Bump version: 0.17.1-beta.0 → 0.17.1-beta.1

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

.bumpversion.cfg

@@ -1,22 +0,0 @@
-[bumpversion]
-current_version = 0.4.17
-commit = True
-message = Bump version: {current_version} → {new_version}
-tag = True
-tag_name = v{new_version}
-
-[bumpversion:file:node/package.json]
-
-[bumpversion:file:nodejs/package.json]
-
-[bumpversion:file:nodejs/npm/darwin-x64/package.json]
-
-[bumpversion:file:nodejs/npm/darwin-arm64/package.json]
-
-[bumpversion:file:nodejs/npm/linux-x64-gnu/package.json]
-
-[bumpversion:file:nodejs/npm/linux-arm64-gnu/package.json]
-
-[bumpversion:file:rust/ffi/node/Cargo.toml]
-
-[bumpversion:file:rust/lancedb/Cargo.toml]

.bumpversion.toml

@@ -0,0 +1,125 @@
+[tool.bumpversion]
+current_version = "0.14.1-beta.0"
+parse = """(?x)
+    (?P<major>0|[1-9]\\d*)\\.
+    (?P<minor>0|[1-9]\\d*)\\.
+    (?P<patch>0|[1-9]\\d*)
+    (?:-(?P<pre_l>[a-zA-Z-]+)\\.(?P<pre_n>0|[1-9]\\d*))?
+"""
+serialize = [
+    "{major}.{minor}.{patch}-{pre_l}.{pre_n}",
+    "{major}.{minor}.{patch}",
+]
+search = "{current_version}"
+replace = "{new_version}"
+regex = false
+ignore_missing_version = false
+ignore_missing_files = false
+tag = true
+sign_tags = false
+tag_name = "v{new_version}"
+tag_message = "Bump version: {current_version} → {new_version}"
+allow_dirty = true
+commit = true
+message = "Bump version: {current_version} → {new_version}"
+commit_args = ""
+
+# Java maven files
+pre_commit_hooks = [
+  """
+    NEW_VERSION="${BVHOOK_NEW_MAJOR}.${BVHOOK_NEW_MINOR}.${BVHOOK_NEW_PATCH}"
+    if [ ! -z "$BVHOOK_NEW_PRE_L" ] && [ ! -z "$BVHOOK_NEW_PRE_N" ]; then
+        NEW_VERSION="${NEW_VERSION}-${BVHOOK_NEW_PRE_L}.${BVHOOK_NEW_PRE_N}"
+    fi
+    echo "Constructed new version: $NEW_VERSION"
+    cd java && mvn versions:set -DnewVersion=$NEW_VERSION && mvn versions:commit
+
+    # Check for any modified but unstaged pom.xml files
+    MODIFIED_POMS=$(git ls-files -m | grep pom.xml)
+    if [ ! -z "$MODIFIED_POMS" ]; then
+        echo "The following pom.xml files were modified but not staged. Adding them now:"
+        echo "$MODIFIED_POMS" | while read -r file; do
+            git add "$file"
+            echo "Added: $file"
+        done
+    fi
+    """,
+]
+
+[tool.bumpversion.parts.pre_l]
+optional_value = "final"
+values = ["beta", "final"]
+
+[[tool.bumpversion.files]]
+filename = "node/package.json"
+replace = "\"version\": \"{new_version}\","
+search = "\"version\": \"{current_version}\","
+
+[[tool.bumpversion.files]]
+filename = "nodejs/package.json"
+replace = "\"version\": \"{new_version}\","
+search = "\"version\": \"{current_version}\","
+
+# nodejs binary packages
+[[tool.bumpversion.files]]
+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]]
+filename = "rust/ffi/node/Cargo.toml"
+replace = "\nversion = \"{new_version}\""
+search = "\nversion = \"{current_version}\""
+
+[[tool.bumpversion.files]]
+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

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

.github/labeler.yml

@@ -0,0 +1,33 @@
+version: 1
+appendOnly: true
+# Labels are applied based on conventional commits standard
+# https://www.conventionalcommits.org/en/v1.0.0/
+# These labels are later used in release notes. See .github/release.yml
+labels:
+# If the PR title has an ! before the : it will be considered a breaking change
+# For example, `feat!: add new feature` will be considered a breaking change
+- label: breaking-change
+  title: "^[^:]+!:.*"
+- label: breaking-change
+  body: "BREAKING CHANGE"
+- label: enhancement
+  title: "^feat(\\(.+\\))?!?:.*"
+- label: bug
+  title: "^fix(\\(.+\\))?!?:.*"
+- label: documentation
+  title: "^docs(\\(.+\\))?!?:.*"
+- label: performance
+  title: "^perf(\\(.+\\))?!?:.*"
+- label: ci
+  title: "^ci(\\(.+\\))?!?:.*"
+- label: chore
+  title: "^(chore|test|build|style)(\\(.+\\))?!?:.*"
+- label: Python
+  files:
+    - "^python\\/.*"
+- label: Rust
+  files:
+    - "^rust\\/.*"
+- label: typescript
+  files:
+    - "^node\\/.*"

.github/release_notes.json

@@ -0,0 +1,41 @@
+{
+    "ignore_labels": ["chore"],
+    "pr_template": "- ${{TITLE}} by @${{AUTHOR}} in ${{URL}}",
+    "categories": [
+        {
+            "title": "## 🏆 Highlights",
+            "labels": ["highlight"]
+        },
+        {
+            "title": "## 🛠 Breaking Changes",
+            "labels": ["breaking-change"]
+        },
+        {
+            "title": "## ⚠️ Deprecations ",
+            "labels": ["deprecation"]
+        },
+        {
+            "title": "## 🎉 New Features",
+            "labels": ["enhancement"]
+        },
+        {
+            "title": "## 🐛 Bug Fixes",
+            "labels": ["bug"]
+        },
+        {
+            "title": "## 📚 Documentation",
+            "labels": ["documentation"]
+        },
+        {
+            "title": "## 🚀 Performance Improvements",
+            "labels": ["performance"]
+        },
+        {
+            "title": "## Other Changes"
+        },
+        {
+            "title": "## 🔧 Build and CI",
+            "labels": ["ci"]
+        }
+    ]
+}

.github/workflows/build_linux_wheel/action.yml

@@ -46,6 +46,7 @@ runs:
       with:
         command: build
         working-directory: python
+        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         target: aarch64-unknown-linux-gnu
         manylinux: ${{ inputs.manylinux }}
         args: ${{ inputs.args }}

.github/workflows/build_mac_wheel/action.yml

@@ -21,5 +21,6 @@ runs:
       with:
         command: build
         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

@@ -26,6 +26,7 @@ runs:
       with:
         command: build
         args: ${{ inputs.args }}
+        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         working-directory: python
     - uses: actions/upload-artifact@v3
       with:

.github/workflows/cargo-publish.yml

@@ -1,8 +1,12 @@
 name: Cargo Publish
 
 on:
-  release:
+  push:
-    types: [ published ]
+    tags-ignore:
+      # We don't publish pre-releases for Rust. Crates.io is just a source
+      # distribution, so we don't need to publish pre-releases.
+      - 'v*-beta*'
+      - '*-v*' # for example, python-vX.Y.Z
 
 env:
   # This env var is used by Swatinem/rust-cache@v2 for the cache

.github/workflows/dev.yml

@@ -0,0 +1,81 @@
+name: PR Checks
+
+on:
+  pull_request_target:
+    types: [opened, edited, synchronize, reopened]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  labeler:
+    permissions:
+      pull-requests: write
+    name: Label PR
+    runs-on: ubuntu-latest
+    steps:
+      - uses: srvaroa/labeler@master
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+  commitlint:
+    permissions:
+      pull-requests: write
+    name: Verify PR title / description conforms to semantic-release
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/setup-node@v3
+        with:
+          node-version: "18"
+      # These rules are disabled because Github will always ensure there
+      # is a blank line between the title and the body and Github will
+      # word wrap the description field to ensure a reasonable max line
+      # length.
+      - run: npm install @commitlint/config-conventional
+      - run: >
+          echo 'module.exports = {
+            "rules": {
+              "body-max-line-length": [0, "always", Infinity],
+              "footer-max-line-length": [0, "always", Infinity],
+              "body-leading-blank": [0, "always"]
+            }
+          }' > .commitlintrc.js
+      - run: npx commitlint --extends @commitlint/config-conventional --verbose <<< $COMMIT_MSG
+        env:
+          COMMIT_MSG: >
+            ${{ github.event.pull_request.title }}
+
+            ${{ github.event.pull_request.body }}
+      - if: failure()
+        uses: actions/github-script@v6
+        with:
+          script: |
+            const message = `**ACTION NEEDED**
+              
+              Lance follows the [Conventional Commits specification](https://www.conventionalcommits.org/en/v1.0.0/) for release automation.
+
+              The PR title and description are used as the merge commit message.\
+              Please update your PR title and description to match the specification.
+
+              For details on the error please inspect the "PR Title Check" action.
+              `
+            // Get list of current comments
+            const comments = await github.paginate(github.rest.issues.listComments, {
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: context.issue.number
+            });
+            // Check if this job already commented
+            for (const comment of comments) {
+              if (comment.body === message) {
+                return // Already commented
+              }
+            }
+            // Post the comment about Conventional Commits
+            github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: context.issue.number,
+              body: message
+            })
+            core.setFailed(message)

.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 -e .
+          python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .
-          python -m pip install -r ../docs/requirements.txt
+          python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -r ../docs/requirements.txt
       - name: Set up node
         uses: actions/setup-node@v3
         with:
@@ -72,9 +72,9 @@ jobs:
       - name: Setup Pages
         uses: actions/configure-pages@v2
       - name: Upload artifact
-        uses: actions/upload-pages-artifact@v1
+        uses: actions/upload-pages-artifact@v3
         with:
           path: "docs/site"
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v1
+        uses: actions/deploy-pages@v4

.github/workflows/docs_test.yml

@@ -24,15 +24,19 @@ env:
 jobs:
   test-python:
     name: Test doc python code
-    runs-on: "buildjet-8vcpu-ubuntu-2204"
+    runs-on: ubuntu-24.04
     steps:
     - name: Checkout
       uses: actions/checkout@v4
     - name: Print CPU capabilities
       run: cat /proc/cpuinfo
+    - name: Install protobuf
+      run: |
+        sudo apt update
+        sudo apt install -y protobuf-compiler
     - name: Install dependecies needed for ubuntu
       run: |
-        sudo apt install -y protobuf-compiler libssl-dev
+        sudo apt install -y libssl-dev
         rustup update && rustup default
     - name: Set up Python
       uses: actions/setup-python@v5
@@ -45,7 +49,7 @@ jobs:
     - name: Build Python
       working-directory: docs/test
       run:
-        python -m pip install -r requirements.txt
+        python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -r requirements.txt
     - name: Create test files
       run: |
         cd docs/test
@@ -56,7 +60,7 @@ jobs:
         for d in *; do cd "$d"; echo "$d".py; python "$d".py; cd ..; done
   test-node:
     name: Test doc nodejs code
-    runs-on: "buildjet-8vcpu-ubuntu-2204"
+    runs-on: ubuntu-24.04
     timeout-minutes: 60
     strategy:
       fail-fast: false
@@ -72,9 +76,13 @@ jobs:
       uses: actions/setup-node@v4
       with:
         node-version: 20
+    - name: Install protobuf
+      run: |
+        sudo apt update
+        sudo apt install -y protobuf-compiler
     - name: Install dependecies needed for ubuntu
       run: |
-        sudo apt install -y protobuf-compiler libssl-dev
+        sudo apt install -y libssl-dev
         rustup update && rustup default
     - name: Rust cache
       uses: swatinem/rust-cache@v2

.github/workflows/java-publish.yml

@@ -0,0 +1,114 @@
+name: Build and publish Java packages
+on:
+  release:
+    types: [released]
+  pull_request:
+    paths:
+      - .github/workflows/java-publish.yml
+
+jobs:
+  macos-arm64:
+    name: Build on MacOS Arm64
+    runs-on: macos-14
+    timeout-minutes: 45
+    defaults:
+      run:
+        working-directory: ./java/core/lancedb-jni
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - uses: Swatinem/rust-cache@v2
+      - name: Install dependencies
+        run: |
+          brew install protobuf
+      - name: Build release
+        run: |
+          cargo build --release
+      - uses: actions/upload-artifact@v4
+        with:
+          name: liblancedb_jni_darwin_aarch64.zip
+          path: target/release/liblancedb_jni.dylib
+          retention-days: 1
+          if-no-files-found: error
+  linux-arm64:
+    name: Build on Linux Arm64
+    runs-on: warp-ubuntu-2204-arm64-8x
+    timeout-minutes: 45
+    defaults:
+      run:
+        working-directory: ./java/core/lancedb-jni
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - uses: Swatinem/rust-cache@v2
+      - uses: actions-rust-lang/setup-rust-toolchain@v1
+        with:
+          toolchain: "1.79.0"
+          cache-workspaces: "./java/core/lancedb-jni"
+          # Disable full debug symbol generation to speed up CI build and keep memory down
+          # "1" means line tables only, which is useful for panic tracebacks.
+          rustflags: "-C debuginfo=1"
+      - name: Install dependencies
+        run: |
+          sudo apt -y -qq update
+          sudo apt install -y protobuf-compiler libssl-dev pkg-config
+      - name: Build release
+        run: |
+          cargo build --release
+      - uses: actions/upload-artifact@v4
+        with:
+          name: liblancedb_jni_linux_aarch64.zip
+          path: target/release/liblancedb_jni.so
+          retention-days: 1
+          if-no-files-found: error
+  linux-x86:
+    runs-on: warp-ubuntu-2204-x64-8x
+    timeout-minutes: 30
+    needs: [macos-arm64, linux-arm64]
+    defaults:
+      run:
+        working-directory: ./java
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - uses: Swatinem/rust-cache@v2
+      - name: Set up Java 8
+        uses: actions/setup-java@v4
+        with:
+          distribution: temurin
+          java-version: 8
+          cache: "maven"
+          server-id: ossrh
+          server-username: SONATYPE_USER
+          server-password: SONATYPE_TOKEN
+          gpg-private-key: ${{ secrets.GPG_PRIVATE_KEY }}
+          gpg-passphrase: ${{ secrets.GPG_PASSPHRASE }}
+      - name: Install dependencies
+        run: |
+          sudo apt -y -qq update
+          sudo apt install -y protobuf-compiler libssl-dev pkg-config
+      - name: Download artifact
+        uses: actions/download-artifact@v4
+      - name: Copy native libs
+        run: |
+          mkdir -p ./core/target/classes/nativelib/darwin-aarch64 ./core/target/classes/nativelib/linux-aarch64
+          cp ../liblancedb_jni_darwin_aarch64.zip/liblancedb_jni.dylib ./core/target/classes/nativelib/darwin-aarch64/liblancedb_jni.dylib
+          cp ../liblancedb_jni_linux_aarch64.zip/liblancedb_jni.so ./core/target/classes/nativelib/linux-aarch64/liblancedb_jni.so
+      - name: Dry run
+        if: github.event_name == 'pull_request'
+        run: |
+          mvn --batch-mode -DskipTests package
+      - name: Set github
+        run: |
+          git config --global user.email "LanceDB Github Runner"
+          git config --global user.name "dev+gha@lancedb.com"
+      - name: Publish with Java 8
+        if: github.event_name == 'release'
+        run: |
+          echo "use-agent" >> ~/.gnupg/gpg.conf
+          echo "pinentry-mode loopback" >> ~/.gnupg/gpg.conf
+          export GPG_TTY=$(tty)
+          mvn --batch-mode -DskipTests -DpushChanges=false -Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }} deploy -P deploy-to-ossrh
+        env:
+          SONATYPE_USER: ${{ secrets.SONATYPE_USER }}
+          SONATYPE_TOKEN: ${{ secrets.SONATYPE_TOKEN }}

.github/workflows/java.yml

@@ -0,0 +1,113 @@
+name: Build and Run Java JNI Tests
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - java/**
+  pull_request:
+    paths:
+      - java/**
+      - rust/**
+      - .github/workflows/java.yml
+env:
+  # This env var is used by Swatinem/rust-cache@v2 for the cache
+  # key, so we set it to make sure it is always consistent.
+  CARGO_TERM_COLOR: always
+  # Disable full debug symbol generation to speed up CI build and keep memory down
+  # "1" means line tables only, which is useful for panic tracebacks.
+  RUSTFLAGS: "-C debuginfo=1"
+  RUST_BACKTRACE: "1"
+  # according to: https://matklad.github.io/2021/09/04/fast-rust-builds.html
+  # CI builds are faster with incremental disabled.
+  CARGO_INCREMENTAL: "0"
+  CARGO_BUILD_JOBS: "1"
+jobs:
+  linux-build-java-11:
+    runs-on: ubuntu-22.04
+    name: ubuntu-22.04 + Java 11
+    defaults:
+      run:
+        working-directory: ./java
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: java/core/lancedb-jni
+      - name: Run cargo fmt
+        run: cargo fmt --check
+        working-directory: ./java/core/lancedb-jni
+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+      - name: Install Java 11
+        uses: actions/setup-java@v4
+        with:
+          distribution: temurin
+          java-version: 11
+          cache: "maven"
+      - name: Java Style Check
+        run: mvn checkstyle:check
+      # Disable because of issues in lancedb rust core code
+      # - name: Rust Clippy
+      #   working-directory: java/core/lancedb-jni
+      #   run: cargo clippy --all-targets -- -D warnings
+      - name: Running tests with Java 11
+        run: mvn clean test
+  linux-build-java-17:
+    runs-on: ubuntu-22.04
+    name: ubuntu-22.04 + Java 17
+    defaults:
+      run:
+        working-directory: ./java
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: java/core/lancedb-jni
+      - name: Run cargo fmt
+        run: cargo fmt --check
+        working-directory: ./java/core/lancedb-jni
+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+      - name: Install Java 17
+        uses: actions/setup-java@v4
+        with:
+          distribution: temurin
+          java-version: 17
+          cache: "maven"
+      - run: echo "JAVA_17=$JAVA_HOME" >> $GITHUB_ENV
+      - name: Java Style Check
+        run: mvn checkstyle:check
+      # Disable because of issues in lancedb rust core code
+      # - name: Rust Clippy
+      #   working-directory: java/core/lancedb-jni
+      #   run: cargo clippy --all-targets -- -D warnings
+      - name: Running tests with Java 17
+        run: |
+          export JAVA_TOOL_OPTIONS="$JAVA_TOOL_OPTIONS \
+          -XX:+IgnoreUnrecognizedVMOptions \
+          --add-opens=java.base/java.lang=ALL-UNNAMED \
+          --add-opens=java.base/java.lang.invoke=ALL-UNNAMED \
+          --add-opens=java.base/java.lang.reflect=ALL-UNNAMED \
+          --add-opens=java.base/java.io=ALL-UNNAMED \
+          --add-opens=java.base/java.net=ALL-UNNAMED \
+          --add-opens=java.base/java.nio=ALL-UNNAMED \
+          --add-opens=java.base/java.util=ALL-UNNAMED \
+          --add-opens=java.base/java.util.concurrent=ALL-UNNAMED \
+          --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED \
+          --add-opens=java.base/jdk.internal.ref=ALL-UNNAMED \
+          --add-opens=java.base/sun.nio.ch=ALL-UNNAMED \
+          --add-opens=java.base/sun.nio.cs=ALL-UNNAMED \
+          --add-opens=java.base/sun.security.action=ALL-UNNAMED \
+          --add-opens=java.base/sun.util.calendar=ALL-UNNAMED \
+          --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED \
+          -Djdk.reflect.useDirectMethodHandle=false \
+          -Dio.netty.tryReflectionSetAccessible=true"
+          JAVA_HOME=$JAVA_17 mvn clean test
+  

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

@@ -1,37 +1,62 @@
 name: Create release commit
 
+# This workflow increments versions, tags the version, and pushes it.
+# When a tag is pushed, another workflow is triggered that creates a GH release
+# and uploads the binaries. This workflow is only for creating the tag.
+
+# This script will enforce that a minor version is incremented if there are any
+# breaking changes since the last minor increment. However, it isn't able to
+# differentiate between breaking changes in Node versus Python. If you wish to
+# bypass this check, you can manually increment the version and push the tag.
 on:
   workflow_dispatch:
     inputs:
       dry_run:
         description: 'Dry run (create the local commit/tags but do not push it)'
         required: true
-        default: "false"
+        default: false
-        type: choice
+        type: boolean
-        options:
+      type:
-          - "true"
-          - "false"
-      part:
         description: 'What kind of release is this?'
         required: true
-        default: 'patch'
+        default: 'preview'
         type: choice
         options:
-          - patch
+          - preview
-          - minor
+          - stable
-          - major
+      python:
+        description: 'Make a Python release'
+        required: true
+        default: true
+        type: boolean
+      other:
+        description: 'Make a Node/Rust/Java release'
+        required: true
+        default: true
+        type: boolean
+      bump-minor:
+        description: 'Bump minor version'
+        required: true
+        default: false
+        type: boolean
 
 jobs:
-  bump-version:
+  make-release:
+    # Creates tag and GH release. The GH release will trigger the build and release jobs.
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
     steps:
-      - name: Check out main
+      - name: Output Inputs
-        uses: actions/checkout@v4
+        run: echo "${{ toJSON(github.event.inputs) }}"
+      - uses: actions/checkout@v4
         with:
-          ref: main
-          persist-credentials: false
           fetch-depth: 0
           lfs: true
+          # It's important we use our token here, as the default token will NOT
+          # 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: Set git configs for bumpversion
         shell: bash
         run: |
@@ -41,19 +66,34 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
-      - name: Bump version, create tag and commit
+      - name: Bump Python version
+        if: ${{ inputs.python }}
+        working-directory: python
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         run: |
-          pip install bump2version
+          # Need to get the commit before bumping the version, so we can
-          bumpversion --verbose ${{ inputs.part }}
+          # determine if there are breaking changes in the next step as well.
-      - name: Push new version and tag
+          echo "COMMIT_BEFORE_BUMP=$(git rev-parse HEAD)" >> $GITHUB_ENV
-        if: ${{ inputs.dry_run }} == "false"
+
+          pip install bump-my-version PyGithub packaging
+          bash ../ci/bump_version.sh ${{ inputs.type }} ${{ inputs.bump-minor }} python-v
+      - name: Bump Node/Rust version
+        if: ${{ inputs.other }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          pip install bump-my-version PyGithub packaging
+          bash ci/bump_version.sh ${{ inputs.type }} ${{ inputs.bump-minor }} v $COMMIT_BEFORE_BUMP
+      - name: Push new version tag
+        if: ${{ !inputs.dry_run }}
         uses: ad-m/github-push-action@master
         with:
+          # Need to use PAT here too to trigger next workflow. See comment above.
           github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
-          branch: main
+          branch: ${{ github.ref }}
           tags: true
       - uses: ./.github/workflows/update_package_lock
-        if: ${{ inputs.dry_run }} == "false"
+        if: ${{ !inputs.dry_run && inputs.other }}
         with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          github_token: ${{ secrets.GITHUB_TOKEN }}
-

.github/workflows/nodejs.yml

@@ -52,8 +52,10 @@ jobs:
         cargo fmt --all -- --check
         cargo clippy --all --all-features -- -D warnings
         npm ci
-        npm run lint
+        npm run lint-ci
-        npm run chkformat
+    - name: Lint examples
+      working-directory: nodejs/examples
+      run: npm ci && npm run lint-ci
   linux:
     name: Linux (NodeJS ${{ matrix.node-version }})
     timeout-minutes: 30
@@ -92,6 +94,18 @@ jobs:
       env:
         S3_TEST: "1"
       run: npm run test
+    - name: Setup examples
+      working-directory: nodejs/examples
+      run: npm ci
+    - name: Test examples
+      working-directory: ./
+      env:
+        OPENAI_API_KEY: test
+        OPENAI_BASE_URL: http://0.0.0.0:8000
+      run: |
+        python ci/mock_openai.py &
+        cd nodejs/examples
+        npm test
   macos:
     timeout-minutes: 30
     runs-on: "macos-14"

.github/workflows/npm-publish.yml

@@ -1,11 +1,13 @@
 name: NPM Publish
 
 on:
-  release:
+  push:
-    types: [published]
+    tags:
+      - "v*"
 
 jobs:
   node:
+    name: vectordb Typescript
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -38,6 +40,7 @@ jobs:
             node/vectordb-*.tgz
 
   node-macos:
+    name: vectordb ${{ matrix.config.arch }}
     strategy:
       matrix:
         config:
@@ -68,6 +71,7 @@ jobs:
             node/dist/lancedb-vectordb-darwin*.tgz
 
   nodejs-macos:
+    name: lancedb ${{ matrix.config.arch }}
     strategy:
       matrix:
         config:
@@ -97,8 +101,8 @@ jobs:
           path: |
             nodejs/dist/*.node
 
-  node-linux:
+  node-linux-gnu:
-    name: node-linux (${{ matrix.config.arch}}-unknown-linux-gnu
+    name: vectordb (${{ matrix.config.arch}}-unknown-linux-gnu)
     runs-on: ${{ matrix.config.runner }}
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -110,12 +114,11 @@ jobs:
             runner: ubuntu-latest
           - arch: aarch64
             # For successful fat LTO builds, we need a large runner to avoid OOM errors.
-            runner: buildjet-16vcpu-ubuntu-2204-arm
+            runner: warp-ubuntu-latest-arm64-4x
     steps:
       - name: Checkout
         uses: actions/checkout@v4
-      # Buildjet aarch64 runners have only 1.5 GB RAM per core, vs 3.5 GB per core for
+      # To avoid OOM errors on ARM, we create a swap file.
-      # x86_64 runners. To avoid OOM errors on ARM, we create a swap file.
       - name: Configure aarch64 build
         if: ${{ matrix.config.arch == 'aarch64' }}
         run: |
@@ -130,16 +133,68 @@ jobs:
           free -h
       - name: Build Linux Artifacts
         run: |
-          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }}
+          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }} ${{ matrix.config.arch }}-unknown-linux-gnu
       - name: Upload Linux Artifacts
         uses: actions/upload-artifact@v4
         with:
-          name: node-native-linux-${{ matrix.config.arch }}
+          name: node-native-linux-${{ matrix.config.arch }}-gnu
           path: |
             node/dist/lancedb-vectordb-linux*.tgz
 
-  nodejs-linux:
+  node-linux-musl:
-    name: nodejs-linux (${{ matrix.config.arch}}-unknown-linux-gnu
+    name: vectordb (${{ 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 --default-toolchain 1.80.0
+          echo "source $HOME/.cargo/env" >> saved_env
+          echo "export CC=clang" >> saved_env
+          echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=-crt-static,+avx2,+fma,+f16c -Clinker=clang -Clink-arg=-fuse-ld=mold'" >> saved_env
+      - name: Configure aarch64 build
+        if: ${{ matrix.config.arch == 'aarch64' }}
+        run: |
+          source "$HOME/.cargo/env"
+          rustup target add aarch64-unknown-linux-musl --toolchain 1.80.0
+          crt=$(realpath $(dirname $(rustup which rustc))/../lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained)
+          sysroot_lib=/usr/aarch64-unknown-linux-musl/usr/lib
+          apk_url=https://dl-cdn.alpinelinux.org/alpine/latest-stable/main/aarch64/
+          curl -sSf $apk_url > apk_list
+          for pkg in gcc libgcc musl; do curl -sSf $apk_url$(cat apk_list | grep -oP '(?<=")'$pkg'-\d.*?(?=")') | tar zxf -; done
+          mkdir -p $sysroot_lib
+          echo 'GROUP ( libgcc_s.so.1 -lgcc )' > $sysroot_lib/libgcc_s.so
+          cp usr/lib/libgcc_s.so.1 $sysroot_lib
+          cp usr/lib/gcc/aarch64-alpine-linux-musl/*/libgcc.a $sysroot_lib
+          cp lib/ld-musl-aarch64.so.1 $sysroot_lib/libc.so
+          echo '!<arch>' > $sysroot_lib/libdl.a
+          (cd $crt && cp crti.o crtbeginS.o crtendS.o crtn.o -t $sysroot_lib)
+          echo "export CARGO_BUILD_TARGET=aarch64-unknown-linux-musl" >> saved_env
+          echo "export RUSTFLAGS='-Ctarget-cpu=apple-m1 -Ctarget-feature=-crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=clang -Clink-arg=-fuse-ld=mold -Clink-arg=--target=aarch64-unknown-linux-musl -Clink-arg=--sysroot=/usr/aarch64-unknown-linux-musl -Clink-arg=-lc'" >> saved_env
+      - name: Build Linux Artifacts
+        run: |
+          source ./saved_env
+          bash ci/manylinux_node/build_vectordb.sh ${{ matrix.config.arch }} ${{ matrix.config.arch }}-unknown-linux-musl
+      - name: Upload Linux Artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: node-native-linux-${{ matrix.config.arch }}-musl
+          path: |
+            node/dist/lancedb-vectordb-linux*.tgz
+
+  nodejs-linux-gnu:
+    name: lancedb (${{ matrix.config.arch}}-unknown-linux-gnu
     runs-on: ${{ matrix.config.runner }}
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -175,7 +230,7 @@ jobs:
       - name: Upload Linux Artifacts
         uses: actions/upload-artifact@v4
         with:
-          name: nodejs-native-linux-${{ matrix.config.arch }}
+          name: nodejs-native-linux-${{ matrix.config.arch }}-gnu
           path: |
             nodejs/dist/*.node
       # The generic files are the same in all distros so we just pick
@@ -189,7 +244,64 @@ 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 --default-toolchain 1.80.0
+          echo "source $HOME/.cargo/env" >> saved_env
+          echo "export CC=clang" >> saved_env
+          echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=-crt-static,+avx2,+fma,+f16c -Clinker=clang -Clink-arg=-fuse-ld=mold'" >> saved_env
+          echo "export X86_64_UNKNOWN_LINUX_MUSL_OPENSSL_INCLUDE_DIR=/usr/include" >> saved_env
+          echo "export X86_64_UNKNOWN_LINUX_MUSL_OPENSSL_LIB_DIR=/usr/lib" >> saved_env
+      - name: Configure aarch64 build
+        if: ${{ matrix.config.arch == 'aarch64' }}
+        run: |
+          source "$HOME/.cargo/env"
+          rustup target add aarch64-unknown-linux-musl --toolchain 1.80.0
+          crt=$(realpath $(dirname $(rustup which rustc))/../lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained)
+          sysroot_lib=/usr/aarch64-unknown-linux-musl/usr/lib
+          apk_url=https://dl-cdn.alpinelinux.org/alpine/latest-stable/main/aarch64/
+          curl -sSf $apk_url > apk_list
+          for pkg in gcc libgcc musl openssl-dev openssl-libs-static; do curl -sSf $apk_url$(cat apk_list | grep -oP '(?<=")'$pkg'-\d.*?(?=")') | tar zxf -; done
+          mkdir -p $sysroot_lib
+          echo 'GROUP ( libgcc_s.so.1 -lgcc )' > $sysroot_lib/libgcc_s.so
+          cp usr/lib/libgcc_s.so.1 $sysroot_lib
+          cp usr/lib/gcc/aarch64-alpine-linux-musl/*/libgcc.a $sysroot_lib
+          cp lib/ld-musl-aarch64.so.1 $sysroot_lib/libc.so
+          echo '!<arch>' > $sysroot_lib/libdl.a
+          (cd $crt && cp crti.o crtbeginS.o crtendS.o crtn.o -t $sysroot_lib)
+          echo "export CARGO_BUILD_TARGET=aarch64-unknown-linux-musl" >> saved_env
+          echo "export RUSTFLAGS='-Ctarget-feature=-crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=clang -Clink-arg=-fuse-ld=mold -Clink-arg=--target=aarch64-unknown-linux-musl -Clink-arg=--sysroot=/usr/aarch64-unknown-linux-musl -Clink-arg=-lc'" >> saved_env
+          echo "export AARCH64_UNKNOWN_LINUX_MUSL_OPENSSL_INCLUDE_DIR=$(realpath usr/include)" >> saved_env
+          echo "export AARCH64_UNKNOWN_LINUX_MUSL_OPENSSL_LIB_DIR=$(realpath usr/lib)" >> saved_env
+      - name: Build Linux Artifacts
+        run: |
+          source ./saved_env
+          bash ci/manylinux_node/build_lancedb.sh ${{ matrix.config.arch }}
+      - name: Upload Linux Artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: nodejs-native-linux-${{ matrix.config.arch }}-musl
+          path: |
+            nodejs/dist/*.node
+
   node-windows:
+    name: vectordb ${{ matrix.target }}
     runs-on: windows-2022
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -222,7 +334,53 @@ 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 --default-toolchain 1.80.0
+          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 ${{ matrix.config.arch }}-pc-windows-msvc --toolchain 1.80.0
+          (mkdir -p sysroot && cd sysroot && sh ../ci/sysroot-${{ 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
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -255,8 +413,57 @@ 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 --default-toolchain 1.80.0
+          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 ${{ matrix.config.arch }}-pc-windows-msvc --toolchain 1.80.0
+          (mkdir -p sysroot && cd sysroot && sh ../ci/sysroot-${{ 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:
-    needs: [node, node-macos, node-linux, node-windows]
+    name: vectordb NPM Publish
+    needs: [node, node-macos, node-linux-gnu, node-linux-musl, node-windows, node-windows-arm64]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -274,13 +481,29 @@ jobs:
         env:
           NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
         run: |
+          # 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"
+          fi
+
           mv */*.tgz .
           for filename in *.tgz; do
-            npm publish $filename
+            npm publish $PUBLISH_ARGS $filename
           done
+      - name: Notify Slack Action
+        uses: ravsamhq/notify-slack-action@2.3.0
+        if: ${{ always() }}
+        with:
+          status: ${{ job.status }}
+          notify_when: "failure"
+          notification_title: "{workflow} is failing"
+        env:
+          SLACK_WEBHOOK_URL: ${{ secrets.ACTION_MONITORING_SLACK }}
 
   release-nodejs:
-    needs: [nodejs-macos, nodejs-linux, nodejs-windows]
+    name: lancedb NPM Publish
+    needs: [nodejs-macos, nodejs-linux-gnu, nodejs-linux-musl, nodejs-windows, nodejs-windows-arm64]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -316,11 +539,33 @@ jobs:
       - name: Publish to NPM
         env:
           NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
-        run: npm publish --access public
+        # By default, things are published to the latest tag. This is what is
+        # installed by default if the user does not specify a version. This is
+        # good for stable releases, but for pre-releases, we want to publish to
+        # the "preview" tag so they can install with `npm install lancedb@preview`.
+        # See: https://medium.com/@mbostock/prereleases-and-npm-e778fc5e2420
+        run: |
+          if [[ $GITHUB_REF =~ refs/tags/v(.*)-beta.* ]]; then
+            npm publish --access public --tag preview
+          else
+            npm publish --access public
+          fi
+      - name: Notify Slack Action
+        uses: ravsamhq/notify-slack-action@2.3.0
+        if: ${{ always() }}
+        with:
+          status: ${{ job.status }}
+          notify_when: "failure"
+          notification_title: "{workflow} is failing"
+        env:
+          SLACK_WEBHOOK_URL: ${{ secrets.ACTION_MONITORING_SLACK }}
 
   update-package-lock:
+    if: startsWith(github.ref, 'refs/tags/v')
     needs: [release]
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -331,11 +576,14 @@ jobs:
           lfs: true
       - uses: ./.github/workflows/update_package_lock
         with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          github_token: ${{ secrets.GITHUB_TOKEN }}
 
   update-package-lock-nodejs:
+    if: startsWith(github.ref, 'refs/tags/v')
     needs: [release-nodejs]
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -346,4 +594,71 @@ jobs:
           lfs: true
       - uses: ./.github/workflows/update_package_lock_nodejs
         with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+
+  gh-release:
+    if: startsWith(github.ref, 'refs/tags/v')
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Extract version
+        id: extract_version
+        env:
+          GITHUB_REF: ${{ github.ref }}
+        run: |
+          set -e
+          echo "Extracting tag and version from $GITHUB_REF"
+          if [[ $GITHUB_REF =~ refs/tags/v(.*) ]]; then
+            VERSION=${BASH_REMATCH[1]}
+            TAG=v$VERSION
+            echo "tag=$TAG" >> $GITHUB_OUTPUT
+            echo "version=$VERSION" >> $GITHUB_OUTPUT
+          else
+            echo "Failed to extract version from $GITHUB_REF"
+            exit 1
+          fi
+          echo "Extracted version $VERSION from $GITHUB_REF"
+          if [[ $VERSION =~ beta ]]; then
+            echo "This is a beta release"
+
+            # Get last release (that is not this one)
+            FROM_TAG=$(git tag --sort='version:refname' \
+              | grep ^v \
+              | grep -vF "$TAG" \
+              | python ci/semver_sort.py v \
+              | tail -n 1)
+          else
+            echo "This is a stable release"
+            # Get last stable tag (ignore betas)
+            FROM_TAG=$(git tag --sort='version:refname' \
+              | grep ^v \
+              | grep -vF "$TAG" \
+              | grep -v beta \
+              | python ci/semver_sort.py v \
+              | tail -n 1)
+          fi
+          echo "Found from tag $FROM_TAG"
+          echo "from_tag=$FROM_TAG" >> $GITHUB_OUTPUT
+      - name: Create Release Notes
+        id: release_notes
+        uses: mikepenz/release-changelog-builder-action@v4
+        with:
+          configuration: .github/release_notes.json
+          toTag: ${{ steps.extract_version.outputs.tag }}
+          fromTag: ${{ steps.extract_version.outputs.from_tag }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Create GH release
+        uses: softprops/action-gh-release@v2
+        with:
+          prerelease: ${{ contains('beta', github.ref) }}
+          tag_name: ${{ steps.extract_version.outputs.tag }}
+          token: ${{ secrets.GITHUB_TOKEN }}
+          generate_release_notes: false
+          name: Node/Rust LanceDB v${{ steps.extract_version.outputs.version }}
+          body: ${{ steps.release_notes.outputs.changelog }}

.github/workflows/pypi-publish.yml

@@ -1,18 +1,16 @@
 name: PyPI Publish
 
 on:
-  release:
+  push:
-    types: [published]
+    tags:
+      - 'python-v*'
 
 jobs:
   linux:
-    # Only runs on tags that matches the python-make-release action
-    if: startsWith(github.ref, 'refs/tags/python-v')
     name: Python ${{ matrix.config.platform }} manylinux${{ matrix.config.manylinux }}
     timeout-minutes: 60
     strategy:
       matrix:
-        python-minor-version: ["8"]
         config:
           - platform: x86_64
             manylinux: "2_17"
@@ -34,25 +32,22 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.${{ matrix.python-minor-version }}
+          python-version: 3.8
       - uses: ./.github/workflows/build_linux_wheel
         with:
-          python-minor-version: ${{ matrix.python-minor-version }}
+          python-minor-version: 8
           args: "--release --strip ${{ matrix.config.extra_args }}"
           arm-build: ${{ matrix.config.platform == 'aarch64' }}
           manylinux: ${{ matrix.config.manylinux }}
       - uses: ./.github/workflows/upload_wheel
         with:
-          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          repo: "pypi"
+          fury_token: ${{ secrets.FURY_TOKEN }}
   mac:
-    # Only runs on tags that matches the python-make-release action
-    if: startsWith(github.ref, 'refs/tags/python-v')
     timeout-minutes: 60
     runs-on: ${{ matrix.config.runner }}
     strategy:
       matrix:
-        python-minor-version: ["8"]
         config:
           - target: x86_64-apple-darwin
             runner: macos-13
@@ -63,7 +58,6 @@ jobs:
     steps:
       - uses: actions/checkout@v4
         with:
-          ref: ${{ inputs.ref }}
           fetch-depth: 0
           lfs: true
       - name: Set up Python
@@ -72,38 +66,95 @@ jobs:
           python-version: 3.12
       - uses: ./.github/workflows/build_mac_wheel
         with:
-          python-minor-version: ${{ matrix.python-minor-version }}
+          python-minor-version: 8
           args: "--release --strip --target ${{ matrix.config.target }} --features fp16kernels"
       - uses: ./.github/workflows/upload_wheel
         with:
-          python-minor-version: ${{ matrix.python-minor-version }}
+          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          fury_token: ${{ secrets.FURY_TOKEN }}
-          repo: "pypi"
   windows:
-    # Only runs on tags that matches the python-make-release action
-    if: startsWith(github.ref, 'refs/tags/python-v')
     timeout-minutes: 60
     runs-on: windows-latest
-    strategy:
-      matrix:
-        python-minor-version: ["8"]
     steps:
       - uses: actions/checkout@v4
         with:
-          ref: ${{ inputs.ref }}
           fetch-depth: 0
           lfs: true
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.${{ matrix.python-minor-version }}
+          python-version: 3.12
       - uses: ./.github/workflows/build_windows_wheel
         with:
-          python-minor-version: ${{ matrix.python-minor-version }}
+          python-minor-version: 8
           args: "--release --strip"
           vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
       - uses: ./.github/workflows/upload_wheel
         with:
-          python-minor-version: ${{ matrix.python-minor-version }}
+          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          fury_token: ${{ secrets.FURY_TOKEN }}
-          repo: "pypi"
+  gh-release:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Extract version
+        id: extract_version
+        env:
+          GITHUB_REF: ${{ github.ref }}
+        run: |
+          set -e
+          echo "Extracting tag and version from $GITHUB_REF"
+          if [[ $GITHUB_REF =~ refs/tags/python-v(.*) ]]; then
+            VERSION=${BASH_REMATCH[1]}
+            TAG=python-v$VERSION
+            echo "tag=$TAG" >> $GITHUB_OUTPUT
+            echo "version=$VERSION" >> $GITHUB_OUTPUT
+          else
+            echo "Failed to extract version from $GITHUB_REF"
+            exit 1
+          fi
+          echo "Extracted version $VERSION from $GITHUB_REF"
+          if [[ $VERSION =~ beta ]]; then
+            echo "This is a beta release"
+
+            # Get last release (that is not this one)
+            FROM_TAG=$(git tag --sort='version:refname' \
+              | grep ^python-v \
+              | grep -vF "$TAG" \
+              | python ci/semver_sort.py python-v \
+              | tail -n 1)
+          else
+            echo "This is a stable release"
+            # Get last stable tag (ignore betas)
+            FROM_TAG=$(git tag --sort='version:refname' \
+              | grep ^python-v \
+              | grep -vF "$TAG" \
+              | grep -v beta \
+              | python ci/semver_sort.py python-v \
+              | tail -n 1)
+          fi
+          echo "Found from tag $FROM_TAG"
+          echo "from_tag=$FROM_TAG" >> $GITHUB_OUTPUT
+      - name: Create Python Release Notes
+        id: python_release_notes
+        uses: mikepenz/release-changelog-builder-action@v4
+        with:
+          configuration: .github/release_notes.json
+          toTag: ${{ steps.extract_version.outputs.tag }}
+          fromTag: ${{ steps.extract_version.outputs.from_tag }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Create Python GH release
+        uses: softprops/action-gh-release@v2
+        with:
+          prerelease: ${{ contains('beta', github.ref) }}
+          tag_name: ${{ steps.extract_version.outputs.tag }}
+          token: ${{ secrets.GITHUB_TOKEN }}
+          generate_release_notes: false
+          name: Python LanceDB v${{ steps.extract_version.outputs.version }}
+          body: ${{ steps.python_release_notes.outputs.changelog }}

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

@@ -1,56 +0,0 @@
-name: Python - Create release commit
-
-on:
-  workflow_dispatch:
-    inputs:
-      dry_run:
-        description: 'Dry run (create the local commit/tags but do not push it)'
-        required: true
-        default: "false"
-        type: choice
-        options:
-          - "true"
-          - "false"
-      part:
-        description: 'What kind of release is this?'
-        required: true
-        default: 'patch'
-        type: choice
-        options:
-          - patch
-          - minor
-          - major
-
-jobs:
-  bump-version:
-    runs-on: ubuntu-latest
-    steps:
-    - name: Check out main
-      uses: actions/checkout@v4
-      with:
-        ref: main
-        persist-credentials: false
-        fetch-depth: 0
-        lfs: true
-    - 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
-      uses: actions/setup-python@v5
-      with:
-        python-version: "3.11"
-    - name: Bump version, create tag and commit
-      working-directory: python
-      run: |
-        pip install bump2version
-        bumpversion --verbose ${{ inputs.part }}
-    - name: Push new version and tag
-      if: ${{ inputs.dry_run }} == "false"
-      uses: ad-m/github-push-action@master
-      with:
-        github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
-        branch: main
-        tags: true
-

.github/workflows/python.yml

@@ -33,11 +33,11 @@ jobs:
           python-version: "3.11"
       - name: Install ruff
         run: |
-          pip install ruff==0.2.2
+          pip install ruff==0.5.4
       - name: Format check
         run: ruff format --check .
       - name: Lint
-        run: ruff .
+        run: ruff check .
   doctest:
     name: "Doctest"
     timeout-minutes: 30
@@ -65,7 +65,7 @@ jobs:
           workspaces: python
       - name: Install
         run: |
-          pip install -e .[tests,dev,embeddings]
+          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests,dev,embeddings]
           pip install tantivy
           pip install mlx
       - name: Doctest
@@ -75,7 +75,7 @@ jobs:
     timeout-minutes: 30
     strategy:
       matrix:
-        python-minor-version: ["8", "11"]
+        python-minor-version: ["9", "11"]
     runs-on: "ubuntu-22.04"
     defaults:
       run:
@@ -138,7 +138,7 @@ jobs:
         run: rm -rf target/wheels
   windows:
     name: "Windows: ${{ matrix.config.name }}"
-    timeout-minutes: 30
+    timeout-minutes: 60
     strategy:
       matrix:
         config:
@@ -189,7 +189,7 @@ jobs:
       - name: Install lancedb
         run: |
           pip install "pydantic<2"
-          pip install -e .[tests]
+          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests]
           pip install tantivy
       - name: Run tests
         run: pytest -m "not slow and not s3_test" -x -v --durations=30 python/tests

.github/workflows/run_tests/action.yml

@@ -15,7 +15,7 @@ runs:
     - name: Install lancedb
       shell: bash
       run: |
-        pip3 install $(ls target/wheels/lancedb-*.whl)[tests,dev]
+        pip3 install --extra-index-url https://pypi.fury.io/lancedb/ $(ls target/wheels/lancedb-*.whl)[tests,dev]
     - name: Setup localstack for integration tests
       if: ${{ inputs.integration == 'true' }}
       shell: bash

.github/workflows/rust.yml

@@ -26,68 +26,78 @@ env:
 jobs:
   lint:
     timeout-minutes: 30
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-24.04
     defaults:
       run:
         shell: bash
-        working-directory: rust
     env:
       # Need up-to-date compilers for kernels
-      CC: gcc-12
+      CC: clang-18
-      CXX: g++-12
+      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 --all --all-features -- -D warnings
+        run: cargo clippy --workspace --tests --all-features -- -D warnings
+
   linux:
     timeout-minutes: 30
-    runs-on: ubuntu-22.04
+    # To build all features, we need more disk space than is available
+    # on the free OSS github runner. This is mostly due to the the
+    # sentence-transformers feature.
+    runs-on: ubuntu-2404-4x-x64
     defaults:
       run:
         shell: bash
         working-directory: rust
     env:
       # Need up-to-date compilers for kernels
-      CC: gcc-12
+      CC: clang-18
-      CXX: g++-12
+      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: |
           sudo apt update
           sudo apt install -y protobuf-compiler libssl-dev
-    - name: Build
+      - name: Make Swap
-      run: cargo build --all-features
+        run: |
-    - name: Start S3 integration test environment
+          sudo fallocate -l 16G /swapfile
-      working-directory: .
+          sudo chmod 600 /swapfile
-      run: docker compose up --detach --wait
+          sudo mkswap /swapfile
-    - name: Run tests
+          sudo swapon /swapfile
-      run: cargo test --all-features
+      - name: Start S3 integration test environment
-    - name: Run examples
+        working-directory: .
-      run: cargo run --example simple
+        run: docker compose up --detach --wait
+      - name: Build
+        run: cargo build --all-features
+      - name: Run tests
+        run: cargo test --all-features
+      - name: Run examples
+        run: cargo run --example simple
+
   macos:
     timeout-minutes: 30
     strategy:
       matrix:
-        mac-runner: [ "macos-13", "macos-14" ]
+        mac-runner: ["macos-13", "macos-14"]
     runs-on: "${{ matrix.mac-runner }}"
     defaults:
       run:
@@ -96,8 +106,8 @@ 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
@@ -110,6 +120,7 @@ jobs:
       - name: Run tests
         # Run with everything except the integration tests.
         run: cargo test --features remote,fp16kernels
+
   windows:
     runs-on: windows-2022
     steps:
@@ -131,4 +142,99 @@ jobs:
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
           cargo build
           cargo test
-  
+
+  windows-arm64:
+    runs-on: windows-4x-arm
+    steps:
+      - name: Install Git
+        run: |
+          Invoke-WebRequest -Uri "https://github.com/git-for-windows/git/releases/download/v2.44.0.windows.1/Git-2.44.0-64-bit.exe" -OutFile "git-installer.exe"
+          Start-Process -FilePath "git-installer.exe" -ArgumentList "/VERYSILENT", "/NORESTART" -Wait
+        shell: powershell
+      - name: Add Git to PATH
+        run: |
+          Add-Content $env:GITHUB_PATH "C:\Program Files\Git\bin"
+          $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+        shell: powershell
+      - name: Configure Git symlinks
+        run: git config --global core.symlinks true
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.13"
+      - name: Install Visual Studio Build Tools
+        run: |
+          Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vs_buildtools.exe" -OutFile "vs_buildtools.exe"
+          Start-Process -FilePath "vs_buildtools.exe" -ArgumentList "--quiet", "--wait", "--norestart", "--nocache", `
+            "--installPath", "C:\BuildTools", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Tools.ARM64", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", `
+            "--add", "Microsoft.VisualStudio.Component.Windows11SDK.22621", `
+            "--add", "Microsoft.VisualStudio.Component.VC.ATL", `
+            "--add", "Microsoft.VisualStudio.Component.VC.ATLMFC", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Llvm.Clang" -Wait
+        shell: powershell
+      - name: Add Visual Studio Build Tools to PATH
+        run: |
+          $vsPath = "C:\BuildTools\VC\Tools\MSVC"
+          $latestVersion = (Get-ChildItem $vsPath | Sort-Object {[version]$_.Name} -Descending)[0].Name
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\arm64"
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\x64"
+          Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\arm64"
+          Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\x64"
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\Llvm\x64\bin"
+
+          # Add MSVC runtime libraries to LIB
+          $env:LIB = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\lib\arm64;" + 
+                     "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\arm64;" +
+                     "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\arm64"
+          Add-Content $env:GITHUB_ENV "LIB=$env:LIB"
+
+          # Add INCLUDE paths
+          $env:INCLUDE = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\include;" +
+                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\ucrt;" +
+                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\um;" +
+                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\shared"
+          Add-Content $env:GITHUB_ENV "INCLUDE=$env:INCLUDE"
+        shell: powershell
+      - name: Install Rust
+        run: |
+          Invoke-WebRequest https://win.rustup.rs/x86_64 -OutFile rustup-init.exe
+          .\rustup-init.exe -y --default-host aarch64-pc-windows-msvc
+        shell: powershell
+      - name: Add Rust to PATH
+        run: |
+          Add-Content $env:GITHUB_PATH "$env:USERPROFILE\.cargo\bin"
+        shell: powershell
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: rust
+      - name: Install 7-Zip ARM
+        run: |
+          New-Item -Path 'C:\7zip' -ItemType Directory
+          Invoke-WebRequest https://7-zip.org/a/7z2408-arm64.exe -OutFile C:\7zip\7z-installer.exe
+          Start-Process -FilePath C:\7zip\7z-installer.exe -ArgumentList '/S' -Wait
+        shell: powershell
+      - name: Add 7-Zip to PATH
+        run: Add-Content $env:GITHUB_PATH "C:\Program Files\7-Zip"
+        shell: powershell
+      - name: Install Protoc v21.12
+        working-directory: C:\
+        run: |
+          if (Test-Path 'C:\protoc') {
+            Write-Host "Protoc directory exists, skipping installation"
+            return
+          }
+          New-Item -Path 'C:\protoc' -ItemType Directory
+          Set-Location C:\protoc
+          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+          & 'C:\Program Files\7-Zip\7z.exe' x protoc.zip
+        shell: powershell
+      - name: Add Protoc to PATH
+        run: Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+        shell: powershell
+      - name: Run tests
+        run: |
+          $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
+          cargo build --target aarch64-pc-windows-msvc
+          cargo test --target aarch64-pc-windows-msvc

.github/workflows/upload_wheel/action.yml

@@ -2,28 +2,44 @@ name: upload-wheel
 
 description: "Upload wheels to Pypi"
 inputs:
-  os:
+  pypi_token:
-    required: true
-    description: "ubuntu-22.04 or macos-13"
-  repo:
-    required: false
-    description: "pypi or testpypi"
-    default: "pypi"
-  token:
     required: true
     description: "release token for the repo"
+  fury_token:
+    required: true
+    description: "release token for the fury repo"
 
 runs:
   using: "composite"
   steps:
-    - name: Install dependencies
+  - name: Install dependencies
-      shell: bash
+    shell: bash
-      run: |
+    run: |
-        python -m pip install --upgrade pip
+      python -m pip install --upgrade pip
-        pip install twine
+      pip install twine
-    - name: Publish wheel
+      python3 -m pip install --upgrade pkginfo
-      env:
+  - name: Choose repo
-        TWINE_USERNAME: __token__
+    shell: bash
-        TWINE_PASSWORD: ${{ inputs.token }}
+    id: choose_repo
-      shell: bash
+    run: |
-      run: twine upload --repository ${{ inputs.repo }} target/wheels/lancedb-*.whl
+      if [ ${{ github.ref }} == "*beta*" ]; then
+        echo "repo=fury" >> $GITHUB_OUTPUT
+      else
+        echo "repo=pypi" >> $GITHUB_OUTPUT
+      fi
+  - name: Publish to PyPI
+    shell: bash
+    env:
+      FURY_TOKEN: ${{ inputs.fury_token }}
+      PYPI_TOKEN: ${{ inputs.pypi_token }}
+    run: |
+      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/
+      else
+        twine upload --repository ${{ steps.choose_repo.outputs.repo }} \
+          --username __token__ \
+          --password $PYPI_TOKEN \
+          target/wheels/lancedb-*.whl
+      fi

.gitignore

@@ -4,9 +4,10 @@
 **/__pycache__
 .DS_Store
 venv
+.venv
 
 .vscode
-
+.zed
 rust/target
 rust/Cargo.lock
 

.pre-commit-config.yaml

@@ -10,9 +10,12 @@ repos:
     rev: v0.2.2
     hooks:
     - id: ruff
-- repo: https://github.com/pre-commit/mirrors-prettier
+- repo: local
-  rev: v3.1.0
   hooks:
-    - id: prettier
+    - id: local-biome-check
+      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/.*
+      exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*|nodejs/examples/.*

Cargo.toml

@@ -1,5 +1,11 @@
 [workspace]
-members = ["rust/ffi/node", "rust/lancedb", "nodejs", "python"]
+members = [
+    "rust/ffi/node",
+    "rust/lancedb",
+    "nodejs",
+    "python",
+    "java/core/lancedb-jni",
+]
 # Python package needs to be built by maturin.
 exclude = ["python"]
 resolver = "2"
@@ -12,32 +18,44 @@ repository = "https://github.com/lancedb/lancedb"
 description = "Serverless, low-latency vector database for AI applications"
 keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
+rust-version = "1.80.0"                                                     # TODO: lower this once we upgrade Lance again.
 
 [workspace.dependencies]
-lance = { "version" = "=0.10.15", "features" = ["dynamodb"] }
+lance = { "version" = "=0.20.0", "features" = [
-lance-index = { "version" = "=0.10.15" }
+    "dynamodb",
-lance-linalg = { "version" = "=0.10.15" }
+] }
-lance-testing = { "version" = "=0.10.15" }
+lance-io = "0.20.0"
+lance-index = "0.20.0"
+lance-linalg = "0.20.0"
+lance-table = "0.20.0"
+lance-testing = "0.20.0"
+lance-datafusion = "0.20.0"
+lance-encoding = "0.20.0"
 # Note that this one does not include pyarrow
-arrow = { version = "50.0", optional = false }
+arrow = { version = "53.2", optional = false }
-arrow-array = "50.0"
+arrow-array = "53.2"
-arrow-data = "50.0"
+arrow-data = "53.2"
-arrow-ipc = "50.0"
+arrow-ipc = "53.2"
-arrow-ord = "50.0"
+arrow-ord = "53.2"
-arrow-schema = "50.0"
+arrow-schema = "53.2"
-arrow-arith = "50.0"
+arrow-arith = "53.2"
-arrow-cast = "50.0"
+arrow-cast = "53.2"
 async-trait = "0"
 chrono = "0.4.35"
-half = { "version" = "=2.3.1", default-features = false, features = [
+datafusion-common = "42.0"
+datafusion-physical-plan = "42.0"
+env_logger = "0.10"
+half = { "version" = "=2.4.1", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"
 log = "0.4"
-object_store = "0.9.0"
+moka = { version = "0.11", features = ["future"] }
+object_store = "0.10.2"
 pin-project = "1.0.7"
 snafu = "0.7.4"
 url = "2"
 num-traits = "0.2"
+rand = "0.8"
 regex = "1.10"
 lazy_static = "1"

README.md

@@ -7,9 +7,10 @@
 
 <a href='https://github.com/lancedb/vectordb-recipes/tree/main' target="_blank"><img alt='LanceDB' src='https://img.shields.io/badge/VectorDB_Recipes-100000?style=for-the-badge&logo=LanceDB&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
 <a href='https://lancedb.github.io/lancedb/' target="_blank"><img alt='lancdb' src='https://img.shields.io/badge/DOCS-100000?style=for-the-badge&logo=lancdb&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
-[![Blog](https://img.shields.io/badge/Blog-12100E?style=for-the-badge&logoColor=white)](https://blog.lancedb.com/) 
+[![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) 
+[![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>
 
@@ -20,7 +21,7 @@
 
 <hr />
 
-LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
+LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.
 
 The key features of LanceDB include:
 
@@ -36,7 +37,7 @@ The key features of LanceDB include:
 
 * GPU support in building vector index(*).
 
-* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lanecdb.html), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
+* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/docs/integrations/vectorstores/lancedb/), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
 
 LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
 
@@ -44,26 +45,24 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
 
 **Javascript**
 ```shell
-npm install vectordb
+npm install @lancedb/lancedb
 ```
 
 ```javascript
-const lancedb = require('vectordb');
+import * as lancedb from "@lancedb/lancedb";
-const db = await lancedb.connect('data/sample-lancedb');
 
-const table = await db.createTable({
+const db = await lancedb.connect("data/sample-lancedb");
-  name: 'vectors',
+const table = await db.createTable("vectors", [
-  data:  [
+	{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
-    { id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
+	{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },
-    { id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }
+], {mode: 'overwrite'});
-  ]
-})
 
-const query = table.search([0.1, 0.3]).limit(2);
+
-const results = await query.execute();
+const query = table.vectorSearch([0.1, 0.3]).limit(2);
+const results = await query.toArray();
 
 // You can also search for rows by specific criteria without involving a vector search.
-const rowsByCriteria = await table.search(undefined).where("price >= 10").execute();
+const rowsByCriteria = await table.query().where("price >= 10").toArray();
 ```
 
 **Python**
@@ -83,5 +82,5 @@ result = table.search([100, 100]).limit(2).to_pandas()
 ```
 
 ## Blogs, Tutorials & Videos
-* 📈 <a href="https://blog.eto.ai/benchmarking-random-access-in-lance-ed690757a826">2000x better performance with Lance over Parquet</a>
+* 📈 <a href="https://blog.lancedb.com/benchmarking-random-access-in-lance/">2000x better performance with Lance over Parquet</a>
-* 🤖 <a href="https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb">Build a question and answer bot with LanceDB</a>
+* 🤖 <a href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Youtube-Search-QA-Bot">Build a question and answer bot with LanceDB</a>

ci/build_linux_artifacts.sh

@@ -1,8 +1,9 @@
 #!/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 \
@@ -18,4 +19,4 @@ docker run \
     -v $(pwd):/io -w /io \
     --memory-swap=-1 \
     lancedb-node-manylinux \
-    bash ci/manylinux_node/build.sh $ARCH
+    bash ci/manylinux_node/build_vectordb.sh $ARCH $TARGET_TRIPLE

ci/build_linux_artifacts_nodejs.sh

@@ -4,9 +4,9 @@ ARCH=${1:-x86_64}
 
 # 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_nodejs
+pushd ci/manylinux_node
 docker build \
-    -t lancedb-nodejs-manylinux \
+    -t lancedb-node-manylinux-$ARCH \
     --build-arg="ARCH=$ARCH" \
     --build-arg="DOCKER_USER=$(id -u)" \
     --progress=plain \
@@ -17,5 +17,5 @@ popd
 docker run \
     -v $(pwd):/io -w /io \
     --memory-swap=-1 \
-    lancedb-nodejs-manylinux \
+    lancedb-node-manylinux-$ARCH \
-    bash ci/manylinux_nodejs/build.sh $ARCH
+    bash ci/manylinux_node/build_lancedb.sh $ARCH

ci/build_windows_artifacts.ps1

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

ci/build_windows_artifacts_nodejs.ps1

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

ci/bump_version.sh

@@ -0,0 +1,51 @@
+set -e
+
+RELEASE_TYPE=${1:-"stable"}
+BUMP_MINOR=${2:-false}
+TAG_PREFIX=${3:-"v"} # Such as "python-v"
+HEAD_SHA=${4:-$(git rev-parse HEAD)}
+
+readonly SELF_DIR=$(cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )
+
+PREV_TAG=$(git tag --sort='version:refname' | grep ^$TAG_PREFIX | python $SELF_DIR/semver_sort.py $TAG_PREFIX | tail -n 1)
+echo "Found previous tag $PREV_TAG"
+
+# Initially, we don't want to tag if we are doing stable, because we will bump
+# again later. See comment at end for why.
+if [[ "$RELEASE_TYPE" == 'stable' ]]; then 
+  BUMP_ARGS="--no-tag"
+fi
+
+# If last is stable and not bumping minor
+if [[ $PREV_TAG != *beta* ]]; then
+    if [[ "$BUMP_MINOR" != "false" ]]; then
+      # X.Y.Z -> X.(Y+1).0-beta.0
+      bump-my-version bump -vv $BUMP_ARGS minor
+    else
+      # X.Y.Z -> X.Y.(Z+1)-beta.0
+      bump-my-version bump -vv $BUMP_ARGS patch
+    fi
+else
+  if [[ "$BUMP_MINOR" != "false" ]]; then
+    # X.Y.Z-beta.N -> X.(Y+1).0-beta.0
+    bump-my-version bump -vv $BUMP_ARGS minor
+  else
+    # X.Y.Z-beta.N -> X.Y.Z-beta.(N+1)
+    bump-my-version bump -vv $BUMP_ARGS pre_n
+  fi
+fi
+
+# The above bump will always bump to a pre-release version. If we are releasing
+# a stable version, bump the pre-release level ("pre_l") to make it stable.
+if [[ $RELEASE_TYPE == 'stable' ]]; then
+  # X.Y.Z-beta.N -> X.Y.Z
+  bump-my-version bump -vv pre_l
+fi
+
+# Validate that we have incremented version appropriately for breaking changes
+NEW_TAG=$(git describe --tags --exact-match HEAD)
+NEW_VERSION=$(echo $NEW_TAG | sed "s/^$TAG_PREFIX//")
+LAST_STABLE_RELEASE=$(git tag --sort='version:refname' | grep ^$TAG_PREFIX | grep -v beta | grep -vF "$NEW_TAG" | python $SELF_DIR/semver_sort.py $TAG_PREFIX | tail -n 1)
+LAST_STABLE_VERSION=$(echo $LAST_STABLE_RELEASE | sed "s/^$TAG_PREFIX//")
+
+python $SELF_DIR/check_breaking_changes.py $LAST_STABLE_RELEASE $HEAD_SHA $LAST_STABLE_VERSION $NEW_VERSION

ci/check_breaking_changes.py

@@ -0,0 +1,35 @@
+"""
+Check whether there are any breaking changes in the PRs between the base and head commits.
+If there are, assert that we have incremented the minor version.
+"""
+import argparse
+import os
+from packaging.version import parse
+
+from github import Github
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("base")
+    parser.add_argument("head")
+    parser.add_argument("last_stable_version")
+    parser.add_argument("current_version")
+    args = parser.parse_args()
+
+    repo = Github(os.environ["GITHUB_TOKEN"]).get_repo(os.environ["GITHUB_REPOSITORY"])
+    commits = repo.compare(args.base, args.head).commits
+    prs = (pr for commit in commits for pr in commit.get_pulls())
+
+    for pr in prs:
+        if any(label.name == "breaking-change" for label in pr.labels):
+            print(f"Breaking change in PR: {pr.html_url}")
+            break
+    else:
+        print("No breaking changes found.")
+        exit(0)
+    
+    last_stable_version = parse(args.last_stable_version)
+    current_version = parse(args.current_version)
+    if current_version.minor <= last_stable_version.minor:
+        print("Minor version is not greater than the last stable version.")
+        exit(1)

ci/manylinux_node/Dockerfile

@@ -4,7 +4,7 @@
 # range of linux distributions.
 ARG ARCH=x86_64
 
-FROM quay.io/pypa/manylinux2014_${ARCH}
+FROM quay.io/pypa/manylinux_2_28_${ARCH}
 
 ARG ARCH=x86_64
 ARG DOCKER_USER=default_user
@@ -18,8 +18,8 @@ COPY install_protobuf.sh install_protobuf.sh
 RUN ./install_protobuf.sh ${ARCH}
 
 ENV DOCKER_USER=${DOCKER_USER}
-# Create a group and user
+# Create a group and user, but only if it doesn't exist
-RUN echo ${ARCH} && adduser --user-group --create-home --uid ${DOCKER_USER} build_user
+RUN echo ${ARCH} && id -u ${DOCKER_USER} >/dev/null 2>&1 || adduser --user-group --create-home --uid ${DOCKER_USER} build_user
 
 # We switch to the user to install Rust and Node, since those like to be 
 # installed at the user level.

ci/manylinux_node/build_lancedb.sh

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

ci/manylinux_node/build_vectordb.sh

@@ -2,18 +2,20 @@
 # 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
 
-source $HOME/.bashrc
+#Alpine doesn't have .bashrc
+FILE=$HOME/.bashrc && test -f $FILE && source $FILE
 
 cd node
 npm ci
 npm run build-release
-npm run pack-build
+npm run pack-build -- -t $TARGET_TRIPLE

ci/manylinux_node/install_openssl.sh

@@ -6,7 +6,7 @@
 # /usr/bin/ld: failed to set dynamic section sizes: Bad value
 set -e
 
-git clone -b OpenSSL_1_1_1u \
+git clone -b OpenSSL_1_1_1v \
     --single-branch \
     https://github.com/openssl/openssl.git
 

ci/manylinux_node/prepare_manylinux_node.sh

@@ -8,7 +8,7 @@ install_node() {
 
     source "$HOME"/.bashrc
 
-    nvm install --no-progress 16
+    nvm install --no-progress 18
 }
 
 install_rust() {

ci/manylinux_nodejs/Dockerfile

@@ -1,31 +0,0 @@
-# Many linux dockerfile with Rust, Node, and Lance dependencies installed.
-# This container allows building the node modules native libraries in an
-# environment with a very old glibc, so that we are compatible with a wide
-# range of linux distributions.
-ARG ARCH=x86_64
-
-FROM quay.io/pypa/manylinux2014_${ARCH}
-
-ARG ARCH=x86_64
-ARG DOCKER_USER=default_user
-
-# Install static openssl
-COPY install_openssl.sh install_openssl.sh
-RUN ./install_openssl.sh ${ARCH} > /dev/null
-
-# Protobuf is also installed as root.
-COPY install_protobuf.sh install_protobuf.sh
-RUN ./install_protobuf.sh ${ARCH}
-
-ENV DOCKER_USER=${DOCKER_USER}
-# Create a group and user
-RUN echo ${ARCH} && adduser --user-group --create-home --uid ${DOCKER_USER} build_user
-
-# We switch to the user to install Rust and Node, since those like to be
-# installed at the user level.
-USER ${DOCKER_USER}
-
-COPY prepare_manylinux_node.sh prepare_manylinux_node.sh
-RUN cp /prepare_manylinux_node.sh $HOME/ && \
-    cd $HOME && \
-    ./prepare_manylinux_node.sh ${ARCH}

ci/manylinux_nodejs/install_openssl.sh

@@ -1,26 +0,0 @@
-#!/bin/bash
-# Builds openssl from source so we can statically link to it
-
-# this is to avoid the error we get with the system installation:
-# /usr/bin/ld: <library>: version node not found for symbol SSLeay@@OPENSSL_1.0.1
-# /usr/bin/ld: failed to set dynamic section sizes: Bad value
-set -e
-
-git clone -b OpenSSL_1_1_1u \
-    --single-branch \
-    https://github.com/openssl/openssl.git
-
-pushd openssl
-
-if [[ $1 == x86_64* ]]; then
-    ARCH=linux-x86_64
-else
-    # gnu target
-    ARCH=linux-aarch64
-fi
-
-./Configure no-shared $ARCH
-
-make
-
-make install

ci/manylinux_nodejs/install_protobuf.sh

@@ -1,15 +0,0 @@
-#!/bin/bash
-# Installs protobuf compiler. Should be run as root.
-set -e
-
-if [[ $1 == x86_64* ]]; then
-    ARCH=x86_64
-else
-    # gnu target
-    ARCH=aarch_64
-fi
-
-PB_REL=https://github.com/protocolbuffers/protobuf/releases
-PB_VERSION=23.1
-curl -LO $PB_REL/download/v$PB_VERSION/protoc-$PB_VERSION-linux-$ARCH.zip
-unzip protoc-$PB_VERSION-linux-$ARCH.zip -d /usr/local

ci/manylinux_nodejs/prepare_manylinux_node.sh

@@ -1,21 +0,0 @@
-#!/bin/bash
-set -e
-
-install_node() {
-    echo "Installing node..."
-
-    curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.34.0/install.sh | bash
-
-    source "$HOME"/.bashrc
-
-    nvm install --no-progress 16
-}
-
-install_rust() {
-    echo "Installing rust..."
-    curl https://sh.rustup.rs -sSf | bash -s -- -y
-    export PATH="$PATH:/root/.cargo/bin"
-}
-
-install_node
-install_rust

ci/mock_openai.py

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

ci/semver_sort.py

@@ -0,0 +1,35 @@
+"""
+Takes a list of semver strings and sorts them in ascending order.
+"""
+
+import sys
+from packaging.version import parse, InvalidVersion
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("prefix", default="v")
+    args = parser.parse_args()
+
+    # Read the input from stdin
+    lines = sys.stdin.readlines()
+
+    # Parse the versions
+    versions = []
+    for line in lines:
+        line = line.strip()
+        try:
+            version_str = line.removeprefix(args.prefix)
+            version = parse(version_str)
+        except InvalidVersion:
+            # There are old tags that don't follow the semver format
+            print(f"Invalid version: {line}", file=sys.stderr)
+            continue
+        versions.append((line, version))
+
+    # Sort the versions
+    versions.sort(key=lambda x: x[1])
+
+    # Print the sorted versions as original strings
+    for line, _ in versions:
+        print(line)

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

@@ -0,0 +1,105 @@
+#!/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
+
+# 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 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

@@ -0,0 +1,105 @@
+#!/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

docs/mkdocs.yml

@@ -26,6 +26,7 @@ theme:
     - content.code.copy
     - content.tabs.link
     - content.action.edit
+    - content.tooltips
     - toc.follow
     - navigation.top
     - navigation.tabs
@@ -33,8 +34,10 @@ theme:
     - navigation.footer
     - navigation.tracking
     - navigation.instant
+    - content.footnote.tooltips
   icon:
     repo: fontawesome/brands/github
+    annotation: material/arrow-right-circle
   custom_dir: overrides
 
 plugins:
@@ -52,15 +55,25 @@ 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
   - mkdocs-jupyter
+  - render_swagger:
+      allow_arbitrary_locations: true
 
 markdown_extensions:
   - admonition
   - footnotes
+  - pymdownx.critic
+  - pymdownx.caret
+  - pymdownx.keys
+  - pymdownx.mark
+  - pymdownx.tilde
   - pymdownx.details
   - pymdownx.highlight:
       anchor_linenums: true
@@ -74,7 +87,15 @@ markdown_extensions:
   - pymdownx.tabbed:
       alternate_style: true
   - md_in_html
+  - abbr
   - attr_list
+  - pymdownx.snippets
+  - 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:
@@ -82,34 +103,77 @@ nav:
       - 🏃🏼‍♂️ Quick start: basic.md
       - 📚 Concepts:
           - Vector search: concepts/vector_search.md
-          - Indexing: concepts/index_ivfpq.md
+          - Indexing:
+            - IVFPQ: concepts/index_ivfpq.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
+          - Building a vector index: ann_indexes.md
           - Vector Search: search.md
-          - Full-text search: fts.md
+          - Full-text search (native): 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
               - Comparing Rerankers: hybrid_search/eval.md
               - Airbnb financial data example: notebooks/hybrid_search.ipynb
+          - RAG:
+              - Vanilla RAG: rag/vanilla_rag.md
+              - Multi-head RAG: rag/multi_head_rag.md
+              - Corrective RAG: rag/corrective_rag.md
+              - Agentic RAG: rag/agentic_rag.md
+              - Graph RAG: rag/graph_rag.md
+              - Self RAG: rag/self_rag.md
+              - Adaptive RAG: rag/adaptive_rag.md
+              - SFR RAG: rag/sfr_rag.md
+              - Advanced Techniques:
+                - HyDE: rag/advanced_techniques/hyde.md
+                - FLARE: rag/advanced_techniques/flare.md
           - Reranking:
               - Quickstart: reranking/index.md
               - Cohere Reranker: reranking/cohere.md
               - Linear Combination Reranker: reranking/linear_combination.md
+              - Reciprocal Rank Fusion Reranker: reranking/rrf.md
               - Cross Encoder Reranker: reranking/cross_encoder.md
               - ColBERT Reranker: reranking/colbert.md
+              - 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: notebooks/reproducibility.ipynb
           - Configuring Storage: guides/storage.md
-          - Sync -> Async Migration Guide: migration.md
+          - Migration Guide: migration.md
+          - Tuning retrieval performance:
+              - Choosing right query type: guides/tuning_retrievers/1_query_types.md
+              - Reranking: guides/tuning_retrievers/2_reranking.md
+              - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
       - 🧬 Managing embeddings:
-          - Overview: embeddings/index.md
+          - Understand Embeddings: embeddings/understanding_embeddings.md
+          - Get Started: embeddings/index.md
           - Embedding functions: embeddings/embedding_functions.md
-          - Available models: embeddings/default_embedding_functions.md
+          - Available models:
+              - Overview: embeddings/default_embedding_functions.md
+              - Text Embedding Functions:
+                  - Sentence Transformers: embeddings/available_embedding_models/text_embedding_functions/sentence_transformers.md
+                  - Huggingface Embedding Models: embeddings/available_embedding_models/text_embedding_functions/huggingface_embedding.md
+                  - Ollama Embeddings: embeddings/available_embedding_models/text_embedding_functions/ollama_embedding.md
+                  - OpenAI Embeddings: embeddings/available_embedding_models/text_embedding_functions/openai_embedding.md
+                  - Instructor Embeddings: embeddings/available_embedding_models/text_embedding_functions/instructor_embedding.md
+                  - Gemini Embeddings: embeddings/available_embedding_models/text_embedding_functions/gemini_embedding.md
+                  - Cohere Embeddings: embeddings/available_embedding_models/text_embedding_functions/cohere_embedding.md
+                  - 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
           - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
           - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
@@ -119,22 +183,32 @@ nav:
           - Polars: python/polars_arrow.md
           - DuckDB: python/duckdb.md
           - LangChain:
-            - LangChain 🔗: https://python.langchain.com/docs/integrations/vectorstores/lancedb/
+              - LangChain 🔗: integrations/langchain.md
-            - LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
+              - LangChain demo: notebooks/langchain_demo.ipynb
-          - LlamaIndex 🦙: https://docs.llamaindex.ai/en/stable/examples/vector_stores/LanceDBIndexDemo/
+              - LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
+          - LlamaIndex 🦙:
+              - LlamaIndex docs: integrations/llamaIndex.md
+              - LlamaIndex demo: notebooks/llamaIndex_demo.ipynb
           - Pydantic: python/pydantic.md
           - Voxel51: integrations/voxel51.md
           - PromptTools: integrations/prompttools.md
+          - dlt: integrations/dlt.md
+          - phidata: integrations/phidata.md
       - 🎯 Examples:
           - Overview: examples/index.md
           - 🐍 Python:
               - Overview: examples/examples_python.md
-              - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+              - Build From Scratch: examples/python_examples/build_from_scratch.md
-              - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+              - Multimodal: examples/python_examples/multimodal.md
-              - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+              - Rag: examples/python_examples/rag.md
-              - Example - Calculate CLIP Embeddings with Roboflow Inference: examples/image_embeddings_roboflow.md
+              - Vector Search: examples/python_examples/vector_search.md
-              - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+              - Chatbot: examples/python_examples/chatbot.md
-              - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
+              - Evaluation: examples/python_examples/evaluations.md
+              - AI Agent: examples/python_examples/aiagent.md
+              - Recommender System: examples/python_examples/recommendersystem.md
+              - 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:
               - Overview: examples/examples_js.md
               - Serverless Website Chatbot: examples/serverless_website_chatbot.md
@@ -142,49 +216,94 @@ nav:
               - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
           - 🦀 Rust:
               - Overview: examples/examples_rust.md
+      - 📓 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
-          - 👾 JavaScript (lancedb): javascript/modules.md
+          - 👾 JavaScript (lancedb): js/globals.md
           - 🦀 Rust: https://docs.rs/lancedb/latest/lancedb/
       - ☁️ LanceDB Cloud:
           - Overview: cloud/index.md
           - API reference:
               - 🐍 Python: python/saas-python.md
-              - 👾 JavaScript: javascript/saas-modules.md
+              - 👾 JavaScript: javascript/modules.md
+              - REST API: cloud/rest.md
 
   - Quick start: basic.md
   - Concepts:
       - Vector search: concepts/vector_search.md
-      - Indexing: concepts/index_ivfpq.md
+      - Indexing:
+        - IVFPQ: concepts/index_ivfpq.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: fts.md
+      - Full-text search (native): 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
           - Comparing Rerankers: hybrid_search/eval.md
           - Airbnb financial data example: notebooks/hybrid_search.ipynb
+      - RAG:
+          - Vanilla RAG: rag/vanilla_rag.md
+          - Multi-head RAG: rag/multi_head_rag.md
+          - Corrective RAG: rag/corrective_rag.md
+          - Agentic RAG: rag/agentic_rag.md
+          - Graph RAG: rag/graph_rag.md
+          - Self RAG: rag/self_rag.md
+          - Adaptive RAG: rag/adaptive_rag.md
+          - SFR RAG: rag/sfr_rag.md
+          - Advanced Techniques:
+            - HyDE: rag/advanced_techniques/hyde.md
+            - FLARE: rag/advanced_techniques/flare.md
       - Reranking:
           - Quickstart: reranking/index.md
           - Cohere Reranker: reranking/cohere.md
           - Linear Combination Reranker: reranking/linear_combination.md
+          - Reciprocal Rank Fusion Reranker: reranking/rrf.md
           - Cross Encoder Reranker: reranking/cross_encoder.md
           - ColBERT Reranker: reranking/colbert.md
+          - Jina Reranker: reranking/jina.md
           - OpenAI Reranker: reranking/openai.md
+          - AnswerDotAi Rerankers: reranking/answerdotai.md
           - Building Custom Rerankers: reranking/custom_reranker.md
+          - Example: notebooks/lancedb_reranking.ipynb
       - Filtering: sql.md
       - Versioning & Reproducibility: notebooks/reproducibility.ipynb
       - Configuring Storage: guides/storage.md
-      - Sync -> Async Migration Guide: migration.md
+      - Migration Guide: migration.md
+      - Tuning retrieval performance:
+          - Choosing right query type: guides/tuning_retrievers/1_query_types.md
+          - Reranking: guides/tuning_retrievers/2_reranking.md
+          - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
   - Managing Embeddings:
-      - Overview: embeddings/index.md
+      - Understand Embeddings: embeddings/understanding_embeddings.md
+      - Get Started: embeddings/index.md
       - Embedding functions: embeddings/embedding_functions.md
-      - Available models: embeddings/default_embedding_functions.md
+      - Available models:
+          - Overview: embeddings/default_embedding_functions.md
+          - Text Embedding Functions:
+              - Sentence Transformers: embeddings/available_embedding_models/text_embedding_functions/sentence_transformers.md
+              - Huggingface Embedding Models: embeddings/available_embedding_models/text_embedding_functions/huggingface_embedding.md
+              - Ollama Embeddings: embeddings/available_embedding_models/text_embedding_functions/ollama_embedding.md
+              - OpenAI Embeddings: embeddings/available_embedding_models/text_embedding_functions/openai_embedding.md
+              - Instructor Embeddings: embeddings/available_embedding_models/text_embedding_functions/instructor_embedding.md
+              - Gemini Embeddings: embeddings/available_embedding_models/text_embedding_functions/gemini_embedding.md
+              - Cohere Embeddings: embeddings/available_embedding_models/text_embedding_functions/cohere_embedding.md
+              - 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
+          - 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
       - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
       - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
@@ -193,33 +312,51 @@ nav:
       - Pandas and PyArrow: python/pandas_and_pyarrow.md
       - Polars: python/polars_arrow.md
       - DuckDB: python/duckdb.md
-      - LangChain 🦜️🔗↗: https://python.langchain.com/docs/integrations/vectorstores/lancedb
+      - LangChain 🦜️🔗↗: integrations/langchain.md
       - LangChain.js 🦜️🔗↗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
-      - LlamaIndex 🦙↗: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
+      - LlamaIndex 🦙↗: integrations/llamaIndex.md
       - Pydantic: python/pydantic.md
       - Voxel51: integrations/voxel51.md
       - PromptTools: integrations/prompttools.md
+      - dlt: integrations/dlt.md
+      - phidata: integrations/phidata.md
   - Examples:
       - examples/index.md
-      - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+      - 🐍 Python:
-      - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+          - Overview: examples/examples_python.md
-      - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+          - Build From Scratch: examples/python_examples/build_from_scratch.md
-      - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+          - Multimodal: examples/python_examples/multimodal.md
-      - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
+          - Rag: examples/python_examples/rag.md
-      - YouTube Transcript Search (JS): examples/youtube_transcript_bot_with_nodejs.md
+          - Vector Search: examples/python_examples/vector_search.md
-      - Serverless Chatbot from any website: examples/serverless_website_chatbot.md
+          - Chatbot: examples/python_examples/chatbot.md
-      - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
+          - Evaluation: examples/python_examples/evaluations.md
+          - AI Agent: examples/python_examples/aiagent.md
+          - Recommender System: examples/python_examples/recommendersystem.md
+          - 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:
+          - Overview: examples/examples_js.md
+          - Serverless Website Chatbot: examples/serverless_website_chatbot.md
+          - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
+          - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
+      - 🦀 Rust:
+          - Overview: examples/examples_rust.md
+  - Studies:
+    - studies/overview.md
+    - ↗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
       - Javascript (vectordb): javascript/modules.md
-      - Javascript (lancedb): js/modules.md
+      - Javascript (lancedb): js/globals.md
       - Rust: https://docs.rs/lancedb/latest/lancedb/index.html
   - LanceDB Cloud:
       - Overview: cloud/index.md
       - API reference:
           - 🐍 Python: python/saas-python.md
-          - 👾 JavaScript: javascript/saas-modules.md
+          - 👾 JavaScript: javascript/modules.md
+          - REST API: cloud/rest.md
 
 extra_css:
   - styles/global.css

docs/openapi.yml

@@ -0,0 +1,487 @@
+openapi: 3.1.0
+info:
+  version: 1.0.0
+  title: LanceDB Cloud API
+  description: |
+    LanceDB Cloud API is a RESTful API that allows users to access and modify data stored in LanceDB Cloud.
+    Table actions are considered temporary resource creations and all use POST method.
+  contact:
+    name: LanceDB support
+    url: https://lancedb.com
+    email: contact@lancedb.com
+
+servers:
+  - url: https://{db}.{region}.api.lancedb.com
+    description: LanceDB Cloud REST endpoint.
+    variables:
+      db:
+        default: ""
+        description: the name of DB
+      region:
+        default: "us-east-1"
+        description: the service region of the DB
+
+security:
+  - key_auth: []
+
+components:
+  securitySchemes:
+    key_auth:
+      name: x-api-key
+      type: apiKey
+      in: header
+  parameters:
+    table_name:
+      name: name
+      in: path
+      description: name of the table
+      required: true
+      schema:
+        type: string
+  responses:
+    invalid_request:
+      description: Invalid request
+      content:
+        text/plain:
+          schema:
+            type: string
+    not_found:
+      description: Not found
+      content:
+        text/plain:
+          schema:
+            type: string
+    unauthorized:
+      description: Unauthorized
+      content:
+        text/plain:
+          schema:
+            type: string
+  requestBodies:
+    arrow_stream_buffer:
+      description: Arrow IPC stream buffer
+      required: true
+      content:
+        application/vnd.apache.arrow.stream:
+          schema:
+            type: string
+            format: binary
+
+paths:
+  /v1/table/:
+    get:
+      description: List tables, optionally, with pagination.
+      tags:
+        - Tables
+      summary: List Tables
+      operationId: listTables
+      parameters:
+        - name: limit
+          in: query
+          description: Limits the number of items to return.
+          schema:
+            type: integer
+        - name: page_token
+          in: query
+          description: Specifies the starting position of the next query
+          schema:
+            type: string
+      responses:
+        "200":
+          description: Successfully returned a list of tables in the DB
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  tables:
+                    type: array
+                    items:
+                      type: string
+                  page_token:
+                    type: string
+
+        "400":
+          $ref: "#/components/responses/invalid_request"
+        "401":
+          $ref: "#/components/responses/unauthorized"
+        "404":
+          $ref: "#/components/responses/not_found"
+
+  /v1/table/{name}/create/:
+    post:
+      description: Create a new table
+      summary: Create a new table
+      operationId: createTable
+      tags:
+        - Tables
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+      requestBody:
+        $ref: "#/components/requestBodies/arrow_stream_buffer"
+      responses:
+        "200":
+          description: Table successfully created
+        "400":
+          $ref: "#/components/responses/invalid_request"
+        "401":
+          $ref: "#/components/responses/unauthorized"
+        "404":
+          $ref: "#/components/responses/not_found"
+
+  /v1/table/{name}/query/:
+    post:
+      description: Vector Query
+      url: https://{db-uri}.{aws-region}.api.lancedb.com/v1/table/{name}/query/
+      tags:
+        - Data
+      summary: Vector Query
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              properties:
+                vector:
+                  type: FixedSizeList
+                  description: |
+                    The targetted vector to search for. Required.
+                vector_column:
+                  type: string
+                  description: |
+                    The column to query, it can be inferred from the schema if there is only one vector column.
+                prefilter:
+                  type: boolean
+                  description: |
+                    Whether to prefilter the data. Optional.
+                k:
+                  type: integer
+                  description: |
+                    The number of search results to return. Default is 10.
+                distance_type:
+                  type: string
+                  description: |
+                    The distance metric to use for search. L2, Cosine, Dot and Hamming are supported. Default is L2.
+                bypass_vector_index:
+                  type: boolean
+                  description: |
+                    Whether to bypass vector index. Optional.
+                filter:
+                  type: string
+                  description: |
+                    A filter expression that specifies the rows to query. Optional.
+                columns:
+                  type: array
+                  items:
+                    type: string
+                  description: |
+                    The columns to return. Optional.
+                nprobe:
+                  type: integer
+                  description: |
+                    The number of probes to use for search. Optional.
+                refine_factor:
+                  type: integer
+                  description: |
+                    The refine factor to use for search. Optional.
+                  default: null
+                fast_search:
+                  type: boolean
+                  description: |
+                    Whether to use fast search. Optional.
+                  default: false
+              required:
+                - vector
+
+      responses:
+        "200":
+          description: top k results if query is successfully executed
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  results:
+                    type: array
+                    items:
+                      type: object
+                      properties:
+                        id:
+                          type: integer
+                        selected_col_1_to_return:
+                          type: col_1_type
+                        selected_col_n_to_return:
+                          type: col_n_type
+                        _distance:
+                          type: float
+
+        "400":
+          $ref: "#/components/responses/invalid_request"
+        "401":
+          $ref: "#/components/responses/unauthorized"
+        "404":
+          $ref: "#/components/responses/not_found"
+
+  /v1/table/{name}/insert/:
+    post:
+      description: Insert new data to the Table.
+      tags:
+        - Data
+      operationId: insertData
+      summary: Insert new data.
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+      requestBody:
+        $ref: "#/components/requestBodies/arrow_stream_buffer"
+      responses:
+        "200":
+          description: Insert successful
+        "400":
+          $ref: "#/components/responses/invalid_request"
+        "401":
+          $ref: "#/components/responses/unauthorized"
+        "404":
+          $ref: "#/components/responses/not_found"
+  /v1/table/{name}/merge_insert/:
+    post:
+      description: Create a "merge insert" operation
+        This operation can add rows, update rows, and remove rows all in a single
+        transaction. See python method `lancedb.table.Table.merge_insert` for examples.
+      tags:
+        - Data
+      summary: Merge Insert
+      operationId: mergeInsert
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+        - name: on
+          in: query
+          description: |
+            The column to use as the primary key for the merge operation.
+          required: true
+          schema:
+            type: string
+        - name: when_matched_update_all
+          in: query
+          description: |
+            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.
+          required: false
+          schema:
+            type: boolean
+        - name: when_matched_update_all_filt
+          in: query
+          description: |
+            If present then only rows that satisfy the filter expression will
+            be updated
+          required: false
+          schema:
+            type: string
+        - name: when_not_matched_insert_all
+          in: query
+          description: |
+            Rows that exist only in the source table (new data) will be
+            inserted into the target table (old data).
+          required: false
+          schema:
+            type: boolean
+        - name: when_not_matched_by_source_delete
+          in: query
+          description: |
+            Rows that exist only in the target table (old data) will be
+            deleted. An optional condition (`when_not_matched_by_source_delete_filt`)
+            can be provided to limit what data is deleted.
+          required: false
+          schema:
+            type: boolean
+        - name: when_not_matched_by_source_delete_filt
+          in: query
+          description: |
+            The filter expression that specifies the rows to delete.
+          required: false
+          schema:
+            type: string
+      requestBody:
+        $ref: "#/components/requestBodies/arrow_stream_buffer"
+      responses:
+        "200":
+          description: Merge Insert successful
+        "400":
+          $ref: "#/components/responses/invalid_request"
+        "401":
+          $ref: "#/components/responses/unauthorized"
+        "404":
+          $ref: "#/components/responses/not_found"
+  /v1/table/{name}/delete/:
+    post:
+      description: Delete rows from a table.
+      tags:
+        - Data
+      summary: Delete rows from a table
+      operationId: deleteData
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              properties:
+                predicate:
+                  type: string
+                  description: |
+                    A filter expression that specifies the rows to delete.
+      responses:
+        "200":
+          description: Delete successful
+        "401":
+          $ref: "#/components/responses/unauthorized"
+  /v1/table/{name}/drop/:
+    post:
+      description: Drop a table
+      tags:
+        - Tables
+      summary: Drop a table
+      operationId: dropTable
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+      requestBody:
+        $ref: "#/components/requestBodies/arrow_stream_buffer"
+      responses:
+        "200":
+          description: Drop successful
+        "401":
+          $ref: "#/components/responses/unauthorized"
+
+  /v1/table/{name}/describe/:
+    post:
+      description: Describe a table and return Table Information.
+      tags:
+        - Tables
+      summary: Describe a table
+      operationId: describeTable
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+      responses:
+        "200":
+          description: Table information
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  table:
+                    type: string
+                  version:
+                    type: integer
+                  schema:
+                    type: string
+                  stats:
+                    type: object
+        "401":
+          $ref: "#/components/responses/unauthorized"
+        "404":
+          $ref: "#/components/responses/not_found"
+
+  /v1/table/{name}/index/list/:
+    post:
+      description: List indexes of a table
+      tags:
+        - Tables
+      summary: List indexes of a table
+      operationId: listIndexes
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+      responses:
+        "200":
+          description: Available list of indexes on the table.
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  indexes:
+                    type: array
+                    items:
+                      type: object
+                      properties:
+                        columns:
+                          type: array
+                          items:
+                            type: string
+                        index_name:
+                          type: string
+                        index_uuid:
+                          type: string
+        "401":
+          $ref: "#/components/responses/unauthorized"
+        "404":
+          $ref: "#/components/responses/not_found"
+  /v1/table/{name}/create_index/:
+    post:
+      description: Create vector index on a Table
+      tags:
+        - Tables
+      summary: Create vector index on a Table
+      operationId: createIndex
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              properties:
+                column:
+                  type: string
+                metric_type:
+                  type: string
+                  nullable: false
+                  description: |
+                    The metric type to use for the index. L2, Cosine, Dot are supported.
+                index_type:
+                  type: string
+      responses:
+        "200":
+          description: Index successfully created
+        "400":
+          $ref: "#/components/responses/invalid_request"
+        "401":
+          $ref: "#/components/responses/unauthorized"
+        "404":
+          $ref: "#/components/responses/not_found"
+  /v1/table/{name}/create_scalar_index/:
+    post:
+      description: Create a scalar index on a table
+      tags:
+        - Tables
+      summary: Create a scalar index on a table
+      operationId: createScalarIndex
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              properties:
+                column:
+                  type: string
+                index_type:
+                  type: string
+                  required: false
+      responses:
+        "200":
+          description: Scalar Index successfully created
+        "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": "0.4.6",
+      "version": "0.12.0",
       "cpu": [
         "x64",
         "arm64"
@@ -31,9 +31,7 @@
         "win32"
       ],
       "dependencies": {
-        "@apache-arrow/ts": "^14.0.2",
         "@neon-rs/load": "^0.0.74",
-        "apache-arrow": "^14.0.2",
         "axios": "^1.4.0"
       },
       "devDependencies": {
@@ -46,6 +44,7 @@
         "@types/temp": "^0.9.1",
         "@types/uuid": "^9.0.3",
         "@typescript-eslint/eslint-plugin": "^5.59.1",
+        "apache-arrow-old": "npm:apache-arrow@13.0.0",
         "cargo-cp-artifact": "^0.1",
         "chai": "^4.3.7",
         "chai-as-promised": "^7.1.1",
@@ -62,15 +61,19 @@
         "ts-node-dev": "^2.0.0",
         "typedoc": "^0.24.7",
         "typedoc-plugin-markdown": "^3.15.3",
-        "typescript": "*",
+        "typescript": "^5.1.0",
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.4.6",
+        "@lancedb/vectordb-darwin-arm64": "0.12.0",
-        "@lancedb/vectordb-darwin-x64": "0.4.6",
+        "@lancedb/vectordb-darwin-x64": "0.12.0",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.4.6",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.12.0",
-        "@lancedb/vectordb-linux-x64-gnu": "0.4.6",
+        "@lancedb/vectordb-linux-x64-gnu": "0.12.0",
-        "@lancedb/vectordb-win32-x64-msvc": "0.4.6"
+        "@lancedb/vectordb-win32-x64-msvc": "0.12.0"
+      },
+      "peerDependencies": {
+        "@apache-arrow/ts": "^14.0.2",
+        "apache-arrow": "^14.0.2"
       }
     },
     "../node/node_modules/apache-arrow": {

docs/requirements.txt

@@ -1,5 +1,7 @@
 mkdocs==1.5.3
 mkdocs-jupyter==0.24.1
 mkdocs-material==9.5.3
-mkdocstrings[python]==0.20.0
+mkdocstrings[python]==0.25.2
-pydantic
+griffe
+mkdocs-render-swagger-plugin
+pydantic

docs/src/ann_indexes.md

@@ -38,13 +38,27 @@ Lance supports `IVF_PQ` index type by default.
     tbl.create_index(num_partitions=256, num_sub_vectors=96)
     ```
 
-=== "Typescript"
+=== "TypeScript"
 
-    ```typescript
+    === "@lancedb/lancedb"
-    --8<--- "docs/src/ann_indexes.ts:import"
 
-    --8<-- "docs/src/ann_indexes.ts:ingest"
+        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.test.ts:ingest"
+        ```
+
+    === "vectordb (deprecated)"
+
+        Creating indexes is done via the [lancedb.Table.createIndex](../javascript/interfaces/Table.md/#createIndex) method.
+
+        ```typescript
+        --8<--- "docs/src/ann_indexes.ts:import"
+
+        --8<-- "docs/src/ann_indexes.ts:ingest"
+        ```
 
 === "Rust"
 
@@ -91,27 +105,27 @@ You can specify the GPU device to train IVF partitions via
 
 === "Linux"
 
-     <!-- skip-test -->
+    <!-- skip-test -->
-     ``` { .python .copy }
+    ``` { .python .copy }
-     # Create index using CUDA on Nvidia GPUs.
+    # Create index using CUDA on Nvidia GPUs.
-     tbl.create_index(
+    tbl.create_index(
-          num_partitions=256,
+        num_partitions=256,
-          num_sub_vectors=96,
+        num_sub_vectors=96,
-          accelerator="cuda"
+        accelerator="cuda"
-     )
+    )
-     ```
+    ```
 
 === "MacOS"
 
-     <!-- skip-test -->
+    <!-- skip-test -->
-     ```python
+    ```python
-     # Create index using MPS on Apple Silicon.
+    # Create index using MPS on Apple Silicon.
-     tbl.create_index(
+    tbl.create_index(
-          num_partitions=256,
+        num_partitions=256,
-          num_sub_vectors=96,
+        num_sub_vectors=96,
-          accelerator="mps"
+        accelerator="mps"
-     )
+    )
-     ```
+    ```
 
 Troubleshooting:
 
@@ -126,13 +140,15 @@ There are a couple of parameters that can be used to fine-tune the search:
 
 - **limit** (default: 10): The amount of results that will be returned
 - **nprobes** (default: 20): The number of probes used. A higher number makes search more accurate but also slower.<br/>
-  Most of the time, setting nprobes to cover 5-10% of the dataset should achieve high recall with low latency.<br/>
+  Most of the time, setting nprobes to cover 5-15% of the dataset should achieve high recall with low latency.<br/>
-  e.g., for 1M vectors divided up into 256 partitions, nprobes should be set to ~20-40.<br/>
+    - _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/>
-  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/>
-  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/>
+    - _For example_, For a dataset of 1 million vectors divided into 256 partitions, setting the `refine_factor` to 200 will initially retrieve the top 4,000 candidates (top k * refine_factor) from all searched partitions. These candidates are then reranked to determine the final top 20 results.<br/>
-  Note: refine_factor is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
+!!! note 
+    Both `nprobes` and `refine_factor` are only applicable if an ANN index is present. If specified on a table without an ANN index, those parameters are ignored.
+
 
 === "Python"
 
@@ -150,11 +166,19 @@ There are a couple of parameters that can be used to fine-tune the search:
     1  [0.48587373, 0.269207, 0.15095535, 0.65531915,...  item 3953  108.393867
     ```
 
-=== "Typescript"
+=== "TypeScript"
 
-    ```typescript
+    === "@lancedb/lancedb"
-    --8<-- "docs/src/ann_indexes.ts:search1"
+
-    ```
+        ```typescript
+        --8<-- "nodejs/examples/ann_indexes.test.ts:search1"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        --8<-- "docs/src/ann_indexes.ts:search1"
+        ```
 
 === "Rust"
 
@@ -172,15 +196,23 @@ You can further filter the elements returned by a search using a where clause.
 
 === "Python"
 
-     ```python
+    ```python
-     tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
+    tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
-     ```
+    ```
 
-=== "Typescript"
+=== "TypeScript"
 
-     ```javascript
+    === "@lancedb/lancedb"
-     --8<-- "docs/src/ann_indexes.ts:search2"
+
-     ```
+        ```typescript
+        --8<-- "nodejs/examples/ann_indexes.test.ts:search2"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```javascript
+        --8<-- "docs/src/ann_indexes.ts:search2"
+        ```
 
 ### Projections (select clause)
 
@@ -188,23 +220,31 @@ You can select the columns returned by the query using a select clause.
 
 === "Python"
 
-     ```python
+    ```python
-     tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
+    tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
-     ```
+    ```
 
 
-     ```text
+    ```text
-                                                   vector _distance
+                                                vector _distance
-     0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
+    0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
-     1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
+    1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
-     ...
+    ...
-     ```
+    ```
 
-=== "Typescript"
+=== "TypeScript"
 
-     ```typescript
+    === "@lancedb/lancedb"
-     --8<-- "docs/src/ann_indexes.ts:search3"
+
-     ```
+        ```typescript
+        --8<-- "nodejs/examples/ann_indexes.test.ts:search3"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        --8<-- "docs/src/ann_indexes.ts:search3"
+        ```
 
 ## FAQ
 
@@ -237,7 +277,15 @@ 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. Because
+`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
 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
+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.
-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/api_reference.md

@@ -4,5 +4,5 @@ The API reference for the LanceDB client SDKs are available at the following loc
 
 - [Python](python/python.md)
 - [JavaScript (legacy vectordb package)](javascript/modules.md)
-- [JavaScript (newer @lancedb/lancedb package)](js/modules.md)
+- [JavaScript (newer @lancedb/lancedb package)](js/globals.md)
 - [Rust](https://docs.rs/lancedb/latest/lancedb/index.html)

docs/src/assets/colab.svg

@@ -0,0 +1 @@
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docs/src/assets/ghost.svg

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docs/src/assets/python.svg

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+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="97.5" height="28" role="img" aria-label="PYTHON"><title>PYTHON</title><g shape-rendering="crispEdges"><rect width="97.5" height="28" fill="#3670a0"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="100"><image x="9" y="7" width="14" height="14" xlink:href="data:image/svg+xml;base64,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"/><text transform="scale(.1)" x="587.5" y="175" textLength="535" fill="#fff" font-weight="bold">PYTHON</text></g></svg>

docs/src/basic.md

@@ -16,11 +16,60 @@
       pip install lancedb
       ```
 
-=== "Typescript"
+=== "Typescript[^1]"
+    === "@lancedb/lancedb"
 
-      ```shell
+        ```shell
-      npm install vectordb
+        npm install @lancedb/lancedb
-      ```
+        ```
+        !!! note "Bundling `@lancedb/lancedb` apps with Webpack"
+
+            Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.
+
+            ```javascript
+            /** @type {import('next').NextConfig} */
+            module.exports = ({
+            webpack(config) {
+                config.externals.push({ '@lancedb/lancedb': '@lancedb/lancedb' })
+                return config;
+            }
+            })
+            ```
+
+        !!! note "Yarn users"
+
+            Unlike other package managers, Yarn does not automatically resolve peer dependencies. If you are using Yarn, you will need to manually install 'apache-arrow':
+
+            ```shell
+            yarn add apache-arrow
+            ```
+
+    === "vectordb (deprecated)"
+
+        ```shell
+        npm install vectordb
+        ```
+        !!! note "Bundling `vectordb` apps with Webpack"
+
+            Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.
+
+            ```javascript
+            /** @type {import('next').NextConfig} */
+            module.exports = ({
+            webpack(config) {
+                config.externals.push({ vectordb: 'vectordb' })
+                return config;
+            }
+            })
+            ```
+
+        !!! note "Yarn users"
+
+            Unlike other package managers, Yarn does not automatically resolve peer dependencies. If you are using Yarn, you will need to manually install 'apache-arrow':
+
+            ```shell
+            yarn add apache-arrow
+            ```
 
 === "Rust"
 
@@ -44,6 +93,43 @@
 
     !!! info "Please also make sure you're using the same version of Arrow as in the [lancedb crate](https://github.com/lancedb/lancedb/blob/main/Cargo.toml)"
 
+### Preview releases
+
+Stable releases are created about every 2 weeks. For the latest features and bug
+fixes, you can install the preview release. These releases receive the same
+level of testing as stable releases, but are not guaranteed to be available for
+more than 6 months after they are released. Once your application is stable, we
+recommend switching to stable releases.
+
+=== "Python"
+
+      ```shell
+      pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
+      ```
+
+=== "Typescript[^1]"
+
+    === "@lancedb/lancedb"
+
+        ```shell
+        npm install @lancedb/lancedb@preview
+        ```
+    === "vectordb (deprecated)"
+
+        ```shell
+        npm install vectordb@preview
+        ```
+
+=== "Rust"
+
+    We don't push preview releases to crates.io, but you can referent the tag
+    in GitHub within your Cargo dependencies:
+
+    ```toml
+    [dependencies]
+    lancedb = { git = "https://github.com/lancedb/lancedb.git", tag = "vX.Y.Z-beta.N" }
+    ```
+
 ## Connect to a database
 
 === "Python"
@@ -63,23 +149,22 @@
         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"
+=== "Typescript[^1]"
 
-    ```typescript
+    === "@lancedb/lancedb"
-    --8<-- "docs/src/basic_legacy.ts:import"
 
-    --8<-- "docs/src/basic_legacy.ts:open_db"
+        ```typescript
-    ```
+        import * as lancedb from "@lancedb/lancedb";
+        import * as arrow from "apache-arrow";
 
-    !!! note "`@lancedb/lancedb` vs. `vectordb`"
+        --8<-- "nodejs/examples/basic.test.ts:connect"
+        ```
 
-        The Javascript SDK was originally released as `vectordb`.  In an effort to
+    === "vectordb (deprecated)"
-        reduce maintenance we are aligning our SDKs.  The new, aligned, Javascript
+
-        API is being released as `lancedb`.  If you are starting new work we encourage
+        ```typescript
-        you to try out `lancedb`.  Once the new API is feature complete we will begin
+        --8<-- "docs/src/basic_legacy.ts:open_db"
-        slowly deprecating `vectordb` in favor of `lancedb`. There is a
+        ```
-        [migration guide](migration.md) detailing the differences which will assist
-        you in this process.
 
 === "Rust"
 
@@ -122,15 +207,23 @@ table.
     --8<-- "python/python/tests/docs/test_basic.py:create_table_async_pandas"
     ```
 
-=== "Typescript"
+=== "Typescript[^1]"
 
-    ```typescript
+    === "@lancedb/lancedb"
-    --8<-- "docs/src/basic_legacy.ts:create_table"
-    ```
 
-    If the table already exists, LanceDB will raise an error by default.
+        ```typescript
-    If you want to overwrite the table, you can pass in `mode="overwrite"`
+        --8<-- "nodejs/examples/basic.test.ts:create_table"
-    to the `createTable` function.
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        --8<-- "docs/src/basic_legacy.ts:create_table"
+        ```
+
+        If the table already exists, LanceDB will raise an error by default.
+        If you want to overwrite the table, you can pass in `mode:"overwrite"`
+        to the `createTable` function.
 
 === "Rust"
 
@@ -150,6 +243,9 @@ table.
 
 !!! info "Under the hood, LanceDB reads in the Apache Arrow data and persists it to disk using the [Lance format](https://www.github.com/lancedb/lance)."
 
+!!! info "Automatic embedding generation with Embedding API"
+    When working with embedding models, it is recommended to use the LanceDB embedding API to automatically create vector representation of the data and queries in the background. See the [quickstart example](#using-the-embedding-api) or the embedding API [guide](./embeddings/)
+
 ### Create an empty table
 
 Sometimes you may not have the data to insert into the table at creation time.
@@ -164,11 +260,22 @@ similar to a `CREATE TABLE` statement in SQL.
       --8<-- "python/python/tests/docs/test_basic.py:create_empty_table_async"
       ```
 
-=== "Typescript"
+    !!! 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).
 
-    ```typescript
+=== "Typescript[^1]"
-    --8<-- "docs/src/basic_legacy.ts:create_empty_table"
+
-    ```
+    === "@lancedb/lancedb"
+
+        ```typescript
+        --8<-- "nodejs/examples/basic.test.ts:create_empty_table"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        --8<-- "docs/src/basic_legacy.ts:create_empty_table"
+        ```
 
 === "Rust"
 
@@ -187,11 +294,19 @@ Once created, you can open a table as follows:
     --8<-- "python/python/tests/docs/test_basic.py:open_table_async"
     ```
 
-=== "Typescript"
+=== "Typescript[^1]"
+    === "@lancedb/lancedb"
+
+        ```typescript
+        --8<-- "nodejs/examples/basic.test.ts:open_table"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        const tbl = await db.openTable("myTable");
+        ```
 
-    ```typescript
-    const tbl = await db.openTable("myTable");
-    ```
 
 === "Rust"
 
@@ -208,11 +323,18 @@ If you forget the name of your table, you can always get a listing of all table
     --8<-- "python/python/tests/docs/test_basic.py:table_names_async"
     ```
 
-=== "Javascript"
+=== "Typescript[^1]"
+    === "@lancedb/lancedb"
 
-    ```javascript
+        ```typescript
-    console.log(await db.tableNames());
+        --8<-- "nodejs/examples/basic.test.ts:table_names"
-    ```
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        console.log(await db.tableNames());
+        ```
 
 === "Rust"
 
@@ -231,11 +353,18 @@ After a table has been created, you can always add more data to it as follows:
     --8<-- "python/python/tests/docs/test_basic.py:add_data_async"
     ```
 
-=== "Typescript"
+=== "Typescript[^1]"
+    === "@lancedb/lancedb"
 
-    ```typescript
+        ```typescript
-    --8<-- "docs/src/basic_legacy.ts:add"
+        --8<-- "nodejs/examples/basic.test.ts:add_data"
-    ```
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        --8<-- "docs/src/basic_legacy.ts:add"
+        ```
 
 === "Rust"
 
@@ -256,11 +385,18 @@ Once you've embedded the query, you can find its nearest neighbors as follows:
 
     This returns a pandas DataFrame with the results.
 
-=== "Typescript"
+=== "Typescript[^1]"
+    === "@lancedb/lancedb"
 
-    ```typescript
+        ```typescript
-    --8<-- "docs/src/basic_legacy.ts:search"
+        --8<-- "nodejs/examples/basic.test.ts:vector_search"
-    ```
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        --8<-- "docs/src/basic_legacy.ts:search"
+        ```
 
 === "Rust"
 
@@ -289,11 +425,18 @@ LanceDB allows you to create an ANN index on a table as follows:
     --8<-- "python/python/tests/docs/test_basic.py:create_index_async"
     ```
 
-=== "Typescript"
+=== "Typescript[^1]"
+    === "@lancedb/lancedb"
 
-    ```{.typescript .ignore}
+        ```typescript
-    --8<-- "docs/src/basic_legacy.ts:create_index"
+        --8<-- "nodejs/examples/basic.test.ts:create_index"
-    ```
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```{.typescript .ignore}
+        --8<-- "docs/src/basic_legacy.ts:create_index"
+        ```
 
 === "Rust"
 
@@ -321,11 +464,19 @@ This can delete any number of rows that match the filter.
     --8<-- "python/python/tests/docs/test_basic.py:delete_rows_async"
     ```
 
-=== "Typescript"
+=== "Typescript[^1]"
 
-    ```typescript
+    === "@lancedb/lancedb"
-    --8<-- "docs/src/basic_legacy.ts:delete"
+
-    ```
+        ```typescript
+        --8<-- "nodejs/examples/basic.test.ts:delete_rows"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        --8<-- "docs/src/basic_legacy.ts:delete"
+        ```
 
 === "Rust"
 
@@ -342,9 +493,15 @@ simple or complex as needed. To see what expressions are supported, see the
 
       Read more: [lancedb.table.Table.delete][]
 
-=== "Javascript"
+=== "Typescript[^1]"
 
-      Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)
+    === "@lancedb/lancedb"
+
+        Read more: [lancedb.Table.delete](javascript/interfaces/Table.md#delete)
+
+    === "vectordb (deprecated)"
+
+        Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)
 
 === "Rust"
 
@@ -356,23 +513,31 @@ Use the `drop_table()` method on the database to remove a table.
 
 === "Python"
 
-      ```python
+    ```python
-      --8<-- "python/python/tests/docs/test_basic.py:drop_table"
+    --8<-- "python/python/tests/docs/test_basic.py:drop_table"
-      --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
+    --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
-      ```
+    ```
 
-      This permanently removes the table and is not recoverable, unlike deleting rows.
+    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,
+    By default, if the table does not exist an exception is raised. To suppress this,
-      you can pass in `ignore_missing=True`.
+    you can pass in `ignore_missing=True`.
 
-=== "Typescript"
+=== "Typescript[^1]"
 
-      ```typescript
+    === "@lancedb/lancedb"
-      --8<-- "docs/src/basic_legacy.ts:drop_table"
-      ```
 
-      This permanently removes the table and is not recoverable, unlike deleting rows.
+        ```typescript
-      If the table does not exist an exception is raised.
+        --8<-- "nodejs/examples/basic.test.ts:drop_table"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        --8<-- "docs/src/basic_legacy.ts:drop_table"
+        ```
+
+        This permanently removes the table and is not recoverable, unlike deleting rows.
+        If the table does not exist an exception is raised.
 
 === "Rust"
 
@@ -380,22 +545,40 @@ Use the `drop_table()` method on the database to remove a table.
     --8<-- "rust/lancedb/examples/simple.rs:drop_table"
     ```
 
-!!! note "Bundling `vectordb` apps with Webpack"
 
-    If you're using the `vectordb` module in JavaScript, since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.
+## Using the Embedding API
+You can use the embedding API when working with embedding models. It automatically vectorizes the data at ingestion and query time and comes with built-in integrations with popular embedding models like Openai, Hugging Face, Sentence Transformers, CLIP and more.
 
-    ```javascript
+=== "Python"
-    /** @type {import('next').NextConfig} */
+
-    module.exports = ({
+    ```python
-    webpack(config) {
+    --8<-- "python/python/tests/docs/test_embeddings_optional.py:imports"
-        config.externals.push({ vectordb: 'vectordb' })
+    --8<-- "python/python/tests/docs/test_embeddings_optional.py:openai_embeddings"
-        return config;
-    }
-    })
     ```
 
+=== "Typescript[^1]"
+
+    === "@lancedb/lancedb"
+
+        ```typescript
+        --8<-- "nodejs/examples/embedding.test.ts:imports"
+        --8<-- "nodejs/examples/embedding.test.ts:openai_embeddings"
+        ```
+
+=== "Rust"
+
+    ```rust
+    --8<-- "rust/lancedb/examples/openai.rs:imports"
+    --8<-- "rust/lancedb/examples/openai.rs:openai_embeddings"
+    ```
+
+Learn about using the existing integrations and creating custom embedding functions in the [embedding API guide](./embeddings/index.md).
+
+
 ## What's next
 
 This section covered the very basics of using LanceDB. If you're learning about vector databases for the first time, you may want to read the page on [indexing](concepts/index_ivfpq.md) to get familiar with the concepts.
 
 If you've already worked with other vector databases, you may want to read the [guides](guides/tables.md) to learn how to work with LanceDB in more detail.
+
+[^1]: The `vectordb` package is a legacy package that is  deprecated in favor of `@lancedb/lancedb`.  The `vectordb` package will continue to receive bug fixes and security updates until September 2024.  We recommend all new projects use `@lancedb/lancedb`.  See the [migration guide](migration.md) for more information.

docs/src/basic_legacy.ts

@@ -1,6 +1,14 @@
 // --8<-- [start:import]
 import * as lancedb from "vectordb";
-import { Schema, Field, Float32, FixedSizeList, Int32, Float16 } from "apache-arrow";
+import {
+  Schema,
+  Field,
+  Float32,
+  FixedSizeList,
+  Int32,
+  Float16,
+} from "apache-arrow";
+import * as arrow from "apache-arrow";
 // --8<-- [end:import]
 import * as fs from "fs";
 import { Table as ArrowTable, Utf8 } from "apache-arrow";
@@ -20,9 +28,33 @@ const example = async () => {
       { vector: [3.1, 4.1], item: "foo", price: 10.0 },
       { vector: [5.9, 26.5], item: "bar", price: 20.0 },
     ],
-    { writeMode: lancedb.WriteMode.Overwrite }
+    { writeMode: lancedb.WriteMode.Overwrite },
   );
   // --8<-- [end:create_table]
+  {
+    // --8<-- [start:create_table_with_schema]
+    const schema = new arrow.Schema([
+      new arrow.Field(
+        "vector",
+        new arrow.FixedSizeList(
+          2,
+          new arrow.Field("item", new arrow.Float32(), true),
+        ),
+      ),
+      new arrow.Field("item", new arrow.Utf8(), true),
+      new arrow.Field("price", new arrow.Float32(), true),
+    ]);
+    const data = [
+      { vector: [3.1, 4.1], item: "foo", price: 10.0 },
+      { vector: [5.9, 26.5], item: "bar", price: 20.0 },
+    ];
+    const tbl = await db.createTable({
+      name: "myTableWithSchema",
+      data,
+      schema,
+    });
+    // --8<-- [end:create_table_with_schema]
+  }
 
   // --8<-- [start:add]
   const newData = Array.from({ length: 500 }, (_, i) => ({
@@ -42,33 +74,35 @@ const example = async () => {
   // --8<-- [end:create_index]
 
   // --8<-- [start:create_empty_table]
-  const schema = new Schema([
+  const schema = new arrow.Schema([
-    new Field("id", new Int32()),
+    new arrow.Field("id", new arrow.Int32()),
-    new Field("name", new Utf8()),
+    new arrow.Field("name", new arrow.Utf8()),
   ]);
+
   const empty_tbl = await db.createTable({ name: "empty_table", schema });
   // --8<-- [end:create_empty_table]
-
+  {
-  // --8<-- [start:create_f16_table]
+    // --8<-- [start:create_f16_table]
-  const dim = 16
+    const dim = 16;
-  const total = 10
+    const total = 10;
-  const f16_schema = new Schema([
+    const schema = new Schema([
-      new Field('id', new Int32()),
+      new Field("id", new Int32()),
       new Field(
-        'vector',
+        "vector",
-        new FixedSizeList(dim, new Field('item', new Float16(), true)),
+        new FixedSizeList(dim, new Field("item", new Float16(), true)),
-        false
+        false,
-      )
+      ),
-    ])
+    ]);
-  const data = lancedb.makeArrowTable(
+    const data = lancedb.makeArrowTable(
       Array.from(Array(total), (_, i) => ({
         id: i,
-        vector: Array.from(Array(dim), Math.random)
+        vector: Array.from(Array(dim), Math.random),
       })),
-      { f16_schema }
+      { schema },
-    )
+    );
-  const table = await db.createTable('f16_tbl', data)
+    const table = await db.createTable("f16_tbl", data);
-  // --8<-- [end:create_f16_table]
+    // --8<-- [end:create_f16_table]
+  }
 
   // --8<-- [start:search]
   const query = await tbl.search([100, 100]).limit(2).execute();

docs/src/cloud/rest.md

@@ -0,0 +1 @@
+!!swagger ../../openapi.yml!!

docs/src/concepts/index_hnsw.md

@@ -0,0 +1,99 @@
+
+# Understanding HNSW index
+
+Approximate Nearest Neighbor (ANN) search is a method for finding data points near a given point in a dataset, though not always the exact nearest one. HNSW is one of the most accurate and fastest Approximate Nearest Neighbour search algorithms, It’s beneficial in high-dimensional spaces where finding the same nearest neighbor would be too slow and costly
+
+[Jump to usage](#usage)
+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.
+* **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.  
+HNSW also combines this with the ideas behind a classic 1-dimensional search data structure: the skip list.
+
+## k-Nearest Neighbor Graphs and k-approximate Nearest neighbor Graphs
+The k-nearest neighbor graph actually predates its use for ANN search. Its construction is quite simple:
+
+* Each vector in the dataset is given an associated vertex.
+* Each vertex has outgoing edges to its k nearest neighbors. That is, the k closest other vertices by Euclidean distance between the two corresponding vectors. This can be thought of as a "friend list" for the vertex.
+* For some applications (including nearest-neighbor search), the incoming edges are also added.
+
+Eventually, it was realized that the following greedy search method over such a graph typically results in good approximate nearest neighbors:
+
+* Given a query vector, start at some fixed "entry point" vertex (e.g. the approximate center node).
+* Look at that vertex's neighbors. If any of them are closer to the query vector than the current vertex, then move to that vertex.
+* Repeat until a local optimum is found.
+
+The above algorithm also generalizes to e.g. top 10 approximate nearest neighbors.
+
+Computing a k-nearest neighbor graph is actually quite slow, taking quadratic time in the dataset size. It was quickly realized that near-identical performance can be achieved using a k-approximate nearest neighbor graph. That is, instead of obtaining the k-nearest neighbors for each vertex, an approximate nearest neighbor search data structure is used to build much faster.  
+In fact, another data structure is not needed: This can be done "incrementally".
+That is, if you start with a k-ANN graph for n-1 vertices, you can extend it to a k-ANN graph for n vertices as well by using the graph to obtain the k-ANN for the new vertex.
+
+One downside of k-NN and k-ANN graphs alone is that one must typically build them with a large value of k to get decent results, resulting in a large index.
+
+
+## HNSW: Hierarchical Navigable Small Worlds
+
+HNSW builds on k-ANN in two main ways:
+
+* Instead of getting the k-approximate nearest neighbors for a large value of k, it sparsifies the k-ANN graph using a carefully chosen "edge pruning" heuristic, allowing for the number of edges per vertex to be limited to a relatively small constant.
+* The "entry point" vertex is chosen dynamically using a recursively constructed data structure on a subset of the data, similarly to a skip list.
+
+This recursive structure can be thought of as separating into layers:
+
+* At the bottom-most layer, an k-ANN graph on the whole dataset is present.
+* At the second layer, a k-ANN graph on a fraction of the dataset (e.g. 10%) is present.
+* At the Lth layer, a k-ANN graph is present. It is over a (constant) fraction (e.g. 10%) of the vectors/vertices present in the L-1th layer.
+
+Then the greedy search routine operates as follows:
+
+* At the top layer (using an arbitrary vertex as an entry point), use the greedy local search routine on the k-ANN graph to get an approximate nearest neighbor at that layer.
+* Using the approximate nearest neighbor found in the previous layer as an entry point, find an approximate nearest neighbor in the next layer with the same method.
+* Repeat until the bottom-most layer is reached. Then use the entry point to find multiple nearest neighbors (e.g. top 10).
+
+
+## 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
+
+```python
+import lancedb
+import numpy as np
+uri = "/tmp/lancedb"
+db = lancedb.connect(uri)
+
+# Create 10,000 sample vectors
+data = [
+    {"vector": row, "item": f"item {i}"}
+    for i, row in enumerate(np.random.random((10_000, 1536)).astype('float32'))
+]
+
+# Add the vectors to a table
+tbl = db.create_table("my_vectors", data=data)
+
+# Create and train the HNSW index for a 1536-dimensional vector
+# Make sure you have enough data in the table for an effective training step
+tbl.create_index(index_type=IVF_HNSW_SQ)
+
+```
+
+### Query the index
+
+```python
+# Search using a random 1536-dimensional embedding
+tbl.search(np.random.random((1536))) \
+    .limit(2) \
+    .to_pandas()
+```

docs/src/concepts/index_ivfpq.md

@@ -58,8 +58,10 @@ 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 the [FAQs](#faq) below 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 [here](../ann_indexes.md/#how-to-choose-num_partitions-and-num_sub_vectors-for-ivf_pq-index) for best practices on choosing these parameters.
 
 
 ### Query the index

docs/src/embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md

@@ -0,0 +1,67 @@
+# Imagebind embeddings
+We have support for [imagebind](https://github.com/facebookresearch/ImageBind) model embeddings. You can download our version of the packaged model via - `pip install imagebind-packaged==0.1.2`.
+
+This function is registered as `imagebind` and supports Audio, Video and Text modalities(extending to Thermal,Depth,IMU data):
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"imagebind_huge"` | Name of the model. |
+| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
+| `normalize` | `bool` | `False` | set to `True` to normalize your inputs before model ingestion. |
+
+Below is an example demonstrating how the API works:
+
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+db = lancedb.connect(tmp_path)
+func = get_registry().get("imagebind").create()
+
+class ImageBindModel(LanceModel):
+    text: str
+    image_uri: str = func.SourceField()
+    audio_path: str
+    vector: Vector(func.ndims()) = func.VectorField()
+
+# add locally accessible image paths
+text_list=["A dog.", "A car", "A bird"]
+image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
+audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]
+
+# Load data
+inputs = [
+    {"text": a, "audio_path": b, "image_uri": c}
+    for a, b, c in zip(text_list, audio_paths, image_paths)
+]
+
+#create table and add data
+table = db.create_table("img_bind", schema=ImageBindModel)
+table.add(inputs)
+```
+
+Now, we can search using any modality:
+
+#### image search
+```python
+query_image = "./assets/dog_image2.jpg" #download an image and enter that path here
+actual = table.search(query_image).limit(1).to_pydantic(ImageBindModel)[0]
+print(actual.text == "dog")
+```
+#### audio search
+
+```python
+query_audio = "./assets/car_audio2.wav" #download an audio clip and enter path here
+actual = table.search(query_audio).limit(1).to_pydantic(ImageBindModel)[0]
+print(actual.text == "car")
+```
+#### Text search
+You can add any input query and fetch the result as follows:
+```python
+query = "an animal which flies and tweets" 
+actual = table.search(query).limit(1).to_pydantic(ImageBindModel)[0]
+print(actual.text == "bird")
+```
+
+If you have any questions about the embeddings API, supported models, or see a relevant model missing, please raise an issue [on GitHub](https://github.com/lancedb/lancedb/issues).

docs/src/embeddings/available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md

@@ -0,0 +1,51 @@
+# Jina Embeddings : Multimodal
+
+Jina embeddings can also be used to embed both text and image data, only some of the models support image data and you can check the list
+under [https://jina.ai/embeddings/](https://jina.ai/embeddings/)
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"jina-clip-v1"` | The model ID of the jina model to use |
+
+Usage Example:
+
+```python
+    import os
+    import requests
+    import lancedb
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.embeddings import get_registry
+    import pandas as pd
+
+    os.environ['JINA_API_KEY'] = 'jina_*'
+
+    db = lancedb.connect("~/.lancedb")
+    func = get_registry().get("jina").create()
+
+
+    class Images(LanceModel):
+        label: str
+        image_uri: str = func.SourceField()  # image uri as the source
+        image_bytes: bytes = func.SourceField()  # image bytes as the source
+        vector: Vector(func.ndims()) = func.VectorField()  # vector column
+        vec_from_bytes: Vector(func.ndims()) = func.VectorField()  # Another vector column
+
+
+    table = db.create_table("images", schema=Images)
+    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
+    uris = [
+        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
+        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
+        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
+    ]
+    # get each uri as bytes
+    image_bytes = [requests.get(uri).content for uri in uris]
+    table.add(
+      pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
+    )
+```

docs/src/embeddings/available_embedding_models/multimodal_embedding_functions/openclip_embedding.md

@@ -0,0 +1,82 @@
+# OpenClip embeddings
+We support CLIP model embeddings using the open source alternative, [open-clip](https://github.com/mlfoundations/open_clip) which supports various customizations. It is registered as `open-clip` and supports the following customizations:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"ViT-B-32"` | The name of the model. |
+| `pretrained` | `str` | `"laion2b_s34b_b79k"` | The name of the pretrained model to load. |
+| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
+| `batch_size` | `int` | `64` | The number of images to process in a batch. |
+| `normalize` | `bool` | `True` | Whether to normalize the input images before feeding them to the model. |
+
+This embedding function supports ingesting images as both bytes and urls. You can query them using both test and other images.
+
+!!! info
+    LanceDB supports ingesting images directly from accessible links.
+
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+db = lancedb.connect(tmp_path)
+func = get_registry().get("open-clip").create()
+
+class Images(LanceModel):
+    label: str
+    image_uri: str = func.SourceField() # image uri as the source
+    image_bytes: bytes = func.SourceField() # image bytes as the source
+    vector: Vector(func.ndims()) = func.VectorField() # vector column 
+    vec_from_bytes: Vector(func.ndims()) = func.VectorField() # Another vector column 
+
+table = db.create_table("images", schema=Images)
+labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
+uris = [
+    "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+    "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
+    "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+    "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
+    "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+    "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
+]
+# get each uri as bytes
+image_bytes = [requests.get(uri).content for uri in uris]
+table.add(
+    pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
+)
+```
+Now we can search using text from both the default vector column and the custom vector column
+```python
+
+# text search
+actual = table.search("man's best friend").limit(1).to_pydantic(Images)[0]
+print(actual.label) # prints "dog"
+
+frombytes = (
+    table.search("man's best friend", vector_column_name="vec_from_bytes")
+    .limit(1)
+    .to_pydantic(Images)[0]
+)
+print(frombytes.label)
+
+```
+
+Because we're using a multi-modal embedding function, we can also search using images
+
+```python
+# image search
+query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
+image_bytes = requests.get(query_image_uri).content
+query_image = Image.open(io.BytesIO(image_bytes))
+actual = table.search(query_image).limit(1).to_pydantic(Images)[0]
+print(actual.label == "dog")
+
+# image search using a custom vector column
+other = (
+    table.search(query_image, vector_column_name="vec_from_bytes")
+    .limit(1)
+    .to_pydantic(Images)[0]
+)
+print(actual.label)
+
+```

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

@@ -0,0 +1,51 @@
+# AWS Bedrock Text Embedding Functions
+
+AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function.
+You can do so by using `awscli` and also add your session_token:
+```shell
+aws configure
+aws configure set aws_session_token "<your_session_token>"
+```
+to ensure that the credentials are set up correctly, you can run the following command:
+```shell
+aws sts get-caller-identity
+```
+
+Supported Embedding modelIDs are:
+* `amazon.titan-embed-text-v1`
+* `cohere.embed-english-v3`
+* `cohere.embed-multilingual-v3`
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| **name** | str | "amazon.titan-embed-text-v1" | The model ID of the bedrock model to use. Supported base models for Text Embeddings: amazon.titan-embed-text-v1, cohere.embed-english-v3, cohere.embed-multilingual-v3 |
+| **region** | str | "us-east-1" | Optional name of the AWS Region in which the service should be called (e.g., "us-east-1"). |
+| **profile_name** | str | None | Optional name of the AWS profile to use for calling the Bedrock service. If not specified, the default profile will be used. |
+| **assumed_role** | str | None | Optional ARN of an AWS IAM role to assume for calling the Bedrock service. If not specified, the current active credentials will be used. |
+| **role_session_name** | str | "lancedb-embeddings" | Optional name of the AWS IAM role session to use for calling the Bedrock service. If not specified, a "lancedb-embeddings" name will be used. |
+| **runtime** | bool | True | Optional choice of getting different client to perform operations with the Amazon Bedrock service. |
+| **max_retries** | int | 7 | Optional number of retries to perform when a request fails. |
+
+Usage Example:
+
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+import pandas as pd
+
+model = get_registry().get("bedrock-text").create()
+
+class TextModel(LanceModel):
+    text: str = model.SourceField()
+    vector: Vector(model.ndims()) = model.VectorField()
+
+df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
+db = lancedb.connect("tmp_path")
+tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+tbl.add(df)
+rs = tbl.search("hello").limit(1).to_pandas()
+```

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

@@ -0,0 +1,63 @@
+# Cohere Embeddings
+
+Using cohere API requires cohere package, which can be installed using `pip install cohere`. Cohere 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 `COHERE_API_KEY` environment variable to use the Cohere API.
+
+Supported models are:
+
+- embed-english-v3.0
+- embed-multilingual-v3.0
+- embed-english-light-v3.0
+- embed-multilingual-light-v3.0
+- embed-english-v2.0
+- embed-english-light-v2.0
+- embed-multilingual-v2.0
+
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|--------|---------|
+| `name` | `str` | `"embed-english-v2.0"` | The model ID of the cohere model to use. Supported base models for Text Embeddings: embed-english-v3.0, embed-multilingual-v3.0, embed-english-light-v3.0, embed-multilingual-light-v3.0, embed-english-v2.0, embed-english-light-v2.0, embed-multilingual-v2.0 |
+| `source_input_type` | `str` | `"search_document"` | The type of input data to be used for the source column. |
+| `query_input_type` | `str` | `"search_query"` | The type of input data to be used for the query. |
+
+Cohere supports following input types:
+
+| Input Type               | Description                          |
+|-------------------------|---------------------------------------|
+| "`search_document`"     | Used for embeddings stored in a vector|
+|                         | database for search use-cases.        |
+| "`search_query`"        | Used for embeddings of search queries |
+|                         | run against a vector DB               |
+| "`semantic_similarity`" | Specifies the given text will be used |
+|                         | for Semantic Textual Similarity (STS) |
+| "`classification`"      | Used for embeddings passed through a  |
+|                         | text classifier.                      |
+| "`clustering`"          | Used for the embeddings run through a |
+|                         | clustering algorithm                  |
+
+Usage Example:
+    
+```python
+    import lancedb
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+
+    cohere = EmbeddingFunctionRegistry
+        .get_instance()
+        .get("cohere")
+        .create(name="embed-multilingual-v2.0")
+
+    class TextModel(LanceModel):
+        text: str = cohere.SourceField()
+        vector: Vector(cohere.ndims()) =  cohere.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/available_embedding_models/text_embedding_functions/gemini_embedding.md

@@ -0,0 +1,35 @@
+# Gemini Embeddings
+With Google's Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity. For more on how and why you should use embeddings, refer to the Embeddings guide.
+The Gemini Embedding Model API supports various task types:
+
+| Task Type               | Description                                                                                                                                                |
+|-------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| "`retrieval_query`"     | Specifies the given text is a query in a search/retrieval setting.                                                                                         |
+| "`retrieval_document`"  | Specifies the given text is a document in a search/retrieval setting. Using this task type requires a title but is automatically proided by Embeddings API |
+| "`semantic_similarity`" | Specifies the given text will be used for Semantic Textual Similarity (STS).                                                                               |
+| "`classification`"      | Specifies that the embeddings will be used for classification.                                                                                             |
+| "`clusering`"           | Specifies that the embeddings will be used for clustering.                                                                                                 |
+
+
+Usage Example:
+
+```python
+import lancedb
+import pandas as pd
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+
+model = get_registry().get("gemini-text").create()
+
+class TextModel(LanceModel):
+    text: str = model.SourceField()
+    vector: Vector(model.ndims()) = model.VectorField()
+
+df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
+db = lancedb.connect("~/.lancedb")
+tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+tbl.add(df)
+rs = tbl.search("hello").limit(1).to_pandas()
+```

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

@@ -0,0 +1,24 @@
+# Huggingface embedding models
+We offer support for all Hugging Face models (which can be loaded via [transformers](https://huggingface.co/docs/transformers/en/index) library). The default model is `colbert-ir/colbertv2.0` which also has its own special callout - `registry.get("colbert")`. Some Hugging Face models might require custom models defined on the HuggingFace Hub in their own modeling files. You may enable this by setting `trust_remote_code=True`. This option should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. 
+
+Example usage - 
+```python
+import lancedb
+import pandas as pd
+
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+
+model = get_registry().get("huggingface").create(name='facebook/bart-base')
+
+class Words(LanceModel):
+    text: str = model.SourceField()
+    vector: Vector(model.ndims()) = model.VectorField()
+
+df = pd.DataFrame({"text": ["hi hello sayonara", "goodbye world"]})
+table = db.create_table("greets", schema=Words)
+table.add(df)
+query = "old greeting"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```

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

@@ -0,0 +1,75 @@
+# IBM watsonx.ai Embeddings
+
+Generate text embeddings using IBM's watsonx.ai platform.
+
+## Supported Models
+
+You can find a list of supported models at [IBM watsonx.ai Documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx). The currently supported model names are:
+
+- `ibm/slate-125m-english-rtrvr`
+- `ibm/slate-30m-english-rtrvr`
+- `sentence-transformers/all-minilm-l12-v2`
+- `intfloat/multilingual-e5-large`
+
+## Parameters
+
+The following parameters can be passed to the `create` method:
+
+| Parameter  | Type     | Default Value                    | Description                                               |
+|------------|----------|----------------------------------|-----------------------------------------------------------|
+| name       | str      | "ibm/slate-125m-english-rtrvr"   | The model ID of the watsonx.ai model to use               |
+| api_key    | str      | None                             | Optional IBM Cloud API key (or set `WATSONX_API_KEY`)     |
+| project_id | str      | None                             | Optional watsonx project ID (or set `WATSONX_PROJECT_ID`) |
+| url        | str      | None                             | Optional custom URL for the watsonx.ai instance           |
+| params     | dict     | None                             | Optional additional parameters for the embedding model    |
+
+## Usage Example
+
+First, the watsonx.ai library is an optional dependency, so must be installed seperately:
+
+```
+pip install ibm-watsonx-ai
+```
+
+Optionally set environment variables (if not passing credentials to `create` directly):
+
+```sh
+export WATSONX_API_KEY="YOUR_WATSONX_API_KEY"
+export WATSONX_PROJECT_ID="YOUR_WATSONX_PROJECT_ID"
+```
+
+```python
+import os
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import EmbeddingFunctionRegistry
+
+watsonx_embed = EmbeddingFunctionRegistry
+  .get_instance()
+  .get("watsonx")
+  .create(
+    name="ibm/slate-125m-english-rtrvr",
+    # Uncomment and set these if not using environment variables
+    # api_key="your_api_key_here",
+    # project_id="your_project_id_here",
+    # url="your_watsonx_url_here",
+    # params={...},
+  )
+
+class TextModel(LanceModel):
+    text: str = watsonx_embed.SourceField()
+    vector: Vector(watsonx_embed.ndims()) = watsonx_embed.VectorField()
+
+data = [
+    {"text": "hello world"},
+    {"text": "goodbye world"},
+]
+
+db = lancedb.connect("~/.lancedb")
+tbl = db.create_table("watsonx_test", schema=TextModel, mode="overwrite")
+
+tbl.add(data)
+
+rs = tbl.search("hello").limit(1).to_pandas()
+print(rs)
+```

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

@@ -0,0 +1,50 @@
+# Instructor Embeddings
+[Instructor](https://instructor-embedding.github.io/) is an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g. classification, retrieval, clustering, text evaluation, etc.) and domains (e.g. science, finance, etc.) by simply providing the task instruction, without any finetuning.
+
+If you want to calculate customized embeddings for specific sentences, you can follow the unified template to write instructions.
+
+!!! info
+    Represent the `domain` `text_type` for `task_objective`:
+
+    * `domain` is optional, and it specifies the domain of the text, e.g. science, finance, medicine, etc.
+    * `text_type` is required, and it specifies the encoding unit, e.g. sentence, document, paragraph, etc.
+    * `task_objective` is optional, and it specifies the objective of embedding, e.g. retrieve a document, classify the sentence, etc.
+
+More information about the model can be found at the [source URL](https://github.com/xlang-ai/instructor-embedding).
+
+| Argument | Type | Default | Description |
+|---|---|---|---|
+| `name` | `str` | "hkunlp/instructor-base" | The name of the model to use |
+| `batch_size` | `int` | `32` | The batch size to use when generating embeddings |
+| `device` | `str` | `"cpu"` | The device to use when generating embeddings |
+| `show_progress_bar` | `bool` | `True` | Whether to show a progress bar when generating embeddings |
+| `normalize_embeddings` | `bool` | `True` | Whether to normalize the embeddings |
+| `quantize` | `bool` | `False` | Whether to quantize the model |
+| `source_instruction` | `str` | `"represent the docuement for retreival"` | The instruction for the source column |
+| `query_instruction` | `str` | `"represent the document for retreiving the most similar documents"` | The instruction for the query |
+
+
+
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry, InstuctorEmbeddingFunction
+
+instructor = get_registry().get("instructor").create(
+                            source_instruction="represent the docuement for retreival",
+                            query_instruction="represent the document for retreiving the most similar documents"
+                            )
+
+class Schema(LanceModel):
+    vector: Vector(instructor.ndims()) = instructor.VectorField()
+    text: str = instructor.SourceField()
+
+db = lancedb.connect("~/.lancedb")
+tbl = db.create_table("test", schema=Schema, mode="overwrite")
+
+texts = [{"text": "Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that..."},
+        {"text": "The disparate impact theory is especially controversial under the Fair Housing Act because the Act..."},
+        {"text": "Disparate impact in United States labor law refers to practices in employment, housing, and other areas that.."}]
+
+tbl.add(texts)
+```

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

@@ -0,0 +1,39 @@
+# Jina Embeddings
+
+Jina embeddings are used to generate embeddings for text and image data.
+You also need to set the `JINA_API_KEY` environment variable to use the Jina API.
+
+You can find a list of supported models under [https://jina.ai/embeddings/](https://jina.ai/embeddings/)
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"jina-clip-v1"` | The model ID of the jina model to use |
+
+Usage Example:
+
+```python
+    import os
+    import lancedb
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+
+    os.environ['JINA_API_KEY'] = 'jina_*'
+
+    jina_embed = EmbeddingFunctionRegistry.get_instance().get("jina").create(name="jina-embeddings-v2-base-en")
+
+
+    class TextModel(LanceModel):
+        text: str = jina_embed.SourceField()
+        vector: Vector(jina_embed.ndims()) = jina_embed.VectorField()
+
+
+    data = [{"text": "hello world"},
+            {"text": "goodbye world"}]
+
+    db = lancedb.connect("~/.lancedb-2")
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(data)
+```

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

@@ -0,0 +1,37 @@
+# Ollama embeddings
+
+Generate embeddings via the [ollama](https://github.com/ollama/ollama-python) python library. More details:
+
+- [Ollama docs on embeddings](https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings)
+- [Ollama blog on embeddings](https://ollama.com/blog/embedding-models)
+
+| Parameter              | Type                       | Default Value            | Description                                                                                                                                    |
+|------------------------|----------------------------|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|
+| `name`                 | `str`                      | `nomic-embed-text`       | The name of the model.                                                                                                                         |
+| `host`                 | `str`                      | `http://localhost:11434` | The Ollama host to connect to.                                                                                                                 |
+| `options`              | `ollama.Options` or `dict` | `None`                   | Additional model parameters listed in the documentation for the Modelfile such as `temperature`. |
+| `keep_alive`           | `float` or `str`           | `"5m"`                   | Controls how long the model will stay loaded into memory following the request.                                                                |
+| `ollama_client_kwargs` | `dict`                     | `{}`                     | kwargs that can be past to the `ollama.Client`.                                                                                                |
+
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+db = lancedb.connect("/tmp/db")
+func = get_registry().get("ollama").create(name="nomic-embed-text")
+
+class Words(LanceModel):
+    text: str = func.SourceField()
+    vector: Vector(func.ndims()) = func.VectorField()
+
+table = db.create_table("words", schema=Words, mode="overwrite")
+table.add([
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+])
+
+query = "greetings"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```

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

@@ -0,0 +1,35 @@
+# OpenAI embeddings
+
+LanceDB registers the OpenAI embeddings function in the registry by default, as `openai`. Below are the parameters that you can customize when creating the instances:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"text-embedding-ada-002"` | The name of the model. |
+| `dim` | `int` |  Model default   | For OpenAI's newer text-embedding-3 model, we can specify a dimensionality that is smaller than the 1536 size. This feature supports it |
+| `use_azure` | bool | `False` | Set true to use Azure OpenAPI SDK |
+
+
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+db = lancedb.connect("/tmp/db")
+func = get_registry().get("openai").create(name="text-embedding-ada-002")
+
+class Words(LanceModel):
+    text: str = func.SourceField()
+    vector: Vector(func.ndims()) = func.VectorField()
+
+table = db.create_table("words", schema=Words, mode="overwrite")
+table.add(
+    [
+        {"text": "hello world"},
+        {"text": "goodbye world"}
+    ]
+    )
+
+query = "greetings"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```

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

@@ -0,0 +1,174 @@
+# Sentence transformers
+Allows you to set parameters when registering a `sentence-transformers` object.
+
+!!! info
+    Sentence transformer embeddings are normalized by default. It is recommended to use normalized embeddings for similarity search.
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `all-MiniLM-L6-v2` | The name of the model |
+| `device` | `str` | `cpu` | The device to run the model on (can be `cpu` or `gpu`) |
+| `normalize` | `bool` | `True` | Whether to normalize the input text before feeding it to the model |
+| `trust_remote_code` | `bool` | `False` | Whether to trust and execute remote code from the model's Huggingface repository |
+
+
+??? "Check out available sentence-transformer models here!"
+    ```markdown
+    - sentence-transformers/all-MiniLM-L12-v2
+    - sentence-transformers/paraphrase-mpnet-base-v2
+    - sentence-transformers/gtr-t5-base
+    - sentence-transformers/LaBSE
+    - sentence-transformers/all-MiniLM-L6-v2
+    - sentence-transformers/bert-base-nli-max-tokens
+    - sentence-transformers/bert-base-nli-mean-tokens
+    - sentence-transformers/bert-base-nli-stsb-mean-tokens
+    - sentence-transformers/bert-base-wikipedia-sections-mean-tokens
+    - sentence-transformers/bert-large-nli-cls-token
+    - sentence-transformers/bert-large-nli-max-tokens
+    - sentence-transformers/bert-large-nli-mean-tokens
+    - sentence-transformers/bert-large-nli-stsb-mean-tokens
+    - sentence-transformers/distilbert-base-nli-max-tokens
+    - sentence-transformers/distilbert-base-nli-mean-tokens
+    - sentence-transformers/distilbert-base-nli-stsb-mean-tokens
+    - sentence-transformers/distilroberta-base-msmarco-v1
+    - sentence-transformers/distilroberta-base-msmarco-v2
+    - sentence-transformers/nli-bert-base-cls-pooling
+    - sentence-transformers/nli-bert-base-max-pooling
+    - sentence-transformers/nli-bert-base
+    - sentence-transformers/nli-bert-large-cls-pooling
+    - sentence-transformers/nli-bert-large-max-pooling
+    - sentence-transformers/nli-bert-large
+    - sentence-transformers/nli-distilbert-base-max-pooling
+    - sentence-transformers/nli-distilbert-base
+    - sentence-transformers/nli-roberta-base
+    - sentence-transformers/nli-roberta-large
+    - sentence-transformers/roberta-base-nli-mean-tokens
+    - sentence-transformers/roberta-base-nli-stsb-mean-tokens
+    - sentence-transformers/roberta-large-nli-mean-tokens
+    - sentence-transformers/roberta-large-nli-stsb-mean-tokens
+    - sentence-transformers/stsb-bert-base
+    - sentence-transformers/stsb-bert-large
+    - sentence-transformers/stsb-distilbert-base
+    - sentence-transformers/stsb-roberta-base
+    - sentence-transformers/stsb-roberta-large
+    - sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens
+    - sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens
+    - sentence-transformers/xlm-r-base-en-ko-nli-ststb
+    - sentence-transformers/xlm-r-bert-base-nli-mean-tokens
+    - sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens
+    - sentence-transformers/xlm-r-large-en-ko-nli-ststb
+    - sentence-transformers/bert-base-nli-cls-token
+    - sentence-transformers/all-distilroberta-v1
+    - sentence-transformers/multi-qa-MiniLM-L6-dot-v1
+    - sentence-transformers/multi-qa-distilbert-cos-v1
+    - sentence-transformers/multi-qa-distilbert-dot-v1
+    - sentence-transformers/multi-qa-mpnet-base-cos-v1
+    - sentence-transformers/multi-qa-mpnet-base-dot-v1
+    - sentence-transformers/nli-distilroberta-base-v2
+    - sentence-transformers/all-MiniLM-L6-v1
+    - sentence-transformers/all-mpnet-base-v1
+    - sentence-transformers/all-mpnet-base-v2
+    - sentence-transformers/all-roberta-large-v1
+    - sentence-transformers/allenai-specter
+    - sentence-transformers/average_word_embeddings_glove.6B.300d
+    - sentence-transformers/average_word_embeddings_glove.840B.300d
+    - sentence-transformers/average_word_embeddings_komninos
+    - sentence-transformers/average_word_embeddings_levy_dependency
+    - sentence-transformers/clip-ViT-B-32-multilingual-v1
+    - sentence-transformers/clip-ViT-B-32
+    - sentence-transformers/distilbert-base-nli-stsb-quora-ranking
+    - sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking
+    - sentence-transformers/distilroberta-base-paraphrase-v1
+    - sentence-transformers/distiluse-base-multilingual-cased-v1
+    - sentence-transformers/distiluse-base-multilingual-cased-v2
+    - sentence-transformers/distiluse-base-multilingual-cased
+    - sentence-transformers/facebook-dpr-ctx_encoder-multiset-base
+    - sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base
+    - sentence-transformers/facebook-dpr-question_encoder-multiset-base
+    - sentence-transformers/facebook-dpr-question_encoder-single-nq-base
+    - sentence-transformers/gtr-t5-large
+    - sentence-transformers/gtr-t5-xl
+    - sentence-transformers/gtr-t5-xxl
+    - sentence-transformers/msmarco-MiniLM-L-12-v3
+    - sentence-transformers/msmarco-MiniLM-L-6-v3
+    - sentence-transformers/msmarco-MiniLM-L12-cos-v5
+    - sentence-transformers/msmarco-MiniLM-L6-cos-v5
+    - sentence-transformers/msmarco-bert-base-dot-v5
+    - sentence-transformers/msmarco-bert-co-condensor
+    - sentence-transformers/msmarco-distilbert-base-dot-prod-v3
+    - sentence-transformers/msmarco-distilbert-base-tas-b
+    - sentence-transformers/msmarco-distilbert-base-v2
+    - sentence-transformers/msmarco-distilbert-base-v3
+    - sentence-transformers/msmarco-distilbert-base-v4
+    - sentence-transformers/msmarco-distilbert-cos-v5
+    - sentence-transformers/msmarco-distilbert-dot-v5
+    - sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned
+    - sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch
+    - sentence-transformers/msmarco-distilroberta-base-v2
+    - sentence-transformers/msmarco-roberta-base-ance-firstp
+    - sentence-transformers/msmarco-roberta-base-v2
+    - sentence-transformers/msmarco-roberta-base-v3
+    - sentence-transformers/multi-qa-MiniLM-L6-cos-v1
+    - sentence-transformers/nli-mpnet-base-v2
+    - sentence-transformers/nli-roberta-base-v2
+    - sentence-transformers/nq-distilbert-base-v1
+    - sentence-transformers/paraphrase-MiniLM-L12-v2
+    - sentence-transformers/paraphrase-MiniLM-L3-v2
+    - sentence-transformers/paraphrase-MiniLM-L6-v2
+    - sentence-transformers/paraphrase-TinyBERT-L6-v2
+    - sentence-transformers/paraphrase-albert-base-v2
+    - sentence-transformers/paraphrase-albert-small-v2
+    - sentence-transformers/paraphrase-distilroberta-base-v1
+    - sentence-transformers/paraphrase-distilroberta-base-v2
+    - sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+    - sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+    - sentence-transformers/paraphrase-xlm-r-multilingual-v1
+    - sentence-transformers/quora-distilbert-base
+    - sentence-transformers/quora-distilbert-multilingual
+    - sentence-transformers/sentence-t5-base
+    - sentence-transformers/sentence-t5-large
+    - sentence-transformers/sentence-t5-xxl
+    - sentence-transformers/sentence-t5-xl
+    - sentence-transformers/stsb-distilroberta-base-v2
+    - sentence-transformers/stsb-mpnet-base-v2
+    - sentence-transformers/stsb-roberta-base-v2
+    - sentence-transformers/stsb-xlm-r-multilingual
+    - sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
+    - sentence-transformers/clip-ViT-L-14
+    - sentence-transformers/clip-ViT-B-16
+    - sentence-transformers/use-cmlm-multilingual
+    - sentence-transformers/all-MiniLM-L12-v1
+    ```
+
+!!! info
+    You can also load many other model architectures from the library. For example models from sources such as BAAI, nomic, salesforce research, etc.
+    See this HF hub page for all [supported models](https://huggingface.co/models?library=sentence-transformers).
+
+!!! note "BAAI Embeddings example"
+    Here is an example that uses BAAI embedding model from the HuggingFace Hub [supported models](https://huggingface.co/models?library=sentence-transformers)
+    ```python
+    import lancedb
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.embeddings import get_registry
+
+    db = lancedb.connect("/tmp/db")
+    model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
+
+    class Words(LanceModel):
+        text: str = model.SourceField()
+        vector: Vector(model.ndims()) = model.VectorField()
+
+    table = db.create_table("words", schema=Words)
+    table.add(
+        [
+            {"text": "hello world"},
+            {"text": "goodbye world"}
+        ]
+    )
+
+    query = "greetings"
+    actual = table.search(query).limit(1).to_pydantic(Words)[0]
+    print(actual.text)
+    ```
+Visit sentence-transformers [HuggingFace HUB](https://huggingface.co/sentence-transformers) page for more information on the available models.
+

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

@@ -0,0 +1,51 @@
+# VoyageAI Embeddings
+
+Voyage AI provides cutting-edge embedding and rerankers.
+
+
+Using voyageai API requires voyageai package, which can be installed using `pip install voyageai`. Voyage AI embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
+You also need to set the `VOYAGE_API_KEY` environment variable to use the VoyageAI API.
+
+Supported models are:
+
+- voyage-3
+- voyage-3-lite
+- voyage-finance-2
+- voyage-multilingual-2
+- voyage-law-2
+- voyage-code-2
+
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|--------|---------|
+| `name` | `str` | `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

@@ -15,198 +15,226 @@ There is another optional layer of abstraction available: `TextEmbeddingFunction
 
 Let's implement `SentenceTransformerEmbeddings` class. All you need to do is implement the `generate_embeddings()` and `ndims` function to handle the input types you expect and register the class in the global `EmbeddingFunctionRegistry`
 
-```python
-from lancedb.embeddings import register
-from lancedb.util import attempt_import_or_raise
 
-@register("sentence-transformers")
+=== "Python"
-class SentenceTransformerEmbeddings(TextEmbeddingFunction):
-    name: str = "all-MiniLM-L6-v2"
-    # set more default instance vars like device, etc.
 
-    def __init__(self, **kwargs):
+    ```python
-        super().__init__(**kwargs)
+    from lancedb.embeddings import register
-        self._ndims = None
+    from lancedb.util import attempt_import_or_raise
-    
-    def generate_embeddings(self, texts):
-        return self._embedding_model().encode(list(texts), ...).tolist()
 
-    def ndims(self):
+    @register("sentence-transformers")
-        if self._ndims is None:
+    class SentenceTransformerEmbeddings(TextEmbeddingFunction):
-            self._ndims = len(self.generate_embeddings("foo")[0])
+        name: str = "all-MiniLM-L6-v2"
-        return self._ndims
+        # set more default instance vars like device, etc.
 
-    @cached(cache={}) 
+        def __init__(self, **kwargs):
-    def _embedding_model(self):
+            super().__init__(**kwargs)
-        return sentence_transformers.SentenceTransformer(name)
+            self._ndims = None
-```
 
-This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and defaul settings.
+        def generate_embeddings(self, texts):
+            return self._embedding_model().encode(list(texts), ...).tolist()
+
+        def ndims(self):
+            if self._ndims is None:
+                self._ndims = len(self.generate_embeddings("foo")[0])
+            return self._ndims
+
+        @cached(cache={})
+        def _embedding_model(self):
+            return sentence_transformers.SentenceTransformer(name)
+    ```
+
+=== "TypeScript"
+
+    ```ts
+    --8<--- "nodejs/examples/custom_embedding_function.test.ts:imports"
+
+    --8<--- "nodejs/examples/custom_embedding_function.test.ts:embedding_impl"
+    ```
+
+
+This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and default settings.
 
 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
+=== "Python"
-from lancedb.pydantic import LanceModel, Vector
 
-registry = EmbeddingFunctionRegistry.get_instance()
+    ```python
-stransformer = registry.get("sentence-transformers").create()
+    from lancedb.pydantic import LanceModel, Vector
 
-class TextModelSchema(LanceModel):
+    registry = EmbeddingFunctionRegistry.get_instance()
-    vector: Vector(stransformer.ndims) = stransformer.VectorField()
+    stransformer = registry.get("sentence-transformers").create()
-    text: str = stransformer.SourceField()
 
-tbl = db.create_table("table", schema=TextModelSchema)
+    class TextModelSchema(LanceModel):
+        vector: Vector(stransformer.ndims) = stransformer.VectorField()
+        text: str = stransformer.SourceField()
 
-tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
+    tbl = db.create_table("table", schema=TextModelSchema)
-result = tbl.search("world").limit(5)
-```
 
-NOTE:
+    tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
+    result = tbl.search("world").limit(5)
+    ```
 
-You can always implement the `EmbeddingFunction` interface directly if you want or need to, `TextEmbeddingFunction` just makes it much simpler and faster for you to do so, by setting up the boiler plat for text-specific use case
+=== "TypeScript"
+
+    ```ts
+    --8<--- "nodejs/examples/custom_embedding_function.test.ts:call_custom_function"
+    ```
+
+!!! note
+
+    You can always implement the `EmbeddingFunction` interface directly if you want or need to, `TextEmbeddingFunction` just makes it much simpler and faster for you to do so, by setting up the boiler plat for text-specific use case
 
 ## Multi-modal embedding function example
-You can also use the `EmbeddingFunction` interface to implement more complex workflows such as multi-modal embedding function support. LanceDB implements `OpenClipEmeddingFunction` class that suppports multi-modal seach. Here's the implementation that you can use as a reference to build your own multi-modal embedding functions.
+You can also use the `EmbeddingFunction` interface to implement more complex workflows such as multi-modal embedding function support.
 
-```python
+=== "Python"
-@register("open-clip")
-class OpenClipEmbeddings(EmbeddingFunction):
-    name: str = "ViT-B-32"
-    pretrained: str = "laion2b_s34b_b79k"
-    device: str = "cpu"
-    batch_size: int = 64
-    normalize: bool = True
-    _model = PrivateAttr()
-    _preprocess = PrivateAttr()
-    _tokenizer = PrivateAttr()
 
-    def __init__(self, *args, **kwargs):
+    LanceDB implements `OpenClipEmeddingFunction` class that suppports multi-modal seach. Here's the implementation that you can use as a reference to build your own multi-modal embedding functions.
-        super().__init__(*args, **kwargs)
-        open_clip = attempt_import_or_raise("open_clip", "open-clip") # EmbeddingFunction util to import external libs and raise if not found
-        model, _, preprocess = open_clip.create_model_and_transforms(
-            self.name, pretrained=self.pretrained
-        )
-        model.to(self.device)
-        self._model, self._preprocess = model, preprocess
-        self._tokenizer = open_clip.get_tokenizer(self.name)
-        self._ndims = None
 
-    def ndims(self):
+    ```python
-        if self._ndims is None:
+    @register("open-clip")
-            self._ndims = self.generate_text_embeddings("foo").shape[0]
+    class OpenClipEmbeddings(EmbeddingFunction):
-        return self._ndims
+        name: str = "ViT-B-32"
+        pretrained: str = "laion2b_s34b_b79k"
+        device: str = "cpu"
+        batch_size: int = 64
+        normalize: bool = True
+        _model = PrivateAttr()
+        _preprocess = PrivateAttr()
+        _tokenizer = PrivateAttr()
 
-    def compute_query_embeddings(
+        def __init__(self, *args, **kwargs):
-        self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
+            super().__init__(*args, **kwargs)
-    ) -> List[np.ndarray]:
+            open_clip = attempt_import_or_raise("open_clip", "open-clip") # EmbeddingFunction util to import external libs and raise if not found
-        """
+            model, _, preprocess = open_clip.create_model_and_transforms(
-        Compute the embeddings for a given user query
+                self.name, pretrained=self.pretrained
+            )
+            model.to(self.device)
+            self._model, self._preprocess = model, preprocess
+            self._tokenizer = open_clip.get_tokenizer(self.name)
+            self._ndims = None
 
-        Parameters
+        def ndims(self):
-        ----------
+            if self._ndims is None:
-        query : Union[str, PIL.Image.Image]
+                self._ndims = self.generate_text_embeddings("foo").shape[0]
-            The query to embed. A query can be either text or an image.
+            return self._ndims
-        """
+
-        if isinstance(query, str):
+        def compute_query_embeddings(
-            return [self.generate_text_embeddings(query)]
+            self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
-        else:
+        ) -> List[np.ndarray]:
+            """
+            Compute the embeddings for a given user query
+
+            Parameters
+            ----------
+            query : Union[str, PIL.Image.Image]
+                The query to embed. A query can be either text or an image.
+            """
+            if isinstance(query, str):
+                return [self.generate_text_embeddings(query)]
+            else:
+                PIL = attempt_import_or_raise("PIL", "pillow")
+                if isinstance(query, PIL.Image.Image):
+                    return [self.generate_image_embedding(query)]
+                else:
+                    raise TypeError("OpenClip supports str or PIL Image as query")
+
+        def generate_text_embeddings(self, text: str) -> np.ndarray:
+            torch = attempt_import_or_raise("torch")
+            text = self.sanitize_input(text)
+            text = self._tokenizer(text)
+            text.to(self.device)
+            with torch.no_grad():
+                text_features = self._model.encode_text(text.to(self.device))
+                if self.normalize:
+                    text_features /= text_features.norm(dim=-1, keepdim=True)
+                return text_features.cpu().numpy().squeeze()
+
+        def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
+            """
+            Sanitize the input to the embedding function.
+            """
+            if isinstance(images, (str, bytes)):
+                images = [images]
+            elif isinstance(images, pa.Array):
+                images = images.to_pylist()
+            elif isinstance(images, pa.ChunkedArray):
+                images = images.combine_chunks().to_pylist()
+            return images
+
+        def compute_source_embeddings(
+            self, images: IMAGES, *args, **kwargs
+        ) -> List[np.array]:
+            """
+            Get the embeddings for the given images
+            """
+            images = self.sanitize_input(images)
+            embeddings = []
+            for i in range(0, len(images), self.batch_size):
+                j = min(i + self.batch_size, len(images))
+                batch = images[i:j]
+                embeddings.extend(self._parallel_get(batch))
+            return embeddings
+
+        def _parallel_get(self, images: Union[List[str], List[bytes]]) -> List[np.ndarray]:
+            """
+            Issue concurrent requests to retrieve the image data
+            """
+            with concurrent.futures.ThreadPoolExecutor() as executor:
+                futures = [
+                    executor.submit(self.generate_image_embedding, image)
+                    for image in images
+                ]
+                return [future.result() for future in futures]
+
+        def generate_image_embedding(
+            self, image: Union[str, bytes, "PIL.Image.Image"]
+        ) -> np.ndarray:
+            """
+            Generate the embedding for a single image
+
+            Parameters
+            ----------
+            image : Union[str, bytes, PIL.Image.Image]
+                The image to embed. If the image is a str, it is treated as a uri.
+                If the image is bytes, it is treated as the raw image bytes.
+            """
+            torch = attempt_import_or_raise("torch")
+            # TODO handle retry and errors for https
+            image = self._to_pil(image)
+            image = self._preprocess(image).unsqueeze(0)
+            with torch.no_grad():
+                return self._encode_and_normalize_image(image)
+
+        def _to_pil(self, image: Union[str, bytes]):
             PIL = attempt_import_or_raise("PIL", "pillow")
-            if isinstance(query, PIL.Image.Image):
+            if isinstance(image, bytes):
-                return [self.generate_image_embedding(query)]
+                return PIL.Image.open(io.BytesIO(image))
-            else:
+            if isinstance(image, PIL.Image.Image):
-                raise TypeError("OpenClip supports str or PIL Image as query")
+                return image
+            elif isinstance(image, str):
+                parsed = urlparse.urlparse(image)
+                # TODO handle drive letter on windows.
+                if parsed.scheme == "file":
+                    return PIL.Image.open(parsed.path)
+                elif parsed.scheme == "":
+                    return PIL.Image.open(image if os.name == "nt" else parsed.path)
+                elif parsed.scheme.startswith("http"):
+                    return PIL.Image.open(io.BytesIO(url_retrieve(image)))
+                else:
+                    raise NotImplementedError("Only local and http(s) urls are supported")
 
-    def generate_text_embeddings(self, text: str) -> np.ndarray:
+        def _encode_and_normalize_image(self, image_tensor: "torch.Tensor"):
-        torch = attempt_import_or_raise("torch")
+            """
-        text = self.sanitize_input(text)
+            encode a single image tensor and optionally normalize the output
-        text = self._tokenizer(text)
+            """
-        text.to(self.device)
+            image_features = self._model.encode_image(image_tensor)
-        with torch.no_grad():
-            text_features = self._model.encode_text(text.to(self.device))
             if self.normalize:
-                text_features /= text_features.norm(dim=-1, keepdim=True)
+                image_features /= image_features.norm(dim=-1, keepdim=True)
-            return text_features.cpu().numpy().squeeze()
+            return image_features.cpu().numpy().squeeze()
+    ```
 
-    def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
+=== "TypeScript"
-        """
-        Sanitize the input to the embedding function.
-        """
-        if isinstance(images, (str, bytes)):
-            images = [images]
-        elif isinstance(images, pa.Array):
-            images = images.to_pylist()
-        elif isinstance(images, pa.ChunkedArray):
-            images = images.combine_chunks().to_pylist()
-        return images
 
-    def compute_source_embeddings(
+    Coming Soon! See this [issue](https://github.com/lancedb/lancedb/issues/1482) to track the status!
-        self, images: IMAGES, *args, **kwargs
-    ) -> List[np.array]:
-        """
-        Get the embeddings for the given images
-        """
-        images = self.sanitize_input(images)
-        embeddings = []
-        for i in range(0, len(images), self.batch_size):
-            j = min(i + self.batch_size, len(images))
-            batch = images[i:j]
-            embeddings.extend(self._parallel_get(batch))
-        return embeddings
-
-    def _parallel_get(self, images: Union[List[str], List[bytes]]) -> List[np.ndarray]:
-        """
-        Issue concurrent requests to retrieve the image data
-        """
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            futures = [
-                executor.submit(self.generate_image_embedding, image)
-                for image in images
-            ]
-            return [future.result() for future in futures]
-
-    def generate_image_embedding(
-        self, image: Union[str, bytes, "PIL.Image.Image"]
-    ) -> np.ndarray:
-        """
-        Generate the embedding for a single image
-
-        Parameters
-        ----------
-        image : Union[str, bytes, PIL.Image.Image]
-            The image to embed. If the image is a str, it is treated as a uri.
-            If the image is bytes, it is treated as the raw image bytes.
-        """
-        torch = attempt_import_or_raise("torch")
-        # TODO handle retry and errors for https
-        image = self._to_pil(image)
-        image = self._preprocess(image).unsqueeze(0)
-        with torch.no_grad():
-            return self._encode_and_normalize_image(image)
-
-    def _to_pil(self, image: Union[str, bytes]):
-        PIL = attempt_import_or_raise("PIL", "pillow")
-        if isinstance(image, bytes):
-            return PIL.Image.open(io.BytesIO(image))
-        if isinstance(image, PIL.Image.Image):
-            return image
-        elif isinstance(image, str):
-            parsed = urlparse.urlparse(image)
-            # TODO handle drive letter on windows.
-            if parsed.scheme == "file":
-                return PIL.Image.open(parsed.path)
-            elif parsed.scheme == "":
-                return PIL.Image.open(image if os.name == "nt" else parsed.path)
-            elif parsed.scheme.startswith("http"):
-                return PIL.Image.open(io.BytesIO(url_retrieve(image)))
-            else:
-                raise NotImplementedError("Only local and http(s) urls are supported")
-
-    def _encode_and_normalize_image(self, image_tensor: "torch.Tensor"):
-        """
-        encode a single image tensor and optionally normalize the output
-        """
-        image_features = self._model.encode_image(image_tensor)
-        if self.normalize:
-            image_features /= image_features.norm(dim=-1, keepdim=True)
-        return image_features.cpu().numpy().squeeze()
-```

docs/src/embeddings/default_embedding_functions.md

@@ -1,532 +1,86 @@
-There are various embedding functions available out of the box with LanceDB to manage your embeddings implicitly. We're actively working on adding other popular embedding APIs and models.
+# 📚 Available Embedding Models
 
-## Text embedding functions
+There are various embedding functions available out of the box with LanceDB to manage your embeddings implicitly. We're actively working on adding other popular embedding APIs and models. 🚀
-Contains the text embedding functions registered by default.
 
-* Embedding functions have an inbuilt rate limit handler wrapper for source and query embedding function calls that retry with exponential backoff. 
+Before jumping on the list of available models, let's understand how to get an embedding model initialized and configured to use in our code: 
-* Each `EmbeddingFunction` implementation automatically takes `max_retries` as an argument which has the default value of 7.
 
-### Sentence transformers
+!!! example "Example usage"
-Allows you to set parameters when registering a `sentence-transformers` object.
-
-!!! info
-    Sentence transformer embeddings are normalized by default. It is recommended to use normalized embeddings for similarity search.
-
-| Parameter | Type | Default Value | Description |
-|---|---|---|---|
-| `name` | `str` | `all-MiniLM-L6-v2` | The name of the model |
-| `device` | `str` | `cpu` | The device to run the model on (can be `cpu` or `gpu`) |
-| `normalize` | `bool` | `True` | Whether to normalize the input text before feeding it to the model |
-
-
-??? "Check out available sentence-transformer models here!"
-    ```markdown
-    - sentence-transformers/all-MiniLM-L12-v2
-    - sentence-transformers/paraphrase-mpnet-base-v2
-    - sentence-transformers/gtr-t5-base
-    - sentence-transformers/LaBSE
-    - sentence-transformers/all-MiniLM-L6-v2
-    - sentence-transformers/bert-base-nli-max-tokens
-    - sentence-transformers/bert-base-nli-mean-tokens
-    - sentence-transformers/bert-base-nli-stsb-mean-tokens
-    - sentence-transformers/bert-base-wikipedia-sections-mean-tokens
-    - sentence-transformers/bert-large-nli-cls-token
-    - sentence-transformers/bert-large-nli-max-tokens
-    - sentence-transformers/bert-large-nli-mean-tokens
-    - sentence-transformers/bert-large-nli-stsb-mean-tokens
-    - sentence-transformers/distilbert-base-nli-max-tokens
-    - sentence-transformers/distilbert-base-nli-mean-tokens
-    - sentence-transformers/distilbert-base-nli-stsb-mean-tokens
-    - sentence-transformers/distilroberta-base-msmarco-v1
-    - sentence-transformers/distilroberta-base-msmarco-v2
-    - sentence-transformers/nli-bert-base-cls-pooling
-    - sentence-transformers/nli-bert-base-max-pooling
-    - sentence-transformers/nli-bert-base
-    - sentence-transformers/nli-bert-large-cls-pooling
-    - sentence-transformers/nli-bert-large-max-pooling
-    - sentence-transformers/nli-bert-large
-    - sentence-transformers/nli-distilbert-base-max-pooling
-    - sentence-transformers/nli-distilbert-base
-    - sentence-transformers/nli-roberta-base
-    - sentence-transformers/nli-roberta-large
-    - sentence-transformers/roberta-base-nli-mean-tokens
-    - sentence-transformers/roberta-base-nli-stsb-mean-tokens
-    - sentence-transformers/roberta-large-nli-mean-tokens
-    - sentence-transformers/roberta-large-nli-stsb-mean-tokens
-    - sentence-transformers/stsb-bert-base
-    - sentence-transformers/stsb-bert-large
-    - sentence-transformers/stsb-distilbert-base
-    - sentence-transformers/stsb-roberta-base
-    - sentence-transformers/stsb-roberta-large
-    - sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens
-    - sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens
-    - sentence-transformers/xlm-r-base-en-ko-nli-ststb
-    - sentence-transformers/xlm-r-bert-base-nli-mean-tokens
-    - sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens
-    - sentence-transformers/xlm-r-large-en-ko-nli-ststb
-    - sentence-transformers/bert-base-nli-cls-token
-    - sentence-transformers/all-distilroberta-v1
-    - sentence-transformers/multi-qa-MiniLM-L6-dot-v1
-    - sentence-transformers/multi-qa-distilbert-cos-v1
-    - sentence-transformers/multi-qa-distilbert-dot-v1
-    - sentence-transformers/multi-qa-mpnet-base-cos-v1
-    - sentence-transformers/multi-qa-mpnet-base-dot-v1
-    - sentence-transformers/nli-distilroberta-base-v2
-    - sentence-transformers/all-MiniLM-L6-v1
-    - sentence-transformers/all-mpnet-base-v1
-    - sentence-transformers/all-mpnet-base-v2
-    - sentence-transformers/all-roberta-large-v1
-    - sentence-transformers/allenai-specter
-    - sentence-transformers/average_word_embeddings_glove.6B.300d
-    - sentence-transformers/average_word_embeddings_glove.840B.300d
-    - sentence-transformers/average_word_embeddings_komninos
-    - sentence-transformers/average_word_embeddings_levy_dependency
-    - sentence-transformers/clip-ViT-B-32-multilingual-v1
-    - sentence-transformers/clip-ViT-B-32
-    - sentence-transformers/distilbert-base-nli-stsb-quora-ranking
-    - sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking
-    - sentence-transformers/distilroberta-base-paraphrase-v1
-    - sentence-transformers/distiluse-base-multilingual-cased-v1
-    - sentence-transformers/distiluse-base-multilingual-cased-v2
-    - sentence-transformers/distiluse-base-multilingual-cased
-    - sentence-transformers/facebook-dpr-ctx_encoder-multiset-base
-    - sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base
-    - sentence-transformers/facebook-dpr-question_encoder-multiset-base
-    - sentence-transformers/facebook-dpr-question_encoder-single-nq-base
-    - sentence-transformers/gtr-t5-large
-    - sentence-transformers/gtr-t5-xl
-    - sentence-transformers/gtr-t5-xxl
-    - sentence-transformers/msmarco-MiniLM-L-12-v3
-    - sentence-transformers/msmarco-MiniLM-L-6-v3
-    - sentence-transformers/msmarco-MiniLM-L12-cos-v5
-    - sentence-transformers/msmarco-MiniLM-L6-cos-v5
-    - sentence-transformers/msmarco-bert-base-dot-v5
-    - sentence-transformers/msmarco-bert-co-condensor
-    - sentence-transformers/msmarco-distilbert-base-dot-prod-v3
-    - sentence-transformers/msmarco-distilbert-base-tas-b
-    - sentence-transformers/msmarco-distilbert-base-v2
-    - sentence-transformers/msmarco-distilbert-base-v3
-    - sentence-transformers/msmarco-distilbert-base-v4
-    - sentence-transformers/msmarco-distilbert-cos-v5
-    - sentence-transformers/msmarco-distilbert-dot-v5
-    - sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned
-    - sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch
-    - sentence-transformers/msmarco-distilroberta-base-v2
-    - sentence-transformers/msmarco-roberta-base-ance-firstp
-    - sentence-transformers/msmarco-roberta-base-v2
-    - sentence-transformers/msmarco-roberta-base-v3
-    - sentence-transformers/multi-qa-MiniLM-L6-cos-v1
-    - sentence-transformers/nli-mpnet-base-v2
-    - sentence-transformers/nli-roberta-base-v2
-    - sentence-transformers/nq-distilbert-base-v1
-    - sentence-transformers/paraphrase-MiniLM-L12-v2
-    - sentence-transformers/paraphrase-MiniLM-L3-v2
-    - sentence-transformers/paraphrase-MiniLM-L6-v2
-    - sentence-transformers/paraphrase-TinyBERT-L6-v2
-    - sentence-transformers/paraphrase-albert-base-v2
-    - sentence-transformers/paraphrase-albert-small-v2
-    - sentence-transformers/paraphrase-distilroberta-base-v1
-    - sentence-transformers/paraphrase-distilroberta-base-v2
-    - sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
-    - sentence-transformers/paraphrase-multilingual-mpnet-base-v2
-    - sentence-transformers/paraphrase-xlm-r-multilingual-v1
-    - sentence-transformers/quora-distilbert-base
-    - sentence-transformers/quora-distilbert-multilingual
-    - sentence-transformers/sentence-t5-base
-    - sentence-transformers/sentence-t5-large
-    - sentence-transformers/sentence-t5-xxl
-    - sentence-transformers/sentence-t5-xl
-    - sentence-transformers/stsb-distilroberta-base-v2
-    - sentence-transformers/stsb-mpnet-base-v2
-    - sentence-transformers/stsb-roberta-base-v2
-    - sentence-transformers/stsb-xlm-r-multilingual
-    - sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
-    - sentence-transformers/clip-ViT-L-14
-    - sentence-transformers/clip-ViT-B-16
-    - sentence-transformers/use-cmlm-multilingual
-    - sentence-transformers/all-MiniLM-L12-v1
-    ```
-
-!!! info
-    You can also load many other model architectures from the library. For example models from sources such as BAAI, nomic, salesforce research, etc.
-    See this HF hub page for all [supported models](https://huggingface.co/models?library=sentence-transformers).
-
-!!! note "BAAI Embeddings example"
-    Here is an example that uses BAAI embedding model from the HuggingFace Hub [supported models](https://huggingface.co/models?library=sentence-transformers)
     ```python
-    import lancedb
+    model = get_registry()
-    from lancedb.pydantic import LanceModel, Vector
+              .get("openai")
-    from lancedb.embeddings import get_registry
+              .create(name="text-embedding-ada-002")
-
-    db = lancedb.connect("/tmp/db")
-    model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
-
-    class Words(LanceModel):
-        text: str = model.SourceField()
-        vector: Vector(model.ndims()) = model.VectorField()
-
-    table = db.create_table("words", schema=Words)
-    table.add(
-        [
-            {"text": "hello world"},
-            {"text": "goodbye world"}
-        ]
-    )
-
-    query = "greetings"
-    actual = table.search(query).limit(1).to_pydantic(Words)[0]
-    print(actual.text)
     ```
-Visit sentence-transformers [HuggingFace HUB](https://huggingface.co/sentence-transformers) page for more information on the available models.
 
-
+Now let's understand the above syntax: 
-### Huggingface embedding models
-We offer support for all huggingface models (which can be loaded via [transformers](https://huggingface.co/docs/transformers/en/index) library). The default model is `colbert-ir/colbertv2.0` which also has its own special callout - `registry.get("colbert")` 
-
-Example usage - 
 ```python
-import lancedb
+model = get_registry().get("model_id").create(...params)
-import pandas as pd
-
-from lancedb.embeddings import get_registry
-from lancedb.pydantic import LanceModel, Vector
-
-model = get_registry().get("huggingface").create(name='facebook/bart-base')
-
-class TextModel(LanceModel):
-    text: str = model.SourceField()
-    vector: Vector(model.ndims()) = model.VectorField()
-
-df = pd.DataFrame({"text": ["hi hello sayonara", "goodbye world"]})
-table = db.create_table("greets", schema=Words)
-table.add()
-query = "old greeting"
-actual = table.search(query).limit(1).to_pydantic(Words)[0]
-print(actual.text)
 ```
+**This👆 line effectively creates a configured instance of an `embedding function` with `model` of choice that is ready for use.**
 
+- `get_registry()` :  This function call returns an instance of a `EmbeddingFunctionRegistry` object. This registry manages the registration and retrieval of embedding functions.
 
-### OpenAI embeddings
+- `.get("model_id")` : This method call on the registry object and retrieves the **embedding models functions** associated with the `"model_id"` (1) .
-LanceDB registers the OpenAI embeddings function in the registry by default, as `openai`. Below are the parameters that you can customize when creating the instances:
+    { .annotate }
 
-| Parameter | Type | Default Value | Description |
+    1.  Hover over the names in table below to find out the `model_id` of different embedding functions.
-|---|---|---|---|
-| `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 |
 
+- `.create(...params)` : This method call is on the object returned by the `get` method. It instantiates an embedding model function using the **specified parameters**. 
 
-```python
+??? question "What parameters does the `.create(...params)` method accepts?"
-import lancedb
+    **Checkout the documentation of specific embedding models (links in the table below👇) to know what parameters it takes**.
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
 
-db = lancedb.connect("/tmp/db")
+!!! tip "Moving on"
-func = get_registry().get("openai").create(name="text-embedding-ada-002")
+    Now that we know how to get the **desired embedding model** and use it in our code, let's explore the comprehensive **list** of embedding models **supported by LanceDB**, in the tables below.
 
-class Words(LanceModel):
+## Text Embedding Functions 📝 
-    text: str = func.SourceField()
+These functions are registered by default to handle text embeddings.
-    vector: Vector(func.ndims()) = func.VectorField()
 
-table = db.create_table("words", schema=Words, mode="overwrite")
+- 🔄 **Embedding functions** have an inbuilt rate limit handler wrapper for source and query embedding function calls that retry with **exponential backoff**. 
-table.add(
-    [
-        {"text": "hello world"},
-        {"text": "goodbye world"}
-    ]
-    )
 
-query = "greetings"
+- 🌕 Each `EmbeddingFunction` implementation automatically takes `max_retries` as an argument which has the default value of 7. 
-actual = table.search(query).limit(1).to_pydantic(Words)[0]
-print(actual.text)
-```
 
-### Instructor Embeddings
+🌟 **Available Text Embeddings**
-[Instructor](https://instructor-embedding.github.io/) is an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g. classification, retrieval, clustering, text evaluation, etc.) and domains (e.g. science, finance, etc.) by simply providing the task instruction, without any finetuning.
 
-If you want to calculate customized embeddings for specific sentences, you can follow the unified template to write instructions.
+| **Embedding** :material-information-outline:{ title="Hover over the name to find out the model_id" } | **Description** | **Documentation** |
+|-----------|-------------|---------------|
+| [**Sentence Transformers**](available_embedding_models/text_embedding_functions/sentence_transformers.md "sentence-transformers")  | 🧠 **SentenceTransformers** is a Python framework for state-of-the-art sentence, text, and image embeddings. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/sbert_2.png" alt="Sentence Transformers Icon" width="90" height="35">](available_embedding_models/text_embedding_functions/sentence_transformers.md)|                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
+| [**Huggingface Models**](available_embedding_models/text_embedding_functions/huggingface_embedding.md "huggingface") |🤗 We offer support for all **Huggingface** models. The default model is `colbert-ir/colbertv2.0`. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/hugging_face.png" alt="Huggingface Icon" width="130" height="35">](available_embedding_models/text_embedding_functions/huggingface_embedding.md) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
+| [**Ollama Embeddings**](available_embedding_models/text_embedding_functions/ollama_embedding.md "ollama") | 🔍 Generate embeddings via the **Ollama** python library. Ollama supports embedding models, making it possible to build RAG apps. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/Ollama.png" alt="Ollama Icon" width="110" height="35">](available_embedding_models/text_embedding_functions/ollama_embedding.md)|                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
+| [**OpenAI Embeddings**](available_embedding_models/text_embedding_functions/openai_embedding.md "openai")| 🔑 **OpenAI’s** text embeddings measure the relatedness of text strings. **LanceDB** supports state-of-the-art embeddings from OpenAI. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/openai.png" alt="OpenAI Icon" width="100" height="35">](available_embedding_models/text_embedding_functions/openai_embedding.md)|                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
+| [**Instructor Embeddings**](available_embedding_models/text_embedding_functions/instructor_embedding.md "instructor") | 📚 **Instructor**: An instruction-finetuned text embedding model that can generate text embeddings tailored to any task and domains by simply providing the task instruction, without any finetuning. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/instructor_embedding.png" alt="Instructor Embedding Icon" width="140" height="35">](available_embedding_models/text_embedding_functions/instructor_embedding.md) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
+| [**Gemini Embeddings**](available_embedding_models/text_embedding_functions/gemini_embedding.md "gemini-text") | 🌌 Google’s Gemini API generates state-of-the-art embeddings for words, phrases, and sentences. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/gemini.png" alt="Gemini Icon" width="95" height="35">](available_embedding_models/text_embedding_functions/gemini_embedding.md) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
+| [**Cohere Embeddings**](available_embedding_models/text_embedding_functions/cohere_embedding.md "cohere") | 💬 This will help you get started with **Cohere** embedding models using LanceDB. Using cohere API requires cohere package. Install it via `pip`. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/cohere.png" alt="Cohere Icon" width="140" height="35">](available_embedding_models/text_embedding_functions/cohere_embedding.md) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
+| [**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) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               
 
-!!! info
-    Represent the `domain` `text_type` for `task_objective`:
 
-    * `domain` is optional, and it specifies the domain of the text, e.g. science, finance, medicine, etc.
-    * `text_type` is required, and it specifies the encoding unit, e.g. sentence, document, paragraph, etc.
-    * `task_objective` is optional, and it specifies the objective of embedding, e.g. retrieve a document, classify the sentence, etc.
 
-More information about the model can be found at the [source URL](https://github.com/xlang-ai/instructor-embedding).
+[st-key]: "sentence-transformers"
+[hf-key]: "huggingface"
+[ollama-key]: "ollama"
+[openai-key]: "openai"
+[instructor-key]: "instructor"
+[gemini-key]: "gemini-text"
+[cohere-key]: "cohere"
+[jina-key]: "jina"
+[aws-key]: "bedrock-text"
+[watsonx-key]: "watsonx"
+[voyageai-key]: "voyageai"
 
-| Argument | Type | Default | Description |
-|---|---|---|---|
-| `name` | `str` | "hkunlp/instructor-base" | The name of the model to use |
-| `batch_size` | `int` | `32` | The batch size to use when generating embeddings |
-| `device` | `str` | `"cpu"` | The device to use when generating embeddings |
-| `show_progress_bar` | `bool` | `True` | Whether to show a progress bar when generating embeddings |
-| `normalize_embeddings` | `bool` | `True` | Whether to normalize the embeddings |
-| `quantize` | `bool` | `False` | Whether to quantize the model |
-| `source_instruction` | `str` | `"represent the docuement for retreival"` | The instruction for the source column |
-| `query_instruction` | `str` | `"represent the document for retreiving the most similar documents"` | The instruction for the query |
 
+## Multi-modal Embedding Functions🖼️ 
 
+Multi-modal embedding functions allow you to query your table using both images and text. 💬🖼️
 
-```python
+🌐 **Available Multi-modal Embeddings**
-import lancedb
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry, InstuctorEmbeddingFunction
 
-instructor = get_registry().get("instructor").create(
+| Embedding :material-information-outline:{ title="Hover over the name to find out the model_id" }  | Description | Documentation  |
-                            source_instruction="represent the docuement for retreival",
+|-----------|-------------|---------------|
-                            query_instruction="represent the document for retreiving the most similar documents"
+| [**OpenClip Embeddings**](available_embedding_models/multimodal_embedding_functions/openclip_embedding.md "open-clip") | 🎨 We support CLIP model embeddings using the open source alternative, **open-clip** which supports various customizations. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/openclip_github.png" alt="openclip Icon" width="150" height="35">](available_embedding_models/multimodal_embedding_functions/openclip_embedding.md) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
-                            )
+| [**Imagebind Embeddings**](available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md "imageind") | 🌌  We have support for **imagebind model embeddings**. You can download our version of the packaged model via - `pip install imagebind-packaged==0.1.2`. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/imagebind_meta.png" alt="imagebind Icon" width="150" height="35">](available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md)|                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
+| [**Jina Multi-modal Embeddings**](available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md "jina") | 🔗 **Jina embeddings** can also be used to embed both **text** and **image** data, only some of the models support image data and you can check the detailed documentation. 👉 | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/jina.png" alt="jina Icon" width="90" height="35">](available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md) |
 
-class Schema(LanceModel):
+!!! note
-    vector: Vector(instructor.ndims()) = instructor.VectorField()
+    If you'd like to request support for additional **embedding functions**, please feel free to open an issue on our LanceDB [GitHub issue page](https://github.com/lancedb/lancedb/issues).
-    text: str = instructor.SourceField()
-
-db = lancedb.connect("~/.lancedb")
-tbl = db.create_table("test", schema=Schema, mode="overwrite")
-
-texts = [{"text": "Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that..."},
-        {"text": "The disparate impact theory is especially controversial under the Fair Housing Act because the Act..."},
-        {"text": "Disparate impact in United States labor law refers to practices in employment, housing, and other areas that.."}]
-
-tbl.add(texts)
-```
-
-### Gemini Embeddings
-With Google's Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity. For more on how and why you should use embeddings, refer to the Embeddings guide.
-The Gemini Embedding Model API supports various task types:
-
-| Task Type               | Description                                                                                                                                                |
-|-------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| "`retrieval_query`"     | Specifies the given text is a query in a search/retrieval setting.                                                                                         |
-| "`retrieval_document`"  | Specifies the given text is a document in a search/retrieval setting. Using this task type requires a title but is automatically proided by Embeddings API |
-| "`semantic_similarity`" | Specifies the given text will be used for Semantic Textual Similarity (STS).                                                                               |
-| "`classification`"      | Specifies that the embeddings will be used for classification.                                                                                             |
-| "`clusering`"           | Specifies that the embeddings will be used for clustering.                                                                                                 |
-
-
-Usage Example:
-
-```python
-import lancedb
-import pandas as pd
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
-
-
-model = get_registry().get("gemini-text").create()
-
-class TextModel(LanceModel):
-    text: str = model.SourceField()
-    vector: Vector(model.ndims()) = model.VectorField()
-
-df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
-db = lancedb.connect("~/.lancedb")
-tbl = db.create_table("test", schema=TextModel, mode="overwrite")
-
-tbl.add(df)
-rs = tbl.search("hello").limit(1).to_pandas()
-```
-
-### AWS Bedrock Text Embedding Functions
-AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function.
-You can do so by using `awscli` and also add your session_token:
-```shell
-aws configure
-aws configure set aws_session_token "<your_session_token>"
-```
-to ensure that the credentials are set up correctly, you can run the following command:
-```shell
-aws sts get-caller-identity
-```
-
-Supported Embedding modelIDs are:
-* `amazon.titan-embed-text-v1`
-* `cohere.embed-english-v3`
-* `cohere.embed-multilingual-v3`
-
-Supported parameters (to be passed in `create` method) are:
-
-| Parameter | Type | Default Value | Description |
-|---|---|---|---|
-| **name** | str | "amazon.titan-embed-text-v1" | The model ID of the bedrock model to use. Supported base models for Text Embeddings: amazon.titan-embed-text-v1, cohere.embed-english-v3, cohere.embed-multilingual-v3 |
-| **region** | str | "us-east-1" | Optional name of the AWS Region in which the service should be called (e.g., "us-east-1"). |
-| **profile_name** | str | None | Optional name of the AWS profile to use for calling the Bedrock service. If not specified, the default profile will be used. |
-| **assumed_role** | str | None | Optional ARN of an AWS IAM role to assume for calling the Bedrock service. If not specified, the current active credentials will be used. |
-| **role_session_name** | str | "lancedb-embeddings" | Optional name of the AWS IAM role session to use for calling the Bedrock service. If not specified, a "lancedb-embeddings" name will be used. |
-| **runtime** | bool | True | Optional choice of getting different client to perform operations with the Amazon Bedrock service. |
-| **max_retries** | int | 7 | Optional number of retries to perform when a request fails. |
-
-Usage Example:
-
-```python
-import lancedb
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
-
-model = get_registry().get("bedrock-text").create()
-
-class TextModel(LanceModel):
-    text: str = model.SourceField()
-    vector: Vector(model.ndims()) = model.VectorField()
-
-df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
-db = lancedb.connect("tmp_path")
-tbl = db.create_table("test", schema=TextModel, mode="overwrite")
-
-tbl.add(df)
-rs = tbl.search("hello").limit(1).to_pandas()
-```
-
-## Multi-modal embedding functions
-Multi-modal embedding functions allow you to query your table using both images and text.
-
-### OpenClip embeddings
-We support CLIP model embeddings using the open source alternative, [open-clip](https://github.com/mlfoundations/open_clip) which supports various customizations. It is registered as `open-clip` and supports the following customizations:
-
-| Parameter | Type | Default Value | Description |
-|---|---|---|---|
-| `name` | `str` | `"ViT-B-32"` | The name of the model. |
-| `pretrained` | `str` | `"laion2b_s34b_b79k"` | The name of the pretrained model to load. |
-| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
-| `batch_size` | `int` | `64` | The number of images to process in a batch. |
-| `normalize` | `bool` | `True` | Whether to normalize the input images before feeding them to the model. |
-
-This embedding function supports ingesting images as both bytes and urls. You can query them using both test and other images.
-
-!!! info
-    LanceDB supports ingesting images directly from accessible links.
-
-```python
-import lancedb
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
-
-db = lancedb.connect(tmp_path)
-func = get_registry.get("open-clip").create()
-
-class Images(LanceModel):
-    label: str
-    image_uri: str = func.SourceField() # image uri as the source
-    image_bytes: bytes = func.SourceField() # image bytes as the source
-    vector: Vector(func.ndims()) = func.VectorField() # vector column 
-    vec_from_bytes: Vector(func.ndims()) = func.VectorField() # Another vector column 
-
-table = db.create_table("images", schema=Images)
-labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
-uris = [
-    "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
-    "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
-    "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
-    "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
-    "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
-    "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
-]
-# get each uri as bytes
-image_bytes = [requests.get(uri).content for uri in uris]
-table.add(
-    [{"label": labels, "image_uri": uris, "image_bytes": image_bytes}]
-)
-```
-Now we can search using text from both the default vector column and the custom vector column
-```python
-
-# text search
-actual = table.search("man's best friend").limit(1).to_pydantic(Images)[0]
-print(actual.label) # prints "dog"
-
-frombytes = (
-    table.search("man's best friend", vector_column_name="vec_from_bytes")
-    .limit(1)
-    .to_pydantic(Images)[0]
-)
-print(frombytes.label)
-
-```
-
-Because we're using a multi-modal embedding function, we can also search using images
-
-```python
-# image search
-query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
-image_bytes = requests.get(query_image_uri).content
-query_image = Image.open(io.BytesIO(image_bytes))
-actual = table.search(query_image).limit(1).to_pydantic(Images)[0]
-print(actual.label == "dog")
-
-# image search using a custom vector column
-other = (
-    table.search(query_image, vector_column_name="vec_from_bytes")
-    .limit(1)
-    .to_pydantic(Images)[0]
-)
-print(actual.label)
-
-```
-
-### Imagebind embeddings
-We have support for [imagebind](https://github.com/facebookresearch/ImageBind) model embeddings. You can download our version of the packaged model via - `pip install imagebind-packaged==0.1.2`.
-
-This function is registered as `imagebind` and supports Audio, Video and Text modalities(extending to Thermal,Depth,IMU data):
-
-| Parameter | Type | Default Value | Description |
-|---|---|---|---|
-| `name` | `str` | `"imagebind_huge"` | Name of the model. |
-| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
-| `normalize` | `bool` | `False` | set to `True` to normalize your inputs before model ingestion. |
-
-Below is an example demonstrating how the API works:
-
-```python
-import lancedb
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
-
-db = lancedb.connect(tmp_path)
-func = get_registry.get("imagebind").create()
-
-class ImageBindModel(LanceModel):
-    text: str
-    image_uri: str = func.SourceField()
-    audio_path: str
-    vector: Vector(func.ndims()) = func.VectorField()
-
-# add locally accessible image paths
-text_list=["A dog.", "A car", "A bird"]
-image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
-audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]
-
-# Load data
-inputs = [
-    {"text": a, "audio_path": b, "image_uri": c}
-    for a, b, c in zip(text_list, audio_paths, image_paths)
-]
-
-#create table and add data
-table = db.create_table("img_bind", schema=ImageBindModel)
-table.add(inputs)
-```
-
-Now, we can search using any modality:
-
-#### image search
-```python
-query_image = "./assets/dog_image2.jpg" #download an image and enter that path here
-actual = table.search(query_image).limit(1).to_pydantic(ImageBindModel)[0]
-print(actual.text == "dog")
-```
-#### audio search
-
-```python
-query_audio = "./assets/car_audio2.wav" #download an audio clip and enter path here
-actual = table.search(query_audio).limit(1).to_pydantic(ImageBindModel)[0]
-print(actual.text == "car")
-```
-#### Text search
-You can add any input query and fetch the result as follows:
-```python
-query = "an animal which flies and tweets" 
-actual = table.search(query).limit(1).to_pydantic(ImageBindModel)[0]
-print(actual.text == "bird")
-```
-
-If you have any questions about the embeddings API, supported models, or see a relevant model missing, please raise an issue [on GitHub](https://github.com/lancedb/lancedb/issues).

docs/src/embeddings/embedding_functions.md

@@ -2,9 +2,12 @@ Representing multi-modal data as vector embeddings is becoming a standard practi
 
 For this purpose, LanceDB introduces an **embedding functions API**, that allow you simply set up once, during the configuration stage of your project. After this, the table remembers it, effectively making the embedding functions *disappear in the background* so you don't have to worry about manually passing callables, and instead, simply focus on the rest of your data engineering pipeline.
 
+!!! Note "Embedding functions on LanceDB cloud"
+    When using embedding functions with LanceDB cloud, the embeddings will be generated on the source device and sent to the cloud. This means that the source device must have the necessary resources to generate the embeddings.
+
 !!! warning
-    Using the embedding function registry means that you don't have to explicitly generate the embeddings yourself. 
+    Using the embedding function registry means that you don't have to explicitly generate the embeddings yourself.
-    However, if your embedding function changes, you'll have to re-configure your table with the new embedding function 
+    However, if your embedding function changes, you'll have to re-configure your table with the new embedding function
     and regenerate the embeddings. In the future, we plan to support the ability to change the embedding function via
     table metadata and have LanceDB automatically take care of regenerating the embeddings.
 
@@ -13,7 +16,7 @@ For this purpose, LanceDB introduces an **embedding functions API**, that allow
 
 === "Python"
     In the LanceDB python SDK, we define a global embedding function registry with
-    many different embedding models and even more coming soon. 
+    many different embedding models and even more coming soon.
     Here's let's an implementation of CLIP as example.
 
     ```python
@@ -23,20 +26,35 @@ For this purpose, LanceDB introduces an **embedding functions API**, that allow
     clip = registry.get("open-clip").create()
     ```
 
-    You can also define your own embedding function by implementing the `EmbeddingFunction` 
+    You can also define your own embedding function by implementing the `EmbeddingFunction`
     abstract base interface. It subclasses Pydantic Model which can be utilized to write complex schemas simply as we'll see next!
 
-=== "JavaScript""
+=== "TypeScript"
     In the TypeScript SDK, the choices are more limited. For now, only the OpenAI
     embedding function is available.
 
     ```javascript
-    const lancedb = require("vectordb");
+    import * as lancedb from '@lancedb/lancedb'
+    import { getRegistry } from '@lancedb/lancedb/embeddings'
 
     // You need to provide an OpenAI API key
     const apiKey = "sk-..."
     // The embedding function will create embeddings for the 'text' column
-    const embedding = new lancedb.OpenAIEmbeddingFunction('text', apiKey)
+    const func = getRegistry().get("openai").create({apiKey})
+    ```
+=== "Rust"
+    In the Rust SDK, the choices are more limited. For now, only the OpenAI
+    embedding function is available. But unlike the Python and TypeScript SDKs, you need manually register the OpenAI embedding function.
+
+    ```toml
+    // Make sure to include the `openai` feature
+    [dependencies]
+    lancedb = {version = "*", features = ["openai"]}
+    ```
+
+    ```rust
+    --8<-- "rust/lancedb/examples/openai.rs:imports"
+    --8<-- "rust/lancedb/examples/openai.rs:openai_embeddings"
     ```
 
 ## 2. Define the data model or schema
@@ -46,20 +64,20 @@ For this purpose, LanceDB introduces an **embedding functions API**, that allow
 
     ```python
     class Pets(LanceModel):
-        vector: Vector(clip.ndims) = clip.VectorField()
+        vector: Vector(clip.ndims()) = clip.VectorField()
         image_uri: str = clip.SourceField()
     ```
 
     `VectorField` tells LanceDB to use the clip embedding function to generate query embeddings for the `vector` column and `SourceField` ensures that when adding data, we automatically use the specified embedding function to encode `image_uri`.
 
-=== "JavaScript"
+=== "TypeScript"
 
     For the TypeScript SDK, a schema can be inferred from input data, or an explicit
     Arrow schema can be provided.
 
 ## 3. Create table and add data
 
-Now that we have chosen/defined our embedding function and the schema, 
+Now that we have chosen/defined our embedding function and the schema,
 we can create the table and ingest data without needing to explicitly generate
 the embeddings at all:
 
@@ -71,17 +89,26 @@ the embeddings at all:
     table.add([{"image_uri": u} for u in uris])
     ```
 
-=== "JavaScript"
+=== "TypeScript"
 
-    ```javascript
+    === "@lancedb/lancedb"
-    const db = await lancedb.connect("data/sample-lancedb");
-    const data = [
-    { text: "pepperoni"},
-    { text: "pineapple"}
-    ]
 
-    const table = await db.createTable("vectors", data, embedding)
+        ```ts
-    ```
+        --8<-- "nodejs/examples/embedding.test.ts:imports"
+        --8<-- "nodejs/examples/embedding.test.ts:embedding_function"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```ts
+        const db = await lancedb.connect("data/sample-lancedb");
+        const data = [
+            { text: "pepperoni"},
+            { text: "pineapple"}
+        ]
+
+        const table = await db.createTable("vectors", data, embedding)
+        ```
 
 ## 4. Querying your table
 Not only can you forget about the embeddings during ingestion, you also don't
@@ -94,8 +121,8 @@ need to worry about it when you query the table:
     ```python
     results = (
         table.search("dog")
-        .limit(10)
+            .limit(10)
-        .to_pandas()
+            .to_pandas()
     )
     ```
 
@@ -106,22 +133,32 @@ need to worry about it when you query the table:
     query_image = Image.open(p)
     results = (
         table.search(query_image)
-        .limit(10)
+            .limit(10)
-        .to_pandas()
+            .to_pandas()
     )
     ```
 
     Both of the above snippet returns a pandas DataFrame with the 10 closest vectors to the query.
 
-=== "JavaScript"
+=== "TypeScript"
+
+    === "@lancedb/lancedb"
+
+        ```ts
+        const results = await table.search("What's the best pizza topping?")
+            .limit(10)
+            .toArray()
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```ts
+        const results = await table
+            .search("What's the best pizza topping?")
+            .limit(10)
+            .execute()
+        ```
 
-    ```javascript
-    const results = await table
-    .search("What's the best pizza topping?")
-    .limit(10)
-    .execute()
-    ```    
-    
     The above snippet returns an array of records with the top 10 nearest neighbors to the query.
 
 ---
@@ -149,7 +186,7 @@ You can also use the integration for adding utility operations in the schema. Fo
 
 ```python
 class Pets(LanceModel):
-    vector: Vector(clip.ndims) = clip.VectorField()
+    vector: Vector(clip.ndims()) = clip.VectorField()
     image_uri: str = clip.SourceField()
 
     @property
@@ -166,4 +203,4 @@ rs[2].image
 ![](../assets/dog_clip_output.png)
 
 Now that you have the basic idea about LanceDB embedding functions and the embedding function registry,
-let's dive deeper into defining your own [custom functions](./custom_embedding_function.md).
+let's dive deeper into defining your own [custom functions](./custom_embedding_function.md).

docs/src/embeddings/index.md

@@ -1,13 +1,13 @@
-Due to the nature of vector embeddings, they can be used to represent any kind of data, from text to images to audio. 
+Due to the nature of vector embeddings, they can be used to represent any kind of data, from text to images to audio.
-This makes them a very powerful tool for machine learning practitioners. 
+This makes them a very powerful tool for machine learning practitioners.
-However, there's no one-size-fits-all solution for generating embeddings - there are many different libraries and APIs 
+However, there's no one-size-fits-all solution for generating embeddings - there are many different libraries and APIs
 (both commercial and open source) that can be used to generate embeddings from structured/unstructured data.
 
 LanceDB supports 3 methods of working with embeddings.
 
 1. You can manually generate embeddings for the data and queries. This is done outside of LanceDB.
 2. You can use the built-in [embedding functions](./embedding_functions.md) to embed the data and queries in the background.
-3. For python users, you can define your own [custom embedding function](./custom_embedding_function.md)
+3. You can define your own [custom embedding function](./custom_embedding_function.md)
    that extends the default embedding functions.
 
 For python users, there is also a legacy [with_embeddings API](./legacy.md).
@@ -18,57 +18,115 @@ It is retained for compatibility and will be removed in a future version.
 To get started with embeddings, you can use the built-in embedding functions.
 
 ### OpenAI Embedding function
+
 LanceDB registers the OpenAI embeddings function in the registry as `openai`. You can pass any supported model name to the `create`. By default it uses `"text-embedding-ada-002"`.
 
+=== "Python"
+
+    ```python
+    import lancedb
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.embeddings import get_registry
+
+    db = lancedb.connect("/tmp/db")
+    func = get_registry().get("openai").create(name="text-embedding-ada-002")
+
+    class Words(LanceModel):
+        text: str = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("words", schema=Words, mode="overwrite")
+    table.add(
+        [
+            {"text": "hello world"},
+            {"text": "goodbye world"}
+        ]
+        )
+
+    query = "greetings"
+    actual = table.search(query).limit(1).to_pydantic(Words)[0]
+    print(actual.text)
+    ```
+
+=== "TypeScript"
+
+    ```typescript
+    --8<--- "nodejs/examples/embedding.test.ts:imports"
+    --8<--- "nodejs/examples/embedding.test.ts:openai_embeddings"
+    ```
+
+=== "Rust"
+
+    ```rust
+    --8<--- "rust/lancedb/examples/openai.rs:imports"
+    --8<--- "rust/lancedb/examples/openai.rs:openai_embeddings"
+    ```
+
+### Sentence Transformers Embedding function
+LanceDB registers the Sentence Transformers embeddings function in the registry as `sentence-transformers`. You can pass any supported model name to the `create`. By default it uses `"sentence-transformers/paraphrase-MiniLM-L6-v2"`.
+
+=== "Python"
+    ```python
+    import lancedb
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.embeddings import get_registry
+
+    db = lancedb.connect("/tmp/db")
+    model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
+
+    class Words(LanceModel):
+        text: str = model.SourceField()
+        vector: Vector(model.ndims()) = model.VectorField()
+
+    table = db.create_table("words", schema=Words)
+    table.add(
+        [
+            {"text": "hello world"},
+            {"text": "goodbye world"}
+        ]
+    )
+
+    query = "greetings"
+    actual = table.search(query).limit(1).to_pydantic(Words)[0]
+    print(actual.text)
+    ```
+
+=== "TypeScript"
+
+    Coming Soon!
+
+=== "Rust"
+
+    Coming Soon!
+
+### Embedding function with LanceDB cloud
+Embedding functions are now supported on LanceDB cloud. The embeddings will be generated on the source device and sent to the cloud. This means that the source device must have the necessary resources to generate the embeddings. Here's an example using the OpenAI embedding function:
+
 ```python
+import os
 import lancedb
 from lancedb.pydantic import LanceModel, Vector
 from lancedb.embeddings import get_registry
+os.environ['OPENAI_API_KEY'] = "..."
 
-db = lancedb.connect("/tmp/db")
+db = lancedb.connect(
-func = get_registry().get("openai").create(name="text-embedding-ada-002")
+  uri="db://....",
+  api_key="sk_...",
+  region="us-east-1"
+)
+func = get_registry().get("openai").create()
 
 class Words(LanceModel):
     text: str = func.SourceField()
     vector: Vector(func.ndims()) = func.VectorField()
 
-table = db.create_table("words", schema=Words, mode="overwrite")
+table = db.create_table("words", schema=Words)
-table.add(
+table.add([
-    [
+    {"text": "hello world"},
-        {"text": "hello world"},
+    {"text": "goodbye world"}
-        {"text": "goodbye world"}
+])
-    ]
-    )
 
 query = "greetings"
 actual = table.search(query).limit(1).to_pydantic(Words)[0]
 print(actual.text)
 ```
-
-### Sentence Transformers Embedding function
-LanceDB registers the Sentence Transformers embeddings function in the registry as `sentence-transformers`. You can pass any supported model name to the `create`. By default it uses `"sentence-transformers/paraphrase-MiniLM-L6-v2"`.
-
-```python
-import lancedb
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
-
-db = lancedb.connect("/tmp/db")
-model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
-
-class Words(LanceModel):
-    text: str = model.SourceField()
-    vector: Vector(model.ndims()) = model.VectorField()
-
-table = db.create_table("words", schema=Words)
-table.add(
-    [
-        {"text": "hello world"},
-        {"text": "goodbye world"}
-    ]
-)
-
-query = "greetings"
-actual = table.search(query).limit(1).to_pydantic(Words)[0]
-print(actual.text)
-```

docs/src/embeddings/understanding_embeddings.md

@@ -0,0 +1,133 @@
+# Understand Embeddings
+
+The term **dimension** is a synonym for the number of elements in a feature vector. Each feature can be thought of as a different axis in a geometric space. 
+
+High-dimensional data means there are many features(or attributes) in the data.
+
+!!! example
+     1. An image is a data point and it might have thousands of dimensions because each pixel could be considered as a feature. 
+
+     2. Text data, when represented by each word or character, can also lead to high dimensions, especially when considering all possible words in a language.
+
+Embedding captures **meaning and relationships** within data by mapping high-dimensional data into a lower-dimensional space. It captures it by placing inputs that are more **similar in meaning** closer together in the **embedding space**. 
+
+## What are Vector Embeddings?
+
+Vector embeddings is a way to convert complex data, like text, images, or audio into numerical coordinates (called vectors) that can be plotted in an n-dimensional space(embedding space). 
+
+The closer these data points are related in the real world, the closer their corresponding numerical coordinates (vectors) will be to each other in the embedding space. This proximity in the embedding space reflects their semantic similarities, allowing machines to intuitively understand and process the data in a way that mirrors human perception of relationships and meaning.
+
+In a way, it captures the most important aspects of the data while ignoring the less important ones. As a result, tasks like searching for related content or identifying patterns become more efficient and accurate, as the embeddings make it possible to quantify how **closely related** different **data points** are and **reduce** the **computational complexity**.
+
+??? question "Are vectors and embeddings the same thing?"
+
+    When we say “vectors” we mean - **list of numbers** that **represents the data**. 
+    When we say “embeddings” we mean - **list of numbers** that **capture important details and relationships**.
+
+    Although the terms are often used interchangeably, “embeddings” highlight how the data is represented with meaning and structure, while “vector” simply refers to the numerical form of that representation.
+
+## Embedding vs Indexing
+
+We already saw that creating **embeddings** on data is a method of creating **vectors** for a **n-dimensional embedding space** that captures the meaning and relationships inherent in the data.
+
+Once we have these **vectors**, indexing comes into play. Indexing is a method of organizing these vector embeddings, that allows us to quickly and efficiently locate and retrieve them from the entire dataset of vector embeddings.
+
+## What types of data/objects can be embedded?
+
+The following are common types of data that can be embedded:
+
+1. **Text**: Text data includes sentences, paragraphs, documents, or any written content.
+2. **Images**:  Image data encompasses photographs, illustrations, or any visual content.
+3. **Audio**: Audio data includes sounds, music, speech, or any auditory content.
+4. **Video**:  Video data consists of moving images and sound, which can convey complex information.
+
+Large datasets of multi-modal data (text, audio, images, etc.) can be converted into embeddings with the appropriate model.
+
+!!! tip "LanceDB vs Other traditional Vector DBs"
+    While many vector databases primarily focus on the storage and retrieval of vector embeddings, **LanceDB** uses **Lance file format** (operates on a disk-based architecture), which allows for the storage and management of not just embeddings but also **raw file data (bytes)**. This capability means that users can integrate various types of data, including images and text, alongside their vector embeddings in a unified system.
+
+    With the ability to store both vectors and associated file data, LanceDB enhances the querying process. Users can perform semantic searches that not only retrieve similar embeddings but also access related files and metadata, thus streamlining the workflow.
+
+## How does embedding works?
+
+As mentioned, after creating embedding, each data point is represented as a vector in a n-dimensional space (embedding space). The dimensionality of this space can vary depending on the complexity of the data and the specific embedding technique used.
+
+Points that are close to each other in vector space are considered similar (or appear in similar contexts), and points that are far away are considered dissimilar. To quantify this closeness, we use distance as a metric which can be measured in the  following way - 
+
+1. **Euclidean Distance (L2)**: It calculates the straight-line distance between two points (vectors) in a multidimensional space.
+2. **Cosine Similarity**: It measures the cosine of the angle between two vectors, providing a normalized measure of similarity based on their direction.
+3. **Dot product**: It is calculated as the sum of the products of their corresponding components. To measure relatedness it considers both the magnitude and direction of the vectors.
+
+## How do you create and store vector embeddings for your data?
+
+1. **Creating embeddings**: Choose an embedding model, it can be a pre-trained model (open-source or commercial) or you can train a custom embedding model for your scenario. Then feed your preprocessed data into the chosen model to obtain embeddings.
+
+??? question "Popular choices for embedding models"
+    For text data, popular choices are OpenAI’s text-embedding models, Google Gemini text-embedding models, Cohere’s Embed models, and SentenceTransformers, etc.
+
+    For image data, popular choices are CLIP (Contrastive Language–Image Pretraining), Imagebind embeddings by meta (supports audio, video, and image), and Jina multi-modal embeddings, etc.
+
+2. **Storing vector embeddings**: This effectively requires **specialized databases** that can handle the complexity of vector data, as traditional databases often struggle with this task. Vector databases are designed specifically for storing and querying vector embeddings. They optimize for efficient nearest-neighbor searches and provide built-in indexing mechanisms.
+
+!!! tip "Why LanceDB"
+    LanceDB **automates** the entire process of creating and storing embeddings for your data. LanceDB allows you to define and use **embedding functions**, which can be **pre-trained models** or **custom models**. 
+    
+    This enables you to **generate** embeddings tailored to the nature of your data (e.g., text, images) and **store** both the **original data** and **embeddings** in a **structured schema** thus providing efficient querying capabilities for similarity searches.
+
+Let's quickly [get started](./index.md) and learn how to manage embeddings in LanceDB. 
+
+## Bonus: As a developer, what you can create using embeddings?
+
+As a developer, you can create a variety of innovative applications using vector embeddings. Check out the following - 
+
+<div class="grid cards" markdown>
+
+-   __Chatbots__
+
+    ---
+
+    Develop chatbots that utilize embeddings to retrieve relevant context and generate coherent, contextually aware responses to user queries.
+
+    [:octicons-arrow-right-24: Check out examples](../examples/python_examples/chatbot.md)
+
+-   __Recommendation Systems__
+
+    ---
+
+    Develop systems that recommend content (such as articles, movies, or products) based on the similarity of keywords and descriptions, enhancing user experience.
+
+    [:octicons-arrow-right-24: Check out examples](../examples/python_examples/recommendersystem.md)
+
+-   __Vector Search__
+
+    ---
+
+    Build powerful applications that harness the full potential of semantic search, enabling them to retrieve relevant data quickly and effectively. 
+
+    [:octicons-arrow-right-24: Check out examples](../examples/python_examples/vector_search.md)
+
+-   __RAG Applications__
+
+    ---
+
+    Combine the strengths of large language models (LLMs) with retrieval-based approaches to create more useful applications.
+
+    [:octicons-arrow-right-24: Check out examples](../examples/python_examples/rag.md)
+
+-   __Many more examples__
+
+    ---
+
+    Explore applied examples available as Colab notebooks or Python scripts to integrate into your applications.
+
+    [:octicons-arrow-right-24: More](../examples/examples_python.md)
+
+</div>
+
+
+
+
+
+
+
+

docs/src/examples/examples_python.md

@@ -1,17 +1,22 @@
-# Examples: Python
+# Overview : Python Examples 
 
-To help you get started, we provide some examples, projects and applications that use the LanceDB Python API. You can always find the latest examples in our [VectorDB Recipes](https://github.com/lancedb/vectordb-recipes) repository.
+To help you get started, we provide some examples, projects, and applications that use the LanceDB Python API. These examples are designed to get you right into the code with minimal introduction, enabling you to move from an idea to a proof of concept in minutes. 
 
-| Example | Interactive Envs | Scripts  |
+You can find the latest examples in our [VectorDB Recipes](https://github.com/lancedb/vectordb-recipes) repository. 
-|-------- | ---------------- | ------   |
+
-| | | |
+**Introduction**
-| [Youtube transcript search bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/youtube_bot/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/main.py)|
+
-| [Langchain: Code Docs QA bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/main.py) |
+Explore applied examples available as Colab notebooks or Python scripts to integrate into your applications. You can also checkout our blog posts related to the particular example for deeper understanding.
-| [AI Agents: Reducing Hallucination](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/main.py)|
+
-| [Multimodal CLIP: DiffusionDB](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_clip/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_clip/main.py)  |
+| Explore                                         | Description                                                                                                                                                      |
-| [Multimodal CLIP: Youtube videos](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_video_search/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_video_search/main.py)  |
+|----------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Movie Recommender](https://github.com/lancedb/vectordb-recipes/tree/main/examples/movie-recommender/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/movie-recommender/main.py)  |
+| [**Build from Scratch with LanceDB** 🛠️🚀](python_examples/build_from_scratch.md)               | Start building your **GenAI applications** from the **ground up** using **LanceDB's** efficient vector-based document retrieval capabilities! Get started quickly with a solid foundation.      |
-| [Audio Search](https://github.com/lancedb/vectordb-recipes/tree/main/examples/audio_search/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/audio_search/main.py)  |
+| [**Multimodal Search with LanceDB** 🤹‍♂️🔍](python_examples/multimodal.md)               | Combine **text** and **image queries** to find the most relevant results using **LanceDB’s multimodal** capabilities. Leverage the efficient vector-based similarity search.   |
-| [Multimodal Image + Text Search](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_search/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_search/main.py)  |
+| [**RAG (Retrieval-Augmented Generation) with LanceDB** 🔓🧐](python_examples/rag.md) | Build RAG (Retrieval-Augmented Generation) with **LanceDB** for efficient **vector-based information retrieval** and more accurate responses from AI.                    |
-| [Evaluating Prompts with Prompttools](https://github.com/lancedb/vectordb-recipes/tree/main/examples/prompttools-eval-prompts/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |  |
+| [**Vector Search: Efficient Retrieval** 🔓👀](python_examples/vector_search.md) | Use **LanceDB's** vector search capabilities to perform efficient and accurate **similarity searches**, enabling rapid discovery and retrieval of relevant documents in Large datasets.                                                 |
+| [**Chatbot applications with LanceDB** 🤖](python_examples/chatbot.md)                | Create **chatbots** that retrieves relevant context for **coherent and context-aware replies**, enhancing user experience through advanced conversational AI. |
+| [**Evaluation: Assessing Text Performance with Precision** 📊💡](python_examples/evaluations.md) | Develop **evaluation** applications that allows you to input reference and candidate texts to **measure** their performance across various metrics.                                                      |
+| [**AI Agents: Intelligent Collaboration** 🤖](python_examples/aiagent.md)              | Enable **AI agents** to communicate and collaborate efficiently through dense vector representations, achieving shared goals seamlessly.                                         |
+| [**Recommender Systems: Personalized Discovery** 🍿📺](python_examples/recommendersystem.md)      | Deliver **personalized experiences** by efficiently storing and querying item embeddings with **LanceDB's** powerful vector database capabilities.                        |
+| **Miscellaneous Examples🌟** | Find other **unique examples** and **creative solutions** using **LanceDB**, showcasing the flexibility and broad applicability of the platform. |
 

docs/src/examples/index.md

@@ -8,9 +8,15 @@ LanceDB provides language APIs, allowing you to embed a database in your languag
 * 👾 [JavaScript](examples_js.md) examples
 * 🦀 Rust examples (coming soon)
 
-## Applications powered by LanceDB
+## Python Applications powered by LanceDB
 
-| Project Name                                        | Description                                                                                                          | Screenshot                                |
+| Project Name | Description |
-|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
+| --- | --- |
-| [YOLOExplorer](https://github.com/lancedb/yoloexplorer) | Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds                  | ![YOLOExplorer](https://github.com/lancedb/vectordb-recipes/assets/15766192/ae513a29-8f15-4e0b-99a1-ccd8272b6131) |
+| **Ultralytics Explorer 🚀**<br>[![Ultralytics](https://img.shields.io/badge/Ultralytics-Docs-green?labelColor=0f3bc4&style=flat-square&logo=https://cdn.prod.website-files.com/646dd1f1a3703e451ba81ecc/64994922cf2a6385a4bf4489_UltralyticsYOLO_mark_blue.svg&link=https://docs.ultralytics.com/datasets/explorer/)](https://docs.ultralytics.com/datasets/explorer/)<br>[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb) |  - 🔍 **Explore CV Datasets**: Semantic search, SQL queries, vector similarity, natural language.<br>- 🖥️ **GUI & Python API**: Seamless dataset interaction.<br>- ⚡ **Efficient & Scalable**: Leverages LanceDB for large datasets.<br>- 📊 **Detailed Analysis**: Easily analyze data patterns.<br>- 🌐 **Browser GUI Demo**: Create embeddings, search images, run queries. |
-| [Website Chatbot (Deployable Vercel Template)](https://github.com/lancedb/lancedb-vercel-chatbot) | Create a chatbot from the sitemap of any website/docs of your choice. Built using vectorDB serverless native javascript package. | ![Chatbot](../assets/vercel-template.gif)    |
+| **Website Chatbot🤖**<br>[![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/lancedb-vercel-chatbot)<br>[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Flancedb%2Flancedb-vercel-chatbot&amp;env=OPENAI_API_KEY&amp;envDescription=OpenAI%20API%20Key%20for%20chat%20completion.&amp;project-name=lancedb-vercel-chatbot&amp;repository-name=lancedb-vercel-chatbot&amp;demo-title=LanceDB%20Chatbot%20Demo&amp;demo-description=Demo%20website%20chatbot%20with%20LanceDB.&amp;demo-url=https%3A%2F%2Flancedb.vercel.app&amp;demo-image=https%3A%2F%2Fi.imgur.com%2FazVJtvr.png) | - 🌐 **Chatbot from Sitemap/Docs**: Create a chatbot using site or document context.<br>- 🚀 **Embed LanceDB in Next.js**: Lightweight, on-prem storage.<br>- 🧠 **AI-Powered Context Retrieval**: Efficiently access relevant data.<br>- 🔧 **Serverless & Native JS**: Seamless integration with Next.js.<br>- ⚡ **One-Click Deploy on Vercel**: Quick and easy setup.. |
+
+## Nodejs Applications powered by LanceDB
+
+| Project Name | Description |
+| --- | --- |
+| **Langchain Writing Assistant✍️ **<br>[![Github](../assets/github.svg)](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/lanchain_writing_assistant) |  - **📂 Data Source Integration**:  Use your own data by specifying data source file, and the app instantly processes it to provide insights. <br>- **🧠 Intelligent Suggestions**:  Powered by LangChain.js and LanceDB, it improves writing productivity and accuracy.  <br>- **💡 Enhanced Writing Experience**: It delivers real-time contextual insights and factual suggestions while the user writes. |

docs/src/examples/python_examples/aiagent.md

@@ -0,0 +1,27 @@
+# AI Agents: Intelligent Collaboration🤖
+
+Think of a platform where AI Agents can seamlessly exchange information, coordinate over tasks, and achieve shared targets with great efficiency💻📈.
+
+## Vector-Based Coordination: The Technical Advantage
+Leveraging LanceDB's vector-based capabilities, we can enable **AI agents 🤖** to communicate and collaborate through dense vector representations. AI agents can exchange information, coordinate on a task or work towards a common goal, just by giving queries📝.
+
+| **AI Agents** | **Description** | **Links** |
+|:--------------|:----------------|:----------|
+| **AI Agents: Reducing Hallucinationt📊** | 🤖💡 **Reduce AI hallucinations** using Critique-Based Contexting! Learn by Simplifying and Automating tedious workflows by going through fitness trainer agent example.💪 | [![Github](../../assets/github.svg)][hullucination_github] <br>[![Open In Collab](../../assets/colab.svg)][hullucination_colab] <br>[![Python](../../assets/python.svg)][hullucination_python] <br>[![Ghost](../../assets/ghost.svg)][hullucination_ghost] |                                                                                                                                                                                                                                                
+| **AI Trends Searcher: CrewAI🔍️** | 🔍️ Learn about **CrewAI Agents** ! Utilize the features of CrewAI - Role-based Agents, Task Management, and Inter-agent Delegation ! Make AI agents work together to do tricky stuff 😺| [![Github](../../assets/github.svg)][trend_github] <br>[![Open In Collab](../../assets/colab.svg)][trend_colab] <br>[![Ghost](../../assets/ghost.svg)][trend_ghost] |                                                                                                                                                                                                                                                
+| **SuperAgent Autogen🤖** | 💻 AI interactions with the Super Agent! Integrating **Autogen**, **LanceDB**, **LangChain**, **LiteLLM**, and **Ollama** to create AI agent that excels in understanding and processing complex queries.🤖 | [![Github](../../assets/github.svg)][superagent_github] <br>[![Open In Collab](../../assets/colab.svg)][superagent_colab] |
+
+
+[hullucination_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents
+[hullucination_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents/main.ipynb
+[hullucination_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents/main.py
+[hullucination_ghost]: https://blog.lancedb.com/how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f/
+
+[trend_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/AI-Trends-with-CrewAI
+[trend_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/AI-Trends-with-CrewAI/CrewAI_AI_Trends.ipynb 
+[trend_ghost]: https://blog.lancedb.com/track-ai-trends-crewai-agents-rag/
+
+[superagent_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/SuperAgent_Autogen
+[superagent_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/SuperAgent_Autogen/main.ipynb
+
+

docs/src/examples/python_examples/build_from_scratch.md

@@ -0,0 +1,13 @@
+# **Build from Scratch with LanceDB 🛠️🚀**
+
+Start building your GenAI applications from the ground up using **LanceDB's** efficient vector-based document retrieval capabilities! 📑
+
+**Get Started in Minutes ⏱️**
+
+These examples provide a solid foundation for building your own GenAI applications using LanceDB. Jump from idea to **proof of concept** quickly with applied examples. Get started and see what you can create! 💻
+
+| **Build From Scratch**                     | **Description**                                                                                              | **Links**                                                                                                                                                                                                                                                                                                                                                                                                                |
+|:-------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| **Build RAG from Scratch🚀💻**             | 📝 Create a **Retrieval-Augmented Generation** (RAG) model from scratch using LanceDB.                       | [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/RAG-from-Scratch)<br>[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)]()                                                                                                                                  |
+| **Local RAG from Scratch with Llama3🔥💡** | 🐫 Build a local RAG model using **Llama3** and **LanceDB** for fast and efficient text generation.          | [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Local-RAG-from-Scratch)<br>[![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Local-RAG-from-Scratch/rag.py)    |
+| **Multi-Head RAG from Scratch📚💻**        | 🤯 Develop a **Multi-Head RAG model** from scratch, enabling generation of text based on multiple documents. | [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Multi-Head-RAG-from-Scratch)<br>[![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Multi-Head-RAG-from-Scratch) |

docs/src/examples/python_examples/chatbot.md

@@ -0,0 +1,41 @@
+**Chatbot applications with LanceDB 🤖**
+====================================================================
+
+ Create innovative chatbot applications that utilizes LanceDB for efficient vector-based response generation! 🌐✨ 
+ 
+**Introduction 👋✨**
+
+ Users can input their queries, allowing the chatbot to retrieve relevant context seamlessly. 🔍📚 This enables the generation of coherent and context-aware replies that enhance user experience. 🌟🤝 Dive into the world of advanced conversational AI and streamline interactions with powerful data management! 🚀💡
+
+
+| **Chatbot**  | **Description**  | **Links**  |
+|:----------------|:-----------------|:-----------|
+| **Databricks DBRX Website Bot ⚡️** | Engage with the **Hogwarts chatbot**, that uses Open-source RAG with **DBRX**, **LanceDB** and **LLama-index with Hugging Face Embeddings**, to provide interactive and engaging user experiences. ✨ | [![GitHub](../../assets/github.svg)][databricks_github] <br>[![Python](../../assets/python.svg)][databricks_python] |
+| **CLI SDK Manual Chatbot Locally 💻** | CLI chatbot for SDK/hardware documents using **Local RAG** with **LLama3**, **Ollama**, **LanceDB**, and **Openhermes Embeddings**, built with **Phidata** Assistant and Knowledge Base 🤖 | [![GitHub](../../assets/github.svg)][clisdk_github] <br>[![Python](../../assets/python.svg)][clisdk_python] |
+| **Youtube Transcript Search QA Bot 📹** |  Search through **youtube transcripts** using natural language with a Q&A bot, leveraging **LanceDB** for effortless data storage and management 💬 | [![GitHub](../../assets/github.svg)][youtube_github] <br>[![Open In Collab](../../assets/colab.svg)][youtube_colab] <br>[![Python](../../assets/python.svg)][youtube_python] |
+| **Code Documentation Q&A Bot with LangChain 🤖** | Query your own documentation easily using questions in natural language with a Q&A bot, powered by **LangChain** and **LanceDB**, demonstrated with **Numpy 1.26 docs** 📚 | [![GitHub](../../assets/github.svg)][docs_github] <br>[![Open In Collab](../../assets/colab.svg)][docs_colab] <br>[![Python](../../assets/python.svg)][docs_python] |
+| **Context-aware Chatbot using Llama 2 & LanceDB 🤖** | Build **conversational AI** with a **context-aware chatbot**, powered by **Llama 2**, **LanceDB**, and **LangChain**, that enables intuitive and meaningful conversations with your data 📚💬 | [![GitHub](../../assets/github.svg)][aware_github] <br>[![Open In Collab](../../assets/colab.svg)][aware_colab] <br>[![Ghost](../../assets/ghost.svg)][aware_ghost] |
+| **Chat with csv using Hybrid Search 📊** | **Chat** application that interacts with **CSV** and **Excel files** using **LanceDB’s** hybrid search capabilities, performing direct operations on large-scale columnar data efficiently 🚀 | [![GitHub](../../assets/github.svg)][csv_github] <br>[![Open In Collab](../../assets/colab.svg)][csv_colab] <br>[![Ghost](../../assets/ghost.svg)][csv_ghost] |
+
+
+[databricks_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/databricks_DBRX_website_bot
+[databricks_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/databricks_DBRX_website_bot/main.py
+
+[clisdk_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/CLI-SDK-Manual-Chatbot-Locally
+[clisdk_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/CLI-SDK-Manual-Chatbot-Locally/assistant.py
+
+[youtube_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Youtube-Search-QA-Bot
+[youtube_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Youtube-Search-QA-Bot/main.ipynb
+[youtube_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Youtube-Search-QA-Bot/main.py
+
+[docs_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot
+[docs_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot/main.ipynb
+[docs_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot/main.py
+
+[aware_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB
+[aware_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb
+[aware_ghost]: https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755
+
+[csv_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/Chat_with_csv_file
+[csv_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/Chat_with_csv_file/main.ipynb
+[csv_ghost]: https://blog.lancedb.com/p/d8c71df4-e55f-479a-819e-cde13354a6a3/

docs/src/examples/python_examples/evaluations.md

@@ -0,0 +1,21 @@
+**Evaluation: Assessing Text Performance with Precision 📊💡**
+====================================================================
+
+Evaluation is a comprehensive tool designed to measure the performance of text-based inputs, enabling data-driven optimization and improvement 📈. 
+
+**Text Evaluation 101 📚**
+
+Using robust framework for assessing reference and candidate texts across various metrics📊, ensure that the text outputs are high-quality and meet specific requirements and standards📝.
+
+| **Evaluation** | **Description** | **Links** |
+| -------------- | --------------- | --------- |
+| **Evaluating Prompts with Prompttools 🤖** | Compare, visualize & evaluate **embedding functions** (incl. OpenAI) across metrics like latency & custom evaluation 📈📊 | [![Github](../../assets/github.svg)][prompttools_github] <br>[![Open In Collab](../../assets/colab.svg)][prompttools_colab] |
+| **Evaluating RAG with RAGAs and GPT-4o 📊** | Evaluate **RAG pipelines** with cutting-edge metrics and tools, integrate with CI/CD for continuous performance checks, and generate responses with GPT-4o 🤖📈 | [![Github](../../assets/github.svg)][RAGAs_github] <br>[![Open In Collab](../../assets/colab.svg)][RAGAs_colab] |
+
+
+
+[prompttools_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts
+[prompttools_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb
+
+[RAGAs_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Evaluating_RAG_with_RAGAs
+[RAGAs_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Evaluating_RAG_with_RAGAs/Evaluating_RAG_with_RAGAs.ipynb

docs/src/examples/python_examples/multimodal.md

@@ -0,0 +1,28 @@
+# **Multimodal Search with LanceDB 🤹‍♂️🔍**
+
+Using LanceDB's multimodal capabilities, combine text and image queries to find the most relevant results in your corpus ! 🔓💡
+
+**Explore the Future of Search 🚀**
+
+LanceDB supports multimodal search by indexing and querying vector representations of text and image data 🤖. This enables efficient retrieval of relevant documents and images using vector-based similarity search 📊. The platform facilitates cross-modal search, allowing for text-image and image-text retrieval, and supports scalable indexing of high-dimensional vector spaces 💻.
+
+
+
+| **Multimodal**  | **Description**  | **Links**  |
+|:----------------|:-----------------|:-----------|
+| **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/) |
+| **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/)                                                                                                                                                                                                                   |
+
+
+[Clip_diffusionDB_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip_diffusiondb
+[Clip_diffusionDB_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip_diffusiondb/main.ipynb
+[Clip_diffusionDB_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip_diffusiondb/main.py
+[Clip_diffusionDB_ghost]: https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/
+
+
+[Clip_youtube_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search
+[Clip_youtube_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search/main.ipynb
+[Clip_youtube_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search/main.py
+[Clip_youtube_ghost]: https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/

docs/src/examples/python_examples/rag.md

@@ -0,0 +1,83 @@
+**RAG (Retrieval-Augmented Generation) with LanceDB 🔓🧐**
+====================================================================
+
+Build RAG (Retrieval-Augmented Generation) with  LanceDB, a powerful solution for efficient vector-based information retrieval 📊. 
+
+**Experience the Future of Search 🔄**
+
+🤖 RAG enables AI to **retrieve** relevant information from external sources and use it to **generate** more accurate and context-specific responses. 💻 LanceDB provides a robust framework for integrating LLMs with external knowledge sources 📝.
+
+| **RAG**                                      | **Description**                                                                                                                                                  | **Links**                  |
+|----------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|                                                                                                                                                     
+| **RAG with Matryoshka Embeddings and LlamaIndex** 🪆🔗  | Utilize **Matryoshka embeddings** and **LlamaIndex** to improve the efficiency and accuracy of your RAG models. 📈✨ | [![Github](../../assets/github.svg)][matryoshka_github] <br>[![Open In Collab](../../assets/colab.svg)][matryoshka_colab]  |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Improve RAG with Re-ranking** 📈🔄 | Enhance your RAG applications by implementing **re-ranking strategies** for more relevant document retrieval. 📚🔍 | [![Github](../../assets/github.svg)][rag_reranking_github] <br>[![Open In Collab](../../assets/colab.svg)][rag_reranking_colab] <br>[![Ghost](../../assets/ghost.svg)][rag_reranking_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Instruct-Multitask** 🧠🎯 |  Integrate the **Instruct Embedding Model** with LanceDB to streamline your embedding API, reducing redundant code and overhead. 🌐📊 | [![Github](../../assets/github.svg)][instruct_multitask_github] <br>[![Open In Collab](../../assets/colab.svg)][instruct_multitask_colab] <br>[![Python](../../assets/python.svg)][instruct_multitask_python] <br>[![Ghost](../../assets/ghost.svg)][instruct_multitask_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Improve RAG with HyDE** 🌌🔍 | Use **Hypothetical Document Embeddings** for efficient, accurate, and unsupervised dense retrieval. 📄🔍 | [![Github](../../assets/github.svg)][hyde_github] <br>[![Open In Collab](../../assets/colab.svg)][hyde_colab]<br>[![Ghost](../../assets/ghost.svg)][hyde_ghost]                 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Improve RAG with LOTR** 🧙‍♂️📜 | Enhance RAG with **Lord of the Retriever (LOTR)** to address 'Lost in the Middle' challenges, especially in medical data. 🌟📜 | [![Github](../../assets/github.svg)][lotr_github] <br>[![Open In Collab](../../assets/colab.svg)][lotr_colab] <br>[![Ghost](../../assets/ghost.svg)][lotr_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Advanced RAG: Parent Document Retriever** 📑🔗 | Use **Parent Document & Bigger Chunk Retriever** to maintain context and relevance when generating related content. 🎵📄 | [![Github](../../assets/github.svg)][parent_doc_retriever_github] <br>[![Open In Collab](../../assets/colab.svg)][parent_doc_retriever_colab] <br>[![Ghost](../../assets/ghost.svg)][parent_doc_retriever_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Corrective RAG with Langgraph** 🔧📊 | Enhance RAG reliability with **Corrective RAG (CRAG)** by self-reflecting and fact-checking for accurate and trustworthy results. ✅🔍 |[![Github](../../assets/github.svg)][corrective_rag_github] <br>[![Open In Collab](../../assets/colab.svg)][corrective_rag_colab] <br>[![Ghost](../../assets/ghost.svg)][corrective_rag_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Contextual Compression with RAG** 🗜️🧠 | Apply **contextual compression techniques** to condense large documents while retaining essential information. 📄🗜️                                               | [![Github](../../assets/github.svg)][compression_rag_github] <br>[![Open In Collab](../../assets/colab.svg)][compression_rag_colab] <br>[![Ghost](../../assets/ghost.svg)][compression_rag_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Improve RAG with FLARE** 🔥|  Enable users to ask questions directly to **academic papers**, focusing on **ArXiv papers**, with **F**orward-**L**ooking **A**ctive **RE**trieval augmented generation.🚀🌟                                                                       | [![Github](../../assets/github.svg)][flare_github] <br>[![Open In Collab](../../assets/colab.svg)][flare_colab] <br>[![Ghost](../../assets/ghost.svg)][flare_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Query Expansion and Reranker** 🔍🔄 | Enhance RAG with query expansion using Large Language Models and advanced **reranking methods** like **Cross Encoders**, **ColBERT v2**, and **FlashRank** for improved document retrieval precision and recall 🔍📈                                                         | [![Github](../../assets/github.svg)][query_github] <br>[![Open In Collab](../../assets/colab.svg)][query_colab] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **RAG Fusion** ⚡🌐 | Build RAG Fusion, utilize the **RRF algorithm** to rerank documents based on user queries ! Use **LanceDB** as vector database to store and retrieve documents related to queries via **OPENAI Embeddings**⚡🌐                                                                          | [![Github](../../assets/github.svg)][fusion_github] <br>[![Open In Collab](../../assets/colab.svg)][fusion_colab] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Agentic RAG** 🤖📚 | Build autonomous information retrieval with **Agentic RAG**, a framework of **intelligent agents** that collaborate to synthesize, summarize, and compare data across sources, that enables proactive and informed decision-making 🤖📚                                                              | [![Github](../../assets/github.svg)][agentic_github] <br>[![Open In Collab](../../assets/colab.svg)][agentic_colab] |
+
+
+
+
+
+
+
+
+
+
+
+
+[matryoshka_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex
+[matryoshka_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex/RAG_with_MatryoshkaEmbedding_and_Llamaindex.ipynb
+
+[rag_reranking_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/RAG_Reranking
+[rag_reranking_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/RAG_Reranking/main.ipynb
+[rag_reranking_ghost]: https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544
+
+
+[instruct_multitask_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/instruct-multitask
+[instruct_multitask_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/instruct-multitask/main.ipynb
+[instruct_multitask_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/instruct-multitask/main.py
+[instruct_multitask_ghost]: https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543
+
+[hyde_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Advance-RAG-with-HyDE
+[hyde_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance-RAG-with-HyDE/main.ipynb
+[hyde_ghost]: https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb
+
+[lotr_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Advance_RAG_LOTR
+[lotr_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance_RAG_LOTR/main.ipynb
+[lotr_ghost]: https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35
+
+[parent_doc_retriever_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/parent_document_retriever
+[parent_doc_retriever_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/parent_document_retriever/main.ipynb
+[parent_doc_retriever_ghost]: https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6
+
+[corrective_rag_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph
+[corrective_rag_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb
+[corrective_rag_ghost]: https://blog.lancedb.com/implementing-corrective-rag-in-the-easiest-way-2/
+
+[compression_rag_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG
+[compression_rag_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb
+[compression_rag_ghost]: https://blog.lancedb.com/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301/
+
+[flare_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/better-rag-FLAIR
+[flare_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/better-rag-FLAIR/main.ipynb
+[flare_ghost]: https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/
+
+[query_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/QueryExpansion%26Reranker
+[query_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/QueryExpansion&Reranker/main.ipynb
+
+
+[fusion_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/RAG_Fusion
+[fusion_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/RAG_Fusion/main.ipynb
+
+[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

@@ -0,0 +1,37 @@
+**Recommender Systems: Personalized Discovery🍿📺**
+==============================================================
+Deliver personalized experiences with Recommender Systems. 🎁
+
+**Technical Overview📜**
+
+🔍️ LanceDB's powerful vector database capabilities can efficiently store and query item embeddings. Recommender Systems can utilize it and provide personalized recommendations based on user preferences 🤝 and item features 📊 and therefore enhance the user experience.🗂️ 
+
+| **Recommender System** | **Description** | **Links** |
+| ---------------------- | --------------- | --------- |
+| **Movie Recommender System🎬** | 🤝 Use **collaborative filtering** to predict user preferences, assuming similar users will like similar movies, and leverage **Singular Value Decomposition** (SVD) from Numpy for precise matrix factorization and accurate recommendations📊 | [![Github](../../assets/github.svg)][movie_github] <br>[![Open In Collab](../../assets/colab.svg)][movie_colab] <br>[![Python](../../assets/python.svg)][movie_python] |
+| **🎥 Movie Recommendation with Genres** | 🔍 Creates movie embeddings using **Doc2Vec**, capturing genre and characteristic nuances, and leverages VectorDB for efficient storage and querying, enabling accurate genre classification and personalized movie recommendations through **similarity searches**🎥 | [![Github](../../assets/github.svg)][genre_github] <br>[![Open In Collab](../../assets/colab.svg)][genre_colab] <br>[![Ghost](../../assets/ghost.svg)][genre_ghost] |
+| **🛍️ Product Recommender using Collaborative Filtering and LanceDB** | 📈 Using **Collaborative Filtering** and **LanceDB** to analyze your past purchases, recommends products based on user's past purchases. Demonstrated with the Instacart dataset in our example🛒 | [![Github](../../assets/github.svg)][product_github] <br>[![Open In Collab](../../assets/colab.svg)][product_colab] <br>[![Python](../../assets/python.svg)][product_python] |
+| **🔍 Arxiv Search with OpenCLIP and LanceDB** | 💡 Build a semantic search engine for **Arxiv papers** using **LanceDB**, and benchmarks its performance against traditional keyword-based search on **Nomic's Atlas**, to demonstrate the power of semantic search in finding relevant research papers📚 | [![Github](../../assets/github.svg)][arxiv_github] <br>[![Open In Collab](../../assets/colab.svg)][arxiv_colab] <br>[![Python](../../assets/python.svg)][arxiv_python] |
+| **Food Recommendation System🍴** | 🍔 Build a food recommendation system with **LanceDB**, featuring vector-based recommendations, full-text search, hybrid search, and reranking model integration for personalized and accurate food suggestions👌 | [![Github](../../assets/github.svg)][food_github] <br>[![Open In Collab](../../assets/colab.svg)][food_colab] |
+
+[movie_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/movie-recommender
+[movie_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.ipynb
+[movie_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.py
+
+
+[genre_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/movie-recommendation-with-genres
+[genre_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb
+[genre_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
+[product_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/product-recommender/main.ipynb
+[product_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/product-recommender/main.py
+
+
+[arxiv_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/arxiv-recommender
+[arxiv_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/arxiv-recommender/main.ipynb
+[arxiv_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/arxiv-recommender/main.py
+ 
+
+[food_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/Food_recommendation
+[food_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/Food_recommendation/main.ipynb

docs/src/examples/python_examples/vector_search.md

@@ -0,0 +1,80 @@
+**Vector Search: Efficient Retrieval 🔓👀**
+====================================================================
+
+Vector search with LanceDB, is a solution for  efficient and accurate similarity searches in large datasets 📊. 
+
+**Vector Search Capabilities in LanceDB🔝**
+
+LanceDB implements vector search algorithms for efficient document retrieval and analysis 📊. This enables fast and accurate discovery of relevant documents, leveraging dense vector representations 🤖. The platform supports scalable indexing and querying of high-dimensional vector spaces, facilitating precise document matching and retrieval 📈.
+
+| **Vector Search** | **Description** | **Links** |
+|:-----------------|:---------------|:---------|
+| **Inbuilt Hybrid Search 🔄** | Perform hybrid search in **LanceDB** by combining the results of semantic and full-text search via a reranking algorithm of your choice 📊 | [![Github](../../assets/github.svg)][inbuilt_hybrid_search_github] <br>[![Open In Collab](../../assets/colab.svg)][inbuilt_hybrid_search_colab] |                                                                                                                                                                                                                                                                                                    
+| **Hybrid Search with BM25 and LanceDB 💡** | Use **Synergizes BM25's** keyword-focused precision (term frequency, document length normalization, bias-free retrieval) with **LanceDB's** semantic understanding (contextual analysis, query intent alignment) for nuanced search results in complex datasets 📈 | [![Github](../../assets/github.svg)][BM25_github] <br>[![Open In Collab](../../assets/colab.svg)][BM25_colab] <br>[![Ghost](../../assets/ghost.svg)][BM25_ghost] |                                                                                                                                                                                                                                                                                                    
+| **NER-powered Semantic Search 🔎** | Extract and identify essential information from text with Named Entity Recognition **(NER)** methods: Dictionary-Based, Rule-Based, and Deep Learning-Based, to accurately extract and categorize entities, enabling precise semantic search results 🗂️ | [![Github](../../assets/github.svg)][NER_github] <br>[![Open In Collab](../../assets/colab.svg)][NER_colab] <br>[![Ghost](../../assets/ghost.svg)][NER_ghost]|                                                                                                                                                                                                                                                                                                    
+| **Audio Similarity Search using Vector Embeddings 🎵** | Create vector **embeddings of audio files** to find similar audio content, enabling efficient audio similarity search and retrieval in **LanceDB's** vector store 📻 |[![Github](../../assets/github.svg)][audio_search_github] <br>[![Open In Collab](../../assets/colab.svg)][audio_search_colab] <br>[![Python](../../assets/python.svg)][audio_search_python]|                                                                                                                                                                                                                                                                                                    
+| **LanceDB Embeddings API: Multi-lingual Semantic Search 🌎** | Build a universal semantic search table with **LanceDB's Embeddings API**, supporting multiple languages (e.g., English, French) using **cohere's** multi-lingual model, for accurate cross-lingual search results 📄 | [![Github](../../assets/github.svg)][mls_github] <br>[![Open In Collab](../../assets/colab.svg)][mls_colab] <br>[![Python](../../assets/python.svg)][mls_python] |                                                                                                                                                                                                                                                                                                    
+| **Facial Recognition: Face Embeddings 🤖** | Detect, crop, and embed faces using Facenet, then store and query face embeddings in **LanceDB** for efficient facial recognition and top-K matching results 👥 | [![Github](../../assets/github.svg)][fr_github] <br>[![Open In Collab](../../assets/colab.svg)][fr_colab] |                                                                                                                                                                                                                                                                                                    
+| **Sentiment Analysis: Hotel Reviews 🏨** | Analyze customer sentiments towards the hotel industry using **BERT models**, storing sentiment labels, scores, and embeddings in **LanceDB**, enabling queries on customer opinions and potential areas for improvement 💬 | [![Github](../../assets/github.svg)][sentiment_analysis_github] <br>[![Open In Collab](../../assets/colab.svg)][sentiment_analysis_colab] <br>[![Ghost](../../assets/ghost.svg)][sentiment_analysis_ghost] |                                                                                                                                                                                                                                                                                                    
+| **Vector Arithmetic with LanceDB ⚖️** | Perform **vector arithmetic** on embeddings, enabling complex relationships and nuances in data to be captured, and simplifying the process of retrieving semantically similar results 📊 | [![Github](../../assets/github.svg)][arithmetic_github] <br>[![Open In Collab](../../assets/colab.svg)][arithmetic_colab] <br>[![Ghost](../../assets/ghost.svg)][arithmetic_ghost] |                                                                                                                                                                                                                                                                                                    
+| **Imagebind Demo 🖼️** | Explore the multi-modal capabilities of **Imagebind** through a Gradio app, use **LanceDB API** for seamless image search and retrieval experiences 📸 | [![Github](../../assets/github.svg)][imagebind_github] <br> [![Open in Spaces](../../assets/open_hf_space.svg)][imagebind_huggingface] |                                                                                                                                                                                                                                                                                                    
+| **Search Engine using SAM & CLIP 🔍** | Build a search engine within an image using **SAM** and **CLIP** models, enabling object-level search and retrieval, with LanceDB indexing and search capabilities to find the closest match between image embeddings and user queries 📸 | [![Github](../../assets/github.svg)][swi_github] <br>[![Open In Collab](../../assets/colab.svg)][swi_colab] <br>[![Ghost](../../assets/ghost.svg)][swi_ghost] |                                                                                                                                                                                                                                                                                                    
+| **Zero Shot Object Localization and Detection with CLIP 🔎** | Perform object detection on images using **OpenAI's CLIP**, enabling zero-shot localization and detection of objects, with capabilities to split images into patches, parse with CLIP, and plot bounding boxes 📊 | [![Github](../../assets/github.svg)][zsod_github] <br>[![Open In Collab](../../assets/colab.svg)][zsod_colab] |                                                                                                                                                                                                                                                                                                    
+| **Accelerate Vector Search with OpenVINO 🚀** | Boost vector search applications using **OpenVINO**, achieving significant speedups with **CLIP** for text-to-image and image-to-image searching, through PyTorch model optimization, FP16 and INT8 format conversion, and quantization with **OpenVINO NNCF** 📈 | [![Github](../../assets/github.svg)][openvino_github] <br>[![Open In Collab](../../assets/colab.svg)][openvino_colab] <br>[![Ghost](../../assets/ghost.svg)][openvino_ghost] |                                                                                                                                                                                                                                                                                                    
+| **Zero-Shot Image Classification with CLIP and LanceDB 📸** | Achieve zero-shot image classification using **CLIP** and **LanceDB**, enabling models to classify images without prior training on specific use cases, unlocking flexible and adaptable image classification capabilities 🔓 | [![Github](../../assets/github.svg)][zsic_github] <br>[![Open In Collab](../../assets/colab.svg)][zsic_colab] <br>[![Ghost](../../assets/ghost.svg)][zsic_ghost] |                                                                                                                                                                                                                                                                                                    
+
+
+
+
+[inbuilt_hybrid_search_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Inbuilt-Hybrid-Search
+[inbuilt_hybrid_search_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Inbuilt-Hybrid-Search/Inbuilt_Hybrid_Search_with_LanceDB.ipynb
+
+[BM25_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Hybrid_search_bm25_lancedb
+[BM25_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Hybrid_search_bm25_lancedb/main.ipynb
+[BM25_ghost]: https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6
+
+[NER_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search
+[NER_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb
+[NER_ghost]: https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493
+
+[audio_search_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/audio_search
+[audio_search_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/audio_search/main.ipynb
+[audio_search_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/archived_examples/audio_search/main.py
+
+[mls_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/multi-lingual-wiki-qa
+[mls_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/multi-lingual-wiki-qa/main.ipynb
+[mls_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/archived_examples/multi-lingual-wiki-qa/main.py
+
+[fr_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/facial_recognition
+[fr_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/facial_recognition/main.ipynb
+
+[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
+[sentiment_analysis_ghost]: https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6
+
+[arithmetic_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Vector-Arithmetic-with-LanceDB
+[arithmetic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Vector-Arithmetic-with-LanceDB/main.ipynb
+[arithmetic_ghost]: https://blog.lancedb.com/vector-arithmetic-with-lancedb-an-intro-to-vector-embeddings/
+
+[imagebind_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/imagebind_demo
+[imagebind_huggingface]: https://huggingface.co/spaces/raghavd99/imagebind2
+
+[swi_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip
+[swi_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb
+[swi_ghost]: https://blog.lancedb.com/search-within-an-image-331b54e4285e
+
+[zsod_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/zero-shot-object-detection-CLIP
+[zsod_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/zero-shot-object-detection-CLIP/zero_shot_object_detection_clip.ipynb
+
+[openvino_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO
+[openvino_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb
+[openvino_ghost]: https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-lancedb/
+
+[zsic_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/zero-shot-image-classification
+[zsic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/zero-shot-image-classification/main.ipynb
+[zsic_ghost]: https://blog.lancedb.com/zero-shot-image-classification-with-vector-search/
+
+
+
+
+

docs/src/examples/serverless_lancedb_with_s3_and_lambda.md

@@ -25,8 +25,8 @@ s3://eto-public/datasets/sift/vec_data.lance
 Then, we can write a quick Python script to populate our LanceDB Table:
 
 ```python
-import pylance
+import lance
-sift_dataset = pylance.dataset("/path/to/local/vec_data.lance")
+sift_dataset = lance.dataset("/path/to/local/vec_data.lance")
 df = sift_dataset.to_table().to_pandas()
 
 import lancedb

docs/src/fts.md

@@ -1,163 +1,229 @@
-# Full-text search
+# Full-text search (Native FTS)
 
-LanceDB provides support for full-text search via [Tantivy](https://github.com/quickwit-oss/tantivy) (currently Python only), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions. Our goal is to push the FTS integration down to the Rust level in the future, so that it's available for Rust and JavaScript users as well.  Follow along at [this Github issue](https://github.com/lancedb/lance/issues/1195)
+LanceDB provides support for full-text search via Lance, allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
 
-A hybrid search solution combining vector and full-text search is also on the way.
+!!! note
-
+    The Python SDK uses tantivy-based FTS by default, need to pass `use_tantivy=False` to use native FTS.
-## 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 `text` we want to index and query via keyword search.
+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
+=== "Python"
-import lancedb
 
-uri = "data/sample-lancedb"
+    ```python
-db = lancedb.connect(uri)
+    import lancedb
 
-table = db.create_table(
+    uri = "data/sample-lancedb"
-    "my_table",
+    db = lancedb.connect(uri)
-    data=[
-        {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
-        {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
-    ],
-)
-```
 
-## Create FTS index on single column
+    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"},
+        ],
+    )
 
-The FTS index must be created before you can search via keywords.
+    # passing `use_tantivy=False` to use lance FTS index
+    # `use_tantivy=True` by default
+    table.create_fts_index("text", use_tantivy=False)
+    table.search("puppy").limit(10).select(["text"]).to_list()
+    # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
+    # ...
+    ```
 
-```python
+=== "TypeScript"
-table.create_fts_index("text")
-```
 
-To search an FTS index via keywords, LanceDB's `table.search` accepts a string as input:
+    ```typescript
+    import * as lancedb from "@lancedb/lancedb";
+    const uri = "data/sample-lancedb"
+    const db = await lancedb.connect(uri);
 
-```python
+    const data = [
-table.search("puppy").limit(10).select(["text"]).to_list()
+    { vector: [3.1, 4.1], text: "Frodo was a happy puppy" },
-```
+    { vector: [5.9, 26.5], text: "There are several kittens playing" },
+    ];
+    const tbl = await db.createTable("my_table", data, { mode: "overwrite" });
+    await tbl.createIndex("text", {
+        config: lancedb.Index.fts(),
+    });
 
-This returns the result as a list of dictionaries as follows.
+    await tbl
+        .search("puppy", queryType="fts")
+        .select(["text"])
+        .limit(10)
+        .toArray();
+    ```
 
-```python
+=== "Rust"
-[{'text': 'Frodo was a happy puppy', 'score': 0.6931471824645996}]
+
-```
+    ```rust
+    let uri = "data/sample-lancedb";
+    let db = connect(uri).execute().await?;
+    let initial_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
+    let tbl = db
+        .create_table("my_table", initial_data)
+        .execute()
+        .await?;
+    tbl
+        .create_index(&["text"], Index::FTS(FtsIndexBuilder::default()))
+        .execute()
+        .await?;
+
+    tbl
+        .query()
+        .full_text_search(FullTextSearchQuery::new("puppy".to_owned()))
+        .select(lancedb::query::Select::Columns(vec!["text".to_owned()]))
+        .limit(10)
+        .execute()
+        .await?;
+    ```
+
+It would search on all indexed columns by default, so it's useful when there are multiple indexed columns.
+
+Passing `fts_columns="text"` if you want to specify the columns to 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.
 
-## Index multiple columns
+## 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.
 
-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`:
+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 example, to enable stemming for English:
 ```python
-table.create_fts_index(["text1", "text2"])
+table.create_fts_index("text", use_tantivy=True, tokenizer_name="en_stem")
 ```
 
-Note that the search API call does not change - you can search over all indexed columns at once.
+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.
+
+For example, for language with accents, you can specify the tokenizer to use `ascii_folding` to remove accents, e.g. 'é' to 'e':
+```python
+table.create_fts_index("text",
+                        use_tantivy=False,
+                        language="French",
+                        stem=True,
+                        ascii_folding=True)
+```
 
 ## Filtering
 
-Currently the LanceDB full text search feature supports *post-filtering*, meaning filters are
+LanceDB full text search supports to filter the search results by a condition, both pre-filtering and post-filtering are supported.
-applied on top of the full text search results. This can be invoked via the familiar
-`where` syntax:
 
-```python
+This can be invoked via the familiar `where` syntax.
-table.search("puppy").limit(10).where("meta='foo'").to_list()
+ 
-```
+With pre-filtering:
+=== "Python"
 
-## Sorting
+    ```python
+    table.search("puppy").limit(10).where("meta='foo'", prefilte=True).to_list()
+    ```
 
-You can pre-sort the documents by specifying `ordering_field_names` when
+=== "TypeScript"
-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, 
 
-```
+    ```typescript
-table.create_fts_index(["text_field"], ordering_field_names=["sort_by_field"])
+    await tbl
+    .search("puppy")
+    .select(["id", "doc"])
+    .limit(10)
+    .where("meta='foo'")
+    .prefilter(true)
+    .toArray();
+    ```
 
-(table.search("terms", ordering_field_name="sort_by_field")
+=== "Rust"
- .limit(20)
- .to_list())
-```
 
-!!! note
+    ```rust
-    If you wish to specify an ordering field at query time, you must also
+    table
-    have specified it during indexing time. Otherwise at query time, an
+        .query()
-    error will be raised that looks like `ValueError: The field does not exist: xxx`
+        .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?;
+    ```
 
-!!! note
+With post-filtering:
-    The fields to sort on must be of typed unsigned integer, or else you will see 
+=== "Python"
-    an error during indexing that looks like 
-    `TypeError: argument 'value': 'float' object cannot be interpreted as an integer`.
 
-!!! note
+    ```python
-    You can specify multiple fields for ordering at indexing time.
+    table.search("puppy").limit(10).where("meta='foo'", prefilte=False).to_list()
-    But at query time only one ordering field is supported.
+    ```
 
+=== "TypeScript"
+
+    ```typescript
+    await tbl
+    .search("apple")
+    .select(["id", "doc"])
+    .limit(10)
+    .where("meta='foo'")
+    .prefilter(false)
+    .toArray();
+    ```
+
+=== "Rust"
+
+    ```rust
+    table
+        .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?;
+    ```
 
 ## 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 AND Man) AND Sea"`. For more details on the terms
+or a **terms** search query like `old man 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"
+To search for a phrase, the index must be created with `with_position=True`:
-    The query parser will raise an exception on queries that are ambiguous. For example, in the query `they could have been dogs OR cats`, `OR` is capitalized so it's considered a keyword query operator. But it's ambiguous how the left part should be treated. So if you submit this search query as is, you'll get `Syntax Error: they could have been dogs OR cats`.
-
-    ```py
-    # This raises a syntax error
-    table.search("they could have been dogs OR cats")
-    ```
-
-    On the other hand, lowercasing `OR` to `or` will work, because there are no capitalized logical operators and
-    the query is treated as a phrase query.
-
-    ```py
-    # This works!
-    table.search("they could have been dogs or cats")
-    ```
-
-It can be cumbersome to have to remember what will cause a syntax error depending on the type of
-query you want to perform. To make this simpler, when you want to perform a phrase query, you can
-enforce it in one of two ways:
-
-1. Place the double-quoted query inside single quotes. For example, `table.search('"they could have been dogs OR cats"')` is treated as
-a phrase query.
-2. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
-itself contains double quotes. For example, `table.search('the cats OR dogs were not really "pets" at all').phrase_query()`
-is treated as a phrase query.
-
-In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested
-double quotes replaced by single quotes.
-
-## Configurations
-
-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
+table.create_fts_index("text", use_tantivy=False, with_position=True)
-heap = 1024 * 1024 * 512
-table.create_fts_index(["text1", "text2"], writer_heap_size=heap, replace=True)
 ```
+This will allow you to search for phrases, but it will also significantly increase the index size and indexing time.
 
-## Current limitations
 
-1. Currently we do not yet support incremental writes.
+## Incremental indexing
-   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.
+LanceDB supports incremental indexing, which means you can add new records to the table without reindexing the entire table.
-   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.
+This can make the query more efficient, especially when the table is large and the new records are relatively small.
+
+=== "Python"
+
+    ```python
+    table.add([{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}])
+    table.optimize()
+    ```
+
+=== "TypeScript"
+
+    ```typescript
+    await tbl.add([{ vector: [3.1, 4.1], text: "Frodo was a happy puppy" }]);
+    await tbl.optimize();
+    ```
+
+=== "Rust"
+
+    ```rust
+    let more_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
+    tbl.add(more_data).execute().await?;
+    tbl.optimize(OptimizeAction::All).execute().await?;
+    ```
+!!! note
+
+    New data added after creating the FTS index will appear in search results while incremental index is still progress, but with increased latency due to a flat search on the unindexed portion. LanceDB Cloud automates this merging process, minimizing the impact on search speed. 

docs/src/fts_tantivy.md

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

@@ -0,0 +1,147 @@
+# Building a 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
+over scalar columns.
+
+- `BTREE`: The most common type is BTREE. The index stores a copy of the
+  column in sorted order. This sorted copy allows a binary search to be used to
+  satisfy queries.
+- `BITMAP`: this index stores a bitmap for each unique value in the column. It 
+  uses a series of bits to indicate whether a value is present in a row of a table
+- `LABEL_LIST`: a special index that can be used on `List<T>` columns to
+  support queries with `array_contains_all` and `array_contains_any`
+  using an underlying bitmap index.
+  For example, a column that contains lists of tags (e.g. `["tag1", "tag2", "tag3"]`) can be indexed with a `LABEL_LIST` index.
+
+!!! tips "How to choose the right scalar index type"
+
+    `BTREE`: This index is good for scalar columns with mostly distinct values and does best when the query is highly selective.
+    
+    `BITMAP`: This index works best for low-cardinality numeric or string columns, where the number of unique values is small (i.e., less than a few thousands).
+    
+    `LABEL_LIST`: This index should be used for columns containing list-type data.
+
+| Data Type                                                       | Filter                                    | Index Type   |
+| --------------------------------------------------------------- | ----------------------------------------- | ------------ |
+| Numeric, String, Temporal                                       | `<`, `=`, `>`, `in`, `between`, `is null` | `BTREE`      |
+| Boolean, numbers or strings with fewer than 1,000 unique values | `<`, `=`, `>`, `in`, `between`, `is null` | `BITMAP`     |
+| List of low cardinality of numbers or strings                   | `array_has_any`, `array_has_all`          | `LABEL_LIST` |
+
+### Create a scalar index
+=== "Python"
+
+    ```python
+    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"]}
+    ]
+
+    db = lancedb.connect("./db")
+    table = db.create_table("books", books)
+    table.create_scalar_index("book_id")  # BTree by default
+    table.create_scalar_index("publisher", index_type="BITMAP")
+    ```
+
+=== "Typescript"
+
+    === "@lancedb/lancedb"
+
+        ```js
+        const db = await lancedb.connect("data");
+        const tbl = await db.openTable("my_vectors");
+
+        await tbl.create_index("book_id");
+        await tlb.create_index("publisher", { config: lancedb.Index.bitmap() })
+        ```
+
+The following scan will be faster if the column `book_id` has a scalar index:
+
+=== "Python"
+
+    ```python
+    import lancedb
+
+    table = db.open_table("books")
+    my_df = table.search().where("book_id = 2").to_pandas()
+    ```
+
+=== "Typescript"
+
+    === "@lancedb/lancedb"
+
+        ```js
+        const db = await lancedb.connect("data");
+        const tbl = await db.openTable("books");
+
+        await tbl
+          .query()
+          .where("book_id = 2")
+          .limit(10)
+          .toArray();
+        ```
+
+Scalar indices can also speed up scans containing a vector search or full text search, and a prefilter:
+
+=== "Python"
+
+    ```python
+    import lancedb
+
+    data = [
+      {"book_id": 1, "vector": [1, 2]},
+      {"book_id": 2, "vector": [3, 4]},
+      {"book_id": 3, "vector": [5, 6]}
+    ]
+    table = db.create_table("book_with_embeddings", data)
+
+    (
+        table.search([1, 2])
+        .where("book_id != 3", prefilter=True)
+        .to_pandas()
+    )
+    ```
+
+=== "Typescript"
+
+    === "@lancedb/lancedb"
+
+        ```js
+        const db = await lancedb.connect("data/lance");
+        const tbl = await db.openTable("book_with_embeddings");
+
+        await tbl.search(Array(1536).fill(1.2))
+          .where("book_id != 3")  // prefilter is default behavior.
+          .limit(10)
+          .toArray();
+        ```
+### Update a scalar index
+Updating the table data (adding, deleting, or modifying records) requires that you also update the scalar index. This can be done by calling `optimize`, which will trigger an update to the existing scalar index.
+=== "Python"
+
+    ```python
+    table.add([{"vector": [7, 8], "book_id": 4}])
+    table.optimize()
+    ```
+
+=== "TypeScript"
+
+    ```typescript
+    await tbl.add([{ vector: [7, 8], book_id: 4 }]);
+    await tbl.optimize();
+    ```
+
+=== "Rust"
+
+    ```rust
+    let more_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
+    tbl.add(more_data).execute().await?;
+    tbl.optimize(OptimizeAction::All).execute().await?;
+    ```
+
+!!! note
+
+    New data added after creating the scalar index will still appear in search results if optimize is not used, but with increased latency due to a flat search on the unindexed portion. LanceDB Cloud automates the optimize process, minimizing the impact on search speed.

docs/src/guides/storage.md

@@ -27,33 +27,62 @@ LanceDB OSS supports object stores such as AWS S3 (and compatible stores), Azure
 
     Azure Blob Storage:
 
+    <!-- skip-test -->
     ```python
     import lancedb
     db = lancedb.connect("az://bucket/path")
     ```
+    Note that for Azure, storage credentials must be configured. See [below](#azure-blob-storage) for more details.
 
-=== "JavaScript"
 
-    AWS S3:
+=== "TypeScript"
 
-    ```javascript
+    === "@lancedb/lancedb"
-    const lancedb = require("lancedb");
-    const db = await lancedb.connect("s3://bucket/path");
-    ```
 
-    Google Cloud Storage:
+        AWS S3:
 
-    ```javascript
+        ```ts
-    const lancedb = require("lancedb");
+        import * as lancedb from "@lancedb/lancedb";
-    const db = await lancedb.connect("gs://bucket/path");
+        const db = await lancedb.connect("s3://bucket/path");
-    ```
+        ```
 
-    Azure Blob Storage:
+        Google Cloud Storage:
 
-    ```javascript
+        ```ts
-    const lancedb = require("lancedb");
+        import * as lancedb from "@lancedb/lancedb";
-    const db = await lancedb.connect("az://bucket/path");
+        const db = await lancedb.connect("gs://bucket/path");
-    ```
+        ```
+
+        Azure Blob Storage:
+
+        ```ts
+        import * as lancedb from "@lancedb/lancedb";
+        const db = await lancedb.connect("az://bucket/path");
+        ```
+
+
+    === "vectordb (deprecated)"
+
+        AWS S3:
+
+        ```ts
+        const lancedb = require("lancedb");
+        const db = await lancedb.connect("s3://bucket/path");
+        ```
+
+        Google Cloud Storage:
+
+        ```ts
+        const lancedb = require("lancedb");
+        const db = await lancedb.connect("gs://bucket/path");
+        ```
+
+        Azure Blob Storage:
+
+        ```ts
+        const lancedb = require("lancedb");
+        const db = await lancedb.connect("az://bucket/path");
+        ```
 
 In most cases, when running in the respective cloud and permissions are set up correctly, no additional configuration is required. When running outside of the respective cloud, authentication credentials must be provided. Credentials and other configuration options can be set in two ways: first, by setting environment variables. And second, by passing a `storage_options` object to the `connect` function. For example, to increase the request timeout to 60 seconds, you can set the `TIMEOUT` environment variable to `60s`:
 
@@ -61,11 +90,6 @@ 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"
@@ -78,13 +102,26 @@ If you only want this to apply to one particular connection, you can pass the `s
     )
     ```
 
-=== "JavaScript"
+=== "TypeScript"
 
-    ```javascript
+    === "@lancedb/lancedb"
-    const lancedb = require("lancedb");
+
-    const db = await lancedb.connect("s3://bucket/path",
+        ```ts
-                                     {storageOptions: {timeout: "60s"}});
+        import * as lancedb from "@lancedb/lancedb";
-    ```
+
+        const db = await lancedb.connect("s3://bucket/path", {
+            storageOptions: {timeout: "60s"}
+        });
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```ts
+        const lancedb = require("lancedb");
+        const db = await lancedb.connect("s3://bucket/path", {
+            storageOptions: {timeout: "60s"}
+        });
+        ```
 
 Getting even more specific, you can set the `timeout` for only a particular table:
 
@@ -101,18 +138,33 @@ Getting even more specific, you can set the `timeout` for only a particular tabl
     )
     ```
 
-=== "JavaScript"
+=== "TypeScript"
 
-    <!-- skip-test -->
+    === "@lancedb/lancedb"
-    ```javascript
+
-    const lancedb = require("lancedb");
+        <!-- skip-test -->
-    const db = await lancedb.connect("s3://bucket/path");
+        ```ts
-    const table = db.createTable(
+        import * as lancedb from "@lancedb/lancedb";
-        "table",
+        const db = await lancedb.connect("s3://bucket/path");
-        [{ a: 1, b: 2}],
+        const table = db.createTable(
-        {storageOptions: {timeout: "60s"}}
+            "table",
-    );
+            [{ a: 1, b: 2}],
-    ```
+            {storageOptions: {timeout: "60s"}}
+        );
+        ```
+
+    === "vectordb (deprecated)"
+
+        <!-- skip-test -->
+        ```ts
+        const lancedb = require("lancedb");
+        const db = await lancedb.connect("s3://bucket/path");
+        const table = db.createTable(
+            "table",
+            [{ a: 1, b: 2}],
+            {storageOptions: {timeout: "60s"}}
+        );
+        ```
 
 !!! info "Storage option casing"
 
@@ -135,7 +187,6 @@ There are several options that can be set for all object stores, mostly related
 | `proxy_ca_certificate`     | PEM-formatted CA certificate for proxy connections.                                                |
 | `proxy_excludes`           | List of hosts that bypass the proxy. This is a comma-separated list of domains and IP masks. Any subdomain of the provided domain will be bypassed. For example, `example.com, 192.168.1.0/24` would bypass `https://api.example.com`, `https://www.example.com`, and any IP in the range `192.168.1.0/24`. |
 
-
 ### AWS S3
 
 To configure credentials for AWS S3, you can use the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` keys. Region can also be set, but it is not mandatory when using AWS.
@@ -155,21 +206,39 @@ These can be set as environment variables or passed in the `storage_options` par
     )
     ```
 
-=== "JavaScript"
+=== "TypeScript"
 
-    ```javascript
+    === "@lancedb/lancedb"
-    const lancedb = require("lancedb");
+
-    const db = await lancedb.connect(
+        ```ts
-        "s3://bucket/path",
+        import * as lancedb from "@lancedb/lancedb";
-        {
+        const db = await lancedb.connect(
-            storageOptions: {
+            "s3://bucket/path",
-                awsAccessKeyId: "my-access-key",
+            {
-                awsSecretAccessKey: "my-secret-key",
+                storageOptions: {
-                awsSessionToken: "my-session-token",
+                    awsAccessKeyId: "my-access-key",
+                    awsSecretAccessKey: "my-secret-key",
+                    awsSessionToken: "my-session-token",
+                }
             }
-        }
+        );
-    );
+        ```
-    ```
+
+    === "vectordb (deprecated)"
+
+        ```ts
+        const lancedb = require("lancedb");
+        const db = await lancedb.connect(
+            "s3://bucket/path",
+            {
+                storageOptions: {
+                    awsAccessKeyId: "my-access-key",
+                    awsSecretAccessKey: "my-secret-key",
+                    awsSessionToken: "my-session-token",
+                }
+            }
+        );
+        ```
 
 Alternatively, if you are using AWS SSO, you can use the `AWS_PROFILE` and `AWS_DEFAULT_REGION` environment variables.
 
@@ -188,7 +257,6 @@ The following keys can be used as both environment variables or keys in the `sto
 | `aws_sse_kms_key_id`                | The KMS key ID to use for server-side encryption. If set, `aws_server_side_encryption` must be `"aws:kms"` or `"aws:kms:dsse"`. |
 | `aws_sse_bucket_key_enabled`        | Whether to use bucket keys for server-side encryption.                                               |
 
-
 !!! tip "Automatic cleanup for failed writes"
 
     LanceDB uses [multi-part uploads](https://docs.aws.amazon.com/AmazonS3/latest/userguide/mpuoverview.html) when writing data to S3 in order to maximize write speed. LanceDB will abort these uploads when it shuts down gracefully, such as when cancelled by keyboard interrupt. However, in the rare case that LanceDB crashes, it is possible that some data will be left lingering in your account. To cleanup this data, we recommend (as AWS themselves do) that you setup a lifecycle rule to delete in-progress uploads after 7 days. See the AWS guide:
@@ -265,6 +333,108 @@ For **read-only access**, LanceDB will need a policy such as:
 }
 ```
 
+#### DynamoDB Commit Store for concurrent writes
+
+By default, S3 does not support concurrent writes. Having two or more processes
+writing to the same table at the same time can lead to data corruption. This is
+because S3, unlike other object stores, does not have any atomic put or copy
+operation.
+
+To enable concurrent writes, you can configure LanceDB to use a DynamoDB table
+as a commit store. This table will be used to coordinate writes between
+different processes. To enable this feature, you must modify your connection
+URI to use the `s3+ddb` scheme and add a query parameter `ddbTableName` with the
+name of the table to use.
+
+=== "Python"
+
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
+    )
+    ```
+
+=== "JavaScript"
+
+    ```javascript
+    const lancedb = require("lancedb");
+
+    const db = await lancedb.connect(
+        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
+    );
+    ```
+
+The DynamoDB table must be created with the following schema:
+
+- Hash key: `base_uri` (string)
+- Range key: `version` (number)
+
+You can create this programmatically with:
+
+=== "Python"
+
+    <!-- skip-test -->
+    ```python
+    import boto3
+
+    dynamodb = boto3.client("dynamodb")
+    table = dynamodb.create_table(
+        TableName=table_name,
+        KeySchema=[
+            {"AttributeName": "base_uri", "KeyType": "HASH"},
+            {"AttributeName": "version", "KeyType": "RANGE"},
+        ],
+        AttributeDefinitions=[
+            {"AttributeName": "base_uri", "AttributeType": "S"},
+            {"AttributeName": "version", "AttributeType": "N"},
+        ],
+        ProvisionedThroughput={"ReadCapacityUnits": 1, "WriteCapacityUnits": 1},
+    )
+    ```
+
+=== "JavaScript"
+
+    <!-- skip-test -->
+    ```javascript
+    import {
+      CreateTableCommand,
+      DynamoDBClient,
+    } from "@aws-sdk/client-dynamodb";
+
+    const dynamodb = new DynamoDBClient({
+      region: CONFIG.awsRegion,
+      credentials: {
+        accessKeyId: CONFIG.awsAccessKeyId,
+        secretAccessKey: CONFIG.awsSecretAccessKey,
+      },
+      endpoint: CONFIG.awsEndpoint,
+    });
+    const command = new CreateTableCommand({
+      TableName: table_name,
+      AttributeDefinitions: [
+        {
+          AttributeName: "base_uri",
+          AttributeType: "S",
+        },
+        {
+          AttributeName: "version",
+          AttributeType: "N",
+        },
+      ],
+      KeySchema: [
+        { AttributeName: "base_uri", KeyType: "HASH" },
+        { AttributeName: "version", KeyType: "RANGE" },
+      ],
+      ProvisionedThroughput: {
+        ReadCapacityUnits: 1,
+        WriteCapacityUnits: 1,
+      },
+    });
+    await client.send(command);
+    ```
+
+
 #### S3-compatible stores
 
 LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you must specify both region and endpoint:
@@ -282,26 +452,51 @@ LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you m
     )
     ```
 
-=== "JavaScript"
+=== "TypeScript"
 
-    ```javascript
+    === "@lancedb/lancedb"
-    const lancedb = require("lancedb");
+
-    const db = await lancedb.connect(
+        ```ts
-        "s3://bucket/path",
+        import * as lancedb from "@lancedb/lancedb";
-        {
+        const db = await lancedb.connect(
-            storageOptions: {
+            "s3://bucket/path",
-                region: "us-east-1",
+            {
-                endpoint: "http://minio:9000",
+                storageOptions: {
+                    region: "us-east-1",
+                    endpoint: "http://minio:9000",
+                }
             }
-        }
+        );
-    );
+        ```
-    ```
+
+    === "vectordb (deprecated)"
+
+        ```ts
+        const lancedb = require("lancedb");
+        const db = await lancedb.connect(
+            "s3://bucket/path",
+            {
+                storageOptions: {
+                    region: "us-east-1",
+                    endpoint: "http://minio:9000",
+                }
+            }
+        );
+        ```
 
 This can also be done with the ``AWS_ENDPOINT`` and ``AWS_DEFAULT_REGION`` environment variables.
 
+!!! tip "Local servers"
+
+    For local development, the server often has a `http` endpoint rather than a
+    secure `https` endpoint. In this case, you must also set the `ALLOW_HTTP`
+    environment variable to `true` to allow non-TLS connections, or pass the
+    storage option `allow_http` as `true`. If you do not do this, you will get
+    an error like `URL scheme is not allowed`.
+
 #### S3 Express
 
-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.
+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).
 
 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**.
 
@@ -318,21 +513,37 @@ To configure LanceDB to use an S3 Express endpoint, you must set the storage opt
     )
     ```
 
-=== "JavaScript"
+=== "TypeScript"
 
-    ```javascript
+    === "@lancedb/lancedb"
-    const lancedb = require("lancedb");
+
-    const db = await lancedb.connect(
+        ```ts
-        "s3://my-bucket--use1-az4--x-s3/path",
+        import * as lancedb from "@lancedb/lancedb";
-        {
+        const db = await lancedb.connect(
-            storageOptions: {
+            "s3://my-bucket--use1-az4--x-s3/path",
-                region: "us-east-1",
+            {
-                s3Express: "true",
+                storageOptions: {
+                    region: "us-east-1",
+                    s3Express: "true",
+                }
             }
-        }
+        );
-    );
+        ```
-    ```
 
+    === "vectordb (deprecated)"
+
+        ```ts
+        const lancedb = require("lancedb");
+        const db = await lancedb.connect(
+            "s3://my-bucket--use1-az4--x-s3/path",
+            {
+                storageOptions: {
+                    region: "us-east-1",
+                    s3Express: "true",
+                }
+            }
+        );
+        ```
 
 ### Google Cloud Storage
 
@@ -351,26 +562,40 @@ GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environme
     )
     ```
 
-=== "JavaScript"
+=== "TypeScript"
 
-    ```javascript
+    === "@lancedb/lancedb"
-    const lancedb = require("lancedb");
+
-    const db = await lancedb.connect(
+        ```ts
-        "gs://my-bucket/my-database",
+        import * as lancedb from "@lancedb/lancedb";
-        {
+        const db = await lancedb.connect(
-            storageOptions: {
+            "gs://my-bucket/my-database",
-                serviceAccount: "path/to/service-account.json",
+            {
+                storageOptions: {
+                    serviceAccount: "path/to/service-account.json",
+                }
             }
-        }
+        );
-    );
+        ```
-    ```
 
+    === "vectordb (deprecated)"
+
+        ```ts
+        const lancedb = require("lancedb");
+        const db = await lancedb.connect(
+            "gs://my-bucket/my-database",
+            {
+                storageOptions: {
+                    serviceAccount: "path/to/service-account.json",
+                }
+            }
+        );
+        ```
 
 !!! info "HTTP/2 support"
 
     By default, GCS uses HTTP/1 for communication, as opposed to HTTP/2. This improves maximum throughput significantly. However, if you wish to use HTTP/2 for some reason, you can set the environment variable `HTTP1_ONLY` to `false`.
 
-
 The following keys can be used as both environment variables or keys in the `storage_options` parameter:
 <!-- source: https://docs.rs/object_store/latest/object_store/gcp/enum.GoogleConfigKey.html -->
 
@@ -380,7 +605,6 @@ The following keys can be used as both environment variables or keys in the `sto
 | ``google_service_account_key``        | The serialized service account key.          |
 | ``google_application_credentials``    | Path to the application credentials.         |
 
-
 ### Azure Blob Storage
 
 Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_ACCOUNT_NAME`and `AZURE_STORAGE_ACCOUNT_KEY` environment variables. Alternatively, you can pass the account name and key in the `storage_options` parameter:
@@ -399,20 +623,37 @@ Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_A
     )
     ```
 
-=== "JavaScript"
+=== "TypeScript"
 
-    ```javascript
+    === "@lancedb/lancedb"
-    const lancedb = require("lancedb");
+
-    const db = await lancedb.connect(
+        ```ts
-        "az://my-container/my-database",
+        import * as lancedb from "@lancedb/lancedb";
-        {
+        const db = await lancedb.connect(
-            storageOptions: {
+            "az://my-container/my-database",
-                accountName: "some-account",
+            {
-                accountKey: "some-key",
+                storageOptions: {
+                    accountName: "some-account",
+                    accountKey: "some-key",
+                }
             }
-        }
+        );
-    );
+        ```
-    ```
+
+    === "vectordb (deprecated)"
+
+        ```ts
+        const lancedb = require("lancedb");
+        const db = await lancedb.connect(
+            "az://my-container/my-database",
+            {
+                storageOptions: {
+                    accountName: "some-account",
+                    accountKey: "some-key",
+                }
+            }
+        );
+        ```
 
 These keys can be used as both environment variables or keys in the `storage_options` parameter:
 
@@ -437,4 +678,4 @@ These keys can be used as both environment variables or keys in the `storage_opt
 | ``azure_use_azure_cli``               | Use azure cli for acquiring access token.                                                        |
 | ``azure_disable_tagging``             | Disables tagging objects. This can be desirable if not supported by the backing store.           |
 
-<!-- TODO: demonstrate how to configure networked file systems for optimal performance -->
+<!-- TODO: demonstrate how to configure networked file systems for optimal performance -->

docs/src/guides/tables.md

@@ -3,32 +3,46 @@
 
 A Table is a collection of Records in a LanceDB Database. Tables in Lance have a schema that defines the columns and their types. These schemas can include nested columns and can evolve over time.
 
-This guide will show how to create tables, insert data into them, and update the data.  
+This guide will show how to create tables, insert data into them, and update the data.
 
 
 ## Creating a LanceDB Table
 
+Initialize a LanceDB connection and create a table
+
 === "Python"
-    Initialize a LanceDB connection and create a table using one of the many methods listed below.
 
     ```python
     import lancedb
     db = lancedb.connect("./.lancedb")
     ```
 
-=== "Javascript"
-
-    Initialize a VectorDB connection and create a table using one of the many methods listed below.
-
-    ```javascript
-    const lancedb = require("vectordb");
-
-    const uri = "data/sample-lancedb";
-    const db = await lancedb.connect(uri);
-    ```
-
     LanceDB allows ingesting data from various sources - `dict`, `list[dict]`, `pd.DataFrame`, `pa.Table` or a `Iterator[pa.RecordBatch]`. Let's take a look at some of the these.
 
+=== "Typescript[^1]"
+
+    === "@lancedb/lancedb"
+
+        ```typescript
+        import * as lancedb from "@lancedb/lancedb";
+        import * as arrow from "apache-arrow";
+
+        const uri = "data/sample-lancedb";
+        const db = await lancedb.connect(uri);
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        const lancedb = require("vectordb");
+        const arrow = require("apache-arrow");
+
+        const uri = "data/sample-lancedb";
+        const db = await lancedb.connect(uri);
+        ```
+
+
+
 ### From list of tuples or dictionaries
 
 === "Python"
@@ -45,102 +59,150 @@ This guide will show how to create tables, insert data into them, and update the
 
     db["my_table"].head()
     ```
+
     !!! info "Note"
-        If the table already exists, LanceDB will raise an error by default. 
+        If the table already exists, LanceDB will raise an error by default.
 
         `create_table` supports an optional `exist_ok` parameter. When set to True
         and the table exists, then it simply opens the existing table. The data you
         passed in will NOT be appended to the table in that case.
 
-        ```python
+    ```python
-        db.create_table("name", data, exist_ok=True)
+    db.create_table("name", data, exist_ok=True)
-        ```
-        
-        Sometimes you want to make sure that you start fresh. If you want to 
-        overwrite the table, you can pass in mode="overwrite" to the createTable function.
-
-        ```python
-        db.create_table("name", data, mode="overwrite")
-        ```
-
-=== "Javascript"
-    You can create a LanceDB table in JavaScript using an array of JSON records as follows.
-
-    ```javascript
-    const tb = await db.createTable("my_table", [{
-        "vector": [3.1, 4.1],
-        "item": "foo",
-        "price": 10.0
-    }, {
-        "vector": [5.9, 26.5],
-        "item": "bar",
-        "price": 20.0
-    }]);
-    ```
-    !!! info "Note"
-    If the table already exists, LanceDB will raise an error by default. If you want to overwrite the table, you need to specify the `WriteMode` in the createTable function.
-
-    ```javascript
-    const table = await con.createTable(tableName, data, { writeMode: WriteMode.Overwrite })
     ```
 
-    ### From a Pandas DataFrame
+    Sometimes you want to make sure that you start fresh. If you want to
+    overwrite the table, you can pass in mode="overwrite" to the createTable function.
 
     ```python
-    import pandas as pd
+    db.create_table("name", data, mode="overwrite")
-
-    data = pd.DataFrame({
-        "vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],
-        "lat": [45.5, 40.1],
-        "long": [-122.7, -74.1]
-    })
-
-    db.create_table("my_table", data)
-
-    db["my_table"].head()
     ```
-    !!! info "Note"
+
+=== "Typescript[^1]"
+    You can create a LanceDB table in JavaScript using an array of records as follows.
+
+    === "@lancedb/lancedb"
+
+
+        ```ts
+        --8<-- "nodejs/examples/basic.test.ts:create_table"
+        ```
+
+        This will infer the schema from the provided data. If you want to explicitly provide a schema, you can use `apache-arrow` to declare a schema
+
+        ```ts
+        --8<-- "nodejs/examples/basic.test.ts:create_table_with_schema"
+        ```
+
+        !!! info "Note"
+            `createTable` supports an optional `existsOk` parameter. When set to true
+            and the table exists, then it simply opens the existing table. The data you
+            passed in will NOT be appended to the table in that case.
+
+        ```ts
+        --8<-- "nodejs/examples/basic.test.ts:create_table_exists_ok"
+        ```
+
+        Sometimes you want to make sure that you start fresh. If you want to
+        overwrite the table, you can pass in mode: "overwrite" to the createTable function.
+
+        ```ts
+        --8<-- "nodejs/examples/basic.test.ts:create_table_overwrite"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```ts
+        --8<-- "docs/src/basic_legacy.ts:create_table"
+        ```
+
+        This will infer the schema from the provided data. If you want to explicitly provide a schema, you can use apache-arrow to declare a schema
+
+
+
+        ```ts
+        --8<-- "docs/src/basic_legacy.ts:create_table_with_schema"
+        ```
+
+        !!! warning
+            `existsOk` is not available in `vectordb`
+
+
+
+            If the table already exists, vectordb will raise an error by default.
+            You can use `writeMode: WriteMode.Overwrite` to overwrite the table.
+            But this will delete the existing table and create a new one with the same name.
+
+
+        Sometimes you want to make sure that you start fresh.
+
+        If you want to overwrite the table, you can pass in `writeMode: lancedb.WriteMode.Overwrite` to the createTable function.
+
+        ```ts
+        const table = await con.createTable(tableName, data, {
+            writeMode: WriteMode.Overwrite
+        })
+        ```
+
+### From a Pandas DataFrame
+
+```python
+import pandas as pd
+
+data = pd.DataFrame({
+    "vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],
+    "lat": [45.5, 40.1],
+    "long": [-122.7, -74.1]
+})
+
+db.create_table("my_table", data)
+
+db["my_table"].head()
+```
+
+!!! info "Note"
     Data is converted to Arrow before being written to disk. For maximum control over how data is saved, either provide the PyArrow schema to convert to or else provide a PyArrow Table directly.
 
-    The **`vector`** column needs to be a [Vector](../python/pydantic.md#vector-field) (defined as [pyarrow.FixedSizeList](https://arrow.apache.org/docs/python/generated/pyarrow.list_.html)) type. 
+The **`vector`** column needs to be a [Vector](../python/pydantic.md#vector-field) (defined as [pyarrow.FixedSizeList](https://arrow.apache.org/docs/python/generated/pyarrow.list_.html)) type.
 
-    ```python
+```python
-    custom_schema = pa.schema([
+custom_schema = pa.schema([
-    pa.field("vector", pa.list_(pa.float32(), 4)),
+pa.field("vector", pa.list_(pa.float32(), 4)),
-    pa.field("lat", pa.float32()),
+pa.field("lat", pa.float32()),
-    pa.field("long", pa.float32())
+pa.field("long", pa.float32())
-    ])
+])
 
-    table = db.create_table("my_table", data, schema=custom_schema)
+table = db.create_table("my_table", data, schema=custom_schema)
-    ```
+```
 
 ### From a Polars DataFrame
 
-    LanceDB supports [Polars](https://pola.rs/), a modern, fast DataFrame library
+LanceDB supports [Polars](https://pola.rs/), a modern, fast DataFrame library
-    written in Rust. Just like in Pandas, the Polars integration is enabled by PyArrow
+written in Rust. Just like in Pandas, the Polars integration is enabled by PyArrow
-    under the hood. A deeper integration between LanceDB Tables and Polars DataFrames
+under the hood. A deeper integration between LanceDB Tables and Polars DataFrames
-    is on the way.
+is on the way.
 
-    ```python
+```python
-    import polars as pl
+import polars as pl
 
-    data = pl.DataFrame({
+data = pl.DataFrame({
-        "vector": [[3.1, 4.1], [5.9, 26.5]],
+    "vector": [[3.1, 4.1], [5.9, 26.5]],
-        "item": ["foo", "bar"],
+    "item": ["foo", "bar"],
-        "price": [10.0, 20.0]
+    "price": [10.0, 20.0]
-    })
+})
-    table = db.create_table("pl_table", data=data)
+table = db.create_table("pl_table", data=data)
-    ```
+```
 
 ### From an Arrow Table
+You can also create LanceDB tables directly from Arrow tables.
+LanceDB supports float16 data type!
+
 === "Python"
-    You can also create LanceDB tables directly from Arrow tables. 
-    LanceDB supports float16 data type!
 
     ```python
     import pyarrows as pa
     import numpy as np
-    
+
     dim = 16
     total = 2
     schema = pa.schema(
@@ -160,13 +222,19 @@ This guide will show how to create tables, insert data into them, and update the
     tbl = db.create_table("f16_tbl", data, schema=schema)
     ```
 
-=== "Javascript"
+=== "Typescript[^1]"
-    You can also create LanceDB tables directly from Arrow tables. 
-    LanceDB supports Float16 data type!
 
-    ```javascript
+    === "@lancedb/lancedb"
-    --8<-- "docs/src/basic_legacy.ts:create_f16_table"
+
-    ```
+        ```typescript
+        --8<-- "nodejs/examples/basic.test.ts:create_f16_table"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        --8<-- "docs/src/basic_legacy.ts:create_f16_table"
+        ```
 
 ### From Pydantic Models
 
@@ -206,7 +274,7 @@ table = db.create_table(table_name, schema=Content)
 
 Sometimes your data model may contain nested objects.
 For example, you may want to store the document string
-and the document soure name as a nested Document object:
+and the document source name as a nested Document object:
 
 ```python
 class Document(BaseModel):
@@ -225,7 +293,7 @@ class NestedSchema(LanceModel):
 tbl = db.create_table("nested_table", schema=NestedSchema, mode="overwrite")
 ```
 
-This creates a struct column called "document" that has two subfields 
+This creates a struct column called "document" that has two subfields
 called "content" and "source":
 
 ```
@@ -236,7 +304,7 @@ vector: fixed_size_list<item: float>[1536] not null
     child 0, item: float
 document: struct<content: string not null, source: string not null> not null
     child 0, content: string not null
-    child 1, source: string not null    
+    child 1, source: string not null
 ```
 
 #### Validators
@@ -261,7 +329,7 @@ class TestModel(LanceModel):
     @classmethod
     def tz_must_match(cls, dt: datetime) -> datetime:
         assert dt.tzinfo == tz
-        return dt        
+        return dt
 
 ok = TestModel(dt_with_tz=datetime.now(tz))
 
@@ -329,25 +397,25 @@ You can also use iterators of other types like Pandas DataFrame or Pylists direc
     tbl = db.open_table("my_table")
     ```
 
-=== "JavaScript"
+=== "Typescript[^1]"
+
     If you forget the name of your table, you can always get a listing of all table names.
 
-    ```javascript
+    ```typescript
     console.log(await db.tableNames());
     ```
 
     Then, you can open any existing tables.
 
-    ```javascript
+    ```typescript
     const tbl = await db.openTable("my_table");
     ```
 
 ## Creating empty table
+You can create an empty table for scenarios where you want to add data to the table later. An example would be when you want to collect data from a stream/external file and then add it to a table in batches.
 
 === "Python"
-    In Python, you can create an empty table for scenarios where you want to add data to the table later. An example would be when you want to collect data from a stream/external file and then add it to a table in batches.
 
-    ```python
 
     An empty table can be initialized via a PyArrow schema.
 
@@ -364,8 +432,8 @@ You can also use iterators of other types like Pandas DataFrame or Pylists direc
     tbl = db.create_table("empty_table_add", schema=schema)
     ```
 
-    Alternatively, you can also use Pydantic to specify the schema for the empty table. Note that we do not 
+    Alternatively, you can also use Pydantic to specify the schema for the empty table. Note that we do not
-    directly import `pydantic` but instead use `lancedb.pydantic` which is a subclass of `pydantic.BaseModel` 
+    directly import `pydantic` but instead use `lancedb.pydantic` which is a subclass of `pydantic.BaseModel`
     that has been extended to support LanceDB specific types like `Vector`.
 
     ```python
@@ -382,9 +450,23 @@ You can also use iterators of other types like Pandas DataFrame or Pylists direc
 
     Once the empty table has been created, you can add data to it via the various methods listed in the [Adding to a table](#adding-to-a-table) section.
 
+=== "Typescript[^1]"
+
+    === "@lancedb/lancedb"
+
+        ```typescript
+        --8<-- "nodejs/examples/basic.test.ts:create_empty_table"
+        ```
+
+    === "vectordb (deprecated)"
+
+        ```typescript
+        --8<-- "docs/src/basic_legacy.ts:create_empty_table"
+        ```
+
 ## Adding to a table
 
-After a table has been created, you can always add more data to it using the various methods available.
+After a table has been created, you can always add more data to it using the `add` method
 
 === "Python"
     You can add any of the valid data structures accepted by LanceDB table, i.e, `dict`, `list[dict]`, `pd.DataFrame`, or `Iterator[pa.RecordBatch]`. Below are some examples.
@@ -452,8 +534,27 @@ After a table has been created, you can always add more data to it using the var
     tbl.add(pydantic_model_items)
     ```
 
+    ??? "Ingesting Pydantic models with LanceDB embedding API"
+        When using LanceDB's embedding API, you can add Pydantic models directly to the table. LanceDB will automatically convert the `vector` field to a vector before adding it to the table. You need to specify the default value of `vector` field as None to allow LanceDB to automatically vectorize the data.
 
-=== "JavaScript"
+        ```python
+        import lancedb
+        from lancedb.pydantic import LanceModel, Vector
+        from lancedb.embeddings import get_registry
+
+        db = lancedb.connect("~/tmp")
+        embed_fcn = get_registry().get("huggingface").create(name="BAAI/bge-small-en-v1.5")
+
+        class Schema(LanceModel):
+            text: str = embed_fcn.SourceField()
+            vector: Vector(embed_fcn.ndims()) = embed_fcn.VectorField(default=None)
+
+        tbl = db.create_table("my_table", schema=Schema, mode="overwrite")
+        models = [Schema(text="hello"), Schema(text="world")]
+        tbl.add(models)
+        ```
+
+=== "Typescript[^1]"
 
     ```javascript
     await tbl.add(
@@ -509,15 +610,15 @@ Use the `delete()` method on tables to delete rows from a table. To choose which
     # 0  3  [5.0, 6.0]
     ```
 
-=== "JavaScript"
+=== "Typescript[^1]"
 
-    ```javascript
+    ```ts
     await tbl.delete('item = "fizz"')
     ```
 
     ### Deleting row with specific column value
 
-    ```javascript
+    ```ts
     const con = await lancedb.connect("./.lancedb")
     const data = [
       {id: 1, vector: [1, 2]},
@@ -531,7 +632,7 @@ Use the `delete()` method on tables to delete rows from a table. To choose which
 
     ### Delete from a list of values
 
-    ```javascript
+    ```ts
     const to_remove = [1, 5];
     await tbl.delete(`id IN (${to_remove.join(",")})`)
     await tbl.countRows() // Returns 1
@@ -588,26 +689,49 @@ This can be used to update zero to all rows depending on how many rows match the
     2  2  [10.0, 10.0]
     ```
 
-=== "JavaScript/Typescript"
+=== "Typescript[^1]"
 
-    API Reference: [vectordb.Table.update](../javascript/interfaces/Table.md/#update)
+    === "@lancedb/lancedb"
 
-    ```javascript
+        API Reference: [lancedb.Table.update](../js/classes/Table.md/#update)
-    const lancedb = require("vectordb");
 
-    const db = await lancedb.connect("./.lancedb");
+        ```ts
+        import * as lancedb from "@lancedb/lancedb";
 
-    const data = [
+        const db = await lancedb.connect("./.lancedb");
-      {x: 1, vector: [1, 2]},
-      {x: 2, vector: [3, 4]},
-      {x: 3, vector: [5, 6]},
-    ];
-    const tbl = await db.createTable("my_table", data)
 
-    await tbl.update({ where: "x = 2", values: {vector: [10, 10]} })
+        const data = [
-    ```
+            {x: 1, vector: [1, 2]},
+            {x: 2, vector: [3, 4]},
+            {x: 3, vector: [5, 6]},
+        ];
+        const tbl = await db.createTable("my_table", data)
 
-The `values` parameter is used to provide the new values for the columns as literal values. You can also use the `values_sql` / `valuesSql` parameter to provide SQL expressions for the new values. For example, you can use `values_sql="x + 1"` to increment the value of the `x` column by 1.
+        await tbl.update({vector: [10, 10]}, { where: "x = 2"})
+        ```
+
+    === "vectordb (deprecated)"
+
+        API Reference: [vectordb.Table.update](../javascript/interfaces/Table.md/#update)
+
+        ```ts
+        const lancedb = require("vectordb");
+
+        const db = await lancedb.connect("./.lancedb");
+
+        const data = [
+            {x: 1, vector: [1, 2]},
+            {x: 2, vector: [3, 4]},
+            {x: 3, vector: [5, 6]},
+        ];
+        const tbl = await db.createTable("my_table", data)
+
+        await tbl.update({ where: "x = 2", values: {vector: [10, 10]} })
+        ```
+
+#### Updating using a sql query
+
+  The `values` parameter is used to provide the new values for the columns as literal values. You can also use the `values_sql` / `valuesSql` parameter to provide SQL expressions for the new values. For example, you can use `values_sql="x + 1"` to increment the value of the `x` column by 1.
 
 === "Python"
 
@@ -626,16 +750,163 @@ The `values` parameter is used to provide the new values for the columns as lite
     2  3  [10.0, 10.0]
     ```
 
-=== "JavaScript/Typescript"
+=== "Typescript[^1]"
 
-    ```javascript
+    === "@lancedb/lancedb"
-    await tbl.update({ valuesSql: { x: "x + 1" } })
+
-    ```
+        Coming Soon!
+
+    === "vectordb (deprecated)"
+
+        ```ts
+        await tbl.update({ valuesSql: { x: "x + 1" } })
+        ```
 
 !!! info "Note"
 
     When rows are updated, they are moved out of the index. The row will still show up in ANN queries, but the query will not be as fast as it would be if the row was in the index. If you update a large proportion of rows, consider rebuilding the index afterwards.
 
+## Drop a table
+
+Use the `drop_table()` method on the database to remove a table.
+
+=== "Python"
+
+      ```python
+      --8<-- "python/python/tests/docs/test_basic.py:drop_table"
+      --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,
+      you can pass in `ignore_missing=True`.
+
+=== "TypeScript"
+
+      ```typescript
+      --8<-- "docs/src/basic_legacy.ts:drop_table"
+      ```
+
+      This permanently removes the table and is not recoverable, unlike deleting rows.
+      If the table does not exist an exception is raised.
+
+## Changing schemas
+
+While tables must have a schema specified when they are created, you can
+change the schema over time. There's three methods to alter the schema of
+a table:
+
+* `add_columns`: Add new columns to the table
+* `alter_columns`: Alter the name, nullability, or data type of a column
+* `drop_columns`: Drop columns from the table
+
+### Adding new columns
+
+You can add new columns to the table with the `add_columns` method. New columns
+are filled with values based on a SQL expression. For example, you can add a new
+column `y` to the table and fill it with the value of `x + 1`.
+
+=== "Python"
+
+    ```python
+    table.add_columns({"double_price": "price * 2"})
+    ```
+    **API Reference:** [lancedb.table.Table.add_columns][]
+
+=== "Typescript"
+
+    ```typescript
+    --8<-- "nodejs/examples/basic.test.ts:add_columns"
+    ```
+    **API Reference:** [lancedb.Table.addColumns](../js/classes/Table.md/#addcolumns)
+
+If you want to fill it with null, you can use `cast(NULL as <data_type>)` as
+the SQL expression to fill the column with nulls, while controlling the data
+type of the column. Available data types are base on the
+[DataFusion data types](https://datafusion.apache.org/user-guide/sql/data_types.html).
+You can use any of the SQL types, such as `BIGINT`:
+
+```sql
+cast(NULL as BIGINT)
+```
+
+Using Arrow data types and the `arrow_typeof` function is not yet supported.
+
+<!-- TODO: we could provide a better formula for filling with nulls:
+   https://github.com/lancedb/lance/issues/3175
+-->
+
+### Altering existing columns
+
+You can alter the name, nullability, or data type of a column with the `alter_columns`
+method.
+
+Changing the name or nullability of a column just updates the metadata. Because
+of this, it's a fast operation. Changing the data type of a column requires
+rewriting the column, which can be a heavy operation.
+
+=== "Python"
+
+    ```python
+    import pyarrow as pa
+    table.alter_column({"path": "double_price", "rename": "dbl_price",
+                        "data_type": pa.float32(), "nullable": False})
+    ```
+    **API Reference:** [lancedb.table.Table.alter_columns][]
+
+=== "Typescript"
+
+    ```typescript
+    --8<-- "nodejs/examples/basic.test.ts:alter_columns"
+    ```
+    **API Reference:** [lancedb.Table.alterColumns](../js/classes/Table.md/#altercolumns)
+
+### Dropping columns
+
+You can drop columns from the table with the `drop_columns` method. This will
+will remove the column from the schema.
+
+<!-- TODO: Provide guidance on how to reduce disk usage once optimize helps here
+    waiting on: https://github.com/lancedb/lance/issues/3177
+-->
+
+=== "Python"
+
+    ```python
+    table.drop_columns(["dbl_price"])
+    ```
+    **API Reference:** [lancedb.table.Table.drop_columns][]
+
+=== "Typescript"
+
+    ```typescript
+    --8<-- "nodejs/examples/basic.test.ts:drop_columns"
+    ```
+    **API Reference:** [lancedb.Table.dropColumns](../js/classes/Table.md/#altercolumns)
+
+
+## Handling bad vectors
+
+In LanceDB Python, you can use the `on_bad_vectors` parameter to choose how
+invalid vector values are handled. Invalid vectors are vectors that are not valid
+because:
+
+1. They are the wrong dimension
+2. They contain NaN values
+3. They are null but are on a non-nullable field
+
+By default, LanceDB will raise an error if it encounters a bad vector. You can
+also choose one of the following options:
+
+* `drop`: Ignore rows with bad vectors
+* `fill`: Replace bad values (NaNs) or missing values (too few dimensions) with
+    the fill value specified in the `fill_value` parameter. An input like
+    `[1.0, NaN, 3.0]` will be replaced with `[1.0, 0.0, 3.0]` if `fill_value=0.0`.
+* `null`: Replace bad vectors with null (only works if the column is nullable).
+    A bad vector `[1.0, NaN, 3.0]` will be replaced with `null` if the column is
+    nullable. If the vector column is non-nullable, then bad vectors will cause an
+    error
+
 ## Consistency
 
 In LanceDB OSS, users can set the `read_consistency_interval` parameter on connections to achieve different levels of read consistency. This parameter determines how frequently the database synchronizes with the underlying storage system to check for updates made by other processes. If another process updates a table, the database will not see the changes until the next synchronization.
@@ -651,7 +922,7 @@ There are three possible settings for `read_consistency_interval`:
     This is only tune-able in LanceDB OSS. In LanceDB Cloud, readers are always eventually consistent.
 
 === "Python"
-    
+
     To set strong consistency, use `timedelta(0)`:
 
     ```python
@@ -673,33 +944,35 @@ There are three possible settings for `read_consistency_interval`:
     ```python
     db = lancedb.connect("./.lancedb")
     table = db.open_table("my_table")
-    
+
     # (Other writes happen to my_table from another process)
 
     # Check for updates
     table.checkout_latest()
     ```
 
-=== "JavaScript/Typescript"
+=== "Typescript[^1]"
 
     To set strong consistency, use `0`:
 
-    ```javascript
+    ```ts
     const db = await lancedb.connect({ uri: "./.lancedb", readConsistencyInterval: 0 });
     const table = await db.openTable("my_table");
     ```
 
     For eventual consistency, specify the update interval as seconds:
 
-    ```javascript
+    ```ts
     const db = await lancedb.connect({ uri: "./.lancedb", readConsistencyInterval: 5 });
     const table = await db.openTable("my_table");
     ```
 
-<!-- Node doesn't yet support the version time travel: https://github.com/lancedb/lancedb/issues/1007 
+<!-- Node doesn't yet support the version time travel: https://github.com/lancedb/lancedb/issues/1007
     Once it does, we can show manual consistency check for Node as well.
 -->
 
 ## What's next?
 
-Learn the best practices on creating an ANN index and getting the most out of it.
+Learn the best practices on creating an ANN index and getting the most out of it.
+
+[^1]: The `vectordb` package is a legacy package that is  deprecated in favor of `@lancedb/lancedb`.  The `vectordb` package will continue to receive bug fixes and security updates until September 2024.  We recommend all new projects use `@lancedb/lancedb`.  See the [migration guide](../migration.md) for more information.

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

@@ -0,0 +1,131 @@
+## 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/>
+
+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:
+
+* Using different query types
+* Using hybrid search
+* Fine-tuning the embedding models
+* Using different embedding models
+
+Using different embedding models is something that's very specific to the use case and the data. So we will not discuss it here. In this section, we will discuss the first three techniques.
+
+
+!!! note "Note"
+    We'll be using a simple metric called "hit-rate" for evaluating the performance of the retriever across this guide. Hit-rate is the percentage of queries for which the retriever returned the correct answer in the top-k results. For example, if the retriever returned the correct answer in the top-3 results for 70% of the queries, then the hit-rate@3 is 0.7.
+
+
+## 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)
+
+### 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. 
+
+```python
+import lancedb
+import pandas as pd
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import Vector, LanceModel
+
+db = lancedb.connect("~/lancedb/query_types")
+df = pd.read_csv("data_qa.csv")
+
+embed_fcn = get_registry().get("huggingface").create(name="BAAI/bge-small-en-v1.")
+
+class Schema(LanceModel):
+    context: str = embed_fcn.SourceField()
+    vector: Vector(embed_fcn.ndims()) = embed_fcn.VectorField()
+
+table = db.create_table("qa", schema=Schema)
+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.
+
+* <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.
+
+    ```python
+    table.search(quries[0]).limit(5).to_pandas()
+    ```
+
+    Vector or semantic search is useful when you want to find documents that are similar to the query in terms of meaning.
+
+---
+
+* <b> Full-text Search: </b>
+    
+    FTS requires creating an index on the column you want to search on. `replace=True` will replace the existing index if it exists.
+    Once the index is created, you can search using the `fts` query type.
+    ```python
+    table.create_fts_index("context", replace=True)
+    table.search(quries[0], query_type="fts").limit(5).to_pandas()
+    ```
+
+    Full-text search is useful when you want to find documents that contain the query terms.
+
+---
+
+* <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.
+    ```python
+    table.search(quries[0], query_type="hybrid").limit(5).to_pandas()
+    ```
+    Hybrid search requires a reranker to combine and rank the results from vector and full-text search. We'll cover reranking as a concept in the next section.
+
+    Hybrid search is useful when you want to combine the benefits of both vector and full-text search.
+
+    !!! 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/)
+
+    
+
+### Hit rate evaluation results
+
+Now that we have seen how to run different query types on the dataset, let's evaluate the hit-rate of each query type on the dataset.
+For brevity, the entire evaluation script is not shown here. You can find the complete evaluation and benchmarking utility scripts [here](https://github.com/lancedb/ragged).
+
+Here are the hit-rate results for the dataset:
+
+| Query Type | Hit-rate@5 |
+| --- | --- |
+| Vector Search | 0.640 |
+| Full-text Search | 0.595 |
+| Hybrid Search (w/ LinearCombinationReranker) | 0.645 |
+
+**Choosing query type** is very specific to the use case and the data. This synthetic dataset has been generated to be semantically challenging, i.e, the queries don't have a lot of keywords in common with the context. So, vector search performs better than full-text search. However, in real-world scenarios, full-text search might perform better than vector search. Hybrid search is a good choice when you want to combine the benefits of both vector and full-text search.
+
+### Evaluation results on other datasets
+
+The hit-rate results can vary based on the dataset and the query type. Here are the hit-rate results for the other datasets using the same embedding function.
+
+* <b> SQuAD Dataset: </b>
+
+    | Query Type | Hit-rate@5 |
+    | --- | --- |
+    | Vector Search | 0.822 |
+    | Full-text Search | 0.835 |
+    | Hybrid Search (w/ LinearCombinationReranker) | 0.8874 |
+
+* <b> Uber10K sec filing Dataset: </b>
+
+    | Query Type | Hit-rate@5 |
+    | --- | --- |
+    | Vector Search | 0.608 |
+    | Full-text Search | 0.82 |
+    | Hybrid Search (w/ LinearCombinationReranker) | 0.80 |
+
+In these standard datasets, FTS seems to perform much better than vector search because the queries have a lot of keywords in common with the context. So, in general choosing the query type is very specific to the use case and the data.
+
+

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

@@ -0,0 +1,80 @@
+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/>
+
+## Reranking search results
+You can rerank any search results using a reranker. The syntax for reranking is as follows:
+
+```python
+from lancedb.rerankers import LinearCombinationReranker
+
+reranker = LinearCombinationReranker()
+table.search(quries[0], query_type="hybrid").rerank(reranker=reranker).limit(5).to_pandas()
+```
+Based on the `query_type`, the `rerank()` function can accept other arguments as well. For example, hybrid search accepts a `normalize` param to determine the score normalization method.
+
+!!! note "Note"
+    LanceDB provides a `Reranker` base class that can be extended to implement custom rerankers. Each reranker must implement the `rerank_hybrid` method. `rerank_vector` and `rerank_fts` methods are optional. For example, the `LinearCombinationReranker` only implements the `rerank_hybrid` method and so it can only be used for reranking hybrid search results.
+
+## Choosing a Reranker
+There are many rerankers available in LanceDB like `CrossEncoderReranker`, `CohereReranker`, and `ColBERT`. The choice of reranker depends on the dataset and the application. You can even implement you own custom reranker by extending the `Reranker` class. For more details about each available reranker and performance comparison, refer to the [rerankers](https://lancedb.github.io/lancedb/reranking/) documentation.
+
+In this example, we'll use the `CohereReranker` to rerank the search results. It requires  `cohere` to be installed and `COHERE_API_KEY` to be set in the environment. To get your API key, sign up on [Cohere](https://cohere.ai/).
+
+```python
+from lancedb.rerankers import CohereReranker
+
+# use Cohere reranker v3
+reranker = CohereReranker(model_name="rerank-english-v3.0") # default model is "rerank-english-v2.0"
+```
+
+### Reranking search results
+Now we can rerank all query type results using the `CohereReranker`:
+
+```python
+
+# rerank hybrid search results
+table.search(quries[0], query_type="hybrid").rerank(reranker=reranker).limit(5).to_pandas()
+
+# rerank vector search results
+table.search(quries[0], query_type="vector").rerank(reranker=reranker).limit(5).to_pandas()
+
+# rerank fts search results
+table.search(quries[0], query_type="fts").rerank(reranker=reranker).limit(5).to_pandas()
+```
+
+Each reranker can accept additional arguments. For example, `CohereReranker` accepts `top_k` and `batch_size` params to control the number of documents to rerank and the batch size for reranking respectively. Similarly, a custom reranker can accept any number of arguments based on the implementation. For example, a reranker can accept a `filter` that implements some custom logic to filter out documents before reranking.
+
+## Results
+
+Let us take a look at the same datasets from the previous sections, using the same embedding table but with Cohere reranker applied to all query types.
+
+!!! note "Note"
+    When reranking fts or vector search results, the search results are over-fetched by a factor of 2 and then reranked. From the reranked set, `top_k` (5 in this case) results are taken. This is done because reranking will have no effect on the hit-rate if we only fetch the `top_k` results.
+
+### Synthetic LLama2 paper dataset
+
+| Query Type | Hit-rate@5 |
+| --- | --- |
+| Vector |  0.640 |
+| FTS   |  0.595  |
+| Reranked vector | 0.677    |
+| Reranked fts  | 0.672    |
+| Hybrid | 0.759 |
+
+### SQuAD Dataset
+
+
+### Uber10K sec filing Dataset
+
+| Query Type | Hit-rate@5 |
+| --- | --- |
+| Vector |  0.608 |
+| FTS   |  0.824  |
+| Reranked vector | 0.671    |
+| Reranked fts  | 0.843    |
+| Hybrid | 0.849 |
+
+
+
+

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

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+## 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/>
+
+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.
+
+We'll use the same dataset as in the previous sections. Start off by splitting the dataset into training and validation sets:
+```python
+from sklearn.model_selection import train_test_split
+
+train_df, validation_df = train_test_split("data_qa.csv", test_size=0.2, random_state=42)
+
+train_df.to_csv("data_train.csv", index=False)
+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. 
+
+
+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
+from llama_index.finetuning import generate_qa_embedding_pairs
+from llama_index.core.evaluation import EmbeddingQAFinetuneDataset
+
+def load_corpus(file):
+    loader = PagedCSVReader(encoding="utf-8")
+    docs = loader.load_data(file=Path(file))
+
+    parser = SentenceSplitter()
+    nodes = parser.get_nodes_from_documents(docs)
+
+    return nodes
+
+from llama_index.llms.openai import OpenAI
+
+
+train_dataset = generate_qa_embedding_pairs(
+    llm=OpenAI(model="gpt-3.5-turbo"), nodes=train_nodes, verbose=False
+)
+val_dataset = generate_qa_embedding_pairs(
+    llm=OpenAI(model="gpt-3.5-turbo"), nodes=val_nodes, verbose=False
+)
+```
+
+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
+
+finetune_engine = SentenceTransformersFinetuneEngine(
+    train_dataset,
+    model_id="BAAI/bge-small-en-v1.5",
+    model_output_path="tuned_model",
+    val_dataset=val_dataset,
+)
+finetune_engine.finetune()
+embed_model = finetune_engine.get_finetuned_model()
+```
+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. 
+On performing the same hit-rate evaluation as before, we see a significant improvement in the hit-rate across all query types.
+
+### Baseline
+| Query Type | Hit-rate@5 |
+| --- | --- |
+| Vector Search | 0.640 |
+| Full-text Search | 0.595 |
+| Reranked Vector Search | 0.677 |
+| Reranked Full-text Search | 0.672 |
+| Hybrid Search (w/ CohereReranker) | 0.759|
+
+### Fine-tuned model ( 2 iterations )
+| Query Type | Hit-rate@5 |
+| --- | --- |
+| Vector Search | 0.672 |
+| Full-text Search | 0.595 |
+| Reranked Vector Search | 0.754 |
+| Reranked Full-text Search | 0.672|
+| Hybrid Search (w/ CohereReranker) | 0.768 |