test

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.18.0"
+current_version = "0.11.0-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.
@@ -66,47 +66,6 @@ glob = "nodejs/npm/*/package.json"
 replace = "\"version\": \"{new_version}\","
 search = "\"version\": \"{current_version}\","
 
-# vectodb node binary packages
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-darwin-arm64\": \"{new_version}\""
-search = "\"@lancedb/vectordb-darwin-arm64\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-darwin-x64\": \"{new_version}\""
-search = "\"@lancedb/vectordb-darwin-x64\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-linux-arm64-gnu\": \"{new_version}\""
-search = "\"@lancedb/vectordb-linux-arm64-gnu\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-linux-x64-gnu\": \"{new_version}\""
-search = "\"@lancedb/vectordb-linux-x64-gnu\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-linux-arm64-musl\": \"{new_version}\""
-search = "\"@lancedb/vectordb-linux-arm64-musl\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-linux-x64-musl\": \"{new_version}\""
-search = "\"@lancedb/vectordb-linux-x64-musl\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-win32-x64-msvc\": \"{new_version}\""
-search = "\"@lancedb/vectordb-win32-x64-msvc\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-win32-arm64-msvc\": \"{new_version}\""
-search = "\"@lancedb/vectordb-win32-arm64-msvc\": \"{current_version}\""
-
 # Cargo files
 # ------------
 [[tool.bumpversion.files]]
@@ -118,8 +77,3 @@ search = "\nversion = \"{current_version}\""
 filename = "rust/lancedb/Cargo.toml"
 replace = "\nversion = \"{new_version}\""
 search = "\nversion = \"{current_version}\""
-
-[[tool.bumpversion.files]]
-filename = "nodejs/Cargo.toml"
-replace = "\nversion = \"{new_version}\""
-search = "\nversion = \"{current_version}\""

.cargo/config.toml

@@ -31,9 +31,6 @@ rustflags = [
 [target.x86_64-unknown-linux-gnu]
 rustflags = ["-C", "target-cpu=haswell", "-C", "target-feature=+avx2,+fma,+f16c"]
 
-[target.x86_64-unknown-linux-musl]
-rustflags = ["-C", "target-cpu=haswell", "-C", "target-feature=-crt-static,+avx2,+fma,+f16c"]
-
 [target.aarch64-apple-darwin]
 rustflags = ["-C", "target-cpu=apple-m1", "-C", "target-feature=+neon,+fp16,+fhm,+dotprod"]
 
@@ -41,7 +38,3 @@ rustflags = ["-C", "target-cpu=apple-m1", "-C", "target-feature=+neon,+fp16,+fhm
 # not found errors on systems that are missing it.
 [target.x86_64-pc-windows-msvc]
 rustflags = ["-Ctarget-feature=+crt-static"]
-
-# Experimental target for Arm64 Windows
-[target.aarch64-pc-windows-msvc]
-rustflags = ["-Ctarget-feature=+crt-static"]

.github/workflows/build_linux_wheel/action.yml

@@ -52,7 +52,12 @@ runs:
         args: ${{ inputs.args }}
         before-script-linux: |
           set -e
-          yum install -y openssl-devel clang \
+          apt install -y unzip
-            && curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-aarch_64.zip > /tmp/protoc.zip \
+          if [ $(uname -m) = "x86_64" ]; then
+            PROTOC_ARCH="x86_64"
+          else
+            PROTOC_ARCH="aarch_64"
+          fi
+          curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-$PROTOC_ARCH.zip > /tmp/protoc.zip \
             && unzip /tmp/protoc.zip -d /usr/local \
             && rm /tmp/protoc.zip

.github/workflows/build_mac_wheel/action.yml

@@ -20,7 +20,7 @@ runs:
       uses: PyO3/maturin-action@v1
       with:
         command: build
-        # TODO: pass through interpreter
         args: ${{ inputs.args }}
         docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         working-directory: python
+        interpreter: 3.${{ inputs.python-minor-version }}

.github/workflows/build_windows_wheel/action.yml

@@ -28,7 +28,7 @@ runs:
         args: ${{ inputs.args }}
         docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         working-directory: python
-    - uses: actions/upload-artifact@v4
+    - uses: actions/upload-artifact@v3
       with:
         name: windows-wheels
         path: python\target\wheels

.github/workflows/docs.yml

@@ -31,7 +31,7 @@ jobs:
       - name: Install dependecies needed for ubuntu
         run: |
           sudo apt install -y protobuf-compiler libssl-dev
-          rustup update && rustup default
+          rustup update && rustup default        
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
@@ -41,8 +41,8 @@ jobs:
       - name: Build Python
         working-directory: python
         run: |
-          python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .
+          python -m pip install -e .
-          python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -r ../docs/requirements.txt
+          python -m pip install -r ../docs/requirements.txt
       - name: Set up node
         uses: actions/setup-node@v3
         with:
@@ -72,9 +72,9 @@ jobs:
       - name: Setup Pages
         uses: actions/configure-pages@v2
       - name: Upload artifact
-        uses: actions/upload-pages-artifact@v3
+        uses: actions/upload-pages-artifact@v1
         with:
           path: "docs/site"
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v4
+        uses: actions/deploy-pages@v1

.github/workflows/docs_test.yml

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

.github/workflows/license-header-check.yml

@@ -1,31 +0,0 @@
-name: Check license headers
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    paths:
-      - rust/**
-      - python/**
-      - nodejs/**
-      - java/**
-      - .github/workflows/license-header-check.yml
-jobs:
-  check-licenses:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-      - name: Install license-header-checker
-        working-directory: /tmp
-        run: |
-            curl -s https://raw.githubusercontent.com/lluissm/license-header-checker/master/install.sh | bash
-            mv /tmp/bin/license-header-checker /usr/local/bin/
-      - name: Check license headers (rust)
-        run: license-header-checker -a -v ./rust/license_header.txt ./ rs && [[ -z `git status -s` ]]
-      - name: Check license headers (python)
-        run: license-header-checker -a -v ./python/license_header.txt python py && [[ -z `git status -s` ]]
-      - name: Check license headers (typescript)
-        run: license-header-checker -a -v ./nodejs/license_header.txt nodejs ts && [[ -z `git status -s` ]]
-      - name: Check license headers (java)
-        run: license-header-checker -a -v ./nodejs/license_header.txt java java && [[ -z `git status -s` ]]

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

@@ -43,7 +43,7 @@ on:
 jobs:
   make-release:
     # Creates tag and GH release. The GH release will trigger the build and release jobs.
-    runs-on: ubuntu-24.04
+    runs-on: ubuntu-latest
     permissions:
       contents: write
     steps:
@@ -57,14 +57,15 @@ jobs:
           # trigger any workflows watching for new tags. See:
           # https://docs.github.com/en/actions/using-workflows/triggering-a-workflow#triggering-a-workflow-from-a-workflow
           token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
-      - name: Validate Lance dependency is at stable version
-        if: ${{ inputs.type == 'stable' }}
-        run: python ci/validate_stable_lance.py
       - name: Set git configs for bumpversion
         shell: bash
         run: |
           git config user.name 'Lance Release'
           git config user.email 'lance-dev@lancedb.com'
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
       - name: Bump Python version
         if: ${{ inputs.python }}
         working-directory: python
@@ -96,7 +97,3 @@ jobs:
         if: ${{ !inputs.dry_run && inputs.other }}
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
-      - uses: ./.github/workflows/update_package_lock_nodejs
-        if: ${{ !inputs.dry_run && inputs.other }}
-        with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/nodejs.yml

@@ -53,9 +53,6 @@ jobs:
         cargo clippy --all --all-features -- -D warnings
         npm ci
         npm run lint-ci
-    - name: Lint examples
-      working-directory: nodejs/examples
-      run: npm ci && npm run lint-ci
   linux:
     name: Linux (NodeJS ${{ matrix.node-version }})
     timeout-minutes: 30
@@ -94,30 +91,6 @@ jobs:
       env:
         S3_TEST: "1"
       run: npm run test
-    - name: Setup examples
-      working-directory: nodejs/examples
-      run: npm ci
-    - name: Test examples
-      working-directory: ./
-      env:
-        OPENAI_API_KEY: test
-        OPENAI_BASE_URL: http://0.0.0.0:8000
-      run: |
-        python ci/mock_openai.py &
-        cd nodejs/examples
-        npm test
-    - name: Check docs
-      run: |
-        # We run this as part of the job because the binary needs to be built
-        # first to export the types of the native code.
-        set -e
-        npm ci
-        npm run docs
-        if ! git diff --exit-code; then
-          echo "Docs need to be updated"
-          echo "Run 'npm run docs', fix any warnings, and commit the changes."
-          exit 1
-        fi
   macos:
     timeout-minutes: 30
     runs-on: "macos-14"

.github/workflows/npm-publish.yml

@@ -101,7 +101,7 @@ jobs:
           path: |
             nodejs/dist/*.node
 
-  node-linux-gnu:
+  node-linux:
     name: vectordb (${{ matrix.config.arch}}-unknown-linux-gnu)
     runs-on: ${{ matrix.config.runner }}
     # Only runs on tags that matches the make-release action
@@ -133,67 +133,15 @@ jobs:
           free -h
       - name: Build Linux Artifacts
         run: |
-          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }} ${{ matrix.config.arch }}-unknown-linux-gnu
+          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }}
       - name: Upload Linux Artifacts
         uses: actions/upload-artifact@v4
         with:
-          name: node-native-linux-${{ matrix.config.arch }}-gnu
+          name: node-native-linux-${{ matrix.config.arch }}
           path: |
             node/dist/lancedb-vectordb-linux*.tgz
 
-  node-linux-musl:
+  nodejs-linux:
-    name: vectordb (${{ matrix.config.arch}}-unknown-linux-musl)
-    runs-on: ubuntu-latest
-    container: alpine:edge
-    # Only runs on tags that matches the make-release action
-    if: startsWith(github.ref, 'refs/tags/v')
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - arch: x86_64
-          - arch: aarch64
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install common dependencies
-        run: |
-          apk add protobuf-dev curl clang mold grep npm bash
-          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y
-          echo "source $HOME/.cargo/env" >> saved_env
-          echo "export CC=clang" >> saved_env
-          echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=-crt-static,+avx2,+fma,+f16c -Clinker=clang -Clink-arg=-fuse-ld=mold'" >> saved_env
-      - name: Configure aarch64 build
-        if: ${{ matrix.config.arch == 'aarch64' }}
-        run: |
-          source "$HOME/.cargo/env"
-          rustup target add aarch64-unknown-linux-musl
-          crt=$(realpath $(dirname $(rustup which rustc))/../lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained)
-          sysroot_lib=/usr/aarch64-unknown-linux-musl/usr/lib
-          apk_url=https://dl-cdn.alpinelinux.org/alpine/latest-stable/main/aarch64/
-          curl -sSf $apk_url > apk_list
-          for pkg in gcc libgcc musl; do curl -sSf $apk_url$(cat apk_list | grep -oP '(?<=")'$pkg'-\d.*?(?=")') | tar zxf -; done
-          mkdir -p $sysroot_lib
-          echo 'GROUP ( libgcc_s.so.1 -lgcc )' > $sysroot_lib/libgcc_s.so
-          cp usr/lib/libgcc_s.so.1 $sysroot_lib
-          cp usr/lib/gcc/aarch64-alpine-linux-musl/*/libgcc.a $sysroot_lib
-          cp lib/ld-musl-aarch64.so.1 $sysroot_lib/libc.so
-          echo '!<arch>' > $sysroot_lib/libdl.a
-          (cd $crt && cp crti.o crtbeginS.o crtendS.o crtn.o -t $sysroot_lib)
-          echo "export CARGO_BUILD_TARGET=aarch64-unknown-linux-musl" >> saved_env
-          echo "export RUSTFLAGS='-Ctarget-cpu=apple-m1 -Ctarget-feature=-crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=clang -Clink-arg=-fuse-ld=mold -Clink-arg=--target=aarch64-unknown-linux-musl -Clink-arg=--sysroot=/usr/aarch64-unknown-linux-musl -Clink-arg=-lc'" >> saved_env
-      - name: Build Linux Artifacts
-        run: |
-          source ./saved_env
-          bash ci/manylinux_node/build_vectordb.sh ${{ matrix.config.arch }} ${{ matrix.config.arch }}-unknown-linux-musl
-      - name: Upload Linux Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: node-native-linux-${{ matrix.config.arch }}-musl
-          path: |
-            node/dist/lancedb-vectordb-linux*.tgz
-
-  nodejs-linux-gnu:
     name: lancedb (${{ matrix.config.arch}}-unknown-linux-gnu
     runs-on: ${{ matrix.config.runner }}
     # Only runs on tags that matches the make-release action
@@ -230,7 +178,7 @@ jobs:
       - name: Upload Linux Artifacts
         uses: actions/upload-artifact@v4
         with:
-          name: nodejs-native-linux-${{ matrix.config.arch }}-gnu
+          name: nodejs-native-linux-${{ matrix.config.arch }}
           path: |
             nodejs/dist/*.node
       # The generic files are the same in all distros so we just pick
@@ -244,62 +192,6 @@ jobs:
             nodejs/dist/*
             !nodejs/dist/*.node
 
-  nodejs-linux-musl:
-    name: lancedb (${{ matrix.config.arch}}-unknown-linux-musl
-    runs-on: ubuntu-latest
-    container: alpine:edge
-    # Only runs on tags that matches the make-release action
-    if: startsWith(github.ref, 'refs/tags/v')
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - arch: x86_64
-          - arch: aarch64
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install common dependencies
-        run: |
-          apk add protobuf-dev curl clang mold grep npm bash openssl-dev openssl-libs-static
-          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y
-          echo "source $HOME/.cargo/env" >> saved_env
-          echo "export CC=clang" >> saved_env
-          echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=-crt-static,+avx2,+fma,+f16c -Clinker=clang -Clink-arg=-fuse-ld=mold'" >> saved_env
-          echo "export X86_64_UNKNOWN_LINUX_MUSL_OPENSSL_INCLUDE_DIR=/usr/include" >> saved_env
-          echo "export X86_64_UNKNOWN_LINUX_MUSL_OPENSSL_LIB_DIR=/usr/lib" >> saved_env
-      - name: Configure aarch64 build
-        if: ${{ matrix.config.arch == 'aarch64' }}
-        run: |
-          source "$HOME/.cargo/env"
-          rustup target add aarch64-unknown-linux-musl
-          crt=$(realpath $(dirname $(rustup which rustc))/../lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained)
-          sysroot_lib=/usr/aarch64-unknown-linux-musl/usr/lib
-          apk_url=https://dl-cdn.alpinelinux.org/alpine/latest-stable/main/aarch64/
-          curl -sSf $apk_url > apk_list
-          for pkg in gcc libgcc musl openssl-dev openssl-libs-static; do curl -sSf $apk_url$(cat apk_list | grep -oP '(?<=")'$pkg'-\d.*?(?=")') | tar zxf -; done
-          mkdir -p $sysroot_lib
-          echo 'GROUP ( libgcc_s.so.1 -lgcc )' > $sysroot_lib/libgcc_s.so
-          cp usr/lib/libgcc_s.so.1 $sysroot_lib
-          cp usr/lib/gcc/aarch64-alpine-linux-musl/*/libgcc.a $sysroot_lib
-          cp lib/ld-musl-aarch64.so.1 $sysroot_lib/libc.so
-          echo '!<arch>' > $sysroot_lib/libdl.a
-          (cd $crt && cp crti.o crtbeginS.o crtendS.o crtn.o -t $sysroot_lib)
-          echo "export CARGO_BUILD_TARGET=aarch64-unknown-linux-musl" >> saved_env
-          echo "export RUSTFLAGS='-Ctarget-feature=-crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=clang -Clink-arg=-fuse-ld=mold -Clink-arg=--target=aarch64-unknown-linux-musl -Clink-arg=--sysroot=/usr/aarch64-unknown-linux-musl -Clink-arg=-lc'" >> saved_env
-          echo "export AARCH64_UNKNOWN_LINUX_MUSL_OPENSSL_INCLUDE_DIR=$(realpath usr/include)" >> saved_env
-          echo "export AARCH64_UNKNOWN_LINUX_MUSL_OPENSSL_LIB_DIR=$(realpath usr/lib)" >> saved_env
-      - name: Build Linux Artifacts
-        run: |
-          source ./saved_env
-          bash ci/manylinux_node/build_lancedb.sh ${{ matrix.config.arch }}
-      - name: Upload Linux Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: nodejs-native-linux-${{ matrix.config.arch }}-musl
-          path: |
-            nodejs/dist/*.node
-
   node-windows:
     name: vectordb ${{ matrix.target }}
     runs-on: windows-2022
@@ -334,51 +226,6 @@ jobs:
           path: |
             node/dist/lancedb-vectordb-win32*.tgz
 
-  node-windows-arm64:
-    name: vectordb ${{ matrix.config.arch }}-pc-windows-msvc
-    # if: startsWith(github.ref, 'refs/tags/v')
-    runs-on: ubuntu-latest
-    container: alpine:edge
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          # - arch: x86_64
-          - arch: aarch64
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install dependencies
-        run: |
-          apk add protobuf-dev curl clang lld llvm19 grep npm bash msitools sed
-          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y
-          echo "source $HOME/.cargo/env" >> saved_env
-          echo "export CC=clang" >> saved_env
-          echo "export AR=llvm-ar" >> saved_env
-          source "$HOME/.cargo/env"
-          rustup target add ${{ matrix.config.arch }}-pc-windows-msvc
-          (mkdir -p sysroot && cd sysroot && sh ../ci/sysroot-${{ matrix.config.arch }}-pc-windows-msvc.sh)
-          echo "export C_INCLUDE_PATH=/usr/${{ matrix.config.arch }}-pc-windows-msvc/usr/include" >> saved_env
-          echo "export CARGO_BUILD_TARGET=${{ matrix.config.arch }}-pc-windows-msvc" >> saved_env
-      - name: Configure x86_64 build
-        if: ${{ matrix.config.arch == 'x86_64' }}
-        run: |
-          echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=+crt-static,+avx2,+fma,+f16c -Clinker=lld -Clink-arg=/LIBPATH:/usr/x86_64-pc-windows-msvc/usr/lib'" >> saved_env
-      - name: Configure aarch64 build
-        if: ${{ matrix.config.arch == 'aarch64' }}
-        run: |
-          echo "export RUSTFLAGS='-Ctarget-feature=+crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=lld -Clink-arg=/LIBPATH:/usr/aarch64-pc-windows-msvc/usr/lib -Clink-arg=arm64rt.lib'" >> saved_env
-      - name: Build Windows Artifacts
-        run: |
-          source ./saved_env
-          bash ci/manylinux_node/build_vectordb.sh ${{ matrix.config.arch }} ${{ matrix.config.arch }}-pc-windows-msvc
-      - name: Upload Windows Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: node-native-windows-${{ matrix.config.arch }}
-          path: |
-            node/dist/lancedb-vectordb-win32*.tgz
-
   nodejs-windows:
     name: lancedb ${{ matrix.target }}
     runs-on: windows-2022
@@ -413,57 +260,9 @@ jobs:
           path: |
             nodejs/dist/*.node
 
-  nodejs-windows-arm64:
-    name: lancedb ${{ matrix.config.arch }}-pc-windows-msvc
-    # Only runs on tags that matches the make-release action
-    # if: startsWith(github.ref, 'refs/tags/v')
-    runs-on: ubuntu-latest
-    container: alpine:edge
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          # - arch: x86_64
-          - arch: aarch64
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install dependencies
-        run: |
-          apk add protobuf-dev curl clang lld llvm19 grep npm bash msitools sed
-          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y
-          echo "source $HOME/.cargo/env" >> saved_env
-          echo "export CC=clang" >> saved_env
-          echo "export AR=llvm-ar" >> saved_env
-          source "$HOME/.cargo/env"
-          rustup target add ${{ matrix.config.arch }}-pc-windows-msvc
-          (mkdir -p sysroot && cd sysroot && sh ../ci/sysroot-${{ matrix.config.arch }}-pc-windows-msvc.sh)
-          echo "export C_INCLUDE_PATH=/usr/${{ matrix.config.arch }}-pc-windows-msvc/usr/include" >> saved_env
-          echo "export CARGO_BUILD_TARGET=${{ matrix.config.arch }}-pc-windows-msvc" >> saved_env
-          printf '#!/bin/sh\ncargo "$@"' > $HOME/.cargo/bin/cargo-xwin
-          chmod u+x $HOME/.cargo/bin/cargo-xwin
-      - name: Configure x86_64 build
-        if: ${{ matrix.config.arch == 'x86_64' }}
-        run: |
-          echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=+crt-static,+avx2,+fma,+f16c -Clinker=lld -Clink-arg=/LIBPATH:/usr/x86_64-pc-windows-msvc/usr/lib'" >> saved_env
-      - name: Configure aarch64 build
-        if: ${{ matrix.config.arch == 'aarch64' }}
-        run: |
-          echo "export RUSTFLAGS='-Ctarget-feature=+crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=lld -Clink-arg=/LIBPATH:/usr/aarch64-pc-windows-msvc/usr/lib -Clink-arg=arm64rt.lib'" >> saved_env
-      - name: Build Windows Artifacts
-        run: |
-          source ./saved_env
-          bash ci/manylinux_node/build_lancedb.sh ${{ matrix.config.arch }}
-      - name: Upload Windows Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: nodejs-native-windows-${{ matrix.config.arch }}
-          path: |
-            nodejs/dist/*.node
-
   release:
     name: vectordb NPM Publish
-    needs: [node, node-macos, node-linux-gnu, node-linux-musl, node-windows, node-windows-arm64]
+    needs: [node, node-macos, node-linux, node-windows]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -481,7 +280,7 @@ jobs:
         env:
           NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
         run: |
-          # Tag beta as "preview" instead of default "latest". See lancedb
+          # Tag beta as "preview" instead of default "latest". See lancedb 
           # npm publish step for more info.
           if [[ $GITHUB_REF =~ refs/tags/v(.*)-beta.* ]]; then
             PUBLISH_ARGS="--tag preview"
@@ -503,7 +302,7 @@ jobs:
 
   release-nodejs:
     name: lancedb NPM Publish
-    needs: [nodejs-macos, nodejs-linux-gnu, nodejs-linux-musl, nodejs-windows, nodejs-windows-arm64]
+    needs: [nodejs-macos, nodejs-linux, nodejs-windows]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -561,7 +360,6 @@ jobs:
           SLACK_WEBHOOK_URL: ${{ secrets.ACTION_MONITORING_SLACK }}
 
   update-package-lock:
-    if: startsWith(github.ref, 'refs/tags/v')
     needs: [release]
     runs-on: ubuntu-latest
     permissions:
@@ -571,7 +369,7 @@ jobs:
         uses: actions/checkout@v4
         with:
           ref: main
-          token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          persist-credentials: false
           fetch-depth: 0
           lfs: true
       - uses: ./.github/workflows/update_package_lock
@@ -579,7 +377,6 @@ jobs:
           github_token: ${{ secrets.GITHUB_TOKEN }}
 
   update-package-lock-nodejs:
-    if: startsWith(github.ref, 'refs/tags/v')
     needs: [release-nodejs]
     runs-on: ubuntu-latest
     permissions:
@@ -589,7 +386,7 @@ jobs:
         uses: actions/checkout@v4
         with:
           ref: main
-          token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          persist-credentials: false
           fetch-depth: 0
           lfs: true
       - uses: ./.github/workflows/update_package_lock_nodejs
@@ -597,7 +394,6 @@ jobs:
           github_token: ${{ secrets.GITHUB_TOKEN }}
 
   gh-release:
-    if: startsWith(github.ref, 'refs/tags/v')
     runs-on: ubuntu-latest
     permissions:
       contents: write

.github/workflows/pypi-publish.yml

@@ -15,21 +15,15 @@ jobs:
           - platform: x86_64
             manylinux: "2_17"
             extra_args: ""
-            runner: ubuntu-22.04
           - platform: x86_64
             manylinux: "2_28"
             extra_args: "--features fp16kernels"
-            runner: ubuntu-22.04
           - platform: aarch64
-            manylinux: "2_17"
+            manylinux: "2_24"
             extra_args: ""
-            # For successful fat LTO builds, we need a large runner to avoid OOM errors.
+          # We don't build fp16 kernels for aarch64, because it uses 
-            runner: ubuntu-2404-8x-arm64
+          # cross compilation image, which doesn't have a new enough compiler.
-          - platform: aarch64
+    runs-on: "ubuntu-22.04"
-            manylinux: "2_28"
-            extra_args: "--features fp16kernels"
-            runner: ubuntu-2404-8x-arm64
-    runs-on: ${{ matrix.config.runner }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -89,7 +83,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.12
+          python-version: 3.8
       - uses: ./.github/workflows/build_windows_wheel
         with:
           python-minor-version: 8

.github/workflows/python.yml

@@ -30,17 +30,16 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.12"
+          python-version: "3.11"
       - name: Install ruff
         run: |
-          pip install ruff==0.9.9
+          pip install ruff==0.5.4
       - name: Format check
         run: ruff format --check .
       - name: Lint
         run: ruff check .
-
+  doctest:
-  type-check:
+    name: "Doctest"
-    name: "Type Check"
     timeout-minutes: 30
     runs-on: "ubuntu-22.04"
     defaults:
@@ -55,36 +54,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.12"
+          python-version: "3.11"
-      - name: Install protobuf compiler
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler
-          pip install toml
-      - name: Install dependencies
-        run: |
-          python ../ci/parse_requirements.py pyproject.toml --extras dev,tests,embeddings > requirements.txt
-          pip install -r requirements.txt
-      - name: Run pyright
-        run: pyright
-
-  doctest:
-    name: "Doctest"
-    timeout-minutes: 30
-    runs-on: "ubuntu-24.04"
-    defaults:
-      run:
-        shell: bash
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
           cache: "pip"
       - name: Install protobuf
         run: |
@@ -105,8 +75,8 @@ jobs:
     timeout-minutes: 30
     strategy:
       matrix:
-        python-minor-version: ["9", "12"]
+        python-minor-version: ["9", "11"]
-    runs-on: "ubuntu-24.04"
+    runs-on: "ubuntu-22.04"
     defaults:
       run:
         shell: bash
@@ -157,7 +127,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.12"
+          python-version: "3.11"
       - uses: Swatinem/rust-cache@v2
         with:
           workspaces: python
@@ -168,7 +138,7 @@ jobs:
         run: rm -rf target/wheels
   windows:
     name: "Windows: ${{ matrix.config.name }}"
-    timeout-minutes: 60
+    timeout-minutes: 30
     strategy:
       matrix:
         config:
@@ -187,7 +157,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.12"
+          python-version: "3.11"
       - uses: Swatinem/rust-cache@v2
         with:
           workspaces: python
@@ -198,7 +168,7 @@ jobs:
         run: rm -rf target/wheels
   pydantic1x:
     timeout-minutes: 30
-    runs-on: "ubuntu-24.04"
+    runs-on: "ubuntu-22.04"
     defaults:
       run:
         shell: bash

.github/workflows/rust.yml

@@ -22,7 +22,6 @@ env:
   # "1" means line tables only, which is useful for panic tracebacks.
   RUSTFLAGS: "-C debuginfo=1"
   RUST_BACKTRACE: "1"
-  CARGO_INCREMENTAL: 0
 
 jobs:
   lint:
@@ -36,49 +35,21 @@ jobs:
       CC: clang-18
       CXX: clang++-18
     steps:
-      - uses: actions/checkout@v4
+    - uses: actions/checkout@v4
-        with:
+      with:
           fetch-depth: 0
           lfs: true
-      - uses: Swatinem/rust-cache@v2
+    - uses: Swatinem/rust-cache@v2
-        with:
+      with:
-          workspaces: rust
+        workspaces: rust
-      - name: Install dependencies
+    - name: Install dependencies
-        run: |
+      run: |
           sudo apt update
           sudo apt install -y protobuf-compiler libssl-dev
-      - name: Run format
+    - name: Run format
-        run: cargo fmt --all -- --check
+      run: cargo fmt --all -- --check
-      - name: Run clippy
+    - name: Run clippy
-        run: cargo clippy --workspace --tests --all-features -- -D warnings
+      run: cargo clippy --workspace --tests --all-features -- -D warnings
-
-  build-no-lock:
-    runs-on: ubuntu-24.04
-    timeout-minutes: 30
-    env:
-      # Need up-to-date compilers for kernels
-      CC: clang
-      CXX: clang++
-    steps:
-      - uses: actions/checkout@v4
-      # Building without a lock file often requires the latest Rust version since downstream
-      # dependencies may have updated their minimum Rust version.
-      - uses: actions-rust-lang/setup-rust-toolchain@v1
-        with:
-          toolchain: "stable"
-      # Remove cargo.lock to force a fresh build
-      - name: Remove Cargo.lock
-        run: rm -f Cargo.lock
-      - uses: rui314/setup-mold@v1
-      - uses: Swatinem/rust-cache@v2
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler libssl-dev
-      - name: Build all
-        run: |
-          cargo build --benches --all-features --tests
-
   linux:
     timeout-minutes: 30
     # To build all features, we need more disk space than is available
@@ -94,41 +65,37 @@ jobs:
       CC: clang-18
       CXX: clang++-18
     steps:
-      - uses: actions/checkout@v4
+    - uses: actions/checkout@v4
-        with:
+      with:
           fetch-depth: 0
           lfs: true
-      - uses: Swatinem/rust-cache@v2
+    - uses: Swatinem/rust-cache@v2
-        with:
+      with:
           workspaces: rust
-      - name: Install dependencies
+    - name: Install dependencies
-        run: |
+      run: |
-          # This shaves 2 minutes off this step in CI. This doesn't seem to be
+          sudo apt update
-          # necessary in standard runners, but it is in the 4x runners.
-          sudo rm /var/lib/man-db/auto-update
           sudo apt install -y protobuf-compiler libssl-dev
-      - uses: rui314/setup-mold@v1
+    - name: Make Swap
-      - name: Make Swap
+      run: |
-        run: |
+        sudo fallocate -l 16G /swapfile
-          sudo fallocate -l 16G /swapfile
+        sudo chmod 600 /swapfile
-          sudo chmod 600 /swapfile
+        sudo mkswap /swapfile
-          sudo mkswap /swapfile
+        sudo swapon /swapfile
-          sudo swapon /swapfile
+    - name: Start S3 integration test environment
-      - name: Start S3 integration test environment
+      working-directory: .
-        working-directory: .
+      run: docker compose up --detach --wait
-        run: docker compose up --detach --wait
+    - name: Build
-      - name: Build
+      run: cargo build --all-features
-        run: cargo build --all-features --tests --locked --examples
+    - name: Run tests
-      - name: Run tests
+      run: cargo test --all-features
-        run: cargo test --all-features --locked
+    - name: Run examples
-      - name: Run examples
+      run: cargo run --example simple
-        run: cargo run --example simple --locked
-
   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:
@@ -137,8 +104,8 @@ jobs:
     steps:
       - uses: actions/checkout@v4
         with:
-          fetch-depth: 0
+            fetch-depth: 0
-          lfs: true
+            lfs: true
       - name: CPU features
         run: sysctl -a | grep cpu
       - uses: Swatinem/rust-cache@v2
@@ -146,15 +113,11 @@ jobs:
           workspaces: rust
       - name: Install dependencies
         run: brew install protobuf
+      - name: Build
+        run: cargo build --all-features
       - name: Run tests
-        run: |
+        # Run with everything except the integration tests.
-          # Don't run the s3 integration tests since docker isn't available
+        run: cargo test --features remote,fp16kernels
-          # on this image.
-          ALL_FEATURES=`cargo metadata --format-version=1 --no-deps \
-            | jq -r '.packages[] | .features | keys | .[]' \
-            | grep -v s3-test | sort | uniq | paste -s -d "," -`
-          cargo test --features $ALL_FEATURES --locked
-
   windows:
     runs-on: windows-2022
     steps:
@@ -174,170 +137,5 @@ jobs:
       - name: Run tests
         run: |
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
-          cargo test --features remote --locked
+          cargo build
-
+          cargo test
-  windows-arm64-cross:
-    # We cross compile in Node releases, so we want to make sure
-    # this can run successfully.
-    runs-on: ubuntu-latest
-    container: alpine:edge
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install dependencies (part 1)
-        run: |
-          set -e
-          apk add protobuf-dev curl clang lld llvm19 grep npm bash msitools sed
-      - name: Install rust
-        uses: actions-rust-lang/setup-rust-toolchain@v1
-        with:
-          target: aarch64-pc-windows-msvc
-      - name: Install dependencies (part 2)
-        run: |
-          set -e
-          mkdir -p sysroot
-          cd sysroot
-          sh ../ci/sysroot-aarch64-pc-windows-msvc.sh
-      - name: Check
-        env:
-          CC: clang
-          AR: llvm-ar
-          C_INCLUDE_PATH: /usr/aarch64-pc-windows-msvc/usr/include
-          CARGO_BUILD_TARGET: aarch64-pc-windows-msvc
-          RUSTFLAGS: -Ctarget-feature=+crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=lld -Clink-arg=/LIBPATH:/usr/aarch64-pc-windows-msvc/usr/lib -Clink-arg=arm64rt.lib
-        run: |
-          source $HOME/.cargo/env
-          cargo check --features remote --locked
-
-  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 --default-toolchain 1.83.0
-        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 = $env:VCPKG_INSTALLATION_ROOT
-          cargo test --target aarch64-pc-windows-msvc --features remote --locked
-
-  msrv:
-    # Check the minimum supported Rust version
-    name: MSRV Check - Rust v${{ matrix.msrv }}
-    runs-on: ubuntu-24.04
-    strategy:
-      matrix:
-        msrv: ["1.78.0"] # This should match up with rust-version in Cargo.toml
-    env:
-      # Need up-to-date compilers for kernels
-      CC: clang-18
-      CXX: clang++-18
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: true
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler libssl-dev
-      - name: Install ${{ matrix.msrv }}
-        uses: dtolnay/rust-toolchain@master
-        with:
-          toolchain: ${{ matrix.msrv }}
-      - name: Downgrade  dependencies
-        # These packages have newer requirements for MSRV
-        run: |
-          cargo update -p aws-sdk-bedrockruntime --precise 1.64.0
-          cargo update -p aws-sdk-dynamodb --precise 1.55.0
-          cargo update -p aws-config --precise 1.5.10
-          cargo update -p aws-sdk-kms --precise 1.51.0
-          cargo update -p aws-sdk-s3 --precise 1.65.0
-          cargo update -p aws-sdk-sso --precise 1.50.0
-          cargo update -p aws-sdk-ssooidc --precise 1.51.0
-          cargo update -p aws-sdk-sts --precise 1.51.0
-          cargo update -p home --precise 0.5.9
-      - name: cargo +${{ matrix.msrv }} check
-        run: cargo check --workspace --tests --benches --all-features

.github/workflows/upload_wheel/action.yml

@@ -17,12 +17,11 @@ runs:
     run: |
       python -m pip install --upgrade pip
       pip install twine
-      python3 -m pip install --upgrade pkginfo
   - name: Choose repo
     shell: bash
     id: choose_repo
     run: |
-      if [[ ${{ github.ref }} == *beta* ]]; then
+      if [ ${{ github.ref }} == "*beta*" ]; then
         echo "repo=fury" >> $GITHUB_OUTPUT
       else
         echo "repo=pypi" >> $GITHUB_OUTPUT
@@ -33,7 +32,7 @@ runs:
       FURY_TOKEN: ${{ inputs.fury_token }}
       PYPI_TOKEN: ${{ inputs.pypi_token }}
     run: |
-      if [[ ${{ steps.choose_repo.outputs.repo }} == fury ]]; then
+      if [ ${{ steps.choose_repo.outputs.repo }} == "fury" ]; then
         WHEEL=$(ls target/wheels/lancedb-*.whl 2> /dev/null | head -n 1)
         echo "Uploading $WHEEL to Fury"
         curl -f -F package=@$WHEEL https://$FURY_TOKEN@push.fury.io/lancedb/

.gitignore

@@ -9,6 +9,7 @@ venv
 .vscode
 .zed
 rust/target
+rust/Cargo.lock
 
 site
 
@@ -41,3 +42,5 @@ dist
 target
 
 **/sccache.log
+
+Cargo.lock

.pre-commit-config.yaml

@@ -1,27 +1,21 @@
 repos:
-  - repo: https://github.com/pre-commit/pre-commit-hooks
+-   repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v3.2.0
     hooks:
-      - id: check-yaml
+    -   id: check-yaml
-      - id: end-of-file-fixer
+    -   id: end-of-file-fixer
-      - id: trailing-whitespace
+    -   id: trailing-whitespace
-  - repo: https://github.com/astral-sh/ruff-pre-commit
+-   repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.9.9
+    rev: v0.2.2
     hooks:
-      - id: ruff
+    - id: ruff
-  # - repo: https://github.com/RobertCraigie/pyright-python
+- repo: local
-  #   rev: v1.1.395
+  hooks:
-  #   hooks:
+    - id: local-biome-check
-  #     - id: pyright
+      name: biome check
-  #       args: ["--project", "python"]
+      entry: npx @biomejs/biome@1.8.3 check --config-path nodejs/biome.json nodejs/
-  #       additional_dependencies: [pyarrow-stubs]
+      language: system
-  - repo: local
+      types: [text]
-    hooks:
+      files: "nodejs/.*"
-      - id: local-biome-check
+      exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*|nodejs/examples/.*
-        name: biome check
-        entry: npx @biomejs/biome@1.8.3 check --config-path nodejs/biome.json nodejs/
-        language: system
-        types: [text]
-        files: "nodejs/.*"
-        exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*|nodejs/examples/.*

CONTRIBUTING.md

@@ -1,78 +0,0 @@
-# Contributing to LanceDB
-
-LanceDB is an open-source project and we welcome contributions from the community.
-This document outlines the process for contributing to LanceDB.
-
-## Reporting Issues
-
-If you encounter a bug or have a feature request, please open an issue on the
-[GitHub issue tracker](https://github.com/lancedb/lancedb).
-
-## Picking an issue
-
-We track issues on the GitHub issue tracker. If you are looking for something to
-work on, check the [good first issue](https://github.com/lancedb/lancedb/contribute) label. These issues are typically the best described and have the smallest scope.
-
-If there's an issue you are interested in working on, please leave a comment on the issue. This will help us avoid duplicate work. Additionally, if you have questions about the issue, please ask them in the issue comments. We are happy to provide guidance on how to approach the issue.
-
-## Configuring Git
-
-First, fork the repository on GitHub, then clone your fork:
-
-```bash
-git clone https://github.com/<username>/lancedb.git
-cd lancedb
-```
-
-Then add the main repository as a remote:
-
-```bash
-git remote add upstream https://github.com/lancedb/lancedb.git
-git fetch upstream
-```
-
-## Setting up your development environment
-
-We have development environments for Python, Typescript, and Java. Each environment has its own setup instructions.
-
-* [Python](python/CONTRIBUTING.md)
-* [Typescript](nodejs/CONTRIBUTING.md)
-<!-- TODO: add Java contributing guide -->
-* [Documentation](docs/README.md)
-
-
-## Best practices for pull requests
-
-For the best chance of having your pull request accepted, please follow these guidelines:
-
-1. Unit test all bug fixes and new features. Your code will not be merged if it
-   doesn't have tests.
-1. If you change the public API, update the documentation in the `docs` directory.
-1. Aim to minimize the number of changes in each pull request. Keep to solving
-   one problem at a time, when possible.
-1. Before marking a pull request ready-for-review, do a self review of your code.
-   Is it clear why you are making the changes? Are the changes easy to understand?
-1. Use [conventional commit messages](https://www.conventionalcommits.org/en/) as pull request titles. Examples:
-    * New feature: `feat: adding foo API`
-    * Bug fix: `fix: issue with foo API`
-    * Documentation change: `docs: adding foo API documentation`
-1. If your pull request is a work in progress, leave the pull request as a draft.
-   We will assume the pull request is ready for review when it is opened.
-1. When writing tests, test the error cases. Make sure they have understandable
-   error messages.
-
-## Project structure
-
-The core library is written in Rust. The Python, Typescript, and Java libraries
-are wrappers around the Rust library.
-
-* `src/lancedb`: Rust library source code
-* `python`: Python package source code
-* `nodejs`: Typescript package source code
-* `node`: **Deprecated** Typescript package source code
-* `java`: Java package source code
-* `docs`: Documentation source code
-
-## Release process
-
-For information on the release process, see: [release_process.md](release_process.md)

Cargo.toml

@@ -18,55 +18,39 @@ repository = "https://github.com/lancedb/lancedb"
 description = "Serverless, low-latency vector database for AI applications"
 keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
-rust-version = "1.78.0"
 
 [workspace.dependencies]
-lance = { "version" = "=0.24.1", "features" = ["dynamodb"] }
+lance = { "version" = "=0.18.0", "features" = ["dynamodb"] }
-lance-io = { version = "=0.24.1" }
+lance-index = { "version" = "=0.18.0" }
-lance-index = { version = "=0.24.1" }
+lance-linalg = { "version" = "=0.18.0" }
-lance-linalg = { version = "=0.24.1" }
+lance-table = { "version" = "=0.18.0" }
-lance-table = { version = "=0.24.1" }
+lance-testing = { "version" = "=0.18.0" }
-lance-testing = { version = "=0.24.1" }
+lance-datafusion = { "version" = "=0.18.0" }
-lance-datafusion = { version = "=0.24.1" }
+lance-encoding = { "version" = "=0.18.0" }
-lance-encoding = { version = "=0.24.1" }
 # Note that this one does not include pyarrow
-arrow = { version = "54.1", optional = false }
+arrow = { version = "52.2", optional = false }
-arrow-array = "54.1"
+arrow-array = "52.2"
-arrow-data = "54.1"
+arrow-data = "52.2"
-arrow-ipc = "54.1"
+arrow-ipc = "52.2"
-arrow-ord = "54.1"
+arrow-ord = "52.2"
-arrow-schema = "54.1"
+arrow-schema = "52.2"
-arrow-arith = "54.1"
+arrow-arith = "52.2"
-arrow-cast = "54.1"
+arrow-cast = "52.2"
 async-trait = "0"
-datafusion = { version = "45.0", default-features = false }
+chrono = "0.4.35"
-datafusion-catalog = "45.0"
+datafusion-common = "40.0"
-datafusion-common = { version = "45.0", default-features = false }
+datafusion-physical-plan = "40.0"
-datafusion-execution = "45.0"
-datafusion-expr = "45.0"
-datafusion-physical-plan = "45.0"
-env_logger = "0.11"
 half = { "version" = "=2.4.1", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"
 log = "0.4"
-moka = { version = "0.12", features = ["future"] }
+moka = { version = "0.11", features = ["future"] }
-object_store = "0.11.0"
+object_store = "0.10.2"
 pin-project = "1.0.7"
-snafu = "0.8"
+snafu = "0.7.4"
 url = "2"
 num-traits = "0.2"
 rand = "0.8"
 regex = "1.10"
 lazy_static = "1"
-semver = "1.0.25"
-
-# Temporary pins to work around downstream issues
-# https://github.com/apache/arrow-rs/commit/2fddf85afcd20110ce783ed5b4cdeb82293da30b
-chrono = "=0.4.39"
-# https://github.com/RustCrypto/formats/issues/1684
-base64ct = "=1.6.0"
-
-# Workaround for: https://github.com/eira-fransham/crunchy/issues/13
-crunchy = "=0.2.2"

README.md

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

ci/build_linux_artifacts.sh

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

ci/build_windows_artifacts.ps1

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

ci/build_windows_artifacts_nodejs.ps1

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

ci/manylinux_node/build_lancedb.sh

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

ci/manylinux_node/build_vectordb.sh

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

ci/mock_openai.py

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

ci/parse_requirements.py

@@ -1,41 +0,0 @@
-import argparse
-import toml
-
-
-def parse_dependencies(pyproject_path, extras=None):
-    with open(pyproject_path, "r") as file:
-        pyproject = toml.load(file)
-
-    dependencies = pyproject.get("project", {}).get("dependencies", [])
-    for dependency in dependencies:
-        print(dependency)
-
-    optional_dependencies = pyproject.get("project", {}).get(
-        "optional-dependencies", {}
-    )
-
-    if extras:
-        for extra in extras.split(","):
-            for dep in optional_dependencies.get(extra, []):
-                print(dep)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Generate requirements.txt from pyproject.toml"
-    )
-    parser.add_argument("path", type=str, help="Path to pyproject.toml")
-    parser.add_argument(
-        "--extras",
-        type=str,
-        help="Comma-separated list of extras to include",
-        default="",
-    )
-
-    args = parser.parse_args()
-
-    parse_dependencies(args.path, args.extras)
-
-
-if __name__ == "__main__":
-    main()

ci/sysroot-aarch64-pc-windows-msvc.sh

@@ -1,105 +0,0 @@
-#!/bin/sh
-
-# https://github.com/mstorsjo/msvc-wine/blob/master/vsdownload.py
-# https://github.com/mozilla/gecko-dev/blob/6027d1d91f2d3204a3992633b3ef730ff005fc64/build/vs/vs2022-car.yaml
-
-# function dl() {
-# 	curl -O https://download.visualstudio.microsoft.com/download/pr/$1
-# }
-
-# [[.h]]
-
-# "id": "Win11SDK_10.0.26100"
-# "version": "10.0.26100.7"
-
-# libucrt.lib
-
-# example: <assert.h>
-# dir: ucrt/
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/2ee3a5fc6e9fc832af7295b138e93839/universal%20crt%20headers%20libraries%20and%20sources-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/b1aa09b90fe314aceb090f6ec7626624/16ab2ea2187acffa6435e334796c8c89.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/400609bb0ff5804e36dbe6dcd42a7f01/6ee7bbee8435130a869cf971694fd9e2.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/2ac327317abb865a0e3f56b2faefa918/78fa3c824c2c48bd4a49ab5969adaaf7.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/f034bc0b2680f67dccd4bfeea3d0f932/7afc7b670accd8e3cc94cfffd516f5cb.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/7ed5e12f9d50f80825a8b27838cf4c7f/96076045170fe5db6d5dcf14b6f6688e.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/764edc185a696bda9e07df8891dddbbb/a1e2a83aa8a71c48c742eeaff6e71928.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/66854bedc6dbd5ccb5dd82c8e2412231/b2f03f34ff83ec013b9e45c7cd8e8a73.cab
-
-# example: <windows.h>
-# dir: um/
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/b286efac4d83a54fc49190bddef1edc9/windows%20sdk%20for%20windows%20store%20apps%20headers-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/e0dc3811d92ab96fcb72bf63d6c08d71/766c0ffd568bbb31bf7fb6793383e24a.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/613503da4b5628768497822826aed39f/8125ee239710f33ea485965f76fae646.cab
-
-# example: <winapifamily.h>
-# dir: /shared
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/122979f0348d3a2a36b6aa1a111d5d0c/windows%20sdk%20for%20windows%20store%20apps%20headers%20onecoreuap-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/766e04beecdfccff39e91dd9eb32834a/e89e3dcbb016928c7e426238337d69eb.cab
-
-
-# "id": "Microsoft.VisualC.14.16.CRT.Headers"
-# "version": "14.16.27045"
-
-# example: <vcruntime.h>
-# dir: MSVC/
-curl -O https://download.visualstudio.microsoft.com/download/pr/bac0afd7-cc9e-4182-8a83-9898fa20e092/87bbe41e09a2f83711e72696f49681429327eb7a4b90618c35667a6ba2e2880e/Microsoft.VisualC.14.16.CRT.Headers.vsix
-
-# [[.lib]]
-
-# advapi32.lib bcrypt.lib kernel32.lib ntdll.lib user32.lib uuid.lib ws2_32.lib userenv.lib cfgmgr32.lib runtimeobject.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/944c4153b849a1f7d0c0404a4f1c05ea/windows%20sdk%20for%20windows%20store%20apps%20libs-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/5306aed3e1a38d1e8bef5934edeb2a9b/05047a45609f311645eebcac2739fc4c.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/13c8a73a0f5a6474040b26d016a26fab/13d68b8a7b6678a368e2d13ff4027521.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/149578fb3b621cdb61ee1813b9b3e791/463ad1b0783ebda908fd6c16a4abfe93.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/5c986c4f393c6b09d5aec3b539e9fb4a/5a22e5cde814b041749fb271547f4dd5.cab
-
-# dbghelp.lib fwpuclnt.lib arm64rt.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/7a332420d812f7c1d41da865ae5a7c52/windows%20sdk%20desktop%20libs%20arm64-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/19de98ed4a79938d0045d19c047936b3/3e2f7be479e3679d700ce0782e4cc318.cab
-
-# libcmt.lib libvcruntime.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/bac0afd7-cc9e-4182-8a83-9898fa20e092/227f40682a88dc5fa0ccb9cadc9ad30af99ad1f1a75db63407587d079f60d035/Microsoft.VisualC.14.16.CRT.ARM64.Desktop.vsix
-
-
-msiextract universal%20crt%20headers%20libraries%20and%20sources-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20headers-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20headers%20onecoreuap-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20libs-x86_en-us.msi
-msiextract windows%20sdk%20desktop%20libs%20arm64-x86_en-us.msi
-unzip -o Microsoft.VisualC.14.16.CRT.Headers.vsix
-unzip -o Microsoft.VisualC.14.16.CRT.ARM64.Desktop.vsix
-
-mkdir -p /usr/aarch64-pc-windows-msvc/usr/include
-mkdir -p /usr/aarch64-pc-windows-msvc/usr/lib
-
-# lowercase folder/file names
-echo "$(find . -regex ".*/[^/]*[A-Z][^/]*")" | xargs -I{} sh -c 'mv "$(echo "{}" | sed -E '"'"'s/(.*\/)/\L\1/'"'"')" "$(echo "{}" | tr [A-Z] [a-z])"'
-
-# .h
-(cd 'program files/windows kits/10/include/10.0.26100.0' && cp -r ucrt/* um/* shared/* -t /usr/aarch64-pc-windows-msvc/usr/include)
-
-cp -r contents/vc/tools/msvc/14.16.27023/include/* /usr/aarch64-pc-windows-msvc/usr/include
-
-# lowercase #include "" and #include <>
-find /usr/aarch64-pc-windows-msvc/usr/include -type f -exec sed -i -E 's/(#include <[^<>]*?[A-Z][^<>]*?>)|(#include "[^"]*?[A-Z][^"]*?")/\L\1\2/' "{}" ';'
-
-# ARM intrinsics
-# original dir: MSVC/
-
-# '__n128x4' redefined in arm_neon.h
-# "arm64_neon.h" included from intrin.h
-
-(cd /usr/lib/llvm19/lib/clang/19/include && cp arm_neon.h intrin.h -t /usr/aarch64-pc-windows-msvc/usr/include)
-
-# .lib
-
-# _Interlocked intrinsics
-# must always link with arm64rt.lib
-# reason: https://developercommunity.visualstudio.com/t/libucrtlibstreamobj-error-lnk2001-unresolved-exter/1544787#T-ND1599818
-# I don't understand the 'correct' fix for this, arm64rt.lib is supposed to be the workaround
-
-(cd 'program files/windows kits/10/lib/10.0.26100.0/um/arm64' && cp advapi32.lib bcrypt.lib kernel32.lib ntdll.lib user32.lib uuid.lib ws2_32.lib userenv.lib cfgmgr32.lib runtimeobject.lib dbghelp.lib fwpuclnt.lib arm64rt.lib -t /usr/aarch64-pc-windows-msvc/usr/lib)
-
-(cd 'contents/vc/tools/msvc/14.16.27023/lib/arm64' && cp libcmt.lib libvcruntime.lib -t /usr/aarch64-pc-windows-msvc/usr/lib)
-
-cp 'program files/windows kits/10/lib/10.0.26100.0/ucrt/arm64/libucrt.lib' /usr/aarch64-pc-windows-msvc/usr/lib

ci/sysroot-x86_64-pc-windows-msvc.sh

@@ -1,105 +0,0 @@
-#!/bin/sh
-
-# https://github.com/mstorsjo/msvc-wine/blob/master/vsdownload.py
-# https://github.com/mozilla/gecko-dev/blob/6027d1d91f2d3204a3992633b3ef730ff005fc64/build/vs/vs2022-car.yaml
-
-# function dl() {
-# 	curl -O https://download.visualstudio.microsoft.com/download/pr/$1
-# }
-
-# [[.h]]
-
-# "id": "Win11SDK_10.0.26100"
-# "version": "10.0.26100.7"
-
-# libucrt.lib
-
-# example: <assert.h>
-# dir: ucrt/
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/2ee3a5fc6e9fc832af7295b138e93839/universal%20crt%20headers%20libraries%20and%20sources-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/b1aa09b90fe314aceb090f6ec7626624/16ab2ea2187acffa6435e334796c8c89.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/400609bb0ff5804e36dbe6dcd42a7f01/6ee7bbee8435130a869cf971694fd9e2.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/2ac327317abb865a0e3f56b2faefa918/78fa3c824c2c48bd4a49ab5969adaaf7.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/f034bc0b2680f67dccd4bfeea3d0f932/7afc7b670accd8e3cc94cfffd516f5cb.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/7ed5e12f9d50f80825a8b27838cf4c7f/96076045170fe5db6d5dcf14b6f6688e.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/764edc185a696bda9e07df8891dddbbb/a1e2a83aa8a71c48c742eeaff6e71928.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/66854bedc6dbd5ccb5dd82c8e2412231/b2f03f34ff83ec013b9e45c7cd8e8a73.cab
-
-# example: <windows.h>
-# dir: um/
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/b286efac4d83a54fc49190bddef1edc9/windows%20sdk%20for%20windows%20store%20apps%20headers-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/e0dc3811d92ab96fcb72bf63d6c08d71/766c0ffd568bbb31bf7fb6793383e24a.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/613503da4b5628768497822826aed39f/8125ee239710f33ea485965f76fae646.cab
-
-# example: <winapifamily.h>
-# dir: /shared
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/122979f0348d3a2a36b6aa1a111d5d0c/windows%20sdk%20for%20windows%20store%20apps%20headers%20onecoreuap-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/766e04beecdfccff39e91dd9eb32834a/e89e3dcbb016928c7e426238337d69eb.cab
-
-
-# "id": "Microsoft.VisualC.14.16.CRT.Headers"
-# "version": "14.16.27045"
-
-# example: <vcruntime.h>
-# dir: MSVC/
-curl -O https://download.visualstudio.microsoft.com/download/pr/bac0afd7-cc9e-4182-8a83-9898fa20e092/87bbe41e09a2f83711e72696f49681429327eb7a4b90618c35667a6ba2e2880e/Microsoft.VisualC.14.16.CRT.Headers.vsix
-
-# [[.lib]]
-
-# advapi32.lib bcrypt.lib kernel32.lib ntdll.lib user32.lib uuid.lib ws2_32.lib userenv.lib cfgmgr32.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/944c4153b849a1f7d0c0404a4f1c05ea/windows%20sdk%20for%20windows%20store%20apps%20libs-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/5306aed3e1a38d1e8bef5934edeb2a9b/05047a45609f311645eebcac2739fc4c.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/13c8a73a0f5a6474040b26d016a26fab/13d68b8a7b6678a368e2d13ff4027521.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/149578fb3b621cdb61ee1813b9b3e791/463ad1b0783ebda908fd6c16a4abfe93.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/5c986c4f393c6b09d5aec3b539e9fb4a/5a22e5cde814b041749fb271547f4dd5.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/bfc3904a0195453419ae4dfea7abd6fb/e10768bb6e9d0ea730280336b697da66.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/637f9f3be880c71f9e3ca07b4d67345c/f9b24c8280986c0683fbceca5326d806.cab
-
-# dbghelp.lib fwpuclnt.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/9f51690d5aa804b1340ce12d1ec80f89/windows%20sdk%20desktop%20libs%20x64-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/d3a7df4ca3303a698640a29e558a5e5b/58314d0646d7e1a25e97c902166c3155.cab
-
-# libcmt.lib libvcruntime.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/bac0afd7-cc9e-4182-8a83-9898fa20e092/8728f21ae09940f1f4b4ee47b4a596be2509e2a47d2f0c83bbec0ea37d69644b/Microsoft.VisualC.14.16.CRT.x64.Desktop.vsix
-
-
-msiextract universal%20crt%20headers%20libraries%20and%20sources-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20headers-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20headers%20onecoreuap-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20libs-x86_en-us.msi
-msiextract windows%20sdk%20desktop%20libs%20x64-x86_en-us.msi
-unzip -o Microsoft.VisualC.14.16.CRT.Headers.vsix
-unzip -o Microsoft.VisualC.14.16.CRT.x64.Desktop.vsix
-
-mkdir -p /usr/x86_64-pc-windows-msvc/usr/include
-mkdir -p /usr/x86_64-pc-windows-msvc/usr/lib
-
-# lowercase folder/file names
-echo "$(find . -regex ".*/[^/]*[A-Z][^/]*")" | xargs -I{} sh -c 'mv "$(echo "{}" | sed -E '"'"'s/(.*\/)/\L\1/'"'"')" "$(echo "{}" | tr [A-Z] [a-z])"'
-
-# .h
-(cd 'program files/windows kits/10/include/10.0.26100.0' && cp -r ucrt/* um/* shared/* -t /usr/x86_64-pc-windows-msvc/usr/include)
-
-cp -r contents/vc/tools/msvc/14.16.27023/include/* /usr/x86_64-pc-windows-msvc/usr/include
-
-# lowercase #include "" and #include <>
-find /usr/x86_64-pc-windows-msvc/usr/include -type f -exec sed -i -E 's/(#include <[^<>]*?[A-Z][^<>]*?>)|(#include "[^"]*?[A-Z][^"]*?")/\L\1\2/' "{}" ';'
-
-# x86 intrinsics
-# original dir: MSVC/
-
-# '_mm_movemask_epi8' defined in emmintrin.h
-# '__v4sf' defined in xmmintrin.h
-# '__v2si' defined in mmintrin.h
-# '__m128d' redefined in immintrin.h
-# '__m128i' redefined in intrin.h
-# '_mm_comlt_epu8' defined in ammintrin.h
-
-(cd /usr/lib/llvm19/lib/clang/19/include && cp emmintrin.h xmmintrin.h mmintrin.h immintrin.h intrin.h ammintrin.h -t /usr/x86_64-pc-windows-msvc/usr/include)
-
-# .lib
-(cd 'program files/windows kits/10/lib/10.0.26100.0/um/x64' && cp advapi32.lib bcrypt.lib kernel32.lib ntdll.lib user32.lib uuid.lib ws2_32.lib userenv.lib cfgmgr32.lib dbghelp.lib fwpuclnt.lib -t /usr/x86_64-pc-windows-msvc/usr/lib)
-
-(cd 'contents/vc/tools/msvc/14.16.27023/lib/x64' && cp libcmt.lib libvcruntime.lib -t /usr/x86_64-pc-windows-msvc/usr/lib)
-
-cp 'program files/windows kits/10/lib/10.0.26100.0/ucrt/x64/libucrt.lib' /usr/x86_64-pc-windows-msvc/usr/lib

ci/validate_stable_lance.py

@@ -1,34 +0,0 @@
-import tomllib
-
-found_preview_lance = False
-
-with open("Cargo.toml", "rb") as f:
-    cargo_data = tomllib.load(f)
-
-    for name, dep in cargo_data["workspace"]["dependencies"].items():
-        if name == "lance" or name.startswith("lance-"):
-            if isinstance(dep, str):
-                version = dep
-            elif isinstance(dep, dict):
-                # Version doesn't have the beta tag in it, so we instead look
-                # at the git tag.
-                version = dep.get('tag', dep.get('version'))
-            else:
-                raise ValueError("Unexpected type for dependency: " + str(dep))
-
-            if "beta" in version:
-                found_preview_lance = True
-                print(f"Dependency '{name}' is a preview version: {version}")
-
-with open("python/pyproject.toml", "rb") as f:
-    py_proj_data = tomllib.load(f)
-
-    for dep in py_proj_data["project"]["dependencies"]:
-        if dep.startswith("pylance"):
-            if "b" in dep:
-                found_preview_lance = True
-                print(f"Dependency '{dep}' is a preview version")
-            break  # Only one pylance dependency
-
-if found_preview_lance:
-    raise ValueError("Found preview version of Lance in dependencies")

docs/README.md

@@ -9,81 +9,36 @@ unreleased features.
 ## Building the docs
 
 ### Setup
-1. Install LanceDB Python. See setup in [Python contributing guide](../python/CONTRIBUTING.md).
+1. Install LanceDB. From LanceDB repo root: `pip install -e python`
-   Run `make develop` to install the Python package.
+2. Install dependencies. From LanceDB repo root: `pip install -r docs/requirements.txt`
-2. Install documentation dependencies. From LanceDB repo root: `pip install -r docs/requirements.txt`
+3. Make sure you have node and npm setup
+4. Make sure protobuf and libssl are installed
 
-### Preview the docs
+### Building node module and create markdown files
 
-```shell
+See [Javascript docs README](./src/javascript/README.md)
+
+### Build docs
+From LanceDB repo root:
+
+Run: `PYTHONPATH=. mkdocs build -f docs/mkdocs.yml`
+
+If successful, you should see a `docs/site` directory that you can verify locally.
+
+### Run local server
+
+You can run a local server to test the docs prior to deployment by navigating to the `docs` directory and running the following command:
+
+```bash
 cd docs
 mkdocs serve
 ```
 
-If you want to just generate the HTML files:
+### Run doctest for typescript example
 
-```shell
+```bash
-PYTHONPATH=. mkdocs build -f docs/mkdocs.yml
+cd lancedb/docs
-```
+npm i
-
-If successful, you should see a `docs/site` directory that you can verify locally.
-
-## Adding examples
-
-To make sure examples are correct, we put examples in test files so they can be
-run as part of our test suites.
-
-You can see the tests are at:
-
-* Python: `python/python/tests/docs`
-* Typescript: `nodejs/examples/`
-
-### Checking python examples
-
-```shell
-cd python
-pytest -vv python/tests/docs
-```
-
-### Checking typescript examples
-
-The `@lancedb/lancedb` package must be built before running the tests:
-
-```shell
-pushd nodejs
-npm ci
 npm run build
-popd
+npm run all
-```
-
-Then you can run the examples by going to the `nodejs/examples` directory and
-running the tests like a normal npm package:
-
-```shell
-pushd nodejs/examples
-npm ci
-npm test
-popd
-```
-
-## API documentation
-
-### Python
-
-The Python API documentation is organized based on the file `docs/src/python/python.md`.
-We manually add entries there so we can control the organization of the reference page.
-**However, this means any new types must be manually added to the file.** No additional
-steps are needed to generate the API documentation.
-
-### Typescript
-
-The typescript API documentation is generated from the typescript source code using [typedoc](https://typedoc.org/).
-
-When new APIs are added, you must manually re-run the typedoc command to update the API documentation.
-The new files should be checked into the repository.
-
-```shell
-pushd nodejs
-npm run docs
-popd
 ```

docs/mkdocs.yml

@@ -4,9 +4,6 @@ repo_url: https://github.com/lancedb/lancedb
 edit_uri: https://github.com/lancedb/lancedb/tree/main/docs/src
 repo_name: lancedb/lancedb
 docs_dir: src
-watch:
-  - src
-  - ../python/python
 
 theme:
   name: "material"
@@ -58,15 +55,10 @@ plugins:
             show_signature_annotations: true
             show_root_heading: true
             members_order: source
-            docstring_section_style: list
-            signature_crossrefs: true
-            separate_signature: true
           import:
             # for cross references
             - https://arrow.apache.org/docs/objects.inv
             - https://pandas.pydata.org/docs/objects.inv
-            - https://lancedb.github.io/lance/objects.inv
-            - https://docs.pydantic.dev/latest/objects.inv
   - mkdocs-jupyter
   - render_swagger:
       allow_arbitrary_locations: true
@@ -98,9 +90,6 @@ markdown_extensions:
   - pymdownx.emoji:
       emoji_index: !!python/name:material.extensions.emoji.twemoji
       emoji_generator: !!python/name:material.extensions.emoji.to_svg
-  - markdown.extensions.toc:
-      baselevel: 1
-      permalink: ""
 
 nav:
   - Home:
@@ -108,17 +97,16 @@ nav:
       - 🏃🏼‍♂️ Quick start: basic.md
       - 📚 Concepts:
           - Vector search: concepts/vector_search.md
-          - Indexing:
+          - Indexing: 
-              - IVFPQ: concepts/index_ivfpq.md
+            - IVFPQ: concepts/index_ivfpq.md
-              - HNSW: concepts/index_hnsw.md
+            - HNSW: concepts/index_hnsw.md
           - Storage: concepts/storage.md
           - Data management: concepts/data_management.md
       - 🔨 Guides:
           - Working with tables: guides/tables.md
           - Building a vector index: ann_indexes.md
           - Vector Search: search.md
-          - Full-text search (native): fts.md
+          - Full-text search: fts.md
-          - Full-text search (tantivy-based): fts_tantivy.md
           - Building a scalar index: guides/scalar_index.md
           - Hybrid search:
               - Overview: hybrid_search/hybrid_search.md
@@ -134,8 +122,8 @@ nav:
               - Adaptive RAG: rag/adaptive_rag.md
               - SFR RAG: rag/sfr_rag.md
               - Advanced Techniques:
-                  - HyDE: rag/advanced_techniques/hyde.md
+                - HyDE: rag/advanced_techniques/hyde.md
-                  - FLARE: rag/advanced_techniques/flare.md
+                - FLARE: rag/advanced_techniques/flare.md
           - Reranking:
               - Quickstart: reranking/index.md
               - Cohere Reranker: reranking/cohere.md
@@ -146,13 +134,10 @@ nav:
               - Jina Reranker: reranking/jina.md
               - OpenAI Reranker: reranking/openai.md
               - AnswerDotAi Rerankers: reranking/answerdotai.md
-              - Voyage AI Rerankers: reranking/voyageai.md
               - Building Custom Rerankers: reranking/custom_reranker.md
               - Example: notebooks/lancedb_reranking.ipynb
           - Filtering: sql.md
-          - Versioning & Reproducibility:
+          - Versioning & Reproducibility: notebooks/reproducibility.ipynb
-              - sync API: notebooks/reproducibility.ipynb
-              - async API: notebooks/reproducibility_async.ipynb
           - Configuring Storage: guides/storage.md
           - Migration Guide: migration.md
           - Tuning retrieval performance:
@@ -160,10 +145,10 @@ nav:
               - Reranking: guides/tuning_retrievers/2_reranking.md
               - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
       - 🧬 Managing embeddings:
-          - Understand Embeddings: embeddings/understanding_embeddings.md
+          - Understand Embeddings: embeddings/understanding_embeddings.md 
           - Get Started: embeddings/index.md
           - Embedding functions: embeddings/embedding_functions.md
-          - Available models:
+          - Available models: 
               - Overview: embeddings/default_embedding_functions.md
               - Text Embedding Functions:
                   - Sentence Transformers: embeddings/available_embedding_models/text_embedding_functions/sentence_transformers.md
@@ -176,13 +161,11 @@ nav:
                   - Jina Embeddings: embeddings/available_embedding_models/text_embedding_functions/jina_embedding.md
                   - AWS Bedrock Text Embedding Functions: embeddings/available_embedding_models/text_embedding_functions/aws_bedrock_embedding.md
                   - IBM watsonx.ai Embeddings: embeddings/available_embedding_models/text_embedding_functions/ibm_watsonx_ai_embedding.md
-                  - Voyage AI Embeddings: embeddings/available_embedding_models/text_embedding_functions/voyageai_embedding.md
               - Multimodal Embedding Functions:
                   - OpenClip embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/openclip_embedding.md
                   - Imagebind embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md
                   - Jina Embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md
           - User-defined embedding functions: embeddings/custom_embedding_function.md
-          - Variables and secrets: embeddings/variables_and_secrets.md
           - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
           - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
       - 🔌 Integrations:
@@ -214,7 +197,7 @@ nav:
               - Evaluation: examples/python_examples/evaluations.md
               - AI Agent: examples/python_examples/aiagent.md
               - Recommender System: examples/python_examples/recommendersystem.md
-              - Miscellaneous:
+              - Miscellaneous: 
                   - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
                   - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
           - 👾 JavaScript:
@@ -224,10 +207,9 @@ nav:
               - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
           - 🦀 Rust:
               - Overview: examples/examples_rust.md
-      - 📓 Studies:
+      - Studies:
           - ↗Improve retrievers with hybrid search and reranking: https://blog.lancedb.com/hybrid-search-and-reranking-report/
       - 💭 FAQs: faq.md
-      - 🔍 Troubleshooting: troubleshooting.md
       - ⚙️ API reference:
           - 🐍 Python: python/python.md
           - 👾 JavaScript (vectordb): javascript/modules.md
@@ -239,22 +221,20 @@ nav:
               - 🐍 Python: python/saas-python.md
               - 👾 JavaScript: javascript/modules.md
               - REST API: cloud/rest.md
-          - FAQs: cloud/cloud_faq.md
 
   - Quick start: basic.md
   - Concepts:
       - Vector search: concepts/vector_search.md
-      - Indexing:
+      - Indexing: 
-          - IVFPQ: concepts/index_ivfpq.md
+        - IVFPQ: concepts/index_ivfpq.md
-          - HNSW: concepts/index_hnsw.md
+        - HNSW: concepts/index_hnsw.md
       - Storage: concepts/storage.md
       - Data management: concepts/data_management.md
   - Guides:
       - Working with tables: guides/tables.md
       - Building an ANN index: ann_indexes.md
       - Vector Search: search.md
-      - Full-text search (native): fts.md
+      - Full-text search: fts.md
-      - Full-text search (tantivy-based): fts_tantivy.md
       - Building a scalar index: guides/scalar_index.md
       - Hybrid search:
           - Overview: hybrid_search/hybrid_search.md
@@ -270,8 +250,8 @@ nav:
           - Adaptive RAG: rag/adaptive_rag.md
           - SFR RAG: rag/sfr_rag.md
           - Advanced Techniques:
-              - HyDE: rag/advanced_techniques/hyde.md
+            - HyDE: rag/advanced_techniques/hyde.md
-              - FLARE: rag/advanced_techniques/flare.md
+            - FLARE: rag/advanced_techniques/flare.md
       - Reranking:
           - Quickstart: reranking/index.md
           - Cohere Reranker: reranking/cohere.md
@@ -285,9 +265,7 @@ nav:
           - Building Custom Rerankers: reranking/custom_reranker.md
           - Example: notebooks/lancedb_reranking.ipynb
       - Filtering: sql.md
-      - Versioning & Reproducibility:
+      - Versioning & Reproducibility: notebooks/reproducibility.ipynb
-          - sync API: notebooks/reproducibility.ipynb
-          - async API: notebooks/reproducibility_async.ipynb
       - Configuring Storage: guides/storage.md
       - Migration Guide: migration.md
       - Tuning retrieval performance:
@@ -295,10 +273,10 @@ nav:
           - Reranking: guides/tuning_retrievers/2_reranking.md
           - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
   - Managing Embeddings:
-      - Understand Embeddings: embeddings/understanding_embeddings.md
+      - Understand Embeddings: embeddings/understanding_embeddings.md 
       - Get Started: embeddings/index.md
       - Embedding functions: embeddings/embedding_functions.md
-      - Available models:
+      - Available models: 
           - Overview: embeddings/default_embedding_functions.md
           - Text Embedding Functions:
               - Sentence Transformers: embeddings/available_embedding_models/text_embedding_functions/sentence_transformers.md
@@ -316,7 +294,6 @@ nav:
               - Imagebind embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md
               - Jina Embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md
       - User-defined embedding functions: embeddings/custom_embedding_function.md
-      - Variables and secrets: embeddings/variables_and_secrets.md
       - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
       - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
   - Integrations:
@@ -344,7 +321,7 @@ nav:
           - Evaluation: examples/python_examples/evaluations.md
           - AI Agent: examples/python_examples/aiagent.md
           - Recommender System: examples/python_examples/recommendersystem.md
-          - Miscellaneous:
+          - Miscellaneous: 
               - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
               - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
       - 👾 JavaScript:
@@ -355,8 +332,8 @@ nav:
       - 🦀 Rust:
           - Overview: examples/examples_rust.md
   - Studies:
-      - studies/overview.md
+    - studies/overview.md
-      - ↗Improve retrievers with hybrid search and reranking: https://blog.lancedb.com/hybrid-search-and-reranking-report/
+    - ↗Improve retrievers with hybrid search and reranking: https://blog.lancedb.com/hybrid-search-and-reranking-report/
   - API reference:
       - Overview: api_reference.md
       - Python: python/python.md
@@ -369,7 +346,6 @@ nav:
           - 🐍 Python: python/saas-python.md
           - 👾 JavaScript: javascript/modules.md
           - REST API: cloud/rest.md
-      - FAQs: cloud/cloud_faq.md
 
 extra_css:
   - styles/global.css
@@ -377,7 +353,6 @@ extra_css:
 
 extra_javascript:
   - "extra_js/init_ask_ai_widget.js"
-  - "extra_js/reo.js"
 
 extra:
   analytics:
@@ -389,4 +364,5 @@ extra:
     - icon: fontawesome/brands/x-twitter
       link: https://twitter.com/lancedb
     - icon: fontawesome/brands/linkedin
-      link: https://www.linkedin.com/company/lancedb
+      link: https://www.linkedin.com/company/lancedb      
+      

docs/openapi.yml

@@ -38,13 +38,6 @@ components:
       required: true
       schema:
         type: string
-    index_name:
-      name: index_name
-      in: path
-      description: name of the index
-      required: true
-      schema:
-        type: string
   responses:
     invalid_request:
       description: Invalid request
@@ -492,22 +485,3 @@ paths:
           $ref: "#/components/responses/unauthorized"
         "404":
           $ref: "#/components/responses/not_found"
-  /v1/table/{name}/index/{index_name}/drop/:
-    post:
-      description: Drop an index from the table
-      tags:
-        - Tables
-      summary: Drop an index from the table
-      operationId: dropIndex
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-        - $ref: "#/components/parameters/index_name"
-      responses:
-        "200":
-          description: Index successfully dropped
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"

docs/package-lock.json

@@ -19,7 +19,7 @@
     },
     "../node": {
       "name": "vectordb",
-      "version": "0.12.0",
+      "version": "0.4.6",
       "cpu": [
         "x64",
         "arm64"
@@ -31,7 +31,9 @@
         "win32"
       ],
       "dependencies": {
+        "@apache-arrow/ts": "^14.0.2",
         "@neon-rs/load": "^0.0.74",
+        "apache-arrow": "^14.0.2",
         "axios": "^1.4.0"
       },
       "devDependencies": {
@@ -44,7 +46,6 @@
         "@types/temp": "^0.9.1",
         "@types/uuid": "^9.0.3",
         "@typescript-eslint/eslint-plugin": "^5.59.1",
-        "apache-arrow-old": "npm:apache-arrow@13.0.0",
         "cargo-cp-artifact": "^0.1",
         "chai": "^4.3.7",
         "chai-as-promised": "^7.1.1",
@@ -61,19 +62,15 @@
         "ts-node-dev": "^2.0.0",
         "typedoc": "^0.24.7",
         "typedoc-plugin-markdown": "^3.15.3",
-        "typescript": "^5.1.0",
+        "typescript": "*",
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.12.0",
+        "@lancedb/vectordb-darwin-arm64": "0.4.6",
-        "@lancedb/vectordb-darwin-x64": "0.12.0",
+        "@lancedb/vectordb-darwin-x64": "0.4.6",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.12.0",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.4.6",
-        "@lancedb/vectordb-linux-x64-gnu": "0.12.0",
+        "@lancedb/vectordb-linux-x64-gnu": "0.4.6",
-        "@lancedb/vectordb-win32-x64-msvc": "0.12.0"
+        "@lancedb/vectordb-win32-x64-msvc": "0.4.6"
-      },
-      "peerDependencies": {
-        "@apache-arrow/ts": "^14.0.2",
-        "apache-arrow": "^14.0.2"
       }
     },
     "../node/node_modules/apache-arrow": {

docs/src/ann_indexes.md

@@ -18,24 +18,25 @@ See the [indexing](concepts/index_ivfpq.md) concepts guide for more information
 Lance supports `IVF_PQ` index type by default.
 
 === "Python"
-    === "Sync API"
 
-        Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
+    Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
 
-        ```python
+    ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
+    import lancedb
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-numpy"
+    import numpy as np
-        --8<-- "python/python/tests/docs/test_guide_index.py:create_ann_index"
+    uri = "data/sample-lancedb"
-        ```
+    db = lancedb.connect(uri)
-    === "Async API"
-        Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
 
-        ```python
+    # Create 10,000 sample vectors
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
+    data = [{"vector": row, "item": f"item {i}"}
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-numpy"
+        for i, row in enumerate(np.random.random((10_000, 1536)).astype('float32'))]
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb-ivfpq"
+
-        --8<-- "python/python/tests/docs/test_guide_index.py:create_ann_index_async"
+    # Add the vectors to a table
-        ```
+    tbl = db.create_table("my_vectors", data=data)
+
+    # Create and train the index - you need to have enough data in the table for an effective training step
+    tbl.create_index(num_partitions=256, num_sub_vectors=96)
+    ```
 
 === "TypeScript"
 
@@ -44,9 +45,9 @@ Lance supports `IVF_PQ` index type by default.
         Creating indexes is done via the [lancedb.Table.createIndex](../js/classes/Table.md/#createIndex) method.
 
         ```typescript
-        --8<--- "nodejs/examples/ann_indexes.test.ts:import"
+        --8<--- "nodejs/examples/ann_indexes.ts:import"
 
-        --8<-- "nodejs/examples/ann_indexes.test.ts:ingest"
+        --8<-- "nodejs/examples/ann_indexes.ts:ingest"
         ```
 
     === "vectordb (deprecated)"
@@ -82,7 +83,6 @@ The following IVF_PQ paramters can be specified:
 - **num_sub_vectors**: The number of sub-vectors (M) that will be created during Product Quantization (PQ).
   For D dimensional vector, it will be divided into `M` subvectors with dimension `D/M`, each of which is replaced by
   a single PQ code. The default is the dimension of the vector divided by 16.
-- **num_bits**: The number of bits used to encode each sub-vector. Only 4 and 8 are supported. The higher the number of bits, the higher the accuracy of the index, also the slower search. The default is 8.
 
 !!! note
 
@@ -126,9 +126,7 @@ You can specify the GPU device to train IVF partitions via
         accelerator="mps"
     )
     ```
-!!! note
+
-    GPU based indexing is not yet supported with our asynchronous client.
-    
 Troubleshooting:
 
 If you see `AssertionError: Torch not compiled with CUDA enabled`, you need to [install
@@ -142,27 +140,23 @@ There are a couple of parameters that can be used to fine-tune the search:
 
 - **limit** (default: 10): The amount of results that will be returned
 - **nprobes** (default: 20): The number of probes used. A higher number makes search more accurate but also slower.<br/>
-  Most of the time, setting nprobes to cover 5-15% of the dataset should achieve high recall with low latency.<br/>
+  Most of the time, setting nprobes to cover 5-10% 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/>
+  e.g., for 1M vectors divided up into 256 partitions, nprobes should be set to ~20-40.<br/>
-
+  Note: nprobes is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
 - **refine_factor** (default: None): Refine the results by reading extra elements and re-ranking them in memory.<br/>
   A higher number makes search more accurate but also slower. If you find the recall is less than ideal, try refine_factor=10 to start.<br/>
-    - _For example_, For a dataset of 1 million vectors divided into 256 partitions, setting the `refine_factor` to 200 will initially retrieve the top 4,000 candidates (top k * refine_factor) from all searched partitions. These candidates are then reranked to determine the final top 20 results.<br/>
+  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
+  Note: refine_factor is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
-    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"
-    === "Sync API"
 
-        ```python
+    ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search"
+    tbl.search(np.random.random((1536))) \
-        ```
+        .limit(2) \
-    === "Async API"
+        .nprobes(20) \
-
+        .refine_factor(10) \
-        ```python
+        .to_pandas()
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_async"
+    ```
-        ```
 
     ```text
                                               vector       item       _distance
@@ -175,7 +169,7 @@ There are a couple of parameters that can be used to fine-tune the search:
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/ann_indexes.test.ts:search1"
+        --8<-- "nodejs/examples/ann_indexes.ts:search1"
         ```
 
     === "vectordb (deprecated)"
@@ -199,23 +193,17 @@ The search will return the data requested in addition to the distance of each it
 You can further filter the elements returned by a search using a where clause.
 
 === "Python"
-    === "Sync API"
 
-        ```python
+    ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_with_filter"
+    tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
-        ```
+    ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_async_with_filter"
-        ```
 
 === "TypeScript"
 
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/ann_indexes.test.ts:search2"
+        --8<-- "nodejs/examples/ann_indexes.ts:search2"
         ```
 
     === "vectordb (deprecated)"
@@ -230,16 +218,10 @@ You can select the columns returned by the query using a select clause.
 
 === "Python"
 
-    === "Sync API"
+    ```python
+    tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
+    ```
 
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_with_select"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_async_with_select"
-        ```
 
     ```text
                                                 vector _distance
@@ -253,7 +235,7 @@ You can select the columns returned by the query using a select clause.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/ann_indexes.test.ts:search3"
+        --8<-- "nodejs/examples/ann_indexes.ts:search3"
         ```
 
     === "vectordb (deprecated)"
@@ -293,15 +275,7 @@ Product quantization can lead to approximately `16 * sizeof(float32) / 1 = 64` t
 Higher number of partitions could lead to more efficient I/O during queries and better accuracy, but it takes much more time to train.
 On `SIFT-1M` dataset, our benchmark shows that keeping each partition 1K-4K rows lead to a good latency / recall.
 
-`num_sub_vectors` specifies how many Product Quantization (PQ) short codes to generate on each vector. The number should be a factor of the vector dimension. Because
+`num_sub_vectors` specifies how many Product Quantization (PQ) short codes to generate on each vector. Because
 PQ is a lossy compression of the original vector, a higher `num_sub_vectors` usually results in
-less space distortion, and thus yields better accuracy. However, a higher `num_sub_vectors` also causes heavier I/O and more PQ computation, and thus, higher latency. `dimension / num_sub_vectors` should be a multiple of 8 for optimum SIMD efficiency.
+less space distortion, and thus yields better accuracy. However, a higher `num_sub_vectors` also causes heavier I/O and
-
+more PQ computation, and thus, higher latency. `dimension / num_sub_vectors` should be a multiple of 8 for optimum SIMD efficiency.
-!!! note
-    if `num_sub_vectors` is set to be greater than the vector dimension, you will see errors like `attempt to divide by zero`
-
-### How to choose `m` and `ef_construction` for `IVF_HNSW_*` index?
-
-`m` determines the number of connections a new node establishes with its closest neighbors upon entering the graph. Typically, `m` falls within the range of 5 to 48. Lower `m` values are suitable for low-dimensional data or scenarios where recall is less critical. Conversely, higher `m` values are beneficial for high-dimensional data or when high recall is required. In essence, a larger `m` results in a denser graph with increased connectivity, but at the expense of higher memory consumption.
-
-`ef_construction` balances build speed and accuracy. Higher values increase accuracy but slow down the build process. A typical range is 150 to 300. For good search results, a minimum value of 100 is recommended. In most cases, setting this value above 500 offers no additional benefit. Ensure that `ef_construction` is always set to a value equal to or greater than `ef` in the search phase

docs/src/ann_indexes.ts

@@ -3,7 +3,6 @@ import * as vectordb from "vectordb";
 // --8<-- [end:import]
 
 (async () => {
-  console.log("ann_indexes.ts: start");
   // --8<-- [start:ingest]
   const db = await vectordb.connect("data/sample-lancedb");
 
@@ -50,5 +49,5 @@ import * as vectordb from "vectordb";
     .execute();
   // --8<-- [end:search3]
 
-  console.log("ann_indexes.ts: done");
+  console.log("Ann indexes: done");
 })();

docs/src/basic.md

@@ -133,22 +133,21 @@ recommend switching to stable releases.
 ## Connect to a database
 
 === "Python"
-    === "Sync API"
 
-        ```python
+    ```python
-        --8<-- "python/python/tests/docs/test_basic.py:imports"
+    --8<-- "python/python/tests/docs/test_basic.py:imports"
+    --8<-- "python/python/tests/docs/test_basic.py:connect"
 
-        --8<-- "python/python/tests/docs/test_basic.py:set_uri"
+    --8<-- "python/python/tests/docs/test_basic.py:connect_async"
-        --8<-- "python/python/tests/docs/test_basic.py:connect"
+    ```
-        ```
-    === "Async API"
 
-        ```python
+    !!! note "Asynchronous Python API"
-        --8<-- "python/python/tests/docs/test_basic.py:imports"
 
-        --8<-- "python/python/tests/docs/test_basic.py:set_uri"
+        The asynchronous Python API is new and has some slight differences compared
-        --8<-- "python/python/tests/docs/test_basic.py:connect_async"
+        to the synchronous API.  Feel free to start using the asynchronous version.
-        ```
+        Once all features have migrated we will start to move the synchronous API to
+        use the same syntax as the asynchronous API.  To help with this migration we
+        have created a [migration guide](migration.md) detailing the differences.
 
 === "Typescript[^1]"
 
@@ -158,7 +157,7 @@ recommend switching to stable releases.
         import * as lancedb from "@lancedb/lancedb";
         import * as arrow from "apache-arrow";
 
-        --8<-- "nodejs/examples/basic.test.ts:connect"
+        --8<-- "nodejs/examples/basic.ts:connect"
         ```
 
     === "vectordb (deprecated)"
@@ -192,40 +191,28 @@ table.
 
 === "Python"
 
+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:create_table"
+    --8<-- "python/python/tests/docs/test_basic.py:create_table_async"
+    ```
+
     If the table already exists, LanceDB will raise an error by default.
     If you want to overwrite the table, you can pass in `mode="overwrite"`
     to the `create_table` method.
 
-    === "Sync API"
+    You can also pass in a pandas DataFrame directly:
 
-        ```python
+    ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table"
+    --8<-- "python/python/tests/docs/test_basic.py:create_table_pandas"
-        ```
+    --8<-- "python/python/tests/docs/test_basic.py:create_table_async_pandas"
-
+    ```
-        You can also pass in a pandas DataFrame directly:
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table_pandas"
-        ```
-
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table_async"
-        ```
-
-        You can also pass in a pandas DataFrame directly:
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table_async_pandas"
-        ```
 
 === "Typescript[^1]"
 
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_table"
+        --8<-- "nodejs/examples/basic.ts:create_table"
         ```
 
     === "vectordb (deprecated)"
@@ -268,16 +255,10 @@ similar to a `CREATE TABLE` statement in SQL.
 
 === "Python"
 
-    === "Sync API"
+      ```python
-
+      --8<-- "python/python/tests/docs/test_basic.py:create_empty_table"
-        ```python
+      --8<-- "python/python/tests/docs/test_basic.py:create_empty_table_async"
-        --8<-- "python/python/tests/docs/test_basic.py:create_empty_table"
+      ```
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_empty_table_async"
-        ```
 
     !!! note "You can define schema in Pydantic"
         LanceDB comes with Pydantic support, which allows you to define the schema of your data using Pydantic models. This makes it easy to work with LanceDB tables and data. Learn more about all supported types in [tables guide](./guides/tables.md).
@@ -287,7 +268,7 @@ similar to a `CREATE TABLE` statement in SQL.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_empty_table"
+        --8<-- "nodejs/examples/basic.ts:create_empty_table"
         ```
 
     === "vectordb (deprecated)"
@@ -308,22 +289,16 @@ Once created, you can open a table as follows:
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    --8<-- "python/python/tests/docs/test_basic.py:open_table"
-        ```python
+    --8<-- "python/python/tests/docs/test_basic.py:open_table_async"
-        --8<-- "python/python/tests/docs/test_basic.py:open_table"
+    ```
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:open_table_async"
-        ```
 
 === "Typescript[^1]"
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:open_table"
+        --8<-- "nodejs/examples/basic.ts:open_table"
         ```
 
     === "vectordb (deprecated)"
@@ -343,22 +318,16 @@ If you forget the name of your table, you can always get a listing of all table
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    --8<-- "python/python/tests/docs/test_basic.py:table_names"
-        ```python
+    --8<-- "python/python/tests/docs/test_basic.py:table_names_async"
-        --8<-- "python/python/tests/docs/test_basic.py:table_names"
+    ```
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:table_names_async"
-        ```
 
 === "Typescript[^1]"
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:table_names"
+        --8<-- "nodejs/examples/basic.ts:table_names"
         ```
 
     === "vectordb (deprecated)"
@@ -379,22 +348,16 @@ After a table has been created, you can always add more data to it as follows:
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    --8<-- "python/python/tests/docs/test_basic.py:add_data"
-        ```python
+    --8<-- "python/python/tests/docs/test_basic.py:add_data_async"
-        --8<-- "python/python/tests/docs/test_basic.py:add_data"
+    ```
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:add_data_async"
-        ```
 
 === "Typescript[^1]"
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:add_data"
+        --8<-- "nodejs/examples/basic.ts:add_data"
         ```
 
     === "vectordb (deprecated)"
@@ -415,16 +378,10 @@ Once you've embedded the query, you can find its nearest neighbors as follows:
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    --8<-- "python/python/tests/docs/test_basic.py:vector_search"
-        ```python
+    --8<-- "python/python/tests/docs/test_basic.py:vector_search_async"
-        --8<-- "python/python/tests/docs/test_basic.py:vector_search"
+    ```
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:vector_search_async"
-        ```
 
     This returns a pandas DataFrame with the results.
 
@@ -432,7 +389,7 @@ Once you've embedded the query, you can find its nearest neighbors as follows:
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:vector_search"
+        --8<-- "nodejs/examples/basic.ts:vector_search"
         ```
 
     === "vectordb (deprecated)"
@@ -463,22 +420,16 @@ LanceDB allows you to create an ANN index on a table as follows:
 
 === "Python"
 
-    === "Sync API"
+    ```py
-
+    --8<-- "python/python/tests/docs/test_basic.py:create_index"
-        ```python
+    --8<-- "python/python/tests/docs/test_basic.py:create_index_async"
-        --8<-- "python/python/tests/docs/test_basic.py:create_index"
+    ```
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_index_async"
-        ```
 
 === "Typescript[^1]"
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_index"
+        --8<-- "nodejs/examples/basic.ts:create_index"
         ```
 
     === "vectordb (deprecated)"
@@ -508,23 +459,17 @@ This can delete any number of rows that match the filter.
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    --8<-- "python/python/tests/docs/test_basic.py:delete_rows"
-        ```python
+    --8<-- "python/python/tests/docs/test_basic.py:delete_rows_async"
-        --8<-- "python/python/tests/docs/test_basic.py:delete_rows"
+    ```
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:delete_rows_async"
-        ```
 
 === "Typescript[^1]"
 
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:delete_rows"
+        --8<-- "nodejs/examples/basic.ts:delete_rows"
         ```
 
     === "vectordb (deprecated)"
@@ -546,10 +491,7 @@ simple or complex as needed. To see what expressions are supported, see the
 
 === "Python"
 
-    === "Sync API"
+      Read more: [lancedb.table.Table.delete][]
-        Read more: [lancedb.table.Table.delete][]
-    === "Async API"
-        Read more: [lancedb.table.AsyncTable.delete][]
 
 === "Typescript[^1]"
 
@@ -571,16 +513,10 @@ Use the `drop_table()` method on the database to remove a table.
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    --8<-- "python/python/tests/docs/test_basic.py:drop_table"
-        ```python
+    --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
-        --8<-- "python/python/tests/docs/test_basic.py:drop_table"
+    ```
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
-        ```
 
     This permanently removes the table and is not recoverable, unlike deleting rows.
     By default, if the table does not exist an exception is raised. To suppress this,
@@ -591,7 +527,7 @@ Use the `drop_table()` method on the database to remove a table.
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:drop_table"
+        --8<-- "nodejs/examples/basic.ts:drop_table"
         ```
 
     === "vectordb (deprecated)"
@@ -615,25 +551,18 @@ You can use the embedding API when working with embedding models. It automatical
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    --8<-- "python/python/tests/docs/test_embeddings_optional.py:imports"
-        ```python
+    --8<-- "python/python/tests/docs/test_embeddings_optional.py:openai_embeddings"
-        --8<-- "python/python/tests/docs/test_embeddings_optional.py:imports"
+    ```
-
-        --8<-- "python/python/tests/docs/test_embeddings_optional.py:openai_embeddings"
-        ```
-    === "Async API"
-
-        Coming soon to the async API.
-        https://github.com/lancedb/lancedb/issues/1938
 
 === "Typescript[^1]"
 
     === "@lancedb/lancedb"
 
         ```typescript
-        --8<-- "nodejs/examples/embedding.test.ts:imports"
+        --8<-- "nodejs/examples/embedding.ts:imports"
-        --8<-- "nodejs/examples/embedding.test.ts:openai_embeddings"
+        --8<-- "nodejs/examples/embedding.ts:openai_embeddings"
         ```
 
 === "Rust"

docs/src/basic_legacy.ts

@@ -107,6 +107,7 @@ const example = async () => {
   // --8<-- [start:search]
   const query = await tbl.search([100, 100]).limit(2).execute();
   // --8<-- [end:search]
+  console.log(query);
 
   // --8<-- [start:delete]
   await tbl.delete('item = "fizz"');
@@ -118,9 +119,8 @@ const example = async () => {
 };
 
 async function main() {
-  console.log("basic_legacy.ts: start");
   await example();
-  console.log("basic_legacy.ts: done");
+  console.log("Basic example: done");
 }
 
 main();

docs/src/cloud/cloud_faq.md

@@ -1,34 +0,0 @@
-This section provides answers to the most common questions asked about LanceDB Cloud. By following these guidelines, you can ensure a smooth, performant experience with LanceDB Cloud.
-
-### Should I reuse the database connection?
-Yes! It is recommended to establish a single database connection and maintain it throughout your interaction with the tables within. 
-
-LanceDB uses HTTP connections to communicate with the servers. By re-using the Connection object, you avoid the overhead of repeatedly establishing HTTP connections, significantly improving efficiency.
-
-### Should I re-use the `Table` object?
-`table = db.open_table()` should be called once and used for all subsequent table operations. If there are changes to the opened table, `table` always reflect the **latest version** of the data. 
-
-### What should I do if I need to search for rows by `id`?
-LanceDB Cloud currently does not support an ID or primary key column. You are recommended to add a 
-user-defined ID column. To significantly improve the query performance with SQL causes, a scalar BITMAP/BTREE index should be created on this column. 
-
-### What are the vector indexing types supported by LanceDB Cloud?
-We support `IVF_PQ` and `IVF_HNSW_SQ` as the `index_type` which is passed to `create_index`. LanceDB Cloud tunes the indexing parameters automatically to achieve the best tradeoff between query latency and query quality.
-
-### When I add new rows to a table, do I need to manually update the index?
-No! LanceDB Cloud triggers an asynchronous background job to index the new vectors.
-
-Even though indexing is asynchronous, your vectors will still be immediately searchable. LanceDB uses brute-force search to search over unindexed rows. This makes you new data is immediately available, but does increase latency temporarily. To disable the brute-force part of search, set the `fast_search` flag in your query to `true`.
-
-### Do I need to reindex the whole dataset if only a small portion of the data is deleted or updated?
-No! Similar to adding data to the table, LanceDB Cloud triggers an asynchronous background job to update the existing indices. Therefore, no action is needed from users and there is absolutely no 
-downtime expected.
-
-### How do I know whether an index has been created?
-While index creation in LanceDB Cloud is generally fast, querying immediately after a `create_index` call may result in errors. It's recommended to use `list_indices` to verify index creation before querying.
-
-### Why is my query latency higher than expected?
-Multiple factors can impact query latency. To reduce query latency, consider the following:
-- Send pre-warm queries: send a few queries to warm up the cache before an actual user query.
-- Check network latency: LanceDB Cloud is hosted in AWS `us-east-1` region. It is recommended to run queries from an EC2 instance that is in the same region.
-- Create scalar indices: If you are filtering on metadata, it is recommended to create scalar indices on those columns. This will speedup searches with metadata filtering. See [here](../guides/scalar_index.md) for more details on creating a scalar index.

docs/src/concepts/index_hnsw.md

@@ -7,7 +7,7 @@ Approximate Nearest Neighbor (ANN) search is a method for finding data points ne
 There are three main types of ANN search algorithms:
 
 * **Tree-based search algorithms**: Use a tree structure to organize and store data points.
-* **Hash-based search algorithms**: Use a specialized geometric hash table to store and manage data points. These algorithms typically focus on theoretical guarantees, and don't usually perform as well as the other approaches in practice.
+* * **Hash-based search algorithms**: Use a specialized geometric hash table to store and manage data points. These algorithms typically focus on theoretical guarantees, and don't usually perform as well as the other approaches in practice.
 * **Graph-based search algorithms**: Use a graph structure to store data points, which can be a bit complex. 
 
 HNSW is a graph-based algorithm. All graph-based search algorithms rely on the idea of a k-nearest neighbor (or k-approximate nearest neighbor) graph, which we outline below.  
@@ -57,13 +57,6 @@ Then the greedy search routine operates as follows:
 
 ## Usage
 
-There are three key parameters to set when constructing an HNSW index:
-
-* `metric`: Use an `L2` euclidean distance metric. We also support `dot` and `cosine` distance.
-* `m`: The number of neighbors to select for each vector in the HNSW graph.
-* `ef_construction`: The number of candidates to evaluate during the construction of the HNSW graph.
-
-
 We can combine the above concepts to understand how to build and query an HNSW index in LanceDB.
 
 ### Construct index

docs/src/concepts/index_ivfpq.md

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

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

@@ -6,7 +6,6 @@ LanceDB registers the OpenAI embeddings function in the registry by default, as
 |---|---|---|---|
 | `name` | `str` | `"text-embedding-ada-002"` | The name of the model. |
 | `dim` | `int` |  Model default   | For OpenAI's newer text-embedding-3 model, we can specify a dimensionality that is smaller than the 1536 size. This feature supports it |
-| `use_azure` | bool | `False` | Set true to use Azure OpenAPI SDK |
 
 
 ```python

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

@@ -1,51 +0,0 @@
-# VoyageAI Embeddings
-
-Voyage AI provides cutting-edge embedding and rerankers.
-
-
-Using voyageai API requires voyageai package, which can be installed using `pip install voyageai`. Voyage AI embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
-You also need to set the `VOYAGE_API_KEY` environment variable to use the VoyageAI API.
-
-Supported models are:
-
-- voyage-3
-- voyage-3-lite
-- voyage-finance-2
-- voyage-multilingual-2
-- voyage-law-2
-- voyage-code-2
-
-
-Supported parameters (to be passed in `create` method) are:
-
-| Parameter | Type | Default Value | Description |
-|---|---|--------|---------|
-| `name` | `str` | `None` | The model ID of the model to use. Supported base models for Text Embeddings: voyage-3, voyage-3-lite, voyage-finance-2, voyage-multilingual-2, voyage-law-2, voyage-code-2 |
-| `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

@@ -47,22 +47,14 @@ Let's implement `SentenceTransformerEmbeddings` class. All you need to do is imp
 === "TypeScript"
 
     ```ts
-    --8<--- "nodejs/examples/custom_embedding_function.test.ts:imports"
+    --8<--- "nodejs/examples/custom_embedding_function.ts:imports"
 
-    --8<--- "nodejs/examples/custom_embedding_function.test.ts:embedding_impl"
+    --8<--- "nodejs/examples/custom_embedding_function.ts:embedding_impl"
     ```
 
 
 This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and default settings.
 
-!!! danger "Use sensitive keys to prevent leaking secrets"
-    To prevent leaking secrets, such as API keys, you should add any sensitive
-    parameters of an embedding function to the output of the
-    [sensitive_keys()][lancedb.embeddings.base.EmbeddingFunction.sensitive_keys] /
-    [getSensitiveKeys()](../../js/namespaces/embedding/classes/EmbeddingFunction/#getsensitivekeys)
-    method. This prevents users from accidentally instantiating the embedding
-    function with hard-coded secrets.
-
 Now you can use this embedding function to create your table schema and that's it! you can then ingest data and run queries without manually vectorizing the inputs.
 
 === "Python"
@@ -86,7 +78,7 @@ Now you can use this embedding function to create your table schema and that's i
 === "TypeScript"
 
     ```ts
-    --8<--- "nodejs/examples/custom_embedding_function.test.ts:call_custom_function"
+    --8<--- "nodejs/examples/custom_embedding_function.ts:call_custom_function"
     ```
 
 !!! note

docs/src/embeddings/default_embedding_functions.md

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

docs/src/embeddings/embedding_functions.md

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

docs/src/embeddings/index.md

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

docs/src/embeddings/variables_and_secrets.md

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

docs/src/examples/python_examples/chatbot.md

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

docs/src/examples/python_examples/multimodal.md

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

docs/src/examples/python_examples/rag.md

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

docs/src/examples/python_examples/recommendersystem.md

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

docs/src/examples/python_examples/vector_search.md

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

docs/src/extra_js/reo.js

@@ -1 +0,0 @@
-!function(){var e,t,n;e="9627b71b382d201",t=function(){Reo.init({clientID:"9627b71b382d201"})},(n=document.createElement("script")).src="https://static.reo.dev/"+e+"/reo.js",n.defer=!0,n.onload=t,document.head.appendChild(n)}();

docs/src/fts.md

@@ -1,29 +1,49 @@
-# Full-text search (Native FTS)
+# Full-text search
 
-LanceDB provides support for full-text search via Lance, allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
+LanceDB provides support for full-text search via Lance (before via [Tantivy](https://github.com/quickwit-oss/tantivy) (Python only)), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
+
+Currently, the Lance full text search is missing some features that are in the Tantivy full text search. This includes query parser and customizing the tokenizer. Thus, in Python, Tantivy is still the default way to do full text search and many of the instructions below apply just to Tantivy-based indices.
+
+
+## Installation (Only for Tantivy-based FTS)
 
 !!! note
-    The Python SDK uses tantivy-based FTS by default, need to pass `use_tantivy=False` to use native FTS.
+    No need to install the tantivy dependency if using native FTS
+
+To use full-text search, install the dependency [`tantivy-py`](https://github.com/quickwit-oss/tantivy-py):
+
+```sh
+# Say you want to use tantivy==0.20.1
+pip install tantivy==0.20.1
+```
 
 ## Example
 
 Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search, the FTS index must be created before you can search via keywords.
 
 === "Python"
-    === "Sync API"
 
-        ```python
+    ```python
-        --8<-- "python/python/tests/docs/test_search.py:import-lancedb"
+    import lancedb
-        --8<-- "python/python/tests/docs/test_search.py:import-lancedb-fts"
-        --8<-- "python/python/tests/docs/test_search.py:basic_fts"
-        ```
-    === "Async API"
 
-        ```python
+    uri = "data/sample-lancedb"
-        --8<-- "python/python/tests/docs/test_search.py:import-lancedb"
+    db = lancedb.connect(uri)
-        --8<-- "python/python/tests/docs/test_search.py:import-lancedb-fts"
+
-        --8<-- "python/python/tests/docs/test_search.py:basic_fts_async"
+    table = db.create_table(
-        ```
+        "my_table",
+        data=[
+            {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
+            {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
+        ],
+    )
+
+    # passing `use_tantivy=False` to use lance FTS index
+    # `use_tantivy=True` by default
+    table.create_fts_index("text")
+    table.search("puppy").limit(10).select(["text"]).to_list()
+    # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
+    # ...
+    ```
 
 === "TypeScript"
 
@@ -42,7 +62,7 @@ Consider that we have a LanceDB table named `my_table`, whose string column `tex
     });
 
     await tbl
-        .search("puppy", "fts")
+        .search("puppy", queryType="fts")
         .select(["text"])
         .limit(10)
         .toArray();
@@ -73,104 +93,58 @@ Consider that we have a LanceDB table named `my_table`, whose string column `tex
     ```
 
 It would search on all indexed columns by default, so it's useful when there are multiple indexed columns.
+For now, this is supported in tantivy way only.
 
-Passing `fts_columns="text"` if you want to specify the columns to search.
+Passing `fts_columns="text"` if you want to specify the columns to search, but it's not available for Tantivy-based full text search.
 
 !!! note
     LanceDB automatically searches on the existing FTS index if the input to the search is of type `str`. If you provide a vector as input, LanceDB will search the ANN index instead.
 
 ## Tokenization
-By default the text is tokenized by splitting on punctuation and whitespaces, and would filter out words that are with length greater than 40, and lowercase all words.
+By default the text is tokenized by splitting on punctuation and whitespaces and then removing tokens that are longer than 40 chars. For more language specific tokenization then provide the argument tokenizer_name with the 2 letter language code followed by "_stem". So for english it would be "en_stem".
 
-Stemming is useful for improving search results by reducing words to their root form, e.g. "running" to "run". LanceDB supports stemming for multiple languages, you can specify the tokenizer name to enable stemming by the pattern `tokenizer_name="{language_code}_stem"`, e.g. `en_stem` for English.
+For now, only the Tantivy-based FTS index supports to specify the tokenizer, so it's only available in Python with `use_tantivy=True`.
 
-For example, to enable stemming for English:
+=== "use_tantivy=True"
-=== "Sync API"
 
     ```python
-    --8<-- "python/python/tests/docs/test_search.py:fts_config_stem"
+    table.create_fts_index("text", use_tantivy=True, tokenizer_name="en_stem")
     ```
-=== "Async API"
 
-    ```python
+=== "use_tantivy=False"
-    --8<-- "python/python/tests/docs/test_search.py:fts_config_stem_async"
+
-    ```
+    [**Not supported yet**](https://github.com/lancedb/lance/issues/1195)
 
 the following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.
 
-The tokenizer is customizable, you can specify how the tokenizer splits the text, and how it filters out words, etc.
+## Index multiple columns
 
-For example, for language with accents, you can specify the tokenizer to use `ascii_folding` to remove accents, e.g. 'é' to 'e':
+If you have multiple string columns to index, there's no need to combine them manually -- simply pass them all as a list to `create_fts_index`:
-=== "Sync API"
+
+=== "use_tantivy=True"
 
     ```python
-    --8<-- "python/python/tests/docs/test_search.py:fts_config_folding"
+    table.create_fts_index(["text1", "text2"])
     ```
-=== "Async API"
 
-    ```python
+=== "use_tantivy=False"
-    --8<-- "python/python/tests/docs/test_search.py:fts_config_folding_async"
+
-    ```
+    [**Not supported yet**](https://github.com/lancedb/lance/issues/1195)
+
+Note that the search API call does not change - you can search over all indexed columns at once.
 
 ## Filtering
 
-LanceDB full text search supports to filter the search results by a condition, both pre-filtering and post-filtering are supported.
+Currently the LanceDB full text search feature supports *post-filtering*, meaning filters are
+applied on top of the full text search results. This can be invoked via the familiar
+`where` syntax:
 
-This can be invoked via the familiar `where` syntax.
- 
-With pre-filtering:
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    table.search("puppy").limit(10).where("meta='foo'").to_list()
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_prefiltering"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_prefiltering_async"
-        ```
-
-=== "TypeScript"
-
-    ```typescript
-    await tbl
-    .search("puppy")
-    .select(["id", "doc"])
-    .limit(10)
-    .where("meta='foo'")
-    .prefilter(true)
-    .toArray();
     ```
 
-=== "Rust"
-
-    ```rust
-    table
-        .query()
-        .full_text_search(FullTextSearchQuery::new("puppy".to_owned()))
-        .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
-        .limit(10)
-        .only_if("meta='foo'")
-        .execute()
-        .await?;
-    ```
-
-With post-filtering:
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_postfiltering"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_postfiltering_async"
-        ```
-
 === "TypeScript"
 
     ```typescript
@@ -179,7 +153,6 @@ With post-filtering:
     .select(["id", "doc"])
     .limit(10)
     .where("meta='foo'")
-    .prefilter(false)
     .toArray();
     ```
 
@@ -190,69 +163,104 @@ With post-filtering:
         .query()
         .full_text_search(FullTextSearchQuery::new(words[0].to_owned()))
         .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
-        .postfilter()
         .limit(10)
         .only_if("meta='foo'")
         .execute()
         .await?;
     ```
 
+## Sorting
+
+!!! warning "Warn"
+    Sorting is available for only Tantivy-based FTS
+
+You can pre-sort the documents by specifying `ordering_field_names` when
+creating the full-text search index. Once pre-sorted, you can then specify
+`ordering_field_name` while searching to return results sorted by the given
+field. For example,
+
+```python
+table.create_fts_index(["text_field"], use_tantivy=True, ordering_field_names=["sort_by_field"])
+
+(table.search("terms", ordering_field_name="sort_by_field")
+ .limit(20)
+ .to_list())
+```
+
+!!! note
+    If you wish to specify an ordering field at query time, you must also
+    have specified it during indexing time. Otherwise at query time, an
+    error will be raised that looks like `ValueError: The field does not exist: xxx`
+
+!!! note
+    The fields to sort on must be of typed unsigned integer, or else you will see
+    an error during indexing that looks like
+    `TypeError: argument 'value': 'float' object cannot be interpreted as an integer`.
+
+!!! note
+    You can specify multiple fields for ordering at indexing time.
+    But at query time only one ordering field is supported.
+
+
 ## Phrase queries vs. terms queries
 
 !!! warning "Warn"
     Lance-based FTS doesn't support queries using boolean operators `OR`, `AND`.
 
 For full-text search you can specify either a **phrase** query like `"the old man and the sea"`,
-or a **terms** search query like `old man sea`. For more details on the terms
+or a **terms** search query like `"(Old AND Man) AND Sea"`. For more details on the terms
 query syntax, see Tantivy's [query parser rules](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html).
 
-To search for a phrase, the index must be created with `with_position=True`:
+!!! tip "Note"
-=== "Sync API"
+    The query parser will raise an exception on queries that are ambiguous. For example, in the query `they could have been dogs OR cats`, `OR` is capitalized so it's considered a keyword query operator. But it's ambiguous how the left part should be treated. So if you submit this search query as is, you'll get `Syntax Error: they could have been dogs OR cats`.
 
-    ```python
+    ```py
-    --8<-- "python/python/tests/docs/test_search.py:fts_with_position"
+    # This raises a syntax error
-    ```
+    table.search("they could have been dogs OR cats")
-=== "Async API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:fts_with_position_async"
-    ```
-This will allow you to search for phrases, but it will also significantly increase the index size and indexing time.
-
-
-## Incremental indexing
-
-LanceDB supports incremental indexing, which means you can add new records to the table without reindexing the entire table.
-
-This can make the query more efficient, especially when the table is large and the new records are relatively small.
-
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_incremental_index"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_incremental_index_async"
-        ```
-
-=== "TypeScript"
-
-    ```typescript
-    await tbl.add([{ vector: [3.1, 4.1], text: "Frodo was a happy puppy" }]);
-    await tbl.optimize();
     ```
 
-=== "Rust"
+    On the other hand, lowercasing `OR` to `or` will work, because there are no capitalized logical operators and
+    the query is treated as a phrase query.
 
-    ```rust
+    ```py
-    let more_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
+    # This works!
-    tbl.add(more_data).execute().await?;
+    table.search("they could have been dogs or cats")
-    tbl.optimize(OptimizeAction::All).execute().await?;
     ```
-!!! note
 
-    New data added after creating the FTS index will appear in search results while incremental index is still progress, but with increased latency due to a flat search on the unindexed portion. LanceDB Cloud automates this merging process, minimizing the impact on search speed. 
+It can be cumbersome to have to remember what will cause a syntax error depending on the type of
+query you want to perform. To make this simpler, when you want to perform a phrase query, you can
+enforce it in one of two ways:
+
+1. Place the double-quoted query inside single quotes. For example, `table.search('"they could have been dogs OR cats"')` is treated as
+a phrase query.
+1. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
+itself contains double quotes. For example, `table.search('the cats OR dogs were not really "pets" at all').phrase_query()`
+is treated as a phrase query.
+
+In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested
+double quotes replaced by single quotes.
+
+
+## Configurations (Only for Tantivy-based FTS)
+
+By default, LanceDB configures a 1GB heap size limit for creating the index. You can
+reduce this if running on a smaller node, or increase this for faster performance while
+indexing a larger corpus.
+
+```python
+# configure a 512MB heap size
+heap = 1024 * 1024 * 512
+table.create_fts_index(["text1", "text2"], writer_heap_size=heap, replace=True)
+```
+
+## Current limitations
+
+For that Tantivy-based FTS:
+
+1. Currently we do not yet support incremental writes.
+   If you add data after FTS index creation, it won't be reflected
+   in search results until you do a full reindex.
+
+2. We currently only support local filesystem paths for the FTS index.
+   This is a tantivy limitation. We've implemented an object store plugin
+   but there's no way in tantivy-py to specify to use it.

docs/src/fts_tantivy.md

@@ -1,160 +0,0 @@
-# Full-text search (Tantivy-based FTS)
-
-LanceDB also provides support for full-text search via [Tantivy](https://github.com/quickwit-oss/tantivy), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
-
-The tantivy-based FTS is only available in Python synchronous APIs and does not support building indexes on object storage or incremental indexing. If you need these features, try native FTS [native FTS](fts.md).
-
-## Installation
-
-To use full-text search, install the dependency [`tantivy-py`](https://github.com/quickwit-oss/tantivy-py):
-
-```sh
-# Say you want to use tantivy==0.20.1
-pip install tantivy==0.20.1
-```
-
-## Example
-
-Consider that we have a LanceDB table named `my_table`, whose string column `content` we want to index and query via keyword search, the FTS index must be created before you can search via keywords.
-
-```python
-import lancedb
-
-uri = "data/sample-lancedb"
-db = lancedb.connect(uri)
-
-table = db.create_table(
-    "my_table",
-    data=[
-        {"id": 1, "vector": [3.1, 4.1], "title": "happy puppy", "content": "Frodo was a happy puppy", "meta": "foo"},
-        {"id": 2, "vector": [5.9, 26.5], "title": "playing kittens", "content": "There are several kittens playing around the puppy", "meta": "bar"},
-    ],
-)
-
-# passing `use_tantivy=False` to use lance FTS index
-# `use_tantivy=True` by default
-table.create_fts_index("content", use_tantivy=True)
-table.search("puppy").limit(10).select(["content"]).to_list()
-# [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
-# ...
-```
-
-It would search on all indexed columns by default, so it's useful when there are multiple indexed columns.
-
-!!! note
-    LanceDB automatically searches on the existing FTS index if the input to the search is of type `str`. If you provide a vector as input, LanceDB will search the ANN index instead.
-
-## Tokenization
-By default the text is tokenized by splitting on punctuation and whitespaces and then removing tokens that are longer than 40 chars. For more language specific tokenization then provide the argument tokenizer_name with the 2 letter language code followed by "_stem". So for english it would be "en_stem".
-
-```python
-table.create_fts_index("content", use_tantivy=True, tokenizer_name="en_stem", replace=True)
-```
-
-the following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.
-
-## Index multiple columns
-
-If you have multiple string columns to index, there's no need to combine them manually -- simply pass them all as a list to `create_fts_index`:
-
-```python
-table.create_fts_index(["title", "content"], use_tantivy=True, replace=True)
-```
-
-Note that the search API call does not change - you can search over all indexed columns at once.
-
-## Filtering
-
-Currently the LanceDB full text search feature supports *post-filtering*, meaning filters are
-applied on top of the full text search results (see [native FTS](fts.md) if you need pre-filtering). This can be invoked via the familiar
-`where` syntax:
-
-```python
-table.search("puppy").limit(10).where("meta='foo'").to_list()
-```
-
-## Sorting
-
-You can pre-sort the documents by specifying `ordering_field_names` when
-creating the full-text search index. Once pre-sorted, you can then specify
-`ordering_field_name` while searching to return results sorted by the given
-field. For example,
-
-```python
-table.create_fts_index(["content"], use_tantivy=True, ordering_field_names=["id"], replace=True)
-
-(table.search("puppy", ordering_field_name="id")
- .limit(20)
- .to_list())
-```
-
-!!! note
-    If you wish to specify an ordering field at query time, you must also
-    have specified it during indexing time. Otherwise at query time, an
-    error will be raised that looks like `ValueError: The field does not exist: xxx`
-
-!!! note
-    The fields to sort on must be of typed unsigned integer, or else you will see
-    an error during indexing that looks like
-    `TypeError: argument 'value': 'float' object cannot be interpreted as an integer`.
-
-!!! note
-    You can specify multiple fields for ordering at indexing time.
-    But at query time only one ordering field is supported.
-
-
-## Phrase queries vs. terms queries
-
-For full-text search you can specify either a **phrase** query like `"the old man and the sea"`,
-or a **terms** search query like `"(Old AND Man) AND Sea"`. For more details on the terms
-query syntax, see Tantivy's [query parser rules](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html).
-
-!!! tip "Note"
-    The query parser will raise an exception on queries that are ambiguous. For example, in the query `they could have been dogs OR cats`, `OR` is capitalized so it's considered a keyword query operator. But it's ambiguous how the left part should be treated. So if you submit this search query as is, you'll get `Syntax Error: they could have been dogs OR cats`.
-
-    ```py
-    # This raises a syntax error
-    table.search("they could have been dogs OR cats")
-    ```
-
-    On the other hand, lowercasing `OR` to `or` will work, because there are no capitalized logical operators and
-    the query is treated as a phrase query.
-
-    ```py
-    # This works!
-    table.search("they could have been dogs or cats")
-    ```
-
-It can be cumbersome to have to remember what will cause a syntax error depending on the type of
-query you want to perform. To make this simpler, when you want to perform a phrase query, you can
-enforce it in one of two ways:
-
-1. Place the double-quoted query inside single quotes. For example, `table.search('"they could have been dogs OR cats"')` is treated as
-a phrase query.
-1. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
-itself contains double quotes. For example, `table.search('the cats OR dogs were not really "pets" at all').phrase_query()`
-is treated as a phrase query.
-
-In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested
-double quotes replaced by single quotes.
-
-
-## Configurations
-
-By default, LanceDB configures a 1GB heap size limit for creating the index. You can
-reduce this if running on a smaller node, or increase this for faster performance while
-indexing a larger corpus.
-
-```python
-# configure a 512MB heap size
-heap = 1024 * 1024 * 512
-table.create_fts_index(["title", "content"], use_tantivy=True, writer_heap_size=heap, replace=True)
-```
-
-## Current limitations
-
-1. New data added after creating the FTS index will appear in search results, but with increased latency due to a flat search on the unindexed portion. Re-indexing with `create_fts_index` will reduce latency. LanceDB Cloud automates this merging process, minimizing the impact on search speed. 
-
-2. We currently only support local filesystem paths for the FTS index.
-   This is a tantivy limitation. We've implemented an object store plugin
-   but there's no way in tantivy-py to specify to use it.

docs/src/guides/scalar_index.md

@@ -1,51 +1,38 @@
-# Building a Scalar Index
+# Building Scalar Index
 
-Scalar indices organize data by scalar attributes (e.g. numbers, categorical values), enabling fast filtering of vector data. In vector databases, scalar indices accelerate the retrieval of scalar data associated with vectors, thus enhancing the query performance when searching for vectors that meet certain scalar criteria. 
+Similar to many SQL databases, LanceDB supports several types of Scalar indices to accelerate search
-
-Similar to many SQL databases, LanceDB supports several types of scalar indices to accelerate search
 over scalar columns.
 
-- `BTREE`: The most common type is BTREE. The index stores a copy of the
+- `BTREE`: The most common type is BTREE. This index is inspired by the btree data structure
-  column in sorted order. This sorted copy allows a binary search to be used to
+  although only the first few layers of the btree are cached in memory.
-  satisfy queries.
+  It will perform well on columns with a large number of unique values and few rows per value.
-- `BITMAP`: this index stores a bitmap for each unique value in the column. It 
+- `BITMAP`: this index stores a bitmap for each unique value in the column.
-  uses a series of bits to indicate whether a value is present in a row of a table
+  This index is useful for columns with a finite number of unique values and many rows per value.
-- `LABEL_LIST`: a special index that can be used on `List<T>` columns to
+  For example, columns that represent "categories", "labels", or "tags"
-  support queries with `array_contains_all` and `array_contains_any`
+- `LABEL_LIST`: a special index that is used to index list columns whose values have a finite set of possibilities.
-  using an underlying bitmap index.
   For example, a column that contains lists of tags (e.g. `["tag1", "tag2", "tag3"]`) can be indexed with a `LABEL_LIST` index.
 
-!!! tips "How to choose the right scalar index type"
-
-    `BTREE`: This index is good for scalar columns with mostly distinct values and does best when the query is highly selective.
-    
-    `BITMAP`: This index works best for low-cardinality numeric or string columns, where the number of unique values is small (i.e., less than a few thousands).
-    
-    `LABEL_LIST`: This index should be used for columns containing list-type data.
-
 | Data Type                                                       | Filter                                    | Index Type   |
 | --------------------------------------------------------------- | ----------------------------------------- | ------------ |
 | Numeric, String, Temporal                                       | `<`, `=`, `>`, `in`, `between`, `is null` | `BTREE`      |
 | Boolean, numbers or strings with fewer than 1,000 unique values | `<`, `=`, `>`, `in`, `between`, `is null` | `BITMAP`     |
 | List of low cardinality of numbers or strings                   | `array_has_any`, `array_has_all`          | `LABEL_LIST` |
 
-### Create a scalar index
 === "Python"
 
-    === "Sync API"
+    ```python
+    import lancedb
+    books = [
+      {"book_id": 1, "publisher": "plenty of books", "tags": ["fantasy", "adventure"]},
+      {"book_id": 2, "publisher": "book town", "tags": ["non-fiction"]},
+      {"book_id": 3, "publisher": "oreilly", "tags": ["textbook"]}
+    ]
 
-        ```python
+    db = lancedb.connect("./db")
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
+    table = db.create_table("books", books)
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb-btree-bitmap"
+    table.create_scalar_index("book_id")  # BTree by default
-        --8<-- "python/python/tests/docs/test_guide_index.py:basic_scalar_index"
+    table.create_scalar_index("publisher", index_type="BITMAP")
-        ```
+    ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb-btree-bitmap"
-        --8<-- "python/python/tests/docs/test_guide_index.py:basic_scalar_index_async"
-        ```
 
 === "Typescript"
 
@@ -59,22 +46,16 @@ over scalar columns.
         await tlb.create_index("publisher", { config: lancedb.Index.bitmap() })
         ```
 
-The following scan will be faster if the column `book_id` has a scalar index:
+For example, the following scan will be faster if the column `my_col` has a scalar index:
 
 === "Python"
 
-    === "Sync API"
+    ```python
+    import lancedb
 
-        ```python
+    table = db.open_table("books")
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
+    my_df = table.search().where("book_id = 2").to_pandas()
-        --8<-- "python/python/tests/docs/test_guide_index.py:search_with_scalar_index"
+    ```
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:search_with_scalar_index_async"
-        ```
 
 === "Typescript"
 
@@ -95,18 +76,22 @@ Scalar indices can also speed up scans containing a vector search or full text s
 
 === "Python"
 
-    === "Sync API"
+    ```python
+    import lancedb
 
-        ```python
+    data = [
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
+      {"book_id": 1, "vector": [1, 2]},
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_with_scalar_index"
+      {"book_id": 2, "vector": [3, 4]},
-        ```
+      {"book_id": 3, "vector": [5, 6]}
-    === "Async API"
+    ]
+    table = db.create_table("book_with_embeddings", data)
 
-        ```python
+    (
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
+        table.search([1, 2])
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_with_scalar_index_async"
+        .where("book_id != 3", prefilter=True)
-        ```
+        .to_pandas()
+    )
+    ```
 
 === "Typescript"
 
@@ -121,36 +106,3 @@ Scalar indices can also speed up scans containing a vector search or full text s
           .limit(10)
           .toArray();
         ```
-### Update a scalar index
-Updating the table data (adding, deleting, or modifying records) requires that you also update the scalar index. This can be done by calling `optimize`, which will trigger an update to the existing scalar index.
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:update_scalar_index"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:update_scalar_index_async"
-        ```
-
-=== "TypeScript"
-
-    ```typescript
-    await tbl.add([{ vector: [7, 8], book_id: 4 }]);
-    await tbl.optimize();
-    ```
-
-=== "Rust"
-
-    ```rust
-    let more_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
-    tbl.add(more_data).execute().await?;
-    tbl.optimize(OptimizeAction::All).execute().await?;
-    ```
-
-!!! note
-
-    New data added after creating the scalar index will still appear in search results if optimize is not used, but with increased latency due to a flat search on the unindexed portion. LanceDB Cloud automates the optimize process, minimizing the impact on search speed.

docs/src/guides/storage.md

@@ -12,52 +12,25 @@ LanceDB OSS supports object stores such as AWS S3 (and compatible stores), Azure
 === "Python"
 
     AWS S3:
-    === "Sync API"
 
-        ```python
+    ```python
-        import lancedb
+    import lancedb
-        db = lancedb.connect("s3://bucket/path")
+    db = lancedb.connect("s3://bucket/path")
-        ```
+    ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async("s3://bucket/path")
-        ```
 
     Google Cloud Storage:
 
-    === "Sync API"
+    ```python
-
+    import lancedb
-        ```python
+    db = lancedb.connect("gs://bucket/path")
-        import lancedb
+    ```
-        db = lancedb.connect("gs://bucket/path")
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async("gs://bucket/path")
-        ```
 
     Azure Blob Storage:
 
-    <!-- skip-test -->
+    ```python
-    === "Sync API"
+    import lancedb
-
+    db = lancedb.connect("az://bucket/path")
-        ```python
+    ```
-        import lancedb
-        db = lancedb.connect("az://bucket/path")
-        ```
-    <!-- skip-test -->
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async("az://bucket/path")
-        ```
-    Note that for Azure, storage credentials must be configured. See [below](#azure-blob-storage) for more details.
-
 
 === "TypeScript"
 
@@ -114,28 +87,22 @@ In most cases, when running in the respective cloud and permissions are set up c
 export TIMEOUT=60s
 ```
 
+!!! note "`storage_options` availability"
+
+    The `storage_options` parameter is only available in Python *async* API and JavaScript API.
+    It is not yet supported in the Python synchronous API.
+
 If you only want this to apply to one particular connection, you can pass the `storage_options` argument when opening the connection:
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    import lancedb
-        ```python
+    db = await lancedb.connect_async(
-        import lancedb
+        "s3://bucket/path",
-        db = lancedb.connect(
+        storage_options={"timeout": "60s"}
-            "s3://bucket/path",
+    )
-            storage_options={"timeout": "60s"}
+    ```
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3://bucket/path",
-            storage_options={"timeout": "60s"}
-        )
-        ```
 
 === "TypeScript"
 
@@ -163,29 +130,15 @@ Getting even more specific, you can set the `timeout` for only a particular tabl
 === "Python"
 
     <!-- skip-test -->
-    === "Sync API"
+    ```python
-
+    import lancedb
-        ```python
+    db = await lancedb.connect_async("s3://bucket/path")
-        import lancedb
+    table = await db.create_table(
-        db = lancedb.connect("s3://bucket/path")
+        "table",
-        table = db.create_table(
+        [{"a": 1, "b": 2}],
-            "table",
+        storage_options={"timeout": "60s"}
-            [{"a": 1, "b": 2}],
+    )
-            storage_options={"timeout": "60s"}
+    ```
-        )
-        ```
-    <!-- skip-test -->
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async("s3://bucket/path")
-        async_table = await async_db.create_table(
-            "table",
-            [{"a": 1, "b": 2}],
-            storage_options={"timeout": "60s"}
-        )
-        ```
 
 === "TypeScript"
 
@@ -243,32 +196,17 @@ These can be set as environment variables or passed in the `storage_options` par
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    import lancedb
-        ```python
+    db = await lancedb.connect_async(
-        import lancedb
+        "s3://bucket/path",
-        db = lancedb.connect(
+        storage_options={
-            "s3://bucket/path",
+            "aws_access_key_id": "my-access-key",
-            storage_options={
+            "aws_secret_access_key": "my-secret-key",
-                "aws_access_key_id": "my-access-key",
+            "aws_session_token": "my-session-token",
-                "aws_secret_access_key": "my-secret-key",
+        }
-                "aws_session_token": "my-session-token",
+    )
-            }
+    ```
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3://bucket/path",
-            storage_options={
-                "aws_access_key_id": "my-access-key",
-                "aws_secret_access_key": "my-secret-key",
-                "aws_session_token": "my-session-token",
-            }
-        )
-        ```
 
 === "TypeScript"
 
@@ -412,22 +350,12 @@ name of the table to use.
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    import lancedb
-        ```python
+    db = await lancedb.connect_async(
-        import lancedb
+        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-        db = lancedb.connect(
+    )
-            "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
+    ```
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-        )    
-        ```
 
 === "JavaScript"
 
@@ -515,30 +443,16 @@ LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you m
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    import lancedb
-        ```python
+    db = await lancedb.connect_async(
-        import lancedb
+        "s3://bucket/path",
-        db = lancedb.connect(
+        storage_options={
-            "s3://bucket/path",
+            "region": "us-east-1",
-            storage_options={
+            "endpoint": "http://minio:9000",
-                "region": "us-east-1",
+        }
-                "endpoint": "http://minio:9000",
+    )
-            }
+    ```
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3://bucket/path",
-            storage_options={
-                "region": "us-east-1",
-                "endpoint": "http://minio:9000",
-            }
-        )    
-        ```
 
 === "TypeScript"
 
@@ -584,36 +498,22 @@ This can also be done with the ``AWS_ENDPOINT`` and ``AWS_DEFAULT_REGION`` envir
 
 #### S3 Express
 
-LanceDB supports [S3 Express One Zone](https://aws.amazon.com/s3/storage-classes/express-one-zone/) endpoints, but requires additional infrastructure configuration for the compute service, such as EC2 or Lambda. Please refer to [Networking requirements for S3 Express One Zone](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-express-networking.html).
+LanceDB supports [S3 Express One Zone](https://aws.amazon.com/s3/storage-classes/express-one-zone/) endpoints, but requires additional configuration. Also, S3 Express endpoints only support connecting from an EC2 instance within the same region.
 
 To configure LanceDB to use an S3 Express endpoint, you must set the storage option `s3_express`. The bucket name in your table URI should **include the suffix**.
 
 === "Python"
 
-    === "Sync API"
+    ```python
-
+    import lancedb
-        ```python
+    db = await lancedb.connect_async(
-        import lancedb
+        "s3://my-bucket--use1-az4--x-s3/path",
-        db = lancedb.connect(
+        storage_options={
-            "s3://my-bucket--use1-az4--x-s3/path",
+            "region": "us-east-1",
-            storage_options={
+            "s3_express": "true",
-                "region": "us-east-1",
+        }
-                "s3_express": "true",
+    )
-            }
+    ```
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3://my-bucket--use1-az4--x-s3/path",
-            storage_options={
-                "region": "us-east-1",
-                "s3_express": "true",
-            }
-        )    
-        ```
 
 === "TypeScript"
 
@@ -654,29 +554,15 @@ GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environme
 === "Python"
 
     <!-- skip-test -->
-    === "Sync API"
+    ```python
-
+    import lancedb
-        ```python
+    db = await lancedb.connect_async(
-        import lancedb
+        "gs://my-bucket/my-database",
-        db = lancedb.connect(
+        storage_options={
-            "gs://my-bucket/my-database",
+            "service_account": "path/to/service-account.json",
-            storage_options={
+        }
-                "service_account": "path/to/service-account.json",
+    )
-            }
+    ```
-        )
-        ```
-    <!-- skip-test -->
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "gs://my-bucket/my-database",
-            storage_options={
-                "service_account": "path/to/service-account.json",
-            }
-        )    
-        ```
 
 === "TypeScript"
 
@@ -728,31 +614,16 @@ Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_A
 === "Python"
 
     <!-- skip-test -->
-    === "Sync API"
+    ```python
-
+    import lancedb
-        ```python
+    db = await lancedb.connect_async(
-        import lancedb
+        "az://my-container/my-database",
-        db = lancedb.connect(
+        storage_options={
-            "az://my-container/my-database",
+            account_name: "some-account",
-            storage_options={
+            account_key: "some-key",
-                account_name: "some-account",
+        }
-                account_key: "some-key",
+    )
-            }
+    ```
-        )
-        ```
-    <!-- skip-test -->
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "az://my-container/my-database",
-            storage_options={
-                account_name: "some-account",
-                account_key: "some-key",
-            }
-        )   
-        ```
 
 === "TypeScript"
 

docs/src/guides/tables/merge_insert.md

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

docs/src/guides/tuning_retrievers/1_query_types.md

@@ -1,8 +1,8 @@
 ## Improving retriever performance
 
-Try it yourself: <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/lancedb_reranking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
+Try it yourself - <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/lancedb_reranking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
 
-VectorDBs are used as retrievers in recommender or chatbot-based systems for retrieving relevant data based on user queries. For example, retrievers are a critical component of Retrieval Augmented Generation (RAG) acrhitectures. In this section, we will discuss how to improve the performance of retrievers.
+VectorDBs are used as retreivers in recommender or chatbot-based systems for retrieving relevant data based on user queries. For example, retriever is a critical component of Retrieval Augmented Generation (RAG) acrhitectures. In this section, we will discuss how to improve the performance of retrievers.
 
 There are serveral ways to improve the performance of retrievers. Some of the common techniques are:
 
@@ -19,7 +19,7 @@ Using different embedding models is something that's very specific to the use ca
 
 
 ## The dataset
-We'll be using a QA dataset generated using a LLama2 review paper. The dataset contains 221 query, context and answer triplets. The queries and answers are generated using GPT-4 based on a given query. Full script used to generate the dataset can be found on this [repo](https://github.com/lancedb/ragged). It can be downloaded from [here](https://github.com/AyushExel/assets/blob/main/data_qa.csv).
+We'll be using a QA dataset generated using a LLama2 review paper. The dataset contains 221 query, context and answer triplets. The queries and answers are generated using GPT-4 based on a given query. Full script used to generate the dataset can be found on this [repo](https://github.com/lancedb/ragged). It can be downloaded from [here](https://github.com/AyushExel/assets/blob/main/data_qa.csv)
 
 ### Using different query types
 Let's setup the embeddings and the dataset first. We'll use the LanceDB's `huggingface` embeddings integration for this guide. 
@@ -45,14 +45,14 @@ table.add(df[["context"]].to_dict(orient="records"))
 queries = df["query"].tolist()
 ```
 
-Now that we have the dataset and embeddings table set up, here's how you can run different query types on the dataset:
+Now that we have the dataset and embeddings table set up, here's how you can run different query types on the dataset.
 
 * <b> Vector Search: </b>
 
     ```python
     table.search(quries[0], query_type="vector").limit(5).to_pandas()
     ```
-    By default, LanceDB uses vector search query type for searching and it automatically converts the input query to a vector before searching when using embedding API. So, the following statement is equivalent to the above statement:
+    By default, LanceDB uses vector search query type for searching and it automatically converts the input query to a vector before searching when using embedding API. So, the following statement is equivalent to the above statement.
 
     ```python
     table.search(quries[0]).limit(5).to_pandas()
@@ -77,7 +77,7 @@ Now that we have the dataset and embeddings table set up, here's how you can run
 
 * <b> Hybrid Search: </b>
 
-    Hybrid search is a combination of vector and full-text search. Here's how you can run a hybrid search query on the dataset:
+    Hybrid search is a combination of vector and full-text search. Here's how you can run a hybrid search query on the dataset.
     ```python
     table.search(quries[0], query_type="hybrid").limit(5).to_pandas()
     ```
@@ -87,7 +87,7 @@ Now that we have the dataset and embeddings table set up, here's how you can run
 
     !!! note "Note"
         By default, it uses `LinearCombinationReranker` that combines the scores from vector and full-text search using a weighted linear combination. It is the simplest reranker implementation available in LanceDB. You can also use other rerankers like `CrossEncoderReranker` or `CohereReranker` for reranking the results.
-        Learn more about rerankers [here](https://lancedb.github.io/lancedb/reranking/).
+        Learn more about rerankers [here](https://lancedb.github.io/lancedb/reranking/)
 
     
 

docs/src/guides/tuning_retrievers/2_reranking.md

@@ -1,6 +1,6 @@
 Continuing from the previous section, we can now rerank the results using more complex rerankers.
 
-Try it yourself: <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/lancedb_reranking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
+Try it yourself - <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/lancedb_reranking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
 
 ## Reranking search results
 You can rerank any search results using a reranker. The syntax for reranking is as follows:
@@ -62,6 +62,9 @@ Let us take a look at the same datasets from the previous sections, using the sa
 | Reranked fts  | 0.672    |
 | Hybrid | 0.759 |
 
+### SQuAD Dataset
+
+
 ### Uber10K sec filing Dataset
 
 | Query Type | Hit-rate@5 |

docs/src/guides/tuning_retrievers/3_embed_tuning.md

@@ -1,5 +1,5 @@
 ## Finetuning the Embedding Model
-Try it yourself: <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/embedding_tuner.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
+Try it yourself - <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/embedding_tuner.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
 
 Another way to improve retriever performance is to fine-tune the embedding model itself. Fine-tuning the embedding model can help in learning better representations for the documents and queries in the dataset. This can be particularly useful when the dataset is very different from the pre-trained data used to train the embedding model.
 
@@ -16,7 +16,7 @@ validation_df.to_csv("data_val.csv", index=False)
 You can use any tuning API to fine-tune embedding models. In this example, we'll utilise Llama-index as it also comes with utilities for synthetic data generation and training the model. 
 
 
-We parse the dataset as llama-index text nodes and generate synthetic QA pairs from each node:
+Then parse the dataset as llama-index text nodes and generate synthetic QA pairs from each node.
 ```python
 from llama_index.core.node_parser import SentenceSplitter
 from llama_index.readers.file import PagedCSVReader
@@ -43,7 +43,7 @@ val_dataset = generate_qa_embedding_pairs(
 )
 ```
 
-Now we'll use `SentenceTransformersFinetuneEngine` engine to fine-tune the model. You can also use `sentence-transformers` or `transformers` library to fine-tune the model:
+Now we'll use `SentenceTransformersFinetuneEngine` engine to fine-tune the model. You can also use `sentence-transformers` or `transformers` library to fine-tune the model. 
 
 ```python
 from llama_index.finetuning import SentenceTransformersFinetuneEngine
@@ -57,7 +57,7 @@ finetune_engine = SentenceTransformersFinetuneEngine(
 finetune_engine.finetune()
 embed_model = finetune_engine.get_finetuned_model()
 ```
-This saves the fine tuned embedding model in `tuned_model` folder.
+This saves the fine tuned embedding model in `tuned_model` folder. This al
 
 # Evaluation results
 In order to eval the retriever, you can either use this model to ingest the data into LanceDB directly or llama-index's LanceDB integration to create a `VectorStoreIndex` and use it as a retriever. 

docs/src/hybrid_search/eval.md

@@ -3,22 +3,22 @@
 Hybrid Search is a broad (often misused) term. It can mean anything from combining multiple methods for searching, to applying ranking methods to better sort the results. In this blog, we use the definition of "hybrid search" to mean using a combination of keyword-based and vector search.
 
 ## The challenge of (re)ranking search results
-Once you have a group of the most relevant search results from multiple search sources, you'd likely standardize the score and rank them accordingly. This process can also be seen as another independent step: reranking.
+Once you have a group of the most relevant search results from multiple search sources, you'd likely standardize the score and rank them accordingly. This process can also be seen as another independent step - reranking.
 There are two approaches for reranking search results from multiple sources.
 
-* <b>Score-based</b>: Calculate final relevance scores based on a weighted linear combination of individual search algorithm scores. Example: Weighted linear combination of semantic search & keyword-based search results.
+* <b>Score-based</b>: Calculate final relevance scores based on a weighted linear combination of individual search algorithm scores. Example - Weighted linear combination of semantic search & keyword-based search results.
 
-* <b>Relevance-based</b>: Discards the existing scores and calculates the relevance of each search result-query pair. Example: Cross Encoder models
+* <b>Relevance-based</b>: Discards the existing scores and calculates the relevance of each search result - query pair. Example - Cross Encoder models
 
-Even though there are many strategies for reranking search results, none works for all cases. Moreover, evaluating them itself is a challenge. Also, reranking can be dataset or application specific so it's hard to generalize.
+Even though there are many strategies for reranking search results, none works for all cases. Moreover, evaluating them itself is a challenge. Also, reranking can be dataset, application specific so it's hard to generalize.
 
 ### Example evaluation of hybrid search with Reranking
 
-Here's some evaluation numbers from an experiment comparing these rerankers on about 800 queries. It is modified version of an evaluation script from [llama-index](https://github.com/run-llama/finetune-embedding/blob/main/evaluate.ipynb) that measures hit-rate at top-k.
+Here's some evaluation numbers from experiment comparing these re-rankers on about 800 queries. It is modified version of an evaluation script from [llama-index](https://github.com/run-llama/finetune-embedding/blob/main/evaluate.ipynb) that measures hit-rate at top-k.
 
 <b> With OpenAI ada2 embedding </b>
 
-Vector Search baseline: `0.64`
+Vector Search baseline - `0.64`
 
 | Reranker | Top-3 | Top-5 | Top-10 |
 | --- | --- | --- | --- |
@@ -33,7 +33,7 @@ Vector Search baseline: `0.64`
 
 <b> With OpenAI embedding-v3-small </b>
 
-Vector Search baseline: `0.59`
+Vector Search baseline - `0.59`
 
 | Reranker | Top-3 | Top-5 | Top-10 |
 | --- | --- | --- | --- |

docs/src/hybrid_search/hybrid_search.md

@@ -5,46 +5,57 @@ LanceDB supports both semantic and keyword-based search (also termed full-text s
 ## Hybrid search in LanceDB
 You can perform hybrid search in LanceDB by combining the results of semantic and full-text search via a reranking algorithm of your choice. LanceDB provides multiple rerankers out of the box. However, you can always write a custom reranker if your use case need more sophisticated logic .
 
-=== "Sync API"
+```python
+import os
 
-    ```python
+import lancedb
-    --8<-- "python/python/tests/docs/test_search.py:import-os"
+import openai
-    --8<-- "python/python/tests/docs/test_search.py:import-openai"
+from lancedb.embeddings import get_registry
-    --8<-- "python/python/tests/docs/test_search.py:import-lancedb"
+from lancedb.pydantic import LanceModel, Vector
-    --8<-- "python/python/tests/docs/test_search.py:import-embeddings"
-    --8<-- "python/python/tests/docs/test_search.py:import-pydantic"
-    --8<-- "python/python/tests/docs/test_search.py:import-lancedb-fts"
-    --8<-- "python/python/tests/docs/test_search.py:import-openai-embeddings"
-    --8<-- "python/python/tests/docs/test_search.py:class-Documents"
-    --8<-- "python/python/tests/docs/test_search.py:basic_hybrid_search"
-    ```
-=== "Async API"
 
-    ```python
+db = lancedb.connect("~/.lancedb")
-    --8<-- "python/python/tests/docs/test_search.py:import-os"
-    --8<-- "python/python/tests/docs/test_search.py:import-openai"
-    --8<-- "python/python/tests/docs/test_search.py:import-lancedb"
-    --8<-- "python/python/tests/docs/test_search.py:import-embeddings"
-    --8<-- "python/python/tests/docs/test_search.py:import-pydantic"
-    --8<-- "python/python/tests/docs/test_search.py:import-lancedb-fts"
-    --8<-- "python/python/tests/docs/test_search.py:import-openai-embeddings"
-    --8<-- "python/python/tests/docs/test_search.py:class-Documents"
-    --8<-- "python/python/tests/docs/test_search.py:basic_hybrid_search_async"
-    ```
 
+# Ingest embedding function in LanceDB table
+# Configuring the environment variable OPENAI_API_KEY
+if "OPENAI_API_KEY" not in os.environ:
+# OR set the key here as a variable
+    openai.api_key = "sk-..."
+embeddings = get_registry().get("openai").create()
+
+class Documents(LanceModel):
+    vector: Vector(embeddings.ndims()) = embeddings.VectorField()
+    text: str = embeddings.SourceField()
+
+table = db.create_table("documents", schema=Documents)
+
+data = [
+    { "text": "rebel spaceships striking from a hidden base"},
+    { "text": "have won their first victory against the evil Galactic Empire"},
+    { "text": "during the battle rebel spies managed to steal secret plans"},
+    { "text": "to the Empire's ultimate weapon the Death Star"}
+]
+
+# ingest docs with auto-vectorization
+table.add(data)
+
+# Create a fts index before the hybrid search
+table.create_fts_index("text")
+# hybrid search with default re-ranker
+results = table.search("flower moon", query_type="hybrid").to_pandas()
+```
 !!! Note
     You can also pass the vector and text query manually. This is useful if you're not using the embedding API or if you're using a separate embedder service.
 ### Explicitly passing the vector and text query
-=== "Sync API"
+```python
+vector_query = [0.1, 0.2, 0.3, 0.4, 0.5]
+text_query = "flower moon"
+results = table.search(query_type="hybrid")
+                .vector(vector_query)
+                .text(text_query)
+                .limit(5)
+                .to_pandas()
 
-    ```python
+```
-    --8<-- "python/python/tests/docs/test_search.py:hybrid_search_pass_vector_text"
-    ```
-=== "Async API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:hybrid_search_pass_vector_text_async"
-    ```
 
 By default, LanceDB uses `RRFReranker()`, which uses reciprocal rank fusion score, to combine and rerank the results of semantic and full-text search. You can customize the hyperparameters as needed or write your own custom reranker. Here's how you can use any of the available rerankers:
 
@@ -57,7 +68,7 @@ By default, LanceDB uses `RRFReranker()`, which uses reciprocal rank fusion scor
 
 
 ## Available Rerankers
-LanceDB provides a number of rerankers out of the box. You can use any of these rerankers by passing them to the `rerank()` method. 
+LanceDB provides a number of re-rankers out of the box. You can use any of these re-rankers by passing them to the `rerank()` method. 
 Go to [Rerankers](../reranking/index.md) to learn more about using the available rerankers and implementing custom rerankers.
 
 

docs/src/index.md

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

docs/src/integrations/langchain.md

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

docs/src/integrations/phidata.md

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

docs/src/javascript/README.md

@@ -41,6 +41,7 @@ To build everything fresh:
 
 ```bash
 npm install
+npm run tsc
 npm run build
 ```
 
@@ -50,6 +51,18 @@ Then you should be able to run the tests with:
 npm test
 ```
 
+### Rebuilding Rust library
+
+```bash
+npm run build
+```
+
+### Rebuilding Typescript
+
+```bash
+npm run tsc
+```
+
 ### Fix lints
 
 To run the linter and have it automatically fix all errors

docs/src/javascript/classes/DefaultWriteOptions.md

@@ -38,4 +38,4 @@ A [WriteMode](../enums/WriteMode.md) to use on this operation
 
 #### Defined in
 
-[index.ts:1359](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1359)
+[index.ts:1019](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1019)

docs/src/javascript/classes/LocalConnection.md

@@ -30,7 +30,6 @@ A connection to a LanceDB database.
 - [dropTable](LocalConnection.md#droptable)
 - [openTable](LocalConnection.md#opentable)
 - [tableNames](LocalConnection.md#tablenames)
-- [withMiddleware](LocalConnection.md#withmiddleware)
 
 ## Constructors
 
@@ -47,7 +46,7 @@ A connection to a LanceDB database.
 
 #### Defined in
 
-[index.ts:739](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L739)
+[index.ts:489](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L489)
 
 ## Properties
 
@@ -57,7 +56,7 @@ A connection to a LanceDB database.
 
 #### Defined in
 
-[index.ts:737](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L737)
+[index.ts:487](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L487)
 
 ___
 
@@ -75,7 +74,7 @@ ___
 
 #### Defined in
 
-[index.ts:736](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L736)
+[index.ts:486](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L486)
 
 ## Accessors
 
@@ -93,7 +92,7 @@ ___
 
 #### Defined in
 
-[index.ts:744](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L744)
+[index.ts:494](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L494)
 
 ## Methods
 
@@ -114,7 +113,7 @@ Creates a new Table, optionally initializing it with new data.
 | Name | Type |
 | :------ | :------ |
 | `name` | `string` \| [`CreateTableOptions`](../interfaces/CreateTableOptions.md)\<`T`\> |
-| `data?` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] |
+| `data?` | `Record`\<`string`, `unknown`\>[] |
 | `optsOrEmbedding?` | [`WriteOptions`](../interfaces/WriteOptions.md) \| [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)\<`T`\> |
 | `opt?` | [`WriteOptions`](../interfaces/WriteOptions.md) |
 
@@ -128,7 +127,7 @@ Creates a new Table, optionally initializing it with new data.
 
 #### Defined in
 
-[index.ts:788](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L788)
+[index.ts:542](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L542)
 
 ___
 
@@ -159,7 +158,7 @@ ___
 
 #### Defined in
 
-[index.ts:822](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L822)
+[index.ts:576](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L576)
 
 ___
 
@@ -185,7 +184,7 @@ Drop an existing table.
 
 #### Defined in
 
-[index.ts:876](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L876)
+[index.ts:630](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L630)
 
 ___
 
@@ -211,7 +210,7 @@ Open a table in the database.
 
 #### Defined in
 
-[index.ts:760](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L760)
+[index.ts:510](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L510)
 
 ▸ **openTable**\<`T`\>(`name`, `embeddings`): `Promise`\<[`Table`](../interfaces/Table.md)\<`T`\>\>
 
@@ -240,7 +239,7 @@ Connection.openTable
 
 #### Defined in
 
-[index.ts:768](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L768)
+[index.ts:518](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L518)
 
 ▸ **openTable**\<`T`\>(`name`, `embeddings?`): `Promise`\<[`Table`](../interfaces/Table.md)\<`T`\>\>
 
@@ -267,7 +266,7 @@ Connection.openTable
 
 #### Defined in
 
-[index.ts:772](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L772)
+[index.ts:522](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L522)
 
 ___
 
@@ -287,36 +286,4 @@ Get the names of all tables in the database.
 
 #### Defined in
 
-[index.ts:751](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L751)
+[index.ts:501](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L501)
-
-___
-
-### withMiddleware
-
-▸ **withMiddleware**(`middleware`): [`Connection`](../interfaces/Connection.md)
-
-Instrument the behavior of this Connection with middleware.
-
-The middleware will be called in the order they are added.
-
-Currently this functionality is only supported for remote Connections.
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `middleware` | `HttpMiddleware` |
-
-#### Returns
-
-[`Connection`](../interfaces/Connection.md)
-
-- this Connection instrumented by the passed middleware
-
-#### Implementation of
-
-[Connection](../interfaces/Connection.md).[withMiddleware](../interfaces/Connection.md#withmiddleware)
-
-#### Defined in
-
-[index.ts:880](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L880)

docs/src/javascript/classes/LocalTable.md

@@ -37,8 +37,6 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 ### Methods
 
 - [add](LocalTable.md#add)
-- [addColumns](LocalTable.md#addcolumns)
-- [alterColumns](LocalTable.md#altercolumns)
 - [checkElectron](LocalTable.md#checkelectron)
 - [cleanupOldVersions](LocalTable.md#cleanupoldversions)
 - [compactFiles](LocalTable.md#compactfiles)
@@ -46,16 +44,13 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 - [createIndex](LocalTable.md#createindex)
 - [createScalarIndex](LocalTable.md#createscalarindex)
 - [delete](LocalTable.md#delete)
-- [dropColumns](LocalTable.md#dropcolumns)
 - [filter](LocalTable.md#filter)
 - [getSchema](LocalTable.md#getschema)
 - [indexStats](LocalTable.md#indexstats)
 - [listIndices](LocalTable.md#listindices)
-- [mergeInsert](LocalTable.md#mergeinsert)
 - [overwrite](LocalTable.md#overwrite)
 - [search](LocalTable.md#search)
 - [update](LocalTable.md#update)
-- [withMiddleware](LocalTable.md#withmiddleware)
 
 ## Constructors
 
@@ -79,7 +74,7 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 
 #### Defined in
 
-[index.ts:892](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L892)
+[index.ts:642](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L642)
 
 • **new LocalTable**\<`T`\>(`tbl`, `name`, `options`, `embeddings`)
 
@@ -100,7 +95,7 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 
 #### Defined in
 
-[index.ts:899](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L899)
+[index.ts:649](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L649)
 
 ## Properties
 
@@ -110,7 +105,7 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 
 #### Defined in
 
-[index.ts:889](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L889)
+[index.ts:639](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L639)
 
 ___
 
@@ -120,7 +115,7 @@ ___
 
 #### Defined in
 
-[index.ts:888](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L888)
+[index.ts:638](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L638)
 
 ___
 
@@ -130,7 +125,7 @@ ___
 
 #### Defined in
 
-[index.ts:887](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L887)
+[index.ts:637](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L637)
 
 ___
 
@@ -148,7 +143,7 @@ ___
 
 #### Defined in
 
-[index.ts:890](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L890)
+[index.ts:640](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L640)
 
 ___
 
@@ -158,7 +153,7 @@ ___
 
 #### Defined in
 
-[index.ts:886](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L886)
+[index.ts:636](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L636)
 
 ___
 
@@ -184,7 +179,7 @@ Creates a filter query to find all rows matching the specified criteria
 
 #### Defined in
 
-[index.ts:938](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L938)
+[index.ts:688](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L688)
 
 ## Accessors
 
@@ -202,7 +197,7 @@ Creates a filter query to find all rows matching the specified criteria
 
 #### Defined in
 
-[index.ts:918](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L918)
+[index.ts:668](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L668)
 
 ___
 
@@ -220,7 +215,7 @@ ___
 
 #### Defined in
 
-[index.ts:1171](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1171)
+[index.ts:849](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L849)
 
 ## Methods
 
@@ -234,7 +229,7 @@ Insert records into this Table.
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
 
 #### Returns
 
@@ -248,59 +243,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:946](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L946)
+[index.ts:696](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L696)
-
-___
-
-### addColumns
-
-▸ **addColumns**(`newColumnTransforms`): `Promise`\<`void`\>
-
-Add new columns with defined values.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `newColumnTransforms` | \{ `name`: `string` ; `valueSql`: `string`  }[] | pairs of column names and the SQL expression to use to calculate the value of the new column. These expressions will be evaluated for each row in the table, and can reference existing columns in the table. |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[addColumns](../interfaces/Table.md#addcolumns)
-
-#### Defined in
-
-[index.ts:1195](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1195)
-
-___
-
-### alterColumns
-
-▸ **alterColumns**(`columnAlterations`): `Promise`\<`void`\>
-
-Alter the name or nullability of columns.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `columnAlterations` | [`ColumnAlteration`](../interfaces/ColumnAlteration.md)[] | One or more alterations to apply to columns. |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[alterColumns](../interfaces/Table.md#altercolumns)
-
-#### Defined in
-
-[index.ts:1201](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1201)
 
 ___
 
@@ -314,7 +257,7 @@ ___
 
 #### Defined in
 
-[index.ts:1183](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1183)
+[index.ts:861](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L861)
 
 ___
 
@@ -337,7 +280,7 @@ Clean up old versions of the table, freeing disk space.
 
 #### Defined in
 
-[index.ts:1130](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1130)
+[index.ts:808](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L808)
 
 ___
 
@@ -364,22 +307,16 @@ Metrics about the compaction operation.
 
 #### Defined in
 
-[index.ts:1153](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1153)
+[index.ts:831](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L831)
 
 ___
 
 ### countRows
 
-▸ **countRows**(`filter?`): `Promise`\<`number`\>
+▸ **countRows**(): `Promise`\<`number`\>
 
 Returns the number of rows in this table.
 
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `filter?` | `string` |
-
 #### Returns
 
 `Promise`\<`number`\>
@@ -390,7 +327,7 @@ Returns the number of rows in this table.
 
 #### Defined in
 
-[index.ts:1021](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1021)
+[index.ts:749](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L749)
 
 ___
 
@@ -420,13 +357,13 @@ VectorIndexParams.
 
 #### Defined in
 
-[index.ts:1003](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1003)
+[index.ts:734](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L734)
 
 ___
 
 ### createScalarIndex
 
-▸ **createScalarIndex**(`column`, `replace?`): `Promise`\<`void`\>
+▸ **createScalarIndex**(`column`, `replace`): `Promise`\<`void`\>
 
 Create a scalar index on this Table for the given column
 
@@ -435,7 +372,7 @@ Create a scalar index on this Table for the given column
 | Name | Type | Description |
 | :------ | :------ | :------ |
 | `column` | `string` | The column to index |
-| `replace?` | `boolean` | If false, fail if an index already exists on the column it is always set to true for remote connections Scalar indices, like vector indices, can be used to speed up scans. A scalar index can speed up scans that contain filter expressions on the indexed column. For example, the following scan will be faster if the column `my_col` has a scalar index: ```ts const con = await lancedb.connect('./.lancedb'); const table = await con.openTable('images'); const results = await table.where('my_col = 7').execute(); ``` Scalar indices can also speed up scans containing a vector search and a prefilter: ```ts const con = await lancedb.connect('././lancedb'); const table = await con.openTable('images'); const results = await table.search([1.0, 2.0]).where('my_col != 7').prefilter(true); ``` Scalar indices can only speed up scans for basic filters using equality, comparison, range (e.g. `my_col BETWEEN 0 AND 100`), and set membership (e.g. `my_col IN (0, 1, 2)`) Scalar indices can be used if the filter contains multiple indexed columns and the filter criteria are AND'd or OR'd together (e.g. `my_col < 0 AND other_col> 100`) Scalar indices may be used if the filter contains non-indexed columns but, depending on the structure of the filter, they may not be usable. For example, if the column `not_indexed` does not have a scalar index then the filter `my_col = 0 OR not_indexed = 1` will not be able to use any scalar index on `my_col`. |
+| `replace` | `boolean` | If false, fail if an index already exists on the column Scalar indices, like vector indices, can be used to speed up scans. A scalar index can speed up scans that contain filter expressions on the indexed column. For example, the following scan will be faster if the column `my_col` has a scalar index: ```ts const con = await lancedb.connect('./.lancedb'); const table = await con.openTable('images'); const results = await table.where('my_col = 7').execute(); ``` Scalar indices can also speed up scans containing a vector search and a prefilter: ```ts const con = await lancedb.connect('././lancedb'); const table = await con.openTable('images'); const results = await table.search([1.0, 2.0]).where('my_col != 7').prefilter(true); ``` Scalar indices can only speed up scans for basic filters using equality, comparison, range (e.g. `my_col BETWEEN 0 AND 100`), and set membership (e.g. `my_col IN (0, 1, 2)`) Scalar indices can be used if the filter contains multiple indexed columns and the filter criteria are AND'd or OR'd together (e.g. `my_col < 0 AND other_col> 100`) Scalar indices may be used if the filter contains non-indexed columns but, depending on the structure of the filter, they may not be usable. For example, if the column `not_indexed` does not have a scalar index then the filter `my_col = 0 OR not_indexed = 1` will not be able to use any scalar index on `my_col`. |
 
 #### Returns
 
@@ -455,7 +392,7 @@ await table.createScalarIndex('my_col')
 
 #### Defined in
 
-[index.ts:1011](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1011)
+[index.ts:742](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L742)
 
 ___
 
@@ -481,38 +418,7 @@ Delete rows from this table.
 
 #### Defined in
 
-[index.ts:1030](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1030)
+[index.ts:758](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L758)
-
-___
-
-### dropColumns
-
-▸ **dropColumns**(`columnNames`): `Promise`\<`void`\>
-
-Drop one or more columns from the dataset
-
-This is a metadata-only operation and does not remove the data from the
-underlying storage. In order to remove the data, you must subsequently
-call ``compact_files`` to rewrite the data without the removed columns and
-then call ``cleanup_files`` to remove the old files.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `columnNames` | `string`[] | The names of the columns to drop. These can be nested column references (e.g. "a.b.c") or top-level column names (e.g. "a"). |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[dropColumns](../interfaces/Table.md#dropcolumns)
-
-#### Defined in
-
-[index.ts:1205](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1205)
 
 ___
 
@@ -532,13 +438,9 @@ Creates a filter query to find all rows matching the specified criteria
 
 [`Query`](Query.md)\<`T`\>
 
-#### Implementation of
-
-[Table](../interfaces/Table.md).[filter](../interfaces/Table.md#filter)
-
 #### Defined in
 
-[index.ts:934](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L934)
+[index.ts:684](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L684)
 
 ___
 
@@ -552,13 +454,13 @@ ___
 
 #### Defined in
 
-[index.ts:1176](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1176)
+[index.ts:854](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L854)
 
 ___
 
 ### indexStats
 
-▸ **indexStats**(`indexName`): `Promise`\<[`IndexStats`](../interfaces/IndexStats.md)\>
+▸ **indexStats**(`indexUuid`): `Promise`\<[`IndexStats`](../interfaces/IndexStats.md)\>
 
 Get statistics about an index.
 
@@ -566,7 +468,7 @@ Get statistics about an index.
 
 | Name | Type |
 | :------ | :------ |
-| `indexName` | `string` |
+| `indexUuid` | `string` |
 
 #### Returns
 
@@ -578,7 +480,7 @@ Get statistics about an index.
 
 #### Defined in
 
-[index.ts:1167](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1167)
+[index.ts:845](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L845)
 
 ___
 
@@ -598,57 +500,7 @@ List the indicies on this table.
 
 #### Defined in
 
-[index.ts:1163](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1163)
+[index.ts:841](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L841)
-
-___
-
-### mergeInsert
-
-▸ **mergeInsert**(`on`, `data`, `args`): `Promise`\<`void`\>
-
-Runs a "merge insert" operation on the table
-
-This operation can add rows, update rows, and remove rows all in a single
-transaction. It is a very generic tool that can be used to create
-behaviors like "insert if not exists", "update or insert (i.e. upsert)",
-or even replace a portion of existing data with new data (e.g. replace
-all data where month="january")
-
-The merge insert operation works by combining new data from a
-**source table** with existing data in a **target table** by using a
-join.  There are three categories of records.
-
-"Matched" records are records that exist in both the source table and
-the target table. "Not matched" records exist only in the source table
-(e.g. these are new data) "Not matched by source" records exist only
-in the target table (this is old data)
-
-The MergeInsertArgs can be used to customize what should happen for
-each category of data.
-
-Please note that the data may appear to be reordered as part of this
-operation.  This is because updated rows will be deleted from the
-dataset and then reinserted at the end with the new values.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `on` | `string` | a column to join on. This is how records from the source table and target table are matched. |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | the new data to insert |
-| `args` | [`MergeInsertArgs`](../interfaces/MergeInsertArgs.md) | parameters controlling how the operation should behave |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[mergeInsert](../interfaces/Table.md#mergeinsert)
-
-#### Defined in
-
-[index.ts:1065](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1065)
 
 ___
 
@@ -662,7 +514,7 @@ Insert records into this Table, replacing its contents.
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
 
 #### Returns
 
@@ -676,7 +528,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:977](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L977)
+[index.ts:716](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L716)
 
 ___
 
@@ -702,7 +554,7 @@ Creates a search query to find the nearest neighbors of the given search term
 
 #### Defined in
 
-[index.ts:926](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L926)
+[index.ts:676](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L676)
 
 ___
 
@@ -728,36 +580,4 @@ Update rows in this table.
 
 #### Defined in
 
-[index.ts:1043](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1043)
+[index.ts:771](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L771)
-
-___
-
-### withMiddleware
-
-▸ **withMiddleware**(`middleware`): [`Table`](../interfaces/Table.md)\<`T`\>
-
-Instrument the behavior of this Table with middleware.
-
-The middleware will be called in the order they are added.
-
-Currently this functionality is only supported for remote tables.
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `middleware` | `HttpMiddleware` |
-
-#### Returns
-
-[`Table`](../interfaces/Table.md)\<`T`\>
-
-- this Table instrumented by the passed middleware
-
-#### Implementation of
-
-[Table](../interfaces/Table.md).[withMiddleware](../interfaces/Table.md#withmiddleware)
-
-#### Defined in
-
-[index.ts:1209](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1209)

docs/src/javascript/classes/MakeArrowTableOptions.md

@@ -1,82 +0,0 @@
-[vectordb](../README.md) / [Exports](../modules.md) / MakeArrowTableOptions
-
-# Class: MakeArrowTableOptions
-
-Options to control the makeArrowTable call.
-
-## Table of contents
-
-### Constructors
-
-- [constructor](MakeArrowTableOptions.md#constructor)
-
-### Properties
-
-- [dictionaryEncodeStrings](MakeArrowTableOptions.md#dictionaryencodestrings)
-- [embeddings](MakeArrowTableOptions.md#embeddings)
-- [schema](MakeArrowTableOptions.md#schema)
-- [vectorColumns](MakeArrowTableOptions.md#vectorcolumns)
-
-## Constructors
-
-### constructor
-
-• **new MakeArrowTableOptions**(`values?`)
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `values?` | `Partial`\<[`MakeArrowTableOptions`](MakeArrowTableOptions.md)\> |
-
-#### Defined in
-
-[arrow.ts:98](https://github.com/lancedb/lancedb/blob/92179835/node/src/arrow.ts#L98)
-
-## Properties
-
-### dictionaryEncodeStrings
-
-• **dictionaryEncodeStrings**: `boolean` = `false`
-
-If true then string columns will be encoded with dictionary encoding
-
-Set this to true if your string columns tend to repeat the same values
-often.  For more precise control use the `schema` property to specify the
-data type for individual columns.
-
-If `schema` is provided then this property is ignored.
-
-#### Defined in
-
-[arrow.ts:96](https://github.com/lancedb/lancedb/blob/92179835/node/src/arrow.ts#L96)
-
-___
-
-### embeddings
-
-• `Optional` **embeddings**: [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)\<`any`\>
-
-#### Defined in
-
-[arrow.ts:85](https://github.com/lancedb/lancedb/blob/92179835/node/src/arrow.ts#L85)
-
-___
-
-### schema
-
-• `Optional` **schema**: `Schema`\<`any`\>
-
-#### Defined in
-
-[arrow.ts:63](https://github.com/lancedb/lancedb/blob/92179835/node/src/arrow.ts#L63)
-
-___
-
-### vectorColumns
-
-• **vectorColumns**: `Record`\<`string`, `VectorColumnOptions`\>
-
-#### Defined in
-
-[arrow.ts:81](https://github.com/lancedb/lancedb/blob/92179835/node/src/arrow.ts#L81)

docs/src/javascript/classes/OpenAIEmbeddingFunction.md

@@ -40,7 +40,7 @@ An embedding function that automatically creates vector representation for a giv
 
 #### Defined in
 
-[embedding/openai.ts:22](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/openai.ts#L22)
+[embedding/openai.ts:21](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/openai.ts#L21)
 
 ## Properties
 
@@ -50,17 +50,17 @@ An embedding function that automatically creates vector representation for a giv
 
 #### Defined in
 
-[embedding/openai.ts:20](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/openai.ts#L20)
+[embedding/openai.ts:19](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/openai.ts#L19)
 
 ___
 
 ### \_openai
 
-• `Private` `Readonly` **\_openai**: `OpenAI`
+• `Private` `Readonly` **\_openai**: `any`
 
 #### Defined in
 
-[embedding/openai.ts:19](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/openai.ts#L19)
+[embedding/openai.ts:18](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/openai.ts#L18)
 
 ___
 
@@ -76,7 +76,7 @@ The name of the column that will be used as input for the Embedding Function.
 
 #### Defined in
 
-[embedding/openai.ts:56](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/openai.ts#L56)
+[embedding/openai.ts:50](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/openai.ts#L50)
 
 ## Methods
 
@@ -102,4 +102,4 @@ Creates a vector representation for the given values.
 
 #### Defined in
 
-[embedding/openai.ts:43](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/openai.ts#L43)
+[embedding/openai.ts:38](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/openai.ts#L38)

docs/src/javascript/classes/Query.md

@@ -19,7 +19,6 @@ A builder for nearest neighbor queries for LanceDB.
 ### Properties
 
 - [\_embeddings](Query.md#_embeddings)
-- [\_fastSearch](Query.md#_fastsearch)
 - [\_filter](Query.md#_filter)
 - [\_limit](Query.md#_limit)
 - [\_metricType](Query.md#_metrictype)
@@ -35,7 +34,6 @@ A builder for nearest neighbor queries for LanceDB.
 ### Methods
 
 - [execute](Query.md#execute)
-- [fastSearch](Query.md#fastsearch)
 - [filter](Query.md#filter)
 - [isElectron](Query.md#iselectron)
 - [limit](Query.md#limit)
@@ -67,7 +65,7 @@ A builder for nearest neighbor queries for LanceDB.
 
 #### Defined in
 
-[query.ts:39](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L39)
+[query.ts:38](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L38)
 
 ## Properties
 
@@ -77,17 +75,7 @@ A builder for nearest neighbor queries for LanceDB.
 
 #### Defined in
 
-[query.ts:37](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L37)
+[query.ts:36](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L36)
-
-___
-
-### \_fastSearch
-
-• `Private` **\_fastSearch**: `boolean`
-
-#### Defined in
-
-[query.ts:36](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L36)
 
 ___
 
@@ -97,7 +85,7 @@ ___
 
 #### Defined in
 
-[query.ts:33](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L33)
+[query.ts:33](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L33)
 
 ___
 
@@ -107,7 +95,7 @@ ___
 
 #### Defined in
 
-[query.ts:29](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L29)
+[query.ts:29](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L29)
 
 ___
 
@@ -117,7 +105,7 @@ ___
 
 #### Defined in
 
-[query.ts:34](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L34)
+[query.ts:34](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L34)
 
 ___
 
@@ -127,7 +115,7 @@ ___
 
 #### Defined in
 
-[query.ts:31](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L31)
+[query.ts:31](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L31)
 
 ___
 
@@ -137,7 +125,7 @@ ___
 
 #### Defined in
 
-[query.ts:35](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L35)
+[query.ts:35](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L35)
 
 ___
 
@@ -147,7 +135,7 @@ ___
 
 #### Defined in
 
-[query.ts:26](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L26)
+[query.ts:26](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L26)
 
 ___
 
@@ -157,7 +145,7 @@ ___
 
 #### Defined in
 
-[query.ts:28](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L28)
+[query.ts:28](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L28)
 
 ___
 
@@ -167,7 +155,7 @@ ___
 
 #### Defined in
 
-[query.ts:30](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L30)
+[query.ts:30](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L30)
 
 ___
 
@@ -177,7 +165,7 @@ ___
 
 #### Defined in
 
-[query.ts:32](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L32)
+[query.ts:32](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L32)
 
 ___
 
@@ -187,7 +175,7 @@ ___
 
 #### Defined in
 
-[query.ts:27](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L27)
+[query.ts:27](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L27)
 
 ___
 
@@ -213,7 +201,7 @@ A filter statement to be applied to this query.
 
 #### Defined in
 
-[query.ts:90](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L90)
+[query.ts:87](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L87)
 
 ## Methods
 
@@ -235,30 +223,7 @@ Execute the query and return the results as an Array of Objects
 
 #### Defined in
 
-[query.ts:127](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L127)
+[query.ts:115](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L115)
-
-___
-
-### fastSearch
-
-▸ **fastSearch**(`value`): [`Query`](Query.md)\<`T`\>
-
-Skip searching un-indexed data. This can make search faster, but will miss
-any data that is not yet indexed.
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `value` | `boolean` |
-
-#### Returns
-
-[`Query`](Query.md)\<`T`\>
-
-#### Defined in
-
-[query.ts:119](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L119)
 
 ___
 
@@ -280,7 +245,7 @@ A filter statement to be applied to this query.
 
 #### Defined in
 
-[query.ts:85](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L85)
+[query.ts:82](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L82)
 
 ___
 
@@ -294,7 +259,7 @@ ___
 
 #### Defined in
 
-[query.ts:155](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L155)
+[query.ts:142](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L142)
 
 ___
 
@@ -303,7 +268,6 @@ ___
 ▸ **limit**(`value`): [`Query`](Query.md)\<`T`\>
 
 Sets the number of results that will be returned
-default value is 10
 
 #### Parameters
 
@@ -317,7 +281,7 @@ default value is 10
 
 #### Defined in
 
-[query.ts:58](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L58)
+[query.ts:55](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L55)
 
 ___
 
@@ -343,7 +307,7 @@ MetricType for the different options
 
 #### Defined in
 
-[query.ts:105](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L105)
+[query.ts:102](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L102)
 
 ___
 
@@ -365,7 +329,7 @@ The number of probes used. A higher number makes search more accurate but also s
 
 #### Defined in
 
-[query.ts:76](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L76)
+[query.ts:73](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L73)
 
 ___
 
@@ -385,7 +349,7 @@ ___
 
 #### Defined in
 
-[query.ts:110](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L110)
+[query.ts:107](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L107)
 
 ___
 
@@ -407,7 +371,7 @@ Refine the results by reading extra elements and re-ranking them in memory.
 
 #### Defined in
 
-[query.ts:67](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L67)
+[query.ts:64](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L64)
 
 ___
 
@@ -429,4 +393,4 @@ Return only the specified columns.
 
 #### Defined in
 
-[query.ts:96](https://github.com/lancedb/lancedb/blob/92179835/node/src/query.ts#L96)
+[query.ts:93](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/query.ts#L93)

docs/src/javascript/enums/IndexStatus.md

@@ -1,52 +0,0 @@
-[vectordb](../README.md) / [Exports](../modules.md) / IndexStatus
-
-# Enumeration: IndexStatus
-
-## Table of contents
-
-### Enumeration Members
-
-- [Done](IndexStatus.md#done)
-- [Failed](IndexStatus.md#failed)
-- [Indexing](IndexStatus.md#indexing)
-- [Pending](IndexStatus.md#pending)
-
-## Enumeration Members
-
-### Done
-
-• **Done** = ``"done"``
-
-#### Defined in
-
-[index.ts:713](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L713)
-
-___
-
-### Failed
-
-• **Failed** = ``"failed"``
-
-#### Defined in
-
-[index.ts:714](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L714)
-
-___
-
-### Indexing
-
-• **Indexing** = ``"indexing"``
-
-#### Defined in
-
-[index.ts:712](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L712)
-
-___
-
-### Pending
-
-• **Pending** = ``"pending"``
-
-#### Defined in
-
-[index.ts:711](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L711)

docs/src/javascript/enums/MetricType.md

@@ -22,7 +22,7 @@ Cosine distance
 
 #### Defined in
 
-[index.ts:1381](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1381)
+[index.ts:1041](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1041)
 
 ___
 
@@ -34,7 +34,7 @@ Dot product
 
 #### Defined in
 
-[index.ts:1386](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1386)
+[index.ts:1046](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1046)
 
 ___
 
@@ -46,4 +46,4 @@ Euclidean distance
 
 #### Defined in
 
-[index.ts:1376](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1376)
+[index.ts:1036](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1036)

docs/src/javascript/enums/WriteMode.md

@@ -22,7 +22,7 @@ Append new data to the table.
 
 #### Defined in
 
-[index.ts:1347](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1347)
+[index.ts:1007](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1007)
 
 ___
 
@@ -34,7 +34,7 @@ Create a new [Table](../interfaces/Table.md).
 
 #### Defined in
 
-[index.ts:1343](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1343)
+[index.ts:1003](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1003)
 
 ___
 
@@ -46,4 +46,4 @@ Overwrite the existing [Table](../interfaces/Table.md) if presented.
 
 #### Defined in
 
-[index.ts:1345](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1345)
+[index.ts:1005](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1005)

docs/src/javascript/interfaces/AwsCredentials.md

@@ -18,7 +18,7 @@
 
 #### Defined in
 
-[index.ts:68](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L68)
+[index.ts:54](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L54)
 
 ___
 
@@ -28,7 +28,7 @@ ___
 
 #### Defined in
 
-[index.ts:70](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L70)
+[index.ts:56](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L56)
 
 ___
 
@@ -38,4 +38,4 @@ ___
 
 #### Defined in
 
-[index.ts:72](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L72)
+[index.ts:58](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L58)

docs/src/javascript/interfaces/CleanupStats.md

@@ -19,7 +19,7 @@ The number of bytes removed from disk.
 
 #### Defined in
 
-[index.ts:1218](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1218)
+[index.ts:878](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L878)
 
 ___
 
@@ -31,4 +31,4 @@ The number of old table versions removed.
 
 #### Defined in
 
-[index.ts:1222](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1222)
+[index.ts:882](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L882)

docs/src/javascript/interfaces/ColumnAlteration.md

@@ -1,53 +0,0 @@
-[vectordb](../README.md) / [Exports](../modules.md) / ColumnAlteration
-
-# Interface: ColumnAlteration
-
-A definition of a column alteration. The alteration changes the column at
-`path` to have the new name `name`, to be nullable if `nullable` is true,
-and to have the data type `data_type`. At least one of `rename` or `nullable`
-must be provided.
-
-## Table of contents
-
-### Properties
-
-- [nullable](ColumnAlteration.md#nullable)
-- [path](ColumnAlteration.md#path)
-- [rename](ColumnAlteration.md#rename)
-
-## Properties
-
-### nullable
-
-• `Optional` **nullable**: `boolean`
-
-Set the new nullability. Note that a nullable column cannot be made non-nullable.
-
-#### Defined in
-
-[index.ts:638](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L638)
-
-___
-
-### path
-
-• **path**: `string`
-
-The path to the column to alter. This is a dot-separated path to the column.
-If it is a top-level column then it is just the name of the column. If it is
-a nested column then it is the path to the column, e.g. "a.b.c" for a column
-`c` nested inside a column `b` nested inside a column `a`.
-
-#### Defined in
-
-[index.ts:633](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L633)
-
-___
-
-### rename
-
-• `Optional` **rename**: `string`
-
-#### Defined in
-
-[index.ts:634](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L634)

docs/src/javascript/interfaces/CompactionMetrics.md

@@ -22,7 +22,7 @@ fragments added.
 
 #### Defined in
 
-[index.ts:1273](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1273)
+[index.ts:933](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L933)
 
 ___
 
@@ -35,7 +35,7 @@ file.
 
 #### Defined in
 
-[index.ts:1268](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1268)
+[index.ts:928](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L928)
 
 ___
 
@@ -47,7 +47,7 @@ The number of new fragments that were created.
 
 #### Defined in
 
-[index.ts:1263](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1263)
+[index.ts:923](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L923)
 
 ___
 
@@ -59,4 +59,4 @@ The number of fragments that were removed.
 
 #### Defined in
 
-[index.ts:1259](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1259)
+[index.ts:919](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L919)

docs/src/javascript/interfaces/CompactionOptions.md

@@ -24,7 +24,7 @@ Default is true.
 
 #### Defined in
 
-[index.ts:1241](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1241)
+[index.ts:901](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L901)
 
 ___
 
@@ -38,7 +38,7 @@ the deleted rows. Default is 10%.
 
 #### Defined in
 
-[index.ts:1247](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1247)
+[index.ts:907](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L907)
 
 ___
 
@@ -46,11 +46,11 @@ ___
 
 • `Optional` **maxRowsPerGroup**: `number`
 
-The maximum number of T per group. Defaults to 1024.
+The maximum number of rows per group. Defaults to 1024.
 
 #### Defined in
 
-[index.ts:1235](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1235)
+[index.ts:895](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L895)
 
 ___
 
@@ -63,7 +63,7 @@ the number of cores on the machine.
 
 #### Defined in
 
-[index.ts:1252](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1252)
+[index.ts:912](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L912)
 
 ___
 
@@ -77,4 +77,4 @@ Defaults to 1024 * 1024.
 
 #### Defined in
 
-[index.ts:1231](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1231)
+[index.ts:891](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L891)

docs/src/javascript/interfaces/Connection.md

@@ -22,7 +22,6 @@ Connection could be local against filesystem or remote against a server.
 - [dropTable](Connection.md#droptable)
 - [openTable](Connection.md#opentable)
 - [tableNames](Connection.md#tablenames)
-- [withMiddleware](Connection.md#withmiddleware)
 
 ## Properties
 
@@ -32,7 +31,7 @@ Connection could be local against filesystem or remote against a server.
 
 #### Defined in
 
-[index.ts:261](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L261)
+[index.ts:183](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L183)
 
 ## Methods
 
@@ -60,7 +59,7 @@ Creates a new Table, optionally initializing it with new data.
 
 #### Defined in
 
-[index.ts:285](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L285)
+[index.ts:207](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L207)
 
 ▸ **createTable**(`name`, `data`): `Promise`\<[`Table`](Table.md)\<`number`[]\>\>
 
@@ -71,7 +70,7 @@ Creates a new Table and initialize it with new data.
 | Name | Type | Description |
 | :------ | :------ | :------ |
 | `name` | `string` | The name of the table. |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
 
 #### Returns
 
@@ -79,7 +78,7 @@ Creates a new Table and initialize it with new data.
 
 #### Defined in
 
-[index.ts:299](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L299)
+[index.ts:221](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L221)
 
 ▸ **createTable**(`name`, `data`, `options`): `Promise`\<[`Table`](Table.md)\<`number`[]\>\>
 
@@ -90,7 +89,7 @@ Creates a new Table and initialize it with new data.
 | Name | Type | Description |
 | :------ | :------ | :------ |
 | `name` | `string` | The name of the table. |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
 | `options` | [`WriteOptions`](WriteOptions.md) | The write options to use when creating the table. |
 
 #### Returns
@@ -99,7 +98,7 @@ Creates a new Table and initialize it with new data.
 
 #### Defined in
 
-[index.ts:311](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L311)
+[index.ts:233](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L233)
 
 ▸ **createTable**\<`T`\>(`name`, `data`, `embeddings`): `Promise`\<[`Table`](Table.md)\<`T`\>\>
 
@@ -116,7 +115,7 @@ Creates a new Table and initialize it with new data.
 | Name | Type | Description |
 | :------ | :------ | :------ |
 | `name` | `string` | The name of the table. |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
 | `embeddings` | [`EmbeddingFunction`](EmbeddingFunction.md)\<`T`\> | An embedding function to use on this table |
 
 #### Returns
@@ -125,7 +124,7 @@ Creates a new Table and initialize it with new data.
 
 #### Defined in
 
-[index.ts:324](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L324)
+[index.ts:246](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L246)
 
 ▸ **createTable**\<`T`\>(`name`, `data`, `embeddings`, `options`): `Promise`\<[`Table`](Table.md)\<`T`\>\>
 
@@ -142,7 +141,7 @@ Creates a new Table and initialize it with new data.
 | Name | Type | Description |
 | :------ | :------ | :------ |
 | `name` | `string` | The name of the table. |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
 | `embeddings` | [`EmbeddingFunction`](EmbeddingFunction.md)\<`T`\> | An embedding function to use on this table |
 | `options` | [`WriteOptions`](WriteOptions.md) | The write options to use when creating the table. |
 
@@ -152,7 +151,7 @@ Creates a new Table and initialize it with new data.
 
 #### Defined in
 
-[index.ts:337](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L337)
+[index.ts:259](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L259)
 
 ___
 
@@ -174,7 +173,7 @@ Drop an existing table.
 
 #### Defined in
 
-[index.ts:348](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L348)
+[index.ts:270](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L270)
 
 ___
 
@@ -203,7 +202,7 @@ Open a table in the database.
 
 #### Defined in
 
-[index.ts:271](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L271)
+[index.ts:193](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L193)
 
 ___
 
@@ -217,32 +216,4 @@ ___
 
 #### Defined in
 
-[index.ts:263](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L263)
+[index.ts:185](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L185)
-
-___
-
-### withMiddleware
-
-▸ **withMiddleware**(`middleware`): [`Connection`](Connection.md)
-
-Instrument the behavior of this Connection with middleware.
-
-The middleware will be called in the order they are added.
-
-Currently this functionality is only supported for remote Connections.
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `middleware` | `HttpMiddleware` |
-
-#### Returns
-
-[`Connection`](Connection.md)
-
-- this Connection instrumented by the passed middleware
-
-#### Defined in
-
-[index.ts:360](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L360)

docs/src/javascript/interfaces/ConnectionOptions.md

@@ -10,10 +10,7 @@
 - [awsCredentials](ConnectionOptions.md#awscredentials)
 - [awsRegion](ConnectionOptions.md#awsregion)
 - [hostOverride](ConnectionOptions.md#hostoverride)
-- [readConsistencyInterval](ConnectionOptions.md#readconsistencyinterval)
 - [region](ConnectionOptions.md#region)
-- [storageOptions](ConnectionOptions.md#storageoptions)
-- [timeout](ConnectionOptions.md#timeout)
 - [uri](ConnectionOptions.md#uri)
 
 ## Properties
@@ -22,13 +19,9 @@
 
 • `Optional` **apiKey**: `string`
 
-API key for the remote connections
-
-Can also be passed by setting environment variable `LANCEDB_API_KEY`
-
 #### Defined in
 
-[index.ts:112](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L112)
+[index.ts:81](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L81)
 
 ___
 
@@ -40,14 +33,9 @@ User provided AWS crednetials.
 
 If not provided, LanceDB will use the default credentials provider chain.
 
-**`Deprecated`**
-
-Pass `aws_access_key_id`, `aws_secret_access_key`, and `aws_session_token`
-through `storageOptions` instead.
-
 #### Defined in
 
-[index.ts:92](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L92)
+[index.ts:75](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L75)
 
 ___
 
@@ -55,15 +43,11 @@ ___
 
 • `Optional` **awsRegion**: `string`
 
-AWS region to connect to. Default is defaultAwsRegion
+AWS region to connect to. Default is defaultAwsRegion.
-
-**`Deprecated`**
-
-Pass `region` through `storageOptions` instead.
 
 #### Defined in
 
-[index.ts:98](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L98)
+[index.ts:78](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L78)
 
 ___
 
@@ -71,33 +55,13 @@ ___
 
 • `Optional` **hostOverride**: `string`
 
-Override the host URL for the remote connection.
+Override the host URL for the remote connections.
 
 This is useful for local testing.
 
 #### Defined in
 
-[index.ts:122](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L122)
+[index.ts:91](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L91)
-
-___
-
-### readConsistencyInterval
-
-• `Optional` **readConsistencyInterval**: `number`
-
-(For LanceDB OSS only): The interval, in seconds, at which to check for
-updates to the table from other processes. If None, then consistency is not
-checked. For performance reasons, this is the default. For strong
-consistency, set this to zero seconds. Then every read will check for
-updates from other processes. As a compromise, you can set this to a
-non-zero value for eventual consistency. If more than that interval
-has passed since the last check, then the table will be checked for updates.
-Note: this consistency only applies to read operations. Write operations are
-always consistent.
-
-#### Defined in
-
-[index.ts:140](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L140)
 
 ___
 
@@ -105,37 +69,11 @@ ___
 
 • `Optional` **region**: `string`
 
-Region to connect. Default is 'us-east-1'
+Region to connect
 
 #### Defined in
 
-[index.ts:115](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L115)
+[index.ts:84](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L84)
-
-___
-
-### storageOptions
-
-• `Optional` **storageOptions**: `Record`\<`string`, `string`\>
-
-User provided options for object storage. For example, S3 credentials or request timeouts.
-
-The various options are described at https://lancedb.github.io/lancedb/guides/storage/
-
-#### Defined in
-
-[index.ts:105](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L105)
-
-___
-
-### timeout
-
-• `Optional` **timeout**: `number`
-
-Duration in milliseconds for request timeout. Default = 10,000 (10 seconds)
-
-#### Defined in
-
-[index.ts:127](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L127)
 
 ___
 
@@ -147,8 +85,8 @@ LanceDB database URI.
 
 - `/path/to/database` - local database
 - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
-- `db://host:port` - remote database (LanceDB cloud)
+- `db://host:port` - remote database (SaaS)
 
 #### Defined in
 
-[index.ts:83](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L83)
+[index.ts:69](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L69)

docs/src/javascript/interfaces/CreateTableOptions.md

@@ -26,7 +26,7 @@
 
 #### Defined in
 
-[index.ts:163](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L163)
+[index.ts:116](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L116)
 
 ___
 
@@ -36,7 +36,7 @@ ___
 
 #### Defined in
 
-[index.ts:169](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L169)
+[index.ts:122](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L122)
 
 ___
 
@@ -46,7 +46,7 @@ ___
 
 #### Defined in
 
-[index.ts:160](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L160)
+[index.ts:113](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L113)
 
 ___
 
@@ -56,7 +56,7 @@ ___
 
 #### Defined in
 
-[index.ts:166](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L166)
+[index.ts:119](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L119)
 
 ___
 
@@ -66,4 +66,4 @@ ___
 
 #### Defined in
 
-[index.ts:172](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L172)
+[index.ts:125](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L125)

docs/src/javascript/interfaces/EmbeddingFunction.md

@@ -18,29 +18,11 @@ An embedding function that automatically creates vector representation for a giv
 
 ### Properties
 
-- [destColumn](EmbeddingFunction.md#destcolumn)
 - [embed](EmbeddingFunction.md#embed)
-- [embeddingDataType](EmbeddingFunction.md#embeddingdatatype)
-- [embeddingDimension](EmbeddingFunction.md#embeddingdimension)
-- [excludeSource](EmbeddingFunction.md#excludesource)
 - [sourceColumn](EmbeddingFunction.md#sourcecolumn)
 
 ## Properties
 
-### destColumn
-
-• `Optional` **destColumn**: `string`
-
-The name of the column that will contain the embedding
-
-By default this is "vector"
-
-#### Defined in
-
-[embedding/embedding_function.ts:49](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/embedding_function.ts#L49)
-
-___
-
 ### embed
 
 • **embed**: (`data`: `T`[]) => `Promise`\<`number`[][]\>
@@ -63,54 +45,7 @@ Creates a vector representation for the given values.
 
 #### Defined in
 
-[embedding/embedding_function.ts:62](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/embedding_function.ts#L62)
+[embedding/embedding_function.ts:27](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/embedding_function.ts#L27)
-
-___
-
-### embeddingDataType
-
-• `Optional` **embeddingDataType**: `Float`\<`Floats`\>
-
-The data type of the embedding
-
-The embedding function should return `number`.  This will be converted into
-an Arrow float array.  By default this will be Float32 but this property can
-be used to control the conversion.
-
-#### Defined in
-
-[embedding/embedding_function.ts:33](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/embedding_function.ts#L33)
-
-___
-
-### embeddingDimension
-
-• `Optional` **embeddingDimension**: `number`
-
-The dimension of the embedding
-
-This is optional, normally this can be determined by looking at the results of
-`embed`.  If this is not specified, and there is an attempt to apply the embedding
-to an empty table, then that process will fail.
-
-#### Defined in
-
-[embedding/embedding_function.ts:42](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/embedding_function.ts#L42)
-
-___
-
-### excludeSource
-
-• `Optional` **excludeSource**: `boolean`
-
-Should the source column be excluded from the resulting table
-
-By default the source column is included.  Set this to true and
-only the embedding will be stored.
-
-#### Defined in
-
-[embedding/embedding_function.ts:57](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/embedding_function.ts#L57)
 
 ___
 
@@ -122,4 +57,4 @@ The name of the column that will be used as input for the Embedding Function.
 
 #### Defined in
 
-[embedding/embedding_function.ts:24](https://github.com/lancedb/lancedb/blob/92179835/node/src/embedding/embedding_function.ts#L24)
+[embedding/embedding_function.ts:22](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/embedding/embedding_function.ts#L22)

docs/src/javascript/interfaces/IndexStats.md

@@ -6,51 +6,18 @@
 
 ### Properties
 
-- [distanceType](IndexStats.md#distancetype)
-- [indexType](IndexStats.md#indextype)
 - [numIndexedRows](IndexStats.md#numindexedrows)
-- [numIndices](IndexStats.md#numindices)
 - [numUnindexedRows](IndexStats.md#numunindexedrows)
 
 ## Properties
 
-### distanceType
-
-• `Optional` **distanceType**: `string`
-
-#### Defined in
-
-[index.ts:728](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L728)
-
-___
-
-### indexType
-
-• **indexType**: `string`
-
-#### Defined in
-
-[index.ts:727](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L727)
-
-___
-
 ### numIndexedRows
 
 • **numIndexedRows**: ``null`` \| `number`
 
 #### Defined in
 
-[index.ts:725](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L725)
+[index.ts:478](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L478)
-
-___
-
-### numIndices
-
-• `Optional` **numIndices**: `number`
-
-#### Defined in
-
-[index.ts:729](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L729)
 
 ___
 
@@ -60,4 +27,4 @@ ___
 
 #### Defined in
 
-[index.ts:726](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L726)
+[index.ts:479](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L479)

docs/src/javascript/interfaces/IvfPQIndexConfig.md

@@ -29,7 +29,7 @@ The column to be indexed
 
 #### Defined in
 
-[index.ts:1282](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1282)
+[index.ts:942](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L942)
 
 ___
 
@@ -41,7 +41,7 @@ Cache size of the index
 
 #### Defined in
 
-[index.ts:1331](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1331)
+[index.ts:991](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L991)
 
 ___
 
@@ -53,7 +53,7 @@ A unique name for the index
 
 #### Defined in
 
-[index.ts:1287](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1287)
+[index.ts:947](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L947)
 
 ___
 
@@ -65,7 +65,7 @@ The max number of iterations for kmeans training.
 
 #### Defined in
 
-[index.ts:1302](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1302)
+[index.ts:962](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L962)
 
 ___
 
@@ -77,7 +77,7 @@ Max number of iterations to train OPQ, if `use_opq` is true.
 
 #### Defined in
 
-[index.ts:1321](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1321)
+[index.ts:981](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L981)
 
 ___
 
@@ -89,7 +89,7 @@ Metric type, L2 or Cosine
 
 #### Defined in
 
-[index.ts:1292](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1292)
+[index.ts:952](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L952)
 
 ___
 
@@ -101,7 +101,7 @@ The number of bits to present one PQ centroid.
 
 #### Defined in
 
-[index.ts:1316](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1316)
+[index.ts:976](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L976)
 
 ___
 
@@ -113,7 +113,7 @@ The number of partitions this index
 
 #### Defined in
 
-[index.ts:1297](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1297)
+[index.ts:957](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L957)
 
 ___
 
@@ -125,7 +125,7 @@ Number of subvectors to build PQ code
 
 #### Defined in
 
-[index.ts:1312](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1312)
+[index.ts:972](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L972)
 
 ___
 
@@ -137,7 +137,7 @@ Replace an existing index with the same name if it exists.
 
 #### Defined in
 
-[index.ts:1326](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1326)
+[index.ts:986](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L986)
 
 ___
 
@@ -147,7 +147,7 @@ ___
 
 #### Defined in
 
-[index.ts:1333](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1333)
+[index.ts:993](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L993)
 
 ___
 
@@ -159,4 +159,4 @@ Train as optimized product quantization.
 
 #### Defined in
 
-[index.ts:1307](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1307)
+[index.ts:967](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L967)

docs/src/javascript/interfaces/MergeInsertArgs.md

@@ -1,73 +0,0 @@
-[vectordb](../README.md) / [Exports](../modules.md) / MergeInsertArgs
-
-# Interface: MergeInsertArgs
-
-## Table of contents
-
-### Properties
-
-- [whenMatchedUpdateAll](MergeInsertArgs.md#whenmatchedupdateall)
-- [whenNotMatchedBySourceDelete](MergeInsertArgs.md#whennotmatchedbysourcedelete)
-- [whenNotMatchedInsertAll](MergeInsertArgs.md#whennotmatchedinsertall)
-
-## Properties
-
-### whenMatchedUpdateAll
-
-• `Optional` **whenMatchedUpdateAll**: `string` \| `boolean`
-
-If true then rows that exist in both the source table (new data) and
-the target table (old data) will be updated, replacing the old row
-with the corresponding matching row.
-
-If there are multiple matches then the behavior is undefined.
-Currently this causes multiple copies of the row to be created
-but that behavior is subject to change.
-
-Optionally, a filter can be specified.  This should be an SQL
-filter where fields with the prefix "target." refer to fields
-in the target table (old data) and fields with the prefix
-"source." refer to fields in the source table (new data).  For
-example, the filter "target.lastUpdated < source.lastUpdated" will
-only update matched rows when the incoming `lastUpdated` value is
-newer.
-
-Rows that do not match the filter will not be updated.  Rows that
-do not match the filter do become "not matched" rows.
-
-#### Defined in
-
-[index.ts:690](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L690)
-
-___
-
-### whenNotMatchedBySourceDelete
-
-• `Optional` **whenNotMatchedBySourceDelete**: `string` \| `boolean`
-
-If true then rows that exist only in the target table (old data)
-will be deleted.
-
-If this is a string then it will be treated as an SQL filter and
-only rows that both do not match any row in the source table and
-match the given filter will be deleted.
-
-This can be used to replace a selection of existing data with
-new data.
-
-#### Defined in
-
-[index.ts:707](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L707)
-
-___
-
-### whenNotMatchedInsertAll
-
-• `Optional` **whenNotMatchedInsertAll**: `boolean`
-
-If true then rows that exist only in the source table (new data)
-will be inserted into the target table.
-
-#### Defined in
-
-[index.ts:695](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L695)

docs/src/javascript/interfaces/Table.md

@@ -25,26 +25,17 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 - [delete](Table.md#delete)
 - [indexStats](Table.md#indexstats)
 - [listIndices](Table.md#listindices)
-- [mergeInsert](Table.md#mergeinsert)
 - [name](Table.md#name)
 - [overwrite](Table.md#overwrite)
 - [schema](Table.md#schema)
 - [search](Table.md#search)
 - [update](Table.md#update)
 
-### Methods
-
-- [addColumns](Table.md#addcolumns)
-- [alterColumns](Table.md#altercolumns)
-- [dropColumns](Table.md#dropcolumns)
-- [filter](Table.md#filter)
-- [withMiddleware](Table.md#withmiddleware)
-
 ## Properties
 
 ### add
 
-• **add**: (`data`: `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[]) => `Promise`\<`number`\>
+• **add**: (`data`: `Record`\<`string`, `unknown`\>[]) => `Promise`\<`number`\>
 
 #### Type declaration
 
@@ -56,7 +47,7 @@ Insert records into this Table.
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
 
 ##### Returns
 
@@ -66,33 +57,27 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:381](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L381)
+[index.ts:291](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L291)
 
 ___
 
 ### countRows
 
-• **countRows**: (`filter?`: `string`) => `Promise`\<`number`\>
+• **countRows**: () => `Promise`\<`number`\>
 
 #### Type declaration
 
-▸ (`filter?`): `Promise`\<`number`\>
+▸ (): `Promise`\<`number`\>
 
 Returns the number of rows in this table.
 
-##### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `filter?` | `string` |
-
 ##### Returns
 
 `Promise`\<`number`\>
 
 #### Defined in
 
-[index.ts:454](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L454)
+[index.ts:361](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L361)
 
 ___
 
@@ -122,17 +107,17 @@ VectorIndexParams.
 
 #### Defined in
 
-[index.ts:398](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L398)
+[index.ts:306](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L306)
 
 ___
 
 ### createScalarIndex
 
-• **createScalarIndex**: (`column`: `string`, `replace?`: `boolean`) => `Promise`\<`void`\>
+• **createScalarIndex**: (`column`: `string`, `replace`: `boolean`) => `Promise`\<`void`\>
 
 #### Type declaration
 
-▸ (`column`, `replace?`): `Promise`\<`void`\>
+▸ (`column`, `replace`): `Promise`\<`void`\>
 
 Create a scalar index on this Table for the given column
 
@@ -141,7 +126,7 @@ Create a scalar index on this Table for the given column
 | Name | Type | Description |
 | :------ | :------ | :------ |
 | `column` | `string` | The column to index |
-| `replace?` | `boolean` | If false, fail if an index already exists on the column it is always set to true for remote connections Scalar indices, like vector indices, can be used to speed up scans. A scalar index can speed up scans that contain filter expressions on the indexed column. For example, the following scan will be faster if the column `my_col` has a scalar index: ```ts const con = await lancedb.connect('./.lancedb'); const table = await con.openTable('images'); const results = await table.where('my_col = 7').execute(); ``` Scalar indices can also speed up scans containing a vector search and a prefilter: ```ts const con = await lancedb.connect('././lancedb'); const table = await con.openTable('images'); const results = await table.search([1.0, 2.0]).where('my_col != 7').prefilter(true); ``` Scalar indices can only speed up scans for basic filters using equality, comparison, range (e.g. `my_col BETWEEN 0 AND 100`), and set membership (e.g. `my_col IN (0, 1, 2)`) Scalar indices can be used if the filter contains multiple indexed columns and the filter criteria are AND'd or OR'd together (e.g. `my_col < 0 AND other_col> 100`) Scalar indices may be used if the filter contains non-indexed columns but, depending on the structure of the filter, they may not be usable. For example, if the column `not_indexed` does not have a scalar index then the filter `my_col = 0 OR not_indexed = 1` will not be able to use any scalar index on `my_col`. |
+| `replace` | `boolean` | If false, fail if an index already exists on the column Scalar indices, like vector indices, can be used to speed up scans. A scalar index can speed up scans that contain filter expressions on the indexed column. For example, the following scan will be faster if the column `my_col` has a scalar index: ```ts const con = await lancedb.connect('./.lancedb'); const table = await con.openTable('images'); const results = await table.where('my_col = 7').execute(); ``` Scalar indices can also speed up scans containing a vector search and a prefilter: ```ts const con = await lancedb.connect('././lancedb'); const table = await con.openTable('images'); const results = await table.search([1.0, 2.0]).where('my_col != 7').prefilter(true); ``` Scalar indices can only speed up scans for basic filters using equality, comparison, range (e.g. `my_col BETWEEN 0 AND 100`), and set membership (e.g. `my_col IN (0, 1, 2)`) Scalar indices can be used if the filter contains multiple indexed columns and the filter criteria are AND'd or OR'd together (e.g. `my_col < 0 AND other_col> 100`) Scalar indices may be used if the filter contains non-indexed columns but, depending on the structure of the filter, they may not be usable. For example, if the column `not_indexed` does not have a scalar index then the filter `my_col = 0 OR not_indexed = 1` will not be able to use any scalar index on `my_col`. |
 
 ##### Returns
 
@@ -157,7 +142,7 @@ await table.createScalarIndex('my_col')
 
 #### Defined in
 
-[index.ts:449](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L449)
+[index.ts:356](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L356)
 
 ___
 
@@ -209,17 +194,17 @@ await tbl.countRows() // Returns 1
 
 #### Defined in
 
-[index.ts:488](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L488)
+[index.ts:395](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L395)
 
 ___
 
 ### indexStats
 
-• **indexStats**: (`indexName`: `string`) => `Promise`\<[`IndexStats`](IndexStats.md)\>
+• **indexStats**: (`indexUuid`: `string`) => `Promise`\<[`IndexStats`](IndexStats.md)\>
 
 #### Type declaration
 
-▸ (`indexName`): `Promise`\<[`IndexStats`](IndexStats.md)\>
+▸ (`indexUuid`): `Promise`\<[`IndexStats`](IndexStats.md)\>
 
 Get statistics about an index.
 
@@ -227,7 +212,7 @@ Get statistics about an index.
 
 | Name | Type |
 | :------ | :------ |
-| `indexName` | `string` |
+| `indexUuid` | `string` |
 
 ##### Returns
 
@@ -235,7 +220,7 @@ Get statistics about an index.
 
 #### Defined in
 
-[index.ts:567](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L567)
+[index.ts:438](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L438)
 
 ___
 
@@ -255,57 +240,7 @@ List the indicies on this table.
 
 #### Defined in
 
-[index.ts:562](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L562)
+[index.ts:433](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L433)
-
-___
-
-### mergeInsert
-
-• **mergeInsert**: (`on`: `string`, `data`: `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[], `args`: [`MergeInsertArgs`](MergeInsertArgs.md)) => `Promise`\<`void`\>
-
-#### Type declaration
-
-▸ (`on`, `data`, `args`): `Promise`\<`void`\>
-
-Runs a "merge insert" operation on the table
-
-This operation can add rows, update rows, and remove rows all in a single
-transaction. It is a very generic tool that can be used to create
-behaviors like "insert if not exists", "update or insert (i.e. upsert)",
-or even replace a portion of existing data with new data (e.g. replace
-all data where month="january")
-
-The merge insert operation works by combining new data from a
-**source table** with existing data in a **target table** by using a
-join.  There are three categories of records.
-
-"Matched" records are records that exist in both the source table and
-the target table. "Not matched" records exist only in the source table
-(e.g. these are new data) "Not matched by source" records exist only
-in the target table (this is old data)
-
-The MergeInsertArgs can be used to customize what should happen for
-each category of data.
-
-Please note that the data may appear to be reordered as part of this
-operation.  This is because updated rows will be deleted from the
-dataset and then reinserted at the end with the new values.
-
-##### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `on` | `string` | a column to join on. This is how records from the source table and target table are matched. |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | the new data to insert |
-| `args` | [`MergeInsertArgs`](MergeInsertArgs.md) | parameters controlling how the operation should behave |
-
-##### Returns
-
-`Promise`\<`void`\>
-
-#### Defined in
-
-[index.ts:553](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L553)
 
 ___
 
@@ -315,13 +250,13 @@ ___
 
 #### Defined in
 
-[index.ts:367](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L367)
+[index.ts:277](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L277)
 
 ___
 
 ### overwrite
 
-• **overwrite**: (`data`: `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[]) => `Promise`\<`number`\>
+• **overwrite**: (`data`: `Record`\<`string`, `unknown`\>[]) => `Promise`\<`number`\>
 
 #### Type declaration
 
@@ -333,7 +268,7 @@ Insert records into this Table, replacing its contents.
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
+| `data` | `Record`\<`string`, `unknown`\>[] | Records to be inserted into the Table |
 
 ##### Returns
 
@@ -343,7 +278,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:389](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L389)
+[index.ts:299](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L299)
 
 ___
 
@@ -353,7 +288,7 @@ ___
 
 #### Defined in
 
-[index.ts:571](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L571)
+[index.ts:440](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L440)
 
 ___
 
@@ -379,7 +314,7 @@ Creates a search query to find the nearest neighbors of the given search term
 
 #### Defined in
 
-[index.ts:373](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L373)
+[index.ts:283](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L283)
 
 ___
 
@@ -430,123 +365,4 @@ let results = await tbl.search([1, 1]).execute();
 
 #### Defined in
 
-[index.ts:521](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L521)
+[index.ts:428](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L428)
-
-## Methods
-
-### addColumns
-
-▸ **addColumns**(`newColumnTransforms`): `Promise`\<`void`\>
-
-Add new columns with defined values.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `newColumnTransforms` | \{ `name`: `string` ; `valueSql`: `string`  }[] | pairs of column names and the SQL expression to use to calculate the value of the new column. These expressions will be evaluated for each row in the table, and can reference existing columns in the table. |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-#### Defined in
-
-[index.ts:582](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L582)
-
-___
-
-### alterColumns
-
-▸ **alterColumns**(`columnAlterations`): `Promise`\<`void`\>
-
-Alter the name or nullability of columns.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `columnAlterations` | [`ColumnAlteration`](ColumnAlteration.md)[] | One or more alterations to apply to columns. |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-#### Defined in
-
-[index.ts:591](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L591)
-
-___
-
-### dropColumns
-
-▸ **dropColumns**(`columnNames`): `Promise`\<`void`\>
-
-Drop one or more columns from the dataset
-
-This is a metadata-only operation and does not remove the data from the
-underlying storage. In order to remove the data, you must subsequently
-call ``compact_files`` to rewrite the data without the removed columns and
-then call ``cleanup_files`` to remove the old files.
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `columnNames` | `string`[] | The names of the columns to drop. These can be nested column references (e.g. "a.b.c") or top-level column names (e.g. "a"). |
-
-#### Returns
-
-`Promise`\<`void`\>
-
-#### Defined in
-
-[index.ts:605](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L605)
-
-___
-
-### filter
-
-▸ **filter**(`value`): [`Query`](../classes/Query.md)\<`T`\>
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `value` | `string` |
-
-#### Returns
-
-[`Query`](../classes/Query.md)\<`T`\>
-
-#### Defined in
-
-[index.ts:569](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L569)
-
-___
-
-### withMiddleware
-
-▸ **withMiddleware**(`middleware`): [`Table`](Table.md)\<`T`\>
-
-Instrument the behavior of this Table with middleware.
-
-The middleware will be called in the order they are added.
-
-Currently this functionality is only supported for remote tables.
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `middleware` | `HttpMiddleware` |
-
-#### Returns
-
-[`Table`](Table.md)\<`T`\>
-
-- this Table instrumented by the passed middleware
-
-#### Defined in
-
-[index.ts:617](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L617)

docs/src/javascript/interfaces/UpdateArgs.md

@@ -20,7 +20,7 @@ new values to set
 
 #### Defined in
 
-[index.ts:652](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L652)
+[index.ts:454](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L454)
 
 ___
 
@@ -33,4 +33,4 @@ in which case all rows will be updated.
 
 #### Defined in
 
-[index.ts:646](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L646)
+[index.ts:448](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L448)

docs/src/javascript/interfaces/UpdateSqlArgs.md

@@ -20,7 +20,7 @@ new values to set as SQL expressions.
 
 #### Defined in
 
-[index.ts:666](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L666)
+[index.ts:468](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L468)
 
 ___
 
@@ -33,4 +33,4 @@ in which case all rows will be updated.
 
 #### Defined in
 
-[index.ts:660](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L660)
+[index.ts:462](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L462)

docs/src/javascript/interfaces/VectorIndex.md

@@ -8,7 +8,6 @@
 
 - [columns](VectorIndex.md#columns)
 - [name](VectorIndex.md#name)
-- [status](VectorIndex.md#status)
 - [uuid](VectorIndex.md#uuid)
 
 ## Properties
@@ -19,7 +18,7 @@
 
 #### Defined in
 
-[index.ts:718](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L718)
+[index.ts:472](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L472)
 
 ___
 
@@ -29,17 +28,7 @@ ___
 
 #### Defined in
 
-[index.ts:719](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L719)
+[index.ts:473](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L473)
-
-___
-
-### status
-
-• **status**: [`IndexStatus`](../enums/IndexStatus.md)
-
-#### Defined in
-
-[index.ts:721](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L721)
 
 ___
 
@@ -49,4 +38,4 @@ ___
 
 #### Defined in
 
-[index.ts:720](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L720)
+[index.ts:474](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L474)

docs/src/javascript/interfaces/WriteOptions.md

@@ -24,4 +24,4 @@ A [WriteMode](../enums/WriteMode.md) to use on this operation
 
 #### Defined in
 
-[index.ts:1355](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1355)
+[index.ts:1015](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1015)

docs/src/javascript/modules.md

@@ -6,7 +6,6 @@
 
 ### Enumerations
 
-- [IndexStatus](enums/IndexStatus.md)
 - [MetricType](enums/MetricType.md)
 - [WriteMode](enums/WriteMode.md)
 
@@ -15,7 +14,6 @@
 - [DefaultWriteOptions](classes/DefaultWriteOptions.md)
 - [LocalConnection](classes/LocalConnection.md)
 - [LocalTable](classes/LocalTable.md)
-- [MakeArrowTableOptions](classes/MakeArrowTableOptions.md)
 - [OpenAIEmbeddingFunction](classes/OpenAIEmbeddingFunction.md)
 - [Query](classes/Query.md)
 
@@ -23,7 +21,6 @@
 
 - [AwsCredentials](interfaces/AwsCredentials.md)
 - [CleanupStats](interfaces/CleanupStats.md)
-- [ColumnAlteration](interfaces/ColumnAlteration.md)
 - [CompactionMetrics](interfaces/CompactionMetrics.md)
 - [CompactionOptions](interfaces/CompactionOptions.md)
 - [Connection](interfaces/Connection.md)
@@ -32,7 +29,6 @@
 - [EmbeddingFunction](interfaces/EmbeddingFunction.md)
 - [IndexStats](interfaces/IndexStats.md)
 - [IvfPQIndexConfig](interfaces/IvfPQIndexConfig.md)
-- [MergeInsertArgs](interfaces/MergeInsertArgs.md)
 - [Table](interfaces/Table.md)
 - [UpdateArgs](interfaces/UpdateArgs.md)
 - [UpdateSqlArgs](interfaces/UpdateSqlArgs.md)
@@ -46,9 +42,7 @@
 ### Functions
 
 - [connect](modules.md#connect)
-- [convertToTable](modules.md#converttotable)
 - [isWriteOptions](modules.md#iswriteoptions)
-- [makeArrowTable](modules.md#makearrowtable)
 
 ## Type Aliases
 
@@ -58,7 +52,7 @@
 
 #### Defined in
 
-[index.ts:1336](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1336)
+[index.ts:996](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L996)
 
 ## Functions
 
@@ -68,11 +62,11 @@
 
 Connect to a LanceDB instance at the given URI.
 
-Accepted formats:
+Accpeted formats:
 
 - `/path/to/database` - local database
 - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
-- `db://host:port` - remote database (LanceDB cloud)
+- `db://host:port` - remote database (SaaS)
 
 #### Parameters
 
@@ -90,7 +84,7 @@ Accepted formats:
 
 #### Defined in
 
-[index.ts:188](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L188)
+[index.ts:141](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L141)
 
 ▸ **connect**(`opts`): `Promise`\<[`Connection`](interfaces/Connection.md)\>
 
@@ -108,35 +102,7 @@ Connect to a LanceDB instance with connection options.
 
 #### Defined in
 
-[index.ts:194](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L194)
+[index.ts:147](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L147)
-
-___
-
-### convertToTable
-
-▸ **convertToTable**\<`T`\>(`data`, `embeddings?`, `makeTableOptions?`): `Promise`\<`ArrowTable`\>
-
-#### Type parameters
-
-| Name |
-| :------ |
-| `T` |
-
-#### Parameters
-
-| Name | Type |
-| :------ | :------ |
-| `data` | `Record`\<`string`, `unknown`\>[] |
-| `embeddings?` | [`EmbeddingFunction`](interfaces/EmbeddingFunction.md)\<`T`\> |
-| `makeTableOptions?` | `Partial`\<[`MakeArrowTableOptions`](classes/MakeArrowTableOptions.md)\> |
-
-#### Returns
-
-`Promise`\<`ArrowTable`\>
-
-#### Defined in
-
-[arrow.ts:465](https://github.com/lancedb/lancedb/blob/92179835/node/src/arrow.ts#L465)
 
 ___
 
@@ -156,116 +122,4 @@ value is WriteOptions
 
 #### Defined in
 
-[index.ts:1362](https://github.com/lancedb/lancedb/blob/92179835/node/src/index.ts#L1362)
+[index.ts:1022](https://github.com/lancedb/lancedb/blob/c89d5e6/node/src/index.ts#L1022)
-
-___
-
-### makeArrowTable
-
-▸ **makeArrowTable**(`data`, `options?`): `ArrowTable`
-
-An enhanced version of the makeTable function from Apache Arrow
-that supports nested fields and embeddings columns.
-
-This function converts an array of Record<String, any> (row-major JS objects)
-to an Arrow Table (a columnar structure)
-
-Note that it currently does not support nulls.
-
-If a schema is provided then it will be used to determine the resulting array
-types.  Fields will also be reordered to fit the order defined by the schema.
-
-If a schema is not provided then the types will be inferred and the field order
-will be controlled by the order of properties in the first record.
-
-If the input is empty then a schema must be provided to create an empty table.
-
-When a schema is not specified then data types will be inferred.  The inference
-rules are as follows:
-
- - boolean => Bool
- - number => Float64
- - String => Utf8
- - Buffer => Binary
- - Record<String, any> => Struct
- - Array<any> => List
-
-#### Parameters
-
-| Name | Type | Description |
-| :------ | :------ | :------ |
-| `data` | `Record`\<`string`, `any`\>[] | input data |
-| `options?` | `Partial`\<[`MakeArrowTableOptions`](classes/MakeArrowTableOptions.md)\> | options to control the makeArrowTable call. |
-
-#### Returns
-
-`ArrowTable`
-
-**`Example`**
-
-```ts
-
-import { fromTableToBuffer, makeArrowTable } from "../arrow";
-import { Field, FixedSizeList, Float16, Float32, Int32, Schema } from "apache-arrow";
-
-const schema = new Schema([
-  new Field("a", new Int32()),
-  new Field("b", new Float32()),
-  new Field("c", new FixedSizeList(3, new Field("item", new Float16()))),
- ]);
- const table = makeArrowTable([
-   { a: 1, b: 2, c: [1, 2, 3] },
-   { a: 4, b: 5, c: [4, 5, 6] },
-   { a: 7, b: 8, c: [7, 8, 9] },
- ], { schema });
-```
-
-By default it assumes that the column named `vector` is a vector column
-and it will be converted into a fixed size list array of type float32.
-The `vectorColumns` option can be used to support other vector column
-names and data types.
-
-```ts
-
-const schema = new Schema([
-   new Field("a", new Float64()),
-   new Field("b", new Float64()),
-   new Field(
-     "vector",
-     new FixedSizeList(3, new Field("item", new Float32()))
-   ),
- ]);
- const table = makeArrowTable([
-   { a: 1, b: 2, vector: [1, 2, 3] },
-   { a: 4, b: 5, vector: [4, 5, 6] },
-   { a: 7, b: 8, vector: [7, 8, 9] },
- ]);
- assert.deepEqual(table.schema, schema);
-```
-
-You can specify the vector column types and names using the options as well
-
-```typescript
-
-const schema = new Schema([
-   new Field('a', new Float64()),
-   new Field('b', new Float64()),
-   new Field('vec1', new FixedSizeList(3, new Field('item', new Float16()))),
-   new Field('vec2', new FixedSizeList(3, new Field('item', new Float16())))
- ]);
-const table = makeArrowTable([
-   { a: 1, b: 2, vec1: [1, 2, 3], vec2: [2, 4, 6] },
-   { a: 4, b: 5, vec1: [4, 5, 6], vec2: [8, 10, 12] },
-   { a: 7, b: 8, vec1: [7, 8, 9], vec2: [14, 16, 18] }
- ], {
-   vectorColumns: {
-     vec1: { type: new Float16() },
-     vec2: { type: new Float16() }
-   }
- }
-assert.deepEqual(table.schema, schema)
-```
-
-#### Defined in
-
-[arrow.ts:198](https://github.com/lancedb/lancedb/blob/92179835/node/src/arrow.ts#L198)

docs/src/js/.nojekyll

@@ -0,0 +1 @@
+TypeDoc added this file to prevent GitHub Pages from using Jekyll. You can turn off this behavior by setting the `githubPages` option to false.

docs/src/js/README.md

@@ -36,8 +36,41 @@ const results = await table.vectorSearch([0.1, 0.3]).limit(20).toArray();
 console.log(results);
 ```
 
-The [quickstart](https://lancedb.github.io/lancedb/basic/) contains a more complete example.
+The [quickstart](../basic.md) contains a more complete example.
 
 ## Development
 
-See [CONTRIBUTING.md](_media/CONTRIBUTING.md) for information on how to contribute to LanceDB.
+```sh
+npm run build
+npm run test
+```
+
+### Running lint / format
+
+LanceDb uses [biome](https://biomejs.dev/) for linting and formatting. if you are using VSCode you will need to install the official [Biome](https://marketplace.visualstudio.com/items?itemName=biomejs.biome) extension.
+To manually lint your code you can run:
+
+```sh
+npm run lint
+```
+
+to automatically fix all fixable issues:
+
+```sh
+npm run lint-fix
+```
+
+If you do not have your workspace root set to the `nodejs` directory, unfortunately the extension will not work. You can still run the linting and formatting commands manually.
+
+### Generating docs
+
+```sh
+npm run docs
+
+cd ../docs
+# Asssume the virtual environment was created
+# python3 -m venv venv
+# pip install -r requirements.txt
+. ./venv/bin/activate
+mkdocs build
+```