[python] Bump version: 0.6.4 → 0.6.5

changed the error msg for query with wrong vector dim thus need this
change to pass the nodejs tests.

.bumpversion.cfg

@@ -0,0 +1,12 @@
+[bumpversion]
+current_version = 0.4.13
+commit = True
+message = Bump version: {current_version} → {new_version}
+tag = True
+tag_name = v{new_version}
+
+[bumpversion:file:node/package.json]
+
+[bumpversion:file:rust/ffi/node/Cargo.toml]
+
+[bumpversion:file:rust/lancedb/Cargo.toml]

.bumpversion.toml

@@ -1,57 +0,0 @@
-[tool.bumpversion]
-current_version = "0.10.0-beta.1"
-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 = ""
-
-[tool.bumpversion.parts.pre_l]
-values = ["beta", "final"]
-optional_value = "final"
-
-[[tool.bumpversion.files]]
-filename = "node/package.json"
-search = "\"version\": \"{current_version}\","
-replace = "\"version\": \"{new_version}\","
-
-[[tool.bumpversion.files]]
-filename = "nodejs/package.json"
-search = "\"version\": \"{current_version}\","
-replace = "\"version\": \"{new_version}\","
-
-# nodejs binary packages
-[[tool.bumpversion.files]]
-glob = "nodejs/npm/*/package.json"
-search = "\"version\": \"{current_version}\","
-replace = "\"version\": \"{new_version}\","
-
-# Cargo files
-# ------------
-[[tool.bumpversion.files]]
-filename = "rust/ffi/node/Cargo.toml"
-search = "\nversion = \"{current_version}\""
-replace = "\nversion = \"{new_version}\""
-
-[[tool.bumpversion.files]]
-filename = "rust/lancedb/Cargo.toml"
-search = "\nversion = \"{current_version}\""
-replace = "\nversion = \"{new_version}\""

.github/labeler.yml

@@ -1,33 +0,0 @@
-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

@@ -1,41 +0,0 @@
-{
-    "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

@@ -14,10 +14,6 @@ inputs:
     # Note: this does *not* mean the host is arm64, since we might be cross-compiling.
     required: false
     default: "false"
-  manylinux:
-    description: "The manylinux version to build for"
-    required: false
-    default: "2_17"
 runs:
   using: "composite"
   steps:
@@ -32,7 +28,7 @@ runs:
         command: build
         working-directory: python
         target: x86_64-unknown-linux-gnu
-        manylinux: ${{ inputs.manylinux }}
+        manylinux: "2_17"
         args: ${{ inputs.args }}
         before-script-linux: |
           set -e
@@ -46,9 +42,8 @@ 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 }}
+        manylinux: "2_24"
         args: ${{ inputs.args }}
         before-script-linux: |
           set -e

.github/workflows/build_mac_wheel/action.yml

@@ -21,6 +21,5 @@ 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

@@ -26,7 +26,6 @@ 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

@@ -1,20 +1,13 @@
 name: Cargo Publish
 
 on:
-  push:
-    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
+  release:
+    types: [ published ]
 
 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
-  # Up-to-date compilers needed for fp16kernels.
-  CC: gcc-12
-  CXX: g++-12
 
 jobs:
   build:

.github/workflows/dev.yml

@@ -1,81 +0,0 @@
-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_test.yml

@@ -18,25 +18,19 @@ on:
 env:
   # 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 -C target-cpu=haswell -C target-feature=+f16c,+avx2,+fma"
+  RUSTFLAGS: "-C debuginfo=1 -C target-cpu=native -C target-feature=+f16c,+avx2,+fma"
   RUST_BACKTRACE: "1"
 
 jobs:
   test-python:
     name: Test doc python code
-    runs-on: "warp-ubuntu-latest-x64-4x"
+    runs-on: "buildjet-8vcpu-ubuntu-2204"
     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 libssl-dev
+        sudo apt install -y protobuf-compiler libssl-dev
         rustup update && rustup default
     - name: Set up Python
       uses: actions/setup-python@v5
@@ -45,7 +39,7 @@ jobs:
         cache: "pip"
         cache-dependency-path: "docs/test/requirements.txt"
     - name: Rust cache
-      uses: swatinem/rust-cache@v2
+      uses: swatinem/rust-cache@v2        
     - name: Build Python
       working-directory: docs/test
       run:
@@ -60,7 +54,7 @@ jobs:
         for d in *; do cd "$d"; echo "$d".py; python "$d".py; cd ..; done
   test-node:
     name: Test doc nodejs code
-    runs-on: "warp-ubuntu-latest-x64-4x"
+    runs-on: "buildjet-8vcpu-ubuntu-2204"
     timeout-minutes: 60
     strategy:
       fail-fast: false
@@ -70,19 +64,13 @@ jobs:
       with:
         fetch-depth: 0
         lfs: true
-    - name: Print CPU capabilities
-      run: cat /proc/cpuinfo
     - name: Set up Node
       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 libssl-dev
+        sudo apt install -y protobuf-compiler libssl-dev
         rustup update && rustup default
     - name: Rust cache
       uses: swatinem/rust-cache@v2

.github/workflows/java-publish.yml

@@ -1,109 +0,0 @@
-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: ${{ secrets.GPG_PASSPHRASE }}
-      - name: Install dependencies
-        run: |
-          sudo apt -y -qq update
-          sudo apt install -y protobuf-compiler libssl-dev pkg-config
-      - name: Download artifact
-        uses: actions/download-artifact@v4
-      - name: Copy native libs
-        run: |
-          mkdir -p ./core/target/classes/nativelib/darwin-aarch64 ./core/target/classes/nativelib/linux-aarch64
-          cp ../liblancedb_jni_darwin_aarch64.zip/liblancedb_jni.dylib ./core/target/classes/nativelib/darwin-aarch64/liblancedb_jni.dylib
-          cp ../liblancedb_jni_linux_aarch64.zip/liblancedb_jni.so ./core/target/classes/nativelib/linux-aarch64/liblancedb_jni.so
-      - name: Set github
-        run: |
-          git config --global user.email "LanceDB Github Runner"
-          git config --global user.name "dev+gha@lancedb.com"
-      - name: Publish with Java 8
-        run: |
-          echo "use-agent" >> ~/.gnupg/gpg.conf
-          echo "pinentry-mode loopback" >> ~/.gnupg/gpg.conf
-          export GPG_TTY=$(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

@@ -1,113 +0,0 @@
-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

@@ -1,62 +1,37 @@
 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
-        type: boolean
-      type:
-        description: 'What kind of release is this?'
-        required: true
-        default: 'preview'
+        default: "false"
         type: choice
         options:
-          - preview
-          - stable
-      python:
-        description: 'Make a Python release'
+          - "true"
+          - "false"
+      part:
+        description: 'What kind of release is this?'
         required: true
-        default: true
-        type: boolean
-      other:
-        description: 'Make a Node/Rust release'
-        required: true
-        default: true
-        type: boolean
-      bump-minor:
-        description: 'Bump minor version'
-        required: true
-        default: false
-        type: boolean
+        default: 'patch'
+        type: choice
+        options:
+          - patch
+          - minor
+          - major
 
 jobs:
-  make-release:
-    # Creates tag and GH release. The GH release will trigger the build and release jobs.
+  bump-version:
     runs-on: ubuntu-latest
-    permissions:
-      contents: write
     steps:
-      - name: Output Inputs
-        run: echo "${{ toJSON(github.event.inputs) }}"
-      - uses: actions/checkout@v4
+      - name: Check out main
+        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: |
@@ -66,34 +41,19 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
-      - name: Bump Python version
-        if: ${{ inputs.python }}
-        working-directory: python
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Bump version, create tag and commit
         run: |
-          # Need to get the commit before bumping the version, so we can
-          # determine if there are breaking changes in the next step as well.
-          echo "COMMIT_BEFORE_BUMP=$(git rev-parse HEAD)" >> $GITHUB_ENV
-
-          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 }}
+          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:
-          # Need to use PAT here too to trigger next workflow. See comment above.
           github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
-          branch: ${{ github.ref }}
+          branch: main
           tags: true
       - uses: ./.github/workflows/update_package_lock
-        if: ${{ !inputs.dry_run && inputs.other }}
+        if: ${{ inputs.dry_run }} == "false"
         with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+

.github/workflows/node.yml

@@ -20,8 +20,7 @@ env:
   # "1" means line tables only, which is useful for panic tracebacks.
   #
   # Use native CPU to accelerate tests if possible, especially for f16
-  # target-cpu=haswell fixes failing ci build
-  RUSTFLAGS: "-C debuginfo=1 -C target-cpu=haswell -C target-feature=+f16c,+avx2,+fma"
+  RUSTFLAGS: "-C debuginfo=1 -C target-cpu=native -C target-feature=+f16c,+avx2,+fma"
   RUST_BACKTRACE: "1"
 
 jobs:
@@ -107,7 +106,6 @@ jobs:
       AWS_ENDPOINT: http://localhost:4566
       # this one is for dynamodb
       DYNAMODB_ENDPOINT: http://localhost:4566
-      ALLOW_HTTP: true
     steps:
     - uses: actions/checkout@v4
       with:

.github/workflows/nodejs.yml

@@ -28,10 +28,6 @@ jobs:
       run:
         shell: bash
         working-directory: nodejs
-    env:
-      # Need up-to-date compilers for kernels
-      CC: gcc-12
-      CXX: g++-12
     steps:
     - uses: actions/checkout@v4
       with:
@@ -52,7 +48,8 @@ jobs:
         cargo fmt --all -- --check
         cargo clippy --all --all-features -- -D warnings
         npm ci
-        npm run lint-ci
+        npm run lint
+        npm run chkformat
   linux:
     name: Linux (NodeJS ${{ matrix.node-version }})
     timeout-minutes: 30
@@ -84,12 +81,7 @@ jobs:
       run: |
         npm ci
         npm run build
-    - name: Setup localstack
-      working-directory: .
-      run: docker compose up --detach --wait
     - name: Test
-      env:
-        S3_TEST: "1"
       run: npm run test
   macos:
     timeout-minutes: 30

.github/workflows/npm-publish.yml

@@ -1,13 +1,11 @@
 name: NPM Publish
 
 on:
-  push:
-    tags:
-      - "v*"
+  release:
+    types: [ published ]
 
 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')
@@ -21,7 +19,7 @@ jobs:
       - uses: actions/setup-node@v3
         with:
           node-version: 20
-          cache: "npm"
+          cache: 'npm'
           cache-dependency-path: node/package-lock.json
       - name: Install dependencies
         run: |
@@ -33,14 +31,13 @@ jobs:
           npm run tsc
           npm pack
       - name: Upload Linux Artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v3
         with:
           name: node-package
           path: |
             node/vectordb-*.tgz
 
   node-macos:
-    name: vectordb ${{ matrix.config.arch }}
     strategy:
       matrix:
         config:
@@ -64,85 +61,15 @@ jobs:
       - name: Build MacOS native node modules
         run: bash ci/build_macos_artifacts.sh ${{ matrix.config.arch }}
       - name: Upload Darwin Artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v3
         with:
-          name: node-native-darwin-${{ matrix.config.arch }}
+          name: native-darwin
           path: |
             node/dist/lancedb-vectordb-darwin*.tgz
 
-  nodejs-macos:
-    name: lancedb ${{ matrix.config.arch }}
-    strategy:
-      matrix:
-        config:
-          - arch: x86_64-apple-darwin
-            runner: macos-13
-          - arch: aarch64-apple-darwin
-            # xlarge is implicitly arm64.
-            runner: macos-14
-    runs-on: ${{ matrix.config.runner }}
-    # Only runs on tags that matches the make-release action
-    if: startsWith(github.ref, 'refs/tags/v')
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install system dependencies
-        run: brew install protobuf
-      - name: Install npm dependencies
-        run: |
-          cd nodejs
-          npm ci
-      - name: Build MacOS native nodejs modules
-        run: bash ci/build_macos_artifacts_nodejs.sh ${{ matrix.config.arch }}
-      - name: Upload Darwin Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: nodejs-native-darwin-${{ matrix.config.arch }}
-          path: |
-            nodejs/dist/*.node
 
   node-linux:
-    name: vectordb (${{ matrix.config.arch}}-unknown-linux-gnu)
-    runs-on: ${{ matrix.config.runner }}
-    # Only runs on tags that matches the make-release action
-    if: startsWith(github.ref, 'refs/tags/v')
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - arch: x86_64
-            runner: ubuntu-latest
-          - arch: aarch64
-            # For successful fat LTO builds, we need a large runner to avoid OOM errors.
-            runner: warp-ubuntu-latest-arm64-4x
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      # To avoid OOM errors on ARM, we create a swap file.
-      - name: Configure aarch64 build
-        if: ${{ matrix.config.arch == 'aarch64' }}
-        run: |
-          free -h
-          sudo fallocate -l 16G /swapfile
-          sudo chmod 600 /swapfile
-          sudo mkswap /swapfile
-          sudo swapon /swapfile
-          echo "/swapfile swap swap defaults 0 0" >> sudo /etc/fstab
-          # print info
-          swapon --show
-          free -h
-      - name: Build Linux Artifacts
-        run: |
-          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }}
-      - name: Upload Linux Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: node-native-linux-${{ matrix.config.arch }}
-          path: |
-            node/dist/lancedb-vectordb-linux*.tgz
-
-  nodejs-linux:
-    name: lancedb (${{ matrix.config.arch}}-unknown-linux-gnu
+    name: node-linux (${{ 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')
@@ -174,26 +101,15 @@ jobs:
           free -h
       - name: Build Linux Artifacts
         run: |
-          bash ci/build_linux_artifacts_nodejs.sh ${{ matrix.config.arch }}
+          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }}
       - name: Upload Linux Artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v3
         with:
-          name: nodejs-native-linux-${{ matrix.config.arch }}
+          name: native-linux
           path: |
-            nodejs/dist/*.node
-      # The generic files are the same in all distros so we just pick
-      # one to do the upload.
-      - name: Upload Generic Artifacts
-        if: ${{ matrix.config.arch == 'x86_64' }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: nodejs-dist
-          path: |
-            nodejs/dist/*
-            !nodejs/dist/*.node
+            node/dist/lancedb-vectordb-linux*.tgz
 
   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')
@@ -220,150 +136,37 @@ jobs:
       - name: Build Windows native node modules
         run: .\ci\build_windows_artifacts.ps1 ${{ matrix.target }}
       - name: Upload Windows Artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v3
         with:
-          name: node-native-windows
+          name: native-windows
           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')
-    strategy:
-      fail-fast: false
-      matrix:
-        target: [x86_64-pc-windows-msvc]
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install Protoc v21.12
-        working-directory: C:\
-        run: |
-          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
-          7z x protoc.zip
-          Add-Content $env:GITHUB_PATH "C:\protoc\bin"
-        shell: powershell
-      - name: Install npm dependencies
-        run: |
-          cd nodejs
-          npm ci
-      - name: Build Windows native node modules
-        run: .\ci\build_windows_artifacts_nodejs.ps1 ${{ matrix.target }}
-      - name: Upload Windows Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: nodejs-native-windows
-          path: |
-            nodejs/dist/*.node
-
   release:
-    name: vectordb NPM Publish
     needs: [node, node-macos, node-linux, node-windows]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
     steps:
-      - uses: actions/download-artifact@v4
-        with:
-          pattern: node-*
+      - uses: actions/download-artifact@v3
       - name: Display structure of downloaded files
         run: ls -R
       - uses: actions/setup-node@v3
         with:
           node-version: 20
-          registry-url: "https://registry.npmjs.org"
+          registry-url: 'https://registry.npmjs.org'
       - name: Publish to NPM
         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 $PUBLISH_ARGS $filename
+            npm publish $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:
-    name: lancedb NPM Publish
-    needs: [nodejs-macos, nodejs-linux, nodejs-windows]
-    runs-on: ubuntu-latest
-    # Only runs on tags that matches the make-release action
-    if: startsWith(github.ref, 'refs/tags/v')
-    defaults:
-      run:
-        shell: bash
-        working-directory: nodejs
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - uses: actions/download-artifact@v4
-        with:
-          name: nodejs-dist
-          path: nodejs/dist
-      - uses: actions/download-artifact@v4
-        name: Download arch-specific binaries
-        with:
-          pattern: nodejs-*
-          path: nodejs/nodejs-artifacts
-          merge-multiple: true
-      - name: Display structure of downloaded files
-        run: find .
-      - uses: actions/setup-node@v3
-        with:
-          node-version: 20
-          registry-url: "https://registry.npmjs.org"
-      - name: Install napi-rs
-        run: npm install -g @napi-rs/cli
-      - name: Prepare artifacts
-        run: npx napi artifacts -d nodejs-artifacts
-      - name: Display structure of staged files
-        run: find npm
-      - name: Publish to NPM
-        env:
-          NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
-        # 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:
     needs: [release]
     runs-on: ubuntu-latest
-    permissions:
-      contents: write
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -374,87 +177,4 @@ jobs:
           lfs: true
       - uses: ./.github/workflows/update_package_lock
         with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-
-  update-package-lock-nodejs:
-    needs: [release-nodejs]
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          ref: main
-          persist-credentials: false
-          fetch-depth: 0
-          lfs: true
-      - uses: ./.github/workflows/update_package_lock_nodejs
-        with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-
-  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 [[ $GITHUB_REF =~ refs/tags/v(.*) ]]; then
-            VERSION=${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_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}

.github/workflows/pypi-publish.yml

@@ -1,28 +1,18 @@
 name: PyPI Publish
 
 on:
-  push:
-    tags:
-      - 'python-v*'
+  release:
+    types: [published]
 
 jobs:
   linux:
-    name: Python ${{ matrix.config.platform }} manylinux${{ matrix.config.manylinux }}
     timeout-minutes: 60
     strategy:
       matrix:
-        config:
-          - platform: x86_64
-            manylinux: "2_17"
-            extra_args: ""
-          - platform: x86_64
-            manylinux: "2_28"
-            extra_args: "--features fp16kernels"
-          - platform: aarch64
-            manylinux: "2_24"
-            extra_args: ""
-          # We don't build fp16 kernels for aarch64, because it uses 
-          # cross compilation image, which doesn't have a new enough compiler.
+        python-minor-version: ["8"]
+        platform:
+          - x86_64
+          - aarch64
     runs-on: "ubuntu-22.04"
     steps:
       - uses: actions/checkout@v4
@@ -32,22 +22,22 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.${{ matrix.python-minor-version }}
       - uses: ./.github/workflows/build_linux_wheel
         with:
-          python-minor-version: 8
-          args: "--release --strip ${{ matrix.config.extra_args }}"
-          arm-build: ${{ matrix.config.platform == 'aarch64' }}
-          manylinux: ${{ matrix.config.manylinux }}
+          python-minor-version: ${{ matrix.python-minor-version }}
+          args: "--release --strip"
+          arm-build: ${{ matrix.platform == 'aarch64' }}
       - uses: ./.github/workflows/upload_wheel
         with:
-          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          fury_token: ${{ secrets.FURY_TOKEN }}
+          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          repo: "pypi"
   mac:
     timeout-minutes: 60
     runs-on: ${{ matrix.config.runner }}
     strategy:
       matrix:
+        python-minor-version: ["8"]
         config:
           - target: x86_64-apple-darwin
             runner: macos-13
@@ -58,6 +48,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
         with:
+          ref: ${{ inputs.ref }}
           fetch-depth: 0
           lfs: true
       - name: Set up Python
@@ -66,95 +57,36 @@ jobs:
           python-version: 3.12
       - uses: ./.github/workflows/build_mac_wheel
         with:
-          python-minor-version: 8
-          args: "--release --strip --target ${{ matrix.config.target }} --features fp16kernels"
+          python-minor-version: ${{ matrix.python-minor-version }}
+          args: "--release --strip --target ${{ matrix.config.target }}"
       - uses: ./.github/workflows/upload_wheel
         with:
-          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          fury_token: ${{ secrets.FURY_TOKEN }}
+          python-minor-version: ${{ matrix.python-minor-version }}
+          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          repo: "pypi"
   windows:
     timeout-minutes: 60
     runs-on: windows-latest
+    strategy:
+      matrix:
+        python-minor-version: ["8"]
     steps:
       - uses: actions/checkout@v4
         with:
+          ref: ${{ inputs.ref }}
           fetch-depth: 0
           lfs: true
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.${{ matrix.python-minor-version }}
       - uses: ./.github/workflows/build_windows_wheel
         with:
-          python-minor-version: 8
+          python-minor-version: ${{ matrix.python-minor-version }}
           args: "--release --strip"
           vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
       - uses: ./.github/workflows/upload_wheel
         with:
-          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          fury_token: ${{ secrets.FURY_TOKEN }}
-  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 }}
+          python-minor-version: ${{ matrix.python-minor-version }}
+          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          repo: "pypi"

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

@@ -0,0 +1,56 @@
+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==0.5.4
+          pip install ruff==0.2.2
       - name: Format check
         run: ruff format --check .
       - name: Lint
-        run: ruff check .
+        run: ruff .
   doctest:
     name: "Doctest"
     timeout-minutes: 30
@@ -65,7 +65,7 @@ jobs:
           workspaces: python
       - name: Install
         run: |
-          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests,dev,embeddings]
+          pip install -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: ["9", "11"]
+        python-minor-version: ["8", "11"]
     runs-on: "ubuntu-22.04"
     defaults:
       run:
@@ -99,8 +99,6 @@ jobs:
           workspaces: python
       - uses: ./.github/workflows/build_linux_wheel
       - uses: ./.github/workflows/run_tests
-        with:
-          integration: true
       # Make sure wheels are not included in the Rust cache
       - name: Delete wheels
         run: rm -rf target/wheels
@@ -189,7 +187,7 @@ jobs:
       - name: Install lancedb
         run: |
           pip install "pydantic<2"
-          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests]
+          pip install -e .[tests]
           pip install tantivy
       - name: Run tests
-        run: pytest -m "not slow and not s3_test" -x -v --durations=30 python/tests
+        run: pytest -m "not slow" -x -v --durations=30 python/tests

.github/workflows/run_tests/action.yml

@@ -5,27 +5,13 @@ inputs:
   python-minor-version:
     required: true
     description: "8 9 10 11 12"
-  integration:
-    required: false
-    description: "Run integration tests"
-    default: "false"
 runs:
   using: "composite"
   steps:
     - name: Install lancedb
       shell: bash
       run: |
-        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' }}
+        pip3 install $(ls target/wheels/lancedb-*.whl)[tests,dev]
+    - name: pytest
       shell: bash
-      working-directory: .
-      run: docker compose up --detach --wait
-    - name: pytest (with integration)
-      shell: bash
-      if: ${{ inputs.integration == 'true' }}
       run: pytest -m "not slow" -x -v --durations=30 python/python/tests
-    - name: pytest (no integration tests)
-      shell: bash
-      if: ${{ inputs.integration != 'true' }}
-      run: pytest -m "not slow and not s3_test" -x -v --durations=30 python/python/tests

.github/workflows/rust.yml

@@ -31,10 +31,6 @@ jobs:
       run:
         shell: bash
         working-directory: rust
-    env:
-      # Need up-to-date compilers for kernels
-      CC: gcc-12
-      CXX: g++-12
     steps:
     - uses: actions/checkout@v4
       with:
@@ -53,18 +49,11 @@ jobs:
       run: cargo clippy --all --all-features -- -D warnings
   linux:
     timeout-minutes: 30
-    # To build all features, we need more disk space than is available
-    # on the GitHub-provided runner. This is mostly due to the the
-    # sentence-transformers feature.
-    runs-on: warp-ubuntu-latest-x64-4x
+    runs-on: ubuntu-22.04
     defaults:
       run:
         shell: bash
         working-directory: rust
-    env:
-      # Need up-to-date compilers for kernels
-      CC: gcc-12
-      CXX: g++-12
     steps:
     - uses: actions/checkout@v4
       with:
@@ -77,9 +66,6 @@ jobs:
       run: |
           sudo apt update
           sudo apt install -y protobuf-compiler libssl-dev
-    - name: Start S3 integration test environment
-      working-directory: .
-      run: docker compose up --detach --wait
     - name: Build
       run: cargo build --all-features
     - name: Run tests
@@ -111,8 +97,7 @@ jobs:
       - name: Build
         run: cargo build --all-features
       - name: Run tests
-        # Run with everything except the integration tests.
-        run: cargo test --features remote,fp16kernels
+        run: cargo test --all-features
   windows:
     runs-on: windows-2022
     steps:
@@ -134,3 +119,4 @@ jobs:
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
           cargo build
           cargo test
+  

.github/workflows/update_package_lock_nodejs/action.yml

@@ -1,33 +0,0 @@
-name: update_package_lock_nodejs
-description: "Update nodejs's package.lock"
-
-inputs:
-  github_token:
-    required: true
-    description: "github token for the repo"
-
-runs:
-  using: "composite"
-  steps:
-    - uses: actions/setup-node@v3
-      with:
-        node-version: 20
-    - name: Set git configs
-      shell: bash
-      run: |
-        git config user.name 'Lance Release'
-        git config user.email 'lance-dev@lancedb.com'
-    - name: Update package-lock.json file
-      working-directory: ./nodejs
-      run: |
-        npm install
-        git add package-lock.json
-        git commit -m "Updating package-lock.json"
-      shell: bash
-    - name: Push changes
-      if: ${{ inputs.dry_run }} == "false"
-      uses: ad-m/github-push-action@master
-      with:
-        github_token: ${{ inputs.github_token }}
-        branch: main
-        tags: true

.github/workflows/update_package_lock_run_nodejs.yml

@@ -1,19 +0,0 @@
-name: Update NodeJs package-lock.json
-
-on:
-  workflow_dispatch:
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          ref: main
-          persist-credentials: false
-          fetch-depth: 0
-          lfs: true
-      - uses: ./.github/workflows/update_package_lock_nodejs
-        with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}

.github/workflows/upload_wheel/action.yml

@@ -2,43 +2,28 @@ name: upload-wheel
 
 description: "Upload wheels to Pypi"
 inputs:
-  pypi_token:
+  os:
+    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
-    shell: bash
-    run: |
-      python -m pip install --upgrade pip
-      pip install twine
-  - name: Choose repo
-    shell: bash
-    id: choose_repo
-    run: |
-      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
+    - name: Install dependencies
+      shell: bash
+      run: |
+        python -m pip install --upgrade pip
+        pip install twine
+    - name: Publish wheel
+      env:
+        TWINE_USERNAME: __token__
+        TWINE_PASSWORD: ${{ inputs.token }}
+      shell: bash
+      run: twine upload --repository ${{ inputs.repo }} target/wheels/lancedb-*.whl

.gitignore

@@ -4,10 +4,9 @@
 **/__pycache__
 .DS_Store
 venv
-.venv
 
 .vscode
-.zed
+
 rust/target
 rust/Cargo.lock
 
@@ -35,7 +34,6 @@ python/dist
 node/dist
 node/examples/**/package-lock.json
 node/examples/**/dist
-nodejs/lancedb/native*
 dist
 
 ## Rust

.pre-commit-config.yaml

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

Cargo.toml

@@ -1,11 +1,5 @@
 [workspace]
-members = [
-    "rust/ffi/node",
-    "rust/lancedb",
-    "nodejs",
-    "python",
-    "java/core/lancedb-jni",
-]
+members = ["rust/ffi/node", "rust/lancedb", "nodejs", "python"]
 # Python package needs to be built by maturin.
 exclude = ["python"]
 resolver = "2"
@@ -20,34 +14,28 @@ keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
 
 [workspace.dependencies]
-lance = { "version" = "=0.17.0", "features" = ["dynamodb"] }
-lance-index = { "version" = "=0.17.0" }
-lance-linalg = { "version" = "=0.17.0" }
-lance-table = { "version" = "=0.17.0" }
-lance-testing = { "version" = "=0.17.0" }
-lance-datafusion = { "version" = "=0.17.0" }
-lance-encoding = { "version" = "=0.17.0" }
+lance = { "version" = "=0.10.5", "features" = ["dynamodb"] }
+lance-index = { "version" = "=0.10.5" }
+lance-linalg = { "version" = "=0.10.5" }
+lance-testing = { "version" = "=0.10.5" }
 # Note that this one does not include pyarrow
-arrow = { version = "52.2", optional = false }
-arrow-array = "52.2"
-arrow-data = "52.2"
-arrow-ipc = "52.2"
-arrow-ord = "52.2"
-arrow-schema = "52.2"
-arrow-arith = "52.2"
-arrow-cast = "52.2"
+arrow = { version = "50.0", optional = false }
+arrow-array = "50.0"
+arrow-data = "50.0"
+arrow-ipc = "50.0"
+arrow-ord = "50.0"
+arrow-schema = "50.0"
+arrow-arith = "50.0"
+arrow-cast = "50.0"
 async-trait = "0"
 chrono = "0.4.35"
-datafusion-physical-plan = "40.0"
-half = { "version" = "=2.4.1", default-features = false, features = [
+half = { "version" = "=2.3.1", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"
 log = "0.4"
-object_store = "0.10.2"
+object_store = "0.9.0"
 pin-project = "1.0.7"
 snafu = "0.7.4"
 url = "2"
 num-traits = "0.2"
-regex = "1.10"
-lazy_static = "1"

README.md

@@ -1,14 +1,14 @@
 <div align="center">
 <p align="center">
 
-<img width="275" alt="LanceDB Logo" src="https://github.com/lancedb/lancedb/assets/5846846/37d7c7ad-c2fd-4f56-9f16-fffb0d17c73a">
+<img width="275" alt="LanceDB Logo" src="https://user-images.githubusercontent.com/917119/226205734-6063d87a-1ecc-45fe-85be-1dea6383a3d8.png">
 
-**Developer-friendly, database for multimodal AI**
+**Developer-friendly, serverless vector database for AI applications**
 
 <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/)
-[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/zMM32dvNtd)
+[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/) 
+[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/zMM32dvNtd) 
 [![Twitter](https://img.shields.io/badge/Twitter-%231DA1F2.svg?style=for-the-badge&logo=Twitter&logoColor=white)](https://twitter.com/lancedb)
 
 </p>
@@ -20,7 +20,7 @@
 
 <hr />
 
-LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.
+LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
 
 The key features of LanceDB include:
 
@@ -36,7 +36,7 @@ The key features of LanceDB include:
 
 * GPU support in building vector index(*).
 
-* 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.
+* 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.
 
 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.
 
@@ -44,24 +44,26 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
 
 **Javascript**
 ```shell
-npm install @lancedb/lancedb
+npm install vectordb
 ```
 
 ```javascript
-import * as lancedb from "@lancedb/lancedb";
+const lancedb = require('vectordb');
+const db = await lancedb.connect('data/sample-lancedb');
 
-const db = await lancedb.connect("data/sample-lancedb");
-const table = await db.createTable("vectors", [
-	{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
-	{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },
-], {mode: 'overwrite'});
+const table = await db.createTable({
+  name: 'vectors',
+  data:  [
+    { id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
+    { id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }
+  ]
+})
 
-
-const query = table.vectorSearch([0.1, 0.3]).limit(2);
-const results = await query.toArray();
+const query = table.search([0.1, 0.3]).limit(2);
+const results = await query.execute();
 
 // You can also search for rows by specific criteria without involving a vector search.
-const rowsByCriteria = await table.query().where("price >= 10").toArray();
+const rowsByCriteria = await table.search(undefined).where("price >= 10").execute();
 ```
 
 **Python**
@@ -81,5 +83,5 @@ result = table.search([100, 100]).limit(2).to_pandas()
 ```
 
 ## Blogs, Tutorials & Videos
-* 📈 <a href="https://blog.lancedb.com/benchmarking-random-access-in-lance/">2000x better performance with Lance over Parquet</a>
+* 📈 <a href="https://blog.eto.ai/benchmarking-random-access-in-lance-ed690757a826">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>

ci/build_linux_artifacts.sh

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

ci/build_linux_artifacts_nodejs.sh

@@ -1,21 +0,0 @@
-#!/bin/bash
-set -e
-ARCH=${1:-x86_64}
-
-# We pass down the current user so that when we later mount the local files
-# into the container, the files are accessible by the current user.
-pushd ci/manylinux_node
-docker build \
-    -t lancedb-node-manylinux-$ARCH \
-    --build-arg="ARCH=$ARCH" \
-    --build-arg="DOCKER_USER=$(id -u)" \
-    --progress=plain \
-    .
-popd
-
-# We turn on memory swap to avoid OOM killer
-docker run \
-    -v $(pwd):/io -w /io \
-    --memory-swap=-1 \
-    lancedb-node-manylinux-$ARCH \
-    bash ci/manylinux_node/build_lancedb.sh $ARCH

ci/build_macos_artifacts_nodejs.sh

@@ -1,34 +0,0 @@
-# Builds the macOS artifacts (nodejs binaries).
-# Usage: ./ci/build_macos_artifacts_nodejs.sh [target]
-# Targets supported: x86_64-apple-darwin aarch64-apple-darwin
-set -e
-
-prebuild_rust() {
-    # Building here for the sake of easier debugging.
-    pushd rust/lancedb
-    echo "Building rust library for $1"
-    export RUST_BACKTRACE=1
-    cargo build --release --target $1
-    popd
-}
-
-build_node_binaries() {
-    pushd nodejs
-    echo "Building nodejs library for $1"
-    export RUST_TARGET=$1
-    npm run build-release
-    popd
-}
-
-if [ -n "$1" ]; then
-    targets=$1
-else
-    targets="x86_64-apple-darwin aarch64-apple-darwin"
-fi
-
-echo "Building artifacts for targets: $targets"
-for target in $targets
-    do
-    prebuild_rust $target
-    build_node_binaries $target
-done

ci/build_windows_artifacts_nodejs.ps1

@@ -1,41 +0,0 @@
-# Builds the Windows artifacts (nodejs binaries).
-# Usage:  .\ci\build_windows_artifacts_nodejs.ps1 [target]
-# Targets supported:
-# - x86_64-pc-windows-msvc
-# - i686-pc-windows-msvc
-
-function Prebuild-Rust {
-    param (
-        [string]$target
-    )
-
-    # Building here for the sake of easier debugging.
-    Push-Location -Path "rust/lancedb"
-    Write-Host "Building rust library for $target"
-    $env:RUST_BACKTRACE=1
-    cargo build --release --target $target
-    Pop-Location
-}
-
-function Build-NodeBinaries {
-    param (
-        [string]$target
-    )
-
-    Push-Location -Path "nodejs"
-    Write-Host "Building nodejs library for $target"
-    $env:RUST_TARGET=$target
-    npm run build-release
-    Pop-Location
-}
-
-$targets = $args[0]
-if (-not $targets) {
-    $targets = "x86_64-pc-windows-msvc"
-}
-
-Write-Host "Building artifacts for targets: $targets"
-foreach ($target in $targets) {
-    Prebuild-Rust $target
-    Build-NodeBinaries $target
-}

ci/bump_version.sh

@@ -1,51 +0,0 @@
-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

@@ -1,35 +0,0 @@
-"""
-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/manylinux_2_28_${ARCH}
+FROM quay.io/pypa/manylinux2014_${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, but only if it doesn't exist
-RUN echo ${ARCH} && id -u ${DOCKER_USER} >/dev/null 2>&1 || adduser --user-group --create-home --uid ${DOCKER_USER} build_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.

ci/manylinux_node/build_lancedb.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-# Builds the nodejs module for manylinux. Invoked by ci/build_linux_artifacts_nodejs.sh.
-set -e
-ARCH=${1:-x86_64}
-
-if [ "$ARCH" = "x86_64" ]; then
-    export OPENSSL_LIB_DIR=/usr/local/lib64/
-else
-    export OPENSSL_LIB_DIR=/usr/local/lib/
-fi
-export OPENSSL_STATIC=1
-export OPENSSL_INCLUDE_DIR=/usr/local/include/openssl
-
-source $HOME/.bashrc
-
-cd nodejs
-npm ci
-npm run build-release

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_1v \
+git clone -b OpenSSL_1_1_1u \
     --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 18
+    nvm install --no-progress 16
 }
 
 install_rust() {

ci/semver_sort.py

@@ -1,35 +0,0 @@
-"""
-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)

docker-compose.yml

@@ -1,18 +1,18 @@
 version: "3.9"
 services:
   localstack:
-    image: localstack/localstack:3.3
+    image: localstack/localstack:0.14
     ports:
       - 4566:4566
     environment:
-      - SERVICES=s3,dynamodb,kms
+      - SERVICES=s3,dynamodb
       - DEBUG=1
       - LS_LOG=trace
       - DOCKER_HOST=unix:///var/run/docker.sock
       - AWS_ACCESS_KEY_ID=ACCESSKEY
       - AWS_SECRET_ACCESS_KEY=SECRETKEY
     healthcheck:
-      test: [ "CMD", "curl", "-s", "http://localhost:4566/_localstack/health" ]
+      test: [ "CMD", "curl", "-f", "http://localhost:4566/health" ]
       interval: 5s
       retries: 3
       start_period: 10s

docs/mkdocs.yml

@@ -26,7 +26,6 @@ theme:
     - content.code.copy
     - content.tabs.link
     - content.action.edit
-    - content.tooltips
     - toc.follow
     - navigation.top
     - navigation.tabs
@@ -36,282 +35,181 @@ theme:
     - navigation.instant
   icon:
     repo: fontawesome/brands/github
-    annotation: material/arrow-right-circle
   custom_dir: overrides
 
 plugins:
-  - search
-  - autorefs
-  - mkdocstrings:
-      handlers:
-        python:
-          paths: [../python]
-          options:
-            docstring_style: numpy
-            heading_level: 3
-            show_source: true
-            show_symbol_type_in_heading: true
-            show_signature_annotations: true
-            show_root_heading: true
-            members_order: source
-          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
+- search
+- autorefs
+- mkdocstrings:
+    handlers:
+      python:
+        paths: [../python]
+        options:
+          docstring_style: numpy
+          heading_level: 4
+          show_source: true
+          show_symbol_type_in_heading: true
+          show_signature_annotations: true
+          members_order: source
+        import:
+          # for cross references
+          - https://arrow.apache.org/docs/objects.inv
+          - https://pandas.pydata.org/docs/objects.inv
+- mkdocs-jupyter
+- ultralytics:
+    verbose: True
+    enabled: True
+    default_image: "assets/lancedb_and_lance.png" # Default image for all pages
+    add_image: True # Automatically add meta image
+    add_keywords: True # Add page keywords in the header tag
+    add_share_buttons: True # Add social share buttons
+    add_authors: False # Display page authors
+    add_desc: False
+    add_dates: False
 
 markdown_extensions:
-  - admonition
-  - footnotes
-  - pymdownx.details
-  - pymdownx.highlight:
-      anchor_linenums: true
-      line_spans: __span
-      pygments_lang_class: true
-  - pymdownx.inlinehilite
-  - pymdownx.snippets:
-      base_path: ..
-      dedent_subsections: true
-  - pymdownx.superfences
-  - 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
+- admonition
+- footnotes
+- pymdownx.details
+- pymdownx.highlight:
+    anchor_linenums: true
+    line_spans: __span
+    pygments_lang_class: true
+- pymdownx.inlinehilite
+- pymdownx.snippets:
+    base_path: ..
+    dedent_subsections: true
+- pymdownx.superfences
+- pymdownx.tabbed:
+    alternate_style: true
+- md_in_html
+- attr_list
 
 nav:
-  - Home:
-      - LanceDB: index.md
-      - 🏃🏼‍♂️ Quick start: basic.md
-      - 📚 Concepts:
-          - Vector search: concepts/vector_search.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 a vector index: ann_indexes.md
-          - Vector Search: search.md
-          - Full-text search: fts.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
-          - 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
-          - 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
-          - Embedding functions: embeddings/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
-      - 🔌 Integrations:
-          - Tools and data formats: integrations/index.md
-          - Pandas and PyArrow: python/pandas_and_pyarrow.md
-          - Polars: python/polars_arrow.md
-          - DuckDB: python/duckdb.md
-          - LangChain:
-              - LangChain 🔗: integrations/langchain.md
-              - LangChain demo: notebooks/langchain_demo.ipynb
-              - 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
-      - 🎯 Examples:
-          - Overview: examples/index.md
-          - 🐍 Python:
-              - Overview: examples/examples_python.md
-              - Build From Scratch: examples/python_examples/build_from_scratch.md
-              - Multimodal: examples/python_examples/multimodal.md
-              - Rag: examples/python_examples/rag.md
-              - Vector Search: examples/python_examples/vector_search.md
-              - Chatbot: examples/python_examples/chatbot.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:
-          - ↗Improve retrievers with hybrid search and reranking: https://blog.lancedb.com/hybrid-search-and-reranking-report/
-      - 💭 FAQs: faq.md
-      - ⚙️ API reference:
-          - 🐍 Python: python/python.md
-          - 👾 JavaScript (vectordb): 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/modules.md
-              - REST API: cloud/rest.md
+- Home:
+  - LanceDB: index.md
+  - 🏃🏼‍♂️ Quick start: basic.md
+  - 📚 Concepts:
+    - Vector search: concepts/vector_search.md
+    - Indexing: concepts/index_ivfpq.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
+    - Hybrid search:
+      - Overview: hybrid_search/hybrid_search.md
+      - Comparing Rerankers: hybrid_search/eval.md
+      - Airbnb financial data example: notebooks/hybrid_search.ipynb
+    - Filtering: sql.md
+    - Versioning & Reproducibility: notebooks/reproducibility.ipynb
+    - Configuring Storage: guides/storage.md
+  - 🧬 Managing embeddings:
+    - Overview: embeddings/index.md
+    - Embedding functions: embeddings/embedding_functions.md
+    - Available models: embeddings/default_embedding_functions.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
+  - 🔌 Integrations:
+    - Tools and data formats: integrations/index.md
+    - Pandas and PyArrow: python/pandas_and_pyarrow.md
+    - Polars: python/polars_arrow.md
+    - DuckDB: python/duckdb.md
+    - LangChain 🔗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
+    - LangChain JS/TS 🔗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
+    - LlamaIndex 🦙: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
+    - Pydantic: python/pydantic.md
+    - Voxel51: integrations/voxel51.md
+    - PromptTools: integrations/prompttools.md
+  - 🎯 Examples:
+    - Overview: examples/index.md
+    - 🐍 Python:
+      - Overview: examples/examples_python.md
+      - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+      - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+      - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+      - Example - Calculate CLIP Embeddings with Roboflow Inference: examples/image_embeddings_roboflow.md
+      - 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
+  - 🔧 CLI & Config: cli_config.md
+  - 💭 FAQs: faq.md
+  - ⚙️ API reference:
+    - 🐍 Python: python/python.md
+    - 👾 JavaScript: javascript/modules.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
 
-  - Quick start: basic.md
-  - Concepts:
-      - Vector search: concepts/vector_search.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
-      - 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
-      - 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
-      - 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
-      - Embedding functions: embeddings/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
-  - Integrations:
-      - Overview: integrations/index.md
-      - Pandas and PyArrow: python/pandas_and_pyarrow.md
-      - Polars: python/polars_arrow.md
-      - DuckDB: python/duckdb.md
-      - LangChain 🦜️🔗↗: integrations/langchain.md
-      - LangChain.js 🦜️🔗↗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
-      - LlamaIndex 🦙↗: integrations/llamaIndex.md
-      - Pydantic: python/pydantic.md
-      - Voxel51: integrations/voxel51.md
-      - PromptTools: integrations/prompttools.md
-      - dlt: integrations/dlt.md
-  - Examples:
-      - examples/index.md
-      - 🐍 Python:
-          - Overview: examples/examples_python.md
-          - Build From Scratch: examples/python_examples/build_from_scratch.md
-          - Multimodal: examples/python_examples/multimodal.md
-          - Rag: examples/python_examples/rag.md
-          - Vector Search: examples/python_examples/vector_search.md
-          - Chatbot: examples/python_examples/chatbot.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/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/modules.md
-          - REST API: cloud/rest.md
+
+- Quick start: basic.md
+- Concepts:
+    - Vector search: concepts/vector_search.md
+    - Indexing: concepts/index_ivfpq.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
+    - Hybrid search:
+      - Overview: hybrid_search/hybrid_search.md
+      - Comparing Rerankers: hybrid_search/eval.md
+      - Airbnb financial data example: notebooks/hybrid_search.ipynb
+    - Filtering: sql.md
+    - Versioning & Reproducibility: notebooks/reproducibility.ipynb
+    - Configuring Storage: guides/storage.md
+- Managing Embeddings:
+  - Overview: embeddings/index.md
+  - Embedding functions: embeddings/embedding_functions.md
+  - Available models: embeddings/default_embedding_functions.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
+- Integrations:
+  - Overview: integrations/index.md
+  - Pandas and PyArrow: python/pandas_and_pyarrow.md
+  - Polars: python/polars_arrow.md
+  - DuckDB : python/duckdb.md
+  - LangChain 🦜️🔗↗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
+  - LangChain.js 🦜️🔗↗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
+  - LlamaIndex 🦙↗: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
+  - Pydantic: python/pydantic.md
+  - Voxel51: integrations/voxel51.md
+  - PromptTools: integrations/prompttools.md
+- Examples:
+  - examples/index.md
+  - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+  - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+  - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+  - 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
+  - YouTube Transcript Search (JS): examples/youtube_transcript_bot_with_nodejs.md
+  - Serverless Chatbot from any website: examples/serverless_website_chatbot.md
+  - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
+- API reference:
+  - Overview: api_reference.md
+  - Python: python/python.md
+  - Javascript: javascript/modules.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
 
 extra_css:
   - styles/global.css
@@ -324,10 +222,3 @@ extra:
   analytics:
     provider: google
     property: G-B7NFM40W74
-  social:
-    - icon: fontawesome/brands/github
-      link: https://github.com/lancedb/lancedb
-    - icon: fontawesome/brands/x-twitter
-      link: https://twitter.com/lancedb
-    - icon: fontawesome/brands/linkedin
-      link: https://www.linkedin.com/company/lancedb

docs/openapi.yml

@@ -1,487 +0,0 @@
-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/requirements.txt

@@ -1,7 +1,6 @@
 mkdocs==1.5.3
 mkdocs-jupyter==0.24.1
 mkdocs-material==9.5.3
-mkdocstrings[python]==0.25.2
-griffe
-mkdocs-render-swagger-plugin
+mkdocstrings[python]==0.20.0
 pydantic
+mkdocs-ultralytics-plugin==0.0.44

docs/src/ann_indexes.md

@@ -38,27 +38,13 @@ Lance supports `IVF_PQ` index type by default.
     tbl.create_index(num_partitions=256, num_sub_vectors=96)
     ```
 
-=== "TypeScript"
+=== "Typescript"
 
-    === "@lancedb/lancedb"
+    ```typescript
+    --8<--- "docs/src/ann_indexes.ts:import"
 
-        Creating indexes is done via the [lancedb.Table.createIndex](../js/classes/Table.md/#createIndex) method.
-
-        ```typescript
-        --8<--- "nodejs/examples/ann_indexes.ts:import"
-
-        --8<-- "nodejs/examples/ann_indexes.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"
-        ```
+    --8<-- "docs/src/ann_indexes.ts:ingest"
+    ```
 
 === "Rust"
 
@@ -105,27 +91,27 @@ You can specify the GPU device to train IVF partitions via
 
 === "Linux"
 
-    <!-- skip-test -->
-    ``` { .python .copy }
-    # Create index using CUDA on Nvidia GPUs.
-    tbl.create_index(
-        num_partitions=256,
-        num_sub_vectors=96,
-        accelerator="cuda"
-    )
-    ```
+     <!-- skip-test -->
+     ``` { .python .copy }
+     # Create index using CUDA on Nvidia GPUs.
+     tbl.create_index(
+          num_partitions=256,
+          num_sub_vectors=96,
+          accelerator="cuda"
+     )
+     ```
 
 === "MacOS"
 
-    <!-- skip-test -->
-    ```python
-    # Create index using MPS on Apple Silicon.
-    tbl.create_index(
-        num_partitions=256,
-        num_sub_vectors=96,
-        accelerator="mps"
-    )
-    ```
+     <!-- skip-test -->
+     ```python
+     # Create index using MPS on Apple Silicon.
+     tbl.create_index(
+          num_partitions=256,
+          num_sub_vectors=96,
+          accelerator="mps"
+     )
+     ```
 
 Troubleshooting:
 
@@ -164,19 +150,11 @@ 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"
 
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/ann_indexes.ts:search1"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/ann_indexes.ts:search1"
-        ```
+    ```typescript
+    --8<-- "docs/src/ann_indexes.ts:search1"
+    ```
 
 === "Rust"
 
@@ -194,23 +172,15 @@ You can further filter the elements returned by a search using a where clause.
 
 === "Python"
 
-    ```python
-    tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
-    ```
+     ```python
+     tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
+     ```
 
-=== "TypeScript"
+=== "Typescript"
 
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/ann_indexes.ts:search2"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```javascript
-        --8<-- "docs/src/ann_indexes.ts:search2"
-        ```
+     ```javascript
+     --8<-- "docs/src/ann_indexes.ts:search2"
+     ```
 
 ### Projections (select clause)
 
@@ -218,31 +188,23 @@ You can select the columns returned by the query using a select clause.
 
 === "Python"
 
-    ```python
-    tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
-    ```
+     ```python
+     tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
+     ```
 
 
-    ```text
-                                                vector _distance
-    0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
-    1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
-    ...
-    ```
+     ```text
+                                                   vector _distance
+     0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
+     1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
+     ...
+     ```
 
-=== "TypeScript"
+=== "Typescript"
 
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/ann_indexes.ts:search3"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/ann_indexes.ts:search3"
-        ```
+     ```typescript
+     --8<-- "docs/src/ann_indexes.ts:search3"
+     ```
 
 ## FAQ
 

docs/src/api_reference.md

@@ -3,6 +3,5 @@
 The API reference for the LanceDB client SDKs are available at the following locations:
 
 - [Python](python/python.md)
-- [JavaScript (legacy vectordb package)](javascript/modules.md)
-- [JavaScript (newer @lancedb/lancedb package)](js/globals.md)
+- [JavaScript](javascript/modules.md)
 - [Rust](https://docs.rs/lancedb/latest/lancedb/index.html)

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-</svg>

docs/src/assets/python.svg

@@ -1 +0,0 @@
-<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

@@ -16,60 +16,11 @@
       pip install lancedb
       ```
 
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+=== "Typescript"
 
-        ```shell
-        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
-            ```
+      ```shell
+      npm install vectordb
+      ```
 
 === "Rust"
 
@@ -93,79 +44,24 @@
 
     !!! 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"
 
-    ```python
-    --8<-- "python/python/tests/docs/test_basic.py:imports"
-    --8<-- "python/python/tests/docs/test_basic.py:connect"
+      ```python
+      import lancedb
+      uri = "data/sample-lancedb"
+      db = lancedb.connect(uri)
+      ```
 
-    --8<-- "python/python/tests/docs/test_basic.py:connect_async"
+=== "Typescript"
+
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:import"
+
+    --8<-- "docs/src/basic_legacy.ts:open_db"
     ```
 
-    !!! note "Asynchronous Python API"
-
-        The asynchronous Python API is new and has some slight differences compared
-        to the synchronous API.  Feel free to start using the asynchronous version.
-        Once all features have migrated we will start to move the synchronous API to
-        use the same syntax as the asynchronous API.  To help with this migration we
-        have created a [migration guide](migration.md) detailing the differences.
-
-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        import * as lancedb from "@lancedb/lancedb";
-        import * as arrow from "apache-arrow";
-
-        --8<-- "nodejs/examples/basic.ts:connect"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:open_db"
-        ```
-
 === "Rust"
 
     ```rust
@@ -186,14 +82,15 @@ If you need a reminder of the uri, you can call `db.uri()`.
 ### Create a table from initial data
 
 If you have data to insert into the table at creation time, you can simultaneously create a
-table and insert the data into it. The schema of the data will be used as the schema of the
+table and insert the data into it.  The schema of the data will be used as the schema of the
 table.
 
 === "Python"
 
     ```python
-    --8<-- "python/python/tests/docs/test_basic.py:create_table"
-    --8<-- "python/python/tests/docs/test_basic.py:create_table_async"
+    tbl = db.create_table("my_table",
+                    data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                          {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
     ```
 
     If the table already exists, LanceDB will raise an error by default.
@@ -203,27 +100,21 @@ table.
     You can also pass in a pandas DataFrame directly:
 
     ```python
-    --8<-- "python/python/tests/docs/test_basic.py:create_table_pandas"
-    --8<-- "python/python/tests/docs/test_basic.py:create_table_async_pandas"
+    import pandas as pd
+    df = pd.DataFrame([{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                       {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+    tbl = db.create_table("table_from_df", data=df)
     ```
 
-=== "Typescript[^1]"
+=== "Typescript"
 
-    === "@lancedb/lancedb"
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:create_table"
+    ```
 
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_table"
-        ```
-
-    === "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.
+    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"
 
@@ -243,39 +134,26 @@ 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.
 In this case, you can create an empty table and specify the schema, so that you can add
-data to the table at a later time (as long as it conforms to the schema). This is
+data to the table at a later time (as long as it conforms to the schema).  This is
 similar to a `CREATE TABLE` statement in SQL.
 
 === "Python"
 
       ```python
-      --8<-- "python/python/tests/docs/test_basic.py:create_empty_table"
-      --8<-- "python/python/tests/docs/test_basic.py:create_empty_table_async"
+      import pyarrow as pa
+      schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
+      tbl = db.create_table("empty_table", schema=schema)
       ```
 
-    !!! 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]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_empty_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:create_empty_table"
-        ```
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:create_empty_table"
+    ```
 
 === "Rust"
 
@@ -290,23 +168,14 @@ Once created, you can open a table as follows:
 === "Python"
 
     ```python
-    --8<-- "python/python/tests/docs/test_basic.py:open_table"
-    --8<-- "python/python/tests/docs/test_basic.py:open_table_async"
+    tbl = db.open_table("my_table")
     ```
 
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:open_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        const tbl = await db.openTable("myTable");
-        ```
+=== "Typescript"
 
+    ```typescript
+    const tbl = await db.openTable("myTable");
+    ```
 
 === "Rust"
 
@@ -319,22 +188,14 @@ If you forget the name of your table, you can always get a listing of all table
 === "Python"
 
     ```python
-    --8<-- "python/python/tests/docs/test_basic.py:table_names"
-    --8<-- "python/python/tests/docs/test_basic.py:table_names_async"
+    print(db.table_names())
     ```
 
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+=== "Javascript"
 
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:table_names"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        console.log(await db.tableNames());
-        ```
+    ```javascript
+    console.log(await db.tableNames());
+    ```
 
 === "Rust"
 
@@ -349,22 +210,22 @@ After a table has been created, you can always add more data to it as follows:
 === "Python"
 
     ```python
-    --8<-- "python/python/tests/docs/test_basic.py:add_data"
-    --8<-- "python/python/tests/docs/test_basic.py:add_data_async"
+
+    # Option 1: Add a list of dicts to a table
+    data = [{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
+            {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}]
+    tbl.add(data)
+
+    # Option 2: Add a pandas DataFrame to a table
+    df = pd.DataFrame(data)
+    tbl.add(data)
     ```
 
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+=== "Typescript"
 
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:add_data"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:add"
-        ```
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:add"
+    ```
 
 === "Rust"
 
@@ -379,24 +240,16 @@ Once you've embedded the query, you can find its nearest neighbors as follows:
 === "Python"
 
     ```python
-    --8<-- "python/python/tests/docs/test_basic.py:vector_search"
-    --8<-- "python/python/tests/docs/test_basic.py:vector_search_async"
+    tbl.search([100, 100]).limit(2).to_pandas()
     ```
 
     This returns a pandas DataFrame with the results.
 
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+=== "Typescript"
 
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:vector_search"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:search"
-        ```
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:search"
+    ```
 
 === "Rust"
 
@@ -421,35 +274,27 @@ LanceDB allows you to create an ANN index on a table as follows:
 === "Python"
 
     ```py
-    --8<-- "python/python/tests/docs/test_basic.py:create_index"
-    --8<-- "python/python/tests/docs/test_basic.py:create_index_async"
+    tbl.create_index()
     ```
 
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+=== "Typescript"
 
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_index"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```{.typescript .ignore}
-        --8<-- "docs/src/basic_legacy.ts:create_index"
-        ```
+    ```{.typescript .ignore}
+    --8<-- "docs/src/basic_legacy.ts:create_index"
+    ```
 
 === "Rust"
 
     ```rust
-    --8<-- "rust/lancedb/examples/simple.rs:create_index"
+     --8<-- "rust/lancedb/examples/simple.rs:create_index"
     ```
 
 !!! note "Why do I need to create an index manually?"
-LanceDB does not automatically create the ANN index for two reasons. The first is that it's optimized
-for really fast retrievals via a disk-based index, and the second is that data and query workloads can
-be very diverse, so there's no one-size-fits-all index configuration. LanceDB provides many parameters
-to fine-tune index size, query latency and accuracy. See the section on
-[ANN indexes](ann_indexes.md) for more details.
+    LanceDB does not automatically create the ANN index for two reasons. The first is that it's optimized
+    for really fast retrievals via a disk-based index, and the second is that data and query workloads can
+    be very diverse, so there's no one-size-fits-all index configuration. LanceDB provides many parameters
+    to fine-tune index size, query latency and accuracy. See the section on
+    [ANN indexes](ann_indexes.md) for more details.
 
 ## Delete rows from a table
 
@@ -460,23 +305,14 @@ This can delete any number of rows that match the filter.
 === "Python"
 
     ```python
-    --8<-- "python/python/tests/docs/test_basic.py:delete_rows"
-    --8<-- "python/python/tests/docs/test_basic.py:delete_rows_async"
+    tbl.delete('item = "fizz"')
     ```
 
-=== "Typescript[^1]"
+=== "Typescript"
 
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:delete_rows"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:delete"
-        ```
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:delete"
+    ```
 
 === "Rust"
 
@@ -486,22 +322,16 @@ This can delete any number of rows that match the filter.
 
 The deletion predicate is a SQL expression that supports the same expressions
 as the `where()` clause (`only_if()` in Rust) on a search. They can be as
-simple or complex as needed. To see what expressions are supported, see the
+simple or complex as needed.  To see what expressions are supported, see the
 [SQL filters](sql.md) section.
 
 === "Python"
 
       Read more: [lancedb.table.Table.delete][]
 
-=== "Typescript[^1]"
+=== "Javascript"
 
-    === "@lancedb/lancedb"
-
-        Read more: [lancedb.Table.delete](javascript/interfaces/Table.md#delete)
-
-    === "vectordb (deprecated)"
-
-        Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)
+      Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)
 
 === "Rust"
 
@@ -513,31 +343,22 @@ 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"
-    ```
+      ```python
+      db.drop_table("my_table")
+      ```
 
-    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`.
+      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[^1]"
+=== "Typescript"
 
-    === "@lancedb/lancedb"
+      ```typescript
+      --8<-- "docs/src/basic_legacy.ts:drop_table"
+      ```
 
-        ```typescript
-        --8<-- "nodejs/examples/basic.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.
+      This permanently removes the table and is not recoverable, unlike deleting rows.
+      If the table does not exist an exception is raised.
 
 === "Rust"
 
@@ -545,40 +366,22 @@ 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"
 
-## 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.
+    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.
 
-=== "Python"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_embeddings_optional.py:imports"
-    --8<-- "python/python/tests/docs/test_embeddings_optional.py:openai_embeddings"
+    ```javascript
+    /** @type {import('next').NextConfig} */
+    module.exports = ({
+    webpack(config) {
+        config.externals.push({ vectordb: 'vectordb' })
+        return config;
+    }
+    })
     ```
 
-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/embedding.ts:imports"
-        --8<-- "nodejs/examples/embedding.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

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

docs/src/cli_config.md

@@ -0,0 +1,51 @@
+
+# CLI & Config
+
+## LanceDB CLI
+Once lanceDB is installed, you can access the CLI using `lancedb` command on the console.
+
+```
+lancedb
+```
+
+This lists out all the various command-line options available. You can get the usage or help for a particular command.
+
+```
+lancedb {command} --help
+```
+
+## LanceDB config
+LanceDB uses a global config file to store certain settings. These settings are configurable using the lanceDB cli.
+To view your config settings, you can use:
+
+```
+lancedb config
+```
+
+These config parameters can be tuned using the cli.
+
+```
+lancedb {config_name} --{argument}
+```
+
+## LanceDB Opt-in Diagnostics
+When enabled, LanceDB will send anonymous events to help us improve LanceDB. These diagnostics are used only for error reporting and no data is collected. Error & stats allow us to automate certain aspects of bug reporting, prioritization of fixes and feature requests.
+These diagnostics are opt-in and can be enabled or disabled using the `lancedb diagnostics` command. These are enabled by default.
+
+### Get usage help
+
+```
+lancedb diagnostics --help
+```
+
+### Disable diagnostics
+
+```
+lancedb diagnostics --disabled
+```
+
+### Enable diagnostics
+
+```
+lancedb diagnostics --enabled
+```

docs/src/cloud/rest.md

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

docs/src/concepts/index_hnsw.md

@@ -1,92 +0,0 @@
-
-# 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
-
-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/embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md

@@ -1,67 +0,0 @@
-# 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

@@ -1,51 +0,0 @@
-# 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

@@ -1,82 +0,0 @@
-# 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

@@ -1,51 +0,0 @@
-# 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

@@ -1,63 +0,0 @@
-# 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

@@ -1,35 +0,0 @@
-# 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

@@ -1,24 +0,0 @@
-# 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
-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

@@ -1,75 +0,0 @@
-# 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

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

@@ -1,39 +0,0 @@
-# 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

@@ -1,37 +0,0 @@
-# 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

@@ -1,34 +0,0 @@
-# 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 |
-
-
-```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

@@ -1,174 +0,0 @@
-# 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/custom_embedding_function.md

@@ -15,226 +15,198 @@ 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
 
-=== "Python"
+@register("sentence-transformers")
+class SentenceTransformerEmbeddings(TextEmbeddingFunction):
+    name: str = "all-MiniLM-L6-v2"
+    # set more default instance vars like device, etc.
 
-    ```python
-    from lancedb.embeddings import register
-    from lancedb.util import attempt_import_or_raise
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._ndims = None
+    
+    def generate_embeddings(self, texts):
+        return self._embedding_model().encode(list(texts), ...).tolist()
 
-    @register("sentence-transformers")
-    class SentenceTransformerEmbeddings(TextEmbeddingFunction):
-        name: str = "all-MiniLM-L6-v2"
-        # set more default instance vars like device, etc.
+    def ndims(self):
+        if self._ndims is None:
+            self._ndims = len(self.generate_embeddings("foo")[0])
+        return self._ndims
 
-        def __init__(self, **kwargs):
-            super().__init__(**kwargs)
-            self._ndims = None
+    @cached(cache={}) 
+    def _embedding_model(self):
+        return sentence_transformers.SentenceTransformer(name)
+```
 
-        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.ts:imports"
-
-    --8<--- "nodejs/examples/custom_embedding_function.ts:embedding_impl"
-    ```
-
-
-This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and default settings.
+This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and defaul 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
 
-    ```python
-    from lancedb.pydantic import LanceModel, Vector
+registry = EmbeddingFunctionRegistry.get_instance()
+stransformer = registry.get("sentence-transformers").create()
 
-    registry = EmbeddingFunctionRegistry.get_instance()
-    stransformer = registry.get("sentence-transformers").create()
+class TextModelSchema(LanceModel):
+    vector: Vector(stransformer.ndims) = stransformer.VectorField()
+    text: str = stransformer.SourceField()
 
-    class TextModelSchema(LanceModel):
-        vector: Vector(stransformer.ndims) = stransformer.VectorField()
-        text: str = stransformer.SourceField()
+tbl = db.create_table("table", schema=TextModelSchema)
 
-    tbl = db.create_table("table", schema=TextModelSchema)
+tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
+result = tbl.search("world").limit(5)
+```
 
-    tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
-    result = tbl.search("world").limit(5)
-    ```
+NOTE:
 
-=== "TypeScript"
-
-    ```ts
-    --8<--- "nodejs/examples/custom_embedding_function.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
+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.
+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.
 
-=== "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()
 
-    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.
+    def __init__(self, *args, **kwargs):
+        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
 
-    ```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 ndims(self):
+        if self._ndims is None:
+            self._ndims = self.generate_text_embeddings("foo").shape[0]
+        return self._ndims
 
-        def __init__(self, *args, **kwargs):
-            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 compute_query_embeddings(
+        self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
+    ) -> List[np.ndarray]:
+        """
+        Compute the embeddings for a given user query
 
-        def ndims(self):
-            if self._ndims is None:
-                self._ndims = self.generate_text_embeddings("foo").shape[0]
-            return self._ndims
-
-        def compute_query_embeddings(
-            self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
-        ) -> 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]):
+        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(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")
+            if isinstance(query, PIL.Image.Image):
+                return [self.generate_image_embedding(query)]
+            else:
+                raise TypeError("OpenClip supports str or PIL Image as query")
 
-        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)
+    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:
-                image_features /= image_features.norm(dim=-1, keepdim=True)
-            return image_features.cpu().numpy().squeeze()
-    ```
+                text_features /= text_features.norm(dim=-1, keepdim=True)
+            return text_features.cpu().numpy().squeeze()
 
-=== "TypeScript"
+    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
 
-    Coming Soon! See this [issue](https://github.com/lancedb/lancedb/issues/1482) to track the status!
+    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(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

@@ -1,84 +1,355 @@
-# 📚 Available Embedding Models
+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.
 
-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. 🚀
+## Text embedding functions
+Contains the text embedding functions registered by default.
 
-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: 
+* Embedding functions have an inbuilt rate limit handler wrapper for source and query embedding function calls that retry with exponential backoff. 
+* Each `EmbeddingFunction` implementation automatically takes `max_retries` as an argument which has the default value of 7.
+
+### 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 |
 
-!!! example "Example usage"
-    ```python
-    model = get_registry()
-              .get("openai")
-              .create(name="text-embedding-ada-002")
-    ```
 
-Now let's understand the above syntax: 
 ```python
-model = get_registry().get("model_id").create(...params)
+db = lancedb.connect("/tmp/db")
+registry = EmbeddingFunctionRegistry.get_instance()
+func = registry.get("sentence-transformers").create(device="cpu")
+
+class Words(LanceModel):
+    text: str = func.SourceField()
+    vector: Vector(func.ndims()) = func.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)
 ```
-**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
+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:
 
-- `.get("model_id")` : This method call on the registry object and retrieves the **embedding models functions** associated with the `"model_id"` (1) .
-    { .annotate }
+| 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 |
 
-    1.  Hover over the names in table below to find out the `model_id` of different embedding functions.
 
-- `.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
+db = lancedb.connect("/tmp/db")
+registry = EmbeddingFunctionRegistry.get_instance()
+func = registry.get("openai").create()
 
-??? question "What parameters does the `.create(...params)` method accepts?"
-    **Checkout the documentation of specific embedding models (links in the table below👇) to know what parameters it takes**.
+class Words(LanceModel):
+    text: str = func.SourceField()
+    vector: Vector(func.ndims()) = func.VectorField()
 
-!!! tip "Moving on"
-    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.
+table = db.create_table("words", schema=Words)
+table.add(
+    [
+        {"text": "hello world"}
+        {"text": "goodbye world"}
+    ]
+    )
 
-## Text Embedding Functions 📝 
-These functions are registered by default to handle text embeddings.
+query = "greetings"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```
 
-- 🔄 **Embedding functions** have an inbuilt rate limit handler wrapper for source and query embedding function calls that retry with **exponential backoff**. 
+### 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.
 
-- 🌕 Each `EmbeddingFunction` implementation automatically takes `max_retries` as an argument which has the default value of 7. 
+If you want to calculate customized embeddings for specific sentences, you can follow the unified template to write instructions.
 
-🌟 **Available Text Embeddings**
+!!! info
+    Represent the `domain` `text_type` for `task_objective`:
 
-| **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) |
+    * `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 |
 
 
 
-[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"
+```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)
+```
+
+### 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.                                                                                                 |
 
 
-## Multi-modal Embedding Functions🖼️ 
+Usage Example:
 
-Multi-modal embedding functions allow you to query your table using both images and text. 💬🖼️
+```python
+import lancedb
+import pandas as pd
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
 
-🌐 **Available Multi-modal Embeddings**
 
-| Embedding :material-information-outline:{ title="Hover over the name to find out the model_id" }  | Description | Documentation  |
-|-----------|-------------|---------------|
-| [**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) |
+model = get_registry().get("gemini-text").create()
 
-!!! note
-    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).
+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
+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
+
+db = lancedb.connect(tmp_path)
+registry = EmbeddingFunctionRegistry.get_instance()
+func = 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
+db = lancedb.connect(tmp_path)
+registry = EmbeddingFunctionRegistry.get_instance()
+func = 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

@@ -2,12 +2,9 @@ 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.
-    However, if your embedding function changes, you'll have to re-configure your table with the new embedding function
+    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 
     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.
 
@@ -16,7 +13,7 @@ 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
@@ -26,35 +23,20 @@ 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!
 
-=== "TypeScript"
+=== "JavaScript""
     In the TypeScript SDK, the choices are more limited. For now, only the OpenAI
     embedding function is available.
 
     ```javascript
-    import * as lancedb from '@lancedb/lancedb'
-    import { getRegistry } from '@lancedb/lancedb/embeddings'
+    const lancedb = require("vectordb");
 
     // You need to provide an OpenAI API key
     const apiKey = "sk-..."
     // The embedding function will create embeddings for the 'text' column
-    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"
+    const embedding = new lancedb.OpenAIEmbeddingFunction('text', apiKey)
     ```
 
 ## 2. Define the data model or schema
@@ -64,20 +46,20 @@ 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`.
 
-=== "TypeScript"
+=== "JavaScript"
 
     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:
 
@@ -89,26 +71,17 @@ the embeddings at all:
     table.add([{"image_uri": u} for u in uris])
     ```
 
-=== "TypeScript"
+=== "JavaScript"
 
-    === "@lancedb/lancedb"
+    ```javascript
+    const db = await lancedb.connect("data/sample-lancedb");
+    const data = [
+    { text: "pepperoni"},
+    { text: "pineapple"}
+    ]
 
-        ```ts
-        --8<-- "nodejs/examples/embedding.ts:imports"
-        --8<-- "nodejs/examples/embedding.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)
-        ```
+    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
@@ -121,8 +94,8 @@ need to worry about it when you query the table:
     ```python
     results = (
         table.search("dog")
-            .limit(10)
-            .to_pandas()
+        .limit(10)
+        .to_pandas()
     )
     ```
 
@@ -133,32 +106,22 @@ need to worry about it when you query the table:
     query_image = Image.open(p)
     results = (
         table.search(query_image)
-            .limit(10)
-            .to_pandas()
+        .limit(10)
+        .to_pandas()
     )
     ```
 
     Both of the above snippet returns a pandas DataFrame with the 10 closest vectors to the query.
 
-=== "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"
 
+    ```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.
 
 ---
@@ -186,7 +149,7 @@ 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
@@ -203,4 +166,4 @@ 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

@@ -1,134 +1,14 @@
-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.
-However, there's no one-size-fits-all solution for generating embeddings - there are many different libraries and APIs
+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. 
+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. You can define your own [custom embedding function](./custom_embedding_function.md)
+3. For python users, 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).
-It is retained for compatibility and will be removed in a future version.
-
-## Quickstart
-
-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.ts:imports"
-    --8<--- "nodejs/examples/embedding.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(
-  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)
-table.add(
-    [
-        {"text": "hello world"},
-        {"text": "goodbye world"}
-    ]
-    )
-
-query = "greetings"
-actual = table.search(query).limit(1).to_pydantic(Words)[0]
-print(actual.text)
-```
+It is retained for compatibility and will be removed in a future version.

docs/src/examples/examples_python.md

@@ -1,22 +1,17 @@
-# Overview : Python Examples 
+# Examples: Python
 
-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. 
+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.
 
-You can find the latest examples in our [VectorDB Recipes](https://github.com/lancedb/vectordb-recipes) repository. 
-
-**Introduction**
-
-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.
-
-| Explore                                         | Description                                                                                                                                                      |
-|----------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [**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.      |
-| [**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.   |
-| [**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.                    |
-| [**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. |
+| Example | Interactive Envs | Scripts  |
+|-------- | ---------------- | ------   |
+| | | |
+| [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) |
+| [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)  |
+| [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)  |
+| [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 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)  |
+| [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> |  |
 

docs/src/examples/index.md

@@ -10,7 +10,7 @@ LanceDB provides language APIs, allowing you to embed a database in your languag
 
 ## Applications powered by LanceDB
 
-| Project Name | Description |
-| --- | --- |
-| **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🤖**<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.. |
+| Project Name                                        | Description                                                                                                          | Screenshot                                |
+|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
+| [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) |
+| [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)    |

docs/src/examples/python_examples/aiagent.md

@@ -1,27 +0,0 @@
-# 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

@@ -1,13 +0,0 @@
-# **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

@@ -1,41 +0,0 @@
-**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/blob/main/tutorials/Chat_with_csv_file
-[csv_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Chat_with_csv_file/main.ipynb
-[csv_ghost]: https://blog.lancedb.com/p/d8c71df4-e55f-479a-819e-cde13354a6a3/

docs/src/examples/python_examples/evaluations.md

@@ -1,21 +0,0 @@
-**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

@@ -1,28 +0,0 @@
-# **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/blob/main/examples/multimodal_search) <br>[![Open In Collab](../../assets/colab.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.ipynb) <br> [![Python](../../assets/python.svg)](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.py)<br> [![Ghost](../../assets/ghost.svg)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/) |
-| **Cambrian-1: Vision-Centric Image Exploration 🔍👀** | Learn how **Cambrian-1** works, using an example of **Vision-Centric** exploration on images found through vector search ! Work on **Flickr-8k** dataset 🔎                        | [![Kaggle](https://img.shields.io/badge/Kaggle-035a7d?style=for-the-badge&logo=kaggle&logoColor=white)](https://www.kaggle.com/code/prasantdixit/cambrian-1-vision-centric-exploration-of-images/)<br> [![Ghost](../../assets/ghost.svg)](https://blog.lancedb.com/cambrian-1-vision-centric-exploration/)                                                                                                                                                                                                                   |
-
-
-[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

@@ -1,83 +0,0 @@
-**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/blob/main/examples/QueryExpansion&Reranker
-[query_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/QueryExpansion&Reranker/main.ipynb
-
-
-[fusion_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/RAG_Fusion
-[fusion_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/RAG_Fusion/main.ipynb
-
-[agentic_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG
-[agentic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG/main.ipynb
-
-

docs/src/examples/python_examples/recommendersystem.md

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-**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/blob/main/examples/movie-recommendation-with-genres
-[genre_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb
-[genre_ghost]: https://blog.lancedb.com/movie-recommendation-system-using-lancedb-and-doc2vec/
-
-[product_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/product-recommender
-[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/blob/main/examples/Food_recommendation
-[food_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Food_recommendation/main.ipynb

docs/src/examples/python_examples/vector_search.md

@@ -1,80 +0,0 @@
-**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/blob/main/examples/audio_search
-[audio_search_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.ipynb
-[audio_search_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.py
-
-[mls_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa
-[mls_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa/main.ipynb
-[mls_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa/main.py
-
-[fr_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/facial_recognition
-[fr_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/facial_recognition/main.ipynb
-
-[sentiment_analysis_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews
-[sentiment_analysis_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb
-[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/blob/main/examples/zero-shot-image-classification
-[zsic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/zero-shot-image-classification/main.ipynb
-[zsic_ghost]: https://blog.lancedb.com/zero-shot-image-classification-with-vector-search/
-
-
-
-
-

docs/src/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 lance
-sift_dataset = lance.dataset("/path/to/local/vec_data.lance")
+import pylance
+sift_dataset = pylance.dataset("/path/to/local/vec_data.lance")
 df = sift_dataset.to_table().to_pandas()
 
 import lancedb

docs/src/extra_js/init_ask_ai_widget.js

@@ -1,79 +1,11 @@
-// Creates an SVG robot icon (from Lucide)
-function robotSVG() {
-  var svg = document.createElementNS("http://www.w3.org/2000/svg", "svg");
-  svg.setAttribute("width", "24");
-  svg.setAttribute("height", "24");
-  svg.setAttribute("viewBox", "0 0 24 24");
-  svg.setAttribute("fill", "none");
-  svg.setAttribute("stroke", "currentColor");
-  svg.setAttribute("stroke-width", "2");
-  svg.setAttribute("stroke-linecap", "round");
-  svg.setAttribute("stroke-linejoin", "round");
-  svg.setAttribute("class", "lucide lucide-bot-message-square");
-
-  var path1 = document.createElementNS("http://www.w3.org/2000/svg", "path");
-  path1.setAttribute("d", "M12 6V2H8");
-  svg.appendChild(path1);
-
-  var path2 = document.createElementNS("http://www.w3.org/2000/svg", "path");
-  path2.setAttribute("d", "m8 18-4 4V8a2 2 0 0 1 2-2h12a2 2 0 0 1 2 2v8a2 2 0 0 1-2 2Z");
-  svg.appendChild(path2);
-
-  var path3 = document.createElementNS("http://www.w3.org/2000/svg", "path");
-  path3.setAttribute("d", "M2 12h2");
-  svg.appendChild(path3);
-
-  var path4 = document.createElementNS("http://www.w3.org/2000/svg", "path");
-  path4.setAttribute("d", "M9 11v2");
-  svg.appendChild(path4);
-
-  var path5 = document.createElementNS("http://www.w3.org/2000/svg", "path");
-  path5.setAttribute("d", "M15 11v2");
-  svg.appendChild(path5);
-
-  var path6 = document.createElementNS("http://www.w3.org/2000/svg", "path");
-  path6.setAttribute("d", "M20 12h2");
-  svg.appendChild(path6);
-
-  return svg
-}
-
-// Creates the Fluidic Chatbot buttom
-function fluidicButton() {
-  var btn = document.createElement("a");
-  btn.href = "https://asklancedb.com";
-  btn.target = "_blank";
-  btn.style.position = "fixed";
-  btn.style.fontWeight = "bold";
-  btn.style.fontSize = ".8rem";
-  btn.style.right = "10px";
-  btn.style.bottom = "10px";
-  btn.style.width = "80px";
-  btn.style.height = "80px";
-  btn.style.background = "linear-gradient(135deg, #7C5EFF 0%, #625eff 100%)";
-  btn.style.color = "white";
-  btn.style.borderRadius = "5px";
-  btn.style.display = "flex";
-  btn.style.flexDirection = "column";
-  btn.style.justifyContent = "center";
-  btn.style.alignItems = "center";
-  btn.style.zIndex = "1000";
-  btn.style.opacity = "0";
-  btn.style.boxShadow = "0 0 0 rgba(0, 0, 0, 0)";
-  btn.style.transition = "opacity 0.2s ease-in, box-shadow 0.2s ease-in";
-
-  setTimeout(function() {
-      btn.style.opacity = "1";
-      btn.style.boxShadow = "0 0 .2rem #0000001a,0 .2rem .4rem #0003"
-  }, 0);
-
-  return btn
-}
-
-document.addEventListener("DOMContentLoaded", function() {
-  var btn = fluidicButton()
-  btn.appendChild(robotSVG());
-  var text = document.createTextNode("Ask AI");
-  btn.appendChild(text);
-  document.body.appendChild(btn);
-});
+document.addEventListener("DOMContentLoaded", function () {
+    var script = document.createElement("script");
+    script.src = "https://widget.kapa.ai/kapa-widget.bundle.js";
+    script.setAttribute("data-website-id", "c5881fae-cec0-490b-b45e-d83d131d4f25");
+    script.setAttribute("data-project-name", "LanceDB");
+    script.setAttribute("data-project-color", "#000000");
+    script.setAttribute("data-project-logo", "https://avatars.githubusercontent.com/u/108903835?s=200&v=4");
+  script.setAttribute("data-modal-example-questions","Help me create an IVF_PQ index,How do I do an exhaustive search?,How do I create a LanceDB table?,Can I use my own embedding function?");
+    script.async = true;
+    document.head.appendChild(script);
+  });

docs/src/fts.md

@@ -1,14 +1,10 @@
 # Full-text search
 
-LanceDB provides support for full-text search via Lance (before via [Tantivy](https://github.com/quickwit-oss/tantivy) (Python only)), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
+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)
 
-Currently, the Lance full text search is missing some features that are in the Tantivy full text search. This includes phrase queries, re-ranking, and customizing the tokenizer. Thus, in Python, Tantivy is still the default way to do full text search and many of the instructions below apply just to Tantivy-based indices.
+A hybrid search solution combining vector and full-text search is also on the way.
 
-
-## Installation (Only for Tantivy-based FTS)
-
-!!! note
-    No need to install the tantivy dependency if using native FTS
+## Installation
 
 To use full-text search, install the dependency [`tantivy-py`](https://github.com/quickwit-oss/tantivy-py):
 
@@ -19,117 +15,53 @@ pip install tantivy==0.20.1
 
 ## Example
 
-Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search, the FTS index must be created before you can search via keywords.
+Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search.
 
-=== "Python"
+```python
+import lancedb
 
-    ```python
-    import lancedb
+uri = "data/sample-lancedb"
+db = lancedb.connect(uri)
 
-    uri = "data/sample-lancedb"
-    db = lancedb.connect(uri)
+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"},
+    ],
+)
+```
 
-    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"},
-        ],
-    )
+## Create FTS index on single column
 
-    # passing `use_tantivy=False` to use lance FTS index
-    # `use_tantivy=True` by default
-    table.create_fts_index("text")
-    table.search("puppy").limit(10).select(["text"]).to_list()
-    # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
-    # ...
-    ```
+The FTS index must be created before you can search via keywords.
 
-=== "TypeScript"
+```python
+table.create_fts_index("text")
+```
 
-    ```typescript
-    import * as lancedb from "@lancedb/lancedb";
-    const uri = "data/sample-lancedb"
-    const db = await lancedb.connect(uri);
+To search an FTS index via keywords, LanceDB's `table.search` accepts a string as input:
 
-    const data = [
-    { 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(),
-    });
+```python
+table.search("puppy").limit(10).select(["text"]).to_list()
+```
 
-    await tbl
-        .search("puppy", queryType="fts")
-        .select(["text"])
-        .limit(10)
-        .toArray();
-    ```
+This returns the result as a list of dictionaries as follows.
 
-=== "Rust"
-
-    ```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.
-For now, this is supported in tantivy way only.
-
-Passing `fts_columns="text"` if you want to specify the columns to search, but it's not available for Tantivy-based full text search.
+```python
+[{'text': 'Frodo was a happy puppy', 'score': 0.6931471824645996}]
+```
 
 !!! 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".
-
-For now, only the Tantivy-based FTS index supports to specify the tokenizer, so it's only available in Python with `use_tantivy=True`.
-
-=== "use_tantivy=True"
-
-    ```python
-    table.create_fts_index("text", use_tantivy=True, tokenizer_name="en_stem")
-    ```
-
-=== "use_tantivy=False"
-
-    [**Not supported yet**](https://github.com/lancedb/lance/issues/1195)
-
-the following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.
-
 ## 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`:
 
-=== "use_tantivy=True"
-
-    ```python
-    table.create_fts_index(["text1", "text2"])
-    ```
-
-=== "use_tantivy=False"
-
-    [**Not supported yet**](https://github.com/lancedb/lance/issues/1195)
+```python
+table.create_fts_index(["text1", "text2"])
+```
 
 Note that the search API call does not change - you can search over all indexed columns at once.
 
@@ -139,48 +71,19 @@ Currently the LanceDB full text search feature supports *post-filtering*, meanin
 applied on top of the full text search results. This can be invoked via the familiar
 `where` syntax:
 
-=== "Python"
-
-    ```python
-    table.search("puppy").limit(10).where("meta='foo'").to_list()
-    ```
-
-=== "TypeScript"
-
-    ```typescript
-    await tbl
-    .search("apple")
-    .select(["id", "doc"])
-    .limit(10)
-    .where("meta='foo'")
-    .toArray();
-    ```
-
-=== "Rust"
-
-    ```rust
-    table
-        .query()
-        .full_text_search(FullTextSearchQuery::new(words[0].to_owned()))
-        .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
-        .limit(10)
-        .only_if("meta='foo'")
-        .execute()
-        .await?;
-    ```
+```python
+table.search("puppy").limit(10).where("meta='foo'").to_list()
+```
 
 ## Sorting
 
-!!! warning "Warn"
-    Sorting is available for only Tantivy-based FTS
-
 You can pre-sort the documents by specifying `ordering_field_names` when
 creating the full-text search index. Once pre-sorted, you can then specify
 `ordering_field_name` while searching to return results sorted by the given
-field. For example,
+field. For example, 
 
-```python
-table.create_fts_index(["text_field"], use_tantivy=True, ordering_field_names=["sort_by_field"])
+```
+table.create_fts_index(["text_field"], ordering_field_names=["sort_by_field"])
 
 (table.search("terms", ordering_field_name="sort_by_field")
  .limit(20)
@@ -193,8 +96,8 @@ table.create_fts_index(["text_field"], use_tantivy=True, ordering_field_names=["
     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
+    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
@@ -204,9 +107,6 @@ table.create_fts_index(["text_field"], use_tantivy=True, ordering_field_names=["
 
 ## Phrase queries vs. terms queries
 
-!!! warning "Warn"
-    Lance-based FTS doesn't support queries combining by 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
 query syntax, see Tantivy's [query parser rules](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html).
@@ -233,15 +133,14 @@ 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
+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 (Only for Tantivy-based FTS)
+## 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
@@ -255,8 +154,6 @@ table.create_fts_index(["text1", "text2"], writer_heap_size=heap, replace=True)
 
 ## Current limitations
 
-For that Tantivy-based FTS:
-
 1. Currently we do not yet support incremental writes.
    If you add data after FTS index creation, it won't be reflected
    in search results until you do a full reindex.

docs/src/guides/scalar_index.md

@@ -1,108 +0,0 @@
-# Building Scalar Index
-
-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. This index is inspired by the btree data structure
-  although only the first few layers of the btree are cached in memory.
-  It will perform well on columns with a large number of unique values and few rows per value.
-- `BITMAP`: this index stores a bitmap for each unique value in the column.
-  This index is useful for columns with a finite number of unique values and many rows per value.
-  For example, columns that represent "categories", "labels", or "tags"
-- `LABEL_LIST`: a special index that is used to index list columns whose values have a finite set of possibilities.
-  For example, a column that contains lists of tags (e.g. `["tag1", "tag2", "tag3"]`) can be indexed with a `LABEL_LIST` index.
-
-| 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` |
-
-=== "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() })
-        ```
-
-For example, the following scan will be faster if the column `my_col` 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();
-        ```

docs/src/guides/storage.md

@@ -32,232 +32,41 @@ LanceDB OSS supports object stores such as AWS S3 (and compatible stores), Azure
     db = lancedb.connect("az://bucket/path")
     ```
 
-=== "TypeScript"
+=== "JavaScript"
 
-    === "@lancedb/lancedb"
+    AWS S3:
 
-        AWS S3:
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect("s3://bucket/path");
-        ```
-
-        Google Cloud Storage:
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        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`:
-
-```bash
-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"
-
-    ```python
-    import lancedb
-    db = await lancedb.connect_async(
-        "s3://bucket/path",
-        storage_options={"timeout": "60s"}
-    )
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect("s3://bucket/path");
     ```
 
-=== "TypeScript"
+    Google Cloud Storage:
 
-    === "@lancedb/lancedb"
-
-        ```ts
-        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:
-
-=== "Python"
-
-    <!-- skip-test -->
-    ```python
-    import lancedb
-    db = await lancedb.connect_async("s3://bucket/path")
-    table = await db.create_table(
-        "table",
-        [{"a": 1, "b": 2}],
-        storage_options={"timeout": "60s"}
-    )
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect("gs://bucket/path");
     ```
 
-=== "TypeScript"
+    Azure Blob Storage:
 
-    === "@lancedb/lancedb"
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect("az://bucket/path");
+    ```
 
-        <!-- skip-test -->
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect("s3://bucket/path");
-        const table = db.createTable(
-            "table",
-            [{ a: 1, b: 2}],
-            {storageOptions: {timeout: "60s"}}
-        );
-        ```
+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 using environment variables. In general, these environment variables are the same as those used by the respective cloud SDKs. The sections below describe the environment variables that can be used to configure each object store.
 
-    === "vectordb (deprecated)"
+LanceDB OSS uses the [object-store](https://docs.rs/object_store/latest/object_store/) Rust crate for object store access. There are general environment variables that can be used to configure the object store, such as the request timeout and proxy configuration. See the [object_store ClientConfigKey](https://docs.rs/object_store/latest/object_store/enum.ClientConfigKey.html) doc for available configuration options. The environment variables that can be set are the snake-cased versions of these variable names. For example, to set `ProxyUrl` use the environment variable `PROXY_URL`. (Don't let the Rust docs intimidate you! We link to them so you can see an up-to-date list of the available options.)
 
-        <!-- 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"
-
-    The storage option keys are case-insensitive. So `connect_timeout` and `CONNECT_TIMEOUT` are the same setting. Usually lowercase is used in the `storage_options` argument and uppercase is used for environment variables. In the `lancedb` Node package, the keys can also be provided in `camelCase` capitalization. For example, `connectTimeout` is equivalent to `connect_timeout`.
-
-### General configuration
-
-There are several options that can be set for all object stores, mostly related to network client configuration.
-
-<!-- from here: https://docs.rs/object_store/latest/object_store/enum.ClientConfigKey.html -->
-
-| Key                        | Description                                                                                      |
-|----------------------------|--------------------------------------------------------------------------------------------------|
-| `allow_http`               | Allow non-TLS, i.e. non-HTTPS connections. Default: `False`.                                      |
-| `allow_invalid_certificates`| Skip certificate validation on HTTPS connections. Default: `False`.                               |
-| `connect_timeout`          | Timeout for only the connect phase of a Client. Default: `5s`.                                    |
-| `timeout`                  | Timeout for the entire request, from connection until the response body has finished. Default: `30s`. |
-| `user_agent`               | User agent string to use in requests.                                                             |
-| `proxy_url`                | URL of a proxy server to use for requests. Default: `None`.                                       |
-| `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.
-These can be set as environment variables or passed in the `storage_options` parameter:
-
-=== "Python"
-
-    ```python
-    import lancedb
-    db = await lancedb.connect_async(
-        "s3://bucket/path",
-        storage_options={
-            "aws_access_key_id": "my-access-key",
-            "aws_secret_access_key": "my-secret-key",
-            "aws_session_token": "my-session-token",
-        }
-    )
-    ```
-
-=== "TypeScript"
-
-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect(
-            "s3://bucket/path",
-            {
-                storageOptions: {
-                    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",
-                }
-            }
-        );
-        ```
+To configure credentials for AWS S3, you can use the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` environment variables.
 
 Alternatively, if you are using AWS SSO, you can use the `AWS_PROFILE` and `AWS_DEFAULT_REGION` environment variables.
 
-The following keys can be used as both environment variables or keys in the `storage_options` parameter:
-
-| Key                                | Description                                                                                          |
-|------------------------------------|------------------------------------------------------------------------------------------------------|
-| `aws_region` / `region`             | The AWS region the bucket is in. This can be automatically detected when using AWS S3, but must be specified for S3-compatible stores. |
-| `aws_access_key_id` / `access_key_id` | The AWS access key ID to use.                                                                       |
-| `aws_secret_access_key` / `secret_access_key` | The AWS secret access key to use.                                                               |
-| `aws_session_token` / `session_token` | The AWS session token to use.                                                                     |
-| `aws_endpoint` / `endpoint`         | The endpoint to use for S3-compatible stores.                                                       |
-| `aws_virtual_hosted_style_request` / `virtual_hosted_style_request` | Whether to use virtual hosted-style requests, where the bucket name is part of the endpoint. Meant to be used with `aws_endpoint`. Default: `False`. |
-| `aws_s3_express` / `s3_express`     | Whether to use S3 Express One Zone endpoints. Default: `False`. See more details below.             |
-| `aws_server_side_encryption`        | The server-side encryption algorithm to use. Must be one of `"AES256"`, `"aws:kms"`, or `"aws:kms:dsse"`. Default: `None`. |
-| `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.                                               |
+You can see a full list of environment variables [here](https://docs.rs/object_store/latest/object_store/aws/struct.AmazonS3Builder.html#method.from_env).
 
 !!! tip "Automatic cleanup for failed writes"
 
@@ -335,349 +144,24 @@ 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:
+LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you must specify two environment variables: `AWS_ENDPOINT` and `AWS_DEFAULT_REGION`. `AWS_ENDPOINT` should be the URL of the S3-compatible store, and `AWS_DEFAULT_REGION` should be the region to use.
 
-=== "Python"
-
-    ```python
-    import lancedb
-    db = await lancedb.connect_async(
-        "s3://bucket/path",
-        storage_options={
-            "region": "us-east-1",
-            "endpoint": "http://minio:9000",
-        }
-    )
-    ```
-
-=== "TypeScript"
-
-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect(
-            "s3://bucket/path",
-            {
-                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.
-
-To configure LanceDB to use an S3 Express endpoint, you must set the storage option `s3_express`. The bucket name in your table URI should **include the suffix**.
-
-=== "Python"
-
-    ```python
-    import lancedb
-    db = await lancedb.connect_async(
-        "s3://my-bucket--use1-az4--x-s3/path",
-        storage_options={
-            "region": "us-east-1",
-            "s3_express": "true",
-        }
-    )
-    ```
-
-=== "TypeScript"
-
-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect(
-            "s3://my-bucket--use1-az4--x-s3/path",
-            {
-                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",
-                }
-            }
-        );
-        ```
+<!-- TODO: we should also document the use of S3 Express once we fully support it -->
 
 ### Google Cloud Storage
 
-GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environment variable to the path of a JSON file containing the service account credentials. Alternatively, you can pass the path to the JSON file in the `storage_options`:
+GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environment variable to the path of a JSON file containing the service account credentials. There are several aliases for this environment variable, documented [here](https://docs.rs/object_store/latest/object_store/gcp/struct.GoogleCloudStorageBuilder.html#method.from_env).
 
-=== "Python"
-
-    <!-- skip-test -->
-    ```python
-    import lancedb
-    db = await lancedb.connect_async(
-        "gs://my-bucket/my-database",
-        storage_options={
-            "service_account": "path/to/service-account.json",
-        }
-    )
-    ```
-
-=== "TypeScript"
-
-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect(
-            "gs://my-bucket/my-database",
-            {
-                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 -->
-
-| Key                                   | Description                                  |
-|---------------------------------------|----------------------------------------------|
-| ``google_service_account`` / `service_account` | Path to the service account JSON file.       |
-| ``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:
+Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_ACCOUNT_NAME` and ``AZURE_STORAGE_ACCOUNT_KEY`` environment variables. The full list of environment variables that can be set are documented [here](https://docs.rs/object_store/latest/object_store/azure/struct.MicrosoftAzureBuilder.html#method.from_env).
 
-=== "Python"
 
-    <!-- skip-test -->
-    ```python
-    import lancedb
-    db = await lancedb.connect_async(
-        "az://my-container/my-database",
-        storage_options={
-            account_name: "some-account",
-            account_key: "some-key",
-        }
-    )
-    ```
-
-=== "TypeScript"
-
-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect(
-            "az://my-container/my-database",
-            {
-                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:
-
-<!-- source: https://docs.rs/object_store/latest/object_store/azure/enum.AzureConfigKey.html -->
-
-| Key                                   | Description                                                                                      |
-|---------------------------------------|--------------------------------------------------------------------------------------------------|
-| ``azure_storage_account_name``        | The name of the azure storage account.                                                           |
-| ``azure_storage_account_key``         | The serialized service account key.                                                              |
-| ``azure_client_id``                   | Service principal client id for authorizing requests.                                            |
-| ``azure_client_secret``               | Service principal client secret for authorizing requests.                                        |
-| ``azure_tenant_id``                   | Tenant id used in oauth flows.                                                                   |
-| ``azure_storage_sas_key``             | Shared access signature. The signature is expected to be percent-encoded, much like they are provided in the azure storage explorer or azure portal. |
-| ``azure_storage_token``               | Bearer token.                                                                                    |
-| ``azure_storage_use_emulator``        | Use object store with azurite storage emulator.                                                  |
-| ``azure_endpoint``                    | Override the endpoint used to communicate with blob storage.                                      |
-| ``azure_use_fabric_endpoint``         | Use object store with url scheme account.dfs.fabric.microsoft.com.                               |
-| ``azure_msi_endpoint``                | Endpoint to request a imds managed identity token.                                               |
-| ``azure_object_id``                   | Object id for use with managed identity authentication.                                          |
-| ``azure_msi_resource_id``             | Msi resource id for use with managed identity authentication.                                    |
-| ``azure_federated_token_file``        | File containing token for Azure AD workload identity federation.                                 |
-| ``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

@@ -3,46 +3,32 @@
 
 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"
@@ -59,150 +45,102 @@ Initialize a LanceDB connection and create a table
 
     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
-    db.create_table("name", data, exist_ok=True)
+        ```python
+        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 })
     ```
 
-    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.
+    ### From a Pandas DataFrame
 
     ```python
-    db.create_table("name", data, mode="overwrite")
+    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()
     ```
-
-=== "Typescript[^1]"
-    You can create a LanceDB table in JavaScript using an array of records as follows.
-
-    === "@lancedb/lancedb"
-
-
-        ```ts
-        --8<-- "nodejs/examples/basic.ts:create_table"
-        ```
-
-        This will infer the schema from the provided data. If you want to explicitly provide a schema, you can use `apache-arrow` to declare a schema
-
-        ```ts
-        --8<-- "nodejs/examples/basic.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.ts:create_table_exists_ok"
-        ```
-
-        Sometimes you want to make sure that you start fresh. If you want to
-        overwrite the table, you can pass in mode: "overwrite" to the createTable function.
-
-        ```ts
-        --8<-- "nodejs/examples/basic.ts:create_table_overwrite"
-        ```
-
-    === "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"
+    !!! 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
-custom_schema = pa.schema([
-pa.field("vector", pa.list_(pa.float32(), 4)),
-pa.field("lat", pa.float32()),
-pa.field("long", pa.float32())
-])
+    ```python
+    custom_schema = pa.schema([
+    pa.field("vector", pa.list_(pa.float32(), 4)),
+    pa.field("lat", 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
-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
-is on the way.
+    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
+    under the hood. A deeper integration between LanceDB Tables and Polars DataFrames
+    is on the way.
 
-```python
-import polars as pl
+    ```python
+    import polars as pl
 
-data = pl.DataFrame({
-    "vector": [[3.1, 4.1], [5.9, 26.5]],
-    "item": ["foo", "bar"],
-    "price": [10.0, 20.0]
-})
-table = db.create_table("pl_table", data=data)
-```
+    data = pl.DataFrame({
+        "vector": [[3.1, 4.1], [5.9, 26.5]],
+        "item": ["foo", "bar"],
+        "price": [10.0, 20.0]
+    })
+    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(
@@ -222,19 +160,13 @@ LanceDB supports float16 data type!
     tbl = db.create_table("f16_tbl", data, schema=schema)
     ```
 
-=== "Typescript[^1]"
+=== "Javascript"
+    You can also create LanceDB tables directly from Arrow tables. 
+    LanceDB supports Float16 data type!
 
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_f16_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:create_f16_table"
-        ```
+    ```javascript
+    --8<-- "docs/src/basic_legacy.ts:create_f16_table"
+    ```
 
 ### From Pydantic Models
 
@@ -293,7 +225,7 @@ 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":
 
 ```
@@ -304,7 +236,7 @@ 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
@@ -329,7 +261,7 @@ 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))
 
@@ -397,25 +329,25 @@ You can also use iterators of other types like Pandas DataFrame or Pylists direc
     tbl = db.open_table("my_table")
     ```
 
-=== "Typescript[^1]"
-
+=== "JavaScript"
     If you forget the name of your table, you can always get a listing of all table names.
 
-    ```typescript
+    ```javascript
     console.log(await db.tableNames());
     ```
 
     Then, you can open any existing tables.
 
-    ```typescript
+    ```javascript
     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.
 
@@ -432,8 +364,8 @@ You can create an empty table for scenarios where you want to add data to the ta
     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
-    directly import `pydantic` but instead use `lancedb.pydantic` which is a subclass of `pydantic.BaseModel`
+    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` 
     that has been extended to support LanceDB specific types like `Vector`.
 
     ```python
@@ -450,23 +382,9 @@ You can create an empty table for scenarios where you want to add data to the ta
 
     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.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 usind the `add` method
+After a table has been created, you can always add more data to it using the various methods available.
 
 === "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.
@@ -534,27 +452,8 @@ After a table has been created, you can always add more data to it usind the `ad
     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` feild as None to allow LanceDB to automatically vectorize the data.
 
-        ```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"
 
     ```javascript
     await tbl.add(
@@ -610,15 +509,15 @@ Use the `delete()` method on tables to delete rows from a table. To choose which
     # 0  3  [5.0, 6.0]
     ```
 
-=== "Typescript[^1]"
+=== "JavaScript"
 
-    ```ts
+    ```javascript
     await tbl.delete('item = "fizz"')
     ```
 
     ### Deleting row with specific column value
 
-    ```ts
+    ```javascript
     const con = await lancedb.connect("./.lancedb")
     const data = [
       {id: 1, vector: [1, 2]},
@@ -632,7 +531,7 @@ Use the `delete()` method on tables to delete rows from a table. To choose which
 
     ### Delete from a list of values
 
-    ```ts
+    ```javascript
     const to_remove = [1, 5];
     await tbl.delete(`id IN (${to_remove.join(",")})`)
     await tbl.countRows() // Returns 1
@@ -689,49 +588,26 @@ This can be used to update zero to all rows depending on how many rows match the
     2  2  [10.0, 10.0]
     ```
 
-=== "Typescript[^1]"
+=== "JavaScript/Typescript"
 
-    === "@lancedb/lancedb"
+    API Reference: [vectordb.Table.update](../javascript/interfaces/Table.md/#update)
 
-        API Reference: [lancedb.Table.update](../js/classes/Table.md/#update)
+    ```javascript
+    const lancedb = require("vectordb");
 
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
+    const db = await lancedb.connect("./.lancedb");
 
-        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)
 
-        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]} })
+    ```
 
-        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.
+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"
 
@@ -750,47 +626,16 @@ This can be used to update zero to all rows depending on how many rows match the
     2  3  [10.0, 10.0]
     ```
 
-=== "Typescript[^1]"
+=== "JavaScript/Typescript"
 
-    === "@lancedb/lancedb"
-
-        Coming Soon!
-
-    === "vectordb (deprecated)"
-
-        ```ts
-        await tbl.update({ valuesSql: { x: "x + 1" } })
-        ```
+    ```javascript
+    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.
-
-
 ## 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.
@@ -806,7 +651,7 @@ 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
@@ -828,35 +673,33 @@ 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()
     ```
 
-=== "Typescript[^1]"
+=== "JavaScript/Typescript"
 
     To set strong consistency, use `0`:
 
-    ```ts
+    ```javascript
     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:
 
-    ```ts
+    ```javascript
     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.
-
-[^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.
+Learn the best practices on creating an ANN index and getting the most out of it.

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

@@ -1,131 +0,0 @@
-## 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

@@ -1,80 +0,0 @@
-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

@@ -1,82 +0,0 @@
-## 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 |