Merge branch 'main' of https://github.com/lancedb/lancedb into yang/relative-lance-dep

Signed-off-by: BubbleCal <bubble-cal@outlook.com>

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.14.0-beta.0"
+current_version = "0.13.0-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.
@@ -87,16 +87,6 @@ glob = "node/package.json"
 replace = "\"@lancedb/vectordb-linux-x64-gnu\": \"{new_version}\""
 search = "\"@lancedb/vectordb-linux-x64-gnu\": \"{current_version}\""
 
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-linux-arm64-musl\": \"{new_version}\""
-search = "\"@lancedb/vectordb-linux-arm64-musl\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-linux-x64-musl\": \"{new_version}\""
-search = "\"@lancedb/vectordb-linux-x64-musl\": \"{current_version}\""
-
 [[tool.bumpversion.files]]
 glob = "node/package.json"
 replace = "\"@lancedb/vectordb-win32-x64-msvc\": \"{new_version}\""

.cargo/config.toml

@@ -31,9 +31,6 @@ rustflags = [
 [target.x86_64-unknown-linux-gnu]
 rustflags = ["-C", "target-cpu=haswell", "-C", "target-feature=+avx2,+fma,+f16c"]
 
-[target.x86_64-unknown-linux-musl]
-rustflags = ["-C", "target-cpu=haswell", "-C", "target-feature=-crt-static,+avx2,+fma,+f16c"]
-
 [target.aarch64-apple-darwin]
 rustflags = ["-C", "target-cpu=apple-m1", "-C", "target-feature=+neon,+fp16,+fhm,+dotprod"]
 

.github/workflows/docs.yml

@@ -31,7 +31,7 @@ jobs:
       - name: Install dependecies needed for ubuntu
         run: |
           sudo apt install -y protobuf-compiler libssl-dev
-          rustup update && rustup default
+          rustup update && rustup default        
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
@@ -41,8 +41,8 @@ jobs:
       - name: Build Python
         working-directory: python
         run: |
-          python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .
-          python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -r ../docs/requirements.txt
+          python -m pip install -e .
+          python -m pip install -r ../docs/requirements.txt
       - name: Set up node
         uses: actions/setup-node@v3
         with:

.github/workflows/docs_test.yml

@@ -49,7 +49,7 @@ jobs:
     - name: Build Python
       working-directory: docs/test
       run:
-        python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -r requirements.txt
+        python -m pip install -r requirements.txt
     - name: Create test files
       run: |
         cd docs/test

.github/workflows/nodejs.yml

@@ -53,9 +53,6 @@ jobs:
         cargo clippy --all --all-features -- -D warnings
         npm ci
         npm run lint-ci
-    - name: Lint examples
-      working-directory: nodejs/examples
-      run: npm ci && npm run lint-ci
   linux:
     name: Linux (NodeJS ${{ matrix.node-version }})
     timeout-minutes: 30
@@ -94,18 +91,6 @@ jobs:
       env:
         S3_TEST: "1"
       run: npm run test
-    - name: Setup examples
-      working-directory: nodejs/examples
-      run: npm ci
-    - name: Test examples
-      working-directory: ./
-      env:
-        OPENAI_API_KEY: test
-        OPENAI_BASE_URL: http://0.0.0.0:8000
-      run: |
-        python ci/mock_openai.py &
-        cd nodejs/examples
-        npm test
   macos:
     timeout-minutes: 30
     runs-on: "macos-14"

.github/workflows/npm-publish.yml

@@ -101,7 +101,7 @@ jobs:
           path: |
             nodejs/dist/*.node
 
-  node-linux-gnu:
+  node-linux:
     name: vectordb (${{ matrix.config.arch}}-unknown-linux-gnu)
     runs-on: ${{ matrix.config.runner }}
     # Only runs on tags that matches the make-release action
@@ -133,70 +133,15 @@ jobs:
           free -h
       - name: Build Linux Artifacts
         run: |
-          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }} ${{ matrix.config.arch }}-unknown-linux-gnu
+          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }}
       - name: Upload Linux Artifacts
         uses: actions/upload-artifact@v4
         with:
-          name: node-native-linux-${{ matrix.config.arch }}-gnu
+          name: node-native-linux-${{ matrix.config.arch }}
           path: |
             node/dist/lancedb-vectordb-linux*.tgz
 
-  node-linux-musl:
-    name: vectordb (${{ matrix.config.arch}}-unknown-linux-musl)
-    runs-on: ${{ matrix.config.runner }}
-    container: alpine:edge
-    # Only runs on tags that matches the make-release action
-    if: startsWith(github.ref, 'refs/tags/v')
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - arch: x86_64
-            runner: ubuntu-latest
-          - arch: aarch64
-            # 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@v4
-      - name: Install common dependencies
-        run: |
-          apk add protobuf-dev curl clang mold grep npm bash
-          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y --default-toolchain 1.80.0
-          echo "source $HOME/.cargo/env" >> saved_env
-          echo "export CC=clang" >> saved_env
-          echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=-crt-static,+avx2,+fma,+f16c -Clinker=clang -Clink-arg=-fuse-ld=mold'" >> saved_env
-      - name: Configure aarch64 build
-        if: ${{ matrix.config.arch == 'aarch64' }}
-        run: |
-          source "$HOME/.cargo/env"
-          rustup target add aarch64-unknown-linux-musl --toolchain 1.80.0
-          crt=$(realpath $(dirname $(rustup which rustc))/../lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained)
-          sysroot_lib=/usr/aarch64-unknown-linux-musl/usr/lib
-          apk_url=https://dl-cdn.alpinelinux.org/alpine/latest-stable/main/aarch64/
-          curl -sSf $apk_url > apk_list
-          for pkg in gcc libgcc musl; do curl -sSf $apk_url$(cat apk_list | grep -oP '(?<=")'$pkg'-\d.*?(?=")') | tar zxf -; done
-          mkdir -p $sysroot_lib
-          echo 'GROUP ( libgcc_s.so.1 -lgcc )' > $sysroot_lib/libgcc_s.so
-          cp usr/lib/libgcc_s.so.1 $sysroot_lib
-          cp usr/lib/gcc/aarch64-alpine-linux-musl/*/libgcc.a $sysroot_lib
-          cp lib/ld-musl-aarch64.so.1 $sysroot_lib/libc.so
-          echo '!<arch>' > $sysroot_lib/libdl.a
-          (cd $crt && cp crti.o crtbeginS.o crtendS.o crtn.o -t $sysroot_lib)
-          echo "export CARGO_BUILD_TARGET=aarch64-unknown-linux-musl" >> saved_env
-          echo "export RUSTFLAGS='-Ctarget-cpu=apple-m1 -Ctarget-feature=-crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=clang -Clink-arg=-fuse-ld=mold -Clink-arg=--target=aarch64-unknown-linux-musl -Clink-arg=--sysroot=/usr/aarch64-unknown-linux-musl -Clink-arg=-lc'" >> saved_env
-      - name: Build Linux Artifacts
-        run: |
-          source ./saved_env
-          bash ci/manylinux_node/build_vectordb.sh ${{ matrix.config.arch }} ${{ matrix.config.arch }}-unknown-linux-musl
-      - name: Upload Linux Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: node-native-linux-${{ matrix.config.arch }}-musl
-          path: |
-            node/dist/lancedb-vectordb-linux*.tgz
-
-  nodejs-linux-gnu:
+  nodejs-linux:
     name: lancedb (${{ matrix.config.arch}}-unknown-linux-gnu
     runs-on: ${{ matrix.config.runner }}
     # Only runs on tags that matches the make-release action
@@ -233,7 +178,7 @@ jobs:
       - name: Upload Linux Artifacts
         uses: actions/upload-artifact@v4
         with:
-          name: nodejs-native-linux-${{ matrix.config.arch }}-gnu
+          name: nodejs-native-linux-${{ matrix.config.arch }}
           path: |
             nodejs/dist/*.node
       # The generic files are the same in all distros so we just pick
@@ -247,65 +192,6 @@ jobs:
             nodejs/dist/*
             !nodejs/dist/*.node
 
-  nodejs-linux-musl:
-    name: lancedb (${{ matrix.config.arch}}-unknown-linux-musl
-    runs-on: ${{ matrix.config.runner }}
-    container: alpine:edge
-    # Only runs on tags that matches the make-release action
-    if: startsWith(github.ref, 'refs/tags/v')
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - arch: x86_64
-            runner: ubuntu-latest
-          - arch: aarch64
-            # 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@v4
-      - name: Install common dependencies
-        run: |
-          apk add protobuf-dev curl clang mold grep npm bash openssl-dev openssl-libs-static
-          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y --default-toolchain 1.80.0
-          echo "source $HOME/.cargo/env" >> saved_env
-          echo "export CC=clang" >> saved_env
-          echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=-crt-static,+avx2,+fma,+f16c -Clinker=clang -Clink-arg=-fuse-ld=mold'" >> saved_env
-          echo "export X86_64_UNKNOWN_LINUX_MUSL_OPENSSL_INCLUDE_DIR=/usr/include" >> saved_env
-          echo "export X86_64_UNKNOWN_LINUX_MUSL_OPENSSL_LIB_DIR=/usr/lib" >> saved_env
-      - name: Configure aarch64 build
-        if: ${{ matrix.config.arch == 'aarch64' }}
-        run: |
-          source "$HOME/.cargo/env"
-          rustup target add aarch64-unknown-linux-musl --toolchain 1.80.0
-          crt=$(realpath $(dirname $(rustup which rustc))/../lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained)
-          sysroot_lib=/usr/aarch64-unknown-linux-musl/usr/lib
-          apk_url=https://dl-cdn.alpinelinux.org/alpine/latest-stable/main/aarch64/
-          curl -sSf $apk_url > apk_list
-          for pkg in gcc libgcc musl openssl-dev openssl-libs-static; do curl -sSf $apk_url$(cat apk_list | grep -oP '(?<=")'$pkg'-\d.*?(?=")') | tar zxf -; done
-          mkdir -p $sysroot_lib
-          echo 'GROUP ( libgcc_s.so.1 -lgcc )' > $sysroot_lib/libgcc_s.so
-          cp usr/lib/libgcc_s.so.1 $sysroot_lib
-          cp usr/lib/gcc/aarch64-alpine-linux-musl/*/libgcc.a $sysroot_lib
-          cp lib/ld-musl-aarch64.so.1 $sysroot_lib/libc.so
-          echo '!<arch>' > $sysroot_lib/libdl.a
-          (cd $crt && cp crti.o crtbeginS.o crtendS.o crtn.o -t $sysroot_lib)
-          echo "export CARGO_BUILD_TARGET=aarch64-unknown-linux-musl" >> saved_env
-          echo "export RUSTFLAGS='-Ctarget-feature=-crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=clang -Clink-arg=-fuse-ld=mold -Clink-arg=--target=aarch64-unknown-linux-musl -Clink-arg=--sysroot=/usr/aarch64-unknown-linux-musl -Clink-arg=-lc'" >> saved_env
-          echo "export AARCH64_UNKNOWN_LINUX_MUSL_OPENSSL_INCLUDE_DIR=$(realpath usr/include)" >> saved_env
-          echo "export AARCH64_UNKNOWN_LINUX_MUSL_OPENSSL_LIB_DIR=$(realpath usr/lib)" >> saved_env
-      - name: Build Linux Artifacts
-        run: |
-          source ./saved_env
-          bash ci/manylinux_node/build_lancedb.sh ${{ matrix.config.arch }}
-      - name: Upload Linux Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: nodejs-native-linux-${{ matrix.config.arch }}-musl
-          path: |
-            nodejs/dist/*.node
-
   node-windows:
     name: vectordb ${{ matrix.target }}
     runs-on: windows-2022
@@ -340,109 +226,125 @@ jobs:
           path: |
             node/dist/lancedb-vectordb-win32*.tgz
 
-  # TODO: re-enable once working https://github.com/lancedb/lancedb/pull/1831
-  # node-windows-arm64:
-  #   name: vectordb win32-arm64-msvc
-  #   runs-on: windows-4x-arm
-  #   if: startsWith(github.ref, 'refs/tags/v')
-  #   steps:
-  #     - uses: actions/checkout@v4
-  #     - name: Install Git
-  #       run: |
-  #         Invoke-WebRequest -Uri "https://github.com/git-for-windows/git/releases/download/v2.44.0.windows.1/Git-2.44.0-64-bit.exe" -OutFile "git-installer.exe"
-  #         Start-Process -FilePath "git-installer.exe" -ArgumentList "/VERYSILENT", "/NORESTART" -Wait
-  #       shell: powershell
-  #     - name: Add Git to PATH
-  #       run: |
-  #         Add-Content $env:GITHUB_PATH "C:\Program Files\Git\bin"
-  #         $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
-  #       shell: powershell
-  #     - name: Configure Git symlinks
-  #       run: git config --global core.symlinks true
-  #     - uses: actions/checkout@v4
-  #     - uses: actions/setup-python@v5
-  #       with:
-  #         python-version: "3.13"
-  #     - name: Install Visual Studio Build Tools
-  #       run: |
-  #         Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vs_buildtools.exe" -OutFile "vs_buildtools.exe"
-  #         Start-Process -FilePath "vs_buildtools.exe" -ArgumentList "--quiet", "--wait", "--norestart", "--nocache", `
-  #           "--installPath", "C:\BuildTools", `
-  #           "--add", "Microsoft.VisualStudio.Component.VC.Tools.ARM64", `
-  #           "--add", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", `
-  #           "--add", "Microsoft.VisualStudio.Component.Windows11SDK.22621", `
-  #           "--add", "Microsoft.VisualStudio.Component.VC.ATL", `
-  #           "--add", "Microsoft.VisualStudio.Component.VC.ATLMFC", `
-  #           "--add", "Microsoft.VisualStudio.Component.VC.Llvm.Clang" -Wait
-  #       shell: powershell
-  #     - name: Add Visual Studio Build Tools to PATH
-  #       run: |
-  #         $vsPath = "C:\BuildTools\VC\Tools\MSVC"
-  #         $latestVersion = (Get-ChildItem $vsPath | Sort-Object {[version]$_.Name} -Descending)[0].Name
-  #         Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\arm64"
-  #         Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\x64"
-  #         Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\arm64"
-  #         Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\x64"
-  #         Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\Llvm\x64\bin"
+  node-windows-arm64:
+    name: vectordb win32-arm64-msvc
+    runs-on: windows-4x-arm
+    if: startsWith(github.ref, 'refs/tags/v')
+    steps:
+      - uses: actions/checkout@v4
+      - name: Cache installations
+        id: cache-installs
+        uses: actions/cache@v4
+        with:
+          path: |
+            C:\Program Files\Git
+            C:\BuildTools
+            C:\Program Files (x86)\Windows Kits
+            C:\Program Files\7-Zip
+            C:\protoc
+          key: ${{ runner.os }}-arm64-installs-v1
+          restore-keys: |
+            ${{ runner.os }}-arm64-installs-
+      - name: Install Git
+        if: steps.cache-installs.outputs.cache-hit != 'true'
+        run: |
+          Invoke-WebRequest -Uri "https://github.com/git-for-windows/git/releases/download/v2.44.0.windows.1/Git-2.44.0-64-bit.exe" -OutFile "git-installer.exe"
+          Start-Process -FilePath "git-installer.exe" -ArgumentList "/VERYSILENT", "/NORESTART" -Wait
+        shell: powershell
+      - name: Add Git to PATH
+        run: |
+          Add-Content $env:GITHUB_PATH "C:\Program Files\Git\bin"
+          $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+        shell: powershell
+      - name: Configure Git symlinks
+        run: git config --global core.symlinks true
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.13"
+      - name: Install Visual Studio Build Tools
+        if: steps.cache-installs.outputs.cache-hit != 'true'
+        run: |
+          Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vs_buildtools.exe" -OutFile "vs_buildtools.exe"
+          Start-Process -FilePath "vs_buildtools.exe" -ArgumentList "--quiet", "--wait", "--norestart", "--nocache", `
+            "--installPath", "C:\BuildTools", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Tools.ARM64", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", `
+            "--add", "Microsoft.VisualStudio.Component.Windows11SDK.22621", `
+            "--add", "Microsoft.VisualStudio.Component.VC.ATL", `
+            "--add", "Microsoft.VisualStudio.Component.VC.ATLMFC", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Llvm.Clang" -Wait
+        shell: powershell
+      - name: Add Visual Studio Build Tools to PATH
+        run: |
+          $vsPath = "C:\BuildTools\VC\Tools\MSVC"
+          $latestVersion = (Get-ChildItem $vsPath | Sort-Object {[version]$_.Name} -Descending)[0].Name
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\arm64"
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\x64"
+          Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\arm64"
+          Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\x64"
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\Llvm\x64\bin"
 
-  #         # Add MSVC runtime libraries to LIB
-  #         $env:LIB = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\lib\arm64;" +
-  #                    "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\arm64;" +
-  #                    "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\arm64"
-  #         Add-Content $env:GITHUB_ENV "LIB=$env:LIB"
+          # Add MSVC runtime libraries to LIB
+          $env:LIB = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\lib\arm64;" + 
+                     "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\arm64;" +
+                     "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\arm64"
+          Add-Content $env:GITHUB_ENV "LIB=$env:LIB"
 
-  #         # Add INCLUDE paths
-  #         $env:INCLUDE = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\include;" +
-  #                       "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\ucrt;" +
-  #                       "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\um;" +
-  #                       "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\shared"
-  #         Add-Content $env:GITHUB_ENV "INCLUDE=$env:INCLUDE"
-  #       shell: powershell
-  #     - name: Install Rust
-  #       run: |
-  #         Invoke-WebRequest https://win.rustup.rs/x86_64 -OutFile rustup-init.exe
-  #         .\rustup-init.exe -y --default-host aarch64-pc-windows-msvc
-  #       shell: powershell
-  #     - name: Add Rust to PATH
-  #       run: |
-  #         Add-Content $env:GITHUB_PATH "$env:USERPROFILE\.cargo\bin"
-  #       shell: powershell
+          # Add INCLUDE paths
+          $env:INCLUDE = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\include;" +
+                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\ucrt;" +
+                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\um;" +
+                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\shared"
+          Add-Content $env:GITHUB_ENV "INCLUDE=$env:INCLUDE"
+        shell: powershell
+      - name: Install Rust
+        run: |
+          Invoke-WebRequest https://win.rustup.rs/x86_64 -OutFile rustup-init.exe
+          .\rustup-init.exe -y --default-host aarch64-pc-windows-msvc
+        shell: powershell
+      - name: Add Rust to PATH
+        run: |
+          Add-Content $env:GITHUB_PATH "$env:USERPROFILE\.cargo\bin"
+        shell: powershell
 
-  #     - uses: Swatinem/rust-cache@v2
-  #       with:
-  #         workspaces: rust
-  #     - name: Install 7-Zip ARM
-  #       run: |
-  #         New-Item -Path 'C:\7zip' -ItemType Directory
-  #         Invoke-WebRequest https://7-zip.org/a/7z2408-arm64.exe -OutFile C:\7zip\7z-installer.exe
-  #         Start-Process -FilePath C:\7zip\7z-installer.exe -ArgumentList '/S' -Wait
-  #       shell: powershell
-  #     - name: Add 7-Zip to PATH
-  #       run: Add-Content $env:GITHUB_PATH "C:\Program Files\7-Zip"
-  #       shell: powershell
-  #     - name: Install Protoc v21.12
-  #       working-directory: C:\
-  #       run: |
-  #         if (Test-Path 'C:\protoc') {
-  #             Write-Host "Protoc directory exists, skipping installation"
-  #             return
-  #         }
-  #         New-Item -Path 'C:\protoc' -ItemType Directory
-  #         Set-Location C:\protoc
-  #         Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
-  #         & 'C:\Program Files\7-Zip\7z.exe' x protoc.zip
-  #       shell: powershell
-  #     - name: Add Protoc to PATH
-  #       run: Add-Content $env:GITHUB_PATH "C:\protoc\bin"
-  #       shell: powershell
-  #     - name: Build Windows native node modules
-  #       run: .\ci\build_windows_artifacts.ps1 aarch64-pc-windows-msvc
-  #     - name: Upload Windows ARM64 Artifacts
-  #       uses: actions/upload-artifact@v4
-  #       with:
-  #         name: node-native-windows-arm64
-  #         path: |
-  #           node/dist/*.node
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: rust
+      - name: Install 7-Zip ARM
+        if: steps.cache-installs.outputs.cache-hit != 'true'
+        run: |
+          New-Item -Path 'C:\7zip' -ItemType Directory
+          Invoke-WebRequest https://7-zip.org/a/7z2408-arm64.exe -OutFile C:\7zip\7z-installer.exe
+          Start-Process -FilePath C:\7zip\7z-installer.exe -ArgumentList '/S' -Wait
+        shell: powershell
+      - name: Add 7-Zip to PATH
+        run: Add-Content $env:GITHUB_PATH "C:\Program Files\7-Zip"
+        shell: powershell
+      - name: Install Protoc v21.12
+        if: steps.cache-installs.outputs.cache-hit != 'true'
+        working-directory: C:\
+        run: |
+          if (Test-Path 'C:\protoc') {
+              Write-Host "Protoc directory exists, skipping installation"
+              return
+          }
+          New-Item -Path 'C:\protoc' -ItemType Directory
+          Set-Location C:\protoc
+          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+          & 'C:\Program Files\7-Zip\7z.exe' x protoc.zip
+        shell: powershell
+      - name: Add Protoc to PATH
+        run: Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+        shell: powershell
+      - name: Build Windows native node modules
+        run: .\ci\build_windows_artifacts.ps1 aarch64-pc-windows-msvc
+      - name: Upload Windows ARM64 Artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: node-native-windows-arm64
+          path: |
+            node/dist/*.node
 
   nodejs-windows:
     name: lancedb ${{ matrix.target }}
@@ -478,103 +380,119 @@ jobs:
           path: |
             nodejs/dist/*.node
 
-  # TODO: re-enable once working https://github.com/lancedb/lancedb/pull/1831
-  # nodejs-windows-arm64:
-  #   name: lancedb win32-arm64-msvc
-  #   runs-on: windows-4x-arm
-  #   if: startsWith(github.ref, 'refs/tags/v')
-  #   steps:
-  #     - uses: actions/checkout@v4
-  #     - name: Install Git
-  #       run: |
-  #         Invoke-WebRequest -Uri "https://github.com/git-for-windows/git/releases/download/v2.44.0.windows.1/Git-2.44.0-64-bit.exe" -OutFile "git-installer.exe"
-  #         Start-Process -FilePath "git-installer.exe" -ArgumentList "/VERYSILENT", "/NORESTART" -Wait
-  #       shell: powershell
-  #     - name: Add Git to PATH
-  #       run: |
-  #         Add-Content $env:GITHUB_PATH "C:\Program Files\Git\bin"
-  #         $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
-  #       shell: powershell
-  #     - name: Configure Git symlinks
-  #       run: git config --global core.symlinks true
-  #     - uses: actions/checkout@v4
-  #     - uses: actions/setup-python@v5
-  #       with:
-  #         python-version: "3.13"
-  #     - name: Install Visual Studio Build Tools
-  #       run: |
-  #         Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vs_buildtools.exe" -OutFile "vs_buildtools.exe"
-  #         Start-Process -FilePath "vs_buildtools.exe" -ArgumentList "--quiet", "--wait", "--norestart", "--nocache", `
-  #           "--installPath", "C:\BuildTools", `
-  #           "--add", "Microsoft.VisualStudio.Component.VC.Tools.ARM64", `
-  #           "--add", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", `
-  #           "--add", "Microsoft.VisualStudio.Component.Windows11SDK.22621", `
-  #           "--add", "Microsoft.VisualStudio.Component.VC.ATL", `
-  #           "--add", "Microsoft.VisualStudio.Component.VC.ATLMFC", `
-  #           "--add", "Microsoft.VisualStudio.Component.VC.Llvm.Clang" -Wait
-  #       shell: powershell
-  #     - name: Add Visual Studio Build Tools to PATH
-  #       run: |
-  #         $vsPath = "C:\BuildTools\VC\Tools\MSVC"
-  #         $latestVersion = (Get-ChildItem $vsPath | Sort-Object {[version]$_.Name} -Descending)[0].Name
-  #         Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\arm64"
-  #         Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\x64"
-  #         Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\arm64"
-  #         Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\x64"
-  #         Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\Llvm\x64\bin"
+  nodejs-windows-arm64:
+    name: lancedb win32-arm64-msvc
+    runs-on: windows-4x-arm
+    if: startsWith(github.ref, 'refs/tags/v')
+    steps:
+      - uses: actions/checkout@v4
+      - name: Cache installations
+        id: cache-installs
+        uses: actions/cache@v4
+        with:
+          path: |
+            C:\Program Files\Git
+            C:\BuildTools
+            C:\Program Files (x86)\Windows Kits
+            C:\Program Files\7-Zip
+            C:\protoc
+          key: ${{ runner.os }}-arm64-installs-v1
+          restore-keys: |
+            ${{ runner.os }}-arm64-installs-
+      - name: Install Git
+        if: steps.cache-installs.outputs.cache-hit != 'true'
+        run: |
+          Invoke-WebRequest -Uri "https://github.com/git-for-windows/git/releases/download/v2.44.0.windows.1/Git-2.44.0-64-bit.exe" -OutFile "git-installer.exe"
+          Start-Process -FilePath "git-installer.exe" -ArgumentList "/VERYSILENT", "/NORESTART" -Wait
+        shell: powershell
+      - name: Add Git to PATH
+        run: |
+          Add-Content $env:GITHUB_PATH "C:\Program Files\Git\bin"
+          $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+        shell: powershell
+      - name: Configure Git symlinks
+        run: git config --global core.symlinks true
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.13"
+      - name: Install Visual Studio Build Tools
+        if: steps.cache-installs.outputs.cache-hit != 'true'
+        run: |
+          Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vs_buildtools.exe" -OutFile "vs_buildtools.exe"
+          Start-Process -FilePath "vs_buildtools.exe" -ArgumentList "--quiet", "--wait", "--norestart", "--nocache", `
+            "--installPath", "C:\BuildTools", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Tools.ARM64", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", `
+            "--add", "Microsoft.VisualStudio.Component.Windows11SDK.22621", `
+            "--add", "Microsoft.VisualStudio.Component.VC.ATL", `
+            "--add", "Microsoft.VisualStudio.Component.VC.ATLMFC", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Llvm.Clang" -Wait
+        shell: powershell
+      - name: Add Visual Studio Build Tools to PATH
+        run: |
+          $vsPath = "C:\BuildTools\VC\Tools\MSVC"
+          $latestVersion = (Get-ChildItem $vsPath | Sort-Object {[version]$_.Name} -Descending)[0].Name
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\arm64"
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\x64"
+          Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\arm64"
+          Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\x64"
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\Llvm\x64\bin"
 
-  #         $env:LIB = ""
-  #         Add-Content $env:GITHUB_ENV "LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\arm64;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\arm64"
-  #       shell: powershell
-  #     - name: Install Rust
-  #       run: |
-  #         Invoke-WebRequest https://win.rustup.rs/x86_64 -OutFile rustup-init.exe
-  #         .\rustup-init.exe -y --default-host aarch64-pc-windows-msvc
-  #       shell: powershell
-  #     - name: Add Rust to PATH
-  #       run: |
-  #         Add-Content $env:GITHUB_PATH "$env:USERPROFILE\.cargo\bin"
-  #       shell: powershell
+          $env:LIB = ""
+          Add-Content $env:GITHUB_ENV "LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\arm64;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\arm64"
+        shell: powershell
+      - name: Install Rust
+        run: |
+          Invoke-WebRequest https://win.rustup.rs/x86_64 -OutFile rustup-init.exe
+          .\rustup-init.exe -y --default-host aarch64-pc-windows-msvc
+        shell: powershell
+      - name: Add Rust to PATH
+        run: |
+          Add-Content $env:GITHUB_PATH "$env:USERPROFILE\.cargo\bin"
+        shell: powershell
 
-  #     - uses: Swatinem/rust-cache@v2
-  #       with:
-  #         workspaces: rust
-  #     - name: Install 7-Zip ARM
-  #       run: |
-  #         New-Item -Path 'C:\7zip' -ItemType Directory
-  #         Invoke-WebRequest https://7-zip.org/a/7z2408-arm64.exe -OutFile C:\7zip\7z-installer.exe
-  #         Start-Process -FilePath C:\7zip\7z-installer.exe -ArgumentList '/S' -Wait
-  #       shell: powershell
-  #     - name: Add 7-Zip to PATH
-  #       run: Add-Content $env:GITHUB_PATH "C:\Program Files\7-Zip"
-  #       shell: powershell
-  #     - name: Install Protoc v21.12
-  #       working-directory: C:\
-  #       run: |
-  #         if (Test-Path 'C:\protoc') {
-  #             Write-Host "Protoc directory exists, skipping installation"
-  #             return
-  #         }
-  #         New-Item -Path 'C:\protoc' -ItemType Directory
-  #         Set-Location C:\protoc
-  #         Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
-  #         & 'C:\Program Files\7-Zip\7z.exe' x protoc.zip
-  #       shell: powershell
-  #     - name: Add Protoc to PATH
-  #       run: Add-Content $env:GITHUB_PATH "C:\protoc\bin"
-  #       shell: powershell
-  #     - name: Build Windows native node modules
-  #       run: .\ci\build_windows_artifacts_nodejs.ps1 aarch64-pc-windows-msvc
-  #     - name: Upload Windows ARM64 Artifacts
-  #       uses: actions/upload-artifact@v4
-  #       with:
-  #         name: nodejs-native-windows-arm64
-  #         path: |
-  #           nodejs/dist/*.node
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: rust
+      - name: Install 7-Zip ARM
+        if: steps.cache-installs.outputs.cache-hit != 'true'
+        run: |
+          New-Item -Path 'C:\7zip' -ItemType Directory
+          Invoke-WebRequest https://7-zip.org/a/7z2408-arm64.exe -OutFile C:\7zip\7z-installer.exe
+          Start-Process -FilePath C:\7zip\7z-installer.exe -ArgumentList '/S' -Wait
+        shell: powershell
+      - name: Add 7-Zip to PATH
+        run: Add-Content $env:GITHUB_PATH "C:\Program Files\7-Zip"
+        shell: powershell
+      - name: Install Protoc v21.12
+        if: steps.cache-installs.outputs.cache-hit != 'true'
+        working-directory: C:\
+        run: |
+          if (Test-Path 'C:\protoc') {
+              Write-Host "Protoc directory exists, skipping installation"
+              return
+          }
+          New-Item -Path 'C:\protoc' -ItemType Directory
+          Set-Location C:\protoc
+          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+          & 'C:\Program Files\7-Zip\7z.exe' x protoc.zip
+        shell: powershell
+      - name: Add Protoc to PATH
+        run: Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+        shell: powershell
+      - name: Build Windows native node modules
+        run: .\ci\build_windows_artifacts_nodejs.ps1 aarch64-pc-windows-msvc
+      - name: Upload Windows ARM64 Artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: nodejs-native-windows-arm64
+          path: |
+            nodejs/dist/*.node
 
   release:
     name: vectordb NPM Publish
-    needs: [node, node-macos, node-linux-gnu, node-linux-musl, node-windows]
+    needs: [node, node-macos, node-linux, node-windows, node-windows-arm64]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -592,7 +510,7 @@ jobs:
         env:
           NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
         run: |
-          # Tag beta as "preview" instead of default "latest". See lancedb
+          # Tag beta as "preview" instead of default "latest". See lancedb 
           # npm publish step for more info.
           if [[ $GITHUB_REF =~ refs/tags/v(.*)-beta.* ]]; then
             PUBLISH_ARGS="--tag preview"
@@ -614,7 +532,7 @@ jobs:
 
   release-nodejs:
     name: lancedb NPM Publish
-    needs: [nodejs-macos, nodejs-linux-gnu, nodejs-linux-musl, nodejs-windows]
+    needs: [nodejs-macos, nodejs-linux, nodejs-windows, nodejs-windows-arm64]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')

.github/workflows/python.yml

@@ -138,7 +138,7 @@ jobs:
         run: rm -rf target/wheels
   windows:
     name: "Windows: ${{ matrix.config.name }}"
-    timeout-minutes: 60
+    timeout-minutes: 30
     strategy:
       matrix:
         config:

.github/workflows/rust.yml

@@ -50,7 +50,6 @@ jobs:
         run: cargo fmt --all -- --check
       - name: Run clippy
         run: cargo clippy --workspace --tests --all-features -- -D warnings
-
   linux:
     timeout-minutes: 30
     # To build all features, we need more disk space than is available
@@ -92,7 +91,6 @@ jobs:
         run: cargo test --all-features
       - name: Run examples
         run: cargo run --example simple
-
   macos:
     timeout-minutes: 30
     strategy:
@@ -120,7 +118,6 @@ jobs:
       - name: Run tests
         # Run with everything except the integration tests.
         run: cargo test --features remote,fp16kernels
-
   windows:
     runs-on: windows-2022
     steps:
@@ -142,11 +139,24 @@ jobs:
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
           cargo build
           cargo test
-
   windows-arm64:
     runs-on: windows-4x-arm
     steps:
+      - name: Cache installations
+        id: cache-installs
+        uses: actions/cache@v4
+        with:
+          path: |
+            C:\Program Files\Git
+            C:\BuildTools
+            C:\Program Files (x86)\Windows Kits
+            C:\Program Files\7-Zip
+            C:\protoc
+          key: ${{ runner.os }}-arm64-installs-v1
+          restore-keys: |
+            ${{ runner.os }}-arm64-installs-
       - name: Install Git
+        if: steps.cache-installs.outputs.cache-hit != 'true'
         run: |
           Invoke-WebRequest -Uri "https://github.com/git-for-windows/git/releases/download/v2.44.0.windows.1/Git-2.44.0-64-bit.exe" -OutFile "git-installer.exe"
           Start-Process -FilePath "git-installer.exe" -ArgumentList "/VERYSILENT", "/NORESTART" -Wait
@@ -163,6 +173,7 @@ jobs:
         with:
           python-version: "3.13"
       - name: Install Visual Studio Build Tools
+        if: steps.cache-installs.outputs.cache-hit != 'true'
         run: |
           Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vs_buildtools.exe" -OutFile "vs_buildtools.exe"
           Start-Process -FilePath "vs_buildtools.exe" -ArgumentList "--quiet", "--wait", "--norestart", "--nocache", `
@@ -206,10 +217,12 @@ jobs:
         run: |
           Add-Content $env:GITHUB_PATH "$env:USERPROFILE\.cargo\bin"
         shell: powershell
+
       - uses: Swatinem/rust-cache@v2
         with:
           workspaces: rust
       - name: Install 7-Zip ARM
+        if: steps.cache-installs.outputs.cache-hit != 'true'
         run: |
           New-Item -Path 'C:\7zip' -ItemType Directory
           Invoke-WebRequest https://7-zip.org/a/7z2408-arm64.exe -OutFile C:\7zip\7z-installer.exe
@@ -219,11 +232,12 @@ jobs:
         run: Add-Content $env:GITHUB_PATH "C:\Program Files\7-Zip"
         shell: powershell
       - name: Install Protoc v21.12
+        if: steps.cache-installs.outputs.cache-hit != 'true'
         working-directory: C:\
         run: |
           if (Test-Path 'C:\protoc') {
-            Write-Host "Protoc directory exists, skipping installation"
-            return
+              Write-Host "Protoc directory exists, skipping installation"
+              return
           }
           New-Item -Path 'C:\protoc' -ItemType Directory
           Set-Location C:\protoc

Cargo.toml

@@ -18,19 +18,16 @@ repository = "https://github.com/lancedb/lancedb"
 description = "Serverless, low-latency vector database for AI applications"
 keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
-rust-version = "1.80.0"                                                     # TODO: lower this once we upgrade Lance again.
+rust-version = "1.80.0" # TODO: lower this once we upgrade Lance again.
 
 [workspace.dependencies]
-lance = { "version" = "=0.20.0", "features" = [
-    "dynamodb",
-], git = "https://github.com/lancedb/lance.git", tag = "v0.20.0-beta.3" }
-lance-io = { version = "=0.20.0", git = "https://github.com/lancedb/lance.git", tag = "v0.20.0-beta.3" }
-lance-index = { version = "=0.20.0", git = "https://github.com/lancedb/lance.git", tag = "v0.20.0-beta.3" }
-lance-linalg = { version = "=0.20.0", git = "https://github.com/lancedb/lance.git", tag = "v0.20.0-beta.3" }
-lance-table = { version = "=0.20.0", git = "https://github.com/lancedb/lance.git", tag = "v0.20.0-beta.3" }
-lance-testing = { version = "=0.20.0", git = "https://github.com/lancedb/lance.git", tag = "v0.20.0-beta.3" }
-lance-datafusion = { version = "=0.20.0", git = "https://github.com/lancedb/lance.git", tag = "v0.20.0-beta.3" }
-lance-encoding = { version = "=0.20.0", git = "https://github.com/lancedb/lance.git", tag = "v0.20.0-beta.3" }
+lance = { "version" = "=0.19.2", "features" = ["dynamodb"], path = "../lance/rust/lance"}
+lance-index = { "version" = "=0.19.2", path = "../lance/rust/lance-index"}
+lance-linalg = { "version" = "=0.19.2", path = "../lance/rust/lance-linalg"}
+lance-testing = { "version" = "=0.19.2", path = "../lance/rust/lance-testing"}
+lance-datafusion = { "version" = "=0.19.2", path = "../lance/rust/lance-datafusion"}
+lance-encoding = { "version" = "=0.19.2", path = "../lance/rust/lance-encoding"}
+lance-table = { "version" = "=0.19.2", path = "../lance/rust/lance-table"}
 # Note that this one does not include pyarrow
 arrow = { version = "52.2", optional = false }
 arrow-array = "52.2"

ci/build_linux_artifacts.sh

@@ -1,9 +1,8 @@
 #!/bin/bash
 set -e
 ARCH=${1:-x86_64}
-TARGET_TRIPLE=${2:-x86_64-unknown-linux-gnu}
 
-# We pass down the current user so that when we later mount the local files
+# We pass down the current user so that when we later mount the local files 
 # into the container, the files are accessible by the current user.
 pushd ci/manylinux_node
 docker build \
@@ -19,4 +18,4 @@ docker run \
     -v $(pwd):/io -w /io \
     --memory-swap=-1 \
     lancedb-node-manylinux \
-    bash ci/manylinux_node/build_vectordb.sh $ARCH $TARGET_TRIPLE
+    bash ci/manylinux_node/build_vectordb.sh $ARCH

ci/manylinux_node/build_lancedb.sh

@@ -11,8 +11,7 @@ fi
 export OPENSSL_STATIC=1
 export OPENSSL_INCLUDE_DIR=/usr/local/include/openssl
 
-#Alpine doesn't have .bashrc
-FILE=$HOME/.bashrc && test -f $FILE && source $FILE
+source $HOME/.bashrc
 
 cd nodejs
 npm ci

ci/manylinux_node/build_vectordb.sh

@@ -2,20 +2,18 @@
 # Builds the node module for manylinux. Invoked by ci/build_linux_artifacts.sh.
 set -e
 ARCH=${1:-x86_64}
-TARGET_TRIPLE=${2:-x86_64-unknown-linux-gnu}
 
 if [ "$ARCH" = "x86_64" ]; then
     export OPENSSL_LIB_DIR=/usr/local/lib64/
-else
+else 
     export OPENSSL_LIB_DIR=/usr/local/lib/
 fi
 export OPENSSL_STATIC=1
 export OPENSSL_INCLUDE_DIR=/usr/local/include/openssl
 
-#Alpine doesn't have .bashrc
-FILE=$HOME/.bashrc && test -f $FILE && source $FILE
+source $HOME/.bashrc
 
 cd node
 npm ci
 npm run build-release
-npm run pack-build -t $TARGET_TRIPLE
+npm run pack-build

ci/mock_openai.py

@@ -1,57 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-"""A zero-dependency mock OpenAI embeddings API endpoint for testing purposes."""
-import argparse
-import json
-import http.server
-
-
-class MockOpenAIRequestHandler(http.server.BaseHTTPRequestHandler):
-    def do_POST(self):
-        content_length = int(self.headers["Content-Length"])
-        post_data = self.rfile.read(content_length)
-        post_data = json.loads(post_data.decode("utf-8"))
-        # See: https://platform.openai.com/docs/api-reference/embeddings/create
-
-        if isinstance(post_data["input"], str):
-            num_inputs = 1
-        else:
-            num_inputs = len(post_data["input"])
-
-        model = post_data.get("model", "text-embedding-ada-002")
-
-        data = []
-        for i in range(num_inputs):
-            data.append({
-                "object": "embedding",
-                "embedding": [0.1] * 1536,
-                "index": i,
-            })
-
-        response = {
-            "object": "list",
-            "data": data,
-            "model": model,
-            "usage": {
-                "prompt_tokens": 0,
-                "total_tokens": 0,
-            }
-        }
-
-        self.send_response(200)
-        self.send_header("Content-type", "application/json")
-        self.end_headers()
-        self.wfile.write(json.dumps(response).encode("utf-8"))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Mock OpenAI embeddings API endpoint")
-    parser.add_argument("--port", type=int, default=8000, help="Port to listen on")
-    args = parser.parse_args()
-    port = args.port
-
-    print(f"server started on port {port}. Press Ctrl-C to stop.")
-    print(f"To use, set OPENAI_BASE_URL=http://localhost:{port} in your environment.")
-
-    with http.server.HTTPServer(("0.0.0.0", port), MockOpenAIRequestHandler) as server:
-        server.serve_forever()

docs/mkdocs.yml

@@ -55,9 +55,6 @@ plugins:
             show_signature_annotations: true
             show_root_heading: true
             members_order: source
-            docstring_section_style: list
-            signature_crossrefs: true
-            separate_signature: true
           import:
             # for cross references
             - https://arrow.apache.org/docs/objects.inv
@@ -141,7 +138,6 @@ nav:
               - Jina Reranker: reranking/jina.md
               - OpenAI Reranker: reranking/openai.md
               - AnswerDotAi Rerankers: reranking/answerdotai.md
-              - Voyage AI Rerankers: reranking/voyageai.md
               - Building Custom Rerankers: reranking/custom_reranker.md
               - Example: notebooks/lancedb_reranking.ipynb
           - Filtering: sql.md
@@ -169,7 +165,6 @@ nav:
                   - Jina Embeddings: embeddings/available_embedding_models/text_embedding_functions/jina_embedding.md
                   - AWS Bedrock Text Embedding Functions: embeddings/available_embedding_models/text_embedding_functions/aws_bedrock_embedding.md
                   - IBM watsonx.ai Embeddings: embeddings/available_embedding_models/text_embedding_functions/ibm_watsonx_ai_embedding.md
-                  - Voyage AI Embeddings: embeddings/available_embedding_models/text_embedding_functions/voyageai_embedding.md
               - Multimodal Embedding Functions:
                   - OpenClip embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/openclip_embedding.md
                   - Imagebind embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md

docs/package-lock.json

@@ -19,7 +19,7 @@
     },
     "../node": {
       "name": "vectordb",
-      "version": "0.12.0",
+      "version": "0.4.6",
       "cpu": [
         "x64",
         "arm64"
@@ -31,7 +31,9 @@
         "win32"
       ],
       "dependencies": {
+        "@apache-arrow/ts": "^14.0.2",
         "@neon-rs/load": "^0.0.74",
+        "apache-arrow": "^14.0.2",
         "axios": "^1.4.0"
       },
       "devDependencies": {
@@ -44,7 +46,6 @@
         "@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",
@@ -61,19 +62,15 @@
         "ts-node-dev": "^2.0.0",
         "typedoc": "^0.24.7",
         "typedoc-plugin-markdown": "^3.15.3",
-        "typescript": "^5.1.0",
+        "typescript": "*",
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.12.0",
-        "@lancedb/vectordb-darwin-x64": "0.12.0",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.12.0",
-        "@lancedb/vectordb-linux-x64-gnu": "0.12.0",
-        "@lancedb/vectordb-win32-x64-msvc": "0.12.0"
-      },
-      "peerDependencies": {
-        "@apache-arrow/ts": "^14.0.2",
-        "apache-arrow": "^14.0.2"
+        "@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": {

docs/src/ann_indexes.md

@@ -45,9 +45,9 @@ Lance supports `IVF_PQ` index type by default.
         Creating indexes is done via the [lancedb.Table.createIndex](../js/classes/Table.md/#createIndex) method.
 
         ```typescript
-        --8<--- "nodejs/examples/ann_indexes.test.ts:import"
+        --8<--- "nodejs/examples/ann_indexes.ts:import"
 
-        --8<-- "nodejs/examples/ann_indexes.test.ts:ingest"
+        --8<-- "nodejs/examples/ann_indexes.ts:ingest"
         ```
 
     === "vectordb (deprecated)"
@@ -140,15 +140,13 @@ There are a couple of parameters that can be used to fine-tune the search:
 
 - **limit** (default: 10): The amount of results that will be returned
 - **nprobes** (default: 20): The number of probes used. A higher number makes search more accurate but also slower.<br/>
-  Most of the time, setting nprobes to cover 5-15% of the dataset should achieve high recall with low latency.<br/>
-    - _For example_, For a dataset of 1 million vectors divided into 256 partitions, `nprobes` should be set to ~20-40. This value can be adjusted to achieve the optimal balance between search latency and search quality. <br/>
-  
+  Most of the time, setting nprobes to cover 5-10% of the dataset should achieve high recall with low latency.<br/>
+  e.g., for 1M vectors divided up into 256 partitions, nprobes should be set to ~20-40.<br/>
+  Note: nprobes is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
 - **refine_factor** (default: None): Refine the results by reading extra elements and re-ranking them in memory.<br/>
   A higher number makes search more accurate but also slower. If you find the recall is less than ideal, try refine_factor=10 to start.<br/>
-    - _For example_, For a dataset of 1 million vectors divided into 256 partitions, setting the `refine_factor` to 200 will initially retrieve the top 4,000 candidates (top k * refine_factor) from all searched partitions. These candidates are then reranked to determine the final top 20 results.<br/>
-!!! note 
-    Both `nprobes` and `refine_factor` are only applicable if an ANN index is present. If specified on a table without an ANN index, those parameters are ignored.
-
+  e.g., for 1M vectors divided into 256 partitions, if you're looking for top 20, then refine_factor=200 reranks the whole partition.<br/>
+  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"
 
@@ -171,7 +169,7 @@ There are a couple of parameters that can be used to fine-tune the search:
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/ann_indexes.test.ts:search1"
+        --8<-- "nodejs/examples/ann_indexes.ts:search1"
         ```
 
     === "vectordb (deprecated)"
@@ -205,7 +203,7 @@ You can further filter the elements returned by a search using a where clause.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/ann_indexes.test.ts:search2"
+        --8<-- "nodejs/examples/ann_indexes.ts:search2"
         ```
 
     === "vectordb (deprecated)"
@@ -237,7 +235,7 @@ You can select the columns returned by the query using a select clause.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/ann_indexes.test.ts:search3"
+        --8<-- "nodejs/examples/ann_indexes.ts:search3"
         ```
 
     === "vectordb (deprecated)"
@@ -277,15 +275,7 @@ Product quantization can lead to approximately `16 * sizeof(float32) / 1 = 64` t
 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 Product Quantization (PQ) short codes to generate on each vector. The number should be a factor of the vector dimension. Because
+`num_sub_vectors` specifies how many Product Quantization (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.
-
-!!! note
-    if `num_sub_vectors` is set to be greater than the vector dimension, you will see errors like `attempt to divide by zero`
-
-### How to choose `m` and `ef_construction` for `IVF_HNSW_*` index?
-
-`m` determines the number of connections a new node establishes with its closest neighbors upon entering the graph. Typically, `m` falls within the range of 5 to 48. Lower `m` values are suitable for low-dimensional data or scenarios where recall is less critical. Conversely, higher `m` values are beneficial for high-dimensional data or when high recall is required. In essence, a larger `m` results in a denser graph with increased connectivity, but at the expense of higher memory consumption.
-
-`ef_construction` balances build speed and accuracy. Higher values increase accuracy but slow down the build process. A typical range is 150 to 300. For good search results, a minimum value of 100 is recommended. In most cases, setting this value above 500 offers no additional benefit. Ensure that `ef_construction` is always set to a value equal to or greater than `ef` in the search phase
+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/basic.md

@@ -157,7 +157,7 @@ recommend switching to stable releases.
         import * as lancedb from "@lancedb/lancedb";
         import * as arrow from "apache-arrow";
 
-        --8<-- "nodejs/examples/basic.test.ts:connect"
+        --8<-- "nodejs/examples/basic.ts:connect"
         ```
 
     === "vectordb (deprecated)"
@@ -212,7 +212,7 @@ table.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_table"
+        --8<-- "nodejs/examples/basic.ts:create_table"
         ```
 
     === "vectordb (deprecated)"
@@ -268,7 +268,7 @@ similar to a `CREATE TABLE` statement in SQL.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_empty_table"
+        --8<-- "nodejs/examples/basic.ts:create_empty_table"
         ```
 
     === "vectordb (deprecated)"
@@ -298,7 +298,7 @@ Once created, you can open a table as follows:
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:open_table"
+        --8<-- "nodejs/examples/basic.ts:open_table"
         ```
 
     === "vectordb (deprecated)"
@@ -327,7 +327,7 @@ If you forget the name of your table, you can always get a listing of all table
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:table_names"
+        --8<-- "nodejs/examples/basic.ts:table_names"
         ```
 
     === "vectordb (deprecated)"
@@ -357,7 +357,7 @@ After a table has been created, you can always add more data to it as follows:
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:add_data"
+        --8<-- "nodejs/examples/basic.ts:add_data"
         ```
 
     === "vectordb (deprecated)"
@@ -389,7 +389,7 @@ Once you've embedded the query, you can find its nearest neighbors as follows:
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:vector_search"
+        --8<-- "nodejs/examples/basic.ts:vector_search"
         ```
 
     === "vectordb (deprecated)"
@@ -429,7 +429,7 @@ LanceDB allows you to create an ANN index on a table as follows:
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_index"
+        --8<-- "nodejs/examples/basic.ts:create_index"
         ```
 
     === "vectordb (deprecated)"
@@ -469,7 +469,7 @@ This can delete any number of rows that match the filter.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:delete_rows"
+        --8<-- "nodejs/examples/basic.ts:delete_rows"
         ```
 
     === "vectordb (deprecated)"
@@ -527,7 +527,7 @@ Use the `drop_table()` method on the database to remove a table.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:drop_table"
+        --8<-- "nodejs/examples/basic.ts:drop_table"
         ```
 
     === "vectordb (deprecated)"
@@ -561,8 +561,8 @@ You can use the embedding API when working with embedding models. It automatical
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/embedding.test.ts:imports"
-        --8<-- "nodejs/examples/embedding.test.ts:openai_embeddings"
+        --8<-- "nodejs/examples/embedding.ts:imports"
+        --8<-- "nodejs/examples/embedding.ts:openai_embeddings"
         ```
 
 === "Rust"

docs/src/concepts/index_hnsw.md

@@ -57,13 +57,6 @@ Then the greedy search routine operates as follows:
 
 ## Usage
 
-There are three key parameters to set when constructing an HNSW index:
-
-* `metric`: Use an `L2` euclidean distance metric. We also support `dot` and `cosine` distance.
-* `m`: The number of neighbors to select for each vector in the HNSW graph.
-* `ef_construction`: The number of candidates to evaluate during the construction of the HNSW graph.
-
-
 We can combine the above concepts to understand how to build and query an HNSW index in LanceDB.
 
 ### Construct index

docs/src/concepts/index_ivfpq.md

@@ -58,10 +58,8 @@ In Python, the index can be created as follows:
 # Make sure you have enough data in the table for an effective training step
 tbl.create_index(metric="L2", num_partitions=256, num_sub_vectors=96)
 ```
-!!! note
-    `num_partitions`=256 and `num_sub_vectors`=96 does not work for every dataset. Those values needs to be adjusted for your particular dataset.
 
-The `num_partitions` is usually chosen to target a particular number of vectors per partition. `num_sub_vectors` is typically chosen based on the desired recall and the dimensionality of the vector. See [here](../ann_indexes.md/#how-to-choose-num_partitions-and-num_sub_vectors-for-ivf_pq-index) for best practices on choosing these parameters.
+The `num_partitions` is usually chosen to target a particular number of vectors per partition. `num_sub_vectors` is typically chosen based on the desired recall and the dimensionality of the vector. See the [FAQs](#faq) below for best practices on choosing these parameters.
 
 
 ### Query the index

docs/src/embeddings/available_embedding_models/text_embedding_functions/voyageai_embedding.md

@@ -20,7 +20,7 @@ Supported parameters (to be passed in `create` method) are:
 
 | Parameter | Type | Default Value | Description |
 |---|---|--------|---------|
-| `name` | `str` | `None` | The model ID of the model to use. Supported base models for Text Embeddings: voyage-3, voyage-3-lite, voyage-finance-2, voyage-multilingual-2, voyage-law-2, voyage-code-2 |
+| `name` | `str` | `"voyage-3"` | The model ID of the model to use. Supported base models for Text Embeddings: voyage-3, voyage-3-lite, voyage-finance-2, voyage-multilingual-2, voyage-law-2, voyage-code-2 |
 | `input_type` | `str` | `None` | Type of the input text. Default to None. Other options: query, document. |
 | `truncation` | `bool` | `True` | Whether to truncate the input texts to fit within the context length. |
 

docs/src/embeddings/custom_embedding_function.md

@@ -47,9 +47,9 @@ Let's implement `SentenceTransformerEmbeddings` class. All you need to do is imp
 === "TypeScript"
 
     ```ts
-    --8<--- "nodejs/examples/custom_embedding_function.test.ts:imports"
+    --8<--- "nodejs/examples/custom_embedding_function.ts:imports"
 
-    --8<--- "nodejs/examples/custom_embedding_function.test.ts:embedding_impl"
+    --8<--- "nodejs/examples/custom_embedding_function.ts:embedding_impl"
     ```
 
 
@@ -78,7 +78,7 @@ Now you can use this embedding function to create your table schema and that's i
 === "TypeScript"
 
     ```ts
-    --8<--- "nodejs/examples/custom_embedding_function.test.ts:call_custom_function"
+    --8<--- "nodejs/examples/custom_embedding_function.ts:call_custom_function"
     ```
 
 !!! note

docs/src/embeddings/default_embedding_functions.md

@@ -53,7 +53,6 @@ These functions are registered by default to handle text embeddings.
 | [**Jina Embeddings**](available_embedding_models/text_embedding_functions/jina_embedding.md "jina") | 🔗 World-class embedding models to improve your search and RAG systems. You will need **jina api key**. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/jina.png" alt="Jina Icon" width="90" height="35">](available_embedding_models/text_embedding_functions/jina_embedding.md) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
 | [ **AWS Bedrock Functions**](available_embedding_models/text_embedding_functions/aws_bedrock_embedding.md "bedrock-text") | ☁️ AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/aws_bedrock.png" alt="AWS Bedrock Icon" width="120" height="35">](available_embedding_models/text_embedding_functions/aws_bedrock_embedding.md) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
 | [**IBM Watsonx.ai**](available_embedding_models/text_embedding_functions/ibm_watsonx_ai_embedding.md "watsonx") | 💡 Generate text embeddings using IBM's watsonx.ai platform. **Note**: watsonx.ai library is an optional dependency. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/watsonx.png" alt="Watsonx Icon" width="140" height="35">](available_embedding_models/text_embedding_functions/ibm_watsonx_ai_embedding.md) |
-| [**VoyageAI Embeddings**](available_embedding_models/text_embedding_functions/voyageai_embedding.md "voyageai") | 🌕 Voyage AI provides cutting-edge embedding and rerankers. This will help you get started with **VoyageAI** embedding models using LanceDB. Using voyageai API requires voyageai package. Install it via `pip`. | [<img src="https://www.voyageai.com/logo.svg" alt="VoyageAI Icon" width="140" height="35">](available_embedding_models/text_embedding_functions/voyageai_embedding.md) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
 
 
 
@@ -67,7 +66,6 @@ These functions are registered by default to handle text embeddings.
 [jina-key]: "jina"
 [aws-key]: "bedrock-text"
 [watsonx-key]: "watsonx"
-[voyageai-key]: "voyageai"
 
 
 ## Multi-modal Embedding Functions🖼️ 

docs/src/embeddings/embedding_functions.md

@@ -94,8 +94,8 @@ the embeddings at all:
     === "@lancedb/lancedb"
 
         ```ts
-        --8<-- "nodejs/examples/embedding.test.ts:imports"
-        --8<-- "nodejs/examples/embedding.test.ts:embedding_function"
+        --8<-- "nodejs/examples/embedding.ts:imports"
+        --8<-- "nodejs/examples/embedding.ts:embedding_function"
         ```
 
     === "vectordb (deprecated)"
@@ -150,7 +150,7 @@ need to worry about it when you query the table:
             .toArray()
         ```
 
-    === "vectordb (deprecated)"
+    === "vectordb (deprecated)
 
         ```ts
         const results = await table

docs/src/embeddings/index.md

@@ -51,8 +51,8 @@ LanceDB registers the OpenAI embeddings function in the registry as `openai`. Yo
 === "TypeScript"
 
     ```typescript
-    --8<--- "nodejs/examples/embedding.test.ts:imports"
-    --8<--- "nodejs/examples/embedding.test.ts:openai_embeddings"
+    --8<--- "nodejs/examples/embedding.ts:imports"
+    --8<--- "nodejs/examples/embedding.ts:openai_embeddings"
     ```
 
 === "Rust"
@@ -121,10 +121,12 @@ class Words(LanceModel):
     vector: Vector(func.ndims()) = func.VectorField()
 
 table = db.create_table("words", schema=Words)
-table.add([
-    {"text": "hello world"},
-    {"text": "goodbye world"}
-])
+table.add(
+    [
+        {"text": "hello world"},
+        {"text": "goodbye world"}
+    ]
+    )
 
 query = "greetings"
 actual = table.search(query).limit(1).to_pydantic(Words)[0]

docs/src/fts.md

@@ -114,45 +114,12 @@ table.create_fts_index("text",
 
 LanceDB full text search supports to filter the search results by a condition, both pre-filtering and post-filtering are supported.
 
-This can be invoked via the familiar `where` syntax.
- 
-With pre-filtering:
+This can be invoked via the familiar `where` syntax:
+
 === "Python"
 
     ```python
-    table.search("puppy").limit(10).where("meta='foo'", prefilte=True).to_list()
-    ```
-
-=== "TypeScript"
-
-    ```typescript
-    await tbl
-    .search("puppy")
-    .select(["id", "doc"])
-    .limit(10)
-    .where("meta='foo'")
-    .prefilter(true)
-    .toArray();
-    ```
-
-=== "Rust"
-
-    ```rust
-    table
-        .query()
-        .full_text_search(FullTextSearchQuery::new("puppy".to_owned()))
-        .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
-        .limit(10)
-        .only_if("meta='foo'")
-        .execute()
-        .await?;
-    ```
-
-With post-filtering:
-=== "Python"
-
-    ```python
-    table.search("puppy").limit(10).where("meta='foo'", prefilte=False).to_list()
+    table.search("puppy").limit(10).where("meta='foo'").to_list()
     ```
 
 === "TypeScript"
@@ -163,7 +130,6 @@ With post-filtering:
     .select(["id", "doc"])
     .limit(10)
     .where("meta='foo'")
-    .prefilter(false)
     .toArray();
     ```
 
@@ -174,7 +140,6 @@ With post-filtering:
         .query()
         .full_text_search(FullTextSearchQuery::new(words[0].to_owned()))
         .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
-        .postfilter()
         .limit(10)
         .only_if("meta='foo'")
         .execute()
@@ -195,35 +160,3 @@ To search for a phrase, the index must be created with `with_position=True`:
 table.create_fts_index("text", use_tantivy=False, with_position=True)
 ```
 This will allow you to search for phrases, but it will also significantly increase the index size and indexing time.
-
-
-## Incremental indexing
-
-LanceDB supports incremental indexing, which means you can add new records to the table without reindexing the entire table.
-
-This can make the query more efficient, especially when the table is large and the new records are relatively small.
-
-=== "Python"
-
-    ```python
-    table.add([{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}])
-    table.optimize()
-    ```
-
-=== "TypeScript"
-
-    ```typescript
-    await tbl.add([{ vector: [3.1, 4.1], text: "Frodo was a happy puppy" }]);
-    await tbl.optimize();
-    ```
-
-=== "Rust"
-
-    ```rust
-    let more_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
-    tbl.add(more_data).execute().await?;
-    tbl.optimize(OptimizeAction::All).execute().await?;
-    ```
-!!! note
-
-    New data added after creating the FTS index will appear in search results while incremental index is still progress, but with increased latency due to a flat search on the unindexed portion. LanceDB Cloud automates this merging process, minimizing the impact on search speed. 

docs/src/fts_tantivy.md

@@ -153,7 +153,9 @@ table.create_fts_index(["title", "content"], use_tantivy=True, writer_heap_size=
 
 ## Current limitations
 
-1. New data added after creating the FTS index will appear in search results, but with increased latency due to a flat search on the unindexed portion. Re-indexing with `create_fts_index` will reduce latency. LanceDB Cloud automates this merging process, minimizing the impact on search speed. 
+1. Currently we do not yet support incremental writes.
+   If you add data after FTS index creation, it won't be reflected
+   in search results until you do a full reindex.
 
 2. We currently only support local filesystem paths for the FTS index.
    This is a tantivy limitation. We've implemented an object store plugin

docs/src/guides/scalar_index.md

@@ -1,35 +1,23 @@
-# Building a Scalar Index
+# Building Scalar Index
 
-Scalar indices organize data by scalar attributes (e.g. numbers, categorical values), enabling fast filtering of vector data. In vector databases, scalar indices accelerate the retrieval of scalar data associated with vectors, thus enhancing the query performance when searching for vectors that meet certain scalar criteria. 
-
-Similar to many SQL databases, LanceDB supports several types of scalar indices to accelerate search
+Similar to many SQL databases, LanceDB supports several types of Scalar indices to accelerate search
 over scalar columns.
 
-- `BTREE`: The most common type is BTREE. The index stores a copy of the
-  column in sorted order. This sorted copy allows a binary search to be used to
-  satisfy queries.
-- `BITMAP`: this index stores a bitmap for each unique value in the column. It 
-  uses a series of bits to indicate whether a value is present in a row of a table
-- `LABEL_LIST`: a special index that can be used on `List<T>` columns to
-  support queries with `array_contains_all` and `array_contains_any`
-  using an underlying bitmap index.
+- `BTREE`: The most common type is BTREE. This index is inspired by the btree data structure
+  although only the first few layers of the btree are cached in memory.
+  It will perform well on columns with a large number of unique values and few rows per value.
+- `BITMAP`: this index stores a bitmap for each unique value in the column.
+  This index is useful for columns with a finite number of unique values and many rows per value.
+  For example, columns that represent "categories", "labels", or "tags"
+- `LABEL_LIST`: a special index that is used to index list columns whose values have a finite set of possibilities.
   For example, a column that contains lists of tags (e.g. `["tag1", "tag2", "tag3"]`) can be indexed with a `LABEL_LIST` index.
 
-!!! tips "How to choose the right scalar index type"
-
-    `BTREE`: This index is good for scalar columns with mostly distinct values and does best when the query is highly selective.
-    
-    `BITMAP`: This index works best for low-cardinality numeric or string columns, where the number of unique values is small (i.e., less than a few thousands).
-    
-    `LABEL_LIST`: This index should be used for columns containing list-type data.
-
 | Data Type                                                       | Filter                                    | Index Type   |
 | --------------------------------------------------------------- | ----------------------------------------- | ------------ |
 | Numeric, String, Temporal                                       | `<`, `=`, `>`, `in`, `between`, `is null` | `BTREE`      |
 | Boolean, numbers or strings with fewer than 1,000 unique values | `<`, `=`, `>`, `in`, `between`, `is null` | `BITMAP`     |
 | List of low cardinality of numbers or strings                   | `array_has_any`, `array_has_all`          | `LABEL_LIST` |
 
-### Create a scalar index
 === "Python"
 
     ```python
@@ -58,7 +46,7 @@ over scalar columns.
         await tlb.create_index("publisher", { config: lancedb.Index.bitmap() })
         ```
 
-The following scan will be faster if the column `book_id` has a scalar index:
+For example, the following scan will be faster if the column `my_col` has a scalar index:
 
 === "Python"
 
@@ -118,30 +106,3 @@ Scalar indices can also speed up scans containing a vector search or full text s
           .limit(10)
           .toArray();
         ```
-### Update a scalar index
-Updating the table data (adding, deleting, or modifying records) requires that you also update the scalar index. This can be done by calling `optimize`, which will trigger an update to the existing scalar index.
-=== "Python"
-
-    ```python
-    table.add([{"vector": [7, 8], "book_id": 4}])
-    table.optimize()
-    ```
-
-=== "TypeScript"
-
-    ```typescript
-    await tbl.add([{ vector: [7, 8], book_id: 4 }]);
-    await tbl.optimize();
-    ```
-
-=== "Rust"
-
-    ```rust
-    let more_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
-    tbl.add(more_data).execute().await?;
-    tbl.optimize(OptimizeAction::All).execute().await?;
-    ```
-
-!!! note
-
-    New data added after creating the scalar index will still appear in search results if optimize is not used, but with increased latency due to a flat search on the unindexed portion. LanceDB Cloud automates the optimize process, minimizing the impact on search speed.

docs/src/guides/tables.md

@@ -85,13 +85,13 @@ Initialize a LanceDB connection and create a table
 
 
         ```ts
-        --8<-- "nodejs/examples/basic.test.ts:create_table"
+        --8<-- "nodejs/examples/basic.ts:create_table"
         ```
 
         This will infer the schema from the provided data. If you want to explicitly provide a schema, you can use `apache-arrow` to declare a schema
 
         ```ts
-        --8<-- "nodejs/examples/basic.test.ts:create_table_with_schema"
+        --8<-- "nodejs/examples/basic.ts:create_table_with_schema"
         ```
 
         !!! info "Note"
@@ -100,14 +100,14 @@ Initialize a LanceDB connection and create a table
             passed in will NOT be appended to the table in that case.
 
         ```ts
-        --8<-- "nodejs/examples/basic.test.ts:create_table_exists_ok"
+        --8<-- "nodejs/examples/basic.ts:create_table_exists_ok"
         ```
 
         Sometimes you want to make sure that you start fresh. If you want to
         overwrite the table, you can pass in mode: "overwrite" to the createTable function.
 
         ```ts
-        --8<-- "nodejs/examples/basic.test.ts:create_table_overwrite"
+        --8<-- "nodejs/examples/basic.ts:create_table_overwrite"
         ```
 
     === "vectordb (deprecated)"
@@ -227,7 +227,7 @@ LanceDB supports float16 data type!
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_f16_table"
+        --8<-- "nodejs/examples/basic.ts:create_f16_table"
         ```
 
     === "vectordb (deprecated)"
@@ -274,7 +274,7 @@ table = db.create_table(table_name, schema=Content)
 
 Sometimes your data model may contain nested objects.
 For example, you may want to store the document string
-and the document source name as a nested Document object:
+and the document soure name as a nested Document object:
 
 ```python
 class Document(BaseModel):
@@ -455,7 +455,7 @@ You can create an empty table for scenarios where you want to add data to the ta
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_empty_table"
+        --8<-- "nodejs/examples/basic.ts:create_empty_table"
         ```
 
     === "vectordb (deprecated)"
@@ -466,7 +466,7 @@ You can create an empty table for scenarios where you want to add data to the ta
 
 ## Adding to a table
 
-After a table has been created, you can always add more data to it using the `add` method
+After a table has been created, you can always add more data to it usind the `add` method
 
 === "Python"
     You can add any of the valid data structures accepted by LanceDB table, i.e, `dict`, `list[dict]`, `pd.DataFrame`, or `Iterator[pa.RecordBatch]`. Below are some examples.
@@ -535,7 +535,7 @@ After a table has been created, you can always add more data to it using the `ad
     ```
 
     ??? "Ingesting Pydantic models with LanceDB embedding API"
-        When using LanceDB's embedding API, you can add Pydantic models directly to the table. LanceDB will automatically convert the `vector` field to a vector before adding it to the table. You need to specify the default value of `vector` field as None to allow LanceDB to automatically vectorize the data.
+        When using LanceDB's embedding API, you can add Pydantic models directly to the table. LanceDB will automatically convert the `vector` field to a vector before adding it to the table. You need to specify the default value of `vector` feild as None to allow LanceDB to automatically vectorize the data.
 
         ```python
         import lancedb
@@ -790,27 +790,6 @@ Use the `drop_table()` method on the database to remove a table.
       This permanently removes the table and is not recoverable, unlike deleting rows.
       If the table does not exist an exception is raised.
 
-## Handling bad vectors
-
-In LanceDB Python, you can use the `on_bad_vectors` parameter to choose how
-invalid vector values are handled. Invalid vectors are vectors that are not valid
-because:
-
-1. They are the wrong dimension
-2. They contain NaN values
-3. They are null but are on a non-nullable field
-
-By default, LanceDB will raise an error if it encounters a bad vector. You can
-also choose one of the following options:
-
-* `drop`: Ignore rows with bad vectors
-* `fill`: Replace bad values (NaNs) or missing values (too few dimensions) with
-    the fill value specified in the `fill_value` parameter. An input like
-    `[1.0, NaN, 3.0]` will be replaced with `[1.0, 0.0, 3.0]` if `fill_value=0.0`.
-* `null`: Replace bad vectors with null (only works if the column is nullable).
-    A bad vector `[1.0, NaN, 3.0]` will be replaced with `null` if the column is
-    nullable. If the vector column is non-nullable, then bad vectors will cause an
-    error
 
 ## Consistency
 
@@ -880,4 +859,4 @@ There are three possible settings for `read_consistency_interval`:
 
 Learn the best practices on creating an ANN index and getting the most out of it.
 
-[^1]: The `vectordb` package is a legacy package that is  deprecated in favor of `@lancedb/lancedb`.  The `vectordb` package will continue to receive bug fixes and security updates until September 2024.  We recommend all new projects use `@lancedb/lancedb`.  See the [migration guide](../migration.md) for more information.
+[^1]: The `vectordb` package is a legacy package that is  deprecated in favor of `@lancedb/lancedb`.  The `vectordb` package will continue to receive bug fixes and security updates until September 2024.  We recommend all new projects use `@lancedb/lancedb`.  See the [migration guide](migration.md) for more information.

docs/src/python/python.md

@@ -1,16 +1,6 @@
 # Python API Reference
 
-This section contains the API reference for the Python API. There is a
-synchronous and an asynchronous API client.
-
-The general flow of using the API is:
-
-1. Use [lancedb.connect][] or [lancedb.connect_async][] to connect to a database.
-2. Use the returned [lancedb.DBConnection][] or [lancedb.AsyncConnection][] to
-   create or open tables.
-3. Use the returned [lancedb.table.Table][] or [lancedb.AsyncTable][] to query
-   or modify tables.
-
+This section contains the API reference for the OSS Python API.
 
 ## Installation
 

docs/src/reranking/cohere.md

@@ -6,9 +6,6 @@ This re-ranker uses the [Cohere](https://cohere.ai/) API to rerank the search re
 !!! note
     Supported Query Types: Hybrid, Vector, FTS
 
-```shell
-pip install cohere
-```
 
 ```python
 import numpy

docs/src/reranking/index.md

@@ -9,7 +9,6 @@ LanceDB comes with some built-in rerankers. Some of the rerankers that are avail
 | `CrossEncoderReranker` | Uses a cross-encoder model to rerank search results | Vector, FTS, Hybrid |
 | `ColbertReranker` | Uses a colbert model to rerank search results | Vector, FTS, Hybrid |
 | `OpenaiReranker`(Experimental) | Uses OpenAI's chat model to rerank search results | Vector, FTS, Hybrid |
-| `VoyageAIReranker` | Uses voyageai Reranker API to rerank results | Vector, FTS, Hybrid |
 
 
 ## Using a Reranker
@@ -74,7 +73,6 @@ LanceDB comes with some built-in rerankers. Here are some of the rerankers that
 - [Jina Reranker](./jina.md)
 - [AnswerDotAI Rerankers](./answerdotai.md)
 - [Reciprocal Rank Fusion Reranker](./rrf.md)
-- [VoyageAI Reranker](./voyageai.md)
 
 ## Creating Custom Rerankers
 

docs/src/search.md

@@ -58,9 +58,9 @@ db.create_table("my_vectors", data=data)
     === "@lancedb/lancedb"
 
         ```ts
-        --8<-- "nodejs/examples/search.test.ts:import"
+        --8<-- "nodejs/examples/search.ts:import"
 
-        --8<-- "nodejs/examples/search.test.ts:search1"
+        --8<-- "nodejs/examples/search.ts:search1"
         ```
 
 
@@ -89,7 +89,7 @@ By default, `l2` will be used as metric type. You can specify the metric type as
     === "@lancedb/lancedb"
 
         ```ts
-        --8<-- "nodejs/examples/search.test.ts:search2"
+        --8<-- "nodejs/examples/search.ts:search2"
         ```
 
     === "vectordb (deprecated)"

docs/src/sql.md

@@ -7,10 +7,6 @@ performed on the top-k results returned by the vector search. However, pre-filte
 option that performs the filter prior to vector search. This can be useful to narrow down on
 the search space on a very large dataset to reduce query latency.
 
-Note that both pre-filtering and post-filtering can yield false positives. For pre-filtering, if the filter is too selective, it might eliminate relevant items that the vector search would have otherwise identified as a good match. In this case, increasing `nprobes` parameter will help reduce such false positives. It is recommended to set `use_index=false` if you know that the filter is highly selective.
-
-Similarly, a highly selective post-filter can lead to false positives. Increasing both `nprobes` and `refine_factor` can mitigate this issue. When deciding between pre-filtering and post-filtering, pre-filtering is generally the safer choice if you're uncertain.
-
 <!-- Setup Code
 ```python
 import lancedb
@@ -53,7 +49,7 @@ const tbl = await db.createTable('myVectors', data)
     === "@lancedb/lancedb"
 
         ```ts
-        --8<-- "nodejs/examples/filtering.test.ts:search"
+        --8<-- "nodejs/examples/filtering.ts:search"
         ```
 
     === "vectordb (deprecated)"
@@ -61,9 +57,6 @@ const tbl = await db.createTable('myVectors', data)
         ```ts
         --8<-- "docs/src/sql_legacy.ts:search"
         ```
-!!! note
-
-    Creating a [scalar index](guides/scalar_index.md) accelerates filtering
 
 ## SQL filters
 
@@ -98,7 +91,7 @@ For example, the following filter string is acceptable:
     === "@lancedb/lancedb"
 
         ```ts
-        --8<-- "nodejs/examples/filtering.test.ts:vec_search"
+        --8<-- "nodejs/examples/filtering.ts:vec_search"
         ```
 
     === "vectordb (deprecated)"
@@ -176,7 +169,7 @@ You can also filter your data without search.
     === "@lancedb/lancedb"
 
         ```ts
-        --8<-- "nodejs/examples/filtering.test.ts:sql_search"
+        --8<-- "nodejs/examples/filtering.ts:sql_search"
         ```
 
     === "vectordb (deprecated)"

java/core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
         <groupId>com.lancedb</groupId>
         <artifactId>lancedb-parent</artifactId>
-        <version>0.14.0-beta.0</version>
+        <version>0.13.0-beta.1</version>
         <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.14.0-beta.0</version>
+    <version>0.13.0-beta.1</version>
     <packaging>pom</packaging>
 
     <name>LanceDB Parent</name>

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.14.0-beta.0",
+  "version": "0.13.0-beta.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.14.0-beta.0",
+      "version": "0.13.0-beta.1",
       "cpu": [
         "x64",
         "arm64"
@@ -52,14 +52,12 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.14.0-beta.0",
-        "@lancedb/vectordb-darwin-x64": "0.14.0-beta.0",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.14.0-beta.0",
-        "@lancedb/vectordb-linux-arm64-musl": "0.14.0-beta.0",
-        "@lancedb/vectordb-linux-x64-gnu": "0.14.0-beta.0",
-        "@lancedb/vectordb-linux-x64-musl": "0.14.0-beta.0",
-        "@lancedb/vectordb-win32-arm64-msvc": "0.14.0-beta.0",
-        "@lancedb/vectordb-win32-x64-msvc": "0.14.0-beta.0"
+        "@lancedb/vectordb-darwin-arm64": "0.13.0-beta.1",
+        "@lancedb/vectordb-darwin-x64": "0.13.0-beta.1",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.13.0-beta.1",
+        "@lancedb/vectordb-linux-x64-gnu": "0.13.0-beta.1",
+        "@lancedb/vectordb-win32-arm64-msvc": "0.13.0-beta.1",
+        "@lancedb/vectordb-win32-x64-msvc": "0.13.0-beta.1"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",
@@ -329,6 +327,66 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
+    "node_modules/@lancedb/vectordb-darwin-arm64": {
+      "version": "0.13.0-beta.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.13.0-beta.1.tgz",
+      "integrity": "sha512-beOrf6selCzzhLgDG8Nibma4nO/CSnA1wUKRmlJHEPtGcg7PW18z6MP/nfwQMpMR/FLRfTo8pPTbpzss47MiQQ==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-darwin-x64": {
+      "version": "0.13.0-beta.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.13.0-beta.1.tgz",
+      "integrity": "sha512-YdraGRF/RbJRkKh0v3xT03LUhq47T2GtCvJ5gZp8wKlh4pHa8LuhLU0DIdvmG/DT5vuQA+td8HDkBm/e3EOdNg==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
+      "version": "0.13.0-beta.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.13.0-beta.1.tgz",
+      "integrity": "sha512-Pp0O/uhEqof1oLaWrNbv+Ym+q8kBkiCqaA5+2eAZ6a3e9U+Ozkvb0FQrHuyi9adJ5wKQ4NabyQE9BMf2bYpOnQ==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
+      "version": "0.13.0-beta.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.13.0-beta.1.tgz",
+      "integrity": "sha512-y8nxOye4egfWF5FGED9EfkmZ1O5HnRLU4a61B8m5JSpkivO9v2epTcbYN0yt/7ZFCgtqMfJ8VW4Mi7qQcz3KDA==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
+      "version": "0.13.0-beta.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.13.0-beta.1.tgz",
+      "integrity": "sha512-STMDP9dp0TBLkB3ro+16pKcGy6bmbhRuEZZZ1Tp5P75yTPeVh4zIgWkidMdU1qBbEYM7xacnsp9QAwgLnMU/Ow==",
+      "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",
@@ -1443,9 +1501,9 @@
       "dev": true
     },
     "node_modules/cross-spawn": {
-      "version": "7.0.6",
-      "resolved": "https://registry.npmjs.org/cross-spawn/-/cross-spawn-7.0.6.tgz",
-      "integrity": "sha512-uV2QOWP2nWzsy2aMp8aRibhi9dlzF5Hgh5SHaB9OiTGEyDTiJJyx0uy51QXdyWbtAHNua4XJzUKca3OzKUd3vA==",
+      "version": "7.0.3",
+      "resolved": "https://registry.npmjs.org/cross-spawn/-/cross-spawn-7.0.3.tgz",
+      "integrity": "sha512-iRDPJKUPVEND7dHPO8rkbOnPpyDygcDFtWjpeWNCgy8WP2rXcxXL8TskReQl6OrB2G7+UJrags1q15Fudc7G6w==",
       "dev": true,
       "dependencies": {
         "path-key": "^3.1.0",

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.14.0-beta.0",
+  "version": "0.13.0-beta.1",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
@@ -84,20 +84,16 @@
       "aarch64-apple-darwin": "@lancedb/vectordb-darwin-arm64",
       "x86_64-unknown-linux-gnu": "@lancedb/vectordb-linux-x64-gnu",
       "aarch64-unknown-linux-gnu": "@lancedb/vectordb-linux-arm64-gnu",
-      "x86_64-unknown-linux-musl": "@lancedb/vectordb-linux-x64-musl",
-      "aarch64-unknown-linux-musl": "@lancedb/vectordb-linux-arm64-musl",
       "x86_64-pc-windows-msvc": "@lancedb/vectordb-win32-x64-msvc",
       "aarch64-pc-windows-msvc": "@lancedb/vectordb-win32-arm64-msvc"
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-x64": "0.14.0-beta.0",
-    "@lancedb/vectordb-darwin-arm64": "0.14.0-beta.0",
-    "@lancedb/vectordb-linux-x64-gnu": "0.14.0-beta.0",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.14.0-beta.0",
-    "@lancedb/vectordb-linux-x64-musl": "0.14.0-beta.0",
-    "@lancedb/vectordb-linux-arm64-musl": "0.14.0-beta.0",
-    "@lancedb/vectordb-win32-x64-msvc": "0.14.0-beta.0",
-    "@lancedb/vectordb-win32-arm64-msvc": "0.14.0-beta.0"
+    "@lancedb/vectordb-darwin-arm64": "0.13.0-beta.1",
+    "@lancedb/vectordb-darwin-x64": "0.13.0-beta.1",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.13.0-beta.1",
+    "@lancedb/vectordb-linux-x64-gnu": "0.13.0-beta.1",
+    "@lancedb/vectordb-win32-x64-msvc": "0.13.0-beta.1",
+    "@lancedb/vectordb-win32-arm64-msvc": "0.13.0-beta.1"
   }
 }

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.14.0-beta.0"
+version = "0.13.0-beta.1"
 license.workspace = true
 description.workspace = true
 repository.workspace = true

nodejs/__test__/connection.test.ts

@@ -110,10 +110,7 @@ describe("given a connection", () => {
     let table = await db.createTable("test", data, { useLegacyFormat: true });
 
     const isV2 = async (table: Table) => {
-      const data = await table
-        .query()
-        .limit(10000)
-        .toArrow({ maxBatchLength: 100000 });
+      const data = await table.query().toArrow({ maxBatchLength: 100000 });
       console.log(data.batches.length);
       return data.batches.length < 5;
     };

nodejs/__test__/table.test.ts

@@ -187,81 +187,6 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
       },
     );
 
-    // TODO: https://github.com/lancedb/lancedb/issues/1832
-    it.skip("should be able to omit nullable fields", async () => {
-      const db = await connect(tmpDir.name);
-      const schema = new arrow.Schema([
-        new arrow.Field(
-          "vector",
-          new arrow.FixedSizeList(
-            2,
-            new arrow.Field("item", new arrow.Float64()),
-          ),
-          true,
-        ),
-        new arrow.Field("item", new arrow.Utf8(), true),
-        new arrow.Field("price", new arrow.Float64(), false),
-      ]);
-      const table = await db.createEmptyTable("test", schema);
-
-      const data1 = { item: "foo", price: 10.0 };
-      await table.add([data1]);
-      const data2 = { vector: [3.1, 4.1], price: 2.0 };
-      await table.add([data2]);
-      const data3 = { vector: [5.9, 26.5], item: "bar", price: 3.0 };
-      await table.add([data3]);
-
-      let res = await table.query().limit(10).toArray();
-      const resVector = res.map((r) => r.get("vector").toArray());
-      expect(resVector).toEqual([null, data2.vector, data3.vector]);
-      const resItem = res.map((r) => r.get("item").toArray());
-      expect(resItem).toEqual(["foo", null, "bar"]);
-      const resPrice = res.map((r) => r.get("price").toArray());
-      expect(resPrice).toEqual([10.0, 2.0, 3.0]);
-
-      const data4 = { item: "foo" };
-      // We can't omit a column if it's not nullable
-      await expect(table.add([data4])).rejects.toThrow("Invalid user input");
-
-      // But we can alter columns to make them nullable
-      await table.alterColumns([{ path: "price", nullable: true }]);
-      await table.add([data4]);
-
-      res = (await table.query().limit(10).toArray()).map((r) => r.toJSON());
-      expect(res).toEqual([data1, data2, data3, data4]);
-    });
-
-    it("should be able to insert nullable data for non-nullable fields", async () => {
-      const db = await connect(tmpDir.name);
-      const schema = new arrow.Schema([
-        new arrow.Field("x", new arrow.Float64(), false),
-        new arrow.Field("id", new arrow.Utf8(), false),
-      ]);
-      const table = await db.createEmptyTable("test", schema);
-
-      const data1 = { x: 4.1, id: "foo" };
-      await table.add([data1]);
-      const res = (await table.query().toArray())[0];
-      expect(res.x).toEqual(data1.x);
-      expect(res.id).toEqual(data1.id);
-
-      const data2 = { x: null, id: "bar" };
-      await expect(table.add([data2])).rejects.toThrow(
-        "declared as non-nullable but contains null values",
-      );
-
-      // But we can alter columns to make them nullable
-      await table.alterColumns([{ path: "x", nullable: true }]);
-      await table.add([data2]);
-
-      const res2 = await table.query().toArray();
-      expect(res2.length).toBe(2);
-      expect(res2[0].x).toEqual(data1.x);
-      expect(res2[0].id).toEqual(data1.id);
-      expect(res2[1].x).toBeNull();
-      expect(res2[1].id).toEqual(data2.id);
-    });
-
     it("should return the table as an instance of an arrow table", async () => {
       const arrowTbl = await table.toArrow();
       expect(arrowTbl).toBeInstanceOf(ArrowTable);
@@ -477,54 +402,6 @@ describe("When creating an index", () => {
     expect(rst.numRows).toBe(1);
   });
 
-  it("should create and search IVF_HNSW indices", async () => {
-    await tbl.createIndex("vec", {
-      config: Index.hnswSq(),
-    });
-
-    // check index directory
-    const indexDir = path.join(tmpDir.name, "test.lance", "_indices");
-    expect(fs.readdirSync(indexDir)).toHaveLength(1);
-    const indices = await tbl.listIndices();
-    expect(indices.length).toBe(1);
-    expect(indices[0]).toEqual({
-      name: "vec_idx",
-      indexType: "IvfHnswSq",
-      columns: ["vec"],
-    });
-
-    // Search without specifying the column
-    let rst = await tbl
-      .query()
-      .limit(2)
-      .nearestTo(queryVec)
-      .distanceType("dot")
-      .toArrow();
-    expect(rst.numRows).toBe(2);
-
-    // Search using `vectorSearch`
-    rst = await tbl.vectorSearch(queryVec).limit(2).toArrow();
-    expect(rst.numRows).toBe(2);
-
-    // Search with specifying the column
-    const rst2 = await tbl
-      .query()
-      .limit(2)
-      .nearestTo(queryVec)
-      .column("vec")
-      .toArrow();
-    expect(rst2.numRows).toBe(2);
-    expect(rst.toString()).toEqual(rst2.toString());
-
-    // test offset
-    rst = await tbl.query().limit(2).offset(1).nearestTo(queryVec).toArrow();
-    expect(rst.numRows).toBe(1);
-
-    // test ef
-    rst = await tbl.query().limit(2).nearestTo(queryVec).ef(100).toArrow();
-    expect(rst.numRows).toBe(2);
-  });
-
   it("should be able to query unindexed data", async () => {
     await tbl.createIndex("vec");
     await tbl.add([
@@ -585,11 +462,11 @@ describe("When creating an index", () => {
     expect(fs.readdirSync(indexDir)).toHaveLength(1);
 
     for await (const r of tbl.query().where("id > 1").select(["id"])) {
-      expect(r.numRows).toBe(10);
+      expect(r.numRows).toBe(298);
     }
     // should also work with 'filter' alias
     for await (const r of tbl.query().filter("id > 1").select(["id"])) {
-      expect(r.numRows).toBe(10);
+      expect(r.numRows).toBe(298);
     }
   });
 
@@ -1121,18 +998,4 @@ describe("column name options", () => {
     const results = await table.query().where("`camelCase` = 1").toArray();
     expect(results[0].camelCase).toBe(1);
   });
-
-  test("can make multiple vector queries in one go", async () => {
-    const results = await table
-      .query()
-      .nearestTo([0.1, 0.2])
-      .addQueryVector([0.1, 0.2])
-      .limit(1)
-      .toArray();
-    console.log(results);
-    expect(results.length).toBe(2);
-    results.sort((a, b) => a.query_index - b.query_index);
-    expect(results[0].query_index).toBe(0);
-    expect(results[1].query_index).toBe(1);
-  });
 });

nodejs/biome.json

@@ -9,8 +9,7 @@
       "**/native.js",
       "**/native.d.ts",
       "**/npm/**/*",
-      "**/.vscode/**",
-      "./examples/*"
+      "**/.vscode/**"
     ]
   },
   "formatter": {

nodejs/examples/ann_indexes.test.ts

@@ -1,57 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import { expect, test } from "@jest/globals";
-// --8<-- [start:import]
-import * as lancedb from "@lancedb/lancedb";
-import { VectorQuery } from "@lancedb/lancedb";
-// --8<-- [end:import]
-import { withTempDirectory } from "./util.ts";
-
-test("ann index examples", async () => {
-  await withTempDirectory(async (databaseDir) => {
-    // --8<-- [start:ingest]
-    const db = await lancedb.connect(databaseDir);
-
-    const data = Array.from({ length: 5_000 }, (_, i) => ({
-      vector: Array(128).fill(i),
-      id: `${i}`,
-      content: "",
-      longId: `${i}`,
-    }));
-
-    const table = await db.createTable("my_vectors", data, {
-      mode: "overwrite",
-    });
-    await table.createIndex("vector", {
-      config: lancedb.Index.ivfPq({
-        numPartitions: 10,
-        numSubVectors: 16,
-      }),
-    });
-    // --8<-- [end:ingest]
-
-    // --8<-- [start:search1]
-    const search = table.search(Array(128).fill(1.2)).limit(2) as VectorQuery;
-    const results1 = await search.nprobes(20).refineFactor(10).toArray();
-    // --8<-- [end:search1]
-    expect(results1.length).toBe(2);
-
-    // --8<-- [start:search2]
-    const results2 = await table
-      .search(Array(128).fill(1.2))
-      .where("id != '1141'")
-      .limit(2)
-      .toArray();
-    // --8<-- [end:search2]
-    expect(results2.length).toBe(2);
-
-    // --8<-- [start:search3]
-    const results3 = await table
-      .search(Array(128).fill(1.2))
-      .select(["id"])
-      .limit(2)
-      .toArray();
-    // --8<-- [end:search3]
-    expect(results3.length).toBe(2);
-  });
-}, 100_000);

nodejs/examples/ann_indexes.ts

@@ -0,0 +1,49 @@
+// --8<-- [start:import]
+import * as lancedb from "@lancedb/lancedb";
+// --8<-- [end:import]
+
+// --8<-- [start:ingest]
+const db = await lancedb.connect("/tmp/lancedb/");
+
+const data = Array.from({ length: 10_000 }, (_, i) => ({
+  vector: Array(1536).fill(i),
+  id: `${i}`,
+  content: "",
+  longId: `${i}`,
+}));
+
+const table = await db.createTable("my_vectors", data, { mode: "overwrite" });
+await table.createIndex("vector", {
+  config: lancedb.Index.ivfPq({
+    numPartitions: 16,
+    numSubVectors: 48,
+  }),
+});
+// --8<-- [end:ingest]
+
+// --8<-- [start:search1]
+const _results1 = await table
+  .search(Array(1536).fill(1.2))
+  .limit(2)
+  .nprobes(20)
+  .refineFactor(10)
+  .toArray();
+// --8<-- [end:search1]
+
+// --8<-- [start:search2]
+const _results2 = await table
+  .search(Array(1536).fill(1.2))
+  .where("id != '1141'")
+  .limit(2)
+  .toArray();
+// --8<-- [end:search2]
+
+// --8<-- [start:search3]
+const _results3 = await table
+  .search(Array(1536).fill(1.2))
+  .select(["id"])
+  .limit(2)
+  .toArray();
+// --8<-- [end:search3]
+
+console.log("Ann indexes: done");

nodejs/examples/basic.test.ts

@@ -1,175 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import { expect, test } from "@jest/globals";
-// --8<--  [start:imports]
-import * as lancedb from "@lancedb/lancedb";
-import * as arrow from "apache-arrow";
-import {
-  Field,
-  FixedSizeList,
-  Float16,
-  Int32,
-  Schema,
-  Utf8,
-} from "apache-arrow";
-// --8<-- [end:imports]
-import { withTempDirectory } from "./util.ts";
-
-test("basic table examples", async () => {
-  await withTempDirectory(async (databaseDir) => {
-    // --8<-- [start:connect]
-    const db = await lancedb.connect(databaseDir);
-    // --8<-- [end:connect]
-    {
-      // --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 },
-        ],
-        { mode: "overwrite" },
-      );
-      // --8<-- [end:create_table]
-
-      const data = [
-        { vector: [3.1, 4.1], item: "foo", price: 10.0 },
-        { vector: [5.9, 26.5], item: "bar", price: 20.0 },
-      ];
-
-      {
-        // --8<-- [start:create_table_exists_ok]
-        const tbl = await db.createTable("myTable", data, {
-          existOk: true,
-        });
-        // --8<-- [end:create_table_exists_ok]
-        expect(await tbl.countRows()).toBe(2);
-      }
-      {
-        // --8<-- [start:create_table_overwrite]
-        const tbl = await db.createTable("myTable", data, {
-          mode: "overwrite",
-        });
-        // --8<-- [end:create_table_overwrite]
-        expect(await tbl.countRows()).toBe(2);
-      }
-    }
-
-    await db.dropTable("myTable");
-
-    {
-      // --8<-- [start:create_table_with_schema]
-      const schema = new arrow.Schema([
-        new arrow.Field(
-          "vector",
-          new arrow.FixedSizeList(
-            2,
-            new arrow.Field("item", new arrow.Float32(), true),
-          ),
-        ),
-        new arrow.Field("item", new arrow.Utf8(), true),
-        new arrow.Field("price", new arrow.Float32(), true),
-      ]);
-      const data = [
-        { vector: [3.1, 4.1], item: "foo", price: 10.0 },
-        { vector: [5.9, 26.5], item: "bar", price: 20.0 },
-      ];
-      const tbl = await db.createTable("myTable", data, {
-        schema,
-      });
-      // --8<-- [end:create_table_with_schema]
-      expect(await tbl.countRows()).toBe(2);
-    }
-
-    {
-      // --8<-- [start:create_empty_table]
-
-      const schema = new arrow.Schema([
-        new arrow.Field("id", new arrow.Int32()),
-        new arrow.Field("name", new arrow.Utf8()),
-      ]);
-
-      const emptyTbl = await db.createEmptyTable("empty_table", schema);
-      // --8<-- [end:create_empty_table]
-      expect(await emptyTbl.countRows()).toBe(0);
-    }
-    {
-      // --8<-- [start:open_table]
-      const _tbl = await db.openTable("myTable");
-      // --8<-- [end:open_table]
-    }
-
-    {
-      // --8<-- [start:table_names]
-      const tableNames = await db.tableNames();
-      // --8<-- [end:table_names]
-      expect(tableNames).toEqual(["empty_table", "myTable"]);
-    }
-
-    const tbl = await db.openTable("myTable");
-    {
-      // --8<-- [start:add_data]
-      const data = [
-        { vector: [1.3, 1.4], item: "fizz", price: 100.0 },
-        { vector: [9.5, 56.2], item: "buzz", price: 200.0 },
-      ];
-      await tbl.add(data);
-      // --8<-- [end:add_data]
-    }
-    {
-      // --8<-- [start:vector_search]
-      const res = await tbl.search([100, 100]).limit(2).toArray();
-      // --8<-- [end:vector_search]
-      expect(res.length).toBe(2);
-    }
-    {
-      const data = Array.from({ length: 1000 })
-        .fill(null)
-        .map(() => ({
-          vector: [Math.random(), Math.random()],
-          item: "autogen",
-          price: Math.round(Math.random() * 100),
-        }));
-
-      await tbl.add(data);
-    }
-
-    // --8<-- [start:create_index]
-    await tbl.createIndex("vector");
-    // --8<-- [end:create_index]
-
-    // --8<-- [start:delete_rows]
-    await tbl.delete('item = "fizz"');
-    // --8<-- [end:delete_rows]
-
-    // --8<-- [start:drop_table]
-    await db.dropTable("myTable");
-    // --8<-- [end:drop_table]
-    await db.dropTable("empty_table");
-
-    {
-      // --8<-- [start:create_f16_table]
-      const db = await lancedb.connect(databaseDir);
-      const dim = 16;
-      const total = 10;
-      const f16Schema = 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),
-        })),
-        { schema: f16Schema },
-      );
-      const _table = await db.createTable("f16_tbl", data);
-      // --8<-- [end:create_f16_table]
-      await db.dropTable("f16_tbl");
-    }
-  });
-});

nodejs/examples/basic.ts

@@ -0,0 +1,162 @@
+// --8<--  [start:imports]
+import * as lancedb from "@lancedb/lancedb";
+import * as arrow from "apache-arrow";
+import {
+  Field,
+  FixedSizeList,
+  Float16,
+  Int32,
+  Schema,
+  Utf8,
+} from "apache-arrow";
+
+// --8<-- [end:imports]
+
+// --8<-- [start:connect]
+const uri = "/tmp/lancedb/";
+const db = await lancedb.connect(uri);
+// --8<-- [end:connect]
+{
+  // --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 },
+    ],
+    { mode: "overwrite" },
+  );
+  // --8<-- [end:create_table]
+
+  const data = [
+    { vector: [3.1, 4.1], item: "foo", price: 10.0 },
+    { vector: [5.9, 26.5], item: "bar", price: 20.0 },
+  ];
+
+  {
+    // --8<-- [start:create_table_exists_ok]
+    const tbl = await db.createTable("myTable", data, {
+      existsOk: true,
+    });
+    // --8<-- [end:create_table_exists_ok]
+  }
+  {
+    // --8<-- [start:create_table_overwrite]
+    const _tbl = await db.createTable("myTable", data, {
+      mode: "overwrite",
+    });
+    // --8<-- [end:create_table_overwrite]
+  }
+}
+
+{
+  // --8<-- [start:create_table_with_schema]
+  const schema = new arrow.Schema([
+    new arrow.Field(
+      "vector",
+      new arrow.FixedSizeList(
+        2,
+        new arrow.Field("item", new arrow.Float32(), true),
+      ),
+    ),
+    new arrow.Field("item", new arrow.Utf8(), true),
+    new arrow.Field("price", new arrow.Float32(), true),
+  ]);
+  const data = [
+    { vector: [3.1, 4.1], item: "foo", price: 10.0 },
+    { vector: [5.9, 26.5], item: "bar", price: 20.0 },
+  ];
+  const _tbl = await db.createTable("myTable", data, {
+    schema,
+  });
+  // --8<-- [end:create_table_with_schema]
+}
+
+{
+  // --8<-- [start:create_empty_table]
+
+  const schema = new arrow.Schema([
+    new arrow.Field("id", new arrow.Int32()),
+    new arrow.Field("name", new arrow.Utf8()),
+  ]);
+
+  const empty_tbl = await db.createEmptyTable("empty_table", schema);
+  // --8<-- [end:create_empty_table]
+}
+{
+  // --8<-- [start:open_table]
+  const _tbl = await db.openTable("myTable");
+  // --8<-- [end:open_table]
+}
+
+{
+  // --8<-- [start:table_names]
+  const tableNames = await db.tableNames();
+  console.log(tableNames);
+  // --8<-- [end:table_names]
+}
+
+const tbl = await db.openTable("myTable");
+{
+  // --8<-- [start:add_data]
+  const data = [
+    { vector: [1.3, 1.4], item: "fizz", price: 100.0 },
+    { vector: [9.5, 56.2], item: "buzz", price: 200.0 },
+  ];
+  await tbl.add(data);
+  // --8<-- [end:add_data]
+}
+{
+  // --8<-- [start:vector_search]
+  const _res = tbl.search([100, 100]).limit(2).toArray();
+  // --8<-- [end:vector_search]
+}
+{
+  const data = Array.from({ length: 1000 })
+    .fill(null)
+    .map(() => ({
+      vector: [Math.random(), Math.random()],
+      item: "autogen",
+      price: Math.round(Math.random() * 100),
+    }));
+
+  await tbl.add(data);
+}
+
+// --8<-- [start:create_index]
+await tbl.createIndex("vector");
+// --8<-- [end:create_index]
+
+// --8<-- [start:delete_rows]
+await tbl.delete('item = "fizz"');
+// --8<-- [end:delete_rows]
+
+// --8<-- [start:drop_table]
+await db.dropTable("myTable");
+// --8<-- [end:drop_table]
+await db.dropTable("empty_table");
+
+{
+  // --8<-- [start:create_f16_table]
+  const db = await lancedb.connect("/tmp/lancedb");
+  const dim = 16;
+  const total = 10;
+  const f16Schema = 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),
+    })),
+    { schema: f16Schema },
+  );
+  const _table = await db.createTable("f16_tbl", data);
+  // --8<-- [end:create_f16_table]
+  await db.dropTable("f16_tbl");
+}

nodejs/examples/custom_embedding_function.test.ts

@@ -1,76 +0,0 @@
-import { FeatureExtractionPipeline, pipeline } from "@huggingface/transformers";
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import { expect, test } from "@jest/globals";
-// --8<-- [start:imports]
-import * as lancedb from "@lancedb/lancedb";
-import {
-  LanceSchema,
-  TextEmbeddingFunction,
-  getRegistry,
-  register,
-} from "@lancedb/lancedb/embedding";
-// --8<-- [end:imports]
-import { withTempDirectory } from "./util.ts";
-
-// --8<-- [start:embedding_impl]
-@register("sentence-transformers")
-class SentenceTransformersEmbeddings extends TextEmbeddingFunction {
-  name = "Xenova/all-miniLM-L6-v2";
-  #ndims!: number;
-  extractor!: FeatureExtractionPipeline;
-
-  async init() {
-    this.extractor = await pipeline("feature-extraction", this.name, {
-      dtype: "fp32",
-    });
-    this.#ndims = await this.generateEmbeddings(["hello"]).then(
-      (e) => e[0].length,
-    );
-  }
-
-  ndims() {
-    return this.#ndims;
-  }
-
-  toJSON() {
-    return {
-      name: this.name,
-    };
-  }
-  async generateEmbeddings(texts: string[]) {
-    const output = await this.extractor(texts, {
-      pooling: "mean",
-      normalize: true,
-    });
-    return output.tolist();
-  }
-}
-// -8<-- [end:embedding_impl]
-
-test("Registry examples", async () => {
-  await withTempDirectory(async (databaseDir) => {
-    // --8<-- [start:call_custom_function]
-    const registry = getRegistry();
-
-    const sentenceTransformer = await registry
-      .get<SentenceTransformersEmbeddings>("sentence-transformers")!
-      .create();
-
-    const schema = LanceSchema({
-      vector: sentenceTransformer.vectorField(),
-      text: sentenceTransformer.sourceField(),
-    });
-
-    const db = await lancedb.connect(databaseDir);
-    const table = await db.createEmptyTable("table", schema, {
-      mode: "overwrite",
-    });
-
-    await table.add([{ text: "hello" }, { text: "world" }]);
-
-    const results = await table.search("greeting").limit(1).toArray();
-    // -8<-- [end:call_custom_function]
-    expect(results.length).toBe(1);
-  });
-}, 100_000);

nodejs/examples/custom_embedding_function.ts

@@ -0,0 +1,64 @@
+// --8<-- [start:imports]
+import * as lancedb from "@lancedb/lancedb";
+import {
+  LanceSchema,
+  TextEmbeddingFunction,
+  getRegistry,
+  register,
+} from "@lancedb/lancedb/embedding";
+import { pipeline } from "@xenova/transformers";
+// --8<-- [end:imports]
+
+// --8<-- [start:embedding_impl]
+@register("sentence-transformers")
+class SentenceTransformersEmbeddings extends TextEmbeddingFunction {
+  name = "Xenova/all-miniLM-L6-v2";
+  #ndims!: number;
+  extractor: any;
+
+  async init() {
+    this.extractor = await pipeline("feature-extraction", this.name);
+    this.#ndims = await this.generateEmbeddings(["hello"]).then(
+      (e) => e[0].length,
+    );
+  }
+
+  ndims() {
+    return this.#ndims;
+  }
+
+  toJSON() {
+    return {
+      name: this.name,
+    };
+  }
+  async generateEmbeddings(texts: string[]) {
+    const output = await this.extractor(texts, {
+      pooling: "mean",
+      normalize: true,
+    });
+    return output.tolist();
+  }
+}
+// -8<-- [end:embedding_impl]
+
+// --8<-- [start:call_custom_function]
+const registry = getRegistry();
+
+const sentenceTransformer = await registry
+  .get<SentenceTransformersEmbeddings>("sentence-transformers")!
+  .create();
+
+const schema = LanceSchema({
+  vector: sentenceTransformer.vectorField(),
+  text: sentenceTransformer.sourceField(),
+});
+
+const db = await lancedb.connect("/tmp/db");
+const table = await db.createEmptyTable("table", schema, { mode: "overwrite" });
+
+await table.add([{ text: "hello" }, { text: "world" }]);
+
+const results = await table.search("greeting").limit(1).toArray();
+console.log(results[0].text);
+// -8<-- [end:call_custom_function]

nodejs/examples/embedding.test.ts

@@ -1,96 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import { expect, test } from "@jest/globals";
-// --8<-- [start:imports]
-import * as lancedb from "@lancedb/lancedb";
-import "@lancedb/lancedb/embedding/openai";
-import { LanceSchema, getRegistry, register } from "@lancedb/lancedb/embedding";
-import { EmbeddingFunction } from "@lancedb/lancedb/embedding";
-import { type Float, Float32, Utf8 } from "apache-arrow";
-// --8<-- [end:imports]
-import { withTempDirectory } from "./util.ts";
-
-const openAiTest = process.env.OPENAI_API_KEY == null ? test.skip : test;
-
-openAiTest("openai embeddings", async () => {
-  await withTempDirectory(async (databaseDir) => {
-    // --8<-- [start:openai_embeddings]
-    const db = await lancedb.connect(databaseDir);
-    const func = getRegistry()
-      .get("openai")
-      ?.create({ model: "text-embedding-ada-002" }) as EmbeddingFunction;
-
-    const wordsSchema = LanceSchema({
-      text: func.sourceField(new Utf8()),
-      vector: func.vectorField(),
-    });
-    const tbl = await db.createEmptyTable("words", wordsSchema, {
-      mode: "overwrite",
-    });
-    await tbl.add([{ text: "hello world" }, { text: "goodbye world" }]);
-
-    const query = "greetings";
-    const actual = (await tbl.search(query).limit(1).toArray())[0];
-    // --8<-- [end:openai_embeddings]
-    expect(actual).toHaveProperty("text");
-  });
-});
-
-test("custom embedding function", async () => {
-  await withTempDirectory(async (databaseDir) => {
-    // --8<-- [start:embedding_function]
-    const db = await lancedb.connect(databaseDir);
-
-    @register("my_embedding")
-    class MyEmbeddingFunction extends EmbeddingFunction<string> {
-      toJSON(): object {
-        return {};
-      }
-      ndims() {
-        return 3;
-      }
-      embeddingDataType(): Float {
-        return new Float32();
-      }
-      async computeQueryEmbeddings(_data: string) {
-        // This is a placeholder for a real embedding function
-        return [1, 2, 3];
-      }
-      async computeSourceEmbeddings(data: string[]) {
-        // This is a placeholder for a real embedding function
-        return Array.from({ length: data.length }).fill([
-          1, 2, 3,
-        ]) as number[][];
-      }
-    }
-
-    const func = new MyEmbeddingFunction();
-
-    const data = [{ text: "pepperoni" }, { text: "pineapple" }];
-
-    // Option 1: manually specify the embedding function
-    const table = await db.createTable("vectors", data, {
-      embeddingFunction: {
-        function: func,
-        sourceColumn: "text",
-        vectorColumn: "vector",
-      },
-      mode: "overwrite",
-    });
-
-    // Option 2: provide the embedding function through a schema
-
-    const schema = LanceSchema({
-      text: func.sourceField(new Utf8()),
-      vector: func.vectorField(),
-    });
-
-    const table2 = await db.createTable("vectors2", data, {
-      schema,
-      mode: "overwrite",
-    });
-    // --8<-- [end:embedding_function]
-    expect(await table.countRows()).toBe(2);
-    expect(await table2.countRows()).toBe(2);
-  });
-});

nodejs/examples/embedding.ts

@@ -0,0 +1,83 @@
+// --8<-- [start:imports]
+import * as lancedb from "@lancedb/lancedb";
+import { LanceSchema, getRegistry, register } from "@lancedb/lancedb/embedding";
+import { EmbeddingFunction } from "@lancedb/lancedb/embedding";
+import { type Float, Float32, Utf8 } from "apache-arrow";
+// --8<-- [end:imports]
+
+{
+  // --8<-- [start:openai_embeddings]
+
+  const db = await lancedb.connect("/tmp/db");
+  const func = getRegistry()
+    .get("openai")
+    ?.create({ model: "text-embedding-ada-002" }) as EmbeddingFunction;
+
+  const wordsSchema = LanceSchema({
+    text: func.sourceField(new Utf8()),
+    vector: func.vectorField(),
+  });
+  const tbl = await db.createEmptyTable("words", wordsSchema, {
+    mode: "overwrite",
+  });
+  await tbl.add([{ text: "hello world" }, { text: "goodbye world" }]);
+
+  const query = "greetings";
+  const actual = (await (await tbl.search(query)).limit(1).toArray())[0];
+
+  // --8<-- [end:openai_embeddings]
+  console.log("result = ", actual.text);
+}
+
+{
+  // --8<-- [start:embedding_function]
+  const db = await lancedb.connect("/tmp/db");
+
+  @register("my_embedding")
+  class MyEmbeddingFunction extends EmbeddingFunction<string> {
+    toJSON(): object {
+      return {};
+    }
+    ndims() {
+      return 3;
+    }
+    embeddingDataType(): Float {
+      return new Float32();
+    }
+    async computeQueryEmbeddings(_data: string) {
+      // This is a placeholder for a real embedding function
+      return [1, 2, 3];
+    }
+    async computeSourceEmbeddings(data: string[]) {
+      // This is a placeholder for a real embedding function
+      return Array.from({ length: data.length }).fill([1, 2, 3]) as number[][];
+    }
+  }
+
+  const func = new MyEmbeddingFunction();
+
+  const data = [{ text: "pepperoni" }, { text: "pineapple" }];
+
+  // Option 1: manually specify the embedding function
+  const table = await db.createTable("vectors", data, {
+    embeddingFunction: {
+      function: func,
+      sourceColumn: "text",
+      vectorColumn: "vector",
+    },
+    mode: "overwrite",
+  });
+
+  // Option 2: provide the embedding function through a schema
+
+  const schema = LanceSchema({
+    text: func.sourceField(new Utf8()),
+    vector: func.vectorField(),
+  });
+
+  const table2 = await db.createTable("vectors2", data, {
+    schema,
+    mode: "overwrite",
+  });
+  // --8<-- [end:embedding_function]
+}

nodejs/examples/filtering.test.ts

@@ -1,42 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import { expect, test } from "@jest/globals";
-import * as lancedb from "@lancedb/lancedb";
-import { withTempDirectory } from "./util.ts";
-
-test("filtering examples", async () => {
-  await withTempDirectory(async (databaseDir) => {
-    const db = await lancedb.connect(databaseDir);
-
-    const data = Array.from({ length: 10_000 }, (_, i) => ({
-      vector: Array(1536).fill(i),
-      id: i,
-      item: `item ${i}`,
-      strId: `${i}`,
-    }));
-
-    const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
-
-    // --8<-- [start:search]
-    const _result = await tbl
-      .search(Array(1536).fill(0.5))
-      .limit(1)
-      .where("id = 10")
-      .toArray();
-    // --8<-- [end:search]
-
-    // --8<-- [start:vec_search]
-    const result = await (
-      tbl.search(Array(1536).fill(0)) as lancedb.VectorQuery
-    )
-      .where("(item IN ('item 0', 'item 2')) AND (id > 10)")
-      .postfilter()
-      .toArray();
-    // --8<-- [end:vec_search]
-    expect(result.length).toBe(0);
-
-    // --8<-- [start:sql_search]
-    await tbl.query().where("id = 10").limit(10).toArray();
-    // --8<-- [end:sql_search]
-  });
-});

nodejs/examples/filtering.ts

@@ -0,0 +1,34 @@
+import * as lancedb from "@lancedb/lancedb";
+
+const db = await lancedb.connect("data/sample-lancedb");
+
+const data = Array.from({ length: 10_000 }, (_, i) => ({
+  vector: Array(1536).fill(i),
+  id: i,
+  item: `item ${i}`,
+  strId: `${i}`,
+}));
+
+const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
+
+// --8<-- [start:search]
+const _result = await tbl
+  .search(Array(1536).fill(0.5))
+  .limit(1)
+  .where("id = 10")
+  .toArray();
+// --8<-- [end:search]
+
+// --8<-- [start:vec_search]
+await tbl
+  .search(Array(1536).fill(0))
+  .where("(item IN ('item 0', 'item 2')) AND (id > 10)")
+  .postfilter()
+  .toArray();
+// --8<-- [end:vec_search]
+
+// --8<-- [start:sql_search]
+await tbl.query().where("id = 10").limit(10).toArray();
+// --8<-- [end:sql_search]
+
+console.log("SQL search: done");

nodejs/examples/full_text_search.test.ts

@@ -1,45 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import { expect, test } from "@jest/globals";
-import * as lancedb from "@lancedb/lancedb";
-import { withTempDirectory } from "./util.ts";
-
-test("full text search", async () => {
-  await withTempDirectory(async (databaseDir) => {
-    const db = await lancedb.connect(databaseDir);
-
-    const words = [
-      "apple",
-      "banana",
-      "cherry",
-      "date",
-      "elderberry",
-      "fig",
-      "grape",
-    ];
-
-    const data = Array.from({ length: 10_000 }, (_, i) => ({
-      vector: Array(1536).fill(i),
-      id: i,
-      item: `item ${i}`,
-      strId: `${i}`,
-      doc: words[i % words.length],
-    }));
-
-    const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
-
-    await tbl.createIndex("doc", {
-      config: lancedb.Index.fts(),
-    });
-
-    // --8<-- [start:full_text_search]
-    const result = await tbl
-      .query()
-      .nearestToText("apple")
-      .select(["id", "doc"])
-      .limit(10)
-      .toArray();
-    expect(result.length).toBe(10);
-    // --8<-- [end:full_text_search]
-  });
-});

nodejs/examples/full_text_search.ts

@@ -0,0 +1,52 @@
+// Copyright 2024 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import * as lancedb from "@lancedb/lancedb";
+
+const db = await lancedb.connect("data/sample-lancedb");
+
+const words = [
+  "apple",
+  "banana",
+  "cherry",
+  "date",
+  "elderberry",
+  "fig",
+  "grape",
+];
+
+const data = Array.from({ length: 10_000 }, (_, i) => ({
+  vector: Array(1536).fill(i),
+  id: i,
+  item: `item ${i}`,
+  strId: `${i}`,
+  doc: words[i % words.length],
+}));
+
+const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
+
+await tbl.createIndex("doc", {
+  config: lancedb.Index.fts(),
+});
+
+// --8<-- [start:full_text_search]
+let result = await tbl
+  .search("apple")
+  .select(["id", "doc"])
+  .limit(10)
+  .toArray();
+console.log(result);
+// --8<-- [end:full_text_search]
+
+console.log("SQL search: done");

nodejs/examples/jest.config.cjs

@@ -1,6 +0,0 @@
-/** @type {import('ts-jest').JestConfigWithTsJest} */
-module.exports = {
-  preset: "ts-jest",
-  testEnvironment: "node",
-  testPathIgnorePatterns: ["./dist"],
-};

nodejs/examples/jsconfig.json

@@ -0,0 +1,27 @@
+{
+  "compilerOptions": {
+    // Enable latest features
+    "lib": ["ESNext", "DOM"],
+    "target": "ESNext",
+    "module": "ESNext",
+    "moduleDetection": "force",
+    "jsx": "react-jsx",
+    "allowJs": true,
+
+    // Bundler mode
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "verbatimModuleSyntax": true,
+    "noEmit": true,
+
+    // Best practices
+    "strict": true,
+    "skipLibCheck": true,
+    "noFallthroughCasesInSwitch": true,
+
+    // Some stricter flags (disabled by default)
+    "noUnusedLocals": false,
+    "noUnusedParameters": false,
+    "noPropertyAccessFromIndexSignature": false
+  }
+}

nodejs/examples/package.json

@@ -5,29 +5,24 @@
   "main": "index.js",
   "type": "module",
   "scripts": {
-    "//1": "--experimental-vm-modules is needed to run jest with sentence-transformers",
-    "//2": "--testEnvironment is needed to run jest with sentence-transformers",
-    "//3": "See: https://github.com/huggingface/transformers.js/issues/57",
-    "test": "node --experimental-vm-modules node_modules/.bin/jest --testEnvironment jest-environment-node-single-context --verbose",
-    "lint": "biome check *.ts && biome format *.ts",
-    "lint-ci": "biome ci .",
-    "lint-fix": "biome check --write *.ts && npm run format",
-    "format": "biome format --write *.ts"
+    "test": "echo \"Error: no test specified\" && exit 1"
   },
   "author": "Lance Devs",
   "license": "Apache-2.0",
   "dependencies": {
-    "@huggingface/transformers": "^3.0.2",
-    "@lancedb/lancedb": "file:../dist",
-    "openai": "^4.29.2",
-    "sharp": "^0.33.5"
+    "@lancedb/lancedb": "file:../",
+    "@xenova/transformers": "^2.17.2"
   },
   "devDependencies": {
-    "@biomejs/biome": "^1.7.3",
-    "@jest/globals": "^29.7.0",
-    "jest": "^29.7.0",
-    "jest-environment-node-single-context": "^29.4.0",
-    "ts-jest": "^29.2.5",
     "typescript": "^5.5.4"
+  },
+  "compilerOptions": {
+    "target": "ESNext",
+    "module": "ESNext",
+    "moduleResolution": "Node",
+    "strict": true,
+    "esModuleInterop": true,
+    "skipLibCheck": true,
+    "forceConsistentCasingInFileNames": true
   }
 }

nodejs/examples/search.test.ts

@@ -1,42 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import { expect, test } from "@jest/globals";
-// --8<-- [start:import]
-import * as lancedb from "@lancedb/lancedb";
-// --8<-- [end:import]
-import { withTempDirectory } from "./util.ts";
-
-test("full text search", async () => {
-  await withTempDirectory(async (databaseDir) => {
-    {
-      const db = await lancedb.connect(databaseDir);
-
-      const data = Array.from({ length: 10_000 }, (_, i) => ({
-        vector: Array(128).fill(i),
-        id: `${i}`,
-        content: "",
-        longId: `${i}`,
-      }));
-
-      await db.createTable("my_vectors", data);
-    }
-
-    // --8<-- [start:search1]
-    const db = await lancedb.connect(databaseDir);
-    const tbl = await db.openTable("my_vectors");
-
-    const results1 = await tbl.search(Array(128).fill(1.2)).limit(10).toArray();
-    // --8<-- [end:search1]
-    expect(results1.length).toBe(10);
-
-    // --8<-- [start:search2]
-    const results2 = await (
-      tbl.search(Array(128).fill(1.2)) as lancedb.VectorQuery
-    )
-      .distanceType("cosine")
-      .limit(10)
-      .toArray();
-    // --8<-- [end:search2]
-    expect(results2.length).toBe(10);
-  });
-});

nodejs/examples/search.ts

@@ -0,0 +1,38 @@
+// --8<-- [end:import]
+import * as fs from "node:fs";
+// --8<-- [start:import]
+import * as lancedb from "@lancedb/lancedb";
+
+async function setup() {
+  fs.rmSync("data/sample-lancedb", { recursive: true, force: true });
+  const db = await lancedb.connect("data/sample-lancedb");
+
+  const data = Array.from({ length: 10_000 }, (_, i) => ({
+    vector: Array(1536).fill(i),
+    id: `${i}`,
+    content: "",
+    longId: `${i}`,
+  }));
+
+  await db.createTable("my_vectors", data);
+}
+
+await setup();
+
+// --8<-- [start:search1]
+const db = await lancedb.connect("data/sample-lancedb");
+const tbl = await db.openTable("my_vectors");
+
+const _results1 = await tbl.search(Array(1536).fill(1.2)).limit(10).toArray();
+// --8<-- [end:search1]
+
+// --8<-- [start:search2]
+const _results2 = await tbl
+  .search(Array(1536).fill(1.2))
+  .distanceType("cosine")
+  .limit(10)
+  .toArray();
+console.log(_results2);
+// --8<-- [end:search2]
+
+console.log("search: done");

nodejs/examples/sentence-transformers.js

@@ -0,0 +1,50 @@
+import * as lancedb from "@lancedb/lancedb";
+
+import { LanceSchema, getRegistry } from "@lancedb/lancedb/embedding";
+import { Utf8 } from "apache-arrow";
+
+const db = await lancedb.connect("/tmp/db");
+const func = await getRegistry().get("huggingface").create();
+
+const facts = [
+  "Albert Einstein was a theoretical physicist.",
+  "The capital of France is Paris.",
+  "The Great Wall of China is one of the Seven Wonders of the World.",
+  "Python is a popular programming language.",
+  "Mount Everest is the highest mountain in the world.",
+  "Leonardo da Vinci painted the Mona Lisa.",
+  "Shakespeare wrote Hamlet.",
+  "The human body has 206 bones.",
+  "The speed of light is approximately 299,792 kilometers per second.",
+  "Water boils at 100 degrees Celsius.",
+  "The Earth orbits the Sun.",
+  "The Pyramids of Giza are located in Egypt.",
+  "Coffee is one of the most popular beverages in the world.",
+  "Tokyo is the capital city of Japan.",
+  "Photosynthesis is the process by which plants make their food.",
+  "The Pacific Ocean is the largest ocean on Earth.",
+  "Mozart was a prolific composer of classical music.",
+  "The Internet is a global network of computers.",
+  "Basketball is a sport played with a ball and a hoop.",
+  "The first computer virus was created in 1983.",
+  "Artificial neural networks are inspired by the human brain.",
+  "Deep learning is a subset of machine learning.",
+  "IBM's Watson won Jeopardy! in 2011.",
+  "The first computer programmer was Ada Lovelace.",
+  "The first chatbot was ELIZA, created in the 1960s.",
+].map((text) => ({ text }));
+
+const factsSchema = LanceSchema({
+  text: func.sourceField(new Utf8()),
+  vector: func.vectorField(),
+});
+
+const tbl = await db.createTable("facts", facts, {
+  mode: "overwrite",
+  schema: factsSchema,
+});
+
+const query = "How many bones are in the human body?";
+const actual = await tbl.search(query).limit(1).toArray();
+
+console.log("Answer: ", actual[0]["text"]);

nodejs/examples/sentence-transformers.test.ts

@@ -1,63 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import { expect, test } from "@jest/globals";
-import { withTempDirectory } from "./util.ts";
-
-import * as lancedb from "@lancedb/lancedb";
-import "@lancedb/lancedb/embedding/transformers";
-import { LanceSchema, getRegistry } from "@lancedb/lancedb/embedding";
-import { EmbeddingFunction } from "@lancedb/lancedb/embedding";
-import { Utf8 } from "apache-arrow";
-
-test("full text search", async () => {
-  await withTempDirectory(async (databaseDir) => {
-    const db = await lancedb.connect(databaseDir);
-    console.log(getRegistry());
-    const func = (await getRegistry()
-      .get("huggingface")
-      ?.create()) as EmbeddingFunction;
-
-    const facts = [
-      "Albert Einstein was a theoretical physicist.",
-      "The capital of France is Paris.",
-      "The Great Wall of China is one of the Seven Wonders of the World.",
-      "Python is a popular programming language.",
-      "Mount Everest is the highest mountain in the world.",
-      "Leonardo da Vinci painted the Mona Lisa.",
-      "Shakespeare wrote Hamlet.",
-      "The human body has 206 bones.",
-      "The speed of light is approximately 299,792 kilometers per second.",
-      "Water boils at 100 degrees Celsius.",
-      "The Earth orbits the Sun.",
-      "The Pyramids of Giza are located in Egypt.",
-      "Coffee is one of the most popular beverages in the world.",
-      "Tokyo is the capital city of Japan.",
-      "Photosynthesis is the process by which plants make their food.",
-      "The Pacific Ocean is the largest ocean on Earth.",
-      "Mozart was a prolific composer of classical music.",
-      "The Internet is a global network of computers.",
-      "Basketball is a sport played with a ball and a hoop.",
-      "The first computer virus was created in 1983.",
-      "Artificial neural networks are inspired by the human brain.",
-      "Deep learning is a subset of machine learning.",
-      "IBM's Watson won Jeopardy! in 2011.",
-      "The first computer programmer was Ada Lovelace.",
-      "The first chatbot was ELIZA, created in the 1960s.",
-    ].map((text) => ({ text }));
-
-    const factsSchema = LanceSchema({
-      text: func.sourceField(new Utf8()),
-      vector: func.vectorField(),
-    });
-
-    const tbl = await db.createTable("facts", facts, {
-      mode: "overwrite",
-      schema: factsSchema,
-    });
-
-    const query = "How many bones are in the human body?";
-    const actual = await tbl.search(query).limit(1).toArray();
-
-    expect(actual[0]["text"]).toBe("The human body has 206 bones.");
-  });
-}, 100_000);

nodejs/examples/tsconfig.json

@@ -1,17 +0,0 @@
-{
-  "include": ["*.test.ts"],
-  "compilerOptions": {
-    "target": "es2022",
-    "module": "NodeNext",
-    "declaration": true,
-    "outDir": "./dist",
-    "strict": true,
-    "allowJs": true,
-    "resolveJsonModule": true,
-    "emitDecoratorMetadata": true,
-    "experimentalDecorators": true,
-    "moduleResolution": "NodeNext",
-    "allowImportingTsExtensions": true,
-    "emitDeclarationOnly": true
-  }
-}

nodejs/examples/util.ts

@@ -1,16 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import * as fs from "fs";
-import { tmpdir } from "os";
-import * as path from "path";
-
-export async function withTempDirectory(
-  fn: (tempDir: string) => Promise<void>,
-) {
-  const tmpDirPath = fs.mkdtempSync(path.join(tmpdir(), "temp-dir-"));
-  try {
-    await fn(tmpDirPath);
-  } finally {
-    fs.rmSync(tmpDirPath, { recursive: true });
-  }
-}

nodejs/jest.config.js

@@ -4,5 +4,4 @@ module.exports = {
   testEnvironment: "node",
   moduleDirectories: ["node_modules", "./dist"],
   moduleFileExtensions: ["js", "ts"],
-  modulePathIgnorePatterns: ["<rootDir>/examples/"],
 };

nodejs/lancedb/embedding/index.ts

@@ -19,6 +19,9 @@ import { EmbeddingFunctionConfig, getRegistry } from "./registry";
 
 export { EmbeddingFunction, TextEmbeddingFunction } from "./embedding_function";
 
+// We need to explicitly export '*' so that the `register` decorator actually registers the class.
+export * from "./openai";
+export * from "./transformers";
 export * from "./registry";
 
 /**

nodejs/lancedb/embedding/registry.ts

@@ -17,6 +17,8 @@ import {
   type EmbeddingFunctionConstructor,
 } from "./embedding_function";
 import "reflect-metadata";
+import { OpenAIEmbeddingFunction } from "./openai";
+import { TransformersEmbeddingFunction } from "./transformers";
 
 type CreateReturnType<T> = T extends { init: () => Promise<void> }
   ? Promise<T>
@@ -71,6 +73,10 @@ export class EmbeddingFunctionRegistry {
     };
   }
 
+  get(name: "openai"): EmbeddingFunctionCreate<OpenAIEmbeddingFunction>;
+  get(
+    name: "huggingface",
+  ): EmbeddingFunctionCreate<TransformersEmbeddingFunction>;
   get<T extends EmbeddingFunction<unknown>>(
     name: string,
   ): EmbeddingFunctionCreate<T> | undefined;

nodejs/lancedb/embedding/transformers.ts

@@ -47,8 +47,8 @@ export class TransformersEmbeddingFunction extends EmbeddingFunction<
   string,
   Partial<XenovaTransformerOptions>
 > {
-  #model?: import("@huggingface/transformers").PreTrainedModel;
-  #tokenizer?: import("@huggingface/transformers").PreTrainedTokenizer;
+  #model?: import("@xenova/transformers").PreTrainedModel;
+  #tokenizer?: import("@xenova/transformers").PreTrainedTokenizer;
   #modelName: XenovaTransformerOptions["model"];
   #initialized = false;
   #tokenizerOptions: XenovaTransformerOptions["tokenizerOptions"];
@@ -92,19 +92,18 @@ export class TransformersEmbeddingFunction extends EmbeddingFunction<
     try {
       // SAFETY:
       // since typescript transpiles `import` to `require`, we need to do this in an unsafe way
-      // We can't use `require` because `@huggingface/transformers` is an ESM module
+      // We can't use `require` because `@xenova/transformers` is an ESM module
       // and we can't use `import` directly because typescript will transpile it to `require`.
       // and we want to remain compatible with both ESM and CJS modules
       // so we use `eval` to bypass typescript for this specific import.
-      transformers = await eval('import("@huggingface/transformers")');
+      transformers = await eval('import("@xenova/transformers")');
     } catch (e) {
-      throw new Error(`error loading @huggingface/transformers\nReason: ${e}`);
+      throw new Error(`error loading @xenova/transformers\nReason: ${e}`);
     }
 
     try {
       this.#model = await transformers.AutoModel.from_pretrained(
         this.#modelName,
-        { dtype: "fp32" },
       );
     } catch (e) {
       throw new Error(
@@ -129,8 +128,7 @@ export class TransformersEmbeddingFunction extends EmbeddingFunction<
     } else {
       const config = this.#model!.config;
 
-      // biome-ignore lint/style/useNamingConvention: we don't control this name.
-      const ndims = (config as unknown as { hidden_size: number }).hidden_size;
+      const ndims = config["hidden_size"];
       if (!ndims) {
         throw new Error(
           "hidden_size not found in model config, you may need to manually specify the embedding dimensions. ",
@@ -185,7 +183,7 @@ export class TransformersEmbeddingFunction extends EmbeddingFunction<
 }
 
 const tensorDiv = (
-  src: import("@huggingface/transformers").Tensor,
+  src: import("@xenova/transformers").Tensor,
   divBy: number,
 ) => {
   for (let i = 0; i < src.data.length; ++i) {

nodejs/lancedb/query.ts

@@ -385,20 +385,6 @@ export class VectorQuery extends QueryBase<NativeVectorQuery> {
     return this;
   }
 
-  /**
-   * Set the number of candidates to consider during the search
-   *
-   * This argument is only used when the vector column has an HNSW index.
-   * If there is no index then this value is ignored.
-   *
-   * Increasing this value will increase the recall of your query but will
-   * also increase the latency of your query. The default value is 1.5*limit.
-   */
-  ef(ef: number): VectorQuery {
-    super.doCall((inner) => inner.ef(ef));
-    return this;
-  }
-
   /**
    * Set the vector column to query
    *
@@ -506,42 +492,6 @@ export class VectorQuery extends QueryBase<NativeVectorQuery> {
     super.doCall((inner) => inner.bypassVectorIndex());
     return this;
   }
-
-  /*
-   * Add a query vector to the search
-   *
-   * This method can be called multiple times to add multiple query vectors
-   * to the search. If multiple query vectors are added, then they will be searched
-   * in parallel, and the results will be concatenated. A column called `query_index`
-   * will be added to indicate the index of the query vector that produced the result.
-   *
-   * Performance wise, this is equivalent to running multiple queries concurrently.
-   */
-  addQueryVector(vector: IntoVector): VectorQuery {
-    if (vector instanceof Promise) {
-      const res = (async () => {
-        try {
-          const v = await vector;
-          const arr = Float32Array.from(v);
-          //
-          // biome-ignore lint/suspicious/noExplicitAny: we need to get the `inner`, but js has no package scoping
-          const value: any = this.addQueryVector(arr);
-          const inner = value.inner as
-            | NativeVectorQuery
-            | Promise<NativeVectorQuery>;
-          return inner;
-        } catch (e) {
-          return Promise.reject(e);
-        }
-      })();
-      return new VectorQuery(res);
-    } else {
-      super.doCall((inner) => {
-        inner.addQueryVector(Float32Array.from(vector));
-      });
-      return this;
-    }
-  }
 }
 
 /** A builder for LanceDB queries. */
@@ -621,9 +571,4 @@ export class Query extends QueryBase<NativeQuery> {
       return new VectorQuery(vectorQuery);
     }
   }
-
-  nearestToText(query: string, columns?: string[]): Query {
-    this.doCall((inner) => inner.fullTextSearch(query, columns));
-    return this;
-  }
 }

nodejs/lancedb/table.ts

@@ -87,12 +87,6 @@ export interface OptimizeOptions {
   deleteUnverified: boolean;
 }
 
-export interface Version {
-  version: number;
-  timestamp: Date;
-  metadata: Record<string, string>;
-}
-
 /**
  * A Table is a collection of Records in a LanceDB Database.
  *
@@ -366,11 +360,6 @@ export abstract class Table {
    */
   abstract checkoutLatest(): Promise<void>;
 
-  /**
-   * List all the versions of the table
-   */
-  abstract listVersions(): Promise<Version[]>;
-
   /**
    * Restore the table to the currently checked out version
    *
@@ -670,14 +659,6 @@ export class LocalTable extends Table {
     await this.inner.checkoutLatest();
   }
 
-  async listVersions(): Promise<Version[]> {
-    return (await this.inner.listVersions()).map((version) => ({
-      version: version.version,
-      timestamp: new Date(version.timestamp / 1000),
-      metadata: version.metadata,
-    }));
-  }
-
   async restore(): Promise<void> {
     await this.inner.restore();
   }

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.14.0-beta.0",
+	"version": "0.13.0-beta.1",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.14.0-beta.0",
+	"version": "0.13.0-beta.1",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.14.0-beta.0",
+	"version": "0.13.0-beta.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

nodejs/npm/linux-arm64-musl/README.md

@@ -1,3 +0,0 @@
-# `@lancedb/lancedb-linux-arm64-musl`
-
-This is the **aarch64-unknown-linux-musl** binary for `@lancedb/lancedb`

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

@@ -1,13 +0,0 @@
-{
-	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.14.0-beta.0",
-	"os": ["linux"],
-	"cpu": ["arm64"],
-	"main": "lancedb.linux-arm64-musl.node",
-	"files": ["lancedb.linux-arm64-musl.node"],
-	"license": "Apache 2.0",
-	"engines": {
-		"node": ">= 18"
-	},
-	"libc": ["musl"]
-}

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.14.0-beta.0",
+	"version": "0.13.0-beta.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/linux-x64-musl/README.md

@@ -1,3 +0,0 @@
-# `@lancedb/lancedb-linux-x64-musl`
-
-This is the **x86_64-unknown-linux-musl** binary for `@lancedb/lancedb`

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

@@ -1,13 +0,0 @@
-{
-	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.14.0-beta.0",
-	"os": ["linux"],
-	"cpu": ["x64"],
-	"main": "lancedb.linux-x64-musl.node",
-	"files": ["lancedb.linux-x64-musl.node"],
-	"license": "Apache 2.0",
-	"engines": {
-		"node": ">= 18"
-	},
-	"libc": ["musl"]
-}

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

@@ -1,6 +1,6 @@
 {
   "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.14.0-beta.0",
+  "version": "0.13.0-beta.1",
   "os": [
     "win32"
   ],

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.14.0-beta.0",
+	"version": "0.13.0-beta.1",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package.json

@@ -10,13 +10,11 @@
     "vector database",
     "ann"
   ],
-  "version": "0.14.0-beta.0",
+  "version": "0.13.0-beta.1",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",
-    "./embedding": "./dist/embedding/index.js",
-    "./embedding/openai": "./dist/embedding/openai.js",
-    "./embedding/transformers": "./dist/embedding/transformers.js"
+    "./embedding": "./dist/embedding/index.js"
   },
   "types": "dist/index.d.ts",
   "napi": {
@@ -24,12 +22,10 @@
     "triples": {
       "defaults": false,
       "additional": [
-        "x86_64-apple-darwin",
         "aarch64-apple-darwin",
-        "x86_64-unknown-linux-gnu",
         "aarch64-unknown-linux-gnu",
-        "x86_64-unknown-linux-musl",
-        "aarch64-unknown-linux-musl",
+        "x86_64-apple-darwin",
+        "x86_64-unknown-linux-gnu",
         "x86_64-pc-windows-msvc"
       ]
     }
@@ -89,7 +85,7 @@
     "reflect-metadata": "^0.2.2"
   },
   "optionalDependencies": {
-    "@huggingface/transformers": "^3.0.2",
+    "@xenova/transformers": ">=2.17 < 3",
     "openai": "^4.29.2"
   },
   "peerDependencies": {

nodejs/src/query.rs

@@ -135,16 +135,6 @@ impl VectorQuery {
         self.inner = self.inner.clone().column(&column);
     }
 
-    #[napi]
-    pub fn add_query_vector(&mut self, vector: Float32Array) -> Result<()> {
-        self.inner = self
-            .inner
-            .clone()
-            .add_query_vector(vector.as_ref())
-            .default_error()?;
-        Ok(())
-    }
-
     #[napi]
     pub fn distance_type(&mut self, distance_type: String) -> napi::Result<()> {
         let distance_type = parse_distance_type(distance_type)?;
@@ -167,11 +157,6 @@ impl VectorQuery {
         self.inner = self.inner.clone().nprobes(nprobe as usize);
     }
 
-    #[napi]
-    pub fn ef(&mut self, ef: u32) {
-        self.inner = self.inner.clone().ef(ef as usize);
-    }
-
     #[napi]
     pub fn bypass_vector_index(&mut self) {
         self.inner = self.inner.clone().bypass_vector_index()

nodejs/src/table.rs

@@ -12,8 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use std::collections::HashMap;
-
 use arrow_ipc::writer::FileWriter;
 use lancedb::ipc::ipc_file_to_batches;
 use lancedb::table::{
@@ -228,28 +226,6 @@ impl Table {
         self.inner_ref()?.checkout_latest().await.default_error()
     }
 
-    #[napi(catch_unwind)]
-    pub async fn list_versions(&self) -> napi::Result<Vec<Version>> {
-        self.inner_ref()?
-            .list_versions()
-            .await
-            .map(|versions| {
-                versions
-                    .iter()
-                    .map(|version| Version {
-                        version: version.version as i64,
-                        timestamp: version.timestamp.timestamp_micros(),
-                        metadata: version
-                            .metadata
-                            .iter()
-                            .map(|(k, v)| (k.clone(), v.clone()))
-                            .collect(),
-                    })
-                    .collect()
-            })
-            .default_error()
-    }
-
     #[napi(catch_unwind)]
     pub async fn restore(&self) -> napi::Result<()> {
         self.inner_ref()?.restore().await.default_error()
@@ -490,10 +466,3 @@ impl From<lancedb::index::IndexStatistics> for IndexStatistics {
         }
     }
 }
-
-#[napi(object)]
-pub struct Version {
-    pub version: i64,
-    pub timestamp: i64,
-    pub metadata: HashMap<String, String>,
-}

nodejs/tsconfig.json

@@ -12,7 +12,7 @@
     "experimentalDecorators": true,
     "moduleResolution": "Node"
   },
-  "exclude": ["./dist/*", "./examples/*"],
+  "exclude": ["./dist/*"],
   "typedocOptions": {
     "entryPoints": ["lancedb/index.ts"],
     "out": "../docs/src/javascript/",

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.17.0-beta.1"
+current_version = "0.16.0-beta.0"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.17.0-beta.1"
+version = "0.16.0-beta.0"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
@@ -15,19 +15,13 @@ crate-type = ["cdylib"]
 
 [dependencies]
 arrow = { version = "52.1", features = ["pyarrow"] }
-lancedb = { path = "../rust/lancedb", default-features = false }
+lancedb = { path = "../rust/lancedb" }
 env_logger.workspace = true
-pyo3 = { version = "0.21", features = [
-    "extension-module",
-    "abi3-py39",
-    "gil-refs"
-] }
+pyo3 = { version = "0.21", features = ["extension-module", "abi3-py38", "gil-refs"] }
 # Using this fork for now: https://github.com/awestlake87/pyo3-asyncio/issues/119
 # pyo3-asyncio = { version = "0.20", features = ["attributes", "tokio-runtime"] }
-pyo3-asyncio-0-21 = { version = "0.21.0", features = [
-    "attributes",
-    "tokio-runtime"
-] }
+pyo3-asyncio-0-21 = { version = "0.21.0", features = ["attributes", "tokio-runtime"] }
+
 pin-project = "1.1.5"
 futures.workspace = true
 tokio = { version = "1.36.0", features = ["sync"] }
@@ -35,14 +29,10 @@ tokio = { version = "1.36.0", features = ["sync"] }
 [build-dependencies]
 pyo3-build-config = { version = "0.20.3", features = [
     "extension-module",
-    "abi3-py39",
+    "abi3-py38",
 ] }
 
 [features]
-default = ["default-tls", "remote"]
+default = ["remote"]
 fp16kernels = ["lancedb/fp16kernels"]
 remote = ["lancedb/remote"]
-# TLS
-default-tls = ["lancedb/default-tls"]
-native-tls = ["lancedb/native-tls"]
-rustls-tls = ["lancedb/rustls-tls"]

python/pyproject.toml

@@ -3,7 +3,8 @@ name = "lancedb"
 # version in Cargo.toml
 dependencies = [
     "deprecation",
-    "pylance==0.20.0b3",
+    "nest-asyncio~=1.0",
+    "pylance==0.19.2-beta.3",
     "tqdm>=4.27.0",
     "pydantic>=1.10",
     "packaging",
@@ -30,6 +31,7 @@ classifiers = [
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",

python/python/lancedb/embeddings/openai.py

@@ -83,33 +83,25 @@ class OpenAIEmbeddings(TextEmbeddingFunction):
         """
         openai = attempt_import_or_raise("openai")
 
-        valid_texts = []
-        valid_indices = []
-        for idx, text in enumerate(texts):
-            if text:
-                valid_texts.append(text)
-                valid_indices.append(idx)
-
         # TODO retry, rate limit, token limit
         try:
-            kwargs = {
-                "input": valid_texts,
-                "model": self.name,
-            }
-            if self.name != "text-embedding-ada-002":
-                kwargs["dimensions"] = self.dim
-
-            rs = self._openai_client.embeddings.create(**kwargs)
-            valid_embeddings = {
-                idx: v.embedding for v, idx in zip(rs.data, valid_indices)
-            }
+            if self.name == "text-embedding-ada-002":
+                rs = self._openai_client.embeddings.create(input=texts, model=self.name)
+            else:
+                kwargs = {
+                    "input": texts,
+                    "model": self.name,
+                }
+                if self.dim:
+                    kwargs["dimensions"] = self.dim
+                rs = self._openai_client.embeddings.create(**kwargs)
         except openai.BadRequestError:
             logging.exception("Bad request: %s", texts)
             return [None] * len(texts)
         except Exception:
             logging.exception("OpenAI embeddings error")
             raise
-        return [valid_embeddings.get(idx, None) for idx in range(len(texts))]
+        return [v.embedding for v in rs.data]
 
     @cached_property
     def _openai_client(self):

python/python/lancedb/embeddings/registry.py

@@ -1,6 +1,15 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
+#  Copyright (c) 2023. LanceDB Developers
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
 import json
 from typing import Dict, Optional
 
@@ -161,7 +170,7 @@ def register(name):
     return __REGISTRY__.get_instance().register(name)
 
 
-def get_registry() -> EmbeddingFunctionRegistry:
+def get_registry():
     """
     Utility function to get the global instance of the registry
 

python/python/lancedb/index.py

@@ -110,7 +110,16 @@ class FTS:
         remove_stop_words: bool = False,
         ascii_folding: bool = False,
     ):
-        self._inner = LanceDbIndex.fts(with_position=with_position)
+        self._inner = LanceDbIndex.fts(
+            with_position=with_position,
+            base_tokenizer=base_tokenizer,
+            language=language,
+            max_token_length=max_token_length,
+            lower_case=lower_case,
+            stem=stem,
+            remove_stop_words=remove_stop_words,
+            ascii_folding=ascii_folding,
+        )
 
 
 class HnswPq:

python/python/lancedb/pydantic.py

@@ -1,5 +1,15 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+#  Copyright 2023 LanceDB Developers
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
 
 """Pydantic (v1 / v2) adapter for LanceDB"""
 
@@ -20,7 +30,6 @@ from typing import (
     Type,
     Union,
     _GenericAlias,
-    GenericAlias,
 )
 
 import numpy as np
@@ -66,7 +75,7 @@ def vector(dim: int, value_type: pa.DataType = pa.float32()):
 
 
 def Vector(
-    dim: int, value_type: pa.DataType = pa.float32(), nullable: bool = True
+    dim: int, value_type: pa.DataType = pa.float32()
 ) -> Type[FixedSizeListMixin]:
     """Pydantic Vector Type.
 
@@ -79,8 +88,6 @@ def Vector(
         The dimension of the vector.
     value_type : pyarrow.DataType, optional
         The value type of the vector, by default pa.float32()
-    nullable : bool, optional
-        Whether the vector is nullable, by default it is True.
 
     Examples
     --------
@@ -96,7 +103,7 @@ def Vector(
     >>> assert schema == pa.schema([
     ...     pa.field("id", pa.int64(), False),
     ...     pa.field("url", pa.utf8(), False),
-    ...     pa.field("embeddings", pa.list_(pa.float32(), 768))
+    ...     pa.field("embeddings", pa.list_(pa.float32(), 768), False)
     ... ])
     """
 
@@ -105,10 +112,6 @@ def Vector(
         def __repr__(self):
             return f"FixedSizeList(dim={dim})"
 
-        @staticmethod
-        def nullable() -> bool:
-            return nullable
-
         @staticmethod
         def dim() -> int:
             return dim
@@ -202,7 +205,9 @@ else:
 def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
     """Convert a Pydantic FieldInfo to Arrow DataType"""
 
-    if isinstance(field.annotation, (_GenericAlias, GenericAlias)):
+    if isinstance(field.annotation, _GenericAlias) or (
+        sys.version_info > (3, 9) and isinstance(field.annotation, types.GenericAlias)
+    ):
         origin = field.annotation.__origin__
         args = field.annotation.__args__
         if origin is list:
@@ -230,7 +235,7 @@ def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
 
 def is_nullable(field: FieldInfo) -> bool:
     """Check if a Pydantic FieldInfo is nullable."""
-    if isinstance(field.annotation, (_GenericAlias, GenericAlias)):
+    if isinstance(field.annotation, _GenericAlias):
         origin = field.annotation.__origin__
         args = field.annotation.__args__
         if origin == Union:
@@ -241,10 +246,6 @@ def is_nullable(field: FieldInfo) -> bool:
         for typ in args:
             if typ is type(None):
                 return True
-    elif inspect.isclass(field.annotation) and issubclass(
-        field.annotation, FixedSizeListMixin
-    ):
-        return field.annotation.nullable()
     return False
 
 

python/python/lancedb/query.py

@@ -131,8 +131,6 @@ class Query(pydantic.BaseModel):
 
     fast_search: bool = False
 
-    ef: Optional[int] = None
-
 
 class LanceQueryBuilder(ABC):
     """An abstract query builder. Subclasses are defined for vector search,
@@ -259,7 +257,6 @@ class LanceQueryBuilder(ABC):
         self._with_row_id = False
         self._vector = None
         self._text = None
-        self._ef = None
 
     @deprecation.deprecated(
         deprecated_in="0.3.1",
@@ -370,13 +367,11 @@ class LanceQueryBuilder(ABC):
         ----------
         limit: int
             The maximum number of results to return.
-            The default query limit is 10 results.
-            For ANN/KNN queries, you must specify a limit.
-            Entering 0, a negative number, or None will reset
-            the limit to the default value of 10.
-            *WARNING* if you have a large dataset, setting
-            the limit to a large number, e.g. the table size,
-            can potentially result in reading a
+            By default the query is limited to the first 10.
+            Call this method and pass 0, a negative value,
+            or None to remove the limit.
+            *WARNING* if you have a large dataset, removing
+            the limit can potentially result in reading a
             large amount of data into memory and cause
             out of memory issues.
 
@@ -643,28 +638,6 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         self._nprobes = nprobes
         return self
 
-    def ef(self, ef: int) -> LanceVectorQueryBuilder:
-        """Set the number of candidates to consider during search.
-
-        Higher values will yield better recall (more likely to find vectors if
-        they exist) at the expense of latency.
-
-        This only applies to the HNSW-related index.
-        The default value is 1.5 * limit.
-
-        Parameters
-        ----------
-        ef: int
-            The number of candidates to consider during search.
-
-        Returns
-        -------
-        LanceVectorQueryBuilder
-            The LanceQueryBuilder object.
-        """
-        self._ef = ef
-        return self
-
     def refine_factor(self, refine_factor: int) -> LanceVectorQueryBuilder:
         """Set the refine factor to use, increasing the number of vectors sampled.
 
@@ -727,7 +700,6 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
             with_row_id=self._with_row_id,
             offset=self._offset,
             fast_search=self._fast_search,
-            ef=self._ef,
         )
         result_set = self._table._execute_query(query, batch_size)
         if self._reranker is not None:
@@ -971,16 +943,12 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
 
 class LanceEmptyQueryBuilder(LanceQueryBuilder):
     def to_arrow(self) -> pa.Table:
-        query = Query(
+        ds = self._table.to_lance()
+        return ds.to_table(
             columns=self._columns,
             filter=self._where,
-            k=self._limit or 10,
-            with_row_id=self._with_row_id,
-            vector=[],
-            # not actually respected in remote query
-            offset=self._offset or 0,
+            limit=self._limit,
         )
-        return self._table._execute_query(query).read_all()
 
     def rerank(self, reranker: Reranker) -> LanceEmptyQueryBuilder:
         """Rerank the results using the specified reranker.
@@ -1099,8 +1067,6 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
             self._vector_query.nprobes(self._nprobes)
         if self._refine_factor:
             self._vector_query.refine_factor(self._refine_factor)
-        if self._ef:
-            self._vector_query.ef(self._ef)
 
         with ThreadPoolExecutor() as executor:
             fts_future = executor.submit(self._fts_query.with_row_id(True).to_arrow)
@@ -1227,29 +1193,6 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         self._nprobes = nprobes
         return self
 
-    def ef(self, ef: int) -> LanceHybridQueryBuilder:
-        """
-        Set the number of candidates to consider during search.
-
-        Higher values will yield better recall (more likely to find vectors if
-        they exist) at the expense of latency.
-
-        This only applies to the HNSW-related index.
-        The default value is 1.5 * limit.
-
-        Parameters
-        ----------
-        ef: int
-            The number of candidates to consider during search.
-
-        Returns
-        -------
-        LanceHybridQueryBuilder
-            The LanceHybridQueryBuilder object.
-        """
-        self._ef = ef
-        return self
-
     def metric(self, metric: Literal["L2", "cosine", "dot"]) -> LanceHybridQueryBuilder:
         """Set the distance metric to use.
 
@@ -1502,11 +1445,10 @@ class AsyncQueryBase(object):
         ...     print(plan)
         >>> asyncio.run(doctest_example()) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
         ProjectionExec: expr=[vector@0 as vector, _distance@2 as _distance]
-          GlobalLimitExec: skip=0, fetch=10
-            FilterExec: _distance@2 IS NOT NULL
-              SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], preserve_partitioning=[false]
-                KNNVectorDistance: metric=l2
-                  LanceScan: uri=..., projection=[vector], row_id=true, row_addr=false, ordered=false
+          FilterExec: _distance@2 IS NOT NULL
+            SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], preserve_partitioning=[false]
+              KNNVectorDistance: metric=l2
+                LanceScan: uri=..., projection=[vector], row_id=true, row_addr=false, ordered=false
 
         Parameters
         ----------
@@ -1549,8 +1491,7 @@ class AsyncQuery(AsyncQueryBase):
         return pa.array(vec)
 
     def nearest_to(
-        self,
-        query_vector: Union[VEC, Tuple, List[VEC]],
+        self, query_vector: Optional[Union[VEC, Tuple]] = None
     ) -> AsyncVectorQuery:
         """
         Find the nearest vectors to the given query vector.
@@ -1588,33 +1529,10 @@ class AsyncQuery(AsyncQueryBase):
 
         Vector searches always have a [limit][].  If `limit` has not been called then
         a default `limit` of 10 will be used.
-
-        Typically, a single vector is passed in as the query. However, you can also
-        pass in multiple vectors.  This can be useful if you want to find the nearest
-        vectors to multiple query vectors. This is not expected to be faster than
-        making multiple queries concurrently; it is just a convenience method.
-        If multiple vectors are passed in then an additional column `query_index`
-        will be added to the results.  This column will contain the index of the
-        query vector that the result is nearest to.
         """
-        if query_vector is None:
-            raise ValueError("query_vector can not be None")
-
-        if (
-            isinstance(query_vector, list)
-            and len(query_vector) > 0
-            and not isinstance(query_vector[0], (float, int))
-        ):
-            # multiple have been passed
-            query_vectors = [AsyncQuery._query_vec_to_array(v) for v in query_vector]
-            new_self = self._inner.nearest_to(query_vectors[0])
-            for v in query_vectors[1:]:
-                new_self.add_query_vector(v)
-            return AsyncVectorQuery(new_self)
-        else:
-            return AsyncVectorQuery(
-                self._inner.nearest_to(AsyncQuery._query_vec_to_array(query_vector))
-            )
+        return AsyncVectorQuery(
+            self._inner.nearest_to(AsyncQuery._query_vec_to_array(query_vector))
+        )
 
     def nearest_to_text(
         self, query: str, columns: Union[str, List[str]] = []
@@ -1676,7 +1594,7 @@ class AsyncVectorQuery(AsyncQueryBase):
         """
         Set the number of partitions to search (probe)
 
-        This argument is only used when the vector column has an IVF-based index.
+        This argument is only used when the vector column has an IVF PQ index.
         If there is no index then this value is ignored.
 
         The IVF stage of IVF PQ divides the input into partitions (clusters) of
@@ -1698,21 +1616,6 @@ class AsyncVectorQuery(AsyncQueryBase):
         self._inner.nprobes(nprobes)
         return self
 
-    def ef(self, ef: int) -> AsyncVectorQuery:
-        """
-        Set the number of candidates to consider during search
-
-        This argument is only used when the vector column has an HNSW index.
-        If there is no index then this value is ignored.
-
-        Increasing this value will increase the recall of your query but will also
-        increase the latency of your query.  The default value is 1.5 * limit.  This
-        default is good for many cases but the best value to use will depend on your
-        data and the recall that you need to achieve.
-        """
-        self._inner.ef(ef)
-        return self
-
     def refine_factor(self, refine_factor: int) -> AsyncVectorQuery:
         """
         A multiplier to control how many additional rows are taken during the refine

python/python/lancedb/remote/background_loop.py

@@ -1,25 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-import asyncio
-import threading
-
-
-class BackgroundEventLoop:
-    """
-    A background event loop that can run futures.
-
-    Used to bridge sync and async code, without messing with users event loops.
-    """
-
-    def __init__(self):
-        self.loop = asyncio.new_event_loop()
-        self.thread = threading.Thread(
-            target=self.loop.run_forever,
-            name="LanceDBBackgroundEventLoop",
-            daemon=True,
-        )
-        self.thread.start()
-
-    def run(self, future):
-        return asyncio.run_coroutine_threadsafe(future, self.loop).result()

python/python/lancedb/remote/db.py

@@ -11,6 +11,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
+import asyncio
 from datetime import timedelta
 import logging
 from concurrent.futures import ThreadPoolExecutor
@@ -20,7 +21,6 @@ import warnings
 
 from lancedb import connect_async
 from lancedb.remote import ClientConfig
-from lancedb.remote.background_loop import BackgroundEventLoop
 import pyarrow as pa
 from overrides import override
 
@@ -31,8 +31,6 @@ from ..pydantic import LanceModel
 from ..table import Table
 from ..util import validate_table_name
 
-LOOP = BackgroundEventLoop()
-
 
 class RemoteDBConnection(DBConnection):
     """A connection to a remote LanceDB database."""
@@ -88,9 +86,18 @@ class RemoteDBConnection(DBConnection):
             raise ValueError(f"Invalid scheme: {parsed.scheme}, only accepts db://")
         self.db_name = parsed.netloc
 
+        import nest_asyncio
+
+        nest_asyncio.apply()
+        try:
+            self._loop = asyncio.get_running_loop()
+        except RuntimeError:
+            self._loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(self._loop)
+
         self.client_config = client_config
 
-        self._conn = LOOP.run(
+        self._conn = self._loop.run_until_complete(
             connect_async(
                 db_url,
                 api_key=api_key,
@@ -120,7 +127,9 @@ class RemoteDBConnection(DBConnection):
         -------
         An iterator of table names.
         """
-        return LOOP.run(self._conn.table_names(start_after=page_token, limit=limit))
+        return self._loop.run_until_complete(
+            self._conn.table_names(start_after=page_token, limit=limit)
+        )
 
     @override
     def open_table(self, name: str, *, index_cache_size: Optional[int] = None) -> Table:
@@ -143,8 +152,8 @@ class RemoteDBConnection(DBConnection):
                 " (there is no local cache to configure)"
             )
 
-        table = LOOP.run(self._conn.open_table(name))
-        return RemoteTable(table, self.db_name)
+        table = self._loop.run_until_complete(self._conn.open_table(name))
+        return RemoteTable(table, self.db_name, self._loop)
 
     @override
     def create_table(
@@ -259,7 +268,7 @@ class RemoteDBConnection(DBConnection):
 
         from .table import RemoteTable
 
-        table = LOOP.run(
+        table = self._loop.run_until_complete(
             self._conn.create_table(
                 name,
                 data,
@@ -269,7 +278,7 @@ class RemoteDBConnection(DBConnection):
                 fill_value=fill_value,
             )
         )
-        return RemoteTable(table, self.db_name)
+        return RemoteTable(table, self.db_name, self._loop)
 
     @override
     def drop_table(self, name: str):
@@ -280,7 +289,7 @@ class RemoteDBConnection(DBConnection):
         name: str
             The name of the table.
         """
-        LOOP.run(self._conn.drop_table(name))
+        self._loop.run_until_complete(self._conn.drop_table(name))
 
     @override
     def rename_table(self, cur_name: str, new_name: str):
@@ -293,7 +302,7 @@ class RemoteDBConnection(DBConnection):
         new_name: str
             The new name of the table.
         """
-        LOOP.run(self._conn.rename_table(cur_name, new_name))
+        self._loop.run_until_complete(self._conn.rename_table(cur_name, new_name))
 
     async def close(self):
         """Close the connection to the database."""

python/python/lancedb/remote/table.py

@@ -12,12 +12,12 @@
 #  limitations under the License.
 
 from datetime import timedelta
+import asyncio
 import logging
 from functools import cached_property
 from typing import Dict, Iterable, List, Optional, Union, Literal
 
 from lancedb.index import FTS, BTree, Bitmap, HnswPq, HnswSq, IvfPq, LabelList
-from lancedb.remote.db import LOOP
 import pyarrow as pa
 
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
@@ -33,7 +33,9 @@ class RemoteTable(Table):
         self,
         table: AsyncTable,
         db_name: str,
+        loop: Optional[asyncio.AbstractEventLoop] = None,
     ):
+        self._loop = loop
         self._table = table
         self.db_name = db_name
 
@@ -54,12 +56,12 @@ class RemoteTable(Table):
         of this Table
 
         """
-        return LOOP.run(self._table.schema())
+        return self._loop.run_until_complete(self._table.schema())
 
     @property
     def version(self) -> int:
         """Get the current version of the table"""
-        return LOOP.run(self._table.version())
+        return self._loop.run_until_complete(self._table.version())
 
     @cached_property
     def embedding_functions(self) -> dict:
@@ -76,10 +78,6 @@ class RemoteTable(Table):
             self.schema.metadata
         )
 
-    def list_versions(self):
-        """List all versions of the table"""
-        return self._loop.run_until_complete(self._table.list_versions())
-
     def to_arrow(self) -> pa.Table:
         """to_arrow() is not yet supported on LanceDB cloud."""
         raise NotImplementedError("to_arrow() is not yet supported on LanceDB cloud.")
@@ -88,19 +86,13 @@ class RemoteTable(Table):
         """to_pandas() is not yet supported on LanceDB cloud."""
         return NotImplementedError("to_pandas() is not yet supported on LanceDB cloud.")
 
-    def checkout(self, version):
-        return self._loop.run_until_complete(self._table.checkout(version))
-
-    def checkout_latest(self):
-        return self._loop.run_until_complete(self._table.checkout_latest())
-
     def list_indices(self):
         """List all the indices on the table"""
-        return LOOP.run(self._table.list_indices())
+        return self._loop.run_until_complete(self._table.list_indices())
 
     def index_stats(self, index_uuid: str):
         """List all the stats of a specified index"""
-        return LOOP.run(self._table.index_stats(index_uuid))
+        return self._loop.run_until_complete(self._table.index_stats(index_uuid))
 
     def create_scalar_index(
         self,
@@ -130,7 +122,9 @@ class RemoteTable(Table):
         else:
             raise ValueError(f"Unknown index type: {index_type}")
 
-        LOOP.run(self._table.create_index(column, config=config, replace=replace))
+        self._loop.run_until_complete(
+            self._table.create_index(column, config=config, replace=replace)
+        )
 
     def create_fts_index(
         self,
@@ -138,9 +132,28 @@ class RemoteTable(Table):
         *,
         replace: bool = False,
         with_position: bool = True,
+        # tokenizer configs:
+        base_tokenizer: str = "simple",
+        language: str = "English",
+        max_token_length: Optional[int] = 40,
+        lower_case: bool = True,
+        stem: bool = False,
+        remove_stop_words: bool = False,
+        ascii_folding: bool = False,
     ):
-        config = FTS(with_position=with_position)
-        LOOP.run(self._table.create_index(column, config=config, replace=replace))
+        config = FTS(
+            with_position=with_position,
+            base_tokenizer=base_tokenizer,
+            language=language,
+            max_token_length=max_token_length,
+            lower_case=lower_case,
+            stem=stem,
+            remove_stop_words=remove_stop_words,
+            ascii_folding=ascii_folding,
+        )
+        self._loop.run_until_complete(
+            self._table.create_index(column, config=config, replace=replace)
+        )
 
     def create_index(
         self,
@@ -221,7 +234,9 @@ class RemoteTable(Table):
                 " 'IVF_PQ', 'IVF_HNSW_PQ', 'IVF_HNSW_SQ'"
             )
 
-        LOOP.run(self._table.create_index(vector_column_name, config=config))
+        self._loop.run_until_complete(
+            self._table.create_index(vector_column_name, config=config)
+        )
 
     def add(
         self,
@@ -253,7 +268,7 @@ class RemoteTable(Table):
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
 
         """
-        LOOP.run(
+        self._loop.run_until_complete(
             self._table.add(
                 data, mode=mode, on_bad_vectors=on_bad_vectors, fill_value=fill_value
             )
@@ -329,6 +344,10 @@ class RemoteTable(Table):
             - and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
+        # empty query builder is not supported in saas, raise error
+        if query is None and query_type != "hybrid":
+            raise ValueError("Empty query is not supported")
+
         return LanceQueryBuilder.create(
             self,
             query,
@@ -341,7 +360,9 @@ class RemoteTable(Table):
     def _execute_query(
         self, query: Query, batch_size: Optional[int] = None
     ) -> pa.RecordBatchReader:
-        return LOOP.run(self._table._execute_query(query, batch_size=batch_size))
+        return self._loop.run_until_complete(
+            self._table._execute_query(query, batch_size=batch_size)
+        )
 
     def merge_insert(self, on: Union[str, Iterable[str]]) -> LanceMergeInsertBuilder:
         """Returns a [`LanceMergeInsertBuilder`][lancedb.merge.LanceMergeInsertBuilder]
@@ -358,7 +379,9 @@ class RemoteTable(Table):
         on_bad_vectors: str,
         fill_value: float,
     ):
-        LOOP.run(self._table._do_merge(merge, new_data, on_bad_vectors, fill_value))
+        self._loop.run_until_complete(
+            self._table._do_merge(merge, new_data, on_bad_vectors, fill_value)
+        )
 
     def delete(self, predicate: str):
         """Delete rows from the table.
@@ -407,7 +430,7 @@ class RemoteTable(Table):
            x      vector  _distance # doctest: +SKIP
         0  2  [3.0, 4.0]       85.0 # doctest: +SKIP
         """
-        LOOP.run(self._table.delete(predicate))
+        self._loop.run_until_complete(self._table.delete(predicate))
 
     def update(
         self,
@@ -457,7 +480,7 @@ class RemoteTable(Table):
         2  2  [10.0, 10.0] # doctest: +SKIP
 
         """
-        LOOP.run(
+        self._loop.run_until_complete(
             self._table.update(where=where, updates=values, updates_sql=values_sql)
         )
 
@@ -487,7 +510,7 @@ class RemoteTable(Table):
         )
 
     def count_rows(self, filter: Optional[str] = None) -> int:
-        return LOOP.run(self._table.count_rows(filter))
+        return self._loop.run_until_complete(self._table.count_rows(filter))
 
     def add_columns(self, transforms: Dict[str, str]):
         raise NotImplementedError(

python/python/lancedb/rerankers/cohere.py

@@ -41,7 +41,7 @@ class CohereReranker(Reranker):
 
     def __init__(
         self,
-        model_name: str = "rerank-english-v3.0",
+        model_name: str = "rerank-english-v2.0",
         column: str = "text",
         top_n: Union[int, None] = None,
         return_score="relevance",

python/python/lancedb/rerankers/voyageai.py

@@ -13,7 +13,7 @@
 
 import os
 from functools import cached_property
-from typing import Optional
+from typing import Union, Optional
 
 import pyarrow as pa
 

python/python/lancedb/table.py

@@ -8,7 +8,7 @@ import inspect
 import time
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from datetime import datetime, timedelta
+from datetime import timedelta
 from functools import cached_property
 from typing import (
     TYPE_CHECKING,
@@ -73,21 +73,6 @@ pl = safe_import_polars()
 QueryType = Literal["vector", "fts", "hybrid", "auto"]
 
 
-def _pd_schema_without_embedding_funcs(
-    schema: Optional[pa.Schema], columns: List[str]
-) -> Optional[pa.Schema]:
-    """Return a schema without any embedding function columns"""
-    if schema is None:
-        return None
-    embedding_functions = EmbeddingFunctionRegistry.get_instance().parse_functions(
-        schema.metadata
-    )
-    if not embedding_functions:
-        return schema
-    columns = set(columns)
-    return pa.schema([field for field in schema if field.name in columns])
-
-
 def _coerce_to_table(data, schema: Optional[pa.Schema] = None) -> pa.Table:
     if _check_for_hugging_face(data):
         # Huggingface datasets
@@ -118,10 +103,10 @@ def _coerce_to_table(data, schema: Optional[pa.Schema] = None) -> pa.Table:
         elif isinstance(data[0], pa.RecordBatch):
             return pa.Table.from_batches(data, schema=schema)
         else:
-            return pa.Table.from_pylist(data, schema=schema)
+            return pa.Table.from_pylist(data)
     elif _check_for_pandas(data) and isinstance(data, pd.DataFrame):
-        raw_schema = _pd_schema_without_embedding_funcs(schema, data.columns.to_list())
-        table = pa.Table.from_pandas(data, preserve_index=False, schema=raw_schema)
+        # Do not add schema here, since schema may contains the vector column
+        table = pa.Table.from_pandas(data, preserve_index=False)
         # Do not serialize Pandas metadata
         meta = table.schema.metadata if table.schema.metadata is not None else {}
         meta = {k: v for k, v in meta.items() if k != b"pandas"}
@@ -187,8 +172,6 @@ def sanitize_create_table(
         schema = schema.to_arrow_schema()
 
     if data is not None:
-        if metadata is None and schema is not None:
-            metadata = schema.metadata
         data, schema = _sanitize_data(
             data,
             schema,
@@ -1012,39 +995,6 @@ class Table(ABC):
             The names of the columns to drop.
         """
 
-    @abstractmethod
-    def checkout(self):
-        """
-        Checks out a specific version of the Table
-
-        Any read operation on the table will now access the data at the checked out
-        version. As a consequence, calling this method will disable any read consistency
-        interval that was previously set.
-
-        This is a read-only operation that turns the table into a sort of "view"
-        or "detached head".  Other table instances will not be affected.  To make the
-        change permanent you can use the `[Self::restore]` method.
-
-        Any operation that modifies the table will fail while the table is in a checked
-        out state.
-
-        To return the table to a normal state use `[Self::checkout_latest]`
-        """
-
-    @abstractmethod
-    def checkout_latest(self):
-        """
-        Ensures the table is pointing at the latest version
-
-        This can be used to manually update a table when the read_consistency_interval
-        is None
-        It can also be used to undo a `[Self::checkout]` operation
-        """
-
-    @abstractmethod
-    def list_versions(self):
-        """List all versions of the table"""
-
     @cached_property
     def _dataset_uri(self) -> str:
         return _table_uri(self._conn.uri, self.name)
@@ -1600,7 +1550,7 @@ class LanceTable(Table):
             "append" and "overwrite".
         on_bad_vectors: str, default "error"
             What to do if any of the vectors are not the same size or contains NaNs.
-            One of "error", "drop", "fill", "null".
+            One of "error", "drop", "fill".
         fill_value: float, default 0.
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
 
@@ -1884,7 +1834,7 @@ class LanceTable(Table):
             data but will validate against any schema that's specified.
         on_bad_vectors: str, default "error"
             What to do if any of the vectors are not the same size or contains NaNs.
-            One of "error", "drop", "fill", "null".
+            One of "error", "drop", "fill".
         fill_value: float, default 0.
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
         embedding_functions: list of EmbeddingFunctionModel, default None
@@ -1992,7 +1942,6 @@ class LanceTable(Table):
                 "metric": query.metric,
                 "nprobes": query.nprobes,
                 "refine_factor": query.refine_factor,
-                "ef": query.ef,
             }
         return ds.scanner(
             columns=query.columns,
@@ -2185,11 +2134,13 @@ def _sanitize_schema(
         vector column to fixed_size_list(float32) if necessary.
     on_bad_vectors: str, default "error"
         What to do if any of the vectors are not the same size or contains NaNs.
-        One of "error", "drop", "fill", "null".
+        One of "error", "drop", "fill".
     fill_value: float, default 0.
         The value to use when filling vectors. Only used if on_bad_vectors="fill".
     """
     if schema is not None:
+        if data.schema == schema:
+            return data
         # cast the columns to the expected types
         data = data.combine_chunks()
         for field in schema:
@@ -2209,7 +2160,6 @@ def _sanitize_schema(
                     vector_column_name=field.name,
                     on_bad_vectors=on_bad_vectors,
                     fill_value=fill_value,
-                    table_schema=schema,
                 )
         return pa.Table.from_arrays(
             [data[name] for name in schema.names], schema=schema
@@ -2230,7 +2180,6 @@ def _sanitize_schema(
 def _sanitize_vector_column(
     data: pa.Table,
     vector_column_name: str,
-    table_schema: Optional[pa.Schema] = None,
     on_bad_vectors: str = "error",
     fill_value: float = 0.0,
 ) -> pa.Table:
@@ -2245,16 +2194,12 @@ def _sanitize_vector_column(
         The name of the vector column.
     on_bad_vectors: str, default "error"
         What to do if any of the vectors are not the same size or contains NaNs.
-        One of "error", "drop", "fill", "null".
+        One of "error", "drop", "fill".
     fill_value: float, default 0.0
         The value to use when filling vectors. Only used if on_bad_vectors="fill".
     """
     # ChunkedArray is annoying to work with, so we combine chunks here
     vec_arr = data[vector_column_name].combine_chunks()
-    if table_schema is not None:
-        field = table_schema.field(vector_column_name)
-    else:
-        field = None
     typ = data[vector_column_name].type
     if pa.types.is_list(typ) or pa.types.is_large_list(typ):
         # if it's a variable size list array,
@@ -2281,11 +2226,7 @@ def _sanitize_vector_column(
                 data, fill_value, on_bad_vectors, vec_arr, vector_column_name
             )
     else:
-        if (
-            field is not None
-            and not field.nullable
-            and pc.any(pc.is_null(vec_arr.values)).as_py()
-        ) or (pc.any(pc.is_nan(vec_arr.values)).as_py()):
+        if pc.any(pc.is_null(vec_arr.values, nan_is_null=True)).as_py():
             data = _sanitize_nans(
                 data, fill_value, on_bad_vectors, vec_arr, vector_column_name
             )
@@ -2329,12 +2270,6 @@ def _sanitize_jagged(data, fill_value, on_bad_vectors, vec_arr, vector_column_na
         )
     elif on_bad_vectors == "drop":
         data = data.filter(correct_ndims)
-    elif on_bad_vectors == "null":
-        data = data.set_column(
-            data.column_names.index(vector_column_name),
-            vector_column_name,
-            pc.if_else(correct_ndims, vec_arr, pa.scalar(None)),
-        )
     return data
 
 
@@ -2351,8 +2286,7 @@ def _sanitize_nans(
         raise ValueError(
             f"Vector column {vector_column_name} has NaNs. "
             "Set on_bad_vectors='drop' to remove them, or "
-            "set on_bad_vectors='fill' and fill_value=<value> to replace them. "
-            "Or set on_bad_vectors='null' to replace them with null."
+            "set on_bad_vectors='fill' and fill_value=<value> to replace them."
         )
     elif on_bad_vectors == "fill":
         if fill_value is None:
@@ -2372,17 +2306,6 @@ def _sanitize_nans(
         np_arr = np_arr.reshape(-1, vec_arr.type.list_size)
         not_nulls = np.any(np_arr, axis=1)
         data = data.filter(~not_nulls)
-    elif on_bad_vectors == "null":
-        # null = pa.nulls(len(vec_arr)).cast(vec_arr.type)
-        # values = pc.if_else(pc.is_nan(vec_arr.values), fill_value, vec_arr.values)
-        np_arr = np.isnan(vec_arr.values.to_numpy(zero_copy_only=False))
-        np_arr = np_arr.reshape(-1, vec_arr.type.list_size)
-        no_nans = np.any(np_arr, axis=1)
-        data = data.set_column(
-            data.column_names.index(vector_column_name),
-            vector_column_name,
-            pc.if_else(no_nans, vec_arr, pa.scalar(None)),
-        )
     return data
 
 
@@ -2648,7 +2571,7 @@ class AsyncTable:
             "append" and "overwrite".
         on_bad_vectors: str, default "error"
             What to do if any of the vectors are not the same size or contains NaNs.
-            One of "error", "drop", "fill", "null".
+            One of "error", "drop", "fill".
         fill_value: float, default 0.
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
 
@@ -2731,7 +2654,7 @@ class AsyncTable:
 
     def vector_search(
         self,
-        query_vector: Union[VEC, Tuple],
+        query_vector: Optional[Union[VEC, Tuple]] = None,
     ) -> AsyncVectorQuery:
         """
         Search the table with a given query vector.
@@ -2770,8 +2693,6 @@ class AsyncTable:
                 async_query = async_query.refine_factor(query.refine_factor)
             if query.vector_column:
                 async_query = async_query.column(query.vector_column)
-            if query.ef:
-                async_query = async_query.ef(query.ef)
 
         if not query.prefilter:
             async_query = async_query.postfilter()
@@ -2935,19 +2856,6 @@ class AsyncTable:
         """
         return await self._inner.version()
 
-    async def list_versions(self):
-        """
-        List all versions of the table
-        """
-        versions = await self._inner.list_versions()
-        for v in versions:
-            ts_nanos = v["timestamp"]
-            v["timestamp"] = datetime.fromtimestamp(ts_nanos // 1e9) + timedelta(
-                microseconds=(ts_nanos % 1e9) // 1e3
-            )
-
-        return versions
-
     async def checkout(self, version):
         """
         Checks out a specific version of the Table

python/python/tests/test_db.py

@@ -599,9 +599,7 @@ async def test_create_in_v2_mode(tmp_path):
     )
 
     async def is_in_v2_mode(tbl):
-        batches = (
-            await tbl.query().limit(10 * 1024).to_batches(max_batch_length=1024 * 10)
-        )
+        batches = await tbl.query().to_batches(max_batch_length=1024 * 10)
         num_batches = 0
         async for batch in batches:
             num_batches += 1