Bump version: 0.16.0-beta.0 → 0.16.0-beta.1

Support sending empty query types to remote lancedb. also include offset
and limit, where were previously omitted.

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.11.1-beta.1"
+current_version = "0.13.0-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.
@@ -92,6 +92,11 @@ 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

@@ -38,3 +38,7 @@ 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

@@ -41,8 +41,8 @@ jobs:
       - name: Build Python
         working-directory: python
         run: |
-          python -m pip install -e .
-          python -m pip install -r ../docs/requirements.txt
+          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
       - 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 -r requirements.txt
+        python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -r requirements.txt
     - name: Create test files
       run: |
         cd docs/test

.github/workflows/nodejs.yml

@@ -53,6 +53,9 @@ 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
@@ -91,6 +94,19 @@ 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 &
+        ss -ltnp | grep :8000
+        cd nodejs/examples
+        npm test
   macos:
     timeout-minutes: 30
     runs-on: "macos-14"

.github/workflows/npm-publish.yml

@@ -226,6 +226,109 @@ jobs:
           path: |
             node/dist/lancedb-vectordb-win32*.tgz
 
+  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
@@ -260,9 +363,102 @@ jobs:
           path: |
             nodejs/dist/*.node
 
+  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]
+    needs: [node, node-macos, node-linux, node-windows, node-windows-arm64]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -302,7 +498,7 @@ jobs:
 
   release-nodejs:
     name: lancedb NPM Publish
-    needs: [nodejs-macos, nodejs-linux, nodejs-windows]
+    needs: [nodejs-macos, nodejs-linux, nodejs-windows, nodejs-windows-arm64]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')

.github/workflows/python.yml

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

.github/workflows/rust.yml

@@ -50,6 +50,7 @@ jobs:
         run: cargo fmt --all -- --check
       - name: Run clippy
         run: cargo clippy --workspace --tests --all-features -- -D warnings
+
   linux:
     timeout-minutes: 30
     # To build all features, we need more disk space than is available
@@ -91,11 +92,12 @@ jobs:
         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:
@@ -118,6 +120,7 @@ jobs:
       - name: Run tests
         # Run with everything except the integration tests.
         run: cargo test --features remote,fp16kernels
+
   windows:
     runs-on: windows-2022
     steps:
@@ -139,3 +142,99 @@ 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

@@ -21,13 +21,15 @@ categories = ["database-implementations"]
 rust-version = "1.80.0" # TODO: lower this once we upgrade Lance again.
 
 [workspace.dependencies]
-lance = { "version" = "=0.19.1", "features" = ["dynamodb"] }
-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" }
+lance = { "version" = "=0.19.2", "features" = [
+    "dynamodb",
+], git = "https://github.com/lancedb/lance.git", tag = "v0.19.2" }
+lance-index = { "version" = "=0.19.2", git = "https://github.com/lancedb/lance.git", tag = "v0.19.2" }
+lance-linalg = { "version" = "=0.19.2", git = "https://github.com/lancedb/lance.git", tag = "v0.19.2" }
+lance-table = { "version" = "=0.19.2", git = "https://github.com/lancedb/lance.git", tag = "v0.19.2" }
+lance-testing = { "version" = "=0.19.2", git = "https://github.com/lancedb/lance.git", tag = "v0.19.2" }
+lance-datafusion = { "version" = "=0.19.2", git = "https://github.com/lancedb/lance.git", tag = "v0.19.2" }
+lance-encoding = { "version" = "=0.19.2", git = "https://github.com/lancedb/lance.git", tag = "v0.19.2" }
 # Note that this one does not include pyarrow
 arrow = { version = "52.2", optional = false }
 arrow-array = "52.2"

README.md

@@ -10,6 +10,7 @@
 [![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

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

ci/build_windows_artifacts_nodejs.ps1

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

ci/mock_openai.py

@@ -0,0 +1,57 @@
+# 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/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.ts:import"
+        --8<--- "nodejs/examples/ann_indexes.test.ts:import"
 
-        --8<-- "nodejs/examples/ann_indexes.ts:ingest"
+        --8<-- "nodejs/examples/ann_indexes.test.ts:ingest"
         ```
 
     === "vectordb (deprecated)"
@@ -140,13 +140,15 @@ 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 5-10% 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.
+  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/>
+  
 - **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/>
-  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.
+    - _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.
+
 
 === "Python"
 
@@ -169,7 +171,7 @@ There are a couple of parameters that can be used to fine-tune the search:
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/ann_indexes.ts:search1"
+        --8<-- "nodejs/examples/ann_indexes.test.ts:search1"
         ```
 
     === "vectordb (deprecated)"
@@ -203,7 +205,7 @@ You can further filter the elements returned by a search using a where clause.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/ann_indexes.ts:search2"
+        --8<-- "nodejs/examples/ann_indexes.test.ts:search2"
         ```
 
     === "vectordb (deprecated)"
@@ -235,7 +237,7 @@ You can select the columns returned by the query using a select clause.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/ann_indexes.ts:search3"
+        --8<-- "nodejs/examples/ann_indexes.test.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.ts:connect"
+        --8<-- "nodejs/examples/basic.test.ts:connect"
         ```
 
     === "vectordb (deprecated)"
@@ -212,7 +212,7 @@ table.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_table"
+        --8<-- "nodejs/examples/basic.test.ts:create_table"
         ```
 
     === "vectordb (deprecated)"
@@ -268,7 +268,7 @@ similar to a `CREATE TABLE` statement in SQL.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_empty_table"
+        --8<-- "nodejs/examples/basic.test.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.ts:open_table"
+        --8<-- "nodejs/examples/basic.test.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.ts:table_names"
+        --8<-- "nodejs/examples/basic.test.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.ts:add_data"
+        --8<-- "nodejs/examples/basic.test.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.ts:vector_search"
+        --8<-- "nodejs/examples/basic.test.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.ts:create_index"
+        --8<-- "nodejs/examples/basic.test.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.ts:delete_rows"
+        --8<-- "nodejs/examples/basic.test.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.ts:drop_table"
+        --8<-- "nodejs/examples/basic.test.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.ts:imports"
-        --8<-- "nodejs/examples/embedding.ts:openai_embeddings"
+        --8<-- "nodejs/examples/embedding.test.ts:imports"
+        --8<-- "nodejs/examples/embedding.test.ts:openai_embeddings"
         ```
 
 === "Rust"

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

@@ -0,0 +1,51 @@
+# 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.ts:imports"
+    --8<--- "nodejs/examples/custom_embedding_function.test.ts:imports"
 
-    --8<--- "nodejs/examples/custom_embedding_function.ts:embedding_impl"
+    --8<--- "nodejs/examples/custom_embedding_function.test.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.ts:call_custom_function"
+    --8<--- "nodejs/examples/custom_embedding_function.test.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.ts:imports"
-        --8<-- "nodejs/examples/embedding.ts:embedding_function"
+        --8<-- "nodejs/examples/embedding.test.ts:imports"
+        --8<-- "nodejs/examples/embedding.test.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.ts:imports"
-    --8<--- "nodejs/examples/embedding.ts:openai_embeddings"
+    --8<--- "nodejs/examples/embedding.test.ts:imports"
+    --8<--- "nodejs/examples/embedding.test.ts:openai_embeddings"
     ```
 
 === "Rust"
@@ -121,12 +121,10 @@ class Words(LanceModel):
     vector: Vector(func.ndims()) = func.VectorField()
 
 table = db.create_table("words", schema=Words)
-table.add(
-    [
+table.add([
     {"text": "hello world"},
     {"text": "goodbye world"}
-    ]
-    )
+])
 
 query = "greetings"
 actual = table.search(query).limit(1).to_pydantic(Words)[0]

docs/src/guides/tables.md

@@ -85,13 +85,13 @@ Initialize a LanceDB connection and create a table
 
 
         ```ts
-        --8<-- "nodejs/examples/basic.ts:create_table"
+        --8<-- "nodejs/examples/basic.test.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.ts:create_table_with_schema"
+        --8<-- "nodejs/examples/basic.test.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.ts:create_table_exists_ok"
+        --8<-- "nodejs/examples/basic.test.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.ts:create_table_overwrite"
+        --8<-- "nodejs/examples/basic.test.ts:create_table_overwrite"
         ```
 
     === "vectordb (deprecated)"
@@ -227,7 +227,7 @@ LanceDB supports float16 data type!
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_f16_table"
+        --8<-- "nodejs/examples/basic.test.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.ts:create_empty_table"
+        --8<-- "nodejs/examples/basic.test.ts:create_empty_table"
         ```
 
     === "vectordb (deprecated)"

docs/src/reranking/voyageai.md

@@ -0,0 +1,77 @@
+# 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.ts:import"
+        --8<-- "nodejs/examples/search.test.ts:import"
 
-        --8<-- "nodejs/examples/search.ts:search1"
+        --8<-- "nodejs/examples/search.test.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.ts:search2"
+        --8<-- "nodejs/examples/search.test.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.ts:search"
+        --8<-- "nodejs/examples/filtering.test.ts:search"
         ```
 
     === "vectordb (deprecated)"
@@ -91,7 +91,7 @@ For example, the following filter string is acceptable:
     === "@lancedb/lancedb"
 
         ```ts
-        --8<-- "nodejs/examples/filtering.ts:vec_search"
+        --8<-- "nodejs/examples/filtering.test.ts:vec_search"
         ```
 
     === "vectordb (deprecated)"
@@ -169,7 +169,7 @@ You can also filter your data without search.
     === "@lancedb/lancedb"
 
         ```ts
-        --8<-- "nodejs/examples/filtering.ts:sql_search"
+        --8<-- "nodejs/examples/filtering.test.ts:sql_search"
         ```
 
     === "vectordb (deprecated)"

java/core/pom.xml

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

java/pom.xml

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

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.11.1-beta.1",
+  "version": "0.13.0-beta.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.11.1-beta.1",
+      "version": "0.13.0-beta.1",
       "cpu": [
         "x64",
         "arm64"
@@ -52,11 +52,12 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@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"
+        "@lancedb/vectordb-darwin-arm64": "0.13.0-beta.1",
+        "@lancedb/vectordb-darwin-x64": "0.13.0-beta.1",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.13.0-beta.1",
+        "@lancedb/vectordb-linux-x64-gnu": "0.13.0-beta.1",
+        "@lancedb/vectordb-win32-arm64-msvc": "0.13.0-beta.1",
+        "@lancedb/vectordb-win32-x64-msvc": "0.13.0-beta.1"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",
@@ -327,65 +328,60 @@
       }
     },
     "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.11.1-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.11.1-beta.1.tgz",
-      "integrity": "sha512-q9jcCbmcz45UHmjgecL6zK82WaqUJsARfniwXXPcnd8ooISVhPkgN+RVKv6edwI9T0PV+xVRYq+LQLlZu5fyxw==",
+      "version": "0.13.0-beta.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.13.0-beta.1.tgz",
+      "integrity": "sha512-beOrf6selCzzhLgDG8Nibma4nO/CSnA1wUKRmlJHEPtGcg7PW18z6MP/nfwQMpMR/FLRfTo8pPTbpzss47MiQQ==",
       "cpu": [
         "arm64"
       ],
-      "license": "Apache-2.0",
       "optional": true,
       "os": [
         "darwin"
       ]
     },
     "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.11.1-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.11.1-beta.1.tgz",
-      "integrity": "sha512-E5tCTS5TaTkssTPa+gdnFxZJ1f60jnSIJXhqufNFZk4s+IMViwR1BPqaqE++WY5c1uBI55ef1862CROKDKX4gg==",
+      "version": "0.13.0-beta.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.13.0-beta.1.tgz",
+      "integrity": "sha512-YdraGRF/RbJRkKh0v3xT03LUhq47T2GtCvJ5gZp8wKlh4pHa8LuhLU0DIdvmG/DT5vuQA+td8HDkBm/e3EOdNg==",
       "cpu": [
         "x64"
       ],
-      "license": "Apache-2.0",
       "optional": true,
       "os": [
         "darwin"
       ]
     },
     "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.11.1-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.11.1-beta.1.tgz",
-      "integrity": "sha512-Obohy6TH31Uq+fp6ZisHR7iAsvgVPqBExrycVcIJqrLZnIe88N9OWUwBXkmfMAw/2hNJFwD4tU7+4U2FcBWX4w==",
+      "version": "0.13.0-beta.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.13.0-beta.1.tgz",
+      "integrity": "sha512-Pp0O/uhEqof1oLaWrNbv+Ym+q8kBkiCqaA5+2eAZ6a3e9U+Ozkvb0FQrHuyi9adJ5wKQ4NabyQE9BMf2bYpOnQ==",
       "cpu": [
         "arm64"
       ],
-      "license": "Apache-2.0",
       "optional": true,
       "os": [
         "linux"
       ]
     },
     "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.11.1-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.11.1-beta.1.tgz",
-      "integrity": "sha512-3Meu0dgrzNrnBVVQhxkUSAOhQNmgtKHvOvmrRLUicV+X19hd33udihgxVpZZb9mpXenJ8lZsS+Jq6R0hWqntag==",
+      "version": "0.13.0-beta.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.13.0-beta.1.tgz",
+      "integrity": "sha512-y8nxOye4egfWF5FGED9EfkmZ1O5HnRLU4a61B8m5JSpkivO9v2epTcbYN0yt/7ZFCgtqMfJ8VW4Mi7qQcz3KDA==",
       "cpu": [
         "x64"
       ],
-      "license": "Apache-2.0",
       "optional": true,
       "os": [
         "linux"
       ]
     },
     "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.11.1-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.11.1-beta.1.tgz",
-      "integrity": "sha512-BafZ9OJPQXsS7JW0weAl12wC+827AiRjfUrE5tvrYWZah2OwCF2U2g6uJ3x4pxfwEGsv5xcHFqgxlS7ttFkh+Q==",
+      "version": "0.13.0-beta.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.13.0-beta.1.tgz",
+      "integrity": "sha512-STMDP9dp0TBLkB3ro+16pKcGy6bmbhRuEZZZ1Tp5P75yTPeVh4zIgWkidMdU1qBbEYM7xacnsp9QAwgLnMU/Ow==",
       "cpu": [
         "x64"
       ],
-      "license": "Apache-2.0",
       "optional": true,
       "os": [
         "win32"

node/package.json

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

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 AxiosResponse, type ResponseType } from 'axios'
+import axios, { type AxiosError, 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('error: ', err)
+      console.error(serializeErrorAsJson(err))
       if (err.response === undefined) {
         throw new Error(`Network Error: ${err.message as string}`)
       }
@@ -247,7 +247,8 @@ export class HttpLancedbClient {
 
       // return response
     } catch (err: any) {
-      console.error('error: ', err)
+      console.error(serializeErrorAsJson(err))
+
       if (err.response === undefined) {
         throw new Error(`Network Error: ${err.message as string}`)
       }
@@ -287,3 +288,15 @@ 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 = "0.11.1-beta.1"
+version = "0.13.0-beta.1"
 license.workspace = true
 description.workspace = true
 repository.workspace = true
@@ -18,7 +18,7 @@ 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

nodejs/__test__/table.test.ts

@@ -402,6 +402,40 @@ 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({
@@ -964,4 +998,18 @@ 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

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

nodejs/examples/ann_indexes.test.ts

@@ -0,0 +1,57 @@
+// 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

@@ -1,49 +0,0 @@
-// --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

@@ -0,0 +1,175 @@
+// 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

@@ -1,162 +0,0 @@
-// --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

@@ -0,0 +1,76 @@
+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

@@ -1,64 +0,0 @@
-// --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

@@ -0,0 +1,96 @@
+// 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

@@ -1,83 +0,0 @@
-// --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

@@ -0,0 +1,42 @@
+// 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

@@ -1,34 +0,0 @@
-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

@@ -0,0 +1,45 @@
+// 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

@@ -1,52 +0,0 @@
-// 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

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

nodejs/examples/jsconfig.json

@@ -1,27 +0,0 @@
-{
-  "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

@@ -5,24 +5,29 @@
   "main": "index.js",
   "type": "module",
   "scripts": {
-    "test": "echo \"Error: no test specified\" && exit 1"
+    "//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"
   },
   "author": "Lance Devs",
   "license": "Apache-2.0",
   "dependencies": {
-    "@lancedb/lancedb": "file:../",
-    "@xenova/transformers": "^2.17.2"
+    "@huggingface/transformers": "^3.0.2",
+    "@lancedb/lancedb": "file:../dist",
+    "openai": "^4.29.2",
+    "sharp": "^0.33.5"
   },
   "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

@@ -0,0 +1,42 @@
+// 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

@@ -1,38 +0,0 @@
-// --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

@@ -1,50 +0,0 @@
-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

@@ -0,0 +1,59 @@
+// 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 { Utf8 } from "apache-arrow";
+
+test("full text search", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+    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();
+
+    expect(actual[0]["text"]).toBe("The human body has 206 bones.");
+  });
+});

nodejs/examples/tsconfig.json

@@ -0,0 +1,17 @@
+{
+  "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

@@ -0,0 +1,16 @@
+// 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

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

nodejs/lancedb/embedding/transformers.ts

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

nodejs/lancedb/query.ts

@@ -239,6 +239,29 @@ 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> {
@@ -469,6 +492,42 @@ 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. */
@@ -548,4 +607,9 @@ 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": "0.11.1-beta.1",
+	"version": "0.13.0-beta.1",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

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

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.11.1-beta.1",
+	"version": "0.13.0-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": "0.11.1-beta.1",
+	"version": "0.13.0-beta.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

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

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

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

@@ -0,0 +1,18 @@
+{
+  "name": "@lancedb/lancedb-win32-arm64-msvc",
+  "version": "0.13.0-beta.1",
+  "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": "0.11.1-beta.1",
+	"version": "0.13.0-beta.1",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package.json

@@ -10,7 +10,7 @@
     "vector database",
     "ann"
   ],
-  "version": "0.11.1-beta.1",
+  "version": "0.13.0-beta.1",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",
@@ -85,7 +85,7 @@
     "reflect-metadata": "^0.2.2"
   },
   "optionalDependencies": {
-    "@xenova/transformers": ">=2.17 < 3",
+    "@huggingface/transformers": "^3.0.2",
     "openai": "^4.29.2"
   },
   "peerDependencies": {

nodejs/src/lib.rs

@@ -82,7 +82,7 @@ pub struct OpenTableOptions {
 #[napi::module_init]
 fn init() {
     let env = Env::new()
-        .filter_or("LANCEDB_LOG", "trace")
+        .filter_or("LANCEDB_LOG", "warn")
         .write_style("LANCEDB_LOG_STYLE");
     env_logger::init_from_env(env);
 }

nodejs/src/query.rs

@@ -80,6 +80,16 @@ 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,
@@ -125,6 +135,16 @@ 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)?;
@@ -183,6 +203,16 @@ 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,

nodejs/tsconfig.json

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

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.15.0"
+current_version = "0.16.0-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 = "0.15.0"
+version = "0.16.0-beta.1"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/pyproject.toml

@@ -3,13 +3,11 @@ name = "lancedb"
 # version in Cargo.toml
 dependencies = [
     "deprecation",
-    "pylance==0.19.1",
-    "requests>=2.31.0",
+    "nest-asyncio~=1.0",
+    "pylance==0.19.2",
     "tqdm>=4.27.0",
     "pydantic>=1.10",
-    "attrs>=21.3.0",
     "packaging",
-    "cachetools",
     "overrides>=0.7",
 ]
 description = "lancedb"
@@ -61,6 +59,7 @@ 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

@@ -19,12 +19,10 @@ 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.db import RemoteDBConnection
+from .remote import ClientConfig
 from .schema import vector
 from .table import AsyncTable
 
@@ -37,6 +35,7 @@ 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.
@@ -64,14 +63,10 @@ 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.
-    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).
+    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.
 
     Examples
     --------
@@ -94,6 +89,8 @@ 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")
@@ -106,7 +103,9 @@ 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

@@ -36,6 +36,8 @@ 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/db.py

@@ -817,6 +817,18 @@ 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

@@ -27,3 +27,4 @@ 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

@@ -13,7 +13,6 @@
 
 import os
 import io
-import requests
 import base64
 from urllib.parse import urlparse
 from pathlib import Path
@@ -226,6 +225,8 @@ 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

@@ -1,15 +1,6 @@
-#  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.
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
 import json
 from typing import Dict, Optional
 
@@ -170,7 +161,7 @@ def register(name):
     return __REGISTRY__.get_instance().register(name)
 
 
-def get_registry():
+def get_registry() -> EmbeddingFunctionRegistry:
     """
     Utility function to get the global instance of the registry
 

python/python/lancedb/embeddings/voyageai.py

@@ -0,0 +1,127 @@
+#  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 os
+from typing import ClassVar, List, Union
+
+import numpy as np
+
+from ..util import attempt_import_or_raise
+from .base import TextEmbeddingFunction
+from .registry import register
+from .utils import api_key_not_found_help, TEXT
+
+
+@register("voyageai")
+class VoyageAIEmbeddingFunction(TextEmbeddingFunction):
+    """
+    An embedding function that uses the VoyageAI API
+
+    https://docs.voyageai.com/docs/embeddings
+
+    Parameters
+    ----------
+    name: str
+        The name of the model to use. List of acceptable models:
+
+            * voyage-3
+            * voyage-3-lite
+            * voyage-finance-2
+            * voyage-multilingual-2
+            * voyage-law-2
+            * voyage-code-2
+
+
+    Examples
+    --------
+    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)
+
+    """
+
+    name: str
+    client: ClassVar = None
+
+    def ndims(self):
+        if self.name == "voyage-3-lite":
+            return 512
+        elif self.name == "voyage-code-2":
+            return 1536
+        elif self.name in [
+            "voyage-3",
+            "voyage-finance-2",
+            "voyage-multilingual-2",
+            "voyage-law-2",
+        ]:
+            return 1024
+        else:
+            raise ValueError(f"Model {self.name} not supported")
+
+    def compute_query_embeddings(self, query: str, *args, **kwargs) -> List[np.array]:
+        return self.compute_source_embeddings(query, input_type="query")
+
+    def compute_source_embeddings(self, texts: TEXT, *args, **kwargs) -> List[np.array]:
+        texts = self.sanitize_input(texts)
+        input_type = (
+            kwargs.get("input_type") or "document"
+        )  # assume source input type if not passed by `compute_query_embeddings`
+        return self.generate_embeddings(texts, input_type=input_type)
+
+    def generate_embeddings(
+        self, texts: Union[List[str], np.ndarray], *args, **kwargs
+    ) -> List[np.array]:
+        """
+        Get the embeddings for the given texts
+
+        Parameters
+        ----------
+        texts: list[str] or np.ndarray (of str)
+            The texts to embed
+        input_type: Optional[str]
+
+        truncation: Optional[bool]
+        """
+        VoyageAIEmbeddingFunction._init_client()
+        rs = VoyageAIEmbeddingFunction.client.embed(
+            texts=texts, model=self.name, **kwargs
+        )
+
+        return [emb for emb in rs.embeddings]
+
+    @staticmethod
+    def _init_client():
+        if VoyageAIEmbeddingFunction.client is None:
+            voyageai = attempt_import_or_raise("voyageai")
+            if os.environ.get("VOYAGE_API_KEY") is None:
+                api_key_not_found_help("voyageai")
+            VoyageAIEmbeddingFunction.client = voyageai.Client(
+                os.environ["VOYAGE_API_KEY"]
+            )

python/python/lancedb/index.py

@@ -467,6 +467,8 @@ class IvfPq:
 
             The default value is 256.
         """
+        if distance_type is not None:
+            distance_type = distance_type.lower()
         self._inner = LanceDbIndex.ivf_pq(
             distance_type=distance_type,
             num_partitions=num_partitions,

python/python/lancedb/query.py

@@ -481,6 +481,7 @@ class LanceQueryBuilder(ABC):
         >>> plan = table.search(query).explain_plan(True)
         >>> print(plan) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
         ProjectionExec: expr=[vector@0 as vector, _distance@2 as _distance]
+        GlobalLimitExec: skip=0, fetch=10
           FilterExec: _distance@2 IS NOT NULL
             SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], preserve_partitioning=[false]
               KNNVectorDistance: metric=l2
@@ -500,7 +501,16 @@ class LanceQueryBuilder(ABC):
             nearest={
                 "column": self._vector_column,
                 "q": self._query,
+                "k": self._limit,
+                "metric": self._metric,
+                "nprobes": self._nprobes,
+                "refine_factor": self._refine_factor,
             },
+            prefilter=self._prefilter,
+            filter=self._str_query,
+            limit=self._limit,
+            with_row_id=self._with_row_id,
+            offset=self._offset,
         ).explain_plan(verbose)
 
     def vector(self, vector: Union[np.ndarray, list]) -> LanceQueryBuilder:
@@ -933,12 +943,16 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
 
 class LanceEmptyQueryBuilder(LanceQueryBuilder):
     def to_arrow(self) -> pa.Table:
-        ds = self._table.to_lance()
-        return ds.to_table(
+        query = Query(
             columns=self._columns,
             filter=self._where,
-            limit=self._limit,
+            k=self._limit or 10,
+            with_row_id=self._with_row_id,
+            vector=[],
+            # not actually respected in remote query
+            offset=self._offset or 0,
         )
+        return self._table._execute_query(query).read_all()
 
     def rerank(self, reranker: Reranker) -> LanceEmptyQueryBuilder:
         """Rerank the results using the specified reranker.
@@ -1315,6 +1329,48 @@ class AsyncQueryBase(object):
         self._inner.offset(offset)
         return self
 
+    def fast_search(self) -> AsyncQuery:
+        """
+        Skip searching un-indexed data.
+
+        This can make queries faster, but will miss any data that has not been
+        indexed.
+
+        !!! tip
+            You can add new data into an existing index by calling
+            [AsyncTable.optimize][lancedb.table.AsyncTable.optimize].
+        """
+        self._inner.fast_search()
+        return self
+
+    def with_row_id(self) -> AsyncQuery:
+        """
+        Include the _rowid column in the results.
+        """
+        self._inner.with_row_id()
+        return self
+
+    def postfilter(self) -> AsyncQuery:
+        """
+        If this is called then filtering will happen after the search instead of
+        before.
+        By default filtering will be performed before the search.  This is how
+        filtering is typically understood to work.  This prefilter step does add some
+        additional latency.  Creating a scalar index on the filter column(s) can
+        often improve this latency.  However, sometimes a filter is too complex or
+        scalar indices cannot be applied to the column.  In these cases postfiltering
+        can be used instead of prefiltering to improve latency.
+        Post filtering applies the filter to the results of the search.  This
+        means we only run the filter on a much smaller set of data.  However, it can
+        cause the query to return fewer than `limit` results (or even no results) if
+        none of the nearest results match the filter.
+        Post filtering happens during the "refine stage" (described in more detail in
+        @see {@link VectorQuery#refineFactor}).  This means that setting a higher refine
+        factor can often help restore some of the results lost by post filtering.
+        """
+        self._inner.postfilter()
+        return self
+
     async def to_batches(
         self, *, max_batch_length: Optional[int] = None
     ) -> AsyncRecordBatchReader:
@@ -1439,7 +1495,7 @@ class AsyncQuery(AsyncQueryBase):
         return pa.array(vec)
 
     def nearest_to(
-        self, query_vector: Optional[Union[VEC, Tuple]] = None
+        self, query_vector: Optional[Union[VEC, Tuple, List[VEC]]] = None
     ) -> AsyncVectorQuery:
         """
         Find the nearest vectors to the given query vector.
@@ -1477,7 +1533,27 @@ class AsyncQuery(AsyncQueryBase):
 
         Vector searches always have a [limit][].  If `limit` has not been called then
         a default `limit` of 10 will be used.
+
+        Typically, a single vector is passed in as the query. However, you can also
+        pass in multiple vectors.  This can be useful if you want to find the nearest
+        vectors to multiple query vectors. This is not expected to be faster than
+        making multiple queries concurrently; it is just a convenience method.
+        If multiple vectors are passed in then an additional column `query_index`
+        will be added to the results.  This column will contain the index of the
+        query vector that the result is nearest to.
         """
+        if (
+            isinstance(query_vector, list)
+            and len(query_vector) > 0
+            and not isinstance(query_vector[0], (float, int))
+        ):
+            # multiple have been passed
+            query_vectors = [AsyncQuery._query_vec_to_array(v) for v in query_vector]
+            new_self = self._inner.nearest_to(query_vectors[0])
+            for v in query_vectors[1:]:
+                new_self.add_query_vector(v)
+            return AsyncVectorQuery(new_self)
+        else:
             return AsyncVectorQuery(
                 self._inner.nearest_to(AsyncQuery._query_vec_to_array(query_vector))
             )
@@ -1618,30 +1694,6 @@ class AsyncVectorQuery(AsyncQueryBase):
         self._inner.distance_type(distance_type)
         return self
 
-    def postfilter(self) -> AsyncVectorQuery:
-        """
-        If this is called then filtering will happen after the vector search instead of
-        before.
-
-        By default filtering will be performed before the vector search.  This is how
-        filtering is typically understood to work.  This prefilter step does add some
-        additional latency.  Creating a scalar index on the filter column(s) can
-        often improve this latency.  However, sometimes a filter is too complex or
-        scalar indices cannot be applied to the column.  In these cases postfiltering
-        can be used instead of prefiltering to improve latency.
-
-        Post filtering applies the filter to the results of the vector search.  This
-        means we only run the filter on a much smaller set of data.  However, it can
-        cause the query to return fewer than `limit` results (or even no results) if
-        none of the nearest results match the filter.
-
-        Post filtering happens during the "refine stage" (described in more detail in
-        @see {@link VectorQuery#refineFactor}).  This means that setting a higher refine
-        factor can often help restore some of the results lost by post filtering.
-        """
-        self._inner.postfilter()
-        return self
-
     def bypass_vector_index(self) -> AsyncVectorQuery:
         """
         If this is called then any vector index is skipped

python/python/lancedb/remote/__init__.py

@@ -11,62 +11,13 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import abc
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from datetime import timedelta
 from typing import List, Optional
 
-import attrs
 from lancedb import __version__
-import pyarrow as pa
-from pydantic import BaseModel
 
-from lancedb.common import VECTOR_COLUMN_NAME
-
-__all__ = ["LanceDBClient", "VectorQuery", "VectorQueryResult"]
-
-
-class VectorQuery(BaseModel):
-    # vector to search for
-    vector: List[float]
-
-    # sql filter to refine the query with
-    filter: Optional[str] = None
-
-    # top k results to return
-    k: int
-
-    # # metrics
-    _metric: str = "L2"
-
-    # which columns to return in the results
-    columns: Optional[List[str]] = None
-
-    # optional query parameters for tuning the results,
-    # e.g. `{"nprobes": "10", "refine_factor": "10"}`
-    nprobes: int = 10
-
-    refine_factor: Optional[int] = None
-
-    vector_column: str = VECTOR_COLUMN_NAME
-
-    fast_search: bool = False
-
-
-@attrs.define
-class VectorQueryResult:
-    # for now the response is directly seralized into a pandas dataframe
-    tbl: pa.Table
-
-    def to_arrow(self) -> pa.Table:
-        return self.tbl
-
-
-class LanceDBClient(abc.ABC):
-    @abc.abstractmethod
-    def query(self, table_name: str, query: VectorQuery) -> VectorQueryResult:
-        """Query the LanceDB server for the given table and query."""
-        pass
+__all__ = ["TimeoutConfig", "RetryConfig", "ClientConfig"]
 
 
 @dataclass
@@ -165,8 +116,8 @@ class RetryConfig:
 @dataclass
 class ClientConfig:
     user_agent: str = f"LanceDB-Python-Client/{__version__}"
-    retry_config: Optional[RetryConfig] = None
-    timeout_config: Optional[TimeoutConfig] = None
+    retry_config: RetryConfig = field(default_factory=RetryConfig)
+    timeout_config: Optional[TimeoutConfig] = field(default_factory=TimeoutConfig)
 
     def __post_init__(self):
         if isinstance(self.retry_config, dict):

python/python/lancedb/remote/arrow.py

@@ -1,25 +0,0 @@
-#  Copyright 2023 LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
-from typing import Iterable, Union
-import pyarrow as pa
-
-
-def to_ipc_binary(table: Union[pa.Table, Iterable[pa.RecordBatch]]) -> bytes:
-    """Serialize a PyArrow Table to IPC binary."""
-    sink = pa.BufferOutputStream()
-    if isinstance(table, Iterable):
-        table = pa.Table.from_batches(table)
-    with pa.ipc.new_stream(sink, table.schema) as writer:
-        writer.write_table(table)
-    return sink.getvalue().to_pybytes()

python/python/lancedb/remote/client.py

@@ -1,269 +0,0 @@
-#  Copyright 2023 LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
-
-import functools
-import logging
-import os
-from typing import Any, Callable, Dict, List, Optional, Union
-from urllib.parse import urljoin
-
-import attrs
-import pyarrow as pa
-import requests
-from pydantic import BaseModel
-from requests.adapters import HTTPAdapter
-from urllib3 import Retry
-
-from lancedb.common import Credential
-from lancedb.remote import VectorQuery, VectorQueryResult
-from lancedb.remote.connection_timeout import LanceDBClientHTTPAdapterFactory
-from lancedb.remote.errors import LanceDBClientError
-
-ARROW_STREAM_CONTENT_TYPE = "application/vnd.apache.arrow.stream"
-
-
-def _check_not_closed(f):
-    @functools.wraps(f)
-    def wrapped(self, *args, **kwargs):
-        if self.closed:
-            raise ValueError("Connection is closed")
-        return f(self, *args, **kwargs)
-
-    return wrapped
-
-
-def _read_ipc(resp: requests.Response) -> pa.Table:
-    resp_body = resp.content
-    with pa.ipc.open_file(pa.BufferReader(resp_body)) as reader:
-        return reader.read_all()
-
-
-@attrs.define(slots=False)
-class RestfulLanceDBClient:
-    db_name: str
-    region: str
-    api_key: Credential
-    host_override: Optional[str] = attrs.field(default=None)
-
-    closed: bool = attrs.field(default=False, init=False)
-
-    connection_timeout: float = attrs.field(default=120.0, kw_only=True)
-    read_timeout: float = attrs.field(default=300.0, kw_only=True)
-
-    @functools.cached_property
-    def session(self) -> requests.Session:
-        sess = requests.Session()
-
-        retry_adapter_instance = retry_adapter(retry_adapter_options())
-        sess.mount(urljoin(self.url, "/v1/table/"), retry_adapter_instance)
-
-        adapter_class = LanceDBClientHTTPAdapterFactory()
-        sess.mount("https://", adapter_class())
-        return sess
-
-    @property
-    def url(self) -> str:
-        return (
-            self.host_override
-            or f"https://{self.db_name}.{self.region}.api.lancedb.com"
-        )
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        self.close()
-        return False  # Do not suppress exceptions
-
-    def close(self):
-        self.session.close()
-        self.closed = True
-
-    @functools.cached_property
-    def headers(self) -> Dict[str, str]:
-        headers = {
-            "x-api-key": self.api_key,
-        }
-        if self.region == "local":  # Local test mode
-            headers["Host"] = f"{self.db_name}.{self.region}.api.lancedb.com"
-        if self.host_override:
-            headers["x-lancedb-database"] = self.db_name
-        return headers
-
-    @staticmethod
-    def _check_status(resp: requests.Response):
-        # Leaving request id empty for now, as we'll be replacing this impl
-        # with the Rust one shortly.
-        if resp.status_code == 404:
-            raise LanceDBClientError(
-                f"Not found: {resp.text}", request_id="", status_code=404
-            )
-        elif 400 <= resp.status_code < 500:
-            raise LanceDBClientError(
-                f"Bad Request: {resp.status_code}, error: {resp.text}",
-                request_id="",
-                status_code=resp.status_code,
-            )
-        elif 500 <= resp.status_code < 600:
-            raise LanceDBClientError(
-                f"Internal Server Error: {resp.status_code}, error: {resp.text}",
-                request_id="",
-                status_code=resp.status_code,
-            )
-        elif resp.status_code != 200:
-            raise LanceDBClientError(
-                f"Unknown Error: {resp.status_code}, error: {resp.text}",
-                request_id="",
-                status_code=resp.status_code,
-            )
-
-    @_check_not_closed
-    def get(self, uri: str, params: Union[Dict[str, Any], BaseModel] = None):
-        """Send a GET request and returns the deserialized response payload."""
-        if isinstance(params, BaseModel):
-            params: Dict[str, Any] = params.dict(exclude_none=True)
-        with self.session.get(
-            urljoin(self.url, uri),
-            params=params,
-            headers=self.headers,
-            timeout=(self.connection_timeout, self.read_timeout),
-        ) as resp:
-            self._check_status(resp)
-            return resp.json()
-
-    @_check_not_closed
-    def post(
-        self,
-        uri: str,
-        data: Optional[Union[Dict[str, Any], BaseModel, bytes]] = None,
-        params: Optional[Dict[str, Any]] = None,
-        content_type: Optional[str] = None,
-        deserialize: Callable = lambda resp: resp.json(),
-        request_id: Optional[str] = None,
-    ) -> Dict[str, Any]:
-        """Send a POST request and returns the deserialized response payload.
-
-        Parameters
-        ----------
-        uri : str
-            The uri to send the POST request to.
-        data: Union[Dict[str, Any], BaseModel]
-        request_id: Optional[str]
-            Optional client side request id to be sent in the request headers.
-
-        """
-        if isinstance(data, BaseModel):
-            data: Dict[str, Any] = data.dict(exclude_none=True)
-        if isinstance(data, bytes):
-            req_kwargs = {"data": data}
-        else:
-            req_kwargs = {"json": data}
-
-        headers = self.headers.copy()
-        if content_type is not None:
-            headers["content-type"] = content_type
-        if request_id is not None:
-            headers["x-request-id"] = request_id
-        with self.session.post(
-            urljoin(self.url, uri),
-            headers=headers,
-            params=params,
-            timeout=(self.connection_timeout, self.read_timeout),
-            **req_kwargs,
-        ) as resp:
-            self._check_status(resp)
-            return deserialize(resp)
-
-    @_check_not_closed
-    def list_tables(self, limit: int, page_token: Optional[str] = None) -> List[str]:
-        """List all tables in the database."""
-        if page_token is None:
-            page_token = ""
-        json = self.get("/v1/table/", {"limit": limit, "page_token": page_token})
-        return json["tables"]
-
-    @_check_not_closed
-    def query(self, table_name: str, query: VectorQuery) -> VectorQueryResult:
-        """Query a table."""
-        tbl = self.post(f"/v1/table/{table_name}/query/", query, deserialize=_read_ipc)
-        return VectorQueryResult(tbl)
-
-    def mount_retry_adapter_for_table(self, table_name: str) -> None:
-        """
-        Adds an http adapter to session that will retry retryable requests to the table.
-        """
-        retry_options = retry_adapter_options(methods=["GET", "POST"])
-        retry_adapter_instance = retry_adapter(retry_options)
-        session = self.session
-
-        session.mount(
-            urljoin(self.url, f"/v1/table/{table_name}/query/"), retry_adapter_instance
-        )
-        session.mount(
-            urljoin(self.url, f"/v1/table/{table_name}/describe/"),
-            retry_adapter_instance,
-        )
-        session.mount(
-            urljoin(self.url, f"/v1/table/{table_name}/index/list/"),
-            retry_adapter_instance,
-        )
-
-
-def retry_adapter_options(methods=["GET"]) -> Dict[str, Any]:
-    return {
-        "retries": int(os.environ.get("LANCE_CLIENT_MAX_RETRIES", "3")),
-        "connect_retries": int(os.environ.get("LANCE_CLIENT_CONNECT_RETRIES", "3")),
-        "read_retries": int(os.environ.get("LANCE_CLIENT_READ_RETRIES", "3")),
-        "backoff_factor": float(
-            os.environ.get("LANCE_CLIENT_RETRY_BACKOFF_FACTOR", "0.25")
-        ),
-        "backoff_jitter": float(
-            os.environ.get("LANCE_CLIENT_RETRY_BACKOFF_JITTER", "0.25")
-        ),
-        "statuses": [
-            int(i.strip())
-            for i in os.environ.get(
-                "LANCE_CLIENT_RETRY_STATUSES", "429, 500, 502, 503"
-            ).split(",")
-        ],
-        "methods": methods,
-    }
-
-
-def retry_adapter(options: Dict[str, Any]) -> HTTPAdapter:
-    total_retries = options["retries"]
-    connect_retries = options["connect_retries"]
-    read_retries = options["read_retries"]
-    backoff_factor = options["backoff_factor"]
-    backoff_jitter = options["backoff_jitter"]
-    statuses = options["statuses"]
-    methods = frozenset(options["methods"])
-    logging.debug(
-        f"Setting up retry adapter with {total_retries} retries,"  # noqa G003
-        + f"connect retries {connect_retries}, read retries {read_retries},"
-        + f"backoff factor {backoff_factor}, statuses {statuses}, "
-        + f"methods {methods}"
-    )
-
-    return HTTPAdapter(
-        max_retries=Retry(
-            total=total_retries,
-            connect=connect_retries,
-            read=read_retries,
-            backoff_factor=backoff_factor,
-            backoff_jitter=backoff_jitter,
-            status_forcelist=statuses,
-            allowed_methods=methods,
-        )
-    )

python/python/lancedb/remote/connection_timeout.py

@@ -1,115 +0,0 @@
-#  Copyright 2024 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.
-
-# This module contains an adapter that will close connections if they have not been
-# used before a certain timeout. This is necessary because some load balancers will
-# close connections after a certain amount of time, but the request module may not yet
-# have received the FIN/ACK and will try to reuse the connection.
-#
-# TODO some of the code here can be simplified if/when this PR is merged:
-# https://github.com/urllib3/urllib3/pull/3275
-
-import datetime
-import logging
-import os
-
-from requests.adapters import HTTPAdapter
-from urllib3.connection import HTTPSConnection
-from urllib3.connectionpool import HTTPSConnectionPool
-from urllib3.poolmanager import PoolManager
-
-
-def get_client_connection_timeout() -> int:
-    return int(os.environ.get("LANCE_CLIENT_CONNECTION_TIMEOUT", "300"))
-
-
-class LanceDBHTTPSConnection(HTTPSConnection):
-    """
-    HTTPSConnection that tracks the last time it was used.
-    """
-
-    idle_timeout: datetime.timedelta
-    last_activity: datetime.datetime
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.last_activity = datetime.datetime.now()
-
-    def request(self, *args, **kwargs):
-        self.last_activity = datetime.datetime.now()
-        super().request(*args, **kwargs)
-
-    def is_expired(self):
-        return datetime.datetime.now() - self.last_activity > self.idle_timeout
-
-
-def LanceDBHTTPSConnectionPoolFactory(client_idle_timeout: int):
-    """
-    Creates a connection pool class that can be used to close idle connections.
-    """
-
-    class LanceDBHTTPSConnectionPool(HTTPSConnectionPool):
-        # override the connection class
-        ConnectionCls = LanceDBHTTPSConnection
-
-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-
-        def _get_conn(self, timeout: float | None = None):
-            logging.debug("Getting https connection")
-            conn = super()._get_conn(timeout)
-            if conn.is_expired():
-                logging.debug("Closing expired connection")
-                conn.close()
-
-            return conn
-
-        def _new_conn(self):
-            conn = super()._new_conn()
-            conn.idle_timeout = datetime.timedelta(seconds=client_idle_timeout)
-            return conn
-
-    return LanceDBHTTPSConnectionPool
-
-
-class LanceDBClientPoolManager(PoolManager):
-    def __init__(
-        self, client_idle_timeout: int, num_pools: int, maxsize: int, **kwargs
-    ):
-        super().__init__(num_pools=num_pools, maxsize=maxsize, **kwargs)
-        # inject our connection pool impl
-        connection_pool_class = LanceDBHTTPSConnectionPoolFactory(
-            client_idle_timeout=client_idle_timeout
-        )
-        self.pool_classes_by_scheme["https"] = connection_pool_class
-
-
-def LanceDBClientHTTPAdapterFactory():
-    """
-    Creates an HTTPAdapter class that can be used to close idle connections
-    """
-
-    # closure over the timeout
-    client_idle_timeout = get_client_connection_timeout()
-
-    class LanceDBClientRequestHTTPAdapter(HTTPAdapter):
-        def init_poolmanager(self, connections, maxsize, block=False):
-            # inject our pool manager impl
-            self.poolmanager = LanceDBClientPoolManager(
-                client_idle_timeout=client_idle_timeout,
-                num_pools=connections,
-                maxsize=maxsize,
-                block=block,
-            )
-
-    return LanceDBClientRequestHTTPAdapter

python/python/lancedb/remote/db.py

@@ -11,13 +11,16 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
+import asyncio
+from datetime import timedelta
 import logging
-import uuid
 from concurrent.futures import ThreadPoolExecutor
-from typing import Iterable, List, Optional, Union
+from typing import Any, Dict, Iterable, List, Optional, Union
 from urllib.parse import urlparse
+import warnings
 
-from cachetools import TTLCache
+from lancedb import connect_async
+from lancedb.remote import ClientConfig
 import pyarrow as pa
 from overrides import override
 
@@ -25,10 +28,8 @@ from ..common import DATA
 from ..db import DBConnection
 from ..embeddings import EmbeddingFunctionConfig
 from ..pydantic import LanceModel
-from ..table import Table, sanitize_create_table
+from ..table import Table
 from ..util import validate_table_name
-from .arrow import to_ipc_binary
-from .client import ARROW_STREAM_CONTENT_TYPE, RestfulLanceDBClient
 
 
 class RemoteDBConnection(DBConnection):
@@ -41,26 +42,70 @@ class RemoteDBConnection(DBConnection):
         region: str,
         host_override: Optional[str] = None,
         request_thread_pool: Optional[ThreadPoolExecutor] = None,
-        connection_timeout: float = 120.0,
-        read_timeout: float = 300.0,
+        client_config: Union[ClientConfig, Dict[str, Any], None] = None,
+        connection_timeout: Optional[float] = None,
+        read_timeout: Optional[float] = None,
     ):
         """Connect to a remote LanceDB database."""
+
+        if isinstance(client_config, dict):
+            client_config = ClientConfig(**client_config)
+        elif client_config is None:
+            client_config = ClientConfig()
+
+        # These are legacy options from the old Python-based client. We keep them
+        # here for backwards compatibility, but will remove them in a future release.
+        if request_thread_pool is not None:
+            warnings.warn(
+                "request_thread_pool is no longer used and will be removed in "
+                "a future release.",
+                DeprecationWarning,
+            )
+
+        if connection_timeout is not None:
+            warnings.warn(
+                "connection_timeout is deprecated and will be removed in a future "
+                "release. Please use client_config.timeout_config.connect_timeout "
+                "instead.",
+                DeprecationWarning,
+            )
+            client_config.timeout_config.connect_timeout = timedelta(
+                seconds=connection_timeout
+            )
+
+        if read_timeout is not None:
+            warnings.warn(
+                "read_timeout is deprecated and will be removed in a future release. "
+                "Please use client_config.timeout_config.read_timeout instead.",
+                DeprecationWarning,
+            )
+            client_config.timeout_config.read_timeout = timedelta(seconds=read_timeout)
+
         parsed = urlparse(db_url)
         if parsed.scheme != "db":
             raise ValueError(f"Invalid scheme: {parsed.scheme}, only accepts db://")
-        self._uri = str(db_url)
         self.db_name = parsed.netloc
-        self.api_key = api_key
-        self._client = RestfulLanceDBClient(
-            self.db_name,
-            region,
-            api_key,
-            host_override,
-            connection_timeout=connection_timeout,
-            read_timeout=read_timeout,
+
+        import nest_asyncio
+
+        nest_asyncio.apply()
+        try:
+            self._loop = asyncio.get_running_loop()
+        except RuntimeError:
+            self._loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(self._loop)
+
+        self.client_config = client_config
+
+        self._conn = self._loop.run_until_complete(
+            connect_async(
+                db_url,
+                api_key=api_key,
+                region=region,
+                host_override=host_override,
+                client_config=client_config,
+            )
         )
-        self._request_thread_pool = request_thread_pool
-        self._table_cache = TTLCache(maxsize=10000, ttl=300)
 
     def __repr__(self) -> str:
         return f"RemoteConnect(name={self.db_name})"
@@ -82,16 +127,9 @@ class RemoteDBConnection(DBConnection):
         -------
         An iterator of table names.
         """
-        while True:
-            result = self._client.list_tables(limit, page_token)
-
-            if len(result) > 0:
-                page_token = result[len(result) - 1]
-            else:
-                break
-            for item in result:
-                self._table_cache[item] = True
-                yield item
+        return self._loop.run_until_complete(
+            self._conn.table_names(start_after=page_token, limit=limit)
+        )
 
     @override
     def open_table(self, name: str, *, index_cache_size: Optional[int] = None) -> Table:
@@ -108,20 +146,14 @@ class RemoteDBConnection(DBConnection):
         """
         from .table import RemoteTable
 
-        self._client.mount_retry_adapter_for_table(name)
-
         if index_cache_size is not None:
             logging.info(
                 "index_cache_size is ignored in LanceDb Cloud"
                 " (there is no local cache to configure)"
             )
 
-        # check if table exists
-        if self._table_cache.get(name) is None:
-            self._client.post(f"/v1/table/{name}/describe/")
-            self._table_cache[name] = True
-
-        return RemoteTable(self, name)
+        table = self._loop.run_until_complete(self._conn.open_table(name))
+        return RemoteTable(table, self.db_name, self._loop)
 
     @override
     def create_table(
@@ -233,27 +265,20 @@ class RemoteDBConnection(DBConnection):
                 "Please vote https://github.com/lancedb/lancedb/issues/626 "
                 "for this feature."
             )
-        if mode is not None:
-            logging.warning("mode is not yet supported on LanceDB Cloud.")
-
-        data, schema = sanitize_create_table(
-            data, schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
-        )
 
         from .table import RemoteTable
 
-        data = to_ipc_binary(data)
-        request_id = uuid.uuid4().hex
-
-        self._client.post(
-            f"/v1/table/{name}/create/",
-            data=data,
-            request_id=request_id,
-            content_type=ARROW_STREAM_CONTENT_TYPE,
+        table = self._loop.run_until_complete(
+            self._conn.create_table(
+                name,
+                data,
+                mode=mode,
+                schema=schema,
+                on_bad_vectors=on_bad_vectors,
+                fill_value=fill_value,
             )
-
-        self._table_cache[name] = True
-        return RemoteTable(self, name)
+        )
+        return RemoteTable(table, self.db_name, self._loop)
 
     @override
     def drop_table(self, name: str):
@@ -264,11 +289,7 @@ class RemoteDBConnection(DBConnection):
         name: str
             The name of the table.
         """
-
-        self._client.post(
-            f"/v1/table/{name}/drop/",
-        )
-        self._table_cache.pop(name, default=None)
+        self._loop.run_until_complete(self._conn.drop_table(name))
 
     @override
     def rename_table(self, cur_name: str, new_name: str):
@@ -281,12 +302,7 @@ class RemoteDBConnection(DBConnection):
         new_name: str
             The new name of the table.
         """
-        self._client.post(
-            f"/v1/table/{cur_name}/rename/",
-            data={"new_table_name": new_name},
-        )
-        self._table_cache.pop(cur_name, default=None)
-        self._table_cache[new_name] = True
+        self._loop.run_until_complete(self._conn.rename_table(cur_name, new_name))
 
     async def close(self):
         """Close the connection to the database."""

python/python/lancedb/remote/table.py

@@ -11,53 +11,57 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
+from datetime import timedelta
+import asyncio
 import logging
-import uuid
-from concurrent.futures import Future
 from functools import cached_property
 from typing import Dict, Iterable, List, Optional, Union, Literal
 
+from lancedb.index import FTS, BTree, Bitmap, HnswPq, HnswSq, IvfPq, LabelList
 import pyarrow as pa
-from lance import json_to_schema
 
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
 from lancedb.merge import LanceMergeInsertBuilder
 from lancedb.embeddings import EmbeddingFunctionRegistry
 
 from ..query import LanceVectorQueryBuilder, LanceQueryBuilder
-from ..table import Query, Table, _sanitize_data
-from ..util import value_to_sql, infer_vector_column_name
-from .arrow import to_ipc_binary
-from .client import ARROW_STREAM_CONTENT_TYPE
-from .db import RemoteDBConnection
+from ..table import AsyncTable, Query, Table
 
 
 class RemoteTable(Table):
-    def __init__(self, conn: RemoteDBConnection, name: str):
-        self._conn = conn
-        self.name = name
+    def __init__(
+        self,
+        table: AsyncTable,
+        db_name: str,
+        loop: Optional[asyncio.AbstractEventLoop] = None,
+    ):
+        self._loop = loop
+        self._table = table
+        self.db_name = db_name
+
+    @property
+    def name(self) -> str:
+        """The name of the table"""
+        return self._table.name
 
     def __repr__(self) -> str:
-        return f"RemoteTable({self._conn.db_name}.{self.name})"
+        return f"RemoteTable({self.db_name}.{self.name})"
 
     def __len__(self) -> int:
         self.count_rows(None)
 
-    @cached_property
+    @property
     def schema(self) -> pa.Schema:
         """The [Arrow Schema](https://arrow.apache.org/docs/python/api/datatypes.html#)
         of this Table
 
         """
-        resp = self._conn._client.post(f"/v1/table/{self.name}/describe/")
-        schema = json_to_schema(resp["schema"])
-        return schema
+        return self._loop.run_until_complete(self._table.schema())
 
     @property
     def version(self) -> int:
         """Get the current version of the table"""
-        resp = self._conn._client.post(f"/v1/table/{self.name}/describe/")
-        return resp["version"]
+        return self._loop.run_until_complete(self._table.version())
 
     @cached_property
     def embedding_functions(self) -> dict:
@@ -84,20 +88,18 @@ class RemoteTable(Table):
 
     def list_indices(self):
         """List all the indices on the table"""
-        resp = self._conn._client.post(f"/v1/table/{self.name}/index/list/")
-        return resp
+        return self._loop.run_until_complete(self._table.list_indices())
 
     def index_stats(self, index_uuid: str):
         """List all the stats of a specified index"""
-        resp = self._conn._client.post(
-            f"/v1/table/{self.name}/index/{index_uuid}/stats/"
-        )
-        return resp
+        return self._loop.run_until_complete(self._table.index_stats(index_uuid))
 
     def create_scalar_index(
         self,
         column: str,
         index_type: Literal["BTREE", "BITMAP", "LABEL_LIST", "scalar"] = "scalar",
+        *,
+        replace: bool = False,
     ):
         """Creates a scalar index
         Parameters
@@ -107,20 +109,23 @@ class RemoteTable(Table):
             or string column.
         index_type : str
             The index type of the scalar index. Must be "scalar" (BTREE),
-            "BTREE", "BITMAP", or "LABEL_LIST"
+            "BTREE", "BITMAP", or "LABEL_LIST",
+        replace : bool
+            If True, replace the existing index with the new one.
         """
+        if index_type == "scalar" or index_type == "BTREE":
+            config = BTree()
+        elif index_type == "BITMAP":
+            config = Bitmap()
+        elif index_type == "LABEL_LIST":
+            config = LabelList()
+        else:
+            raise ValueError(f"Unknown index type: {index_type}")
 
-        data = {
-            "column": column,
-            "index_type": index_type,
-            "replace": True,
-        }
-        resp = self._conn._client.post(
-            f"/v1/table/{self.name}/create_scalar_index/", data=data
+        self._loop.run_until_complete(
+            self._table.create_index(column, config=config, replace=replace)
         )
 
-        return resp
-
     def create_fts_index(
         self,
         column: str,
@@ -128,15 +133,10 @@ class RemoteTable(Table):
         replace: bool = False,
         with_position: bool = True,
     ):
-        data = {
-            "column": column,
-            "index_type": "FTS",
-            "replace": replace,
-        }
-        resp = self._conn._client.post(
-            f"/v1/table/{self.name}/create_index/", data=data
+        config = FTS(with_position=with_position)
+        self._loop.run_until_complete(
+            self._table.create_index(column, config=config, replace=replace)
         )
-        return resp
 
     def create_index(
         self,
@@ -204,17 +204,22 @@ class RemoteTable(Table):
                 "Existing indexes will always be replaced."
             )
 
-        data = {
-            "column": vector_column_name,
-            "index_type": index_type,
-            "metric_type": metric,
-            "index_cache_size": index_cache_size,
-        }
-        resp = self._conn._client.post(
-            f"/v1/table/{self.name}/create_index/", data=data
+        index_type = index_type.upper()
+        if index_type == "VECTOR" or index_type == "IVF_PQ":
+            config = IvfPq(distance_type=metric)
+        elif index_type == "IVF_HNSW_PQ":
+            config = HnswPq(distance_type=metric)
+        elif index_type == "IVF_HNSW_SQ":
+            config = HnswSq(distance_type=metric)
+        else:
+            raise ValueError(
+                f"Unknown vector index type: {index_type}. Valid options are"
+                " 'IVF_PQ', 'IVF_HNSW_PQ', 'IVF_HNSW_SQ'"
             )
 
-        return resp
+        self._loop.run_until_complete(
+            self._table.create_index(vector_column_name, config=config)
+        )
 
     def add(
         self,
@@ -246,22 +251,10 @@ class RemoteTable(Table):
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
 
         """
-        data, _ = _sanitize_data(
-            data,
-            self.schema,
-            metadata=self.schema.metadata,
-            on_bad_vectors=on_bad_vectors,
-            fill_value=fill_value,
+        self._loop.run_until_complete(
+            self._table.add(
+                data, mode=mode, on_bad_vectors=on_bad_vectors, fill_value=fill_value
             )
-        payload = to_ipc_binary(data)
-
-        request_id = uuid.uuid4().hex
-
-        self._conn._client.post(
-            f"/v1/table/{self.name}/insert/",
-            data=payload,
-            params={"request_id": request_id, "mode": mode},
-            content_type=ARROW_STREAM_CONTENT_TYPE,
         )
 
     def search(
@@ -334,16 +327,6 @@ class RemoteTable(Table):
             - and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
-        # empty query builder is not supported in saas, raise error
-        if query is None and query_type != "hybrid":
-            raise ValueError("Empty query is not supported")
-        vector_column_name = infer_vector_column_name(
-            schema=self.schema,
-            query_type=query_type,
-            query=query,
-            vector_column_name=vector_column_name,
-        )
-
         return LanceQueryBuilder.create(
             self,
             query,
@@ -356,38 +339,10 @@ class RemoteTable(Table):
     def _execute_query(
         self, query: Query, batch_size: Optional[int] = None
     ) -> pa.RecordBatchReader:
-        if (
-            query.vector is not None
-            and len(query.vector) > 0
-            and not isinstance(query.vector[0], float)
-        ):
-            if self._conn._request_thread_pool is None:
-
-                def submit(name, q):
-                    f = Future()
-                    f.set_result(self._conn._client.query(name, q))
-                    return f
-
-            else:
-
-                def submit(name, q):
-                    return self._conn._request_thread_pool.submit(
-                        self._conn._client.query, name, q
+        return self._loop.run_until_complete(
+            self._table._execute_query(query, batch_size=batch_size)
         )
 
-            results = []
-            for v in query.vector:
-                v = list(v)
-                q = query.copy()
-                q.vector = v
-                results.append(submit(self.name, q))
-            return pa.concat_tables(
-                [add_index(r.result().to_arrow(), i) for i, r in enumerate(results)]
-            ).to_reader()
-        else:
-            result = self._conn._client.query(self.name, query)
-            return result.to_arrow().to_reader()
-
     def merge_insert(self, on: Union[str, Iterable[str]]) -> LanceMergeInsertBuilder:
         """Returns a [`LanceMergeInsertBuilder`][lancedb.merge.LanceMergeInsertBuilder]
         that can be used to create a "merge insert" operation.
@@ -403,42 +358,8 @@ class RemoteTable(Table):
         on_bad_vectors: str,
         fill_value: float,
     ):
-        data, _ = _sanitize_data(
-            new_data,
-            self.schema,
-            metadata=None,
-            on_bad_vectors=on_bad_vectors,
-            fill_value=fill_value,
-        )
-        payload = to_ipc_binary(data)
-
-        params = {}
-        if len(merge._on) != 1:
-            raise ValueError(
-                "RemoteTable only supports a single on key in merge_insert"
-            )
-        params["on"] = merge._on[0]
-        params["when_matched_update_all"] = str(merge._when_matched_update_all).lower()
-        if merge._when_matched_update_all_condition is not None:
-            params["when_matched_update_all_filt"] = (
-                merge._when_matched_update_all_condition
-            )
-        params["when_not_matched_insert_all"] = str(
-            merge._when_not_matched_insert_all
-        ).lower()
-        params["when_not_matched_by_source_delete"] = str(
-            merge._when_not_matched_by_source_delete
-        ).lower()
-        if merge._when_not_matched_by_source_condition is not None:
-            params["when_not_matched_by_source_delete_filt"] = (
-                merge._when_not_matched_by_source_condition
-            )
-
-        self._conn._client.post(
-            f"/v1/table/{self.name}/merge_insert/",
-            data=payload,
-            params=params,
-            content_type=ARROW_STREAM_CONTENT_TYPE,
+        self._loop.run_until_complete(
+            self._table._do_merge(merge, new_data, on_bad_vectors, fill_value)
         )
 
     def delete(self, predicate: str):
@@ -488,8 +409,7 @@ class RemoteTable(Table):
            x      vector  _distance # doctest: +SKIP
         0  2  [3.0, 4.0]       85.0 # doctest: +SKIP
         """
-        payload = {"predicate": predicate}
-        self._conn._client.post(f"/v1/table/{self.name}/delete/", data=payload)
+        self._loop.run_until_complete(self._table.delete(predicate))
 
     def update(
         self,
@@ -539,18 +459,9 @@ class RemoteTable(Table):
         2  2  [10.0, 10.0] # doctest: +SKIP
 
         """
-        if values is not None and values_sql is not None:
-            raise ValueError("Only one of values or values_sql can be provided")
-        if values is None and values_sql is None:
-            raise ValueError("Either values or values_sql must be provided")
-
-        if values is not None:
-            updates = [[k, value_to_sql(v)] for k, v in values.items()]
-        else:
-            updates = [[k, v] for k, v in values_sql.items()]
-
-        payload = {"predicate": where, "updates": updates}
-        self._conn._client.post(f"/v1/table/{self.name}/update/", data=payload)
+        self._loop.run_until_complete(
+            self._table.update(where=where, updates=values, updates_sql=values_sql)
+        )
 
     def cleanup_old_versions(self, *_):
         """cleanup_old_versions() is not supported on the LanceDB cloud"""
@@ -564,12 +475,21 @@ class RemoteTable(Table):
             "compact_files() is not supported on the LanceDB cloud"
         )
 
-    def count_rows(self, filter: Optional[str] = None) -> int:
-        payload = {"predicate": filter}
-        resp = self._conn._client.post(
-            f"/v1/table/{self.name}/count_rows/", data=payload
+    def optimize(
+        self,
+        *,
+        cleanup_older_than: Optional[timedelta] = None,
+        delete_unverified: bool = False,
+    ):
+        """optimize() is not supported on the LanceDB cloud.
+        Indices are optimized automatically."""
+        raise NotImplementedError(
+            "optimize() is not supported on the LanceDB cloud. "
+            "Indices are optimized automatically."
         )
-        return resp
+
+    def count_rows(self, filter: Optional[str] = None) -> int:
+        return self._loop.run_until_complete(self._table.count_rows(filter))
 
     def add_columns(self, transforms: Dict[str, str]):
         raise NotImplementedError(

python/python/lancedb/rerankers/__init__.py

@@ -7,6 +7,7 @@ from .openai import OpenaiReranker
 from .jinaai import JinaReranker
 from .rrf import RRFReranker
 from .answerdotai import AnswerdotaiRerankers
+from .voyageai import VoyageAIReranker
 
 __all__ = [
     "Reranker",
@@ -18,4 +19,5 @@ __all__ = [
     "JinaReranker",
     "RRFReranker",
     "AnswerdotaiRerankers",
+    "VoyageAIReranker",
 ]

python/python/lancedb/rerankers/jinaai.py

@@ -12,7 +12,6 @@
 #  limitations under the License.
 
 import os
-import requests
 from functools import cached_property
 from typing import Union
 
@@ -57,6 +56,8 @@ class JinaReranker(Reranker):
 
     @cached_property
     def _client(self):
+        import requests
+
         if os.environ.get("JINA_API_KEY") is None and self.api_key is None:
             raise ValueError(
                 "JINA_API_KEY not set. Either set it in your environment or \

python/python/lancedb/rerankers/linear_combination.py

@@ -11,7 +11,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-from numpy import NaN
+from numpy import nan
 import pyarrow as pa
 
 from .base import Reranker
@@ -71,7 +71,7 @@ class LinearCombinationReranker(Reranker):
             elif self.score == "all":
                 results = results.append_column(
                     "_distance",
-                    pa.array([NaN] * len(fts_results), type=pa.float32()),
+                    pa.array([nan] * len(fts_results), type=pa.float32()),
                 )
             return results
 
@@ -92,7 +92,7 @@ class LinearCombinationReranker(Reranker):
             elif self.score == "all":
                 results = results.append_column(
                     "_score",
-                    pa.array([NaN] * len(vector_results), type=pa.float32()),
+                    pa.array([nan] * len(vector_results), type=pa.float32()),
                 )
             return results
 

python/python/lancedb/rerankers/voyageai.py

@@ -0,0 +1,133 @@
+#  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 os
+from functools import cached_property
+from typing import Optional
+
+import pyarrow as pa
+
+from ..util import attempt_import_or_raise
+from .base import Reranker
+
+
+class VoyageAIReranker(Reranker):
+    """
+    Reranks the results using the VoyageAI Rerank API.
+    https://docs.voyageai.com/docs/reranker
+
+    Parameters
+    ----------
+    model_name : str, default "rerank-english-v2.0"
+        The name of the cross encoder model to use. Available voyageai models are:
+        - rerank-2
+        - rerank-2-lite
+    column : str, default "text"
+        The name of the column to use as input to the cross encoder model.
+    top_n : int, default None
+        The number of results to return. If None, will return all results.
+    return_score : str, default "relevance"
+        options are "relevance" or "all". Only "relevance" is supported for now.
+    api_key : str, default None
+        The API key to use. If None, will use the OPENAI_API_KEY environment variable.
+    truncation : Optional[bool], default None
+    """
+
+    def __init__(
+        self,
+        model_name: str,
+        column: str = "text",
+        top_n: Optional[int] = None,
+        return_score="relevance",
+        api_key: Optional[str] = None,
+        truncation: Optional[bool] = True,
+    ):
+        super().__init__(return_score)
+        self.model_name = model_name
+        self.column = column
+        self.top_n = top_n
+        self.api_key = api_key
+        self.truncation = truncation
+
+    @cached_property
+    def _client(self):
+        voyageai = attempt_import_or_raise("voyageai")
+        if os.environ.get("VOYAGE_API_KEY") is None and self.api_key is None:
+            raise ValueError(
+                "VOYAGE_API_KEY not set. Either set it in your environment or \
+                pass it as `api_key` argument to the VoyageAIReranker."
+            )
+        return voyageai.Client(
+            api_key=os.environ.get("VOYAGE_API_KEY") or self.api_key,
+        )
+
+    def _rerank(self, result_set: pa.Table, query: str):
+        docs = result_set[self.column].to_pylist()
+        response = self._client.rerank(
+            query=query,
+            documents=docs,
+            top_k=self.top_n,
+            model=self.model_name,
+            truncation=self.truncation,
+        )
+        results = (
+            response.results
+        )  # returns list (text, idx, relevance) attributes sorted descending by score
+        indices, scores = list(
+            zip(*[(result.index, result.relevance_score) for result in results])
+        )  # tuples
+        result_set = result_set.take(list(indices))
+        # add the scores
+        result_set = result_set.append_column(
+            "_relevance_score", pa.array(scores, type=pa.float32())
+        )
+
+        return result_set
+
+    def rerank_hybrid(
+        self,
+        query: str,
+        vector_results: pa.Table,
+        fts_results: pa.Table,
+    ):
+        combined_results = self.merge_results(vector_results, fts_results)
+        combined_results = self._rerank(combined_results, query)
+        if self.score == "relevance":
+            combined_results = self._keep_relevance_score(combined_results)
+        elif self.score == "all":
+            raise NotImplementedError(
+                "return_score='all' not implemented for voyageai reranker"
+            )
+        return combined_results
+
+    def rerank_vector(
+        self,
+        query: str,
+        vector_results: pa.Table,
+    ):
+        result_set = self._rerank(vector_results, query)
+        if self.score == "relevance":
+            result_set = result_set.drop_columns(["_distance"])
+
+        return result_set
+
+    def rerank_fts(
+        self,
+        query: str,
+        fts_results: pa.Table,
+    ):
+        result_set = self._rerank(fts_results, query)
+        if self.score == "relevance":
+            result_set = result_set.drop_columns(["_score"])
+
+        return result_set

python/python/lancedb/table.py

@@ -3,6 +3,7 @@
 
 from __future__ import annotations
 
+import asyncio
 import inspect
 import time
 from abc import ABC, abstractmethod
@@ -32,7 +33,7 @@ import pyarrow.fs as pa_fs
 from lance import LanceDataset
 from lance.dependencies import _check_for_hugging_face
 
-from .common import DATA, VEC, VECTOR_COLUMN_NAME
+from .common import DATA, VEC, VECTOR_COLUMN_NAME, sanitize_uri
 from .embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
 from .merge import LanceMergeInsertBuilder
 from .pydantic import LanceModel, model_to_dict
@@ -57,12 +58,14 @@ from .util import (
 )
 from .index import lang_mapping
 
+from ._lancedb import connect as lancedb_connect
+
 if TYPE_CHECKING:
     import PIL
     from lance.dataset import CleanupStats, ReaderLike
     from ._lancedb import Table as LanceDBTable, OptimizeStats
     from .db import LanceDBConnection
-    from .index import BTree, IndexConfig, IvfPq, Bitmap, LabelList, FTS
+    from .index import BTree, IndexConfig, IvfPq, Bitmap, LabelList, FTS, HnswPq, HnswSq
 
 pd = safe_import_pandas()
 pl = safe_import_polars()
@@ -70,6 +73,21 @@ pl = safe_import_polars()
 QueryType = Literal["vector", "fts", "hybrid", "auto"]
 
 
+def _pd_schema_without_embedding_funcs(
+    schema: Optional[pa.Schema], columns: List[str]
+) -> Optional[pa.Schema]:
+    """Return a schema without any embedding function columns"""
+    if schema is None:
+        return None
+    embedding_functions = EmbeddingFunctionRegistry.get_instance().parse_functions(
+        schema.metadata
+    )
+    if not embedding_functions:
+        return schema
+    columns = set(columns)
+    return pa.schema([field for field in schema if field.name in columns])
+
+
 def _coerce_to_table(data, schema: Optional[pa.Schema] = None) -> pa.Table:
     if _check_for_hugging_face(data):
         # Huggingface datasets
@@ -100,10 +118,10 @@ def _coerce_to_table(data, schema: Optional[pa.Schema] = None) -> pa.Table:
         elif isinstance(data[0], pa.RecordBatch):
             return pa.Table.from_batches(data, schema=schema)
         else:
-            return pa.Table.from_pylist(data)
+            return pa.Table.from_pylist(data, schema=schema)
     elif _check_for_pandas(data) and isinstance(data, pd.DataFrame):
-        # Do not add schema here, since schema may contains the vector column
-        table = pa.Table.from_pandas(data, preserve_index=False)
+        raw_schema = _pd_schema_without_embedding_funcs(schema, data.columns.to_list())
+        table = pa.Table.from_pandas(data, preserve_index=False, schema=raw_schema)
         # Do not serialize Pandas metadata
         meta = table.schema.metadata if table.schema.metadata is not None else {}
         meta = {k: v for k, v in meta.items() if k != b"pandas"}
@@ -169,6 +187,8 @@ def sanitize_create_table(
         schema = schema.to_arrow_schema()
 
     if data is not None:
+        if metadata is None and schema is not None:
+            metadata = schema.metadata
         data, schema = _sanitize_data(
             data,
             schema,
@@ -893,6 +913,55 @@ class Table(ABC):
         For most cases, the default should be fine.
         """
 
+    @abstractmethod
+    def optimize(
+        self,
+        *,
+        cleanup_older_than: Optional[timedelta] = None,
+        delete_unverified: bool = False,
+    ):
+        """
+        Optimize the on-disk data and indices for better performance.
+
+        Modeled after ``VACUUM`` in PostgreSQL.
+
+        Optimization covers three operations:
+
+         * Compaction: Merges small files into larger ones
+         * Prune: Removes old versions of the dataset
+         * Index: Optimizes the indices, adding new data to existing indices
+
+        Parameters
+        ----------
+        cleanup_older_than: timedelta, optional default 7 days
+            All files belonging to versions older than this will be removed.  Set
+            to 0 days to remove all versions except the latest.  The latest version
+            is never removed.
+        delete_unverified: bool, default False
+            Files leftover from a failed transaction may appear to be part of an
+            in-progress operation (e.g. appending new data) and these files will not
+            be deleted unless they are at least 7 days old. If delete_unverified is True
+            then these files will be deleted regardless of their age.
+
+        Experimental API
+        ----------------
+
+        The optimization process is undergoing active development and may change.
+        Our goal with these changes is to improve the performance of optimization and
+        reduce the complexity.
+
+        That being said, it is essential today to run optimize if you want the best
+        performance.  It should be stable and safe to use in production, but it our
+        hope that the API may be simplified (or not even need to be called) in the
+        future.
+
+        The frequency an application shoudl call optimize is based on the frequency of
+        data modifications.  If data is frequently added, deleted, or updated then
+        optimize should be run frequently.  A good rule of thumb is to run optimize if
+        you have added or modified 100,000 or more records or run more than 20 data
+        modification operations.
+        """
+
     @abstractmethod
     def add_columns(self, transforms: Dict[str, str]):
         """
@@ -948,7 +1017,9 @@ class Table(ABC):
         return _table_uri(self._conn.uri, self.name)
 
     def _get_fts_index_path(self) -> Tuple[str, pa_fs.FileSystem, bool]:
-        if get_uri_scheme(self._dataset_uri) != "file":
+        from .remote.table import RemoteTable
+
+        if isinstance(self, RemoteTable) or get_uri_scheme(self._dataset_uri) != "file":
             return ("", None, False)
         path = join_uri(self._dataset_uri, "_indices", "fts")
         fs, path = fs_from_uri(path)
@@ -1969,6 +2040,83 @@ class LanceTable(Table):
         """
         return self.to_lance().optimize.compact_files(*args, **kwargs)
 
+    def optimize(
+        self,
+        *,
+        cleanup_older_than: Optional[timedelta] = None,
+        delete_unverified: bool = False,
+    ):
+        """
+        Optimize the on-disk data and indices for better performance.
+
+        Modeled after ``VACUUM`` in PostgreSQL.
+
+        Optimization covers three operations:
+
+         * Compaction: Merges small files into larger ones
+         * Prune: Removes old versions of the dataset
+         * Index: Optimizes the indices, adding new data to existing indices
+
+        Parameters
+        ----------
+        cleanup_older_than: timedelta, optional default 7 days
+            All files belonging to versions older than this will be removed.  Set
+            to 0 days to remove all versions except the latest.  The latest version
+            is never removed.
+        delete_unverified: bool, default False
+            Files leftover from a failed transaction may appear to be part of an
+            in-progress operation (e.g. appending new data) and these files will not
+            be deleted unless they are at least 7 days old. If delete_unverified is True
+            then these files will be deleted regardless of their age.
+
+        Experimental API
+        ----------------
+
+        The optimization process is undergoing active development and may change.
+        Our goal with these changes is to improve the performance of optimization and
+        reduce the complexity.
+
+        That being said, it is essential today to run optimize if you want the best
+        performance.  It should be stable and safe to use in production, but it our
+        hope that the API may be simplified (or not even need to be called) in the
+        future.
+
+        The frequency an application shoudl call optimize is based on the frequency of
+        data modifications.  If data is frequently added, deleted, or updated then
+        optimize should be run frequently.  A good rule of thumb is to run optimize if
+        you have added or modified 100,000 or more records or run more than 20 data
+        modification operations.
+        """
+        try:
+            asyncio.get_running_loop()
+            raise AssertionError(
+                "Synchronous method called in asynchronous context. "
+                "If you are writing an asynchronous application "
+                "then please use the asynchronous APIs"
+            )
+
+        except RuntimeError:
+            asyncio.run(
+                self._async_optimize(
+                    cleanup_older_than=cleanup_older_than,
+                    delete_unverified=delete_unverified,
+                )
+            )
+            self.checkout_latest()
+
+    async def _async_optimize(
+        self,
+        cleanup_older_than: Optional[timedelta] = None,
+        delete_unverified: bool = False,
+    ):
+        conn = await lancedb_connect(
+            sanitize_uri(self._conn.uri),
+        )
+        table = AsyncTable(await conn.open_table(self.name))
+        return await table.optimize(
+            cleanup_older_than=cleanup_older_than, delete_unverified=delete_unverified
+        )
+
     def add_columns(self, transforms: Dict[str, str]):
         self._dataset_mut.add_columns(transforms)
 
@@ -2382,7 +2530,9 @@ class AsyncTable:
         column: str,
         *,
         replace: Optional[bool] = None,
-        config: Optional[Union[IvfPq, BTree, Bitmap, LabelList, FTS]] = None,
+        config: Optional[
+            Union[IvfPq, HnswPq, HnswSq, BTree, Bitmap, LabelList, FTS]
+        ] = None,
     ):
         """Create an index to speed up queries
 
@@ -2535,7 +2685,44 @@ class AsyncTable:
     async def _execute_query(
         self, query: Query, batch_size: Optional[int] = None
     ) -> pa.RecordBatchReader:
-        pass
+        # The sync remote table calls into this method, so we need to map the
+        # query to the async version of the query and run that here. This is only
+        # used for that code path right now.
+        async_query = self.query().limit(query.k)
+        if query.offset > 0:
+            async_query = async_query.offset(query.offset)
+        if query.columns:
+            async_query = async_query.select(query.columns)
+        if query.filter:
+            async_query = async_query.where(query.filter)
+        if query.fast_search:
+            async_query = async_query.fast_search()
+        if query.with_row_id:
+            async_query = async_query.with_row_id()
+
+        if query.vector:
+            async_query = (
+                async_query.nearest_to(query.vector)
+                .distance_type(query.metric)
+                .nprobes(query.nprobes)
+            )
+            if query.refine_factor:
+                async_query = async_query.refine_factor(query.refine_factor)
+            if query.vector_column:
+                async_query = async_query.column(query.vector_column)
+
+        if not query.prefilter:
+            async_query = async_query.postfilter()
+
+        if isinstance(query.full_text_query, str):
+            async_query = async_query.nearest_to_text(query.full_text_query)
+        elif isinstance(query.full_text_query, dict):
+            fts_query = query.full_text_query["query"]
+            fts_columns = query.full_text_query.get("columns", []) or []
+            async_query = async_query.nearest_to_text(fts_query, columns=fts_columns)
+
+        table = await async_query.to_arrow()
+        return table.to_reader()
 
     async def _do_merge(
         self,
@@ -2781,7 +2968,7 @@ class AsyncTable:
             cleanup_older_than = round(cleanup_older_than.total_seconds() * 1000)
         return await self._inner.optimize(cleanup_older_than, delete_unverified)
 
-    async def list_indices(self) -> IndexConfig:
+    async def list_indices(self) -> Iterable[IndexConfig]:
         """
         List all indices that have been created with Self::create_index
         """
@@ -2865,3 +3052,8 @@ class IndexStatistics:
     ]
     distance_type: Optional[Literal["l2", "cosine", "dot"]] = None
     num_indices: Optional[int] = None
+
+    # This exists for backwards compatibility with an older API, which returned
+    # a dictionary instead of a class.
+    def __getitem__(self, key):
+        return getattr(self, key)

python/python/tests/test_embeddings.py

@@ -1,15 +1,6 @@
-#  Copyright 2023 LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
 from typing import List, Union
 from unittest.mock import MagicMock, patch
 
@@ -18,6 +9,7 @@ import lancedb
 import numpy as np
 import pyarrow as pa
 import pytest
+import pandas as pd
 from lancedb.conftest import MockTextEmbeddingFunction
 from lancedb.embeddings import (
     EmbeddingFunctionConfig,
@@ -129,6 +121,142 @@ def test_embedding_with_bad_results(tmp_path):
     # assert tbl["vector"].null_count == 1
 
 
+def test_with_existing_vectors(tmp_path):
+    @register("mock-embedding")
+    class MockEmbeddingFunction(TextEmbeddingFunction):
+        def ndims(self):
+            return 128
+
+        def generate_embeddings(
+            self, texts: Union[List[str], np.ndarray]
+        ) -> List[np.array]:
+            return [np.random.randn(self.ndims()).tolist() for _ in range(len(texts))]
+
+    registry = get_registry()
+    model = registry.get("mock-embedding").create()
+
+    class Schema(LanceModel):
+        text: str = model.SourceField()
+        vector: Vector(model.ndims()) = model.VectorField()
+
+    db = lancedb.connect(tmp_path)
+    tbl = db.create_table("test", schema=Schema, mode="overwrite")
+    tbl.add([{"text": "hello world", "vector": np.zeros(128).tolist()}])
+
+    embeddings = tbl.to_arrow()["vector"].to_pylist()
+    assert not np.any(embeddings), "all zeros"
+
+
+def test_embedding_function_with_pandas(tmp_path):
+    @register("mock-embedding")
+    class _MockEmbeddingFunction(TextEmbeddingFunction):
+        def ndims(self):
+            return 128
+
+        def generate_embeddings(
+            self, texts: Union[List[str], np.ndarray]
+        ) -> List[np.array]:
+            return [np.random.randn(self.ndims()).tolist() for _ in range(len(texts))]
+
+    registery = get_registry()
+    func = registery.get("mock-embedding").create()
+
+    class TestSchema(LanceModel):
+        text: str = func.SourceField()
+        val: int
+        vector: Vector(func.ndims()) = func.VectorField()
+
+    df = pd.DataFrame(
+        {
+            "text": ["hello world", "goodbye world"],
+            "val": [1, 2],
+            "not-used": ["s1", "s3"],
+        }
+    )
+    db = lancedb.connect(tmp_path)
+    tbl = db.create_table("test", schema=TestSchema, mode="overwrite", data=df)
+    schema = tbl.schema
+    assert schema.field("text").type == pa.string()
+    assert schema.field("val").type == pa.int64()
+    assert schema.field("vector").type == pa.list_(pa.float32(), 128)
+
+    df = pd.DataFrame(
+        {
+            "text": ["extra", "more"],
+            "val": [4, 5],
+            "misc-col": ["s1", "s3"],
+        }
+    )
+    tbl.add(df)
+
+    assert tbl.count_rows() == 4
+    embeddings = tbl.to_arrow()["vector"]
+    assert embeddings.null_count == 0
+
+    df = pd.DataFrame(
+        {
+            "text": ["with", "embeddings"],
+            "val": [6, 7],
+            "vector": [np.zeros(128).tolist(), np.zeros(128).tolist()],
+        }
+    )
+    tbl.add(df)
+
+    embeddings = tbl.search().where("val > 5").to_arrow()["vector"].to_pylist()
+    assert not np.any(embeddings), "all zeros"
+
+
+def test_multiple_embeddings_for_pandas(tmp_path):
+    @register("mock-embedding")
+    class MockFunc1(TextEmbeddingFunction):
+        def ndims(self):
+            return 128
+
+        def generate_embeddings(
+            self, texts: Union[List[str], np.ndarray]
+        ) -> List[np.array]:
+            return [np.random.randn(self.ndims()).tolist() for _ in range(len(texts))]
+
+    @register("mock-embedding2")
+    class MockFunc2(TextEmbeddingFunction):
+        def ndims(self):
+            return 512
+
+        def generate_embeddings(
+            self, texts: Union[List[str], np.ndarray]
+        ) -> List[np.array]:
+            return [np.random.randn(self.ndims()).tolist() for _ in range(len(texts))]
+
+    registery = get_registry()
+    func1 = registery.get("mock-embedding").create()
+    func2 = registery.get("mock-embedding2").create()
+
+    class TestSchema(LanceModel):
+        text: str = func1.SourceField()
+        val: int
+        vec1: Vector(func1.ndims()) = func1.VectorField()
+        prompt: str = func2.SourceField()
+        vec2: Vector(func2.ndims()) = func2.VectorField()
+
+    df = pd.DataFrame(
+        {
+            "text": ["hello world", "goodbye world"],
+            "val": [1, 2],
+            "prompt": ["hello", "goodbye"],
+        }
+    )
+    db = lancedb.connect(tmp_path)
+    tbl = db.create_table("test", schema=TestSchema, mode="overwrite", data=df)
+
+    schema = tbl.schema
+    assert schema.field("text").type == pa.string()
+    assert schema.field("val").type == pa.int64()
+    assert schema.field("vec1").type == pa.list_(pa.float32(), 128)
+    assert schema.field("prompt").type == pa.string()
+    assert schema.field("vec2").type == pa.list_(pa.float32(), 512)
+    assert tbl.count_rows() == 2
+
+
 @pytest.mark.slow
 def test_embedding_function_rate_limit(tmp_path):
     def _get_schema_from_model(model):
@@ -196,6 +324,7 @@ def test_add_optional_vector(tmp_path):
         "ollama",
         "cohere",
         "instructor",
+        "voyageai",
     ],
 )
 def test_embedding_function_safe_model_dump(embedding_type):

python/python/tests/test_embeddings_slow.py

@@ -18,7 +18,6 @@ import lancedb
 import numpy as np
 import pandas as pd
 import pytest
-import requests
 from lancedb.embeddings import get_registry
 from lancedb.pydantic import LanceModel, Vector
 
@@ -108,6 +107,7 @@ def test_basic_text_embeddings(alias, tmp_path):
 
 @pytest.mark.slow
 def test_openclip(tmp_path):
+    import requests
     from PIL import Image
 
     db = lancedb.connect(tmp_path)
@@ -481,3 +481,22 @@ def test_ollama_embedding(tmp_path):
         json.dumps(dumped_model)
     except TypeError:
         pytest.fail("Failed to JSON serialize the dumped model")
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(
+    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
+)
+def test_voyageai_embedding_function():
+    voyageai = get_registry().get("voyageai").create(name="voyage-3", max_retries=0)
+
+    class TextModel(LanceModel):
+        text: str = voyageai.SourceField()
+        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
+
+    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
+    db = lancedb.connect("~/lancedb")
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(df)
+    assert len(tbl.to_pandas()["vector"][0]) == voyageai.ndims()

python/python/tests/test_fts.py

@@ -235,6 +235,29 @@ async def test_search_fts_async(async_table):
     results = await async_table.query().nearest_to_text("puppy").limit(5).to_list()
     assert len(results) == 5
 
+    expected_count = await async_table.count_rows(
+        "count > 5000 and contains(text, 'puppy')"
+    )
+    expected_count = min(expected_count, 10)
+
+    limited_results_pre_filter = await (
+        async_table.query()
+        .nearest_to_text("puppy")
+        .where("count > 5000")
+        .limit(10)
+        .to_list()
+    )
+    assert len(limited_results_pre_filter) == expected_count
+    limited_results_post_filter = await (
+        async_table.query()
+        .nearest_to_text("puppy")
+        .where("count > 5000")
+        .limit(10)
+        .postfilter()
+        .to_list()
+    )
+    assert len(limited_results_post_filter) <= expected_count
+
 
 @pytest.mark.asyncio
 async def test_search_fts_specify_column_async(async_table):

python/python/tests/test_index.py

@@ -49,7 +49,7 @@ async def test_create_scalar_index(some_table: AsyncTable):
     # Can recreate if replace=True
     await some_table.create_index("id", replace=True)
     indices = await some_table.list_indices()
-    assert str(indices) == '[Index(BTree, columns=["id"])]'
+    assert str(indices) == '[Index(BTree, columns=["id"], name="id_idx")]'
     assert len(indices) == 1
     assert indices[0].index_type == "BTree"
     assert indices[0].columns == ["id"]
@@ -64,7 +64,7 @@ async def test_create_scalar_index(some_table: AsyncTable):
 async def test_create_bitmap_index(some_table: AsyncTable):
     await some_table.create_index("id", config=Bitmap())
     indices = await some_table.list_indices()
-    assert str(indices) == '[Index(Bitmap, columns=["id"])]'
+    assert str(indices) == '[Index(Bitmap, columns=["id"], name="id_idx")]'
     indices = await some_table.list_indices()
     assert len(indices) == 1
     index_name = indices[0].name
@@ -80,7 +80,7 @@ async def test_create_bitmap_index(some_table: AsyncTable):
 async def test_create_label_list_index(some_table: AsyncTable):
     await some_table.create_index("tags", config=LabelList())
     indices = await some_table.list_indices()
-    assert str(indices) == '[Index(LabelList, columns=["tags"])]'
+    assert str(indices) == '[Index(LabelList, columns=["tags"], name="tags_idx")]'
 
 
 @pytest.mark.asyncio

python/python/tests/test_query.py

@@ -17,6 +17,7 @@ from typing import Optional
 
 import lance
 import lancedb
+from lancedb.index import IvfPq
 import numpy as np
 import pandas.testing as tm
 import pyarrow as pa
@@ -330,6 +331,12 @@ async def test_query_async(table_async: AsyncTable):
     # Also check an empty query
     await check_query(table_async.query().where("id < 0"), expected_num_rows=0)
 
+    # with row id
+    await check_query(
+        table_async.query().select(["id", "vector"]).with_row_id(),
+        expected_columns=["id", "vector", "_rowid"],
+    )
+
 
 @pytest.mark.asyncio
 async def test_query_to_arrow_async(table_async: AsyncTable):
@@ -358,6 +365,25 @@ async def test_query_to_pandas_async(table_async: AsyncTable):
     assert df.shape == (0, 4)
 
 
+@pytest.mark.asyncio
+async def test_fast_search_async(tmp_path):
+    db = await lancedb.connect_async(tmp_path)
+    vectors = pa.FixedShapeTensorArray.from_numpy_ndarray(
+        np.random.rand(256, 32)
+    ).storage
+    table = await db.create_table("test", pa.table({"vector": vectors}))
+    await table.create_index(
+        "vector", config=IvfPq(num_partitions=1, num_sub_vectors=1)
+    )
+    await table.add(pa.table({"vector": vectors}))
+
+    q = [1.0] * 32
+    plan = await table.query().nearest_to(q).explain_plan(True)
+    assert "LanceScan" in plan
+    plan = await table.query().nearest_to(q).fast_search().explain_plan(True)
+    assert "LanceScan" not in plan
+
+
 def test_explain_plan(table):
     q = LanceVectorQueryBuilder(table, [0, 0], "vector")
     plan = q.explain_plan(verbose=True)

python/python/tests/test_remote_client.py

@@ -1,96 +0,0 @@
-#  Copyright 2023 LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
-import attrs
-import numpy as np
-import pandas as pd
-import pyarrow as pa
-import pytest
-from aiohttp import web
-from lancedb.remote.client import RestfulLanceDBClient, VectorQuery
-
-
-@attrs.define
-class MockLanceDBServer:
-    runner: web.AppRunner = attrs.field(init=False)
-    site: web.TCPSite = attrs.field(init=False)
-
-    async def query_handler(self, request: web.Request) -> web.Response:
-        table_name = request.match_info["table_name"]
-        assert table_name == "test_table"
-
-        await request.json()
-        # TODO: do some matching
-
-        vecs = pd.Series([np.random.rand(128) for x in range(10)], name="vector")
-        ids = pd.Series(range(10), name="id")
-        df = pd.DataFrame([vecs, ids]).T
-
-        batch = pa.RecordBatch.from_pandas(
-            df,
-            schema=pa.schema(
-                [
-                    pa.field("vector", pa.list_(pa.float32(), 128)),
-                    pa.field("id", pa.int64()),
-                ]
-            ),
-        )
-
-        sink = pa.BufferOutputStream()
-        with pa.ipc.new_file(sink, batch.schema) as writer:
-            writer.write_batch(batch)
-
-        return web.Response(body=sink.getvalue().to_pybytes())
-
-    async def setup(self):
-        app = web.Application()
-        app.add_routes([web.post("/table/{table_name}", self.query_handler)])
-        self.runner = web.AppRunner(app)
-        await self.runner.setup()
-        self.site = web.TCPSite(self.runner, "localhost", 8111)
-
-    async def start(self):
-        await self.site.start()
-
-    async def stop(self):
-        await self.runner.cleanup()
-
-
-@pytest.mark.skip(reason="flaky somehow, fix later")
-@pytest.mark.asyncio
-async def test_e2e_with_mock_server():
-    mock_server = MockLanceDBServer()
-    await mock_server.setup()
-    await mock_server.start()
-
-    try:
-        with RestfulLanceDBClient("lancedb+http://localhost:8111") as client:
-            df = (
-                await client.query(
-                    "test_table",
-                    VectorQuery(
-                        vector=np.random.rand(128).tolist(),
-                        k=10,
-                        _metric="L2",
-                        columns=["id", "vector"],
-                    ),
-                )
-            ).to_pandas()
-
-            assert "vector" in df.columns
-            assert "id" in df.columns
-
-        assert client.closed
-    finally:
-        # make sure we don't leak resources
-        await mock_server.stop()

python/python/tests/test_remote_db.py

@@ -2,91 +2,19 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 import contextlib
+from datetime import timedelta
 import http.server
+import json
 import threading
 from unittest.mock import MagicMock
 import uuid
 
 import lancedb
+from lancedb.conftest import MockTextEmbeddingFunction
+from lancedb.remote import ClientConfig
 from lancedb.remote.errors import HttpError, RetryError
-import pyarrow as pa
-from lancedb.remote.client import VectorQuery, VectorQueryResult
 import pytest
-
-
-class FakeLanceDBClient:
-    def close(self):
-        pass
-
-    def query(self, table_name: str, query: VectorQuery) -> VectorQueryResult:
-        assert table_name == "test"
-        t = pa.schema([]).empty_table()
-        return VectorQueryResult(t)
-
-    def post(self, path: str):
-        pass
-
-    def mount_retry_adapter_for_table(self, table_name: str):
-        pass
-
-
-def test_remote_db():
-    conn = lancedb.connect("db://client-will-be-injected", api_key="fake")
-    setattr(conn, "_client", FakeLanceDBClient())
-
-    table = conn["test"]
-    table.schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 2))])
-    table.search([1.0, 2.0]).to_pandas()
-
-
-def test_create_empty_table():
-    client = MagicMock()
-    conn = lancedb.connect("db://client-will-be-injected", api_key="fake")
-
-    conn._client = client
-
-    schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 2))])
-
-    client.post.return_value = {"status": "ok"}
-    table = conn.create_table("test", schema=schema)
-    assert table.name == "test"
-    assert client.post.call_args[0][0] == "/v1/table/test/create/"
-
-    json_schema = {
-        "fields": [
-            {
-                "name": "vector",
-                "nullable": True,
-                "type": {
-                    "type": "fixed_size_list",
-                    "fields": [
-                        {"name": "item", "nullable": True, "type": {"type": "float"}}
-                    ],
-                    "length": 2,
-                },
-            },
-        ]
-    }
-    client.post.return_value = {"schema": json_schema}
-    assert table.schema == schema
-    assert client.post.call_args[0][0] == "/v1/table/test/describe/"
-
-    client.post.return_value = 0
-    assert table.count_rows(None) == 0
-
-
-def test_create_table_with_recordbatches():
-    client = MagicMock()
-    conn = lancedb.connect("db://client-will-be-injected", api_key="fake")
-
-    conn._client = client
-
-    batch = pa.RecordBatch.from_arrays([pa.array([[1.0, 2.0], [3.0, 4.0]])], ["vector"])
-
-    client.post.return_value = {"status": "ok"}
-    table = conn.create_table("test", [batch], schema=batch.schema)
-    assert table.name == "test"
-    assert client.post.call_args[0][0] == "/v1/table/test/create/"
+import pyarrow as pa
 
 
 def make_mock_http_handler(handler):
@@ -100,8 +28,35 @@ def make_mock_http_handler(handler):
     return MockLanceDBHandler
 
 
+@contextlib.contextmanager
+def mock_lancedb_connection(handler):
+    with http.server.HTTPServer(
+        ("localhost", 8080), make_mock_http_handler(handler)
+    ) as server:
+        handle = threading.Thread(target=server.serve_forever)
+        handle.start()
+
+        db = lancedb.connect(
+            "db://dev",
+            api_key="fake",
+            host_override="http://localhost:8080",
+            client_config={
+                "retry_config": {"retries": 2},
+                "timeout_config": {
+                    "connect_timeout": 1,
+                },
+            },
+        )
+
+        try:
+            yield db
+        finally:
+            server.shutdown()
+            handle.join()
+
+
 @contextlib.asynccontextmanager
-async def mock_lancedb_connection(handler):
+async def mock_lancedb_connection_async(handler):
     with http.server.HTTPServer(
         ("localhost", 8080), make_mock_http_handler(handler)
     ) as server:
@@ -143,7 +98,7 @@ async def test_async_remote_db():
         request.end_headers()
         request.wfile.write(b'{"tables": []}')
 
-    async with mock_lancedb_connection(handler) as db:
+    async with mock_lancedb_connection_async(handler) as db:
         table_names = await db.table_names()
         assert table_names == []
 
@@ -159,12 +114,12 @@ async def test_http_error():
         request.end_headers()
         request.wfile.write(b"Internal Server Error")
 
-    async with mock_lancedb_connection(handler) as db:
-        with pytest.raises(HttpError, match="Internal Server Error") as exc_info:
+    async with mock_lancedb_connection_async(handler) as db:
+        with pytest.raises(HttpError) as exc_info:
             await db.table_names()
 
         assert exc_info.value.request_id == request_id_holder["request_id"]
-        assert exc_info.value.status_code == 507
+        assert "Internal Server Error" in str(exc_info.value)
 
 
 @pytest.mark.asyncio
@@ -178,15 +133,253 @@ async def test_retry_error():
         request.end_headers()
         request.wfile.write(b"Try again later")
 
-    async with mock_lancedb_connection(handler) as db:
-        with pytest.raises(RetryError, match="Hit retry limit") as exc_info:
+    async with mock_lancedb_connection_async(handler) as db:
+        with pytest.raises(RetryError) as exc_info:
             await db.table_names()
 
         assert exc_info.value.request_id == request_id_holder["request_id"]
-        assert exc_info.value.status_code == 429
 
         cause = exc_info.value.__cause__
         assert isinstance(cause, HttpError)
         assert "Try again later" in str(cause)
         assert cause.request_id == request_id_holder["request_id"]
         assert cause.status_code == 429
+
+
+@contextlib.contextmanager
+def query_test_table(query_handler):
+    def handler(request):
+        if request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b"{}")
+        elif request.path == "/v1/table/test/query/":
+            content_len = int(request.headers.get("Content-Length"))
+            body = request.rfile.read(content_len)
+            body = json.loads(body)
+
+            data = query_handler(body)
+
+            request.send_response(200)
+            request.send_header("Content-Type", "application/vnd.apache.arrow.file")
+            request.end_headers()
+
+            with pa.ipc.new_file(request.wfile, schema=data.schema) as f:
+                f.write_table(data)
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    with mock_lancedb_connection(handler) as db:
+        assert repr(db) == "RemoteConnect(name=dev)"
+        table = db.open_table("test")
+        assert repr(table) == "RemoteTable(dev.test)"
+        yield table
+
+
+def test_query_sync_minimal():
+    def handler(body):
+        assert body == {
+            "distance_type": "l2",
+            "k": 10,
+            "prefilter": False,
+            "refine_factor": None,
+            "vector": [1.0, 2.0, 3.0],
+            "nprobes": 20,
+        }
+
+        return pa.table({"id": [1, 2, 3]})
+
+    with query_test_table(handler) as table:
+        data = table.search([1, 2, 3]).to_list()
+        expected = [{"id": 1}, {"id": 2}, {"id": 3}]
+        assert data == expected
+
+
+def test_query_sync_empty_query():
+    def handler(body):
+        assert body == {
+            "k": 10,
+            "filter": "true",
+            "vector": [],
+            "columns": ["id"],
+        }
+
+        return pa.table({"id": [1, 2, 3]})
+
+    with query_test_table(handler) as table:
+        data = table.search(None).where("true").select(["id"]).limit(10).to_list()
+        expected = [{"id": 1}, {"id": 2}, {"id": 3}]
+        assert data == expected
+
+
+def test_query_sync_maximal():
+    def handler(body):
+        assert body == {
+            "distance_type": "cosine",
+            "k": 42,
+            "prefilter": True,
+            "refine_factor": 10,
+            "vector": [1.0, 2.0, 3.0],
+            "nprobes": 5,
+            "filter": "id > 0",
+            "columns": ["id", "name"],
+            "vector_column": "vector2",
+            "fast_search": True,
+            "with_row_id": True,
+        }
+
+        return pa.table({"id": [1, 2, 3], "name": ["a", "b", "c"]})
+
+    with query_test_table(handler) as table:
+        (
+            table.search([1, 2, 3], vector_column_name="vector2", fast_search=True)
+            .metric("cosine")
+            .limit(42)
+            .refine_factor(10)
+            .nprobes(5)
+            .where("id > 0", prefilter=True)
+            .with_row_id(True)
+            .select(["id", "name"])
+            .to_list()
+        )
+
+
+def test_query_sync_multiple_vectors():
+    def handler(_body):
+        return pa.table({"id": [1]})
+
+    with query_test_table(handler) as table:
+        results = table.search([[1, 2, 3], [4, 5, 6]]).limit(1).to_list()
+        assert len(results) == 2
+        results.sort(key=lambda x: x["query_index"])
+        assert results == [{"id": 1, "query_index": 0}, {"id": 1, "query_index": 1}]
+
+
+def test_query_sync_fts():
+    def handler(body):
+        assert body == {
+            "full_text_query": {
+                "query": "puppy",
+                "columns": [],
+            },
+            "k": 10,
+            "vector": [],
+        }
+
+        return pa.table({"id": [1, 2, 3]})
+
+    with query_test_table(handler) as table:
+        (table.search("puppy", query_type="fts").to_list())
+
+    def handler(body):
+        assert body == {
+            "full_text_query": {
+                "query": "puppy",
+                "columns": ["name", "description"],
+            },
+            "k": 42,
+            "vector": [],
+            "with_row_id": True,
+        }
+
+        return pa.table({"id": [1, 2, 3]})
+
+    with query_test_table(handler) as table:
+        (
+            table.search("puppy", query_type="fts", fts_columns=["name", "description"])
+            .with_row_id(True)
+            .limit(42)
+            .to_list()
+        )
+
+
+def test_query_sync_hybrid():
+    def handler(body):
+        if "full_text_query" in body:
+            # FTS query
+            assert body == {
+                "full_text_query": {
+                    "query": "puppy",
+                    "columns": [],
+                },
+                "k": 42,
+                "vector": [],
+                "with_row_id": True,
+            }
+            return pa.table({"_rowid": [1, 2, 3], "_score": [0.1, 0.2, 0.3]})
+        else:
+            # Vector query
+            assert body == {
+                "distance_type": "l2",
+                "k": 42,
+                "prefilter": False,
+                "refine_factor": None,
+                "vector": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                "nprobes": 20,
+                "with_row_id": True,
+            }
+            return pa.table({"_rowid": [1, 2, 3], "_distance": [0.1, 0.2, 0.3]})
+
+    with query_test_table(handler) as table:
+        embedding_func = MockTextEmbeddingFunction()
+        embedding_config = MagicMock()
+        embedding_config.function = embedding_func
+
+        embedding_funcs = MagicMock()
+        embedding_funcs.get = MagicMock(return_value=embedding_config)
+        table.embedding_functions = embedding_funcs
+
+        (table.search("puppy", query_type="hybrid").limit(42).to_list())
+
+
+def test_create_client():
+    mandatory_args = {
+        "uri": "db://dev",
+        "api_key": "fake-api-key",
+        "region": "us-east-1",
+    }
+
+    db = lancedb.connect(**mandatory_args)
+    assert isinstance(db.client_config, ClientConfig)
+
+    db = lancedb.connect(**mandatory_args, client_config={})
+    assert isinstance(db.client_config, ClientConfig)
+
+    db = lancedb.connect(
+        **mandatory_args,
+        client_config=ClientConfig(timeout_config={"connect_timeout": 42}),
+    )
+    assert isinstance(db.client_config, ClientConfig)
+    assert db.client_config.timeout_config.connect_timeout == timedelta(seconds=42)
+
+    db = lancedb.connect(
+        **mandatory_args,
+        client_config={"timeout_config": {"connect_timeout": timedelta(seconds=42)}},
+    )
+    assert isinstance(db.client_config, ClientConfig)
+    assert db.client_config.timeout_config.connect_timeout == timedelta(seconds=42)
+
+    db = lancedb.connect(
+        **mandatory_args, client_config=ClientConfig(retry_config={"retries": 42})
+    )
+    assert isinstance(db.client_config, ClientConfig)
+    assert db.client_config.retry_config.retries == 42
+
+    db = lancedb.connect(
+        **mandatory_args, client_config={"retry_config": {"retries": 42}}
+    )
+    assert isinstance(db.client_config, ClientConfig)
+    assert db.client_config.retry_config.retries == 42
+
+    with pytest.warns(DeprecationWarning):
+        db = lancedb.connect(**mandatory_args, connection_timeout=42)
+        assert db.client_config.timeout_config.connect_timeout == timedelta(seconds=42)
+
+    with pytest.warns(DeprecationWarning):
+        db = lancedb.connect(**mandatory_args, read_timeout=42)
+        assert db.client_config.timeout_config.read_timeout == timedelta(seconds=42)
+
+    with pytest.warns(DeprecationWarning):
+        lancedb.connect(**mandatory_args, request_thread_pool=10)

python/python/tests/test_rerankers.py

@@ -16,6 +16,7 @@ from lancedb.rerankers import (
     OpenaiReranker,
     JinaReranker,
     AnswerdotaiRerankers,
+    VoyageAIReranker,
 )
 from lancedb.table import LanceTable
 
@@ -344,3 +345,14 @@ def test_jina_reranker(tmp_path, use_tantivy):
     table, schema = get_test_table(tmp_path, use_tantivy)
     reranker = JinaReranker()
     _run_test_reranker(reranker, table, "single player experience", None, schema)
+
+
+@pytest.mark.skipif(
+    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
+)
+@pytest.mark.parametrize("use_tantivy", [True, False])
+def test_voyageai_reranker(tmp_path, use_tantivy):
+    pytest.importorskip("voyageai")
+    reranker = VoyageAIReranker(model_name="rerank-2")
+    table, schema = get_test_table(tmp_path, use_tantivy)
+    _run_test_reranker(reranker, table, "single player experience", None, schema)