Bump version: 0.12.0-beta.0 → 0.12.0

This exposes the `LANCEDB_LOG` environment variable in node, so that
users can now turn on logging.

In addition, fixes a bug where only the top-level error from Rust was
being shown. This PR makes sure the full error chain is included in the
error message. In the future, will improve this so the error chain is
set on the [cause](https://nodejs.org/api/errors.html#errorcause)
property of JS errors https://github.com/lancedb/lancedb/issues/1779

Fixes #1774

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.7.0"
+current_version = "0.12.0"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.
@@ -24,34 +24,87 @@ commit = true
 message = "Bump version: {current_version} → {new_version}"
 commit_args = ""
 
+# Java maven files
+pre_commit_hooks = [
+  """
+    NEW_VERSION="${BVHOOK_NEW_MAJOR}.${BVHOOK_NEW_MINOR}.${BVHOOK_NEW_PATCH}"
+    if [ ! -z "$BVHOOK_NEW_PRE_L" ] && [ ! -z "$BVHOOK_NEW_PRE_N" ]; then
+        NEW_VERSION="${NEW_VERSION}-${BVHOOK_NEW_PRE_L}.${BVHOOK_NEW_PRE_N}"
+    fi
+    echo "Constructed new version: $NEW_VERSION"
+    cd java && mvn versions:set -DnewVersion=$NEW_VERSION && mvn versions:commit
+
+    # Check for any modified but unstaged pom.xml files
+    MODIFIED_POMS=$(git ls-files -m | grep pom.xml)
+    if [ ! -z "$MODIFIED_POMS" ]; then
+        echo "The following pom.xml files were modified but not staged. Adding them now:"
+        echo "$MODIFIED_POMS" | while read -r file; do
+            git add "$file"
+            echo "Added: $file"
+        done
+    fi
+    """,
+]
+
 [tool.bumpversion.parts.pre_l]
-values = ["beta", "final"]
 optional_value = "final"
+values = ["beta", "final"]
 
 [[tool.bumpversion.files]]
 filename = "node/package.json"
-search = "\"version\": \"{current_version}\","
 replace = "\"version\": \"{new_version}\","
+search = "\"version\": \"{current_version}\","
 
 [[tool.bumpversion.files]]
 filename = "nodejs/package.json"
-search = "\"version\": \"{current_version}\","
 replace = "\"version\": \"{new_version}\","
+search = "\"version\": \"{current_version}\","
 
 # nodejs binary packages
 [[tool.bumpversion.files]]
 glob = "nodejs/npm/*/package.json"
-search = "\"version\": \"{current_version}\","
 replace = "\"version\": \"{new_version}\","
+search = "\"version\": \"{current_version}\","
+
+# vectodb node binary packages
+[[tool.bumpversion.files]]
+glob = "node/package.json"
+replace = "\"@lancedb/vectordb-darwin-arm64\": \"{new_version}\""
+search = "\"@lancedb/vectordb-darwin-arm64\": \"{current_version}\""
+
+[[tool.bumpversion.files]]
+glob = "node/package.json"
+replace = "\"@lancedb/vectordb-darwin-x64\": \"{new_version}\""
+search = "\"@lancedb/vectordb-darwin-x64\": \"{current_version}\""
+
+[[tool.bumpversion.files]]
+glob = "node/package.json"
+replace = "\"@lancedb/vectordb-linux-arm64-gnu\": \"{new_version}\""
+search = "\"@lancedb/vectordb-linux-arm64-gnu\": \"{current_version}\""
+
+[[tool.bumpversion.files]]
+glob = "node/package.json"
+replace = "\"@lancedb/vectordb-linux-x64-gnu\": \"{new_version}\""
+search = "\"@lancedb/vectordb-linux-x64-gnu\": \"{current_version}\""
+
+[[tool.bumpversion.files]]
+glob = "node/package.json"
+replace = "\"@lancedb/vectordb-win32-x64-msvc\": \"{new_version}\""
+search = "\"@lancedb/vectordb-win32-x64-msvc\": \"{current_version}\""
 
 # Cargo files
 # ------------
 [[tool.bumpversion.files]]
 filename = "rust/ffi/node/Cargo.toml"
-search = "\nversion = \"{current_version}\""
 replace = "\nversion = \"{new_version}\""
+search = "\nversion = \"{current_version}\""
 
 [[tool.bumpversion.files]]
 filename = "rust/lancedb/Cargo.toml"
-search = "\nversion = \"{current_version}\""
 replace = "\nversion = \"{new_version}\""
+search = "\nversion = \"{current_version}\""
+
+[[tool.bumpversion.files]]
+filename = "nodejs/Cargo.toml"
+replace = "\nversion = \"{new_version}\""
+search = "\nversion = \"{current_version}\""

.github/workflows/docs_test.yml

@@ -24,15 +24,19 @@ env:
 jobs:
   test-python:
     name: Test doc python code
-    runs-on: "warp-ubuntu-latest-x64-4x"
+    runs-on: ubuntu-24.04
     steps:
     - name: Checkout
       uses: actions/checkout@v4
     - name: Print CPU capabilities
       run: cat /proc/cpuinfo
+    - name: Install protobuf
+      run: |
+        sudo apt update
+        sudo apt install -y protobuf-compiler
     - name: Install dependecies needed for ubuntu
       run: |
-        sudo apt install -y protobuf-compiler libssl-dev
+        sudo apt install -y libssl-dev
         rustup update && rustup default
     - name: Set up Python
       uses: actions/setup-python@v5
@@ -56,7 +60,7 @@ jobs:
         for d in *; do cd "$d"; echo "$d".py; python "$d".py; cd ..; done
   test-node:
     name: Test doc nodejs code
-    runs-on: "warp-ubuntu-latest-x64-4x"
+    runs-on: ubuntu-24.04
     timeout-minutes: 60
     strategy:
       fail-fast: false
@@ -72,9 +76,13 @@ jobs:
       uses: actions/setup-node@v4
       with:
         node-version: 20
+    - name: Install protobuf
+      run: |
+        sudo apt update
+        sudo apt install -y protobuf-compiler
     - name: Install dependecies needed for ubuntu
       run: |
-        sudo apt install -y protobuf-compiler libssl-dev
+        sudo apt install -y libssl-dev
         rustup update && rustup default
     - name: Rust cache
       uses: swatinem/rust-cache@v2

.github/workflows/java-publish.yml

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

.github/workflows/java.yml

@@ -3,6 +3,8 @@ on:
   push:
     branches:
       - main
+    paths:
+      - java/**
   pull_request:
     paths:
       - java/**
@@ -21,9 +23,42 @@ env:
   CARGO_INCREMENTAL: "0"
   CARGO_BUILD_JOBS: "1"
 jobs:
-  linux-build:
+  linux-build-java-11:
     runs-on: ubuntu-22.04
-    name: ubuntu-22.04 + Java 11 & 17
+    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
@@ -47,20 +82,12 @@ jobs:
           java-version: 17
           cache: "maven"
       - run: echo "JAVA_17=$JAVA_HOME" >> $GITHUB_ENV
-      - name: Install Java 11
-        uses: actions/setup-java@v4
-        with:
-          distribution: temurin
-          java-version: 11
-          cache: "maven"
       - name: Java Style Check
         run: mvn checkstyle:check
       # Disable because of issues in lancedb rust core code
       # - name: Rust Clippy
       #   working-directory: java/core/lancedb-jni
       #   run: cargo clippy --all-targets -- -D warnings
-      - name: Running tests with Java 11
-        run: mvn clean test
       - name: Running tests with Java 17
         run: |
           export JAVA_TOOL_OPTIONS="$JAVA_TOOL_OPTIONS \
@@ -83,3 +110,4 @@ jobs:
           -Djdk.reflect.useDirectMethodHandle=false \
           -Dio.netty.tryReflectionSetAccessible=true"
           JAVA_HOME=$JAVA_17 mvn clean test
+  

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

@@ -30,7 +30,7 @@ on:
         default: true
         type: boolean
       other:
-        description: 'Make a Node/Rust release'
+        description: 'Make a Node/Rust/Java release'
         required: true
         default: true
         type: boolean

.github/workflows/npm-publish.yml

@@ -7,6 +7,7 @@ on:
 
 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')
@@ -39,6 +40,7 @@ jobs:
             node/vectordb-*.tgz
 
   node-macos:
+    name: vectordb ${{ matrix.config.arch }}
     strategy:
       matrix:
         config:
@@ -69,6 +71,7 @@ jobs:
             node/dist/lancedb-vectordb-darwin*.tgz
 
   nodejs-macos:
+    name: lancedb ${{ matrix.config.arch }}
     strategy:
       matrix:
         config:
@@ -99,7 +102,7 @@ jobs:
             nodejs/dist/*.node
 
   node-linux:
-    name: node-linux (${{ matrix.config.arch}}-unknown-linux-gnu
+    name: vectordb (${{ matrix.config.arch}}-unknown-linux-gnu)
     runs-on: ${{ matrix.config.runner }}
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -139,7 +142,7 @@ jobs:
             node/dist/lancedb-vectordb-linux*.tgz
 
   nodejs-linux:
-    name: nodejs-linux (${{ matrix.config.arch}}-unknown-linux-gnu
+    name: lancedb (${{ matrix.config.arch}}-unknown-linux-gnu
     runs-on: ${{ matrix.config.runner }}
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -190,6 +193,7 @@ jobs:
             !nodejs/dist/*.node
 
   node-windows:
+    name: vectordb ${{ matrix.target }}
     runs-on: windows-2022
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -223,6 +227,7 @@ jobs:
             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')
@@ -256,6 +261,7 @@ jobs:
             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
@@ -284,8 +290,18 @@ jobs:
           for filename in *.tgz; do
             npm publish $PUBLISH_ARGS $filename
           done
+      - name: Notify Slack Action
+        uses: ravsamhq/notify-slack-action@2.3.0
+        if: ${{ always() }}
+        with:
+          status: ${{ job.status }}
+          notify_when: "failure"
+          notification_title: "{workflow} is failing"
+        env:
+          SLACK_WEBHOOK_URL: ${{ secrets.ACTION_MONITORING_SLACK }}
 
   release-nodejs:
+    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
@@ -333,6 +349,15 @@ jobs:
           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]

.github/workflows/python.yml

@@ -33,11 +33,11 @@ jobs:
           python-version: "3.11"
       - name: Install ruff
         run: |
-          pip install ruff==0.2.2
+          pip install ruff==0.5.4
       - name: Format check
         run: ruff format --check .
       - name: Lint
-        run: ruff .
+        run: ruff check .
   doctest:
     name: "Doctest"
     timeout-minutes: 30

.github/workflows/rust.yml

@@ -26,15 +26,14 @@ env:
 jobs:
   lint:
     timeout-minutes: 30
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-24.04
     defaults:
       run:
         shell: bash
-        working-directory: rust
     env:
       # Need up-to-date compilers for kernels
-      CC: gcc-12
-      CXX: g++-12
+      CC: clang-18
+      CXX: clang++-18
     steps:
     - uses: actions/checkout@v4
       with:
@@ -50,18 +49,21 @@ jobs:
     - name: Run format
       run: cargo fmt --all -- --check
     - name: Run clippy
-      run: cargo clippy --all --all-features -- -D warnings
+      run: cargo clippy --workspace --tests --all-features -- -D warnings
   linux:
     timeout-minutes: 30
-    runs-on: ubuntu-22.04
+    # To build all features, we need more disk space than is available
+    # on the free OSS github runner. This is mostly due to the the
+    # sentence-transformers feature.
+    runs-on: ubuntu-2404-4x-x64
     defaults:
       run:
         shell: bash
         working-directory: rust
     env:
       # Need up-to-date compilers for kernels
-      CC: gcc-12
-      CXX: g++-12
+      CC: clang-18
+      CXX: clang++-18
     steps:
     - uses: actions/checkout@v4
       with:
@@ -74,6 +76,12 @@ jobs:
       run: |
           sudo apt update
           sudo apt install -y protobuf-compiler libssl-dev
+    - name: Make Swap
+      run: |
+        sudo fallocate -l 16G /swapfile
+        sudo chmod 600 /swapfile
+        sudo mkswap /swapfile
+        sudo swapon /swapfile
     - name: Start S3 integration test environment
       working-directory: .
       run: docker compose up --detach --wait
@@ -131,4 +139,3 @@ jobs:
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
           cargo build
           cargo test
-  

Cargo.toml

@@ -18,34 +18,41 @@ repository = "https://github.com/lancedb/lancedb"
 description = "Serverless, low-latency vector database for AI applications"
 keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
+rust-version = "1.80.0" # TODO: lower this once we upgrade Lance again.
 
 [workspace.dependencies]
-lance = { "version" = "=0.14.1", "features" = ["dynamodb"] }
-lance-index = { "version" = "=0.14.1" }
-lance-linalg = { "version" = "=0.14.1" }
-lance-testing = { "version" = "=0.14.1" }
-lance-datafusion = { "version" = "=0.14.1" }
+lance = { "version" = "=0.19.1", "features" = ["dynamodb"] }
+lance-index = { "version" = "=0.19.1" }
+lance-linalg = { "version" = "=0.19.1" }
+lance-table = { "version" = "=0.19.1" }
+lance-testing = { "version" = "=0.19.1" }
+lance-datafusion = { "version" = "=0.19.1" }
+lance-encoding = { "version" = "=0.19.1" }
 # Note that this one does not include pyarrow
-arrow = { version = "51.0", optional = false }
-arrow-array = "51.0"
-arrow-data = "51.0"
-arrow-ipc = "51.0"
-arrow-ord = "51.0"
-arrow-schema = "51.0"
-arrow-arith = "51.0"
-arrow-cast = "51.0"
+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"
 async-trait = "0"
 chrono = "0.4.35"
-datafusion-physical-plan = "37.1"
+datafusion-common = "41.0"
+datafusion-physical-plan = "41.0"
+env_logger = "0.10"
 half = { "version" = "=2.4.1", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"
 log = "0.4"
-object_store = "0.9.0"
+moka = { version = "0.11", features = ["future"] }
+object_store = "0.10.2"
 pin-project = "1.0.7"
 snafu = "0.7.4"
 url = "2"
 num-traits = "0.2"
+rand = "0.8"
 regex = "1.10"
 lazy_static = "1"

README.md

@@ -7,8 +7,8 @@
 
 <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) 
+[![Blog](https://img.shields.io/badge/Blog-12100E?style=for-the-badge&logoColor=white)](https://blog.lancedb.com/)
+[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/zMM32dvNtd)
 [![Twitter](https://img.shields.io/badge/Twitter-%231DA1F2.svg?style=for-the-badge&logo=Twitter&logoColor=white)](https://twitter.com/lancedb)
 
 </p>
@@ -44,26 +44,24 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
 
 **Javascript**
 ```shell
-npm install vectordb
+npm install @lancedb/lancedb
 ```
 
 ```javascript
-const lancedb = require('vectordb');
-const db = await lancedb.connect('data/sample-lancedb');
+import * as lancedb from "@lancedb/lancedb";
 
-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 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 query = table.search([0.1, 0.3]).limit(2);
-const results = await query.execute();
+
+const query = table.vectorSearch([0.1, 0.3]).limit(2);
+const results = await query.toArray();
 
 // You can also search for rows by specific criteria without involving a vector search.
-const rowsByCriteria = await table.search(undefined).where("price >= 10").execute();
+const rowsByCriteria = await table.query().where("price >= 10").toArray();
 ```
 
 **Python**
@@ -84,4 +82,4 @@ 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://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb">Build a question and answer bot with LanceDB</a>
+* 🤖 <a href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Youtube-Search-QA-Bot">Build a question and answer bot with LanceDB</a>

ci/build_linux_artifacts.sh

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

ci/build_linux_artifacts_nodejs.sh

@@ -4,9 +4,9 @@ ARCH=${1:-x86_64}
 
 # We pass down the current user so that when we later mount the local files
 # into the container, the files are accessible by the current user.
-pushd ci/manylinux_nodejs
+pushd ci/manylinux_node
 docker build \
-    -t lancedb-nodejs-manylinux \
+    -t lancedb-node-manylinux-$ARCH \
     --build-arg="ARCH=$ARCH" \
     --build-arg="DOCKER_USER=$(id -u)" \
     --progress=plain \
@@ -17,5 +17,5 @@ popd
 docker run \
     -v $(pwd):/io -w /io \
     --memory-swap=-1 \
-    lancedb-nodejs-manylinux \
-    bash ci/manylinux_nodejs/build.sh $ARCH
+    lancedb-node-manylinux-$ARCH \
+    bash ci/manylinux_node/build_lancedb.sh $ARCH

ci/manylinux_node/Dockerfile

@@ -4,7 +4,7 @@
 # range of linux distributions.
 ARG ARCH=x86_64
 
-FROM quay.io/pypa/manylinux2014_${ARCH}
+FROM quay.io/pypa/manylinux_2_28_${ARCH}
 
 ARG ARCH=x86_64
 ARG DOCKER_USER=default_user
@@ -18,8 +18,8 @@ COPY install_protobuf.sh install_protobuf.sh
 RUN ./install_protobuf.sh ${ARCH}
 
 ENV DOCKER_USER=${DOCKER_USER}
-# Create a group and user
-RUN echo ${ARCH} && adduser --user-group --create-home --uid ${DOCKER_USER} build_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
 
 # We switch to the user to install Rust and Node, since those like to be 
 # installed at the user level.

ci/manylinux_node/install_openssl.sh

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

ci/manylinux_node/prepare_manylinux_node.sh

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

ci/manylinux_nodejs/Dockerfile

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

ci/manylinux_nodejs/install_openssl.sh

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

ci/manylinux_nodejs/install_protobuf.sh

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

ci/manylinux_nodejs/prepare_manylinux_node.sh

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

docs/mkdocs.yml

@@ -26,6 +26,7 @@ theme:
     - content.code.copy
     - content.tabs.link
     - content.action.edit
+    - content.tooltips
     - toc.follow
     - navigation.top
     - navigation.tabs
@@ -33,8 +34,10 @@ theme:
     - navigation.footer
     - navigation.tracking
     - navigation.instant
+    - content.footnote.tooltips
   icon:
     repo: fontawesome/brands/github
+    annotation: material/arrow-right-circle
   custom_dir: overrides
 
 plugins:
@@ -58,11 +61,16 @@ plugins:
             - https://pandas.pydata.org/docs/objects.inv
   - mkdocs-jupyter
   - render_swagger:
-      allow_arbitrary_locations : true
+      allow_arbitrary_locations: true
 
 markdown_extensions:
   - admonition
   - footnotes
+  - pymdownx.critic
+  - pymdownx.caret
+  - pymdownx.keys
+  - pymdownx.mark
+  - pymdownx.tilde
   - pymdownx.details
   - pymdownx.highlight:
       anchor_linenums: true
@@ -76,7 +84,15 @@ markdown_extensions:
   - pymdownx.tabbed:
       alternate_style: true
   - md_in_html
+  - abbr
   - attr_list
+  - pymdownx.snippets
+  - pymdownx.emoji:
+      emoji_index: !!python/name:material.extensions.emoji.twemoji
+      emoji_generator: !!python/name:material.extensions.emoji.to_svg
+  - markdown.extensions.toc:
+      baselevel: 1
+      permalink: ""
 
 nav:
   - Home:
@@ -84,26 +100,44 @@ nav:
       - 🏃🏼‍♂️ Quick start: basic.md
       - 📚 Concepts:
           - Vector search: concepts/vector_search.md
-          - Indexing: concepts/index_ivfpq.md
+          - Indexing:
+            - IVFPQ: concepts/index_ivfpq.md
+            - HNSW: concepts/index_hnsw.md
           - Storage: concepts/storage.md
           - Data management: concepts/data_management.md
       - 🔨 Guides:
           - Working with tables: guides/tables.md
-          - Building an ANN index: ann_indexes.md
+          - Building a vector index: ann_indexes.md
           - Vector Search: search.md
-          - Full-text search: fts.md
+          - Full-text search (native): fts.md
+          - Full-text search (tantivy-based): fts_tantivy.md
+          - Building a scalar index: guides/scalar_index.md
           - Hybrid search:
               - Overview: hybrid_search/hybrid_search.md
               - Comparing Rerankers: hybrid_search/eval.md
               - Airbnb financial data example: notebooks/hybrid_search.ipynb
+          - RAG:
+              - Vanilla RAG: rag/vanilla_rag.md
+              - Multi-head RAG: rag/multi_head_rag.md
+              - Corrective RAG: rag/corrective_rag.md
+              - Agentic RAG: rag/agentic_rag.md
+              - Graph RAG: rag/graph_rag.md
+              - Self RAG: rag/self_rag.md
+              - Adaptive RAG: rag/adaptive_rag.md
+              - SFR RAG: rag/sfr_rag.md
+              - Advanced Techniques:
+                - HyDE: rag/advanced_techniques/hyde.md
+                - FLARE: rag/advanced_techniques/flare.md
           - Reranking:
               - Quickstart: reranking/index.md
               - Cohere Reranker: reranking/cohere.md
               - Linear Combination Reranker: reranking/linear_combination.md
+              - Reciprocal Rank Fusion Reranker: reranking/rrf.md
               - Cross Encoder Reranker: reranking/cross_encoder.md
               - ColBERT Reranker: reranking/colbert.md
               - Jina Reranker: reranking/jina.md
               - OpenAI Reranker: reranking/openai.md
+              - AnswerDotAi Rerankers: reranking/answerdotai.md
               - Building Custom Rerankers: reranking/custom_reranker.md
               - Example: notebooks/lancedb_reranking.ipynb
           - Filtering: sql.md
@@ -115,9 +149,26 @@ nav:
               - Reranking: guides/tuning_retrievers/2_reranking.md
               - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
       - 🧬 Managing embeddings:
-          - Overview: embeddings/index.md
+          - Understand Embeddings: embeddings/understanding_embeddings.md
+          - Get Started: embeddings/index.md
           - Embedding functions: embeddings/embedding_functions.md
-          - Available models: embeddings/default_embedding_functions.md
+          - Available models:
+              - Overview: embeddings/default_embedding_functions.md
+              - Text Embedding Functions:
+                  - Sentence Transformers: embeddings/available_embedding_models/text_embedding_functions/sentence_transformers.md
+                  - Huggingface Embedding Models: embeddings/available_embedding_models/text_embedding_functions/huggingface_embedding.md
+                  - Ollama Embeddings: embeddings/available_embedding_models/text_embedding_functions/ollama_embedding.md
+                  - OpenAI Embeddings: embeddings/available_embedding_models/text_embedding_functions/openai_embedding.md
+                  - Instructor Embeddings: embeddings/available_embedding_models/text_embedding_functions/instructor_embedding.md
+                  - Gemini Embeddings: embeddings/available_embedding_models/text_embedding_functions/gemini_embedding.md
+                  - Cohere Embeddings: embeddings/available_embedding_models/text_embedding_functions/cohere_embedding.md
+                  - Jina Embeddings: embeddings/available_embedding_models/text_embedding_functions/jina_embedding.md
+                  - AWS Bedrock Text Embedding Functions: embeddings/available_embedding_models/text_embedding_functions/aws_bedrock_embedding.md
+                  - IBM watsonx.ai Embeddings: embeddings/available_embedding_models/text_embedding_functions/ibm_watsonx_ai_embedding.md
+              - Multimodal Embedding Functions:
+                  - OpenClip embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/openclip_embedding.md
+                  - Imagebind embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md
+                  - Jina Embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md
           - User-defined embedding functions: embeddings/custom_embedding_function.md
           - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
           - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
@@ -127,25 +178,32 @@ nav:
           - 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
+              - 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
+              - LlamaIndex docs: integrations/llamaIndex.md
+              - LlamaIndex demo: notebooks/llamaIndex_demo.ipynb
           - Pydantic: python/pydantic.md
           - Voxel51: integrations/voxel51.md
           - PromptTools: integrations/prompttools.md
+          - dlt: integrations/dlt.md
+          - phidata: integrations/phidata.md
       - 🎯 Examples:
           - Overview: examples/index.md
           - 🐍 Python:
               - Overview: examples/examples_python.md
-              - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
-              - 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
+              - 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
@@ -153,11 +211,14 @@ nav:
               - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
           - 🦀 Rust:
               - Overview: examples/examples_rust.md
+      - 📓 Studies:
+          - ↗Improve retrievers with hybrid search and reranking: https://blog.lancedb.com/hybrid-search-and-reranking-report/
       - 💭 FAQs: faq.md
+      - 🔍 Troubleshooting: troubleshooting.md
       - ⚙️ API reference:
           - 🐍 Python: python/python.md
           - 👾 JavaScript (vectordb): javascript/modules.md
-          - 👾 JavaScript (lancedb): javascript/modules.md
+          - 👾 JavaScript (lancedb): js/globals.md
           - 🦀 Rust: https://docs.rs/lancedb/latest/lancedb/
       - ☁️ LanceDB Cloud:
           - Overview: cloud/index.md
@@ -169,26 +230,44 @@ nav:
   - Quick start: basic.md
   - Concepts:
       - Vector search: concepts/vector_search.md
-      - Indexing: concepts/index_ivfpq.md
+      - Indexing:
+        - IVFPQ: concepts/index_ivfpq.md
+        - HNSW: concepts/index_hnsw.md
       - Storage: concepts/storage.md
       - Data management: concepts/data_management.md
   - Guides:
       - Working with tables: guides/tables.md
       - Building an ANN index: ann_indexes.md
       - Vector Search: search.md
-      - Full-text search: fts.md
+      - Full-text search (native): fts.md
+      - Full-text search (tantivy-based): fts_tantivy.md
+      - Building a scalar index: guides/scalar_index.md
       - Hybrid search:
           - Overview: hybrid_search/hybrid_search.md
           - Comparing Rerankers: hybrid_search/eval.md
           - Airbnb financial data example: notebooks/hybrid_search.ipynb
+      - RAG:
+          - Vanilla RAG: rag/vanilla_rag.md
+          - Multi-head RAG: rag/multi_head_rag.md
+          - Corrective RAG: rag/corrective_rag.md
+          - Agentic RAG: rag/agentic_rag.md
+          - Graph RAG: rag/graph_rag.md
+          - Self RAG: rag/self_rag.md
+          - Adaptive RAG: rag/adaptive_rag.md
+          - SFR RAG: rag/sfr_rag.md
+          - Advanced Techniques:
+            - HyDE: rag/advanced_techniques/hyde.md
+            - FLARE: rag/advanced_techniques/flare.md
       - Reranking:
           - Quickstart: reranking/index.md
           - Cohere Reranker: reranking/cohere.md
           - Linear Combination Reranker: reranking/linear_combination.md
+          - Reciprocal Rank Fusion Reranker: reranking/rrf.md
           - Cross Encoder Reranker: reranking/cross_encoder.md
           - ColBERT Reranker: reranking/colbert.md
           - Jina Reranker: reranking/jina.md
           - OpenAI Reranker: reranking/openai.md
+          - AnswerDotAi Rerankers: reranking/answerdotai.md
           - Building Custom Rerankers: reranking/custom_reranker.md
           - Example: notebooks/lancedb_reranking.ipynb
       - Filtering: sql.md
@@ -200,9 +279,26 @@ nav:
           - Reranking: guides/tuning_retrievers/2_reranking.md
           - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
   - Managing Embeddings:
-      - Overview: embeddings/index.md
+      - Understand Embeddings: embeddings/understanding_embeddings.md
+      - Get Started: embeddings/index.md
       - Embedding functions: embeddings/embedding_functions.md
-      - Available models: embeddings/default_embedding_functions.md
+      - Available models:
+          - Overview: embeddings/default_embedding_functions.md
+          - Text Embedding Functions:
+              - Sentence Transformers: embeddings/available_embedding_models/text_embedding_functions/sentence_transformers.md
+              - Huggingface Embedding Models: embeddings/available_embedding_models/text_embedding_functions/huggingface_embedding.md
+              - Ollama Embeddings: embeddings/available_embedding_models/text_embedding_functions/ollama_embedding.md
+              - OpenAI Embeddings: embeddings/available_embedding_models/text_embedding_functions/openai_embedding.md
+              - Instructor Embeddings: embeddings/available_embedding_models/text_embedding_functions/instructor_embedding.md
+              - Gemini Embeddings: embeddings/available_embedding_models/text_embedding_functions/gemini_embedding.md
+              - Cohere Embeddings: embeddings/available_embedding_models/text_embedding_functions/cohere_embedding.md
+              - Jina Embeddings: embeddings/available_embedding_models/text_embedding_functions/jina_embedding.md
+              - AWS Bedrock Text Embedding Functions: embeddings/available_embedding_models/text_embedding_functions/aws_bedrock_embedding.md
+              - IBM watsonx.ai Embeddings: embeddings/available_embedding_models/text_embedding_functions/ibm_watsonx_ai_embedding.md
+          - Multimodal Embedding Functions:
+              - OpenClip embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/openclip_embedding.md
+              - Imagebind embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md
+              - Jina Embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md
       - User-defined embedding functions: embeddings/custom_embedding_function.md
       - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
       - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
@@ -217,21 +313,38 @@ nav:
       - Pydantic: python/pydantic.md
       - Voxel51: integrations/voxel51.md
       - PromptTools: integrations/prompttools.md
+      - dlt: integrations/dlt.md
+      - phidata: integrations/phidata.md
   - Examples:
       - examples/index.md
-      - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
-      - 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
+      - 🐍 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/modules.md
+      - Javascript (lancedb): js/globals.md
       - Rust: https://docs.rs/lancedb/latest/lancedb/index.html
   - LanceDB Cloud:
       - Overview: cloud/index.md

docs/requirements.txt

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

docs/src/api_reference.md

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

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+</svg>

docs/src/assets/python.svg

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

@@ -35,6 +35,15 @@
             }
             })
             ```
+
+        !!! 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
@@ -53,6 +62,15 @@
             }
             })
             ```
+
+        !!! note "Yarn users"
+
+            Unlike other package managers, Yarn does not automatically resolve peer dependencies. If you are using Yarn, you will need to manually install 'apache-arrow':
+
+            ```shell
+            yarn add apache-arrow
+            ```
+
 === "Rust"
 
     ```shell
@@ -554,7 +572,7 @@ You can use the embedding API when working with embedding models. It automatical
     --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/).
+Learn about using the existing integrations and creating custom embedding functions in the [embedding API guide](./embeddings/index.md).
 
 
 ## What's next

docs/src/basic_legacy.ts

@@ -1,6 +1,14 @@
 // --8<-- [start:import]
 import * as lancedb from "vectordb";
-import { Schema, Field, Float32, FixedSizeList, Int32, Float16 } from "apache-arrow";
+import {
+  Schema,
+  Field,
+  Float32,
+  FixedSizeList,
+  Int32,
+  Float16,
+} from "apache-arrow";
+import * as arrow from "apache-arrow";
 // --8<-- [end:import]
 import * as fs from "fs";
 import { Table as ArrowTable, Utf8 } from "apache-arrow";
@@ -20,10 +28,33 @@ const example = async () => {
       { vector: [3.1, 4.1], item: "foo", price: 10.0 },
       { vector: [5.9, 26.5], item: "bar", price: 20.0 },
     ],
-    { writeMode: lancedb.WriteMode.Overwrite }
+    { writeMode: lancedb.WriteMode.Overwrite },
   );
   // --8<-- [end:create_table]
-
+  {
+    // --8<-- [start:create_table_with_schema]
+    const schema = new arrow.Schema([
+      new arrow.Field(
+        "vector",
+        new arrow.FixedSizeList(
+          2,
+          new arrow.Field("item", new arrow.Float32(), true),
+        ),
+      ),
+      new arrow.Field("item", new arrow.Utf8(), true),
+      new arrow.Field("price", new arrow.Float32(), true),
+    ]);
+    const data = [
+      { vector: [3.1, 4.1], item: "foo", price: 10.0 },
+      { vector: [5.9, 26.5], item: "bar", price: 20.0 },
+    ];
+    const tbl = await db.createTable({
+      name: "myTableWithSchema",
+      data,
+      schema,
+    });
+    // --8<-- [end:create_table_with_schema]
+  }
 
   // --8<-- [start:add]
   const newData = Array.from({ length: 500 }, (_, i) => ({
@@ -43,33 +74,35 @@ const example = async () => {
   // --8<-- [end:create_index]
 
   // --8<-- [start:create_empty_table]
-  const schema = new Schema([
-    new Field("id", new Int32()),
-    new Field("name", new Utf8()),
+  const schema = new arrow.Schema([
+    new arrow.Field("id", new arrow.Int32()),
+    new arrow.Field("name", new arrow.Utf8()),
   ]);
+
   const empty_tbl = await db.createTable({ name: "empty_table", schema });
   // --8<-- [end:create_empty_table]
-
-  // --8<-- [start:create_f16_table]
-  const dim = 16
-  const total = 10
-  const f16_schema = new Schema([
-      new Field('id', new Int32()),
+  {
+    // --8<-- [start:create_f16_table]
+    const dim = 16;
+    const total = 10;
+    const 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),
       })),
-      { f16_schema }
-    )
-  const table = await db.createTable('f16_tbl', data)
-  // --8<-- [end:create_f16_table]
+      { 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/concepts/index_hnsw.md

@@ -0,0 +1,92 @@
+
+# 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

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

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

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

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

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

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

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

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

@@ -0,0 +1,63 @@
+# Cohere Embeddings
+
+Using cohere API requires cohere package, which can be installed using `pip install cohere`. Cohere embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
+You also need to set the `COHERE_API_KEY` environment variable to use the Cohere API.
+
+Supported models are:
+
+- embed-english-v3.0
+- embed-multilingual-v3.0
+- embed-english-light-v3.0
+- embed-multilingual-light-v3.0
+- embed-english-v2.0
+- embed-english-light-v2.0
+- embed-multilingual-v2.0
+
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|--------|---------|
+| `name` | `str` | `"embed-english-v2.0"` | The model ID of the cohere model to use. Supported base models for Text Embeddings: embed-english-v3.0, embed-multilingual-v3.0, embed-english-light-v3.0, embed-multilingual-light-v3.0, embed-english-v2.0, embed-english-light-v2.0, embed-multilingual-v2.0 |
+| `source_input_type` | `str` | `"search_document"` | The type of input data to be used for the source column. |
+| `query_input_type` | `str` | `"search_query"` | The type of input data to be used for the query. |
+
+Cohere supports following input types:
+
+| Input Type               | Description                          |
+|-------------------------|---------------------------------------|
+| "`search_document`"     | Used for embeddings stored in a vector|
+|                         | database for search use-cases.        |
+| "`search_query`"        | Used for embeddings of search queries |
+|                         | run against a vector DB               |
+| "`semantic_similarity`" | Specifies the given text will be used |
+|                         | for Semantic Textual Similarity (STS) |
+| "`classification`"      | Used for embeddings passed through a  |
+|                         | text classifier.                      |
+| "`clustering`"          | Used for the embeddings run through a |
+|                         | clustering algorithm                  |
+
+Usage Example:
+    
+```python
+    import lancedb
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+
+    cohere = EmbeddingFunctionRegistry
+        .get_instance()
+        .get("cohere")
+        .create(name="embed-multilingual-v2.0")
+
+    class TextModel(LanceModel):
+        text: str = cohere.SourceField()
+        vector: Vector(cohere.ndims()) =  cohere.VectorField()
+
+    data = [ { "text": "hello world" },
+            { "text": "goodbye world" }]
+
+    db = lancedb.connect("~/.lancedb")
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(data)
+```

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

docs/src/embeddings/custom_embedding_function.md

@@ -15,198 +15,226 @@ There is another optional layer of abstraction available: `TextEmbeddingFunction
 
 Let's implement `SentenceTransformerEmbeddings` class. All you need to do is implement the `generate_embeddings()` and `ndims` function to handle the input types you expect and register the class in the global `EmbeddingFunctionRegistry`
 
-```python
-from lancedb.embeddings import register
-from lancedb.util import attempt_import_or_raise
 
-@register("sentence-transformers")
-class SentenceTransformerEmbeddings(TextEmbeddingFunction):
-    name: str = "all-MiniLM-L6-v2"
-    # set more default instance vars like device, etc.
+=== "Python"
 
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._ndims = None
-    
-    def generate_embeddings(self, texts):
-        return self._embedding_model().encode(list(texts), ...).tolist()
+    ```python
+    from lancedb.embeddings import register
+    from lancedb.util import attempt_import_or_raise
 
-    def ndims(self):
-        if self._ndims is None:
-            self._ndims = len(self.generate_embeddings("foo")[0])
-        return self._ndims
+    @register("sentence-transformers")
+    class SentenceTransformerEmbeddings(TextEmbeddingFunction):
+        name: str = "all-MiniLM-L6-v2"
+        # set more default instance vars like device, etc.
 
-    @cached(cache={}) 
-    def _embedding_model(self):
-        return sentence_transformers.SentenceTransformer(name)
-```
+        def __init__(self, **kwargs):
+            super().__init__(**kwargs)
+            self._ndims = None
 
-This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and defaul settings.
+        def generate_embeddings(self, texts):
+            return self._embedding_model().encode(list(texts), ...).tolist()
+
+        def ndims(self):
+            if self._ndims is None:
+                self._ndims = len(self.generate_embeddings("foo")[0])
+            return self._ndims
+
+        @cached(cache={})
+        def _embedding_model(self):
+            return sentence_transformers.SentenceTransformer(name)
+    ```
+
+=== "TypeScript"
+
+    ```ts
+    --8<--- "nodejs/examples/custom_embedding_function.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.
 
 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
-from lancedb.pydantic import LanceModel, Vector
+=== "Python"
 
-registry = EmbeddingFunctionRegistry.get_instance()
-stransformer = registry.get("sentence-transformers").create()
+    ```python
+    from lancedb.pydantic import LanceModel, Vector
 
-class TextModelSchema(LanceModel):
-    vector: Vector(stransformer.ndims) = stransformer.VectorField()
-    text: str = stransformer.SourceField()
+    registry = EmbeddingFunctionRegistry.get_instance()
+    stransformer = registry.get("sentence-transformers").create()
 
-tbl = db.create_table("table", schema=TextModelSchema)
+    class TextModelSchema(LanceModel):
+        vector: Vector(stransformer.ndims) = stransformer.VectorField()
+        text: str = stransformer.SourceField()
 
-tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
-result = tbl.search("world").limit(5)
-```
+    tbl = db.create_table("table", schema=TextModelSchema)
 
-NOTE:
+    tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
+    result = tbl.search("world").limit(5)
+    ```
 
-You can always implement the `EmbeddingFunction` interface directly if you want or need to, `TextEmbeddingFunction` just makes it much simpler and faster for you to do so, by setting up the boiler plat for text-specific use case
+=== "TypeScript"
+
+    ```ts
+    --8<--- "nodejs/examples/custom_embedding_function.ts:call_custom_function"
+    ```
+
+!!! note
+
+    You can always implement the `EmbeddingFunction` interface directly if you want or need to, `TextEmbeddingFunction` just makes it much simpler and faster for you to do so, by setting up the boiler plat for text-specific use case
 
 ## Multi-modal embedding function example
-You can also use the `EmbeddingFunction` interface to implement more complex workflows such as multi-modal embedding function support. LanceDB implements `OpenClipEmeddingFunction` class that suppports multi-modal seach. Here's the implementation that you can use as a reference to build your own multi-modal embedding functions.
+You can also use the `EmbeddingFunction` interface to implement more complex workflows such as multi-modal embedding function support.
 
-```python
-@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()
+=== "Python"
 
-    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
+    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 ndims(self):
-        if self._ndims is None:
-            self._ndims = self.generate_text_embeddings("foo").shape[0]
-        return self._ndims
+    ```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 compute_query_embeddings(
-        self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
-    ) -> List[np.ndarray]:
-        """
-        Compute the embeddings for a given user query
+        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
 
-        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:
+        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]):
             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")
+            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 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))
+        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:
-                text_features /= text_features.norm(dim=-1, keepdim=True)
-            return text_features.cpu().numpy().squeeze()
+                image_features /= image_features.norm(dim=-1, keepdim=True)
+            return image_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
+=== "TypeScript"
 
-    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()
-```
+    Coming Soon! See this [issue](https://github.com/lancedb/lancedb/issues/1482) to track the status!

docs/src/embeddings/default_embedding_functions.md

@@ -1,722 +1,84 @@
-There are various embedding functions available out of the box with LanceDB to manage your embeddings implicitly. We're actively working on adding other popular embedding APIs and models.
+# 📚 Available Embedding Models
 
-## Text embedding functions
-Contains the text embedding functions registered by default.
+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. 🚀
 
-* 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.
+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: 
 
-### 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 |
-
-
-??? "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)
+!!! example "Example usage"
     ```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.
-
-
-### 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)
-```
-
-
-### 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)
-```
-
-
-### 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)
-```
-
-### 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)
-```
-
-### 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()
-```
-
-### 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)
+    model = get_registry()
+              .get("openai")
+              .create(name="text-embedding-ada-002")
     ```
 
-### 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:
-
+Now let's understand the above syntax: 
 ```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)
+model = get_registry().get("model_id").create(...params)
 ```
+**This👆 line effectively creates a configured instance of an `embedding function` with `model` of choice that is ready for use.**
 
-### 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
-```
+- `get_registry()` :  This function call returns an instance of a `EmbeddingFunctionRegistry` object. This registry manages the registration and retrieval of embedding functions.
 
-Supported Embedding modelIDs are:
-* `amazon.titan-embed-text-v1`
-* `cohere.embed-english-v3`
-* `cohere.embed-multilingual-v3`
+- `.get("model_id")` : This method call on the registry object and retrieves the **embedding models functions** associated with the `"model_id"` (1) .
+    { .annotate }
 
-Supported parameters (to be passed in `create` method) are:
+    1.  Hover over the names in table below to find out the `model_id` of different embedding functions.
 
-| 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. |
+- `.create(...params)` : This method call is on the object returned by the `get` method. It instantiates an embedding model function using the **specified parameters**. 
 
-Usage Example:
+??? 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**.
 
-```python
-import lancedb
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
+!!! 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.
 
-model = get_registry().get("bedrock-text").create()
+## Text Embedding Functions 📝 
+These functions are registered by default to handle text embeddings.
 
-class TextModel(LanceModel):
-    text: str = model.SourceField()
-    vector: Vector(model.ndims()) = model.VectorField()
+- 🔄 **Embedding functions** have an inbuilt rate limit handler wrapper for source and query embedding function calls that retry with **exponential backoff**. 
 
-df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
-db = lancedb.connect("tmp_path")
-tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+- 🌕 Each `EmbeddingFunction` implementation automatically takes `max_retries` as an argument which has the default value of 7. 
 
-tbl.add(df)
-rs = tbl.search("hello").limit(1).to_pandas()
-```
+🌟 **Available Text Embeddings**
 
-## Multi-modal embedding functions
-Multi-modal embedding functions allow you to query your table using both images and text.
-
-### OpenClip embeddings
-We support CLIP model embeddings using the open source alternative, [open-clip](https://github.com/mlfoundations/open_clip) which supports various customizations. It is registered as `open-clip` and supports the following customizations:
-
-| Parameter | Type | Default Value | Description |
-|---|---|---|---|
-| `name` | `str` | `"ViT-B-32"` | The name of the model. |
-| `pretrained` | `str` | `"laion2b_s34b_b79k"` | The name of the pretrained model to load. |
-| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
-| `batch_size` | `int` | `64` | The number of images to process in a batch. |
-| `normalize` | `bool` | `True` | Whether to normalize the input images before feeding them to the model. |
-
-This embedding function supports ingesting images as both bytes and urls. You can query them using both test and other images.
-
-!!! info
-    LanceDB supports ingesting images directly from accessible links.
-
-```python
-import lancedb
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
-
-db = lancedb.connect(tmp_path)
-func = get_registry.get("open-clip").create()
-
-class Images(LanceModel):
-    label: str
-    image_uri: str = func.SourceField() # image uri as the source
-    image_bytes: bytes = func.SourceField() # image bytes as the source
-    vector: Vector(func.ndims()) = func.VectorField() # vector column 
-    vec_from_bytes: Vector(func.ndims()) = func.VectorField() # Another vector column 
-
-table = db.create_table("images", schema=Images)
-labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
-uris = [
-    "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
-    "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
-    "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
-    "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
-    "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
-    "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
-]
-# get each uri as bytes
-image_bytes = [requests.get(uri).content for uri in uris]
-table.add(
-    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)
-
-```
-
-### 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).
-
-### Jina Embeddings
-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()
+| **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) |
 
 
-    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
+
+[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"
 
 
-    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})
-    )
-```
+## Multi-modal Embedding Functions🖼️ 
+
+Multi-modal embedding functions allow you to query your table using both images and text. 💬🖼️
+
+🌐 **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) |
+
+!!! 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).

docs/src/embeddings/embedding_functions.md

@@ -2,8 +2,8 @@ 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 "LanceDB cloud doesn't support embedding functions yet"
-    LanceDB Cloud does not support embedding functions yet. You need to generate embeddings before ingesting into the table or querying.
+!!! 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.

docs/src/embeddings/index.md

@@ -99,28 +99,28 @@ LanceDB registers the Sentence Transformers embeddings function in the registry
 
     Coming Soon!
 
-### Jina Embeddings
-
-LanceDB registers the JinaAI embeddings function in the registry as `jina`. You can pass any supported model name to the `create`. By default it uses `"jina-clip-v1"`.
-`jina-clip-v1` can handle both text and images and other models only support `text`.
-
-You need to pass `JINA_API_KEY` in the environment variable or pass it as `api_key` to `create` method.
+### 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['JINA_API_KEY'] = "jina_*"
+os.environ['OPENAI_API_KEY'] = "..."
 
-db = lancedb.connect("/tmp/db")
-func = get_registry().get("jina").create(name="jina-clip-v1")
+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, mode="overwrite")
+table = db.create_table("words", schema=Words)
 table.add(
     [
         {"text": "hello world"},

docs/src/embeddings/understanding_embeddings.md

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

docs/src/examples/examples_python.md

@@ -1,17 +1,22 @@
-# Examples: Python
+# Overview : Python Examples 
 
-To help you get started, we provide some examples, projects and applications that use the LanceDB Python API. You can always find the latest examples in our [VectorDB Recipes](https://github.com/lancedb/vectordb-recipes) repository.
+To help you get started, we provide some examples, projects, and applications that use the LanceDB Python API. These examples are designed to get you right into the code with minimal introduction, enabling you to move from an idea to a proof of concept in minutes. 
 
-| Example | Interactive Envs | Scripts  |
-|-------- | ---------------- | ------   |
-| | | |
-| [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> |  |
+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. |
 

docs/src/examples/index.md

@@ -8,9 +8,15 @@ LanceDB provides language APIs, allowing you to embed a database in your languag
 * 👾 [JavaScript](examples_js.md) examples
 * 🦀 Rust examples (coming soon)
 
-## Applications powered by LanceDB
+## Python Applications powered by LanceDB
 
-| Project Name                                        | Description                                                                                                          | Screenshot                                |
-|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
-| [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)    |
+| 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.. |
+
+## Nodejs Applications powered by LanceDB
+
+| Project Name | Description |
+| --- | --- |
+| **Langchain Writing Assistant✍️ **<br>[![Github](../assets/github.svg)](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/lanchain_writing_assistant) |  - **📂 Data Source Integration**:  Use your own data by specifying data source file, and the app instantly processes it to provide insights. <br>- **🧠 Intelligent Suggestions**:  Powered by LangChain.js and LanceDB, it improves writing productivity and accuracy.  <br>- **💡 Enhanced Writing Experience**: It delivers real-time contextual insights and factual suggestions while the user writes. |

docs/src/examples/python_examples/aiagent.md

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

docs/src/examples/python_examples/build_from_scratch.md

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

docs/src/examples/python_examples/chatbot.md

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

docs/src/examples/python_examples/evaluations.md

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

docs/src/examples/python_examples/multimodal.md

@@ -0,0 +1,28 @@
+# **Multimodal Search with LanceDB 🤹‍♂️🔍**
+
+Using LanceDB's multimodal capabilities, combine text and image queries to find the most relevant results in your corpus ! 🔓💡
+
+**Explore the Future of Search 🚀**
+
+LanceDB supports multimodal search by indexing and querying vector representations of text and image data 🤖. This enables efficient retrieval of relevant documents and images using vector-based similarity search 📊. The platform facilitates cross-modal search, allowing for text-image and image-text retrieval, and supports scalable indexing of high-dimensional vector spaces 💻.
+
+
+
+| **Multimodal**  | **Description**  | **Links**  |
+|:----------------|:-----------------|:-----------|
+| **Multimodal CLIP: DiffusionDB 🌐💥**    | Multi-Modal Search with **CLIP** and **LanceDB** Using **DiffusionDB** Data for Combined Text and Image Understanding ! 🔓 | [![GitHub](../../assets/github.svg)][Clip_diffusionDB_github] <br>[![Open In Collab](../../assets/colab.svg)][Clip_diffusionDB_colab] <br>[![Python](../../assets/python.svg)][Clip_diffusionDB_python] <br>[![Ghost](../../assets/ghost.svg)][Clip_diffusionDB_ghost] |
+| **Multimodal CLIP: Youtube Videos 📹👀** | Search **Youtube videos** using Multimodal CLIP, finding relevant content with ease and accuracy! 🎯                                                                                                                                                                | [![Github](../../assets/github.svg)][Clip_youtube_github]                                                                                                    <br>[![Open In Collab](../../assets/colab.svg)][Clip_youtube_colab]                                                                                           <br> [![Python](../../assets/python.svg)][Clip_youtube_python]         <br>[![Ghost](../../assets/ghost.svg)][Clip_youtube_python] |
+| **Multimodal Image + Text Search 📸🔍**               | Find **relevant documents** and **images** with a single query using **LanceDB's** multimodal search capabilities, to seamlessly integrate text and visuals ! 🌉 | [![GitHub](../../assets/github.svg)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/multimodal_search) <br>[![Open In Collab](../../assets/colab.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/multimodal_search/main.ipynb) <br> [![Python](../../assets/python.svg)](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.py)<br> [![Ghost](../../assets/ghost.svg)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/) |
+| **Cambrian-1: Vision-Centric Image Exploration 🔍👀** | Learn how **Cambrian-1** works, using an example of **Vision-Centric** exploration on images found through vector search ! Work on **Flickr-8k** dataset 🔎                        | [![Kaggle](https://img.shields.io/badge/Kaggle-035a7d?style=for-the-badge&logo=kaggle&logoColor=white)](https://www.kaggle.com/code/prasantdixit/cambrian-1-vision-centric-exploration-of-images/)<br> [![Ghost](../../assets/ghost.svg)](https://blog.lancedb.com/cambrian-1-vision-centric-exploration/)                                                                                                                                                                                                                   |
+
+
+[Clip_diffusionDB_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip_diffusiondb
+[Clip_diffusionDB_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip_diffusiondb/main.ipynb
+[Clip_diffusionDB_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip_diffusiondb/main.py
+[Clip_diffusionDB_ghost]: https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/
+
+
+[Clip_youtube_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search
+[Clip_youtube_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search/main.ipynb
+[Clip_youtube_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search/main.py
+[Clip_youtube_ghost]: https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/

docs/src/examples/python_examples/rag.md

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

docs/src/examples/python_examples/recommendersystem.md

@@ -0,0 +1,37 @@
+**Recommender Systems: Personalized Discovery🍿📺**
+==============================================================
+Deliver personalized experiences with Recommender Systems. 🎁
+
+**Technical Overview📜**
+
+🔍️ LanceDB's powerful vector database capabilities can efficiently store and query item embeddings. Recommender Systems can utilize it and provide personalized recommendations based on user preferences 🤝 and item features 📊 and therefore enhance the user experience.🗂️ 
+
+| **Recommender System** | **Description** | **Links** |
+| ---------------------- | --------------- | --------- |
+| **Movie Recommender System🎬** | 🤝 Use **collaborative filtering** to predict user preferences, assuming similar users will like similar movies, and leverage **Singular Value Decomposition** (SVD) from Numpy for precise matrix factorization and accurate recommendations📊 | [![Github](../../assets/github.svg)][movie_github] <br>[![Open In Collab](../../assets/colab.svg)][movie_colab] <br>[![Python](../../assets/python.svg)][movie_python] |
+| **🎥 Movie Recommendation with Genres** | 🔍 Creates movie embeddings using **Doc2Vec**, capturing genre and characteristic nuances, and leverages VectorDB for efficient storage and querying, enabling accurate genre classification and personalized movie recommendations through **similarity searches**🎥 | [![Github](../../assets/github.svg)][genre_github] <br>[![Open In Collab](../../assets/colab.svg)][genre_colab] <br>[![Ghost](../../assets/ghost.svg)][genre_ghost] |
+| **🛍️ Product Recommender using Collaborative Filtering and LanceDB** | 📈 Using **Collaborative Filtering** and **LanceDB** to analyze your past purchases, recommends products based on user's past purchases. Demonstrated with the Instacart dataset in our example🛒 | [![Github](../../assets/github.svg)][product_github] <br>[![Open In Collab](../../assets/colab.svg)][product_colab] <br>[![Python](../../assets/python.svg)][product_python] |
+| **🔍 Arxiv Search with OpenCLIP and LanceDB** | 💡 Build a semantic search engine for **Arxiv papers** using **LanceDB**, and benchmarks its performance against traditional keyword-based search on **Nomic's Atlas**, to demonstrate the power of semantic search in finding relevant research papers📚 | [![Github](../../assets/github.svg)][arxiv_github] <br>[![Open In Collab](../../assets/colab.svg)][arxiv_colab] <br>[![Python](../../assets/python.svg)][arxiv_python] |
+| **Food Recommendation System🍴** | 🍔 Build a food recommendation system with **LanceDB**, featuring vector-based recommendations, full-text search, hybrid search, and reranking model integration for personalized and accurate food suggestions👌 | [![Github](../../assets/github.svg)][food_github] <br>[![Open In Collab](../../assets/colab.svg)][food_colab] |
+
+[movie_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/movie-recommender
+[movie_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.ipynb
+[movie_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.py
+
+
+[genre_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/movie-recommendation-with-genres
+[genre_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb
+[genre_ghost]: https://blog.lancedb.com/movie-recommendation-system-using-lancedb-and-doc2vec/
+
+[product_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/product-recommender
+[product_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/product-recommender/main.ipynb
+[product_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/product-recommender/main.py
+
+
+[arxiv_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/arxiv-recommender
+[arxiv_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/arxiv-recommender/main.ipynb
+[arxiv_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/arxiv-recommender/main.py
+ 
+
+[food_github]: https://github.com/lancedb/vectordb-recipes/tree/main/examples/archived_examples/Food_recommendation
+[food_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/archived_examples/Food_recommendation/main.ipynb

docs/src/examples/python_examples/vector_search.md

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

docs/src/examples/serverless_lancedb_with_s3_and_lambda.md

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

docs/src/fts.md

@@ -1,173 +1,162 @@
-# Full-text search
+# Full-text search (Native FTS)
 
-LanceDB provides support for full-text search via [Tantivy](https://github.com/quickwit-oss/tantivy) (currently Python only), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions. Our goal is to push the FTS integration down to the Rust level in the future, so that it's available for Rust and JavaScript users as well.  Follow along at [this Github issue](https://github.com/lancedb/lance/issues/1195)
+LanceDB provides support for full-text search via Lance, allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
 
-
-## Installation
-
-To use full-text search, install the dependency [`tantivy-py`](https://github.com/quickwit-oss/tantivy-py):
-
-```sh
-# Say you want to use tantivy==0.20.1
-pip install tantivy==0.20.1
-```
+!!! note
+    The Python SDK uses tantivy-based FTS by default, need to pass `use_tantivy=False` to use native FTS.
 
 ## Example
 
-Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search.
+Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search, the FTS index must be created before you can search via keywords.
 
-```python
-import lancedb
+=== "Python"
 
-uri = "data/sample-lancedb"
-db = lancedb.connect(uri)
+    ```python
+    import lancedb
 
-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"},
-    ],
-)
-```
+    uri = "data/sample-lancedb"
+    db = lancedb.connect(uri)
 
-## Create FTS index on single column
+    table = db.create_table(
+        "my_table",
+        data=[
+            {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
+            {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
+        ],
+    )
 
-The FTS index must be created before you can search via keywords.
+    # passing `use_tantivy=False` to use lance FTS index
+    # `use_tantivy=True` by default
+    table.create_fts_index("text", use_tantivy=False)
+    table.search("puppy").limit(10).select(["text"]).to_list()
+    # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
+    # ...
+    ```
 
-```python
-table.create_fts_index("text")
-```
+=== "TypeScript"
 
-To search an FTS index via keywords, LanceDB's `table.search` accepts a string as input:
+    ```typescript
+    import * as lancedb from "@lancedb/lancedb";
+    const uri = "data/sample-lancedb"
+    const db = await lancedb.connect(uri);
 
-```python
-table.search("puppy").limit(10).select(["text"]).to_list()
-```
+    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(),
+    });
 
-This returns the result as a list of dictionaries as follows.
+    await tbl
+        .search("puppy", queryType="fts")
+        .select(["text"])
+        .limit(10)
+        .toArray();
+    ```
 
-```python
-[{'text': 'Frodo was a happy puppy', 'score': 0.6931471824645996}]
-```
+=== "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.
+
+Passing `fts_columns="text"` if you want to specify the columns to search.
 
 !!! note
     LanceDB automatically searches on the existing FTS index if the input to the search is of type `str`. If you provide a vector as input, LanceDB will search the ANN index instead.
 
 ## 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".
+By default the text is tokenized by splitting on punctuation and whitespaces, and would filter out words that are with length greater than 40, and lowercase all words.
 
+Stemming is useful for improving search results by reducing words to their root form, e.g. "running" to "run". LanceDB supports stemming for multiple languages, you can specify the tokenizer name to enable stemming by the pattern `tokenizer_name="{language_code}_stem"`, e.g. `en_stem` for English.
+
+For example, to enable stemming for English:
 ```python
-table.create_fts_index("text", tokenizer_name="en_stem")
+table.create_fts_index("text", use_tantivy=True, tokenizer_name="en_stem")
 ```
 
-The following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.
+the following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.
 
+The tokenizer is customizable, you can specify how the tokenizer splits the text, and how it filters out words, etc.
 
-## Index multiple columns
-
-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`:
-
+For example, for language with accents, you can specify the tokenizer to use `ascii_folding` to remove accents, e.g. 'é' to 'e':
 ```python
-table.create_fts_index(["text1", "text2"])
+table.create_fts_index("text",
+                        use_tantivy=False,
+                        language="French",
+                        stem=True,
+                        ascii_folding=True)
 ```
 
-Note that the search API call does not change - you can search over all indexed columns at once.
-
 ## Filtering
 
-Currently the LanceDB full text search feature supports *post-filtering*, meaning filters are
-applied on top of the full text search results. This can be invoked via the familiar
-`where` syntax:
+LanceDB full text search supports to filter the search results by a condition, both pre-filtering and post-filtering are supported.
 
-```python
-table.search("puppy").limit(10).where("meta='foo'").to_list()
-```
+This can be invoked via the familiar `where` syntax:
 
-## Sorting
+=== "Python"
 
-You can pre-sort the documents by specifying `ordering_field_names` when
-creating the full-text search index. Once pre-sorted, you can then specify
-`ordering_field_name` while searching to return results sorted by the given
-field. For example, 
+    ```python
+    table.search("puppy").limit(10).where("meta='foo'").to_list()
+    ```
 
-```
-table.create_fts_index(["text_field"], ordering_field_names=["sort_by_field"])
+=== "TypeScript"
 
-(table.search("terms", ordering_field_name="sort_by_field")
- .limit(20)
- .to_list())
-```
+    ```typescript
+    await tbl
+    .search("apple")
+    .select(["id", "doc"])
+    .limit(10)
+    .where("meta='foo'")
+    .toArray();
+    ```
 
-!!! note
-    If you wish to specify an ordering field at query time, you must also
-    have specified it during indexing time. Otherwise at query time, an
-    error will be raised that looks like `ValueError: The field does not exist: xxx`
-
-!!! note
-    The fields to sort on must be of typed unsigned integer, or else you will see 
-    an error during indexing that looks like 
-    `TypeError: argument 'value': 'float' object cannot be interpreted as an integer`.
-
-!!! note
-    You can specify multiple fields for ordering at indexing time.
-    But at query time only one ordering field is supported.
+=== "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?;
+    ```
 
 ## Phrase queries vs. terms queries
 
+!!! warning "Warn"
+    Lance-based FTS doesn't support queries using boolean operators `OR`, `AND`.
+
 For full-text search you can specify either a **phrase** query like `"the old man and the sea"`,
-or a **terms** search query like `"(Old AND Man) AND Sea"`. For more details on the terms
+or a **terms** search query like `old man sea`. For more details on the terms
 query syntax, see Tantivy's [query parser rules](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html).
 
-!!! tip "Note"
-    The query parser will raise an exception on queries that are ambiguous. For example, in the query `they could have been dogs OR cats`, `OR` is capitalized so it's considered a keyword query operator. But it's ambiguous how the left part should be treated. So if you submit this search query as is, you'll get `Syntax Error: they could have been dogs OR cats`.
-
-    ```py
-    # This raises a syntax error
-    table.search("they could have been dogs OR cats")
-    ```
-
-    On the other hand, lowercasing `OR` to `or` will work, because there are no capitalized logical operators and
-    the query is treated as a phrase query.
-
-    ```py
-    # This works!
-    table.search("they could have been dogs or cats")
-    ```
-
-It can be cumbersome to have to remember what will cause a syntax error depending on the type of
-query you want to perform. To make this simpler, when you want to perform a phrase query, you can
-enforce it in one of two ways:
-
-1. Place the double-quoted query inside single quotes. For example, `table.search('"they could have been dogs OR cats"')` is treated as
-a phrase query.
-2. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
-itself contains double quotes. For example, `table.search('the cats OR dogs were not really "pets" at all').phrase_query()`
-is treated as a phrase query.
-
-In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested
-double quotes replaced by single quotes.
-
-
-## Configurations
-
-By default, LanceDB configures a 1GB heap size limit for creating the index. You can
-reduce this if running on a smaller node, or increase this for faster performance while
-indexing a larger corpus.
-
+To search for a phrase, the index must be created with `with_position=True`:
 ```python
-# configure a 512MB heap size
-heap = 1024 * 1024 * 512
-table.create_fts_index(["text1", "text2"], writer_heap_size=heap, replace=True)
+table.create_fts_index("text", use_tantivy=False, with_position=True)
 ```
-
-## Current limitations
-
-1. Currently we do not yet support incremental writes.
-   If you add data after FTS index creation, it won't be reflected
-   in search results until you do a full reindex.
-
-2. We currently only support local filesystem paths for the FTS index.
-   This is a tantivy limitation. We've implemented an object store plugin
-   but there's no way in tantivy-py to specify to use it.
+This will allow you to search for phrases, but it will also significantly increase the index size and indexing time.

docs/src/fts_tantivy.md

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

docs/src/guides/scalar_index.md

@@ -0,0 +1,108 @@
+# 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

@@ -498,7 +498,7 @@ This can also be done with the ``AWS_ENDPOINT`` and ``AWS_DEFAULT_REGION`` envir
 
 #### S3 Express
 
-LanceDB supports [S3 Express One Zone](https://aws.amazon.com/s3/storage-classes/express-one-zone/) endpoints, but requires additional configuration. Also, S3 Express endpoints only support connecting from an EC2 instance within the same region.
+LanceDB supports [S3 Express One Zone](https://aws.amazon.com/s3/storage-classes/express-one-zone/) endpoints, but requires additional infrastructure configuration for the compute service, such as EC2 or Lambda. Please refer to [Networking requirements for S3 Express One Zone](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-express-networking.html).
 
 To configure LanceDB to use an S3 Express endpoint, you must set the storage option `s3_express`. The bucket name in your table URI should **include the suffix**.
 

docs/src/guides/tables.md

@@ -35,6 +35,7 @@ Initialize a LanceDB connection and create a table
 
         ```typescript
         const lancedb = require("vectordb");
+        const arrow = require("apache-arrow");
 
         const uri = "data/sample-lancedb";
         const db = await lancedb.connect(uri);
@@ -98,7 +99,6 @@ Initialize a LanceDB connection and create a table
             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"
         ```
@@ -116,14 +116,32 @@ Initialize a LanceDB connection and create a table
         --8<-- "docs/src/basic_legacy.ts:create_table"
         ```
 
-        !!! warning
-            `existsOk` option is not supported in `vectordb`
+        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
+
 
-        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
-        const table = await con.createTable(tableName, data, { writeMode: WriteMode.Overwrite })
+        --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
@@ -398,7 +416,6 @@ You can create an empty table for scenarios where you want to add data to the ta
 
 === "Python"
 
-    ```python
 
     An empty table can be initialized via a PyArrow schema.
 

docs/src/hybrid_search/hybrid_search.md

@@ -43,200 +43,32 @@ table.create_fts_index("text")
 # hybrid search with default re-ranker
 results = table.search("flower moon", query_type="hybrid").to_pandas()
 ```
+!!! Note
+    You can also pass the vector and text query manually. This is useful if you're not using the embedding API or if you're using a separate embedder service.
+### Explicitly passing the vector and text query
+```python
+vector_query = [0.1, 0.2, 0.3, 0.4, 0.5]
+text_query = "flower moon"
+results = table.search(query_type="hybrid")
+                .vector(vector_query)
+                .text(text_query)
+                .limit(5)
+                .to_pandas()
 
-By default, LanceDB uses `LinearCombinationReranker(weight=0.7)` to combine and rerank the results of semantic and full-text search. You can customize the hyperparameters as needed or write your own custom reranker. Here's how you can use any of the available rerankers:
+```
+
+By default, LanceDB uses `RRFReranker()`, which uses reciprocal rank fusion score, to combine and rerank the results of semantic and full-text search. You can customize the hyperparameters as needed or write your own custom reranker. Here's how you can use any of the available rerankers:
 
 
 ### `rerank()` arguments
 * `normalize`: `str`, default `"score"`:
     The method to normalize the scores. Can be "rank" or "score". If "rank", the scores are converted to ranks and then normalized. If "score", the scores are normalized directly.
-* `reranker`: `Reranker`, default `LinearCombinationReranker(weight=0.7)`.
+* `reranker`: `Reranker`, default `RRF()`.
     The reranker to use. If not specified, the default reranker is used.
 
 
 ## Available Rerankers
-LanceDB provides a number of re-rankers out of the box. You can use any of these re-rankers by passing them to the `rerank()` method. Here's a list of available re-rankers:
-
-### Linear Combination Reranker
-This is the default re-ranker used by LanceDB. It combines the results of semantic and full-text search using a linear combination of the scores. The weights for the linear combination can be specified. It defaults to 0.7, i.e, 70% weight for semantic search and 30% weight for full-text search.
+LanceDB provides a number of re-rankers out of the box. You can use any of these re-rankers by passing them to the `rerank()` method. 
+Go to [Rerankers](../reranking/index.md) to learn more about using the available rerankers and implementing custom rerankers.
 
 
-```python
-from lancedb.rerankers import LinearCombinationReranker
-
-reranker = LinearCombinationReranker(weight=0.3) # Use 0.3 as the weight for vector search
-
-results = table.search("rebel", query_type="hybrid").rerank(reranker=reranker).to_pandas()
-```
-
-### Arguments
-----------------
-* `weight`: `float`, default `0.7`:
-    The weight to use for the semantic search score. The weight for the full-text search score is `1 - weights`.
-* `fill`: `float`, default `1.0`:
-        The score to give to results that are only in one of the two result sets.This is treated as penalty, so a higher value means a lower score.
-        TODO: We should just hardcode this-- its pretty confusing as we invert scores to calculate final score
-* `return_score` : str, default `"relevance"`
-        options are "relevance" or "all"
-        The type of score to return. If "relevance", will return only the `_relevance_score. If "all", will return all scores from the vector and FTS search along with the relevance score.
-
-### Cohere Reranker
-This re-ranker uses the [Cohere](https://cohere.ai/) API to combine the results of semantic and full-text search. You can use this re-ranker by passing `CohereReranker()` to the `rerank()` method. Note that you'll need to set the `COHERE_API_KEY` environment variable to use this re-ranker.
-
-```python
-from lancedb.rerankers import CohereReranker
-
-reranker = CohereReranker()
-
-results = table.search("vampire weekend", query_type="hybrid").rerank(reranker=reranker).to_pandas()
-```
-
-### Arguments
-----------------
-* `model_name` : str, default `"rerank-english-v2.0"`
-        The name of the cross encoder model to use. Available cohere models are:
-        - rerank-english-v2.0
-        - rerank-multilingual-v2.0
-* `column` : str, default `"text"`
-        The name of the column to use as input to the cross encoder model.
-* `top_n` : str, default `None`
-        The number of results to return. If None, will return all results.
-
-!!! Note
-    Only returns `_relevance_score`. Does not support `return_score = "all"`.
-
-### Cross Encoder Reranker
-This reranker uses the [Sentence Transformers](https://www.sbert.net/) library to combine the results of semantic and full-text search. You can use it by passing `CrossEncoderReranker()` to the `rerank()` method.
-
-```python
-from lancedb.rerankers import CrossEncoderReranker
-
-reranker = CrossEncoderReranker()
-
-results = table.search("harmony hall", query_type="hybrid").rerank(reranker=reranker).to_pandas()
-```
-
-
-### Arguments
-----------------
-* `model` : str, default `"cross-encoder/ms-marco-TinyBERT-L-6"`
-        The name of the cross encoder model to use. Available cross encoder models can be found [here](https://www.sbert.net/docs/pretrained_cross-encoders.html)
-* `column` : str, default `"text"`
-        The name of the column to use as input to the cross encoder model.
-* `device` : str, default `None`
-        The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu".
-
-!!! Note
-    Only returns `_relevance_score`. Does not support `return_score = "all"`.
-
-
-### ColBERT Reranker
-This reranker uses the ColBERT model to combine the results of semantic and full-text search. You can use it by passing `ColbertrReranker()` to the `rerank()` method. 
-
-ColBERT reranker model calculates relevance of given docs against the query and don't take existing fts and vector search scores into account, so it currently only supports `return_score="relevance"`. By default, it looks for `text` column to rerank the results. But you can specify the column name to use as input to the cross encoder model as described below.
-
-```python
-from lancedb.rerankers import ColbertReranker
-
-reranker = ColbertReranker()
-
-results = table.search("harmony hall", query_type="hybrid").rerank(reranker=reranker).to_pandas()
-```
-
-### Arguments
-----------------
-* `model_name` : `str`, default `"colbert-ir/colbertv2.0"`
-        The name of the cross encoder model to use.
-* `column` : `str`, default `"text"`
-        The name of the column to use as input to the cross encoder model.
-* `return_score` : `str`, default `"relevance"`
-        options are `"relevance"` or `"all"`. Only `"relevance"` is supported for now.
-
-!!! Note
-    Only returns `_relevance_score`. Does not support `return_score = "all"`.
-
-### OpenAI Reranker
-This reranker uses the OpenAI API to combine the results of semantic and full-text search. You can use it by passing `OpenaiReranker()` to the `rerank()` method.
-
-!!! Note
-    This prompts chat model to rerank results which is not a dedicated reranker model. This should be treated as experimental.
-
-!!! Tip
-    - You might run out of token limit so set the search `limits` based on your token limit.
-    - It is recommended to use gpt-4-turbo-preview, the default model, older models might lead to undesired behaviour
-
-```python
-from lancedb.rerankers import OpenaiReranker
-
-reranker = OpenaiReranker()
-
-results = table.search("harmony hall", query_type="hybrid").rerank(reranker=reranker).to_pandas()
-```
-
-### Arguments
-----------------
-* `model_name` : `str`, default `"gpt-4-turbo-preview"`
-    The name of the cross encoder model to use.
-* `column` : `str`, default `"text"`
-    The name of the column to use as input to the cross encoder model.
-* `return_score` : `str`, default `"relevance"`
-    options are "relevance" or "all". Only "relevance" is supported for now.
-* `api_key` : `str`, default `None`
-    The API key to use. If None, will use the OPENAI_API_KEY environment variable.
-
-
-## Building Custom Rerankers
-You can build your own custom reranker by subclassing the `Reranker` class and implementing the `rerank_hybrid()` method. Here's an example of a custom reranker that combines the results of semantic and full-text search using a linear combination of the scores.
-
-The `Reranker` base interface comes with a `merge_results()` method that can be used to combine the results of semantic and full-text search. This is a vanilla merging algorithm that simply concatenates the results and removes the duplicates without taking the scores into consideration. It only keeps the first copy of the row encountered. This works well in cases that don't require the scores of semantic and full-text search to combine the results. If you want to use the scores or want to support `return_score="all"`, you'll need to implement your own merging algorithm.
-
-```python
-
-from lancedb.rerankers import Reranker
-import pyarrow as pa
-
-class MyReranker(Reranker):
-    def __init__(self, param1, param2, ..., return_score="relevance"):
-        super().__init__(return_score)
-        self.param1 = param1
-        self.param2 = param2
-    
-    def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table):
-        # Use the built-in merging function
-        combined_result = self.merge_results(vector_results, fts_results)
-        
-        # Do something with the combined results
-        # ...
-
-        # Return the combined results
-        return combined_result
-
-```
-
-### Example of a Custom Reranker
-For the sake of simplicity let's build custom reranker that just enchances the Cohere Reranker by accepting a filter query, and accept other CohereReranker params as kwags.
-
-```python
-
-from typing import List, Union
-import pandas as pd
-from lancedb.rerankers import CohereReranker
-
-class MofidifiedCohereReranker(CohereReranker):
-    def __init__(self, filters: Union[str, List[str]], **kwargs):
-        super().__init__(**kwargs)
-        filters = filters if isinstance(filters, list) else [filters]
-        self.filters = filters
-
-    def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table)-> pa.Table:
-        combined_result = super().rerank_hybrid(query, vector_results, fts_results)
-        df = combined_result.to_pandas()
-        for filter in self.filters:
-            df = df.query("not text.str.contains(@filter)")
-
-        return pa.Table.from_pandas(df)
-
-```
-
-!!! tip
-    The `vector_results` and `fts_results` are pyarrow tables. You can convert them to pandas dataframes using `to_pandas()` method and perform any operations you want. After you are done, you can convert the dataframe back to pyarrow table using `pa.Table.from_pandas()` method and return it.

docs/src/index.md

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

docs/src/integrations/dlt.md

@@ -0,0 +1,142 @@
+# dlt  
+
+[dlt](https://dlthub.com/docs/intro) is an open-source library that you can add to your Python scripts to load data from various and often messy data sources into well-structured, live datasets. dlt's [integration with LanceDB](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb) lets you ingest data from any source (databases, APIs, CSVs, dataframes, JSONs, and more) into LanceDB with a few lines of simple python code. The integration enables automatic normalization of nested data, schema inference, incremental loading and embedding the data. dlt also has integrations with several other tools like dbt, airflow, dagster etc. that can be inserted into your LanceDB workflow.
+
+## How to ingest data into LanceDB  
+
+In this example, we will be fetching movie information from the [Open Movie Database (OMDb) API](https://www.omdbapi.com/) and loading it into a local LanceDB instance. To implement it, you will need an API key for the OMDb API (which can be created freely [here](https://www.omdbapi.com/apikey.aspx)).
+  
+1. **Install `dlt` with LanceDB extras:**  
+    ```sh 
+    pip install dlt[lancedb]
+    ```
+
+2. **Inside an empty directory, initialize a `dlt` project with:**  
+    ```sh
+    dlt init rest_api lancedb
+    ```
+    This will add all the files necessary to create a `dlt` pipeline that can ingest data from any REST API (ex: OMDb API) and load into LanceDB.
+    ```text
+    ├── .dlt
+    │   ├── config.toml
+    │   └── secrets.toml
+    ├── rest_api
+    ├── rest_api_pipeline.py
+    └── requirements.txt
+    ```
+  
+    dlt has a list of pre-built [sources](https://dlthub.com/docs/dlt-ecosystem/verified-sources/) like [SQL databases](https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database), [REST APIs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api), [Google Sheets](https://dlthub.com/docs/dlt-ecosystem/verified-sources/google_sheets), [Notion](https://dlthub.com/docs/dlt-ecosystem/verified-sources/notion) etc., that can be used out-of-the-box by running `dlt init <source_name> lancedb`. Since dlt is a python library, it is also very easy to modify these pre-built sources or to write your own custom source from scratch.
+
+
+3. **Specify necessary credentials and/or embedding model details:**  
+    
+    In order to fetch data from the OMDb API, you will need to pass a valid API key into your pipeline. Depending on whether you're using LanceDB OSS or LanceDB cloud, you also may need to provide the necessary credentials to connect to the LanceDB instance. These can be pasted inside `.dlt/sercrets.toml`. 
+
+    dlt's LanceDB integration also allows you to automatically embed the data during ingestion. Depending on the embedding model chosen, you may need to paste the necessary credentials inside `.dlt/sercrets.toml`:
+    ```toml
+    [sources.rest_api]
+    api_key = "api_key" # Enter the API key for the OMDb API
+
+    [destination.lancedb]
+    embedding_model_provider = "sentence-transformers"
+    embedding_model = "all-MiniLM-L6-v2"
+    [destination.lancedb.credentials]
+    uri = ".lancedb"
+    api_key = "api_key" # API key to connect to LanceDB Cloud. Leave out if you are using LanceDB OSS.
+    embedding_model_provider_api_key = "embedding_model_provider_api_key" # Not needed for providers that don't need authentication (ollama, sentence-transformers).
+    ```
+    See [here](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb#configure-the-destination) for more information and for a list of available models and model providers.  
+
+
+4. **Write the pipeline code inside `rest_api_pipeline.py`:**  
+
+    The following code shows how you can configure dlt's REST API source to connect to the [OMDb API](https://www.omdbapi.com/), fetch all movies with the word "godzilla" in the title, and load it into a LanceDB table. The REST API source allows you to pull data from any API with minimal code, to learn more read the [dlt docs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api).
+
+    ```python   
+
+    # Import necessary modules
+    import dlt
+    from rest_api import rest_api_source
+
+    # Configure the REST API source
+    movies_source = rest_api_source(
+        {
+            "client": {
+                "base_url": "https://www.omdbapi.com/",
+                "auth": { # authentication strategy for the OMDb API
+                    "type": "api_key",
+                    "name": "apikey",
+                    "api_key": dlt.secrets["sources.rest_api.api_token"], # read API credentials directly from secrets.toml
+                    "location": "query"
+                },
+                "paginator": { # pagination strategy for the OMDb API 
+                    "type": "page_number",
+                    "base_page": 1,
+                    "total_path": "totalResults",
+                    "maximum_page": 5
+                }
+            },
+            "resources": [ # list of API endpoints to request
+                {
+                    "name": "movie_search",
+                    "endpoint": {
+                        "path": "/",
+                        "params": {
+                            "s": "godzilla",
+                            "type": "movie"
+                        }
+                    }
+                }
+            ]
+        })
+
+
+    if __name__ == "__main__":
+        # Create a pipeline object
+        pipeline = dlt.pipeline(
+            pipeline_name='movies_pipeline',
+            destination='lancedb', # this tells dlt to load the data into LanceDB
+            dataset_name='movies_data_pipeline',
+        )
+
+        # Run the pipeline
+        load_info = pipeline.run(movies_source)
+
+        # pretty print the information on data that was loaded
+        print(load_info)
+    ```
+
+    The script above will ingest the data into LanceDB as it is, i.e. without creating any embeddings. If we want to embed one of the fields (for example, `"Title"` that contains the movie titles), then we will use dlt's `lancedb_adapter` and modify the script as follows:  
+    
+    - Add the following import statement:
+        ```python
+        from dlt.destinations.adapters import lancedb_adapter
+        ```
+    - Modify the pipeline run like this:
+        ```python
+        load_info = pipeline.run(
+            lancedb_adapter(
+                movies_source,
+                embed="Title",
+            )
+        )
+        ```
+    This will use the embedding model specified inside `.dlt/secrets.toml` to embed the field `"Title"`.
+
+5. **Install necessary dependencies:**  
+    ```sh
+    pip install -r requirements.txt
+    ```  
+
+    Note: You may need to install the dependencies for your embedding models separately.
+    ```sh
+    pip install sentence-transformers
+    ```
+
+6. **Run the pipeline:**
+    Finally, running the following command will ingest the data into your LanceDB instance.
+    ```sh
+    python custom_source.py
+    ```
+
+For more information and advanced usage of dlt's LanceDB integration, read [the dlt documentation](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb).

docs/src/integrations/langchain.md

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

docs/src/integrations/phidata.md

@@ -0,0 +1,383 @@
+**phidata** is a framework for building **AI Assistants** with long-term memory, contextual knowledge, and the ability to take actions using function calling. It helps turn general-purpose LLMs into specialized assistants tailored to your use case by extending its capabilities using **memory**, **knowledge**, and **tools**. 
+
+- **Memory**: Stores chat history in a **database** and enables LLMs to have long-term conversations.
+- **Knowledge**: Stores information in a **vector database** and provides LLMs with business context. (Here we will use LanceDB)
+- **Tools**: Enable LLMs to take actions like pulling data from an **API**, **sending emails** or **querying a database**, etc.
+
+![example](https://raw.githubusercontent.com/lancedb/assets/refs/heads/main/docs/assets/integration/phidata_assistant.png)
+
+Memory & knowledge make LLMs smarter while tools make them autonomous.
+
+LanceDB is a vector database and its integration into phidata makes it easy for us to provide a **knowledge base** to LLMs. It enables us to store information as [embeddings](../embeddings/understanding_embeddings.md) and search for the **results** similar to ours using **query**. 
+
+??? Question "What is Knowledge Base?"
+    Knowledge Base is a database of information that the Assistant can search to improve its responses. This information is stored in a vector database and provides LLMs with business context, which makes them respond in a context-aware manner.
+
+    While any type of storage can act as a knowledge base, vector databases offer the best solution for retrieving relevant results from dense information quickly.
+
+Let's see how using LanceDB inside phidata helps in making LLM more useful:
+
+## Prerequisites: install and import necessary dependencies
+
+**Create a virtual environment**
+
+1. install virtualenv package
+    ```python
+    pip install virtualenv
+    ```
+2. Create a directory for your project and go to the directory and create a virtual environment inside it.
+    ```python
+    mkdir phi
+    ```
+    ```python
+    cd phi
+    ```
+    ```python
+    python -m venv phidata_
+    ```
+
+**Activating virtual environment**
+
+1. from inside the project directory, run the following command to activate the virtual environment.
+    ```python
+    phidata_/Scripts/activate
+    ```
+
+**Install the following packages in the virtual environment**
+```python
+pip install lancedb phidata youtube_transcript_api openai ollama numpy pandas
+```
+
+**Create python files and import necessary libraries**
+
+You need to create two files - `transcript.py` and `ollama_assistant.py` or `openai_assistant.py`
+
+=== "openai_assistant.py"
+
+    ```python
+    import os, openai
+    from rich.prompt import Prompt
+    from phi.assistant import Assistant
+    from phi.knowledge.text import TextKnowledgeBase
+    from phi.vectordb.lancedb import LanceDb
+    from phi.llm.openai import OpenAIChat
+    from phi.embedder.openai import OpenAIEmbedder
+    from transcript import extract_transcript
+
+    if "OPENAI_API_KEY" not in os.environ:
+    # OR set the key here as a variable
+        openai.api_key = "sk-..."
+
+    # The code below creates a file "transcript.txt" in the directory, the txt file will be used below
+    youtube_url = "https://www.youtube.com/watch?v=Xs33-Gzl8Mo" 
+    segment_duration = 20
+    transcript_text,dict_transcript = extract_transcript(youtube_url,segment_duration)
+    ```
+
+=== "ollama_assistant.py"
+
+    ```python
+    from rich.prompt import Prompt
+    from phi.assistant import Assistant
+    from phi.knowledge.text import TextKnowledgeBase
+    from phi.vectordb.lancedb import LanceDb
+    from phi.llm.ollama import Ollama
+    from phi.embedder.ollama import OllamaEmbedder
+    from transcript import extract_transcript
+
+    # The code below creates a file "transcript.txt" in the directory, the txt file will be used below
+    youtube_url = "https://www.youtube.com/watch?v=Xs33-Gzl8Mo"
+    segment_duration = 20
+    transcript_text,dict_transcript = extract_transcript(youtube_url,segment_duration)
+    ```
+
+=== "transcript.py"
+
+    ``` python
+    from youtube_transcript_api import YouTubeTranscriptApi
+    import re
+
+    def smodify(seconds):
+        hours, remainder = divmod(seconds, 3600)
+        minutes, seconds = divmod(remainder, 60)
+        return f"{int(hours):02}:{int(minutes):02}:{int(seconds):02}"
+        
+    def extract_transcript(youtube_url,segment_duration):
+        # Extract video ID from the URL
+        video_id = re.search(r'(?<=v=)[\w-]+', youtube_url)
+        if not video_id:
+            video_id = re.search(r'(?<=be/)[\w-]+', youtube_url)
+        if not video_id:
+            return None
+
+        video_id = video_id.group(0)
+
+        # Attempt to fetch the transcript
+        try:
+            # Try to get the official transcript
+            transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=['en'])
+        except Exception:
+            # If no official transcript is found, try to get auto-generated transcript
+            try:
+                transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
+                for transcript in transcript_list:
+                    transcript = transcript.translate('en').fetch()
+            except Exception:
+                return None
+
+        # Format the transcript into 120s chunks
+        transcript_text,dict_transcript = format_transcript(transcript,segment_duration)
+        # Open the file in write mode, which creates it if it doesn't exist
+        with open("transcript.txt", "w",encoding="utf-8") as file:
+            file.write(transcript_text)
+        return transcript_text,dict_transcript
+
+    def format_transcript(transcript,segment_duration):
+        chunked_transcript = []
+        chunk_dict = []
+        current_chunk = []
+        current_time = 0
+        # 2 minutes in seconds
+        start_time_chunk = 0  # To track the start time of the current chunk
+
+        for segment in transcript:
+            start_time = segment['start']
+            end_time_x = start_time + segment['duration']
+            text = segment['text']
+            
+            # Add text to the current chunk
+            current_chunk.append(text)
+            
+            # Update the current time with the duration of the current segment
+            # The duration of the current segment is given by segment['start'] - start_time_chunk
+            if current_chunk:
+                current_time = start_time - start_time_chunk
+            
+            # If current chunk duration reaches or exceeds 2 minutes, save the chunk
+            if current_time >= segment_duration:
+                # Use the start time of the first segment in the current chunk as the timestamp
+                chunked_transcript.append(f"[{smodify(start_time_chunk)} to {smodify(end_time_x)}] " + " ".join(current_chunk))
+                current_chunk = re.sub(r'[\xa0\n]', lambda x: '' if x.group() == '\xa0' else ' ', "\n".join(current_chunk))
+                chunk_dict.append({"timestamp":f"[{smodify(start_time_chunk)} to {smodify(end_time_x)}]", "text": "".join(current_chunk)})
+                current_chunk = []  # Reset the chunk
+                start_time_chunk = start_time + segment['duration'] # Update the start time for the next chunk
+                current_time = 0  # Reset current time
+
+        # Add any remaining text in the last chunk
+        if current_chunk:
+            chunked_transcript.append(f"[{smodify(start_time_chunk)} to {smodify(end_time_x)}] " + " ".join(current_chunk))
+            current_chunk = re.sub(r'[\xa0\n]', lambda x: '' if x.group() == '\xa0' else ' ', "\n".join(current_chunk))
+            chunk_dict.append({"timestamp":f"[{smodify(start_time_chunk)} to {smodify(end_time_x)}]", "text": "".join(current_chunk)})
+
+        return "\n\n".join(chunked_transcript), chunk_dict
+    ```
+
+!!! warning
+    If creating Ollama assistant, download and install Ollama [from here](https://ollama.com/) and then run the Ollama instance in the background. Also, download the required models using `ollama pull <model-name>`. Check out the models [here](https://ollama.com/library) 
+
+
+**Run the following command to deactivate the virtual environment if needed**
+```python
+deactivate
+```
+
+## **Step 1** - Create a Knowledge Base for AI Assistant using LanceDB
+
+=== "openai_assistant.py"
+
+    ```python
+    # Create knowledge Base with OpenAIEmbedder in LanceDB
+    knowledge_base = TextKnowledgeBase(
+        path="transcript.txt",
+        vector_db=LanceDb(
+            embedder=OpenAIEmbedder(api_key = openai.api_key),
+            table_name="transcript_documents",
+            uri="./t3mp/.lancedb",
+        ),
+        num_documents = 10
+    )
+    ```
+
+=== "ollama_assistant.py"
+
+    ```python
+    # Create knowledge Base with OllamaEmbedder in LanceDB
+    knowledge_base = TextKnowledgeBase(
+        path="transcript.txt",
+        vector_db=LanceDb(
+            embedder=OllamaEmbedder(model="nomic-embed-text",dimensions=768),
+            table_name="transcript_documents",
+            uri="./t2mp/.lancedb",
+        ),
+        num_documents = 10
+    )
+    ```
+Check out the list of **embedders** supported by **phidata** and their usage [here](https://docs.phidata.com/embedder/introduction).
+
+Here we have used `TextKnowledgeBase`, which loads text/docx files to the knowledge base.
+
+Let's see all the parameters that `TextKnowledgeBase` takes -
+
+| Name| Type | Purpose | Default |
+|:----|:-----|:--------|:--------|
+|`path`|`Union[str, Path]`| Path to text file(s). It can point to a single text file or a directory of text files.| provided by user |
+|`formats`|`List[str]`| File formats accepted by this knowledge base. |`[".txt"]`|
+|`vector_db`|`VectorDb`| Vector Database for the Knowledge Base. phidata provides a wrapper around many vector DBs, you can import it like this - `from phi.vectordb.lancedb import LanceDb` | provided by user |
+|`num_documents`|`int`| Number of results (documents/vectors) that vector search should return. |`5`|
+|`reader`|`TextReader`| phidata provides many types of reader objects which read data, clean it and create chunks of data, encapsulate each chunk inside an object of the `Document` class, and return **`List[Document]`**.  | `TextReader()` |
+|`optimize_on`|`int`| It is used to specify the number of documents on which to optimize the vector database. Supposed to create an index. |`1000`|
+
+??? Tip "Wonder! What is `Document` class?"
+    We know that, before storing the data in vectorDB, we need to split the data into smaller chunks upon which embeddings will be created and these embeddings along with the chunks will be stored in vectorDB. When the user queries over the vectorDB, some of these embeddings will be returned as the result based on the semantic similarity with the query.
+    
+    When the user queries over vectorDB, the queries are converted into embeddings, and a nearest neighbor search is performed over these query embeddings which returns the embeddings that correspond to most semantically similar chunks(parts of our data) present in vectorDB. 
+
+    Here, a “Document” is a class in phidata. Since there is an option to let phidata create and manage embeddings, it splits our data into smaller chunks(as expected). It does not directly create embeddings on it. Instead, it takes each chunk and encapsulates it inside the object of the `Document` class along with various other metadata related to the chunk. Then embeddings are created on these `Document` objects and stored in vectorDB.
+
+    ```python
+    class Document(BaseModel):
+        """Model for managing a document"""
+
+        content: str # <--- here data of chunk is stored 
+        id: Optional[str] = None
+        name: Optional[str] = None
+        meta_data: Dict[str, Any] = {}
+        embedder: Optional[Embedder] = None
+        embedding: Optional[List[float]] = None
+        usage: Optional[Dict[str, Any]] = None
+    ```
+
+However, using phidata you can load many other types of data in the knowledge base(other than text). Check out [phidata Knowledge Base](https://docs.phidata.com/knowledge/introduction) for more information.
+
+Let's dig deeper into the `vector_db` parameter and see what parameters `LanceDb` takes -
+
+| Name| Type | Purpose | Default |
+|:----|:-----|:--------|:--------|
+|`embedder`|`Embedder`| phidata provides many Embedders that abstract the interaction with embedding APIs and utilize it to generate embeddings. Check out other embedders [here](https://docs.phidata.com/embedder/introduction) | `OpenAIEmbedder` |
+|`distance`|`List[str]`| The choice of distance metric used to calculate the similarity between vectors, which directly impacts search results and performance in vector databases. |`Distance.cosine`|
+|`connection`|`lancedb.db.LanceTable`| LanceTable can be accessed through `.connection`. You can connect to an existing table of LanceDB, created outside of phidata, and utilize it. If not provided, it creates a new table using `table_name` parameter and adds it to `connection`. |`None`|
+|`uri`|`str`| It specifies the directory location of **LanceDB database** and establishes a connection that can be used to interact with the database. | `"/tmp/lancedb"` |
+|`table_name`|`str`|  If `connection` is not provided, it initializes and connects to a new **LanceDB table** with a specified(or default) name in the database present at `uri`. |`"phi"`|
+|`nprobes`|`int`| It refers to the number of partitions that the search algorithm examines to find the nearest neighbors of a given query vector. Higher values will yield better recall (more likely to find vectors if they exist) at the expense of latency. |`20`|
+
+
+!!! note
+    Since we just initialized the KnowledgeBase. The VectorDB table that corresponds to this Knowledge Base is not yet populated with our data. It will be populated in **Step 3**, once we perform the `load` operation. 
+    
+    You can check the state of the LanceDB table using - `knowledge_base.vector_db.connection.to_pandas()`
+
+Now that the Knowledge Base is initialized, , we can go to **step 2**.
+
+## **Step 2** -  Create an assistant with our choice of LLM and reference to the knowledge base.
+
+
+=== "openai_assistant.py"
+
+    ```python
+    # define an assistant with gpt-4o-mini llm and reference to the knowledge base created above
+    assistant = Assistant(
+        llm=OpenAIChat(model="gpt-4o-mini", max_tokens=1000, temperature=0.3,api_key = openai.api_key),
+        description="""You are an Expert in explaining youtube video transcripts. You are a bot that takes transcript of a video and answer the question based on it.
+        
+        This is transcript for the above timestamp: {relevant_document}
+        The user input is: {user_input}
+        generate highlights only when asked.
+        When asked to generate highlights from the video, understand the context for each timestamp and create key highlight points, answer in following way - 
+        [timestamp] - highlight 1
+        [timestamp] - highlight 2
+        ... so on
+        
+        Your task is to understand the user question, and provide an answer using the provided contexts. Your answers are correct, high-quality, and written by an domain expert. If the provided context does not contain the answer, simply state,'The provided context does not have the answer.'""",
+        knowledge_base=knowledge_base,
+        add_references_to_prompt=True,
+    )
+    ```
+
+=== "ollama_assistant.py"
+
+    ```python
+    # define an assistant with llama3.1 llm and reference to the knowledge base created above
+    assistant = Assistant(
+        llm=Ollama(model="llama3.1"),
+        description="""You are an Expert in explaining youtube video transcripts. You are a bot that takes transcript of a video and answer the question based on it.
+
+        This is transcript for the above timestamp: {relevant_document}
+        The user input is: {user_input}
+        generate highlights only when asked.
+        When asked to generate highlights from the video, understand the context for each timestamp and create key highlight points, answer in following way - 
+        [timestamp] - highlight 1
+        [timestamp] - highlight 2
+        ... so on
+
+        Your task is to understand the user question, and provide an answer using the provided contexts. Your answers are correct, high-quality, and written by an domain expert. If the provided context does not contain the answer, simply state,'The provided context does not have the answer.'""",
+        knowledge_base=knowledge_base,
+        add_references_to_prompt=True,
+    )
+    ```
+
+Assistants add **memory**, **knowledge**, and **tools** to LLMs. Here we will add only **knowledge** in this example. 
+
+Whenever we will give a query to LLM, the assistant will retrieve relevant information from our **Knowledge Base**(table in LanceDB) and pass it to LLM along with the user query in a structured way. 
+
+- The `add_references_to_prompt=True` always adds information from the knowledge base to the prompt, regardless of whether it is relevant to the question.
+
+To know more about an creating assistant in phidata, check out [phidata docs](https://docs.phidata.com/assistants/introduction) here.
+
+## **Step 3** - Load data to Knowledge Base.
+
+```python
+# load out data into the knowledge_base (populating the LanceTable)
+assistant.knowledge_base.load(recreate=False)
+```
+The above code loads the data to the Knowledge Base(LanceDB Table) and now it is ready to be used by the assistant. 
+
+| Name| Type | Purpose | Default |
+|:----|:-----|:--------|:--------|
+|`recreate`|`bool`| If True, it drops the existing table and recreates the table in the vectorDB. |`False`|
+|`upsert`|`bool`| If True and the vectorDB supports upsert, it will upsert documents to the vector db. | `False` |
+|`skip_existing`|`bool`| If True, skips documents that already exist in the vectorDB when inserting. |`True`|
+
+??? tip "What is upsert?"
+    Upsert is a database operation that combines "update" and "insert". It updates existing records if a document with the same identifier does exist, or inserts new records if no matching record exists. This is useful for maintaining the most current information without manually checking for existence.
+
+During the Load operation, phidata directly interacts with the LanceDB library and performs the loading of the table with our data in the following steps -     
+
+1. **Creates** and **initializes** the table if it does not exist.
+
+2. Then it **splits** our data into smaller **chunks**.
+
+    ??? question "How do they create chunks?"
+        **phidata** provides many types of **Knowledge Bases** based on the type of data. Most of them :material-information-outline:{ title="except LlamaIndexKnowledgeBase and LangChainKnowledgeBase"} has a property method called `document_lists` of type `Iterator[List[Document]]`. During the load operation, this property method is invoked. It traverses on the data provided by us (in this case, a text file(s)) using `reader`. Then it **reads**, **creates chunks**, and **encapsulates** each chunk inside a `Document` object and yields **lists of `Document` objects** that contain our data.
+
+3. Then **embeddings** are created on these chunks are **inserted** into the LanceDB Table 
+
+    ??? question "How do they insert your data as different rows in LanceDB Table?"
+        The chunks of your data are in the form - **lists of `Document` objects**. It was yielded in the step above.
+
+        for each `Document` in `List[Document]`, it does the following operations:
+        
+        - Creates embedding on `Document`.
+        - Cleans the **content attribute**(chunks of our data is here) of `Document`.
+        - Prepares data by creating `id` and loading `payload` with the metadata related to this chunk. (1)
+            { .annotate }
+
+            1.  Three columns will be added to the table - `"id"`, `"vector"`, and `"payload"` (payload contains various metadata including **`content`**)
+                
+        - Then add this data to LanceTable. 
+
+4. Now the internal state of `knowledge_base` is changed (embeddings are created and loaded in the table ) and it **ready to be used by assistant**.
+
+## **Step 4** - Start a cli chatbot with access to the Knowledge base  
+
+```python
+# start cli chatbot with knowledge base
+assistant.print_response("Ask me about something from the knowledge base")
+while True:
+    message = Prompt.ask(f"[bold] :sunglasses: User [/bold]")
+    if message in ("exit", "bye"):
+        break
+    assistant.print_response(message, markdown=True)
+```  
+
+
+For more information and amazing cookbooks of phidata, read the [phidata documentation](https://docs.phidata.com/introduction) and also visit [LanceDB x phidata docmentation](https://docs.phidata.com/vectordb/lancedb).

docs/src/integrations/voxel51.md

@@ -1,13 +1,73 @@
 # FiftyOne
 
-FiftyOne is an open source toolkit for building high-quality datasets and computer vision models. It provides an API to create LanceDB tables and run similarity queries, both programmatically in Python and via point-and-click in the App.
+FiftyOne is an open source toolkit that enables users to curate better data and build better models. It includes tools for data exploration, visualization, and management, as well as features for collaboration and sharing. 
+
+Any developers, data scientists, and researchers who work with computer vision and machine learning can use FiftyOne to improve the quality of their datasets and deliver insights about their models.
+
 
 ![example](../assets/voxel.gif)
 
-## Basic recipe
+**FiftyOne** provides an API to create LanceDB tables and run similarity queries, both **programmatically in Python** and via **point-and-click in the App**.
 
-The basic workflow shown below uses LanceDB to create a similarity index on your FiftyOne
-datasets:
+Let's get started and see how to use **LanceDB** to create a **similarity index** on your FiftyOne datasets.
+
+## Overview 
+
+**[Embeddings](../embeddings/understanding_embeddings.md)** are foundational to all of the **vector search** features. In FiftyOne, embeddings are managed by the [**FiftyOne Brain**](https://docs.voxel51.com/user_guide/brain.html) that provides powerful machine learning techniques designed to transform how you curate your data from an art into a measurable science. 
+
+!!!question "Have you ever wanted to find the images most similar to an image in your dataset?"
+    The **FiftyOne Brain** makes computing **visual similarity** really easy. You can compute the similarity of samples in your dataset using an embedding model and store the results in the **brain key**. 
+
+    You can then sort your samples by similarity or use this information to find potential duplicate images.
+
+Here we will be doing the following : 
+
+1. **Create Index** - In order to run similarity queries against our media, we need to **index** the data. We can do this via the `compute_similarity()` function. 
+
+    -  In the function, specify the **model** you want to use to generate the embedding vectors, and what **vector search engine** you want to use on the **backend** (here LanceDB). 
+    
+    !!!tip
+        You can also give the similarity index a name(`brain_key`), which is useful if you want to run vector searches against multiple indexes.
+
+2. **Query** - Once you have generated your similarity index, you can query your dataset with `sort_by_similarity()`. The query can be any of the following:
+
+    - An ID (sample or patch)
+    - A query vector of same dimension as the index
+    - A list of IDs (samples or patches)
+    - A text prompt (search semantically)
+
+## Prerequisites: install necessary dependencies
+
+1. **Create and activate a virtual environment**
+
+    Install virtualenv package and run the following command in your project directory.
+    ```python
+    python -m venv fiftyone_
+    ```
+    From inside the project directory run the following to activate the virtual environment.
+    === "Windows"
+
+        ```python
+        fiftyone_/Scripts/activate
+        ```
+
+    === "macOS/Linux"
+
+        ```python
+        source fiftyone_/Scripts/activate
+        ```
+
+2. **Install the following packages in the virtual environment**
+
+    To install FiftyOne, ensure you have activated any virtual environment that you are using, then run
+    ```python
+    pip install fiftyone
+    ```
+
+
+## Understand basic workflow
+
+The basic workflow shown below uses LanceDB to create a similarity index on your FiftyOne datasets:
 
 1. Load a dataset into FiftyOne.
 
@@ -19,14 +79,10 @@ datasets:
 
 5. If desired, delete the table.
 
-The example below demonstrates this workflow.
+## Quick Example
 
-!!! Note
+Let's jump on a quick example that demonstrates this workflow.
 
-    Install the LanceDB Python client to run the code shown below.
-    ```
-    pip install lancedb
-    ```
 
 ```python
 
@@ -36,7 +92,10 @@ import fiftyone.zoo as foz
 
 # Step 1: Load your data into FiftyOne
 dataset = foz.load_zoo_dataset("quickstart")
+```
+Make sure you install torch ([guide here](https://pytorch.org/get-started/locally/)) before proceeding. 
 
+```python
 # Steps 2 and 3: Compute embeddings and create a similarity index
 lancedb_index = fob.compute_similarity(
     dataset, 
@@ -45,8 +104,11 @@ lancedb_index = fob.compute_similarity(
     backend="lancedb",
 )
 ```
-Once the similarity index has been generated, we can query our data in FiftyOne
-by specifying the `brain_key`:
+
+!!! note
+    Running the code above will download the clip model (2.6Gb)
+
+Once the similarity index has been generated, we can query our data in FiftyOne by specifying the `brain_key`:
 
 ```python
 # Step 4: Query your data
@@ -56,7 +118,22 @@ view = dataset.sort_by_similarity(
     brain_key="lancedb_index",
     k=10,  # limit to 10 most similar samples
 )
+```
+The returned result are of type - `DatasetView`.
 
+!!! note
+    `DatasetView` does not hold its contents in-memory. Views simply store the rule(s) that are applied to extract the content of interest from the underlying Dataset when the view is iterated/aggregated on.
+
+    This means, for example, that the contents of a `DatasetView` may change as the underlying Dataset is modified.
+
+??? question "Can you query a view instead of dataset?"
+    Yes, you can also query a view.
+
+    Performing a similarity search on a `DatasetView` will only return results from the view; if the view contains samples that were not included in the index, they will never be included in the result.
+
+    This means that you can index an entire Dataset once and then perform searches on subsets of the dataset by constructing views that contain the images of interest.
+
+```python
 # Step 5 (optional): Cleanup
 
 # Delete the LanceDB table
@@ -66,4 +143,90 @@ lancedb_index.cleanup()
 dataset.delete_brain_run("lancedb_index")
 ```
 
+
+## Using LanceDB backend
+By default, calling `compute_similarity()` or `sort_by_similarity()` will use an sklearn backend.
+
+To use the LanceDB backend, simply set the optional `backend` parameter of `compute_similarity()` to `"lancedb"`:
+
+```python
+import fiftyone.brain as fob
+#... rest of the code
+fob.compute_similarity(..., backend="lancedb", ...)
+```
+
+Alternatively, you can configure FiftyOne to use the LanceDB backend by setting the following environment variable.
+
+In your terminal, set the environment variable using:
+=== "Windows"
+
+    ```python
+    $Env:FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND="lancedb" //powershell
+
+    set FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND=lancedb //cmd
+    ```
+
+=== "macOS/Linux"
+
+    ```python
+    export FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND=lancedb
+    ```
+
+!!! note
+    This will only run during the terminal session. Once terminal is closed, environment variable is deleted.
+
+Alternatively, you can **permanently** configure FiftyOne to use the LanceDB backend creating a `brain_config.json` at `~/.fiftyone/brain_config.json`. The JSON file may contain any desired subset of config fields that you wish to customize.
+
+```json
+{
+    "default_similarity_backend": "lancedb"
+}
+```
+This will override the default `brain_config` and will set it according to your customization. You can check the configuration by running the following code : 
+
+```python
+import fiftyone.brain as fob
+# Print your current brain config
+print(fob.brain_config)
+```
+
+## LanceDB config parameters
+
+The LanceDB backend supports query parameters that can be used to customize your similarity queries. These parameters include:
+
+| Name| Purpose | Default |
+|:----|:--------|:--------|
+|**table_name**|The name of the LanceDB table to use. If none is provided, a new table will be created|`None`|
+|**metric**|The embedding distance metric to use when creating a new table. The supported values are ("cosine", "euclidean")|`"cosine"`|
+|**uri**| The database URI to use. In this Database URI, tables will be created. |`"/tmp/lancedb"`|
+
+There are two ways to specify/customize the parameters:
+
+1. **Using `brain_config.json` file** 
+
+    ```json
+    {
+        "similarity_backends": {
+            "lancedb": {
+                "table_name": "your-table",
+                "metric": "euclidean",
+                "uri": "/tmp/lancedb"
+            }
+        }
+    }
+    ```
+
+2. **Directly passing to `compute_similarity()` to configure a specific new index** : 
+
+    ```python
+    lancedb_index = fob.compute_similarity(
+        ...
+        backend="lancedb",
+        brain_key="lancedb_index",
+        table_name="your-table",
+        metric="euclidean",
+        uri="/tmp/lancedb",
+    )
+    ```
+
 For a much more in depth walkthrough of the integration, visit the LanceDB x Voxel51 [docs page](https://docs.voxel51.com/integrations/lancedb.html).

docs/src/javascript/README.md

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

docs/src/javascript/classes/DefaultWriteOptions.md

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

docs/src/javascript/classes/LocalConnection.md

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

docs/src/javascript/classes/LocalTable.md

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

docs/src/javascript/classes/MakeArrowTableOptions.md

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

docs/src/javascript/classes/OpenAIEmbeddingFunction.md

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

docs/src/javascript/classes/Query.md

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

docs/src/javascript/enums/IndexStatus.md

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

docs/src/javascript/enums/MetricType.md

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

docs/src/javascript/enums/WriteMode.md

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

docs/src/javascript/interfaces/AwsCredentials.md

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

docs/src/javascript/interfaces/CleanupStats.md

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

docs/src/javascript/interfaces/ColumnAlteration.md

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

docs/src/javascript/interfaces/CompactionMetrics.md

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

docs/src/javascript/interfaces/CompactionOptions.md

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

docs/src/javascript/interfaces/Connection.md

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

docs/src/javascript/interfaces/ConnectionOptions.md

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

docs/src/javascript/interfaces/CreateTableOptions.md

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

docs/src/javascript/interfaces/EmbeddingFunction.md

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

docs/src/javascript/interfaces/IndexStats.md

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

docs/src/javascript/interfaces/IvfPQIndexConfig.md

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

docs/src/javascript/interfaces/MergeInsertArgs.md

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

docs/src/javascript/interfaces/Table.md

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

docs/src/javascript/interfaces/UpdateArgs.md

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

docs/src/javascript/interfaces/UpdateSqlArgs.md

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

docs/src/javascript/interfaces/VectorIndex.md

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

docs/src/javascript/interfaces/WriteOptions.md

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

docs/src/javascript/modules.md

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