fix: error during deserialization of "INVERTED" index type

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.13.0-beta.2"
+current_version = "0.11.1-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.
@@ -92,11 +92,6 @@ glob = "node/package.json"
 replace = "\"@lancedb/vectordb-win32-x64-msvc\": \"{new_version}\""
 search = "\"@lancedb/vectordb-win32-x64-msvc\": \"{current_version}\""
 
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-win32-arm64-msvc\": \"{new_version}\""
-search = "\"@lancedb/vectordb-win32-arm64-msvc\": \"{current_version}\""
-
 # Cargo files
 # ------------
 [[tool.bumpversion.files]]

.cargo/config.toml

@@ -38,7 +38,3 @@ rustflags = ["-C", "target-cpu=apple-m1", "-C", "target-feature=+neon,+fp16,+fhm
 # not found errors on systems that are missing it.
 [target.x86_64-pc-windows-msvc]
 rustflags = ["-Ctarget-feature=+crt-static"]
-
-# Experimental target for Arm64 Windows
-[target.aarch64-pc-windows-msvc]
-rustflags = ["-Ctarget-feature=+crt-static"]

.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

@@ -226,110 +226,6 @@ 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"
-
-  #         # Add MSVC runtime libraries to LIB
-  #         $env:LIB = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\lib\arm64;" +
-  #                    "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\arm64;" +
-  #                    "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\arm64"
-  #         Add-Content $env:GITHUB_ENV "LIB=$env:LIB"
-
-  #         # Add INCLUDE paths
-  #         $env:INCLUDE = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\include;" +
-  #                       "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\ucrt;" +
-  #                       "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\um;" +
-  #                       "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\shared"
-  #         Add-Content $env:GITHUB_ENV "INCLUDE=$env:INCLUDE"
-  #       shell: powershell
-  #     - name: Install Rust
-  #       run: |
-  #         Invoke-WebRequest https://win.rustup.rs/x86_64 -OutFile rustup-init.exe
-  #         .\rustup-init.exe -y --default-host aarch64-pc-windows-msvc
-  #       shell: powershell
-  #     - name: Add Rust to PATH
-  #       run: |
-  #         Add-Content $env:GITHUB_PATH "$env:USERPROFILE\.cargo\bin"
-  #       shell: powershell
-
-  #     - uses: Swatinem/rust-cache@v2
-  #       with:
-  #         workspaces: rust
-  #     - name: Install 7-Zip ARM
-  #       run: |
-  #         New-Item -Path 'C:\7zip' -ItemType Directory
-  #         Invoke-WebRequest https://7-zip.org/a/7z2408-arm64.exe -OutFile C:\7zip\7z-installer.exe
-  #         Start-Process -FilePath C:\7zip\7z-installer.exe -ArgumentList '/S' -Wait
-  #       shell: powershell
-  #     - name: Add 7-Zip to PATH
-  #       run: Add-Content $env:GITHUB_PATH "C:\Program Files\7-Zip"
-  #       shell: powershell
-  #     - name: Install Protoc v21.12
-  #       working-directory: C:\
-  #       run: |
-  #         if (Test-Path 'C:\protoc') {
-  #             Write-Host "Protoc directory exists, skipping installation"
-  #             return
-  #         }
-  #         New-Item -Path 'C:\protoc' -ItemType Directory
-  #         Set-Location C:\protoc
-  #         Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
-  #         & 'C:\Program Files\7-Zip\7z.exe' x protoc.zip
-  #       shell: powershell
-  #     - name: Add Protoc to PATH
-  #       run: Add-Content $env:GITHUB_PATH "C:\protoc\bin"
-  #       shell: powershell
-  #     - name: 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 }}
     runs-on: windows-2022
@@ -364,103 +260,9 @@ 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"
-
-  #         $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
-
   release:
     name: vectordb NPM Publish
-    needs: [node, node-macos, node-linux, node-windows, node-windows-arm64]
+    needs: [node, node-macos, node-linux, node-windows]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -478,7 +280,7 @@ 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"
@@ -500,7 +302,7 @@ jobs:
 
   release-nodejs:
     name: lancedb NPM Publish
-    needs: [nodejs-macos, nodejs-linux, nodejs-windows, nodejs-windows-arm64]
+    needs: [nodejs-macos, nodejs-linux, nodejs-windows]
     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

@@ -35,22 +35,21 @@ jobs:
       CC: clang-18
       CXX: clang++-18
     steps:
-      - uses: actions/checkout@v4
-        with:
+    - uses: actions/checkout@v4
+      with:
           fetch-depth: 0
           lfs: true
-      - uses: Swatinem/rust-cache@v2
-        with:
-          workspaces: rust
-      - name: Install dependencies
-        run: |
+    - uses: Swatinem/rust-cache@v2
+      with:
+        workspaces: rust
+    - name: Install dependencies
+      run: |
           sudo apt update
           sudo apt install -y protobuf-compiler libssl-dev
-      - name: Run format
-        run: cargo fmt --all -- --check
-      - name: Run clippy
-        run: cargo clippy --workspace --tests --all-features -- -D warnings
-
+    - name: Run format
+      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
@@ -66,38 +65,37 @@ jobs:
       CC: clang-18
       CXX: clang++-18
     steps:
-      - uses: actions/checkout@v4
-        with:
+    - uses: actions/checkout@v4
+      with:
           fetch-depth: 0
           lfs: true
-      - uses: Swatinem/rust-cache@v2
-        with:
+    - uses: Swatinem/rust-cache@v2
+      with:
           workspaces: rust
-      - name: Install dependencies
-        run: |
+    - name: Install dependencies
+      run: |
           sudo apt update
           sudo apt install -y protobuf-compiler libssl-dev
-      - name: Make Swap
-        run: |
-          sudo fallocate -l 16G /swapfile
-          sudo chmod 600 /swapfile
-          sudo mkswap /swapfile
-          sudo swapon /swapfile
-      - name: Start S3 integration test environment
-        working-directory: .
-        run: docker compose up --detach --wait
-      - name: Build
-        run: cargo build --all-features
-      - name: Run tests
-        run: cargo test --all-features
-      - name: Run examples
-        run: cargo run --example simple
-
+    - name: Make Swap
+      run: |
+        sudo fallocate -l 16G /swapfile
+        sudo chmod 600 /swapfile
+        sudo mkswap /swapfile
+        sudo swapon /swapfile
+    - name: Start S3 integration test environment
+      working-directory: .
+      run: docker compose up --detach --wait
+    - name: Build
+      run: cargo build --all-features
+    - name: Run tests
+      run: cargo test --all-features
+    - name: Run examples
+      run: cargo run --example simple
   macos:
     timeout-minutes: 30
     strategy:
       matrix:
-        mac-runner: ["macos-13", "macos-14"]
+        mac-runner: [ "macos-13", "macos-14" ]
     runs-on: "${{ matrix.mac-runner }}"
     defaults:
       run:
@@ -106,8 +104,8 @@ jobs:
     steps:
       - uses: actions/checkout@v4
         with:
-          fetch-depth: 0
-          lfs: true
+            fetch-depth: 0
+            lfs: true
       - name: CPU features
         run: sysctl -a | grep cpu
       - uses: Swatinem/rust-cache@v2
@@ -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:
@@ -142,99 +139,3 @@ jobs:
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
           cargo build
           cargo test
-
-  windows-arm64:
-    runs-on: windows-4x-arm
-    steps:
-      - name: Install Git
-        run: |
-          Invoke-WebRequest -Uri "https://github.com/git-for-windows/git/releases/download/v2.44.0.windows.1/Git-2.44.0-64-bit.exe" -OutFile "git-installer.exe"
-          Start-Process -FilePath "git-installer.exe" -ArgumentList "/VERYSILENT", "/NORESTART" -Wait
-        shell: powershell
-      - name: Add Git to PATH
-        run: |
-          Add-Content $env:GITHUB_PATH "C:\Program Files\Git\bin"
-          $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
-        shell: powershell
-      - name: Configure Git symlinks
-        run: git config --global core.symlinks true
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.13"
-      - name: Install Visual Studio Build Tools
-        run: |
-          Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vs_buildtools.exe" -OutFile "vs_buildtools.exe"
-          Start-Process -FilePath "vs_buildtools.exe" -ArgumentList "--quiet", "--wait", "--norestart", "--nocache", `
-            "--installPath", "C:\BuildTools", `
-            "--add", "Microsoft.VisualStudio.Component.VC.Tools.ARM64", `
-            "--add", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", `
-            "--add", "Microsoft.VisualStudio.Component.Windows11SDK.22621", `
-            "--add", "Microsoft.VisualStudio.Component.VC.ATL", `
-            "--add", "Microsoft.VisualStudio.Component.VC.ATLMFC", `
-            "--add", "Microsoft.VisualStudio.Component.VC.Llvm.Clang" -Wait
-        shell: powershell
-      - name: Add Visual Studio Build Tools to PATH
-        run: |
-          $vsPath = "C:\BuildTools\VC\Tools\MSVC"
-          $latestVersion = (Get-ChildItem $vsPath | Sort-Object {[version]$_.Name} -Descending)[0].Name
-          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\arm64"
-          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\x64"
-          Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\arm64"
-          Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\x64"
-          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\Llvm\x64\bin"
-
-          # Add MSVC runtime libraries to LIB
-          $env:LIB = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\lib\arm64;" + 
-                     "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\arm64;" +
-                     "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\arm64"
-          Add-Content $env:GITHUB_ENV "LIB=$env:LIB"
-
-          # Add INCLUDE paths
-          $env:INCLUDE = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\include;" +
-                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\ucrt;" +
-                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\um;" +
-                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\shared"
-          Add-Content $env:GITHUB_ENV "INCLUDE=$env:INCLUDE"
-        shell: powershell
-      - name: Install Rust
-        run: |
-          Invoke-WebRequest https://win.rustup.rs/x86_64 -OutFile rustup-init.exe
-          .\rustup-init.exe -y --default-host aarch64-pc-windows-msvc
-        shell: powershell
-      - name: Add Rust to PATH
-        run: |
-          Add-Content $env:GITHUB_PATH "$env:USERPROFILE\.cargo\bin"
-        shell: powershell
-      - uses: Swatinem/rust-cache@v2
-        with:
-          workspaces: rust
-      - name: Install 7-Zip ARM
-        run: |
-          New-Item -Path 'C:\7zip' -ItemType Directory
-          Invoke-WebRequest https://7-zip.org/a/7z2408-arm64.exe -OutFile C:\7zip\7z-installer.exe
-          Start-Process -FilePath C:\7zip\7z-installer.exe -ArgumentList '/S' -Wait
-        shell: powershell
-      - name: Add 7-Zip to PATH
-        run: Add-Content $env:GITHUB_PATH "C:\Program Files\7-Zip"
-        shell: powershell
-      - name: Install Protoc v21.12
-        working-directory: C:\
-        run: |
-          if (Test-Path 'C:\protoc') {
-            Write-Host "Protoc directory exists, skipping installation"
-            return
-          }
-          New-Item -Path 'C:\protoc' -ItemType Directory
-          Set-Location C:\protoc
-          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
-          & 'C:\Program Files\7-Zip\7z.exe' x protoc.zip
-        shell: powershell
-      - name: Add Protoc to PATH
-        run: Add-Content $env:GITHUB_PATH "C:\protoc\bin"
-        shell: powershell
-      - name: Run tests
-        run: |
-          $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
-          cargo build --target aarch64-pc-windows-msvc
-          cargo test --target aarch64-pc-windows-msvc

Cargo.toml

@@ -18,18 +18,17 @@ 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.
 
 [workspace.dependencies]
-lance = { "version" = "=0.19.2", "features" = [
+lance = { "version" = "=0.19.1", "features" = [
     "dynamodb",
 ]}
-lance-index = "=0.19.2"
-lance-linalg = "=0.19.2"
-lance-table = "=0.19.2"
-lance-testing = "=0.19.2"
-lance-datafusion = "=0.19.2"
-lance-encoding = "=0.19.2"
+lance-index = { "version" = "=0.19.1"}
+lance-linalg = { "version" = "=0.19.1"}
+lance-table = { "version" = "=0.19.1"}
+lance-testing = { "version" = "=0.19.1"}
+lance-datafusion = { "version" = "=0.19.1" }
+lance-encoding = { "version" = "=0.19.1" }
 # Note that this one does not include pyarrow
 arrow = { version = "52.2", optional = false }
 arrow-array = "52.2"
@@ -43,7 +42,6 @@ async-trait = "0"
 chrono = "0.4.35"
 datafusion-common = "41.0"
 datafusion-physical-plan = "41.0"
-env_logger = "0.10"
 half = { "version" = "=2.4.1", default-features = false, features = [
     "num-traits",
 ] }

README.md

@@ -10,7 +10,6 @@
 [![Blog](https://img.shields.io/badge/Blog-12100E?style=for-the-badge&logoColor=white)](https://blog.lancedb.com/)
 [![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/zMM32dvNtd)
 [![Twitter](https://img.shields.io/badge/Twitter-%231DA1F2.svg?style=for-the-badge&logo=Twitter&logoColor=white)](https://twitter.com/lancedb)
-[![Gurubase](https://img.shields.io/badge/Gurubase-Ask%20LanceDB%20Guru-006BFF?style=for-the-badge)](https://gurubase.io/g/lancedb)
 
 </p>
 

ci/build_windows_artifacts.ps1

@@ -3,7 +3,6 @@
 # Targets supported:
 # - x86_64-pc-windows-msvc
 # - i686-pc-windows-msvc
-# - aarch64-pc-windows-msvc
 
 function Prebuild-Rust {
     param (
@@ -32,7 +31,7 @@ function Build-NodeBinaries {
 
 $targets = $args[0]
 if (-not $targets) {
-    $targets = "x86_64-pc-windows-msvc", "aarch64-pc-windows-msvc"
+    $targets = "x86_64-pc-windows-msvc"
 }
 
 Write-Host "Building artifacts for targets: $targets"

ci/build_windows_artifacts_nodejs.ps1

@@ -3,7 +3,6 @@
 # Targets supported:
 # - x86_64-pc-windows-msvc
 # - i686-pc-windows-msvc
-# - aarch64-pc-windows-msvc
 
 function Prebuild-Rust {
     param (
@@ -32,7 +31,7 @@ function Build-NodeBinaries {
 
 $targets = $args[0]
 if (-not $targets) {
-    $targets = "x86_64-pc-windows-msvc", "aarch64-pc-windows-msvc"
+    $targets = "x86_64-pc-windows-msvc"
 }
 
 Write-Host "Building artifacts for targets: $targets"

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

@@ -100,7 +100,7 @@ nav:
       - 🏃🏼‍♂️ Quick start: basic.md
       - 📚 Concepts:
           - Vector search: concepts/vector_search.md
-          - Indexing:
+          - Indexing: 
             - IVFPQ: concepts/index_ivfpq.md
             - HNSW: concepts/index_hnsw.md
           - Storage: concepts/storage.md
@@ -109,8 +109,7 @@ nav:
           - Working with tables: guides/tables.md
           - Building a vector index: ann_indexes.md
           - Vector Search: search.md
-          - Full-text search (native): fts.md
-          - Full-text search (tantivy-based): fts_tantivy.md
+          - Full-text search: fts.md
           - Building a scalar index: guides/scalar_index.md
           - Hybrid search:
               - Overview: hybrid_search/hybrid_search.md
@@ -149,10 +148,10 @@ nav:
               - Reranking: guides/tuning_retrievers/2_reranking.md
               - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
       - 🧬 Managing embeddings:
-          - Understand Embeddings: embeddings/understanding_embeddings.md
+          - Understand Embeddings: embeddings/understanding_embeddings.md 
           - Get Started: embeddings/index.md
           - Embedding functions: embeddings/embedding_functions.md
-          - Available models:
+          - Available models: 
               - Overview: embeddings/default_embedding_functions.md
               - Text Embedding Functions:
                   - Sentence Transformers: embeddings/available_embedding_models/text_embedding_functions/sentence_transformers.md
@@ -201,7 +200,7 @@ nav:
               - Evaluation: examples/python_examples/evaluations.md
               - AI Agent: examples/python_examples/aiagent.md
               - Recommender System: examples/python_examples/recommendersystem.md
-              - Miscellaneous:
+              - Miscellaneous: 
                   - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
                   - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
           - 👾 JavaScript:
@@ -211,10 +210,9 @@ nav:
               - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
           - 🦀 Rust:
               - Overview: examples/examples_rust.md
-      - 📓 Studies:
+      - Studies:
           - ↗Improve retrievers with hybrid search and reranking: https://blog.lancedb.com/hybrid-search-and-reranking-report/
       - 💭 FAQs: faq.md
-      - 🔍 Troubleshooting: troubleshooting.md
       - ⚙️ API reference:
           - 🐍 Python: python/python.md
           - 👾 JavaScript (vectordb): javascript/modules.md
@@ -230,7 +228,7 @@ nav:
   - Quick start: basic.md
   - Concepts:
       - Vector search: concepts/vector_search.md
-      - Indexing:
+      - Indexing: 
         - IVFPQ: concepts/index_ivfpq.md
         - HNSW: concepts/index_hnsw.md
       - Storage: concepts/storage.md
@@ -239,8 +237,7 @@ nav:
       - Working with tables: guides/tables.md
       - Building an ANN index: ann_indexes.md
       - Vector Search: search.md
-      - Full-text search (native): fts.md
-      - Full-text search (tantivy-based): fts_tantivy.md
+      - Full-text search: fts.md
       - Building a scalar index: guides/scalar_index.md
       - Hybrid search:
           - Overview: hybrid_search/hybrid_search.md
@@ -279,10 +276,10 @@ nav:
           - Reranking: guides/tuning_retrievers/2_reranking.md
           - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
   - Managing Embeddings:
-      - Understand Embeddings: embeddings/understanding_embeddings.md
+      - Understand Embeddings: embeddings/understanding_embeddings.md 
       - Get Started: embeddings/index.md
       - Embedding functions: embeddings/embedding_functions.md
-      - Available models:
+      - Available models: 
           - Overview: embeddings/default_embedding_functions.md
           - Text Embedding Functions:
               - Sentence Transformers: embeddings/available_embedding_models/text_embedding_functions/sentence_transformers.md
@@ -327,7 +324,7 @@ nav:
           - Evaluation: examples/python_examples/evaluations.md
           - AI Agent: examples/python_examples/aiagent.md
           - Recommender System: examples/python_examples/recommendersystem.md
-          - Miscellaneous:
+          - Miscellaneous: 
               - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
               - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
       - 👾 JavaScript:
@@ -370,4 +367,5 @@ extra:
     - icon: fontawesome/brands/x-twitter
       link: https://twitter.com/lancedb
     - icon: fontawesome/brands/linkedin
-      link: https://www.linkedin.com/company/lancedb
+      link: https://www.linkedin.com/company/lancedb      
+      

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)"

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/embeddings/available_embedding_models/text_embedding_functions/voyageai_embedding.md

@@ -1,51 +0,0 @@
-# VoyageAI Embeddings
-
-Voyage AI provides cutting-edge embedding and rerankers.
-
-
-Using voyageai API requires voyageai package, which can be installed using `pip install voyageai`. Voyage AI embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
-You also need to set the `VOYAGE_API_KEY` environment variable to use the VoyageAI API.
-
-Supported models are:
-
-- voyage-3
-- voyage-3-lite
-- voyage-finance-2
-- voyage-multilingual-2
-- voyage-law-2
-- voyage-code-2
-
-
-Supported parameters (to be passed in `create` method) are:
-
-| Parameter | Type | Default Value | Description |
-|---|---|--------|---------|
-| `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. |
-
-
-Usage Example:
-    
-```python
-    import lancedb
-    from lancedb.pydantic import LanceModel, Vector
-    from lancedb.embeddings import EmbeddingFunctionRegistry
-
-    voyageai = EmbeddingFunctionRegistry
-        .get_instance()
-        .get("voyageai")
-        .create(name="voyage-3")
-
-    class TextModel(LanceModel):
-        text: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) =  voyageai.VectorField()
-
-    data = [ { "text": "hello world" },
-            { "text": "goodbye world" }]
-
-    db = lancedb.connect("~/.lancedb")
-    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
-
-    tbl.add(data)
-```

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/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/examples/python_examples/chatbot.md

@@ -36,6 +36,6 @@
 [aware_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb
 [aware_ghost]: https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755
 
-[csv_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/Chat_with_csv_file
-[csv_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/Chat_with_csv_file/main.ipynb
+[csv_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Chat_with_csv_file
+[csv_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Chat_with_csv_file/main.ipynb
 [csv_ghost]: https://blog.lancedb.com/p/d8c71df4-e55f-479a-819e-cde13354a6a3/

docs/src/examples/python_examples/multimodal.md

@@ -12,7 +12,7 @@ LanceDB supports multimodal search by indexing and querying vector representatio
 |:----------------|:-----------------|:-----------|
 | **Multimodal CLIP: DiffusionDB 🌐💥**    | Multi-Modal Search with **CLIP** and **LanceDB** Using **DiffusionDB** Data for Combined Text and Image Understanding ! 🔓 | [![GitHub](../../assets/github.svg)][Clip_diffusionDB_github] <br>[![Open In Collab](../../assets/colab.svg)][Clip_diffusionDB_colab] <br>[![Python](../../assets/python.svg)][Clip_diffusionDB_python] <br>[![Ghost](../../assets/ghost.svg)][Clip_diffusionDB_ghost] |
 | **Multimodal CLIP: Youtube Videos 📹👀** | Search **Youtube videos** using Multimodal CLIP, finding relevant content with ease and accuracy! 🎯                                                                                                                                                                | [![Github](../../assets/github.svg)][Clip_youtube_github]                                                                                                    <br>[![Open In Collab](../../assets/colab.svg)][Clip_youtube_colab]                                                                                           <br> [![Python](../../assets/python.svg)][Clip_youtube_python]         <br>[![Ghost](../../assets/ghost.svg)][Clip_youtube_python] |
-| **Multimodal Image + Text Search 📸🔍**               | Find **relevant documents** and **images** with a single query using **LanceDB's** multimodal search capabilities, to seamlessly integrate text and visuals ! 🌉 | [![GitHub](../../assets/github.svg)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/multimodal_search) <br>[![Open In Collab](../../assets/colab.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/multimodal_search/main.ipynb) <br> [![Python](../../assets/python.svg)](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.py)<br> [![Ghost](../../assets/ghost.svg)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/) |
+| **Multimodal Image + Text Search 📸🔍**               | Find **relevant documents** and **images** with a single query using **LanceDB's** multimodal search capabilities, to seamlessly integrate text and visuals ! 🌉 | [![GitHub](../../assets/github.svg)](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search) <br>[![Open In Collab](../../assets/colab.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.ipynb) <br> [![Python](../../assets/python.svg)](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.py)<br> [![Ghost](../../assets/ghost.svg)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/) |
 | **Cambrian-1: Vision-Centric Image Exploration 🔍👀** | Learn how **Cambrian-1** works, using an example of **Vision-Centric** exploration on images found through vector search ! Work on **Flickr-8k** dataset 🔎                        | [![Kaggle](https://img.shields.io/badge/Kaggle-035a7d?style=for-the-badge&logo=kaggle&logoColor=white)](https://www.kaggle.com/code/prasantdixit/cambrian-1-vision-centric-exploration-of-images/)<br> [![Ghost](../../assets/ghost.svg)](https://blog.lancedb.com/cambrian-1-vision-centric-exploration/)                                                                                                                                                                                                                   |
 
 

docs/src/examples/python_examples/rag.md

@@ -70,12 +70,12 @@ Build RAG (Retrieval-Augmented Generation) with  LanceDB, a powerful solution fo
 [flare_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/better-rag-FLAIR/main.ipynb
 [flare_ghost]: https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/
 
-[query_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/QueryExpansion%26Reranker
-[query_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/QueryExpansion&Reranker/main.ipynb
+[query_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/QueryExpansion&Reranker
+[query_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/QueryExpansion&Reranker/main.ipynb
 
 
-[fusion_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/RAG_Fusion
-[fusion_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/RAG_Fusion/main.ipynb
+[fusion_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/RAG_Fusion
+[fusion_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/RAG_Fusion/main.ipynb
 
 [agentic_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG
 [agentic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG/main.ipynb

docs/src/examples/python_examples/recommendersystem.md

@@ -19,8 +19,8 @@ Deliver personalized experiences with Recommender Systems. 🎁
 [movie_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.py
 
 
-[genre_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/movie-recommendation-with-genres
-[genre_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb
+[genre_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/movie-recommendation-with-genres
+[genre_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb
 [genre_ghost]: https://blog.lancedb.com/movie-recommendation-system-using-lancedb-and-doc2vec/
 
 [product_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/product-recommender
@@ -33,5 +33,5 @@ Deliver personalized experiences with Recommender Systems. 🎁
 [arxiv_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/arxiv-recommender/main.py
  
 
-[food_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/Food_recommendation
-[food_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/Food_recommendation/main.ipynb
+[food_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Food_recommendation
+[food_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Food_recommendation/main.ipynb

docs/src/examples/python_examples/vector_search.md

@@ -37,16 +37,16 @@ LanceDB implements vector search algorithms for efficient document retrieval and
 [NER_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb
 [NER_ghost]: https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493
 
-[audio_search_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/audio_search
-[audio_search_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/audio_search/main.ipynb
-[audio_search_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/archived_examples/audio_search/main.py
+[audio_search_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/audio_search
+[audio_search_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.ipynb
+[audio_search_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.py
 
-[mls_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/multi-lingual-wiki-qa
-[mls_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/multi-lingual-wiki-qa/main.ipynb
-[mls_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/archived_examples/multi-lingual-wiki-qa/main.py
+[mls_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa
+[mls_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa/main.ipynb
+[mls_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa/main.py
 
-[fr_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/facial_recognition
-[fr_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/facial_recognition/main.ipynb
+[fr_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/facial_recognition
+[fr_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/facial_recognition/main.ipynb
 
 [sentiment_analysis_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews
 [sentiment_analysis_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb
@@ -70,8 +70,8 @@ LanceDB implements vector search algorithms for efficient document retrieval and
 [openvino_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb
 [openvino_ghost]: https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-lancedb/
 
-[zsic_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/zero-shot-image-classification
-[zsic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/zero-shot-image-classification/main.ipynb
+[zsic_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/zero-shot-image-classification
+[zsic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/zero-shot-image-classification/main.ipynb
 [zsic_ghost]: https://blog.lancedb.com/zero-shot-image-classification-with-vector-search/
 
 

docs/src/fts.md

@@ -1,9 +1,21 @@
-# Full-text search (Native FTS)
+# Full-text search
 
-LanceDB provides support for full-text search via Lance, allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
+LanceDB provides support for full-text search via Lance (before via [Tantivy](https://github.com/quickwit-oss/tantivy) (Python only)), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
+
+Currently, the Lance full text search is missing some features that are in the Tantivy full text search. This includes query parser and customizing the tokenizer. Thus, in Python, Tantivy is still the default way to do full text search and many of the instructions below apply just to Tantivy-based indices.
+
+
+## Installation (Only for Tantivy-based FTS)
 
 !!! note
-    The Python SDK uses tantivy-based FTS by default, need to pass `use_tantivy=False` to use native FTS.
+    No need to install the tantivy dependency if using native FTS
+
+To use full-text search, install the dependency [`tantivy-py`](https://github.com/quickwit-oss/tantivy-py):
+
+```sh
+# Say you want to use tantivy==0.20.1
+pip install tantivy==0.20.1
+```
 
 ## Example
 
@@ -27,7 +39,7 @@ Consider that we have a LanceDB table named `my_table`, whose string column `tex
 
     # passing `use_tantivy=False` to use lance FTS index
     # `use_tantivy=True` by default
-    table.create_fts_index("text", use_tantivy=False)
+    table.create_fts_index("text")
     table.search("puppy").limit(10).select(["text"]).to_list()
     # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
     # ...
@@ -81,40 +93,51 @@ Consider that we have a LanceDB table named `my_table`, whose string column `tex
     ```
 
 It would search on all indexed columns by default, so it's useful when there are multiple indexed columns.
+For now, this is supported in tantivy way only.
 
-Passing `fts_columns="text"` if you want to specify the columns to search.
+Passing `fts_columns="text"` if you want to specify the columns to search, but it's not available for Tantivy-based full text search.
 
 !!! note
     LanceDB automatically searches on the existing FTS index if the input to the search is of type `str`. If you provide a vector as input, LanceDB will search the ANN index instead.
 
 ## Tokenization
-By default the text is tokenized by splitting on punctuation and whitespaces, and would filter out words that are with length greater than 40, and lowercase all words.
+By default the text is tokenized by splitting on punctuation and whitespaces and then removing tokens that are longer than 40 chars. For more language specific tokenization then provide the argument tokenizer_name with the 2 letter language code followed by "_stem". So for english it would be "en_stem".
 
-Stemming is useful for improving search results by reducing words to their root form, e.g. "running" to "run". LanceDB supports stemming for multiple languages, you can specify the tokenizer name to enable stemming by the pattern `tokenizer_name="{language_code}_stem"`, e.g. `en_stem` for English.
+For now, only the Tantivy-based FTS index supports to specify the tokenizer, so it's only available in Python with `use_tantivy=True`.
 
-For example, to enable stemming for English:
-```python
-table.create_fts_index("text", use_tantivy=True, tokenizer_name="en_stem")
-```
+=== "use_tantivy=True"
+
+    ```python
+    table.create_fts_index("text", use_tantivy=True, tokenizer_name="en_stem")
+    ```
+
+=== "use_tantivy=False"
+
+    [**Not supported yet**](https://github.com/lancedb/lance/issues/1195)
 
 the following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.
 
-The tokenizer is customizable, you can specify how the tokenizer splits the text, and how it filters out words, etc.
+## Index multiple columns
 
-For example, for language with accents, you can specify the tokenizer to use `ascii_folding` to remove accents, e.g. 'é' to 'e':
-```python
-table.create_fts_index("text",
-                        use_tantivy=False,
-                        language="French",
-                        stem=True,
-                        ascii_folding=True)
-```
+If you have multiple string columns to index, there's no need to combine them manually -- simply pass them all as a list to `create_fts_index`:
+
+=== "use_tantivy=True"
+
+    ```python
+    table.create_fts_index(["text1", "text2"])
+    ```
+
+=== "use_tantivy=False"
+
+    [**Not supported yet**](https://github.com/lancedb/lance/issues/1195)
+
+Note that the search API call does not change - you can search over all indexed columns at once.
 
 ## Filtering
 
-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:
+Currently the LanceDB full text search feature supports *post-filtering*, meaning filters are
+applied on top of the full text search results. This can be invoked via the familiar
+`where` syntax:
 
 === "Python"
 
@@ -146,17 +169,98 @@ This can be invoked via the familiar `where` syntax:
         .await?;
     ```
 
+## Sorting
+
+!!! warning "Warn"
+    Sorting is available for only Tantivy-based FTS
+
+You can pre-sort the documents by specifying `ordering_field_names` when
+creating the full-text search index. Once pre-sorted, you can then specify
+`ordering_field_name` while searching to return results sorted by the given
+field. For example,
+
+```python
+table.create_fts_index(["text_field"], use_tantivy=True, ordering_field_names=["sort_by_field"])
+
+(table.search("terms", ordering_field_name="sort_by_field")
+ .limit(20)
+ .to_list())
+```
+
+!!! note
+    If you wish to specify an ordering field at query time, you must also
+    have specified it during indexing time. Otherwise at query time, an
+    error will be raised that looks like `ValueError: The field does not exist: xxx`
+
+!!! note
+    The fields to sort on must be of typed unsigned integer, or else you will see
+    an error during indexing that looks like
+    `TypeError: argument 'value': 'float' object cannot be interpreted as an integer`.
+
+!!! note
+    You can specify multiple fields for ordering at indexing time.
+    But at query time only one ordering field is supported.
+
+
 ## Phrase queries vs. terms queries
 
 !!! warning "Warn"
     Lance-based FTS doesn't support queries using boolean operators `OR`, `AND`.
 
 For full-text search you can specify either a **phrase** query like `"the old man and the sea"`,
-or a **terms** search query like `old man sea`. For more details on the terms
+or a **terms** search query like `"(Old AND Man) AND Sea"`. For more details on the terms
 query syntax, see Tantivy's [query parser rules](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html).
 
-To search for a phrase, the index must be created with `with_position=True`:
+!!! tip "Note"
+    The query parser will raise an exception on queries that are ambiguous. For example, in the query `they could have been dogs OR cats`, `OR` is capitalized so it's considered a keyword query operator. But it's ambiguous how the left part should be treated. So if you submit this search query as is, you'll get `Syntax Error: they could have been dogs OR cats`.
+
+    ```py
+    # This raises a syntax error
+    table.search("they could have been dogs OR cats")
+    ```
+
+    On the other hand, lowercasing `OR` to `or` will work, because there are no capitalized logical operators and
+    the query is treated as a phrase query.
+
+    ```py
+    # This works!
+    table.search("they could have been dogs or cats")
+    ```
+
+It can be cumbersome to have to remember what will cause a syntax error depending on the type of
+query you want to perform. To make this simpler, when you want to perform a phrase query, you can
+enforce it in one of two ways:
+
+1. Place the double-quoted query inside single quotes. For example, `table.search('"they could have been dogs OR cats"')` is treated as
+a phrase query.
+1. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
+itself contains double quotes. For example, `table.search('the cats OR dogs were not really "pets" at all').phrase_query()`
+is treated as a phrase query.
+
+In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested
+double quotes replaced by single quotes.
+
+
+## Configurations (Only for Tantivy-based FTS)
+
+By default, LanceDB configures a 1GB heap size limit for creating the index. You can
+reduce this if running on a smaller node, or increase this for faster performance while
+indexing a larger corpus.
+
 ```python
-table.create_fts_index("text", use_tantivy=False, with_position=True)
+# configure a 512MB heap size
+heap = 1024 * 1024 * 512
+table.create_fts_index(["text1", "text2"], writer_heap_size=heap, replace=True)
 ```
-This will allow you to search for phrases, but it will also significantly increase the index size and indexing time.
+
+## Current limitations
+
+For that Tantivy-based FTS:
+
+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
+   but there's no way in tantivy-py to specify to use it.

docs/src/fts_tantivy.md

@@ -1,162 +0,0 @@
-# Full-text search (Tantivy-based FTS)
-
-LanceDB also provides support for full-text search via [Tantivy](https://github.com/quickwit-oss/tantivy), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
-
-The tantivy-based FTS is only available in Python and does not support building indexes on object storage or incremental indexing. If you need these features, try native FTS [native FTS](fts.md).
-
-## Installation
-
-To use full-text search, install the dependency [`tantivy-py`](https://github.com/quickwit-oss/tantivy-py):
-
-```sh
-# Say you want to use tantivy==0.20.1
-pip install tantivy==0.20.1
-```
-
-## Example
-
-Consider that we have a LanceDB table named `my_table`, whose string column `content` we want to index and query via keyword search, the FTS index must be created before you can search via keywords.
-
-```python
-import lancedb
-
-uri = "data/sample-lancedb"
-db = lancedb.connect(uri)
-
-table = db.create_table(
-    "my_table",
-    data=[
-        {"id": 1, "vector": [3.1, 4.1], "title": "happy puppy", "content": "Frodo was a happy puppy", "meta": "foo"},
-        {"id": 2, "vector": [5.9, 26.5], "title": "playing kittens", "content": "There are several kittens playing around the puppy", "meta": "bar"},
-    ],
-)
-
-# passing `use_tantivy=False` to use lance FTS index
-# `use_tantivy=True` by default
-table.create_fts_index("content", use_tantivy=True)
-table.search("puppy").limit(10).select(["content"]).to_list()
-# [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
-# ...
-```
-
-It would search on all indexed columns by default, so it's useful when there are multiple indexed columns.
-
-!!! note
-    LanceDB automatically searches on the existing FTS index if the input to the search is of type `str`. If you provide a vector as input, LanceDB will search the ANN index instead.
-
-## Tokenization
-By default the text is tokenized by splitting on punctuation and whitespaces and then removing tokens that are longer than 40 chars. For more language specific tokenization then provide the argument tokenizer_name with the 2 letter language code followed by "_stem". So for english it would be "en_stem".
-
-```python
-table.create_fts_index("content", use_tantivy=True, tokenizer_name="en_stem", replace=True)
-```
-
-the following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.
-
-## Index multiple columns
-
-If you have multiple string columns to index, there's no need to combine them manually -- simply pass them all as a list to `create_fts_index`:
-
-```python
-table.create_fts_index(["title", "content"], use_tantivy=True, replace=True)
-```
-
-Note that the search API call does not change - you can search over all indexed columns at once.
-
-## Filtering
-
-Currently the LanceDB full text search feature supports *post-filtering*, meaning filters are
-applied on top of the full text search results (see [native FTS](fts.md) if you need pre-filtering). This can be invoked via the familiar
-`where` syntax:
-
-```python
-table.search("puppy").limit(10).where("meta='foo'").to_list()
-```
-
-## Sorting
-
-You can pre-sort the documents by specifying `ordering_field_names` when
-creating the full-text search index. Once pre-sorted, you can then specify
-`ordering_field_name` while searching to return results sorted by the given
-field. For example,
-
-```python
-table.create_fts_index(["content"], use_tantivy=True, ordering_field_names=["id"], replace=True)
-
-(table.search("puppy", ordering_field_name="id")
- .limit(20)
- .to_list())
-```
-
-!!! note
-    If you wish to specify an ordering field at query time, you must also
-    have specified it during indexing time. Otherwise at query time, an
-    error will be raised that looks like `ValueError: The field does not exist: xxx`
-
-!!! note
-    The fields to sort on must be of typed unsigned integer, or else you will see
-    an error during indexing that looks like
-    `TypeError: argument 'value': 'float' object cannot be interpreted as an integer`.
-
-!!! note
-    You can specify multiple fields for ordering at indexing time.
-    But at query time only one ordering field is supported.
-
-
-## Phrase queries vs. terms queries
-
-For full-text search you can specify either a **phrase** query like `"the old man and the sea"`,
-or a **terms** search query like `"(Old AND Man) AND Sea"`. For more details on the terms
-query syntax, see Tantivy's [query parser rules](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html).
-
-!!! tip "Note"
-    The query parser will raise an exception on queries that are ambiguous. For example, in the query `they could have been dogs OR cats`, `OR` is capitalized so it's considered a keyword query operator. But it's ambiguous how the left part should be treated. So if you submit this search query as is, you'll get `Syntax Error: they could have been dogs OR cats`.
-
-    ```py
-    # This raises a syntax error
-    table.search("they could have been dogs OR cats")
-    ```
-
-    On the other hand, lowercasing `OR` to `or` will work, because there are no capitalized logical operators and
-    the query is treated as a phrase query.
-
-    ```py
-    # This works!
-    table.search("they could have been dogs or cats")
-    ```
-
-It can be cumbersome to have to remember what will cause a syntax error depending on the type of
-query you want to perform. To make this simpler, when you want to perform a phrase query, you can
-enforce it in one of two ways:
-
-1. Place the double-quoted query inside single quotes. For example, `table.search('"they could have been dogs OR cats"')` is treated as
-a phrase query.
-1. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
-itself contains double quotes. For example, `table.search('the cats OR dogs were not really "pets" at all').phrase_query()`
-is treated as a phrase query.
-
-In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested
-double quotes replaced by single quotes.
-
-
-## Configurations
-
-By default, LanceDB configures a 1GB heap size limit for creating the index. You can
-reduce this if running on a smaller node, or increase this for faster performance while
-indexing a larger corpus.
-
-```python
-# configure a 512MB heap size
-heap = 1024 * 1024 * 512
-table.create_fts_index(["title", "content"], use_tantivy=True, writer_heap_size=heap, replace=True)
-```
-
-## Current limitations
-
-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
-   but there's no way in tantivy-py to specify to use it.

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)"
@@ -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)"
@@ -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
 

docs/src/index.md

@@ -49,8 +49,7 @@ The following pages go deeper into the internal of LanceDB and how to use it.
 * [Working with tables](guides/tables.md): Learn how to work with tables and their associated functions
 * [Indexing](ann_indexes.md): Understand how to create indexes
 * [Vector search](search.md): Learn how to perform vector similarity search
-* [Full-text search (native)](fts.md): Learn how to perform full-text search
-* [Full-text search (tantivy-based)](fts_tantivy.md): Learn how to perform full-text search using Tantivy
+* [Full-text search](fts.md): Learn how to perform full-text search
 * [Managing embeddings](embeddings/index.md): Managing embeddings and the embedding functions API in LanceDB
 * [Ecosystem Integrations](integrations/index.md): Integrate LanceDB with other tools in the data ecosystem
 * [Python API Reference](python/python.md): Python OSS and Cloud API references

docs/src/integrations/langchain.md

@@ -1,10 +1,5 @@
-**LangChain** is a framework designed for building applications with large language models (LLMs) by chaining together various components. It supports a range of functionalities including memory, agents, and chat models, enabling developers to create context-aware applications.
-
-![Illustration](https://raw.githubusercontent.com/lancedb/assets/refs/heads/main/docs/assets/integration/langchain_rag.png)
-
-LangChain streamlines these stages (in figure above) by providing pre-built components and tools for integration, memory management, and deployment, allowing developers to focus on application logic rather than underlying complexities.
-
-Integration of **Langchain** with **LanceDB** enables applications to retrieve the most relevant data by comparing query vectors against stored vectors, facilitating effective information retrieval. It results in better and context aware replies and actions by the LLMs.
+# Langchain
+![Illustration](../assets/langchain.png)
 
 ## Quick Start
 You can load your document data using langchain's loaders, for this example we are using `TextLoader` and `OpenAIEmbeddings` as the embedding model. Checkout Complete example here - [LangChain demo](../notebooks/langchain_example.ipynb)
@@ -31,28 +26,20 @@ print(docs[0].page_content)
 
 ## Documentation
 In the above example `LanceDB` vector store class object is created using `from_documents()` method  which is a `classmethod` and returns the initialized class object. 
-
 You can also use `LanceDB.from_texts(texts: List[str],embedding: Embeddings)` class method.  
 
-The exhaustive list of parameters for `LanceDB` vector store are : 
-
-|Name|type|Purpose|default|
-|:----|:----|:----|:----|
-|`connection`| (Optional) `Any` |`lancedb.db.LanceDBConnection` connection object to use.  If not provided, a new connection will be created.|`None`|
-|`embedding`| (Optional) `Embeddings` | Langchain embedding model.|Provided by user.|
-|`uri`| (Optional) `str` |It specifies the directory location of **LanceDB database** and establishes a connection that can be used to interact with the database. |`/tmp/lancedb`|
-|`vector_key` |(Optional) `str`| Column name to use for vector's in the table.|`'vector'`|
-|`id_key` |(Optional) `str`| Column name to use for id's in the table.|`'id'`|
-|`text_key` |(Optional) `str` |Column name to use for text in the table.|`'text'`|
-|`table_name` |(Optional) `str`| Name of your table in the database.|`'vectorstore'`|
-|`api_key` |(Optional `str`) |API key to use for LanceDB cloud database.|`None`|
-|`region` |(Optional) `str`| Region to use for LanceDB cloud database.|Only for LanceDB Cloud : `None`.|
-|`mode` |(Optional) `str` |Mode to use for adding data to the table. Valid values are "append" and "overwrite".|`'overwrite'`|
-|`table`| (Optional) `Any`|You can connect to an existing table of LanceDB, created outside of langchain, and utilize it.|`None`|
-|`distance`|(Optional) `str`|The choice of distance metric used to calculate the similarity between vectors.|`'l2'`|
-|`reranker` |(Optional) `Any`|The reranker to use for LanceDB.|`None`|
-|`relevance_score_fn` |(Optional) `Callable[[float], float]` | Langchain relevance score function to be used.|`None`|
-|`limit`|`int`|Set the maximum number of results to return.|`DEFAULT_K` (it is 4)|
+The exhaustive list of parameters for `LanceDB` vector store are :  
+- `connection`: (Optional) `lancedb.db.LanceDBConnection` connection object to use.  If not provided, a new connection will be created.  
+- `embedding`: Langchain embedding model.  
+- `vector_key`: (Optional) Column name to use for vector's in the table. Defaults to `'vector'`.   
+- `id_key`: (Optional) Column name to use for id's in the table. Defaults to `'id'`.  
+- `text_key`: (Optional) Column name to use for text in the table. Defaults to `'text'`.  
+- `table_name`: (Optional) Name of your table in the database. Defaults to `'vectorstore'`.  
+- `api_key`: (Optional) API key to use for LanceDB cloud database. Defaults to `None`.  
+- `region`: (Optional) Region to use for LanceDB cloud database. Only for LanceDB Cloud, defaults to `None`.  
+- `mode`: (Optional) Mode to use for adding data to the table. Defaults to `'overwrite'`.  
+- `reranker`: (Optional) The reranker to use for LanceDB.
+- `relevance_score_fn`: (Optional[Callable[[float], float]]) Langchain relevance score function to be used. Defaults to `None`. 
 
 ```python
 db_url = "db://lang_test" # url of db you created
@@ -64,24 +51,19 @@ vector_store = LanceDB(
     api_key=api_key, #(dont include for local API)
     region=region, #(dont include for local API)
     embedding=embeddings,
-    table_name='langchain_test' # Optional
+    table_name='langchain_test' #Optional
     )
 ```
 
 ### Methods 
 
 ##### add_texts()
+- `texts`: `Iterable` of strings to add to the vectorstore.
+- `metadatas`: Optional `list[dict()]` of metadatas associated with the texts.
+- `ids`: Optional `list` of ids to associate with the texts. 
+- `kwargs`: `Any`
 
-This method turn texts into embedding and add it to the database.
-
-|Name|Purpose|defaults|
-|:---|:---|:---|
-|`texts`|`Iterable` of strings to add to the vectorstore.|Provided by user|
-|`metadatas`|Optional `list[dict()]` of metadatas associated with the texts.|`None`|
-|`ids`|Optional `list` of ids to associate with the texts.|`None`|
-|`kwargs`| Other keyworded arguments provided by the user. |-|
-
-It returns list of ids of the added texts.
+This method adds texts and stores respective embeddings automatically.
 
 ```python
 vector_store.add_texts(texts = ['test_123'], metadatas =[{'source' :'wiki'}]) 
@@ -96,25 +78,14 @@ pd_df.to_csv("docsearch.csv", index=False)
 # you can also create a new vector store object using an older connection object:
 vector_store = LanceDB(connection=tbl, embedding=embeddings)
 ```
-
-------
-
-
 ##### create_index() 
+- `col_name`: `Optional[str] = None`
+- `vector_col`: `Optional[str] = None`
+- `num_partitions`: `Optional[int] = 256`
+- `num_sub_vectors`: `Optional[int] = 96`
+- `index_cache_size`: `Optional[int] = None`
 
-This method creates a scalar(for non-vector cols) or a vector index on a table.
-
-|Name|type|Purpose|defaults|
-|:---|:---|:---|:---|
-|`vector_col`|`Optional[str]`| Provide if you want to create index on a vector column. |`None`|
-|`col_name`|`Optional[str]`| Provide if you want to create index on a non-vector column. |`None`|
-|`metric`|`Optional[str]` |Provide the metric to use for vector index. choice of metrics: 'L2', 'dot', 'cosine'. |`L2`|
-|`num_partitions`|`Optional[int]`|Number of partitions to use for the index.|`256`|
-|`num_sub_vectors`|`Optional[int]` |Number of sub-vectors to use for the index.|`96`|
-|`index_cache_size`|`Optional[int]` |Size of the index cache.|`None`|
-|`name`|`Optional[str]` |Name of the table to create index on.|`None`|
-
-For index creation make sure your table has enough data in it. An ANN index is ususally not needed for datasets ~100K vectors. For large-scale (>1M) or higher dimension vectors, it is beneficial to create an ANN index.
+This method creates an index for the vector store. For index creation make sure your table has enough data in it. An ANN index is ususally not needed for datasets ~100K vectors. For large-scale (>1M) or higher dimension vectors, it is beneficial to create an ANN index.
 
 ```python
 # for creating vector index
@@ -125,63 +96,42 @@ vector_store.create_index(col_name='text')
 
 ```
 
-------
-
 ##### similarity_search()
+- `query`: `str`
+- `k`: `Optional[int] = None`
+- `filter`: `Optional[Dict[str, str]] = None`
+- `fts`: `Optional[bool] = False`
+- `name`: `Optional[str] = None`
+- `kwargs`: `Any`
 
-This method performs similarity search based on **text query**.
-
-| Name    | Type                 | Purpose | Default |
-|---------|----------------------|---------|---------|
-| `query` | `str`                |  A `str` representing the text query that you want to search for in the vector store.         | N/A     |
-| `k`         | `Optional[int]`           | It specifies the number of documents to return.        | `None`    |
-| `filter`    | `Optional[Dict[str, str]]`| It is used to filter the search results by specific metadata criteria.        | `None`    |
-| `fts`   | `Optional[bool]`     |   It indicates whether to perform a full-text search (FTS).      | `False`   |
-| `name`      | `Optional[str]`           | It is used for specifying the name of the table to query. If not provided, it uses the default table set during the initialization of the LanceDB instance.        | `None`    |
-| `kwargs`    | `Any`                     | Other keyworded arguments provided by the user.        | N/A     |
-
-Return documents most similar to the query **without relevance scores**.
+Return documents most similar to the query without relevance scores
 
 ```python
 docs = docsearch.similarity_search(query)
 print(docs[0].page_content)
 ```
 
-------
-
 ##### similarity_search_by_vector()
+- `embedding`: `List[float]`
+- `k`: `Optional[int] = None`
+- `filter`: `Optional[Dict[str, str]] = None`
+- `name`: `Optional[str] = None`
+- `kwargs`: `Any`
 
-The method returns documents that are most similar to the specified **embedding (query) vector**. 
-
-| Name        | Type                      | Purpose | Default |
-|-------------|---------------------------|---------|---------|
-| `embedding` | `List[float]`             | The embedding vector you want to use to search for similar documents in the vector store.         | N/A     |
-| `k`         | `Optional[int]`           | It specifies the number of documents to return.        | `None`    |
-| `filter`    | `Optional[Dict[str, str]]`| It is used to filter the search results by specific metadata criteria.        | `None`    |
-| `name`      | `Optional[str]`           | It is used for specifying the name of the table to query. If not provided, it uses the default table set during the initialization of the LanceDB instance.        | `None`    |
-| `kwargs`    | `Any`                     | Other keyworded arguments provided by the user.        | N/A     |
-
-**It does not provide relevance scores.**
+Returns documents most similar to the query vector.
 
 ```python
 docs = docsearch.similarity_search_by_vector(query)
 print(docs[0].page_content)
 ```
 
-------
-
 ##### similarity_search_with_score()
+- `query`: `str`
+- `k`: `Optional[int] = None`
+- `filter`: `Optional[Dict[str, str]] = None`
+- `kwargs`: `Any`
 
-Returns documents most similar to the **query string** along with their relevance scores.
-
-| Name     | Type                      | Purpose | Default |
-|----------|---------------------------|---------|---------|
-| `query`  | `str`                     |A `str` representing the text query you want to search for in the vector store. This query will be converted into an embedding using the specified embedding function.         | N/A     |
-| `k`      | `Optional[int]`           | It specifies the number of documents to return.  | `None`    |
-| `filter` | `Optional[Dict[str, str]]`|  It is used to filter the search results by specific metadata criteria. This allows you to narrow down the search results based on certain metadata attributes associated with the documents.       | `None`    |
-| `kwargs` | `Any`                     |  Other keyworded arguments provided by the user.       | N/A     |
-
-It gets called by base class's `similarity_search_with_relevance_scores` which selects relevance score based on our `_select_relevance_score_fn`.
+Returns documents most similar to the query string with relevance scores, gets called by base class's `similarity_search_with_relevance_scores` which selects relevance score based on our `_select_relevance_score_fn`.
 
 ```python
 docs = docsearch.similarity_search_with_relevance_scores(query)
@@ -189,21 +139,15 @@ print("relevance score - ", docs[0][1])
 print("text- ", docs[0][0].page_content[:1000])
 ```
 
-------
-
 ##### similarity_search_by_vector_with_relevance_scores()
+- `embedding`: `List[float]`
+- `k`: `Optional[int] = None`
+- `filter`: `Optional[Dict[str, str]] = None`
+- `name`: `Optional[str] = None`
+- `kwargs`: `Any`
 
-Similarity search using **query vector**.
-
-| Name        | Type                      | Purpose | Default |
-|-------------|---------------------------|---------|---------|
-| `embedding` | `List[float]`             | The embedding vector you want to use to search for similar documents in the vector store.        | N/A     |
-| `k`         | `Optional[int]`           |   It specifies the number of documents to return.       | `None`    |
-| `filter`    | `Optional[Dict[str, str]]`|  It is used to filter the search results by specific metadata criteria.       | `None`    |
-| `name`      | `Optional[str]`           |   It is used for specifying the name of the table to query.       | `None`    |
-| `kwargs`    | `Any`                     |   Other keyworded arguments provided by the user.      | N/A     |
-
-The method returns documents most similar to the specified embedding (query) vector, along with their relevance scores.
+Return documents most similar to the query vector with relevance scores.
+Relevance score 
 
 ```python
 docs = docsearch.similarity_search_by_vector_with_relevance_scores(query_embedding)
@@ -211,22 +155,20 @@ print("relevance score - ", docs[0][1])
 print("text- ", docs[0][0].page_content[:1000])
 ```
 
-------
-
 ##### max_marginal_relevance_search()
+- `query`: `str`
+- `k`: `Optional[int] = None`
+- `fetch_k` : Number of Documents to fetch to pass to MMR algorithm, `Optional[int] = None`
+- `lambda_mult`: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5. `float = 0.5`
+- `filter`: `Optional[Dict[str, str]] = None`
+- `kwargs`: `Any`
 
-This method returns docs selected using the maximal marginal relevance(MMR).
+Returns docs selected using the maximal marginal relevance(MMR).
 Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
 
-| Name | Type | Purpose | Default |
-|---------------|-----------------|-----------|---------|
-| `query`       | `str` | Text to look up documents similar to. | N/A     |
-| `k`   | `Optional[int]` | Number of Documents to return.| `4`       |
-| `fetch_k`| `Optional[int]`| Number of Documents to fetch to pass to MMR algorithm.| `None`    |
-| `lambda_mult` | `float`                   | Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. | `0.5`     |
-| `filter`| `Optional[Dict[str, str]]`| Filter by metadata. | `None`    |
-|`kwargs`| Other keyworded arguments provided by the user. |-|
-
 Similarly, `max_marginal_relevance_search_by_vector()` function returns docs most similar to the embedding passed to the function using MMR. instead of a string query you need to pass the embedding to be searched for. 
 
 ```python
@@ -244,19 +186,12 @@ result_texts = [doc.page_content for doc in result]
 print(result_texts)
 ```
 
-------
-
 ##### add_images()
+- `uris` : File path to the image. `List[str]`.
+- `metadatas` : Optional list of metadatas. `(Optional[List[dict]], optional)`
+- `ids` : Optional list of IDs. `(Optional[List[str]], optional)`
 
-This method ddds images by automatically creating their embeddings and adds them to the vectorstore.
-
-| Name       | Type                          | Purpose                        | Default |
-|------------|-------------------------------|--------------------------------|---------|
-| `uris`     | `List[str]`                   | File path to the image         | N/A     |
-| `metadatas`| `Optional[List[dict]]`        | Optional list of metadatas     | `None`    |
-| `ids`      | `Optional[List[str]]`         | Optional list of IDs           | `None`    |
-
-It returns list of IDs of the added images.
+Adds images by automatically creating their embeddings and adds them to the vectorstore.
 
 ```python
 vec_store.add_images(uris=image_uris) 

docs/src/integrations/phidata.md

@@ -45,7 +45,7 @@ Let's see how using LanceDB inside phidata helps in making LLM more useful:
 
 **Install the following packages in the virtual environment**
 ```python
-pip install lancedb phidata youtube_transcript_api openai ollama numpy pandas
+pip install lancedb phidata youtube_transcript_api openai ollama pandas numpy
 ```
 
 **Create python files and import necessary libraries**

docs/src/js/interfaces/FtsOptions.md

@@ -1,25 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / FtsOptions
-
-# Interface: FtsOptions
-
-Options to create an `FTS` index
-
-## Properties
-
-### withPosition?
-
-> `optional` **withPosition**: `boolean`
-
-Whether to store the positions of the term in the document.
-
-If this is true then the index will store the positions of the term in the document.
-This allows phrase queries to be run. But it also increases the size of the index,
-and the time to build the index.
-
-The default value is true.
-
-***

docs/src/reranking/voyageai.md

@@ -1,77 +0,0 @@
-# Voyage AI Reranker
-
-Voyage AI provides cutting-edge embedding and rerankers.
-
-This re-ranker uses the [VoyageAI](https://docs.voyageai.com/docs/) API to rerank the search results. You can use this re-ranker by passing `VoyageAIReranker()` to the `rerank()` method. Note that you'll either need to set the `VOYAGE_API_KEY` environment variable or pass the `api_key` argument to use this re-ranker.
-
-
-!!! note
-    Supported Query Types: Hybrid, Vector, FTS
-
-
-```python
-import numpy
-import lancedb
-from lancedb.embeddings import get_registry
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.rerankers import VoyageAIReranker
-
-embedder = get_registry().get("sentence-transformers").create()
-db = lancedb.connect("~/.lancedb")
-
-class Schema(LanceModel):
-    text: str = embedder.SourceField()
-    vector: Vector(embedder.ndims()) = embedder.VectorField()
-
-data = [
-    {"text": "hello world"},
-    {"text": "goodbye world"}
-    ]
-tbl = db.create_table("test", schema=Schema, mode="overwrite")
-tbl.add(data)
-reranker = VoyageAIReranker(model_name="rerank-2")
-
-# Run vector search with a reranker
-result = tbl.search("hello").rerank(reranker=reranker).to_list() 
-
-# Run FTS search with a reranker
-result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
-
-# Run hybrid search with a reranker
-tbl.create_fts_index("text", replace=True)
-result = tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
-
-```
-
-Accepted Arguments
-----------------
-| Argument | Type | Default | Description |
-| --- | --- | --- | --- |
-| `model_name` | `str` | `None` | The name of the reranker model to use. Available models are: rerank-2, rerank-2-lite |
-| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
-| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
-| `api_key` | `str` | `None` | The API key for the Voyage AI API. If not provided, the `VOYAGE_API_KEY` environment variable is used. |
-| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
-| `truncation` | `bool` | `None` | Whether to truncate the input to satisfy the "context length limit" on the query and the documents. |
-
-
-## Supported Scores for each query type
-You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
-
-### Hybrid Search
-|`return_score`| Status | Description |
-| --- | --- | --- |
-| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
-| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
-
-### Vector Search
-|`return_score`| Status | Description |
-| --- | --- | --- |
-| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
-| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
-
-### FTS Search
-|`return_score`| Status | Description |
-| --- | --- | --- |
-| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
-| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |

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

@@ -49,7 +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)"
@@ -91,7 +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)"
@@ -169,7 +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)"

docs/src/troubleshooting.md

@@ -1,33 +0,0 @@
-## Getting help
-
-The following sections provide various diagnostics and troubleshooting tips for LanceDB.
-These can help you provide additional information when asking questions or making
-error reports.
-
-For trouble shooting, the best place to ask is in our Discord, under the relevant
-language channel. By asking in the language-specific channel, it makes it more
-likely that someone who knows the answer will see your question.
-
-## Enabling logging
-
-To provide more information, especially for LanceDB Cloud related issues, enable
-debug logging. You can set the `LANCEDB_LOG` environment variable:
-
-```shell
-export LANCEDB_LOG=debug
-```
-
-You can turn off colors and formatting in the logs by setting
-
-```shell
-export LANCEDB_LOG_STYLE=never
-```
-
-## Explaining query plans
-
-If you have slow queries or unexpected query results, it can be helpful to
-print the resolved query plan. You can use the `explain_plan` method to do this:
-
-* Python Sync: [LanceQueryBuilder.explain_plan][lancedb.query.LanceQueryBuilder.explain_plan]
-* Python Async: [AsyncQueryBase.explain_plan][lancedb.query.AsyncQueryBase.explain_plan]
-* Node @lancedb/lancedb: [LanceQueryBuilder.explainPlan](/lancedb/js/classes/QueryBase/#explainplan)

docs/test/requirements.txt

@@ -3,7 +3,7 @@ numpy
 pandas
 pylance
 duckdb
-tantivy==0.20.1
 --extra-index-url https://download.pytorch.org/whl/cpu
 torch
 polars>=0.19, <=1.3.0
+

java/core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
         <groupId>com.lancedb</groupId>
         <artifactId>lancedb-parent</artifactId>
-        <version>0.13.0-beta.2</version>
+        <version>0.11.1-beta.1</version>
         <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.13.0-beta.2</version>
+    <version>0.11.1-beta.1</version>
     <packaging>pom</packaging>
 
     <name>LanceDB Parent</name>

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.13.0-beta.2",
+  "version": "0.11.1-beta.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.13.0-beta.2",
+      "version": "0.11.1-beta.0",
       "cpu": [
         "x64",
         "arm64"
@@ -52,12 +52,11 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.13.0-beta.2",
-        "@lancedb/vectordb-darwin-x64": "0.13.0-beta.2",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.13.0-beta.2",
-        "@lancedb/vectordb-linux-x64-gnu": "0.13.0-beta.2",
-        "@lancedb/vectordb-win32-arm64-msvc": "0.13.0-beta.2",
-        "@lancedb/vectordb-win32-x64-msvc": "0.13.0-beta.2"
+        "@lancedb/vectordb-darwin-arm64": "0.11.1-beta.0",
+        "@lancedb/vectordb-darwin-x64": "0.11.1-beta.0",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.11.1-beta.0",
+        "@lancedb/vectordb-linux-x64-gnu": "0.11.1-beta.0",
+        "@lancedb/vectordb-win32-x64-msvc": "0.11.1-beta.0"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.13.0-beta.2",
+  "version": "0.11.1-beta.1",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
@@ -84,16 +84,14 @@
       "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-pc-windows-msvc": "@lancedb/vectordb-win32-x64-msvc",
-      "aarch64-pc-windows-msvc": "@lancedb/vectordb-win32-arm64-msvc"
+      "x86_64-pc-windows-msvc": "@lancedb/vectordb-win32-x64-msvc"
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-arm64": "0.13.0-beta.2",
-    "@lancedb/vectordb-darwin-x64": "0.13.0-beta.2",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.13.0-beta.2",
-    "@lancedb/vectordb-linux-x64-gnu": "0.13.0-beta.2",
-    "@lancedb/vectordb-win32-x64-msvc": "0.13.0-beta.2",
-    "@lancedb/vectordb-win32-arm64-msvc": "0.13.0-beta.2"
+    "@lancedb/vectordb-darwin-arm64": "0.11.1-beta.1",
+    "@lancedb/vectordb-darwin-x64": "0.11.1-beta.1",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.11.1-beta.1",
+    "@lancedb/vectordb-linux-x64-gnu": "0.11.1-beta.1",
+    "@lancedb/vectordb-win32-x64-msvc": "0.11.1-beta.1"
   }
 }

node/src/remote/client.ts

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import axios, { type AxiosError, type AxiosResponse, type ResponseType } from 'axios'
+import axios, { type AxiosResponse, type ResponseType } from 'axios'
 
 import { tableFromIPC, type Table as ArrowTable } from 'apache-arrow'
 
@@ -197,7 +197,7 @@ export class HttpLancedbClient {
       response = await callWithMiddlewares(req, this._middlewares)
       return response
     } catch (err: any) {
-      console.error(serializeErrorAsJson(err))
+      console.error('error: ', err)
       if (err.response === undefined) {
         throw new Error(`Network Error: ${err.message as string}`)
       }
@@ -247,8 +247,7 @@ export class HttpLancedbClient {
 
       // return response
     } catch (err: any) {
-      console.error(serializeErrorAsJson(err))
-
+      console.error('error: ', err)
       if (err.response === undefined) {
         throw new Error(`Network Error: ${err.message as string}`)
       }
@@ -288,15 +287,3 @@ export class HttpLancedbClient {
     return clone
   }
 }
-
-function serializeErrorAsJson(err: AxiosError) {
-  const error = JSON.parse(JSON.stringify(err, Object.getOwnPropertyNames(err)))
-  error.response = err.response != null
-      ? JSON.parse(JSON.stringify(
-        err.response,
-        // config contains the request data, too noisy
-        Object.getOwnPropertyNames(err.response).filter(prop => prop !== 'config')
-      ))
-      : null
-  return JSON.stringify({ error })
-}

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.13.0-beta.2"
+version = "0.11.1-beta.1"
 license.workspace = true
 description.workspace = true
 repository.workspace = true
@@ -13,17 +13,15 @@ crate-type = ["cdylib"]
 
 [dependencies]
 arrow-ipc.workspace = true
-env_logger.workspace = true
 futures.workspace = true
 lancedb = { path = "../rust/lancedb", features = ["remote"] }
 napi = { version = "2.16.8", default-features = false, features = [
     "napi9",
-    "async"
+    "async",
 ] }
 napi-derive = "2.16.4"
 # Prevent dynamic linking of lzma, which comes from datafusion
 lzma-sys = { version = "*", features = ["static"] }
-log.workspace = true
 
 [build-dependencies]
 napi-build = "2.1"

nodejs/__test__/remote.test.ts

@@ -90,29 +90,4 @@ describe("remote connection", () => {
       },
     );
   });
-
-  it("shows the full error messages on retry errors", async () => {
-    await withMockDatabase(
-      (_req, res) => {
-        // We retry on 500 errors, so we return 500s until the client gives up.
-        res.writeHead(500).end("Internal Server Error");
-      },
-      async (db) => {
-        try {
-          await db.tableNames();
-          fail("expected an error");
-          // biome-ignore lint/suspicious/noExplicitAny: skip
-        } catch (e: any) {
-          expect(e.message).toContain("Hit retry limit for request_id=");
-          expect(e.message).toContain("Caused by: Http error");
-          expect(e.message).toContain("500 Internal Server Error");
-        }
-      },
-      {
-        clientConfig: {
-          retryConfig: { retries: 2 },
-        },
-      },
-    );
-  });
 });

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);
@@ -477,40 +402,6 @@ describe("When creating an index", () => {
     expect(rst.numRows).toBe(1);
   });
 
-  it("should be able to query unindexed data", async () => {
-    await tbl.createIndex("vec");
-    await tbl.add([
-      {
-        id: 300,
-        vec: Array(32)
-          .fill(1)
-          .map(() => Math.random()),
-        tags: [],
-      },
-    ]);
-
-    const plan1 = await tbl.query().nearestTo(queryVec).explainPlan(true);
-    expect(plan1).toMatch("LanceScan");
-
-    const plan2 = await tbl
-      .query()
-      .nearestTo(queryVec)
-      .fastSearch()
-      .explainPlan(true);
-    expect(plan2).not.toMatch("LanceScan");
-  });
-
-  it("should be able to query with row id", async () => {
-    const results = await tbl
-      .query()
-      .nearestTo(queryVec)
-      .withRowId()
-      .limit(1)
-      .toArray();
-    expect(results.length).toBe(1);
-    expect(results[0]).toHaveProperty("_rowid");
-  });
-
   it("should allow parameters to be specified", async () => {
     await tbl.createIndex("vec", {
       config: Index.ivfPq({
@@ -1073,18 +964,4 @@ 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

@@ -239,29 +239,6 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
     return this;
   }
 
-  /**
-   * Skip searching un-indexed data. This can make search faster, but will miss
-   * any data that is not yet indexed.
-   *
-   * Use {@link lancedb.Table#optimize} to index all un-indexed data.
-   */
-  fastSearch(): this {
-    this.doCall((inner: NativeQueryType) => inner.fastSearch());
-    return this;
-  }
-
-  /**
-   * Whether to return the row id in the results.
-   *
-   * This column can be used to match results between different queries. For
-   * example, to match results from a full text search and a vector search in
-   * order to perform hybrid search.
-   */
-  withRowId(): this {
-    this.doCall((inner: NativeQueryType) => inner.withRowId());
-    return this;
-  }
-
   protected nativeExecute(
     options?: Partial<QueryExecutionOptions>,
   ): Promise<NativeBatchIterator> {
@@ -492,42 +469,6 @@ 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. */
@@ -607,9 +548,4 @@ 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/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.13.0-beta.2",
+	"version": "0.11.1-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": "0.13.0-beta.2",
+	"version": "0.11.1-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": "0.13.0-beta.2",
+	"version": "0.11.1-beta.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.13.0-beta.2",
+	"version": "0.11.1-beta.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/win32-arm64-msvc/README.md

@@ -1,3 +0,0 @@
-# `@lancedb/lancedb-win32-arm64-msvc`
-
-This is the **aarch64-pc-windows-msvc** binary for `@lancedb/lancedb`

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

@@ -1,18 +0,0 @@
-{
-  "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.13.0-beta.2",
-  "os": [
-    "win32"
-  ],
-  "cpu": [
-    "arm64"
-  ],
-  "main": "lancedb.win32-arm64-msvc.node",
-  "files": [
-    "lancedb.win32-arm64-msvc.node"
-  ],
-  "license": "Apache 2.0",
-  "engines": {
-    "node": ">= 18"
-  }
-}

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.13.0-beta.2",
+	"version": "0.11.1-beta.1",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package.json

@@ -10,13 +10,11 @@
     "vector database",
     "ann"
   ],
-  "version": "0.13.0-beta.2",
+  "version": "0.11.1-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": {
@@ -87,7 +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/connection.rs

@@ -18,7 +18,6 @@ use std::str::FromStr;
 use napi::bindgen_prelude::*;
 use napi_derive::*;
 
-use crate::error::{convert_error, NapiErrorExt};
 use crate::table::Table;
 use crate::ConnectionOptions;
 use lancedb::connection::{
@@ -87,7 +86,12 @@ impl Connection {
             builder = builder.host_override(&host_override);
         }
 
-        Ok(Self::inner_new(builder.execute().await.default_error()?))
+        Ok(Self::inner_new(
+            builder
+                .execute()
+                .await
+                .map_err(|e| napi::Error::from_reason(format!("{}", e)))?,
+        ))
     }
 
     #[napi]
@@ -119,7 +123,9 @@ impl Connection {
         if let Some(limit) = limit {
             op = op.limit(limit);
         }
-        op.execute().await.default_error()
+        op.execute()
+            .await
+            .map_err(|e| napi::Error::from_reason(format!("{}", e)))
     }
 
     /// Create table from a Apache Arrow IPC (file) buffer.
@@ -150,13 +156,17 @@ impl Connection {
         }
         if let Some(data_storage_option) = data_storage_options.as_ref() {
             builder = builder.data_storage_version(
-                LanceFileVersion::from_str(data_storage_option).map_err(|e| convert_error(&e))?,
+                LanceFileVersion::from_str(data_storage_option)
+                    .map_err(|e| napi::Error::from_reason(format!("{}", e)))?,
             );
         }
         if let Some(enable_v2_manifest_paths) = enable_v2_manifest_paths {
             builder = builder.enable_v2_manifest_paths(enable_v2_manifest_paths);
         }
-        let tbl = builder.execute().await.default_error()?;
+        let tbl = builder
+            .execute()
+            .await
+            .map_err(|e| napi::Error::from_reason(format!("{}", e)))?;
         Ok(Table::new(tbl))
     }
 
@@ -185,13 +195,17 @@ impl Connection {
         }
         if let Some(data_storage_option) = data_storage_options.as_ref() {
             builder = builder.data_storage_version(
-                LanceFileVersion::from_str(data_storage_option).map_err(|e| convert_error(&e))?,
+                LanceFileVersion::from_str(data_storage_option)
+                    .map_err(|e| napi::Error::from_reason(format!("{}", e)))?,
             );
         }
         if let Some(enable_v2_manifest_paths) = enable_v2_manifest_paths {
             builder = builder.enable_v2_manifest_paths(enable_v2_manifest_paths);
         }
-        let tbl = builder.execute().await.default_error()?;
+        let tbl = builder
+            .execute()
+            .await
+            .map_err(|e| napi::Error::from_reason(format!("{}", e)))?;
         Ok(Table::new(tbl))
     }
 
@@ -211,13 +225,19 @@ impl Connection {
         if let Some(index_cache_size) = index_cache_size {
             builder = builder.index_cache_size(index_cache_size);
         }
-        let tbl = builder.execute().await.default_error()?;
+        let tbl = builder
+            .execute()
+            .await
+            .map_err(|e| napi::Error::from_reason(format!("{}", e)))?;
         Ok(Table::new(tbl))
     }
 
     /// Drop table with the name. Or raise an error if the table does not exist.
     #[napi(catch_unwind)]
     pub async fn drop_table(&self, name: String) -> napi::Result<()> {
-        self.get_inner()?.drop_table(&name).await.default_error()
+        self.get_inner()?
+            .drop_table(&name)
+            .await
+            .map_err(|e| napi::Error::from_reason(format!("{}", e)))
     }
 }

nodejs/src/error.rs

@@ -7,31 +7,6 @@ pub trait NapiErrorExt<T> {
 
 impl<T> NapiErrorExt<T> for std::result::Result<T, lancedb::Error> {
     fn default_error(self) -> Result<T> {
-        self.map_err(|err| convert_error(&err))
+        self.map_err(|err| napi::Error::from_reason(err.to_string()))
     }
 }
-
-pub fn convert_error(err: &dyn std::error::Error) -> napi::Error {
-    let mut message = err.to_string();
-
-    // Append causes
-    let mut cause = err.source();
-    let mut indent = 2;
-    while let Some(err) = cause {
-        let cause_message = format!("Caused by: {}", err);
-        message.push_str(&indent_string(&cause_message, indent));
-
-        cause = err.source();
-        indent += 2;
-    }
-
-    napi::Error::from_reason(message)
-}
-
-fn indent_string(s: &str, amount: usize) -> String {
-    let indent = " ".repeat(amount);
-    s.lines()
-        .map(|line| format!("{}{}", indent, line))
-        .collect::<Vec<_>>()
-        .join("\n")
-}

nodejs/src/lib.rs

@@ -14,7 +14,6 @@
 
 use std::collections::HashMap;
 
-use env_logger::Env;
 use napi_derive::*;
 
 mod connection;
@@ -78,11 +77,3 @@ pub struct WriteOptions {
 pub struct OpenTableOptions {
     pub storage_options: Option<HashMap<String, String>>,
 }
-
-#[napi::module_init]
-fn init() {
-    let env = Env::new()
-        .filter_or("LANCEDB_LOG", "warn")
-        .write_style("LANCEDB_LOG_STYLE");
-    env_logger::init_from_env(env);
-}

nodejs/src/merge.rs

@@ -2,8 +2,6 @@ use lancedb::{arrow::IntoArrow, ipc::ipc_file_to_batches, table::merge::MergeIns
 use napi::bindgen_prelude::*;
 use napi_derive::napi;
 
-use crate::error::convert_error;
-
 #[napi]
 #[derive(Clone)]
 /// A builder used to create and run a merge insert operation
@@ -37,18 +35,14 @@ impl NativeMergeInsertBuilder {
     pub async fn execute(&self, buf: Buffer) -> napi::Result<()> {
         let data = ipc_file_to_batches(buf.to_vec())
             .and_then(IntoArrow::into_arrow)
-            .map_err(|e| {
-                napi::Error::from_reason(format!("Failed to read IPC file: {}", convert_error(&e)))
-            })?;
+            .map_err(|e| napi::Error::from_reason(format!("Failed to read IPC file: {}", e)))?;
 
         let this = self.clone();
 
-        this.inner.execute(data).await.map_err(|e| {
-            napi::Error::from_reason(format!(
-                "Failed to execute merge insert: {}",
-                convert_error(&e)
-            ))
-        })
+        this.inner
+            .execute(data)
+            .await
+            .map_err(|e| napi::Error::from_reason(format!("Failed to execute merge insert: {}", e)))
     }
 }
 

nodejs/src/query.rs

@@ -22,7 +22,6 @@ use lancedb::query::VectorQuery as LanceDbVectorQuery;
 use napi::bindgen_prelude::*;
 use napi_derive::napi;
 
-use crate::error::convert_error;
 use crate::error::NapiErrorExt;
 use crate::iterator::RecordBatchIterator;
 use crate::util::parse_distance_type;
@@ -80,16 +79,6 @@ impl Query {
         Ok(VectorQuery { inner })
     }
 
-    #[napi]
-    pub fn fast_search(&mut self) {
-        self.inner = self.inner.clone().fast_search();
-    }
-
-    #[napi]
-    pub fn with_row_id(&mut self) {
-        self.inner = self.inner.clone().with_row_id();
-    }
-
     #[napi(catch_unwind)]
     pub async fn execute(
         &self,
@@ -104,10 +93,7 @@ impl Query {
             .execute_with_options(execution_opts)
             .await
             .map_err(|e| {
-                napi::Error::from_reason(format!(
-                    "Failed to execute query stream: {}",
-                    convert_error(&e)
-                ))
+                napi::Error::from_reason(format!("Failed to execute query stream: {}", e))
             })?;
         Ok(RecordBatchIterator::new(inner_stream))
     }
@@ -115,10 +101,7 @@ impl Query {
     #[napi]
     pub async fn explain_plan(&self, verbose: bool) -> napi::Result<String> {
         self.inner.explain_plan(verbose).await.map_err(|e| {
-            napi::Error::from_reason(format!(
-                "Failed to retrieve the query plan: {}",
-                convert_error(&e)
-            ))
+            napi::Error::from_reason(format!("Failed to retrieve the query plan: {}", e))
         })
     }
 }
@@ -135,16 +118,6 @@ 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)?;
@@ -203,16 +176,6 @@ impl VectorQuery {
         self.inner = self.inner.clone().offset(offset as usize);
     }
 
-    #[napi]
-    pub fn fast_search(&mut self) {
-        self.inner = self.inner.clone().fast_search();
-    }
-
-    #[napi]
-    pub fn with_row_id(&mut self) {
-        self.inner = self.inner.clone().with_row_id();
-    }
-
     #[napi(catch_unwind)]
     pub async fn execute(
         &self,
@@ -227,10 +190,7 @@ impl VectorQuery {
             .execute_with_options(execution_opts)
             .await
             .map_err(|e| {
-                napi::Error::from_reason(format!(
-                    "Failed to execute query stream: {}",
-                    convert_error(&e)
-                ))
+                napi::Error::from_reason(format!("Failed to execute query stream: {}", e))
             })?;
         Ok(RecordBatchIterator::new(inner_stream))
     }
@@ -238,10 +198,7 @@ impl VectorQuery {
     #[napi]
     pub async fn explain_plan(&self, verbose: bool) -> napi::Result<String> {
         self.inner.explain_plan(verbose).await.map_err(|e| {
-            napi::Error::from_reason(format!(
-                "Failed to retrieve the query plan: {}",
-                convert_error(&e)
-            ))
+            napi::Error::from_reason(format!("Failed to retrieve the query plan: {}", e))
         })
     }
 }

nodejs/src/table.rs

@@ -72,7 +72,10 @@ impl Table {
     /// Return Schema as empty Arrow IPC file.
     #[napi(catch_unwind)]
     pub async fn schema(&self) -> napi::Result<Buffer> {
-        let schema = self.inner_ref()?.schema().await.default_error()?;
+        let schema =
+            self.inner_ref()?.schema().await.map_err(|e| {
+                napi::Error::from_reason(format!("Failed to create IPC file: {}", e))
+            })?;
         let mut writer = FileWriter::try_new(vec![], &schema)
             .map_err(|e| napi::Error::from_reason(format!("Failed to create IPC file: {}", e)))?;
         writer
@@ -97,7 +100,12 @@ impl Table {
             return Err(napi::Error::from_reason(format!("Invalid mode: {}", mode)));
         };
 
-        op.execute().await.default_error()
+        op.execute().await.map_err(|e| {
+            napi::Error::from_reason(format!(
+                "Failed to add batches to table {}: {}",
+                self.name, e
+            ))
+        })
     }
 
     #[napi(catch_unwind)]
@@ -106,12 +114,22 @@ impl Table {
             .count_rows(filter)
             .await
             .map(|val| val as i64)
-            .default_error()
+            .map_err(|e| {
+                napi::Error::from_reason(format!(
+                    "Failed to count rows in table {}: {}",
+                    self.name, e
+                ))
+            })
     }
 
     #[napi(catch_unwind)]
     pub async fn delete(&self, predicate: String) -> napi::Result<()> {
-        self.inner_ref()?.delete(&predicate).await.default_error()
+        self.inner_ref()?.delete(&predicate).await.map_err(|e| {
+            napi::Error::from_reason(format!(
+                "Failed to delete rows in table {}: predicate={}",
+                self.name, e
+            ))
+        })
     }
 
     #[napi(catch_unwind)]
@@ -169,7 +187,12 @@ impl Table {
         self.inner_ref()?
             .add_columns(transforms, None)
             .await
-            .default_error()?;
+            .map_err(|err| {
+                napi::Error::from_reason(format!(
+                    "Failed to add columns to table {}: {}",
+                    self.name, err
+                ))
+            })?;
         Ok(())
     }
 
@@ -190,7 +213,12 @@ impl Table {
         self.inner_ref()?
             .alter_columns(&alterations)
             .await
-            .default_error()?;
+            .map_err(|err| {
+                napi::Error::from_reason(format!(
+                    "Failed to alter columns in table {}: {}",
+                    self.name, err
+                ))
+            })?;
         Ok(())
     }
 
@@ -200,7 +228,12 @@ impl Table {
         self.inner_ref()?
             .drop_columns(&col_refs)
             .await
-            .default_error()?;
+            .map_err(|err| {
+                napi::Error::from_reason(format!(
+                    "Failed to drop columns from table {}: {}",
+                    self.name, err
+                ))
+            })?;
         Ok(())
     }
 

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 = "0.16.0"
+current_version = "0.14.1-beta.1"
 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 = "0.16.0"
+version = "0.14.1-beta.1"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
@@ -16,7 +16,7 @@ crate-type = ["cdylib"]
 [dependencies]
 arrow = { version = "52.1", features = ["pyarrow"] }
 lancedb = { path = "../rust/lancedb" }
-env_logger.workspace = true
+env_logger = "0.10"
 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"] }

python/pyproject.toml

@@ -3,11 +3,13 @@ name = "lancedb"
 # version in Cargo.toml
 dependencies = [
     "deprecation",
-    "nest-asyncio~=1.0",
-    "pylance==0.19.2",
+    "pylance==0.19.1",
+    "requests>=2.31.0",
     "tqdm>=4.27.0",
     "pydantic>=1.10",
+    "attrs>=21.3.0",
     "packaging",
+    "cachetools",
     "overrides>=0.7",
 ]
 description = "lancedb"
@@ -59,7 +61,6 @@ dev = ["ruff", "pre-commit"]
 docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"]
 clip = ["torch", "pillow", "open-clip"]
 embeddings = [
-    "requests>=2.31.0",
     "openai>=1.6.1",
     "sentence-transformers",
     "torch",

python/python/lancedb/__init__.py

@@ -19,10 +19,12 @@ from typing import Dict, Optional, Union, Any
 
 __version__ = importlib.metadata.version("lancedb")
 
+from lancedb.remote import ClientConfig
+
 from ._lancedb import connect as lancedb_connect
 from .common import URI, sanitize_uri
 from .db import AsyncConnection, DBConnection, LanceDBConnection
-from .remote import ClientConfig
+from .remote.db import RemoteDBConnection
 from .schema import vector
 from .table import AsyncTable
 
@@ -35,7 +37,6 @@ def connect(
     host_override: Optional[str] = None,
     read_consistency_interval: Optional[timedelta] = None,
     request_thread_pool: Optional[Union[int, ThreadPoolExecutor]] = None,
-    client_config: Union[ClientConfig, Dict[str, Any], None] = None,
     **kwargs: Any,
 ) -> DBConnection:
     """Connect to a LanceDB database.
@@ -63,10 +64,14 @@ def connect(
         the last check, then the table will be checked for updates. Note: this
         consistency only applies to read operations. Write operations are
         always consistent.
-    client_config: ClientConfig or dict, optional
-        Configuration options for the LanceDB Cloud HTTP client. If a dict, then
-        the keys are the attributes of the ClientConfig class. If None, then the
-        default configuration is used.
+    request_thread_pool: int or ThreadPoolExecutor, optional
+        The thread pool to use for making batch requests to the LanceDB Cloud API.
+        If an integer, then a ThreadPoolExecutor will be created with that
+        number of threads. If None, then a ThreadPoolExecutor will be created
+        with the default number of threads. If a ThreadPoolExecutor, then that
+        executor will be used for making requests. This is for LanceDB Cloud
+        only and is only used when making batch requests (i.e., passing in
+        multiple queries to the search method at once).
 
     Examples
     --------
@@ -89,8 +94,6 @@ def connect(
     conn : DBConnection
         A connection to a LanceDB database.
     """
-    from .remote.db import RemoteDBConnection
-
     if isinstance(uri, str) and uri.startswith("db://"):
         if api_key is None:
             api_key = os.environ.get("LANCEDB_API_KEY")
@@ -103,9 +106,7 @@ def connect(
             api_key,
             region,
             host_override,
-            # TODO: remove this (deprecation warning downstream)
             request_thread_pool=request_thread_pool,
-            client_config=client_config,
             **kwargs,
         )
 

python/python/lancedb/_lancedb.pyi

@@ -36,8 +36,6 @@ class Connection(object):
         data_storage_version: Optional[str] = None,
         enable_v2_manifest_paths: Optional[bool] = None,
     ) -> Table: ...
-    async def rename_table(self, old_name: str, new_name: str) -> None: ...
-    async def drop_table(self, name: str) -> None: ...
 
 class Table:
     def name(self) -> str: ...

python/python/lancedb/conftest.py

@@ -26,7 +26,7 @@ registry = EmbeddingFunctionRegistry.get_instance()
 @registry.register("test")
 class MockTextEmbeddingFunction(TextEmbeddingFunction):
     """
-    Return the hash of the first 10 characters (normalized)
+    Return the hash of the first 10 characters
     """
 
     def generate_embeddings(self, texts):
@@ -41,23 +41,6 @@ class MockTextEmbeddingFunction(TextEmbeddingFunction):
         return 10
 
 
-@registry.register("nonnorm")
-class MockNonNormTextEmbeddingFunction(TextEmbeddingFunction):
-    """
-    Return the ord of the first 10 characters (not normalized)
-    """
-
-    def generate_embeddings(self, texts):
-        return [self._compute_one_embedding(row) for row in texts]
-
-    def _compute_one_embedding(self, row):
-        emb = np.array([float(ord(c)) for c in row[:10]])
-        return emb if len(emb) == 10 else [0] * 10
-
-    def ndims(self):
-        return 10
-
-
 class RateLimitedAPI:
     rate_limit = 0.1  # 1 request per 0.1 second
     last_request_time = 0

python/python/lancedb/db.py

@@ -817,18 +817,6 @@ class AsyncConnection(object):
         table = await self._inner.open_table(name, storage_options, index_cache_size)
         return AsyncTable(table)
 
-    async def rename_table(self, old_name: str, new_name: str):
-        """Rename a table in the database.
-
-        Parameters
-        ----------
-        old_name: str
-            The current name of the table.
-        new_name: str
-            The new name of the table.
-        """
-        await self._inner.rename_table(old_name, new_name)
-
     async def drop_table(self, name: str):
         """Drop a table from the database.
 

python/python/lancedb/embeddings/__init__.py

@@ -27,4 +27,3 @@ from .imagebind import ImageBindEmbeddings
 from .utils import with_embeddings
 from .jinaai import JinaEmbeddings
 from .watsonx import WatsonxEmbeddings
-from .voyageai import VoyageAIEmbeddingFunction

python/python/lancedb/embeddings/jinaai.py

@@ -13,6 +13,7 @@
 
 import os
 import io
+import requests
 import base64
 from urllib.parse import urlparse
 from pathlib import Path
@@ -225,8 +226,6 @@ class JinaEmbeddings(EmbeddingFunction):
         return [result["embedding"] for result in sorted_embeddings]
 
     def _init_client(self):
-        import requests
-
         if JinaEmbeddings._session is None:
             if self.api_key is None and os.environ.get("JINA_API_KEY") is None:
                 api_key_not_found_help("jina")

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