates a new Table and initialize it with new data.
 | Name | Type | Description |
 | :------ | :------ | :------ |
 | `name` | `string` | The name of the table. |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
 | `embeddings` | [`EmbeddingFunction`](EmbeddingFunction.md)\<`T`\> | An embedding function to use on this table |
 | `options` | [`WriteOptions`](WriteOptions.md) | The write options to use when creating the table. |
 
@@ -152,7 +151,7 @@ Creates a new Table and initialize it with new data.
 
 #### Defined in
 
-[index.ts:264](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L264)
+[index.ts:259](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L259)
 
 ___
 
@@ -174,7 +173,7 @@ Drop an existing table.
 
 #### Defined in
 
-[index.ts:275](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L275)
+[index.ts:270](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L270)
 
 ___
 
@@ -203,7 +202,7 @@ Open a table in the database.
 
 #### Defined in
 
-[index.ts:198](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L198)
+[index.ts:193](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L193)
 
 ___
 
@@ -217,4 +216,4 @@ ___
 
 #### Defined in
 
-[index.ts:190](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L190)
+[index.ts:185](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L185)

docs/src/javascript/interfaces/ConnectionOptions.md

@@ -19,13 +19,9 @@
 
 • `Optional` **apiKey**: `string`
 
-API key for the remote connections
-
-Can also be passed by setting environment variable `LANCEDB_API_KEY`
-
 #### Defined in
 
-[index.ts:88](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L88)
+[index.ts:81](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L81)
 
 ___
 
@@ -39,7 +35,7 @@ If not provided, LanceDB will use the default credentials provider chain.
 
 #### Defined in
 
-[index.ts:78](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L78)
+[index.ts:75](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L75)
 
 ___
 
@@ -51,7 +47,7 @@ AWS region to connect to. Default is defaultAwsRegion.
 
 #### Defined in
 
-[index.ts:81](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L81)
+[index.ts:78](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L78)
 
 ___
 
@@ -59,13 +55,13 @@ ___
 
 • `Optional` **hostOverride**: `string`
 
-Override the host URL for the remote connection.
+Override the host URL for the remote connections.
 
 This is useful for local testing.
 
 #### Defined in
 
-[index.ts:98](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L98)
+[index.ts:91](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L91)
 
 ___
 
@@ -77,7 +73,7 @@ Region to connect
 
 #### Defined in
 
-[index.ts:91](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L91)
+[index.ts:84](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L84)
 
 ___
 
@@ -89,8 +85,8 @@ LanceDB database URI.
 
 - `/path/to/database` - local database
 - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
-- `db://host:port` - remote database (LanceDB cloud)
+- `db://host:port` - remote database (SaaS)
 
 #### Defined in
 
-[index.ts:72](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L72)
+[index.ts:69](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L69)

docs/src/javascript/interfaces/CreateTableOptions.md

@@ -26,7 +26,7 @@
 
 #### Defined in
 
-[index.ts:121](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L121)
+[index.ts:116](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L116)
 
 ___
 
@@ -36,7 +36,7 @@ ___
 
 #### Defined in
 
-[index.ts:127](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L127)
+[index.ts:122](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L122)
 
 ___
 
@@ -46,7 +46,7 @@ ___
 
 #### Defined in
 
-[index.ts:118](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L118)
+[index.ts:113](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L113)
 
 ___
 
@@ -56,7 +56,7 @@ ___
 
 #### Defined in
 
-[index.ts:124](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L124)
+[index.ts:119](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L119)
 
 ___
 
@@ -66,4 +66,4 @@ ___
 
 #### Defined in
 
-[index.ts:130](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L130)
+[index.ts:125](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L125)

docs/src/javascript/interfaces/EmbeddingFunction.md

@@ -45,7 +45,7 @@ Creates a vector representation for the given values.
 
 #### Defined in
 
-[embedding/embedding_function.ts:27](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/embedding/embedding_function.ts#L27)
+[embedding/embedding_function.ts:27](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/embedding_function.ts#L27)
 
 ___
 
@@ -57,4 +57,4 @@ The name of the column that will be used as input for the Embedding Function.
 
 #### Defined in
 
-[embedding/embedding_function.ts:22](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/embedding/embedding_function.ts#L22)
+[embedding/embedding_function.ts:22](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/embedding_function.ts#L22)

docs/src/javascript/interfaces/IndexStats.md

@@ -17,7 +17,7 @@
 
 #### Defined in
 
-[index.ts:485](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L485)
+[index.ts:478](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L478)
 
 ___
 
@@ -27,4 +27,4 @@ ___
 
 #### Defined in
 
-[index.ts:486](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L486)
+[index.ts:479](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L479)

docs/src/javascript/interfaces/IvfPQIndexConfig.md

@@ -29,7 +29,7 @@ The column to be indexed
 
 #### Defined in
 
-[index.ts:968](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L968)
+[index.ts:942](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L942)
 
 ___
 
@@ -41,7 +41,7 @@ Cache size of the index
 
 #### Defined in
 
-[index.ts:1042](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1042)
+[index.ts:991](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L991)
 
 ___
 
@@ -49,11 +49,11 @@ ___
 
 • `Optional` **index\_name**: `string`
 
-Note: this parameter is not supported on LanceDB Cloud
+A unique name for the index
 
 #### Defined in
 
-[index.ts:976](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L976)
+[index.ts:947](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L947)
 
 ___
 
@@ -61,11 +61,11 @@ ___
 
 • `Optional` **max\_iters**: `number`
 
-Note: this parameter is not yet supported on LanceDB Cloud
+The max number of iterations for kmeans training.
 
 #### Defined in
 
-[index.ts:997](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L997)
+[index.ts:962](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L962)
 
 ___
 
@@ -73,11 +73,11 @@ ___
 
 • `Optional` **max\_opq\_iters**: `number`
 
-Note: this parameter is not yet supported on LanceDB Cloud
+Max number of iterations to train OPQ, if `use_opq` is true.
 
 #### Defined in
 
-[index.ts:1029](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1029)
+[index.ts:981](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L981)
 
 ___
 
@@ -89,7 +89,7 @@ Metric type, L2 or Cosine
 
 #### Defined in
 
-[index.ts:981](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L981)
+[index.ts:952](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L952)
 
 ___
 
@@ -97,11 +97,11 @@ ___
 
 • `Optional` **num\_bits**: `number`
 
-Note: this parameter is not yet supported on LanceDB Cloud
+The number of bits to present one PQ centroid.
 
 #### Defined in
 
-[index.ts:1021](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1021)
+[index.ts:976](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L976)
 
 ___
 
@@ -109,11 +109,11 @@ ___
 
 • `Optional` **num\_partitions**: `number`
 
-Note: this parameter is not yet supported on LanceDB Cloud
+The number of partitions this index
 
 #### Defined in
 
-[index.ts:989](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L989)
+[index.ts:957](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L957)
 
 ___
 
@@ -121,11 +121,11 @@ ___
 
 • `Optional` **num\_sub\_vectors**: `number`
 
-Note: this parameter is not yet supported on LanceDB Cloud
+Number of subvectors to build PQ code
 
 #### Defined in
 
-[index.ts:1013](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1013)
+[index.ts:972](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L972)
 
 ___
 
@@ -133,11 +133,11 @@ ___
 
 • `Optional` **replace**: `boolean`
 
-Note: this parameter is not yet supported on LanceDB Cloud
+Replace an existing index with the same name if it exists.
 
 #### Defined in
 
-[index.ts:1037](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1037)
+[index.ts:986](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L986)
 
 ___
 
@@ -147,7 +147,7 @@ ___
 
 #### Defined in
 
-[index.ts:1044](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1044)
+[index.ts:993](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L993)
 
 ___
 
@@ -155,8 +155,8 @@ ___
 
 • `Optional` **use\_opq**: `boolean`
 
-Note: this parameter is not yet supported on LanceDB Cloud
+Train as optimized product quantization.
 
 #### Defined in
 
-[index.ts:1005](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1005)
+[index.ts:967](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L967)

docs/src/javascript/interfaces/Table.md

@@ -13,7 +13,6 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 ## Implemented by
 
 - [`LocalTable`](../classes/LocalTable.md)
-- [`RemoteTable`](../classes/RemoteTable.md)
 
 ## Table of contents
 
@@ -36,7 +35,7 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 
 ### add
 
-• **add**: (`data`: `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[]) => `Promise`\<`number`\>
+• **add**: (`data`: `Record`\<`string`, `unknown`\>[]) => `Promise`\<`number`\>
 
 #### Type declaration
 
@@ -48,7 +47,7 @@ Insert records into this Table.
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
 
 ##### Returns
 
@@ -58,7 +57,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:296](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L296)
+[index.ts:291](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L291)
 
 ___
 
@@ -78,7 +77,7 @@ Returns the number of rows in this table.
 
 #### Defined in
 
-[index.ts:368](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L368)
+[index.ts:361](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L361)
 
 ___
 
@@ -108,7 +107,7 @@ VectorIndexParams.
 
 #### Defined in
 
-[index.ts:313](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L313)
+[index.ts:306](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L306)
 
 ___
 
@@ -143,7 +142,7 @@ await table.createScalarIndex('my_col')
 
 #### Defined in
 
-[index.ts:363](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L363)
+[index.ts:356](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L356)
 
 ___
 
@@ -195,7 +194,7 @@ await tbl.countRows() // Returns 1
 
 #### Defined in
 
-[index.ts:402](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L402)
+[index.ts:395](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L395)
 
 ___
 
@@ -221,7 +220,7 @@ Get statistics about an index.
 
 #### Defined in
 
-[index.ts:445](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L445)
+[index.ts:438](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L438)
 
 ___
 
@@ -241,7 +240,7 @@ List the indicies on this table.
 
 #### Defined in
 
-[index.ts:440](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L440)
+[index.ts:433](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L433)
 
 ___
 
@@ -251,13 +250,13 @@ ___
 
 #### Defined in
 
-[index.ts:282](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L282)
+[index.ts:277](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L277)
 
 ___
 
 ### overwrite
 
-• **overwrite**: (`data`: `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[]) => `Promise`\<`number`\>
+• **overwrite**: (`data`: `Record`\<`string`, `unknown`\>[]) => `Promise`\<`number`\>
 
 #### Type declaration
 
@@ -269,7 +268,7 @@ Insert records into this Table, replacing its contents.
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
 
 ##### Returns
 
@@ -279,7 +278,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:304](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L304)
+[index.ts:299](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L299)
 
 ___
 
@@ -289,7 +288,7 @@ ___
 
 #### Defined in
 
-[index.ts:447](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L447)
+[index.ts:440](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L440)
 
 ___
 
@@ -315,7 +314,7 @@ Creates a search query to find the nearest neighbors of the given search term
 
 #### Defined in
 
-[index.ts:288](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L288)
+[index.ts:283](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L283)
 
 ___
 
@@ -366,4 +365,4 @@ let results = await tbl.search([1, 1]).execute();
 
 #### Defined in
 
-[index.ts:435](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L435)
+[index.ts:428](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L428)

docs/src/javascript/interfaces/UpdateArgs.md

@@ -20,7 +20,7 @@ new values to set
 
 #### Defined in
 
-[index.ts:461](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L461)
+[index.ts:454](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L454)
 
 ___
 
@@ -33,4 +33,4 @@ in which case all rows will be updated.
 
 #### Defined in
 
-[index.ts:455](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L455)
+[index.ts:448](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L448)

docs/src/javascript/interfaces/UpdateSqlArgs.md

@@ -20,7 +20,7 @@ new values to set as SQL expressions.
 
 #### Defined in
 
-[index.ts:475](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L475)
+[index.ts:468](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L468)
 
 ___
 
@@ -33,4 +33,4 @@ in which case all rows will be updated.
 
 #### Defined in
 
-[index.ts:469](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L469)
+[index.ts:462](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L462)

docs/src/javascript/interfaces/VectorIndex.md

@@ -18,7 +18,7 @@
 
 #### Defined in
 
-[index.ts:479](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L479)
+[index.ts:472](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L472)
 
 ___
 
@@ -28,7 +28,7 @@ ___
 
 #### Defined in
 
-[index.ts:480](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L480)
+[index.ts:473](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L473)
 
 ___
 
@@ -38,4 +38,4 @@ ___
 
 #### Defined in
 
-[index.ts:481](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L481)
+[index.ts:474](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L474)

docs/src/javascript/interfaces/WriteOptions.md

@@ -24,4 +24,4 @@ A [WriteMode](../enums/WriteMode.md) to use on this operation
 
 #### Defined in
 
-[index.ts:1066](https://github.com/lancedb/lancedb/blob/5228ca4/node/src/index.ts#L1066)
+[index.ts:1015](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1015)

docs/src/notebooks/DisappearingEmbeddingFunction.ipynb

@@ -290,7 +290,7 @@
         "from lancedb.pydantic import LanceModel, Vector\n",
         "\n",
         "class Pets(LanceModel):\n",
-        "    vector: Vector(clip.ndims) = clip.VectorField()\n",
+        "    vector: Vector(clip.ndims()) = clip.VectorField()\n",
         "    image_uri: str = clip.SourceField()\n",
         "\n",
         "    @property\n",
@@ -360,7 +360,7 @@
         "    table = db.create_table(\"pets\", schema=Pets)\n",
         "    # use a sampling of 1000 images\n",
         "    p = Path(\"~/Downloads/images\").expanduser()\n",
-        "    uris = [str(f) for f in p.iterdir()]\n",
+        "    uris = [str(f) for f in p.glob(\"*.jpg\")]\n",
         "    uris = sample(uris, 1000)\n",
         "    table.add(pd.DataFrame({\"image_uri\": uris}))"
       ]
@@ -543,7 +543,7 @@
       ],
       "source": [
         "from PIL import Image\n",
-        "p = Path(\"/Users/changshe/Downloads/images/samoyed_100.jpg\")\n",
+        "p = Path(\"~/Downloads/images/samoyed_100.jpg\").expanduser()\n",
         "query_image = Image.open(p)\n",
         "query_image"
       ]

docs/src/notebooks/diffusiondb/datagen.py

@@ -23,10 +23,8 @@ from multiprocessing import Pool
 import lance
 import pyarrow as pa
 from datasets import load_dataset
-from PIL import Image
 from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast
 
-import lancedb
 
 MODEL_ID = "openai/clip-vit-base-patch32"
 

docs/src/notebooks/tables_guide.ipynb

@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 2,
    "id": "c1b4e34b-a49c-471d-a343-a5940bb5138a",
    "metadata": {},
    "outputs": [],
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "4e5a8d07-d9a1-48c1-913a-8e0629289579",
    "metadata": {},
    "outputs": [],
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "5df12f66-8d99-43ad-8d0b-22189ec0a6b9",
    "metadata": {},
    "outputs": [
@@ -62,7 +62,7 @@
        "long: [[-122.7,-74.1]]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "f4d87ae9-0ccb-48eb-b31d-bb8f2370e47e",
    "metadata": {},
    "outputs": [
@@ -108,7 +108,7 @@
        "long: [[-122.7,-74.1]]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -135,10 +135,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "25f34bcf-fca0-4431-8601-eac95d1bd347",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2024-01-31T18:59:33Z WARN  lance::dataset] No existing dataset at /Users/qian/Work/LanceDB/lancedb/docs/src/notebooks/.lancedb/table3.lance, it will be created\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -148,7 +155,7 @@
        "long: float"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -171,45 +178,51 @@
    "id": "4df51925-7ca2-4005-9c72-38b3d26240c6",
    "metadata": {},
    "source": [
-    "### From PyArrow Tables\n",
+    "### From an Arrow Table\n",
     "\n",
-    "You can also create LanceDB tables directly from pyarrow tables"
+    "You can also create LanceDB tables directly from pyarrow tables. LanceDB supports float16 type."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "id": "90a880f6-be43-4c9d-ba65-0b05197c0f6f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "vector: fixed_size_list<item: float>[2]\n",
+       "vector: fixed_size_list<item: halffloat>[16]\n",
-       "  child 0, item: float\n",
+       "  child 0, item: halffloat\n",
-       "item: string\n",
+       "text: string"
-       "price: double"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "table = pa.Table.from_arrays(\n",
+    "import numpy as np\n",
+    "\n",
+    "dim = 16\n",
+    "total = 2\n",
+    "schema = pa.schema(\n",
     "    [\n",
-    "            pa.array([[3.1, 4.1], [5.9, 26.5]],\n",
+    "        pa.field(\"vector\", pa.list_(pa.float16(), dim)),\n",
-    "                    pa.list_(pa.float32(), 2)),\n",
+    "        pa.field(\"text\", pa.string())\n",
-    "            pa.array([\"foo\", \"bar\"]),\n",
+    "    ]\n",
-    "            pa.array([10.0, 20.0]),\n",
+    ")\n",
+    "data = pa.Table.from_arrays(\n",
+    "    [\n",
+    "        pa.array([np.random.randn(dim).astype(np.float16) for _ in range(total)],\n",
+    "                pa.list_(pa.float16(), dim)),\n",
+    "        pa.array([\"foo\", \"bar\"])\n",
     "    ],\n",
-    "        [\"vector\", \"item\", \"price\"],\n",
+    "    [\"vector\", \"text\"],\n",
     ")\n",
     "\n",
-    "db = lancedb.connect(\"db\")\n",
+    "tbl = db.create_table(\"f16_tbl\", data, schema=schema)\n",
-    "\n",
-    "tbl = db.create_table(\"test1\", table, mode=\"overwrite\")\n",
     "tbl.schema"
    ]
   },
@@ -225,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "id": "d81121d7-e4b7-447c-a48c-974b6ebb464a",
    "metadata": {},
    "outputs": [
@@ -240,7 +253,7 @@
        "imdb_id: int64 not null"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -282,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "id": "bc247142-4e3c-41a2-b94c-8e00d2c2a508",
    "metadata": {},
    "outputs": [
@@ -292,7 +305,7 @@
        "LanceTable(table4)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -333,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "id": "25ad3523-e0c9-4c28-b3df-38189c4e0e5f",
    "metadata": {},
    "outputs": [
@@ -346,7 +359,7 @@
        "price: double not null"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -385,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "id": "2814173a-eacc-4dd8-a64d-6312b44582cc",
    "metadata": {},
    "outputs": [],
@@ -411,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "id": "df9e13c0-41f6-437f-9dfa-2fd71d3d9c45",
    "metadata": {},
    "outputs": [
@@ -421,7 +434,7 @@
        "['table6', 'table4', 'table5', 'movielens_small']"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -432,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "id": "9343f5ad-6024-42ee-ac2f-6c1471df8679",
    "metadata": {},
    "outputs": [
@@ -541,7 +554,7 @@
        "9  [5.9, 26.5]  bar   20.0"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -564,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "id": "8a56250f-73a1-4c26-a6ad-5c7a0ce3a9ab",
    "metadata": {},
    "outputs": [],
@@ -590,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
    "id": "030c7057-b98e-4e2f-be14-b8c1f927f83c",
    "metadata": {},
    "outputs": [],
@@ -621,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 16,
    "id": "e7a17de2-08d2-41b7-bd05-f63d1045ab1f",
    "metadata": {},
    "outputs": [
@@ -629,16 +642,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "32\n"
+      "22\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "17"
+       "12"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -661,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 17,
    "id": "fe3310bd-08f4-4a22-a63b-b3127d22f9f7",
    "metadata": {},
    "outputs": [
@@ -681,25 +694,20 @@
       "8    [3.1, 4.1]   foo   10.0\n",
       "9    [3.1, 4.1]   foo   10.0\n",
       "10   [3.1, 4.1]   foo   10.0\n",
-      "11   [3.1, 4.1]   foo   10.0\n",
+      "11   [3.1, 4.1]   foo   10.0\n"
-      "12   [3.1, 4.1]   foo   10.0\n",
-      "13   [3.1, 4.1]   foo   10.0\n",
-      "14   [3.1, 4.1]   foo   10.0\n",
-      "15   [3.1, 4.1]   foo   10.0\n",
-      "16   [3.1, 4.1]   foo   10.0\n"
      ]
     },
     {
      "ename": "OSError",
-     "evalue": "LanceError(IO): Error during planning: column foo does not exist",
+     "evalue": "LanceError(IO): Error during planning: column foo does not exist, /Users/runner/work/lance/lance/rust/lance-core/src/error.rs:212:23",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[30], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m to_remove \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mstr\u001b[39m(v) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m to_remove)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(tbl\u001b[38;5;241m.\u001b[39mto_pandas())\n\u001b[0;32m----> 4\u001b[0m \u001b[43mtbl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mitem IN (\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mto_remove\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m tbl\u001b[38;5;241m.\u001b[39mto_pandas()\n",
+      "Cell \u001b[0;32mIn[17], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m to_remove \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mstr\u001b[39m(v) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m to_remove)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(tbl\u001b[38;5;241m.\u001b[39mto_pandas())\n\u001b[0;32m----> 4\u001b[0m \u001b[43mtbl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mitem IN (\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mto_remove\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/lancedb/lancedb/python/lancedb/table.py:610\u001b[0m, in \u001b[0;36mLanceTable.delete\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdelete\u001b[39m(\u001b[38;5;28mself\u001b[39m, where: \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m--> 610\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Work/LanceDB/lancedb/docs/doc-venv/lib/python3.11/site-packages/lancedb/table.py:872\u001b[0m, in \u001b[0;36mLanceTable.delete\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m    871\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdelete\u001b[39m(\u001b[38;5;28mself\u001b[39m, where: \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m--> 872\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/lancedb/lancedb/env/lib/python3.11/site-packages/lance/dataset.py:489\u001b[0m, in \u001b[0;36mLanceDataset.delete\u001b[0;34m(self, predicate)\u001b[0m\n\u001b[1;32m    487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(predicate, pa\u001b[38;5;241m.\u001b[39mcompute\u001b[38;5;241m.\u001b[39mExpression):\n\u001b[1;32m    488\u001b[0m     predicate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(predicate)\n\u001b[0;32m--> 489\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredicate\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Work/LanceDB/lancedb/docs/doc-venv/lib/python3.11/site-packages/lance/dataset.py:596\u001b[0m, in \u001b[0;36mLanceDataset.delete\u001b[0;34m(self, predicate)\u001b[0m\n\u001b[1;32m    594\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(predicate, pa\u001b[38;5;241m.\u001b[39mcompute\u001b[38;5;241m.\u001b[39mExpression):\n\u001b[1;32m    595\u001b[0m     predicate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(predicate)\n\u001b[0;32m--> 596\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredicate\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mOSError\u001b[0m: LanceError(IO): Error during planning: column foo does not exist"
+      "\u001b[0;31mOSError\u001b[0m: LanceError(IO): Error during planning: column foo does not exist, /Users/runner/work/lance/lance/rust/lance-core/src/error.rs:212:23"
      ]
     }
    ],
@@ -712,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "87d5bc21-847f-4c81-b56e-f6dbe5d05aac",
    "metadata": {},
    "outputs": [],
@@ -729,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "id": "9cba4519-eb3a-4941-ab7e-873d762e750f",
    "metadata": {},
    "outputs": [],
@@ -742,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "id": "5bdc9801-d5ed-4871-92d0-88b27108e788",
    "metadata": {},
    "outputs": [
@@ -817,7 +825,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

docs/src/python/duckdb.md

@@ -1,6 +1,9 @@
 # DuckDB
 
-LanceDB is very well-integrated with [DuckDB](https://duckdb.org/), an in-process SQL OLAP database. This integration is done via [Arrow](https://duckdb.org/docs/guides/python/sql_on_arrow) .
+In Python, LanceDB tables can also be queried with [DuckDB](https://duckdb.org/), an in-process SQL OLAP database. This means you can write complex SQL queries to analyze your data in LanceDB.
+
+This integration is done via [Apache Arrow](https://duckdb.org/docs/guides/python/sql_on_arrow), which provides zero-copy data sharing between LanceDB and DuckDB. DuckDB is capable of passing down column selections and basic filters to LanceDB, reducing the amount of data that needs to be scanned to perform your query. Finally, the integration allows streaming data from LanceDB tables, allowing you to aggregate tables that won't fit into memory. All of this uses the same mechanism described in DuckDB's blog post *[DuckDB quacks Arrow](https://duckdb.org/2021/12/03/duck-arrow.html)*.
+
 
 We can demonstrate this by first installing `duckdb` and `lancedb`.
 
@@ -19,14 +22,15 @@ data = [
     {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}
 ]
 table = db.create_table("pd_table", data=data)
-arrow_table = table.to_arrow()
 ```
 
-DuckDB can directly query the `pyarrow.Table` object:
+To query the table, first call `to_lance` to convert the table to a "dataset", which is an object that can be queried by DuckDB. Then all you need to do is reference that dataset by the same name in your SQL query.
 
 ```python
 import duckdb
 
+arrow_table = table.to_lance()
+
 duckdb.query("SELECT * FROM arrow_table")
 ```
 

docs/src/python/python.md

@@ -24,6 +24,12 @@ pip install lancedb
 
 ::: lancedb.query.LanceQueryBuilder
 
+::: lancedb.query.LanceVectorQueryBuilder
+
+::: lancedb.query.LanceFtsQueryBuilder
+
+::: lancedb.query.LanceHybridQueryBuilder
+
 ## Embeddings
 
 ::: lancedb.embeddings.registry.EmbeddingFunctionRegistry
@@ -58,12 +64,26 @@ pip install lancedb
 
 ::: lancedb.schema.vector
 
+::: lancedb.merge.LanceMergeInsertBuilder
+
 ## Integrations
 
-### Pydantic
+## Pydantic
 
 ::: lancedb.pydantic.pydantic_to_schema
 
 ::: lancedb.pydantic.vector
 
 ::: lancedb.pydantic.LanceModel
+
+## Reranking
+
+::: lancedb.rerankers.linear_combination.LinearCombinationReranker
+
+::: lancedb.rerankers.cohere.CohereReranker
+
+::: lancedb.rerankers.colbert.ColbertReranker
+
+::: lancedb.rerankers.cross_encoder.CrossEncoderReranker
+
+::: lancedb.rerankers.openai.OpenaiReranker

docs/src/python/saas-python.md

@@ -0,0 +1,20 @@
+# Python API Reference (SaaS)
+
+This section contains the API reference for the SaaS Python API.
+
+## Installation
+
+```shell
+pip install lancedb
+```
+
+## Connection
+
+::: lancedb.connect
+
+::: lancedb.remote.db.RemoteDBConnection
+
+## Table
+
+::: lancedb.remote.table.RemoteTable
+

docs/src/search.md

@@ -2,9 +2,9 @@
 
 A vector search finds the approximate or exact nearest neighbors to a given query vector.
 
-* In a recommendation system or search engine, you can find similar records to
+- In a recommendation system or search engine, you can find similar records to
   the one you searched.
-* In LLM and other AI applications,
+- In LLM and other AI applications,
   each data point can be represented by [embeddings generated from existing models](embeddings/index.md),
   following which the search returns the most relevant features.
 
@@ -14,16 +14,15 @@ Distance metrics are a measure of the similarity between a pair of vectors.
 Currently, LanceDB supports the following metrics:
 
 | Metric   | Description                                                                 |
-| ----------- | ------------------------------------ |
+| -------- | --------------------------------------------------------------------------- |
 | `l2`     | [Euclidean / L2 distance](https://en.wikipedia.org/wiki/Euclidean_distance) |
 | `cosine` | [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity)        |
 | `dot`    | [Dot Production](https://en.wikipedia.org/wiki/Dot_product)                 |
 
-
 ## Exhaustive search (kNN)
 
-If you do not create a vector index, LanceDB exhaustively scans the *entire* vector space
+If you do not create a vector index, LanceDB exhaustively scans the _entire_ vector space
-and compute the distance to every vector in order to find the exact nearest neighbors. This is effectively a kNN search.
+and computes the distance to every vector in order to find the exact nearest neighbors. This is effectively a kNN search.
 
 <!-- Setup Code
 ```python
@@ -38,22 +37,9 @@ data = [{"vector": row, "item": f"item {i}"}
 db.create_table("my_vectors", data=data)
 ```
 -->
-<!-- Setup Code
-```javascript
-const vectordb_setup = require('vectordb')
-const db_setup = await vectordb_setup.connect('data/sample-lancedb')
-
-let data = []
-for (let i = 0; i < 10_000; i++) {
-     data.push({vector: Array(1536).fill(i), id: `${i}`, content: "", longId: `${i}`},)
-}
-await db_setup.createTable('my_vectors', data)
-```
--->
 
 === "Python"
 
-
     ```python
     import lancedb
     import numpy as np
@@ -70,14 +56,9 @@ await db_setup.createTable('my_vectors', data)
 === "JavaScript"
 
     ```javascript
-    const vectordb = require('vectordb')
+    --8<-- "docs/src/search_legacy.ts:import"
-    const db = await vectordb.connect('data/sample-lancedb')
 
-    const tbl = await db.openTable("my_vectors")
+    --8<-- "docs/src/search_legacy.ts:search1"
-
-    const results_1 = await tbl.search(Array(1536).fill(1.2))
-        .limit(10)
-        .execute()
     ```
 
 By default, `l2` will be used as metric type. You can specify the metric type as
@@ -92,14 +73,10 @@ By default, `l2` will be used as metric type. You can specify the metric type as
         .to_list()
     ```
 
-
 === "JavaScript"
 
     ```javascript
-    const results_2 = await tbl.search(Array(1536).fill(1.2))
+    --8<-- "docs/src/search_legacy.ts:search2"
-        .metricType("cosine")
-        .limit(10)
-        .execute()
     ```
 
 ## Approximate nearest neighbor (ANN) search
@@ -108,7 +85,7 @@ To perform scalable vector retrieval with acceptable latencies, it's common to b
 While the exhaustive search is guaranteed to always return 100% recall, the approximate nature of
 an ANN search means that using an index often involves a trade-off between recall and latency.
 
-See the [IVF_PQ index](./concepts/index_ivfpq.md.md) for a deeper description of how `IVF_PQ`
+See the [IVF_PQ index](./concepts/index_ivfpq.md) for a deeper description of how `IVF_PQ`
 indexes work in LanceDB.
 
 ## Output search results
@@ -117,7 +94,9 @@ LanceDB returns vector search results via different formats commonly used in pyt
 Let's create a LanceDB table with a nested schema:
 
 === "Python"
+
     ```python
+
     from datetime import datetime
     import lancedb
     from lancedb.pydantic import LanceModel, Vector
@@ -205,4 +184,3 @@ Let's create a LanceDB table with a nested schema:
 
     Note that in this case the extra `_distance` field is discarded since
     it's not part of the LanceSchema.
-

docs/src/search_legacy.ts

@@ -0,0 +1,41 @@
+// --8<-- [start:import]
+import * as lancedb from "vectordb";
+// --8<-- [end:import]
+import * as fs from "fs";
+
+async function setup() {
+  fs.rmSync("data/sample-lancedb", { recursive: true, force: true });
+  const db = await lancedb.connect("data/sample-lancedb");
+
+  let data = [];
+  for (let i = 0; i < 10_000; i++) {
+    data.push({
+      vector: Array(1536).fill(i),
+      id: `${i}`,
+      content: "",
+      longId: `${i}`,
+    });
+  }
+  await db.createTable("my_vectors", data);
+}
+
+async () => {
+  await setup();
+
+  // --8<-- [start:search1]
+  const db = await lancedb.connect("data/sample-lancedb");
+  const tbl = await db.openTable("my_vectors");
+
+  const results_1 = await tbl.search(Array(1536).fill(1.2)).limit(10).execute();
+  // --8<-- [end:search1]
+
+  // --8<-- [start:search2]
+  const results_2 = await tbl
+    .search(Array(1536).fill(1.2))
+    .metricType(lancedb.MetricType.Cosine)
+    .limit(10)
+    .execute();
+  // --8<-- [end:search2]
+
+  console.log("search: done");
+};

docs/src/sql.md

@@ -34,6 +34,7 @@ const tbl = await db.createTable('myVectors', data)
 -->
 
 === "Python"
+
     ```py
     result = (
         tbl.search([0.5, 0.2])
@@ -44,12 +45,9 @@ const tbl = await db.createTable('myVectors', data)
     ```
 
 === "JavaScript"
+
     ```javascript
-    let result = await tbl.search(Array(1536).fill(0.5))
+    --8<-- "docs/src/sql_legacy.ts:search"
-        .limit(1)
-        .filter("id = 10")
-        .prefilter(true)
-        .execute()
     ```
 
 ## SQL filters
@@ -60,14 +58,14 @@ It can be used during vector search, update, and deletion operations.
 
 Currently, Lance supports a growing list of SQL expressions.
 
-* ``>``, ``>=``, ``<``, ``<=``, ``=``
+- `>`, `>=`, `<`, `<=`, `=`
-* ``AND``, ``OR``, ``NOT``
+- `AND`, `OR`, `NOT`
-* ``IS NULL``, ``IS NOT NULL``
+- `IS NULL`, `IS NOT NULL`
-* ``IS TRUE``, ``IS NOT TRUE``, ``IS FALSE``, ``IS NOT FALSE``
+- `IS TRUE`, `IS NOT TRUE`, `IS FALSE`, `IS NOT FALSE`
-* ``IN``
+- `IN`
-* ``LIKE``, ``NOT LIKE``
+- `LIKE`, `NOT LIKE`
-* ``CAST``
+- `CAST`
-* ``regexp_match(column, pattern)``
+- `regexp_match(column, pattern)`
 
 For example, the following filter string is acceptable:
 
@@ -82,29 +80,27 @@ For example, the following filter string is acceptable:
 === "Javascript"
 
     ```javascript
-    await tbl.search(Array(1536).fill(0))
+    --8<-- "docs/src/sql_legacy.ts:vec_search"
-       .where("(item IN ('item 0', 'item 2')) AND (id > 10)")
-       .execute()
     ```
 
-
 If your column name contains special characters or is a [SQL Keyword](https://docs.rs/sqlparser/latest/sqlparser/keywords/index.html),
 you can use backtick (`` ` ``) to escape it. For nested fields, each segment of the
 path must be wrapped in backticks.
 
 === "SQL"
+
     ```sql
     `CUBE` = 10 AND `column name with space` IS NOT NULL
       AND `nested with space`.`inner with space` < 2
     ```
 
-!!! warning
+!!!warning "Field names containing periods (`.`) are not supported."
-    Field names containing periods (``.``) are not supported.
 
 Literals for dates, timestamps, and decimals can be written by writing the string
 value after the type name. For example
 
 === "SQL"
+
     ```sql
     date_col = date '2021-01-01'
     and timestamp_col = timestamp '2021-01-01 00:00:00'
@@ -115,48 +111,46 @@ For timestamp columns, the precision can be specified as a number in the type
 parameter. Microsecond precision (6) is the default.
 
 | SQL            | Time unit    |
-|------------------|--------------|
+| -------------- | ------------ |
-| ``timestamp(0)`` | Seconds      |
+| `timestamp(0)` | Seconds      |
-| ``timestamp(3)`` | Milliseconds |
+| `timestamp(3)` | Milliseconds |
-| ``timestamp(6)`` | Microseconds |
+| `timestamp(6)` | Microseconds |
-| ``timestamp(9)`` | Nanoseconds  |
+| `timestamp(9)` | Nanoseconds  |
 
 LanceDB internally stores data in [Apache Arrow](https://arrow.apache.org/) format.
 The mapping from SQL types to Arrow types is:
 
 | SQL type                                                  | Arrow type         |
-|----------|------------|
+| --------------------------------------------------------- | ------------------ |
-| ``boolean`` | ``Boolean`` |
+| `boolean`                                                 | `Boolean`          |
-| ``tinyint`` / ``tinyint unsigned`` | ``Int8`` / ``UInt8`` |
+| `tinyint` / `tinyint unsigned`                            | `Int8` / `UInt8`   |
-| ``smallint`` / ``smallint unsigned`` | ``Int16`` / ``UInt16`` |
+| `smallint` / `smallint unsigned`                          | `Int16` / `UInt16` |
-| ``int`` or ``integer`` / ``int unsigned`` or ``integer unsigned`` | ``Int32`` / ``UInt32`` |
+| `int` or `integer` / `int unsigned` or `integer unsigned` | `Int32` / `UInt32` |
-| ``bigint`` / ``bigint unsigned`` | ``Int64`` / ``UInt64`` |
+| `bigint` / `bigint unsigned`                              | `Int64` / `UInt64` |
-| ``float`` | ``Float32`` |
+| `float`                                                   | `Float32`          |
-| ``double`` | ``Float64`` |
+| `double`                                                  | `Float64`          |
-| ``decimal(precision, scale)`` | ``Decimal128`` |
+| `decimal(precision, scale)`                               | `Decimal128`       |
-| ``date`` | ``Date32`` |
+| `date`                                                    | `Date32`           |
-| ``timestamp`` | ``Timestamp`` [^1] |
+| `timestamp`                                               | `Timestamp` [^1]   |
-| ``string`` | ``Utf8`` |
+| `string`                                                  | `Utf8`             |
-| ``binary`` | ``Binary`` |
+| `binary`                                                  | `Binary`           |
 
 [^1]: See precision mapping in previous table.
 
-
 ## Filtering without Vector Search
 
 You can also filter your data without search.
 
 === "Python"
+
     ```python
     tbl.search().where("id = 10").limit(10).to_arrow()
     ```
 
 === "JavaScript"
+
     ```javascript
-      await tbl.where('id = 10').limit(10).execute()
+    --8<---- "docs/src/sql_legacy.ts:sql_search"
     ```
 
-!!! warning
+!!!warning "If your table is large, this could potentially return a very large amount of data. Please be sure to use a `limit` clause unless you're sure you want to return the whole result set."
-    If your table is large, this could potentially return a very large
-    amount of data. Please be sure to use a `limit` clause unless
-    you're sure you want to return the whole result set.

docs/src/sql_legacy.ts

@@ -0,0 +1,38 @@
+import * as vectordb from "vectordb";
+
+(async () => {
+  const db = await vectordb.connect("data/sample-lancedb");
+
+  let data = [];
+  for (let i = 0; i < 10_000; i++) {
+    data.push({
+      vector: Array(1536).fill(i),
+      id: i,
+      item: `item ${i}`,
+      strId: `${i}`,
+    });
+  }
+  const tbl = await db.createTable("myVectors", data);
+
+  // --8<-- [start:search]
+  let result = await tbl
+    .search(Array(1536).fill(0.5))
+    .limit(1)
+    .filter("id = 10")
+    .prefilter(true)
+    .execute();
+  // --8<-- [end:search]
+
+  // --8<-- [start:vec_search]
+  await tbl
+    .search(Array(1536).fill(0))
+    .where("(item IN ('item 0', 'item 2')) AND (id > 10)")
+    .execute();
+  // --8<-- [end:vec_search]
+
+  // --8<-- [start:sql_search]
+  await tbl.filter("id = 10").limit(10).execute();
+  // --8<-- [end:sql_search]
+
+  console.log("SQL search: done");
+})();

docs/test/md_testing.js

@@ -1,55 +0,0 @@
-const glob = require("glob");
-const fs = require("fs");
-const path = require("path");
-
-const globString = "../src/**/*.md";
-
-const excludedGlobs = [
-  "../src/fts.md",
-  "../src/embedding.md",
-  "../src/examples/*.md",
-  "../src/guides/tables.md",
-  "../src/guides/storage.md",
-  "../src/embeddings/*.md",
-];
-
-const nodePrefix = "javascript";
-const nodeFile = ".js";
-const nodeFolder = "node";
-const asyncPrefix = "(async () => {\n";
-const asyncSuffix = "})();";
-
-function* yieldLines(lines, prefix, suffix) {
-  let inCodeBlock = false;
-  for (const line of lines) {
-    if (line.trim().startsWith(prefix + nodePrefix)) {
-      inCodeBlock = true;
-    } else if (inCodeBlock && line.trim().startsWith(suffix)) {
-      inCodeBlock = false;
-      yield "\n";
-    } else if (inCodeBlock) {
-      yield line;
-    }
-  }
-}
-
-const files = glob.sync(globString, { recursive: true });
-const excludedFiles = glob.sync(excludedGlobs, { recursive: true });
-
-for (const file of files.filter((file) => !excludedFiles.includes(file))) {
-  const lines = [];
-  const data = fs.readFileSync(file, "utf-8");
-  const fileLines = data.split("\n");
-
-  for (const line of yieldLines(fileLines, "```", "```")) {
-    lines.push(line);
-  }
-
-  if (lines.length > 0) {
-    const fileName = path.basename(file, ".md");
-    const outPath = path.join(nodeFolder, fileName, `${fileName}${nodeFile}`);
-    console.log(outPath)
-    fs.mkdirSync(path.dirname(outPath), { recursive: true });
-    fs.writeFileSync(outPath, asyncPrefix + "\n" + lines.join("\n") + asyncSuffix);
-  }
-}

docs/test/md_testing.py

@@ -14,6 +14,7 @@ excluded_globs = [
     "../src/concepts/*.md",
     "../src/ann_indexes.md",
     "../src/basic.md",
+    "../src/hybrid_search/hybrid_search.md",
 ]
 
 python_prefix = "py"
@@ -48,6 +49,7 @@ def yield_lines(lines: Iterator[str], prefix: str, suffix: str):
             if not skip_test:
                 yield line[strip_length:]
 
+
 for file in filter(lambda file: file not in excluded_files, files):
     with open(file, "r") as f:
         lines = list(yield_lines(iter(f), "```", "```"))

docs/test/package.json

@@ -1,13 +0,0 @@
-{
-  "name": "lancedb-docs-test",
-  "version": "1.0.0",
-  "description": "",
-  "author": "",
-  "license": "ISC",
-  "dependencies": {
-    "fs": "^0.0.1-security",
-    "glob": "^10.2.7",
-    "path": "^0.12.7",
-    "vectordb": "https://gitpkg.now.sh/lancedb/lancedb/node?main"
-  }
-}

docs/tsconfig.json

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

node/.eslintrc.js

@@ -13,5 +13,10 @@ module.exports = {
   },
   rules: {
     "@typescript-eslint/method-signature-style": "off",
+    "@typescript-eslint/quotes": "off",
+    "@typescript-eslint/semi": "off",
+    "@typescript-eslint/explicit-function-return-type": "off",
+    "@typescript-eslint/space-before-function-paren": "off",
+    "@typescript-eslint/indent": "off",
   }
 }

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.4.4",
+  "version": "0.4.13",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.4.4",
+      "version": "0.4.13",
       "cpu": [
         "x64",
         "arm64"
@@ -18,9 +18,7 @@
         "win32"
       ],
       "dependencies": {
-        "@apache-arrow/ts": "^14.0.2",
         "@neon-rs/load": "^0.0.74",
-        "apache-arrow": "^14.0.2",
         "axios": "^1.4.0"
       },
       "devDependencies": {
@@ -33,6 +31,7 @@
         "@types/temp": "^0.9.1",
         "@types/uuid": "^9.0.3",
         "@typescript-eslint/eslint-plugin": "^5.59.1",
+        "apache-arrow-old": "npm:apache-arrow@13.0.0",
         "cargo-cp-artifact": "^0.1",
         "chai": "^4.3.7",
         "chai-as-promised": "^7.1.1",
@@ -53,11 +52,15 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.4.4",
+        "@lancedb/vectordb-darwin-arm64": "0.4.13",
-        "@lancedb/vectordb-darwin-x64": "0.4.4",
+        "@lancedb/vectordb-darwin-x64": "0.4.13",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.4.4",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.4.13",
-        "@lancedb/vectordb-linux-x64-gnu": "0.4.4",
+        "@lancedb/vectordb-linux-x64-gnu": "0.4.13",
-        "@lancedb/vectordb-win32-x64-msvc": "0.4.4"
+        "@lancedb/vectordb-win32-x64-msvc": "0.4.13"
+      },
+      "peerDependencies": {
+        "@apache-arrow/ts": "^14.0.2",
+        "apache-arrow": "^14.0.2"
       }
     },
     "node_modules/@75lb/deep-merge": {
@@ -93,6 +96,7 @@
       "version": "14.0.2",
       "resolved": "https://registry.npmjs.org/@apache-arrow/ts/-/ts-14.0.2.tgz",
       "integrity": "sha512-CtwAvLkK0CZv7xsYeCo91ml6PvlfzAmAJZkRYuz2GNBwfYufj5SVi0iuSMwIMkcU/szVwvLdzORSLa5PlF/2ug==",
+      "peer": true,
       "dependencies": {
         "@types/command-line-args": "5.2.0",
         "@types/command-line-usage": "5.0.2",
@@ -109,7 +113,8 @@
     "node_modules/@apache-arrow/ts/node_modules/@types/node": {
       "version": "20.3.0",
       "resolved": "https://registry.npmjs.org/@types/node/-/node-20.3.0.tgz",
-      "integrity": "sha512-cumHmIAf6On83X7yP+LrsEyUOf/YlociZelmpRYaGFydoaPdxdt80MAbu6vWerQT2COCp2nPvHdsbD7tHn/YlQ=="
+      "integrity": "sha512-cumHmIAf6On83X7yP+LrsEyUOf/YlociZelmpRYaGFydoaPdxdt80MAbu6vWerQT2COCp2nPvHdsbD7tHn/YlQ==",
+      "peer": true
     },
     "node_modules/@cargo-messages/android-arm-eabi": {
       "version": "0.0.160",
@@ -328,6 +333,66 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
+    "node_modules/@lancedb/vectordb-darwin-arm64": {
+      "version": "0.4.13",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.4.13.tgz",
+      "integrity": "sha512-JfroNCG8yKIU931Y+x8d0Fp8C9DHUSC5j+CjI+e5err7rTWtie4j3JbsXlWAnPFaFEOg0Xk3BWkSikCvhPGJGg==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-darwin-x64": {
+      "version": "0.4.13",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.4.13.tgz",
+      "integrity": "sha512-dG6IMvfpHpnHdbJ0UffzJ7cZfMiC02MjIi6YJzgx+hKz2UNXWNBIfTvvhqli85mZsGRXL1OYDdYv0K1YzNjXlA==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
+      "version": "0.4.13",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.4.13.tgz",
+      "integrity": "sha512-BRR1VzaMviXby7qmLm0axNZM8eUZF3ZqfvnDKdVRpC3LaRueD6pMXHuC2IUKaFkn7xktf+8BlDZb6foFNEj8bQ==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
+      "version": "0.4.13",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.4.13.tgz",
+      "integrity": "sha512-WnekZ7ZMlria+NODZ6aBCljCFQSe2bBNUS9ZpyFl/Y1vHduSQPuBxM6V7vp2QubC0daq/rifgjDob89DF+x3xw==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
+      "version": "0.4.13",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.4.13.tgz",
+      "integrity": "sha512-3NDpMWBL2ksDHXAraXhowiLqQcNWM5bdbeHwze4+InYMD54hyQ2ODNc+4usxp63Nya9biVnFS27yXULqkzIEqQ==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "win32"
+      ]
+    },
     "node_modules/@neon-rs/cli": {
       "version": "0.0.160",
       "resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.160.tgz",
@@ -888,6 +953,7 @@
       "version": "14.0.2",
       "resolved": "https://registry.npmjs.org/apache-arrow/-/apache-arrow-14.0.2.tgz",
       "integrity": "sha512-EBO2xJN36/XoY81nhLcwCJgFwkboDZeyNQ+OPsG7bCoQjc2BT0aTyH/MR6SrL+LirSNz+cYqjGRlupMMlP1aEg==",
+      "peer": true,
       "dependencies": {
         "@types/command-line-args": "5.2.0",
         "@types/command-line-usage": "5.0.2",
@@ -904,10 +970,39 @@
         "arrow2csv": "bin/arrow2csv.js"
       }
     },
+    "node_modules/apache-arrow-old": {
+      "name": "apache-arrow",
+      "version": "13.0.0",
+      "resolved": "https://registry.npmjs.org/apache-arrow/-/apache-arrow-13.0.0.tgz",
+      "integrity": "sha512-3gvCX0GDawWz6KFNC28p65U+zGh/LZ6ZNKWNu74N6CQlKzxeoWHpi4CgEQsgRSEMuyrIIXi1Ea2syja7dwcHvw==",
+      "dev": true,
+      "dependencies": {
+        "@types/command-line-args": "5.2.0",
+        "@types/command-line-usage": "5.0.2",
+        "@types/node": "20.3.0",
+        "@types/pad-left": "2.1.1",
+        "command-line-args": "5.2.1",
+        "command-line-usage": "7.0.1",
+        "flatbuffers": "23.5.26",
+        "json-bignum": "^0.0.3",
+        "pad-left": "^2.1.0",
+        "tslib": "^2.5.3"
+      },
+      "bin": {
+        "arrow2csv": "bin/arrow2csv.js"
+      }
+    },
+    "node_modules/apache-arrow-old/node_modules/@types/node": {
+      "version": "20.3.0",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.3.0.tgz",
+      "integrity": "sha512-cumHmIAf6On83X7yP+LrsEyUOf/YlociZelmpRYaGFydoaPdxdt80MAbu6vWerQT2COCp2nPvHdsbD7tHn/YlQ==",
+      "dev": true
+    },
     "node_modules/apache-arrow/node_modules/@types/node": {
       "version": "20.3.0",
       "resolved": "https://registry.npmjs.org/@types/node/-/node-20.3.0.tgz",
-      "integrity": "sha512-cumHmIAf6On83X7yP+LrsEyUOf/YlociZelmpRYaGFydoaPdxdt80MAbu6vWerQT2COCp2nPvHdsbD7tHn/YlQ=="
+      "integrity": "sha512-cumHmIAf6On83X7yP+LrsEyUOf/YlociZelmpRYaGFydoaPdxdt80MAbu6vWerQT2COCp2nPvHdsbD7tHn/YlQ==",
+      "peer": true
     },
     "node_modules/arg": {
       "version": "4.1.3",