Bump version: 0.9.0-beta.3 → 0.9.0-beta.4

bump version
Fix fast_search
2025-12-23 05:19:58 +00:00 · 2024-12-20 16:02:53 +00:00 · 2024-12-20 09:44:46 -06:00 · 2024-12-20 09:43:39 -06:00 · 2024-12-20 09:21:05 -06:00 · 2024-12-18 10:21:04 -06:00
235 changed files with 4850 additions and 19108 deletions
--- a/.bumpversion.toml
+++ b/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.10.0-beta.0"
+current_version = "0.5.2"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
--- a/.github/workflows/docs_test.yml
+++ b/.github/workflows/docs_test.yml
@@ -24,7 +24,7 @@ env:
 jobs:
  test-python:
    name: Test doc python code
-    runs-on: "warp-ubuntu-latest-x64-4x"
+    runs-on: "buildjet-8vcpu-ubuntu-2204"
    steps:
    - name: Checkout
      uses: actions/checkout@v4
@@ -56,7 +56,7 @@ jobs:
        for d in *; do cd "$d"; echo "$d".py; python "$d".py; cd ..; done
  test-node:
    name: Test doc nodejs code
-    runs-on: "warp-ubuntu-latest-x64-4x"
+    runs-on: "buildjet-8vcpu-ubuntu-2204"
    timeout-minutes: 60
    strategy:
      fail-fast: false
--- a/.github/workflows/java.yml
+++ b/.github/workflows/java.yml
@@ -3,8 +3,6 @@ on:
  push:
    branches:
      - main
-    paths:
-      - java/**
  pull_request:
    paths:
      - java/**
@@ -23,42 +21,9 @@ env:
  CARGO_INCREMENTAL: "0"
  CARGO_BUILD_JOBS: "1"
 jobs:
-  linux-build-java-11:
+  linux-build:
    runs-on: ubuntu-22.04
-    name: ubuntu-22.04 + Java 11
-    defaults:
-      run:
-        working-directory: ./java
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      - uses: Swatinem/rust-cache@v2
-        with:
-          workspaces: java/core/lancedb-jni
-      - name: Run cargo fmt
-        run: cargo fmt --check
-        working-directory: ./java/core/lancedb-jni
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler libssl-dev
-      - name: Install Java 11
-        uses: actions/setup-java@v4
-        with:
-          distribution: temurin
-          java-version: 11
-          cache: "maven"
-      - name: Java Style Check
-        run: mvn checkstyle:check
-      # Disable because of issues in lancedb rust core code
-      # - name: Rust Clippy
-      #   working-directory: java/core/lancedb-jni
-      #   run: cargo clippy --all-targets -- -D warnings
-      - name: Running tests with Java 11
-        run: mvn clean test
-  linux-build-java-17:
-    runs-on: ubuntu-22.04
-    name: ubuntu-22.04 + Java 17
+    name: ubuntu-22.04 + Java 11 & 17
    defaults:
      run:
        working-directory: ./java
@@ -82,12 +47,20 @@ jobs:
          java-version: 17
          cache: "maven"
      - run: echo "JAVA_17=$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 \
@@ -110,4 +83,3 @@ jobs:
          -Djdk.reflect.useDirectMethodHandle=false \
          -Dio.netty.tryReflectionSetAccessible=true"
          JAVA_HOME=$JAVA_17 mvn clean test
-  
--- a/.github/workflows/npm-publish.yml
+++ b/.github/workflows/npm-publish.yml
@@ -7,7 +7,6 @@ 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')
@@ -40,7 +39,6 @@ jobs:
            node/vectordb-*.tgz

  node-macos:
-    name: vectordb ${{ matrix.config.arch }}
    strategy:
      matrix:
        config:
@@ -71,7 +69,6 @@ jobs:
            node/dist/lancedb-vectordb-darwin*.tgz

  nodejs-macos:
-    name: lancedb ${{ matrix.config.arch }}
    strategy:
      matrix:
        config:
@@ -102,7 +99,7 @@ jobs:
            nodejs/dist/*.node

  node-linux:
-    name: vectordb (${{ matrix.config.arch}}-unknown-linux-gnu)
+    name: node-linux (${{ matrix.config.arch}}-unknown-linux-gnu
    runs-on: ${{ matrix.config.runner }}
    # Only runs on tags that matches the make-release action
    if: startsWith(github.ref, 'refs/tags/v')
@@ -142,7 +139,7 @@ jobs:
            node/dist/lancedb-vectordb-linux*.tgz

  nodejs-linux:
-    name: lancedb (${{ matrix.config.arch}}-unknown-linux-gnu
+    name: nodejs-linux (${{ matrix.config.arch}}-unknown-linux-gnu
    runs-on: ${{ matrix.config.runner }}
    # Only runs on tags that matches the make-release action
    if: startsWith(github.ref, 'refs/tags/v')
@@ -193,7 +190,6 @@ 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')
@@ -227,7 +223,6 @@ 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')
@@ -261,7 +256,6 @@ 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
@@ -290,18 +284,8 @@ 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
@@ -349,15 +333,6 @@ 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]
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -33,11 +33,11 @@ jobs:
          python-version: "3.11"
      - name: Install ruff
        run: |
-          pip install ruff==0.5.4
+          pip install ruff==0.2.2
      - name: Format check
        run: ruff format --check .
      - name: Lint
-        run: ruff check .
+        run: ruff .
  doctest:
    name: "Doctest"
    timeout-minutes: 30
--- a/.github/workflows/rust.yml
+++ b/.github/workflows/rust.yml
@@ -53,10 +53,7 @@ jobs:
      run: cargo clippy --all --all-features -- -D warnings
  linux:
    timeout-minutes: 30
-    # To build all features, we need more disk space than is available
-    # on the GitHub-provided runner. This is mostly due to the the
-    # sentence-transformers feature.
-    runs-on: warp-ubuntu-latest-x64-4x
+    runs-on: ubuntu-22.04
    defaults:
      run:
        shell: bash
@@ -134,3 +131,4 @@ jobs:
          $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
          cargo build
          cargo test
+  
--- a/.gitignore
+++ b/.gitignore
@@ -4,7 +4,6 @@
 **/__pycache__
 .DS_Store
 venv
-.venv

 .vscode
 .zed
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -14,8 +14,8 @@ repos:
  hooks:
    - id: local-biome-check
      name: biome check
-      entry: npx @biomejs/biome@1.8.3 check --config-path nodejs/biome.json nodejs/
+      entry: npx @biomejs/biome@1.7.3 check --config-path nodejs/biome.json nodejs/
      language: system
      types: [text]
      files: "nodejs/.*"
-      exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*|nodejs/examples/.*
+      exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -20,30 +20,29 @@ keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]

 [workspace.dependencies]
-lance = { "version" = "=0.16.1", "features" = ["dynamodb"] }
-lance-index = { "version" = "=0.16.1" }
-lance-linalg = { "version" = "=0.16.1" }
-lance-testing = { "version" = "=0.16.1" }
-lance-datafusion = { "version" = "=0.16.1" }
-lance-encoding = { "version" = "=0.16.1" }
+lance = { "version" = "=0.13.0", "features" = ["dynamodb"] }
+lance-index = { "version" = "=0.13.0" }
+lance-linalg = { "version" = "=0.13.0" }
+lance-testing = { "version" = "=0.13.0" }
+lance-datafusion = { "version" = "=0.13.0" }
 # Note that this one does not include pyarrow
-arrow = { version = "52.2", optional = false }
-arrow-array = "52.2"
-arrow-data = "52.2"
-arrow-ipc = "52.2"
-arrow-ord = "52.2"
-arrow-schema = "52.2"
-arrow-arith = "52.2"
-arrow-cast = "52.2"
+arrow = { version = "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"
 async-trait = "0"
 chrono = "0.4.35"
-datafusion-physical-plan = "40.0"
+datafusion-physical-plan = "37.1"
 half = { "version" = "=2.4.1", default-features = false, features = [
    "num-traits",
 ] }
 futures = "0"
 log = "0.4"
-object_store = "0.10.2"
+object_store = "0.9.0"
 pin-project = "1.0.7"
 snafu = "0.7.4"
 url = "2"
--- a/README.md
+++ b/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>
@@ -44,24 +44,26 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu

 **Javascript**
 ```shell
-npm install @lancedb/lancedb
+npm install vectordb
 ```

 ```javascript
-import * as lancedb from "@lancedb/lancedb";
+const lancedb = require('vectordb');
+const db = await lancedb.connect('data/sample-lancedb');

-const db = await lancedb.connect("data/sample-lancedb");
-const table = await db.createTable("vectors", [
-	{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
-	{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },
-], {mode: 'overwrite'});
+const table = await db.createTable({
+  name: 'vectors',
+  data:  [
+    { id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
+    { id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }
+  ]
+})

-
-const query = table.vectorSearch([0.1, 0.3]).limit(2);
-const results = await query.toArray();
+const query = table.search([0.1, 0.3]).limit(2);
+const results = await query.execute();

 // You can also search for rows by specific criteria without involving a vector search.
-const rowsByCriteria = await table.query().where("price >= 10").toArray();
+const rowsByCriteria = await table.search(undefined).where("price >= 10").execute();
 ```

 **Python**
--- a/ci/build_linux_artifacts.sh
+++ b/ci/build_linux_artifacts.sh
@@ -18,4 +18,4 @@ docker run \
    -v $(pwd):/io -w /io \
    --memory-swap=-1 \
    lancedb-node-manylinux \
-    bash ci/manylinux_node/build_vectordb.sh $ARCH
+    bash ci/manylinux_node/build.sh $ARCH
--- a/ci/build_linux_artifacts_nodejs.sh
+++ b/ci/build_linux_artifacts_nodejs.sh
@@ -4,9 +4,9 @@ ARCH=${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_node
+pushd ci/manylinux_nodejs
 docker build \
-    -t lancedb-node-manylinux-$ARCH \
+    -t lancedb-nodejs-manylinux \
    --build-arg="ARCH=$ARCH" \
    --build-arg="DOCKER_USER=$(id -u)" \
    --progress=plain \
@@ -17,5 +17,5 @@ popd
 docker run \
    -v $(pwd):/io -w /io \
    --memory-swap=-1 \
-    lancedb-node-manylinux-$ARCH \
-    bash ci/manylinux_node/build_lancedb.sh $ARCH
+    lancedb-nodejs-manylinux \
+    bash ci/manylinux_nodejs/build.sh $ARCH
--- a/ci/manylinux_node/Dockerfile
+++ b/ci/manylinux_node/Dockerfile
@@ -4,7 +4,7 @@
 # range of linux distributions.
 ARG ARCH=x86_64

-FROM quay.io/pypa/manylinux_2_28_${ARCH}
+FROM quay.io/pypa/manylinux2014_${ARCH}

 ARG ARCH=x86_64
 ARG DOCKER_USER=default_user
@@ -18,8 +18,8 @@ COPY install_protobuf.sh install_protobuf.sh
 RUN ./install_protobuf.sh ${ARCH}

 ENV DOCKER_USER=${DOCKER_USER}
-# Create a group and user, but only if it doesn't exist
-RUN echo ${ARCH} && id -u ${DOCKER_USER} >/dev/null 2>&1 || adduser --user-group --create-home --uid ${DOCKER_USER} build_user
+# Create a group and user
+RUN echo ${ARCH} && adduser --user-group --create-home --uid ${DOCKER_USER} build_user

 # We switch to the user to install Rust and Node, since those like to be 
 # installed at the user level.
--- a/ci/manylinux_node/build_vectordb.sh
+++ b/ci/manylinux_node/build_vectordb.sh
--- a/ci/manylinux_node/install_openssl.sh
+++ b/ci/manylinux_node/install_openssl.sh
@@ -6,7 +6,7 @@
 # /usr/bin/ld: failed to set dynamic section sizes: Bad value
 set -e

-git clone -b OpenSSL_1_1_1v \
+git clone -b OpenSSL_1_1_1u \
    --single-branch \
    https://github.com/openssl/openssl.git

--- a/ci/manylinux_node/prepare_manylinux_node.sh
+++ b/ci/manylinux_node/prepare_manylinux_node.sh
@@ -8,7 +8,7 @@ install_node() {

    source "$HOME"/.bashrc

-    nvm install --no-progress 18
+    nvm install --no-progress 16
 }

 install_rust() {
--- a/ci/manylinux_nodejs/Dockerfile
+++ b/ci/manylinux_nodejs/Dockerfile
@@ -0,0 +1,31 @@
+# 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}
--- a/ci/manylinux_node/build_lancedb.sh
+++ b/ci/manylinux_node/build_lancedb.sh
--- a/ci/manylinux_nodejs/install_openssl.sh
+++ b/ci/manylinux_nodejs/install_openssl.sh
@@ -0,0 +1,26 @@
+#!/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
--- a/ci/manylinux_nodejs/install_protobuf.sh
+++ b/ci/manylinux_nodejs/install_protobuf.sh
@@ -0,0 +1,15 @@
+#!/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
--- a/ci/manylinux_nodejs/prepare_manylinux_node.sh
+++ b/ci/manylinux_nodejs/prepare_manylinux_node.sh
@@ -0,0 +1,21 @@
+#!/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
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -57,8 +57,6 @@ plugins:
            - https://arrow.apache.org/docs/objects.inv
            - https://pandas.pydata.org/docs/objects.inv
  - mkdocs-jupyter
-  - render_swagger:
-      allow_arbitrary_locations: true

 markdown_extensions:
  - admonition
@@ -89,10 +87,9 @@ nav:
          - Data management: concepts/data_management.md
      - 🔨 Guides:
          - Working with tables: guides/tables.md
-          - Building a vector index: ann_indexes.md
+          - Building an ANN index: ann_indexes.md
          - Vector Search: search.md
          - Full-text search: fts.md
-          - Building a scalar index: guides/scalar_index.md
          - Hybrid search:
              - Overview: hybrid_search/hybrid_search.md
              - Comparing Rerankers: hybrid_search/eval.md
@@ -101,21 +98,17 @@ nav:
              - 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
              - Building Custom Rerankers: reranking/custom_reranker.md
-              - Example: notebooks/lancedb_reranking.ipynb
          - Filtering: sql.md
          - Versioning & Reproducibility: notebooks/reproducibility.ipynb
          - Configuring Storage: guides/storage.md
-          - Migration Guide: migration.md
+          - Sync -> Async Migration Guide: migration.md
          - Tuning retrieval performance:
              - Choosing right query type: guides/tuning_retrievers/1_query_types.md
              - Reranking: guides/tuning_retrievers/2_reranking.md
-              - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
      - 🧬 Managing embeddings:
          - Overview: embeddings/index.md
          - Embedding functions: embeddings/embedding_functions.md
@@ -129,33 +122,24 @@ 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 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:  https://docs.llamaindex.ai/en/stable/examples/vector_stores/LanceDBIndexDemo/
          - Pydantic: python/pydantic.md
          - Voxel51: integrations/voxel51.md
          - PromptTools: integrations/prompttools.md
-          - dlt: integrations/dlt.md
      - 🎯 Examples:
          - Overview: examples/index.md
          - 🐍 Python:
              - Overview: examples/examples_python.md
-              - Build From Scratch: examples/python_examples/build_from_scratch.md
-              - Multimodal: examples/python_examples/multimodal.md
-              - Rag: examples/python_examples/rag.md
-              - Vector Search: examples/python_examples/vector_search.md
-              - Chatbot: examples/python_examples/chatbot.md
-              - Evaluation: examples/python_examples/evaluations.md
-              - AI Agent: examples/python_examples/aiagent.md
-              - Miscellaneous: 
-                  - 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: notebooks/youtube_transcript_search.ipynb
+              - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+              - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+              - Example - Calculate CLIP Embeddings with Roboflow Inference: examples/image_embeddings_roboflow.md
+              - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+              - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
          - 👾 JavaScript:
              - Overview: examples/examples_js.md
              - Serverless Website Chatbot: examples/serverless_website_chatbot.md
@@ -167,14 +151,13 @@ nav:
      - ⚙️ API reference:
          - 🐍 Python: python/python.md
          - 👾 JavaScript (vectordb): javascript/modules.md
-          - 👾 JavaScript (lancedb): js/globals.md
+          - 👾 JavaScript (lancedb): javascript/modules.md
          - 🦀 Rust: https://docs.rs/lancedb/latest/lancedb/
      - ☁️ LanceDB Cloud:
          - Overview: cloud/index.md
          - API reference:
              - 🐍 Python: python/saas-python.md
              - 👾 JavaScript: javascript/modules.md
-              - REST API: cloud/rest.md

  - Quick start: basic.md
  - Concepts:
@@ -187,7 +170,6 @@ nav:
      - Building an ANN index: ann_indexes.md
      - Vector Search: search.md
      - Full-text search: fts.md
-      - Building a scalar index: guides/scalar_index.md
      - Hybrid search:
          - Overview: hybrid_search/hybrid_search.md
          - Comparing Rerankers: hybrid_search/eval.md
@@ -196,21 +178,17 @@ nav:
          - 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
          - Building Custom Rerankers: reranking/custom_reranker.md
-          - Example: notebooks/lancedb_reranking.ipynb
      - Filtering: sql.md
      - Versioning & Reproducibility: notebooks/reproducibility.ipynb
      - Configuring Storage: guides/storage.md
-      - Migration Guide: migration.md
+      - Sync -> Async Migration Guide: migration.md
      - Tuning retrieval performance:
          - Choosing right query type: guides/tuning_retrievers/1_query_types.md
          - Reranking: guides/tuning_retrievers/2_reranking.md
-          - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
  - Managing Embeddings:
      - Overview: embeddings/index.md
      - Embedding functions: embeddings/embedding_functions.md
@@ -223,49 +201,33 @@ nav:
      - Pandas and PyArrow: python/pandas_and_pyarrow.md
      - Polars: python/polars_arrow.md
      - DuckDB: python/duckdb.md
-      - LangChain 🦜️🔗↗: integrations/langchain.md
+      - LangChain 🦜️🔗↗: https://python.langchain.com/docs/integrations/vectorstores/lancedb
      - LangChain.js 🦜️🔗↗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
-      - LlamaIndex 🦙↗: integrations/llamaIndex.md
+      - LlamaIndex 🦙↗: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
      - Pydantic: python/pydantic.md
      - Voxel51: integrations/voxel51.md
      - PromptTools: integrations/prompttools.md
-      - dlt: integrations/dlt.md
  - Examples:
      - examples/index.md
-      - 🐍 Python:
-          - Overview: examples/examples_python.md
-          - Build From Scratch: examples/python_examples/build_from_scratch.md
-          - Multimodal: examples/python_examples/multimodal.md
-          - Rag: examples/python_examples/rag.md
-          - Vector Search: examples/python_examples/vector_search.md
-          - Chatbot: examples/python_examples/chatbot.md
-          - Evaluation: examples/python_examples/evaluations.md
-          - AI Agent: examples/python_examples/aiagent.md
-          - Miscellaneous: 
-              - 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
-      - 👾 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
+      - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+      - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+      - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+      - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+      - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
+      - YouTube Transcript Search (JS): examples/youtube_transcript_bot_with_nodejs.md
+      - Serverless Chatbot from any website: examples/serverless_website_chatbot.md
+      - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
  - API reference:
      - Overview: api_reference.md
      - Python: python/python.md
      - Javascript (vectordb): javascript/modules.md
-      - Javascript (lancedb): js/globals.md
+      - Javascript (lancedb): js/modules.md
      - Rust: https://docs.rs/lancedb/latest/lancedb/index.html
  - LanceDB Cloud:
      - Overview: cloud/index.md
      - API reference:
          - 🐍 Python: python/saas-python.md
          - 👾 JavaScript: javascript/modules.md
-          - REST API: cloud/rest.md

 extra_css:
  - styles/global.css
--- a/docs/openapi.yml
+++ b/docs/openapi.yml
@@ -1,487 +0,0 @@
-openapi: 3.1.0
-info:
-  version: 1.0.0
-  title: LanceDB Cloud API
-  description: |
-    LanceDB Cloud API is a RESTful API that allows users to access and modify data stored in LanceDB Cloud.
-    Table actions are considered temporary resource creations and all use POST method.
-  contact:
-    name: LanceDB support
-    url: https://lancedb.com
-    email: contact@lancedb.com
-
-servers:
-  - url: https://{db}.{region}.api.lancedb.com
-    description: LanceDB Cloud REST endpoint.
-    variables:
-      db:
-        default: ""
-        description: the name of DB
-      region:
-        default: "us-east-1"
-        description: the service region of the DB
-
-security:
-  - key_auth: []
-
-components:
-  securitySchemes:
-    key_auth:
-      name: x-api-key
-      type: apiKey
-      in: header
-  parameters:
-    table_name:
-      name: name
-      in: path
-      description: name of the table
-      required: true
-      schema:
-        type: string
-  responses:
-    invalid_request:
-      description: Invalid request
-      content:
-        text/plain:
-          schema:
-            type: string
-    not_found:
-      description: Not found
-      content:
-        text/plain:
-          schema:
-            type: string
-    unauthorized:
-      description: Unauthorized
-      content:
-        text/plain:
-          schema:
-            type: string
-  requestBodies:
-    arrow_stream_buffer:
-      description: Arrow IPC stream buffer
-      required: true
-      content:
-        application/vnd.apache.arrow.stream:
-          schema:
-            type: string
-            format: binary
-
-paths:
-  /v1/table/:
-    get:
-      description: List tables, optionally, with pagination.
-      tags:
-        - Tables
-      summary: List Tables
-      operationId: listTables
-      parameters:
-        - name: limit
-          in: query
-          description: Limits the number of items to return.
-          schema:
-            type: integer
-        - name: page_token
-          in: query
-          description: Specifies the starting position of the next query
-          schema:
-            type: string
-      responses:
-        "200":
-          description: Successfully returned a list of tables in the DB
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  tables:
-                    type: array
-                    items:
-                      type: string
-                  page_token:
-                    type: string
-
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/create/:
-    post:
-      description: Create a new table
-      summary: Create a new table
-      operationId: createTable
-      tags:
-        - Tables
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Table successfully created
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/query/:
-    post:
-      description: Vector Query
-      url: https://{db-uri}.{aws-region}.api.lancedb.com/v1/table/{name}/query/
-      tags:
-        - Data
-      summary: Vector Query
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                vector:
-                  type: FixedSizeList
-                  description: |
-                    The targetted vector to search for. Required.
-                vector_column:
-                  type: string
-                  description: |
-                    The column to query, it can be inferred from the schema if there is only one vector column.
-                prefilter:
-                  type: boolean
-                  description: |
-                    Whether to prefilter the data. Optional.
-                k:
-                  type: integer
-                  description: |
-                    The number of search results to return. Default is 10.
-                distance_type:
-                  type: string
-                  description: |
-                    The distance metric to use for search. L2, Cosine, Dot and Hamming are supported. Default is L2.
-                bypass_vector_index:
-                  type: boolean
-                  description: |
-                    Whether to bypass vector index. Optional.
-                filter:
-                  type: string
-                  description: |
-                    A filter expression that specifies the rows to query. Optional.
-                columns:
-                  type: array
-                  items:
-                    type: string
-                  description: |
-                    The columns to return. Optional.
-                nprobe:
-                  type: integer
-                  description: |
-                    The number of probes to use for search. Optional.
-                refine_factor:
-                  type: integer
-                  description: |
-                    The refine factor to use for search. Optional.
-                  default: null
-                fast_search:
-                  type: boolean
-                  description: |
-                    Whether to use fast search. Optional.
-                  default: false
-              required:
-                - vector
-
-      responses:
-        "200":
-          description: top k results if query is successfully executed
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  results:
-                    type: array
-                    items:
-                      type: object
-                      properties:
-                        id:
-                          type: integer
-                        selected_col_1_to_return:
-                          type: col_1_type
-                        selected_col_n_to_return:
-                          type: col_n_type
-                        _distance:
-                          type: float
-
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/insert/:
-    post:
-      description: Insert new data to the Table.
-      tags:
-        - Data
-      operationId: insertData
-      summary: Insert new data.
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Insert successful
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/merge_insert/:
-    post:
-      description: Create a "merge insert" operation
-        This operation can add rows, update rows, and remove rows all in a single
-        transaction. See python method `lancedb.table.Table.merge_insert` for examples.
-      tags:
-        - Data
-      summary: Merge Insert
-      operationId: mergeInsert
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-        - name: on
-          in: query
-          description: |
-            The column to use as the primary key for the merge operation.
-          required: true
-          schema:
-            type: string
-        - name: when_matched_update_all
-          in: query
-          description: |
-            Rows that exist in both the source table (new data) and
-            the target table (old data) will be updated, replacing
-            the old row with the corresponding matching row.
-          required: false
-          schema:
-            type: boolean
-        - name: when_matched_update_all_filt
-          in: query
-          description: |
-            If present then only rows that satisfy the filter expression will
-            be updated
-          required: false
-          schema:
-            type: string
-        - name: when_not_matched_insert_all
-          in: query
-          description: |
-            Rows that exist only in the source table (new data) will be
-            inserted into the target table (old data).
-          required: false
-          schema:
-            type: boolean
-        - name: when_not_matched_by_source_delete
-          in: query
-          description: |
-            Rows that exist only in the target table (old data) will be
-            deleted. An optional condition (`when_not_matched_by_source_delete_filt`)
-            can be provided to limit what data is deleted.
-          required: false
-          schema:
-            type: boolean
-        - name: when_not_matched_by_source_delete_filt
-          in: query
-          description: |
-            The filter expression that specifies the rows to delete.
-          required: false
-          schema:
-            type: string
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Merge Insert successful
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/delete/:
-    post:
-      description: Delete rows from a table.
-      tags:
-        - Data
-      summary: Delete rows from a table
-      operationId: deleteData
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                predicate:
-                  type: string
-                  description: |
-                    A filter expression that specifies the rows to delete.
-      responses:
-        "200":
-          description: Delete successful
-        "401":
-          $ref: "#/components/responses/unauthorized"
-  /v1/table/{name}/drop/:
-    post:
-      description: Drop a table
-      tags:
-        - Tables
-      summary: Drop a table
-      operationId: dropTable
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Drop successful
-        "401":
-          $ref: "#/components/responses/unauthorized"
-
-  /v1/table/{name}/describe/:
-    post:
-      description: Describe a table and return Table Information.
-      tags:
-        - Tables
-      summary: Describe a table
-      operationId: describeTable
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      responses:
-        "200":
-          description: Table information
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  table:
-                    type: string
-                  version:
-                    type: integer
-                  schema:
-                    type: string
-                  stats:
-                    type: object
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/index/list/:
-    post:
-      description: List indexes of a table
-      tags:
-        - Tables
-      summary: List indexes of a table
-      operationId: listIndexes
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      responses:
-        "200":
-          description: Available list of indexes on the table.
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  indexes:
-                    type: array
-                    items:
-                      type: object
-                      properties:
-                        columns:
-                          type: array
-                          items:
-                            type: string
-                        index_name:
-                          type: string
-                        index_uuid:
-                          type: string
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/create_index/:
-    post:
-      description: Create vector index on a Table
-      tags:
-        - Tables
-      summary: Create vector index on a Table
-      operationId: createIndex
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                column:
-                  type: string
-                metric_type:
-                  type: string
-                  nullable: false
-                  description: |
-                    The metric type to use for the index. L2, Cosine, Dot are supported.
-                index_type:
-                  type: string
-      responses:
-        "200":
-          description: Index successfully created
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/create_scalar_index/:
-    post:
-      description: Create a scalar index on a table
-      tags:
-        - Tables
-      summary: Create a scalar index on a table
-      operationId: createScalarIndex
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                column:
-                  type: string
-                index_type:
-                  type: string
-                  required: false
-      responses:
-        "200":
-          description: Scalar Index successfully created
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,7 +1,5 @@
 mkdocs==1.5.3
 mkdocs-jupyter==0.24.1
 mkdocs-material==9.5.3
-mkdocstrings[python]==0.25.2
-griffe
-mkdocs-render-swagger-plugin
-pydantic
+mkdocstrings[python]==0.20.0
+pydantic
--- a/docs/src/ann_indexes.md
+++ b/docs/src/ann_indexes.md
@@ -38,27 +38,13 @@ Lance supports `IVF_PQ` index type by default.
    tbl.create_index(num_partitions=256, num_sub_vectors=96)
    ```

-=== "TypeScript"
+=== "Typescript"

-    === "@lancedb/lancedb"
+    ```typescript
+    --8<--- "docs/src/ann_indexes.ts:import"

-        Creating indexes is done via the [lancedb.Table.createIndex](../js/classes/Table.md/#createIndex) method.
-
-        ```typescript
-        --8<--- "nodejs/examples/ann_indexes.ts:import"
-
-        --8<-- "nodejs/examples/ann_indexes.ts:ingest"
-        ```
-
-    === "vectordb (deprecated)"
-
-        Creating indexes is done via the [lancedb.Table.createIndex](../javascript/interfaces/Table.md/#createIndex) method.
-
-        ```typescript
-        --8<--- "docs/src/ann_indexes.ts:import"
-
-        --8<-- "docs/src/ann_indexes.ts:ingest"
-        ```
+    --8<-- "docs/src/ann_indexes.ts:ingest"
+    ```

 === "Rust"

@@ -105,27 +91,27 @@ You can specify the GPU device to train IVF partitions via

 === "Linux"

-    <!-- skip-test -->
-    ``` { .python .copy }
-    # Create index using CUDA on Nvidia GPUs.
-    tbl.create_index(
-        num_partitions=256,
-        num_sub_vectors=96,
-        accelerator="cuda"
-    )
-    ```
+     <!-- skip-test -->
+     ``` { .python .copy }
+     # Create index using CUDA on Nvidia GPUs.
+     tbl.create_index(
+          num_partitions=256,
+          num_sub_vectors=96,
+          accelerator="cuda"
+     )
+     ```

 === "MacOS"

-    <!-- skip-test -->
-    ```python
-    # Create index using MPS on Apple Silicon.
-    tbl.create_index(
-        num_partitions=256,
-        num_sub_vectors=96,
-        accelerator="mps"
-    )
-    ```
+     <!-- skip-test -->
+     ```python
+     # Create index using MPS on Apple Silicon.
+     tbl.create_index(
+          num_partitions=256,
+          num_sub_vectors=96,
+          accelerator="mps"
+     )
+     ```

 Troubleshooting:

@@ -164,19 +150,11 @@ There are a couple of parameters that can be used to fine-tune the search:
    1  [0.48587373, 0.269207, 0.15095535, 0.65531915,...  item 3953  108.393867
    ```

-=== "TypeScript"
+=== "Typescript"

-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/ann_indexes.ts:search1"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/ann_indexes.ts:search1"
-        ```
+    ```typescript
+    --8<-- "docs/src/ann_indexes.ts:search1"
+    ```

 === "Rust"

@@ -194,23 +172,15 @@ You can further filter the elements returned by a search using a where clause.

 === "Python"

-    ```python
-    tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
-    ```
+     ```python
+     tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
+     ```

-=== "TypeScript"
+=== "Typescript"

-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/ann_indexes.ts:search2"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```javascript
-        --8<-- "docs/src/ann_indexes.ts:search2"
-        ```
+     ```javascript
+     --8<-- "docs/src/ann_indexes.ts:search2"
+     ```

 ### Projections (select clause)

@@ -218,31 +188,23 @@ You can select the columns returned by the query using a select clause.

 === "Python"

-    ```python
-    tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
-    ```
+     ```python
+     tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
+     ```


-    ```text
-                                                vector _distance
-    0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
-    1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
-    ...
-    ```
+     ```text
+                                                   vector _distance
+     0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
+     1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
+     ...
+     ```

-=== "TypeScript"
+=== "Typescript"

-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/ann_indexes.ts:search3"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/ann_indexes.ts:search3"
-        ```
+     ```typescript
+     --8<-- "docs/src/ann_indexes.ts:search3"
+     ```

 ## FAQ

--- a/docs/src/api_reference.md
+++ b/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/globals.md)
+- [JavaScript (newer @lancedb/lancedb package)](js/modules.md)
 - [Rust](https://docs.rs/lancedb/latest/lancedb/index.html)
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-</svg>
--- a/docs/src/assets/python.svg
+++ b/docs/src/assets/python.svg
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="97.5" height="28" role="img" aria-label="PYTHON"><title>PYTHON</title><g shape-rendering="crispEdges"><rect width="97.5" height="28" fill="#3670a0"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="100"><image x="9" y="7" width="14" height="14" xlink:href="data:image/svg+xml;base64,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"/><text transform="scale(.1)" x="587.5" y="175" textLength="535" fill="#fff" font-weight="bold">PYTHON</text></g></svg>
--- a/docs/src/basic.md
+++ b/docs/src/basic.md
@@ -16,60 +16,11 @@
      pip install lancedb
      ```

-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+=== "Typescript"

-        ```shell
-        npm install @lancedb/lancedb
-        ```
-        !!! note "Bundling `@lancedb/lancedb` apps with Webpack"
-
-            Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.
-
-            ```javascript
-            /** @type {import('next').NextConfig} */
-            module.exports = ({
-            webpack(config) {
-                config.externals.push({ '@lancedb/lancedb': '@lancedb/lancedb' })
-                return config;
-            }
-            })
-            ```
-
-        !!! note "Yarn users"
-
-            Unlike other package managers, Yarn does not automatically resolve peer dependencies. If you are using Yarn, you will need to manually install 'apache-arrow':
-
-            ```shell
-            yarn add apache-arrow
-            ```
-
-    === "vectordb (deprecated)"
-
-        ```shell
-        npm install vectordb
-        ```
-        !!! note "Bundling `vectordb` apps with Webpack"
-
-            Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.
-
-            ```javascript
-            /** @type {import('next').NextConfig} */
-            module.exports = ({
-            webpack(config) {
-                config.externals.push({ vectordb: 'vectordb' })
-                return config;
-            }
-            })
-            ```
-
-        !!! note "Yarn users"
-
-            Unlike other package managers, Yarn does not automatically resolve peer dependencies. If you are using Yarn, you will need to manually install 'apache-arrow':
-
-            ```shell
-            yarn add apache-arrow
-            ```
+      ```shell
+      npm install vectordb
+      ```

 === "Rust"

@@ -107,21 +58,14 @@ recommend switching to stable releases.
      pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
      ```

-=== "Typescript[^1]"
+=== "Typescript"

-    === "@lancedb/lancedb"
-
-        ```shell
-        npm install @lancedb/lancedb@preview
-        ```
-    === "vectordb (deprecated)"
-
-        ```shell
-        npm install vectordb@preview
-        ```
+      ```shell
+      npm install vectordb@preview
+      ```

 === "Rust"
-
+    
    We don't push preview releases to crates.io, but you can referent the tag
    in GitHub within your Cargo dependencies:

@@ -149,22 +93,23 @@ recommend switching to stable releases.
        use the same syntax as the asynchronous API.  To help with this migration we
        have created a [migration guide](migration.md) detailing the differences.

-=== "Typescript[^1]"
+=== "Typescript"

-    === "@lancedb/lancedb"
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:import"

-        ```typescript
-        import * as lancedb from "@lancedb/lancedb";
-        import * as arrow from "apache-arrow";
+    --8<-- "docs/src/basic_legacy.ts:open_db"
+    ```

-        --8<-- "nodejs/examples/basic.ts:connect"
-        ```
+    !!! note "`@lancedb/lancedb` vs. `vectordb`"

-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:open_db"
-        ```
+        The Javascript SDK was originally released as `vectordb`.  In an effort to
+        reduce maintenance we are aligning our SDKs.  The new, aligned, Javascript
+        API is being released as `lancedb`.  If you are starting new work we encourage
+        you to try out `lancedb`.  Once the new API is feature complete we will begin
+        slowly deprecating `vectordb` in favor of `lancedb`. There is a
+        [migration guide](migration.md) detailing the differences which will assist
+        you in this process.

 === "Rust"

@@ -207,23 +152,15 @@ table.
    --8<-- "python/python/tests/docs/test_basic.py:create_table_async_pandas"
    ```

-=== "Typescript[^1]"
+=== "Typescript"

-    === "@lancedb/lancedb"
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:create_table"
+    ```

-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:create_table"
-        ```
-
-        If the table already exists, LanceDB will raise an error by default.
-        If you want to overwrite the table, you can pass in `mode:"overwrite"`
-        to the `createTable` function.
+    If the table already exists, LanceDB will raise an error by default.
+    If you want to overwrite the table, you can pass in `mode="overwrite"`
+    to the `createTable` function.

 === "Rust"

@@ -263,19 +200,11 @@ similar to a `CREATE TABLE` statement in SQL.
    !!! note "You can define schema in Pydantic"
        LanceDB comes with Pydantic support, which allows you to define the schema of your data using Pydantic models. This makes it easy to work with LanceDB tables and data. Learn more about all supported types in [tables guide](./guides/tables.md).

-=== "Typescript[^1]"
+=== "Typescript"

-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_empty_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:create_empty_table"
-        ```
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:create_empty_table"
+    ```

 === "Rust"

@@ -294,19 +223,11 @@ Once created, you can open a table as follows:
    --8<-- "python/python/tests/docs/test_basic.py:open_table_async"
    ```

-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:open_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        const tbl = await db.openTable("myTable");
-        ```
+=== "Typescript"

+    ```typescript
+    const tbl = await db.openTable("myTable");
+    ```

 === "Rust"

@@ -323,18 +244,11 @@ If you forget the name of your table, you can always get a listing of all table
    --8<-- "python/python/tests/docs/test_basic.py:table_names_async"
    ```

-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+=== "Javascript"

-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:table_names"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        console.log(await db.tableNames());
-        ```
+    ```javascript
+    console.log(await db.tableNames());
+    ```

 === "Rust"

@@ -353,18 +267,11 @@ After a table has been created, you can always add more data to it as follows:
    --8<-- "python/python/tests/docs/test_basic.py:add_data_async"
    ```

-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+=== "Typescript"

-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:add_data"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:add"
-        ```
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:add"
+    ```

 === "Rust"

@@ -385,18 +292,11 @@ Once you've embedded the query, you can find its nearest neighbors as follows:

    This returns a pandas DataFrame with the results.

-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+=== "Typescript"

-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:vector_search"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:search"
-        ```
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:search"
+    ```

 === "Rust"

@@ -425,18 +325,11 @@ LanceDB allows you to create an ANN index on a table as follows:
    --8<-- "python/python/tests/docs/test_basic.py:create_index_async"
    ```

-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+=== "Typescript"

-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_index"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```{.typescript .ignore}
-        --8<-- "docs/src/basic_legacy.ts:create_index"
-        ```
+    ```{.typescript .ignore}
+    --8<-- "docs/src/basic_legacy.ts:create_index"
+    ```

 === "Rust"

@@ -464,19 +357,11 @@ This can delete any number of rows that match the filter.
    --8<-- "python/python/tests/docs/test_basic.py:delete_rows_async"
    ```

-=== "Typescript[^1]"
+=== "Typescript"

-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:delete_rows"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:delete"
-        ```
+    ```typescript
+    --8<-- "docs/src/basic_legacy.ts:delete"
+    ```

 === "Rust"

@@ -493,15 +378,9 @@ simple or complex as needed. To see what expressions are supported, see the

      Read more: [lancedb.table.Table.delete][]

-=== "Typescript[^1]"
+=== "Javascript"

-    === "@lancedb/lancedb"
-
-        Read more: [lancedb.Table.delete](javascript/interfaces/Table.md#delete)
-
-    === "vectordb (deprecated)"
-
-        Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)
+      Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)

 === "Rust"

@@ -513,31 +392,23 @@ Use the `drop_table()` method on the database to remove a table.

 === "Python"

-    ```python
-    --8<-- "python/python/tests/docs/test_basic.py:drop_table"
-    --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
-    ```
+      ```python
+      --8<-- "python/python/tests/docs/test_basic.py:drop_table"
+      --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
+      ```

-    This permanently removes the table and is not recoverable, unlike deleting rows.
-    By default, if the table does not exist an exception is raised. To suppress this,
-    you can pass in `ignore_missing=True`.
+      This permanently removes the table and is not recoverable, unlike deleting rows.
+      By default, if the table does not exist an exception is raised. To suppress this,
+      you can pass in `ignore_missing=True`.

-=== "Typescript[^1]"
+=== "Typescript"

-    === "@lancedb/lancedb"
+      ```typescript
+      --8<-- "docs/src/basic_legacy.ts:drop_table"
+      ```

-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:drop_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:drop_table"
-        ```
-
-        This permanently removes the table and is not recoverable, unlike deleting rows.
-        If the table does not exist an exception is raised.
+      This permanently removes the table and is not recoverable, unlike deleting rows.
+      If the table does not exist an exception is raised.

 === "Rust"

@@ -545,6 +416,19 @@ Use the `drop_table()` method on the database to remove a table.
    --8<-- "rust/lancedb/examples/simple.rs:drop_table"
    ```

+!!! note "Bundling `vectordb` apps with Webpack"
+
+    If you're using the `vectordb` module in JavaScript, since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.
+
+    ```javascript
+    /** @type {import('next').NextConfig} */
+    module.exports = ({
+    webpack(config) {
+        config.externals.push({ vectordb: 'vectordb' })
+        return config;
+    }
+    })
+    ```

 ## Using the Embedding API
 You can use the embedding API when working with embedding models. It automatically vectorizes the data at ingestion and query time and comes with built-in integrations with popular embedding models like Openai, Hugging Face, Sentence Transformers, CLIP and more.
@@ -556,22 +440,6 @@ You can use the embedding API when working with embedding models. It automatical
    --8<-- "python/python/tests/docs/test_embeddings_optional.py:openai_embeddings"
    ```

-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/embedding.ts:imports"
-        --8<-- "nodejs/examples/embedding.ts:openai_embeddings"
-        ```
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/openai.rs:imports"
-    --8<-- "rust/lancedb/examples/openai.rs:openai_embeddings"
-    ```
-
 Learn about using the existing integrations and creating custom embedding functions in the [embedding API guide](./embeddings/).


@@ -580,5 +448,3 @@ Learn about using the existing integrations and creating custom embedding functi
 This section covered the very basics of using LanceDB. If you're learning about vector databases for the first time, you may want to read the page on [indexing](concepts/index_ivfpq.md) to get familiar with the concepts.

 If you've already worked with other vector databases, you may want to read the [guides](guides/tables.md) to learn how to work with LanceDB in more detail.
-
-[^1]: The `vectordb` package is a legacy package that is  deprecated in favor of `@lancedb/lancedb`.  The `vectordb` package will continue to receive bug fixes and security updates until September 2024.  We recommend all new projects use `@lancedb/lancedb`.  See the [migration guide](migration.md) for more information.
--- a/docs/src/basic_legacy.ts
+++ b/docs/src/basic_legacy.ts
@@ -1,14 +1,6 @@
 // --8<-- [start:import]
 import * as lancedb from "vectordb";
-import {
-  Schema,
-  Field,
-  Float32,
-  FixedSizeList,
-  Int32,
-  Float16,
-} from "apache-arrow";
-import * as arrow from "apache-arrow";
+import { Schema, Field, Float32, FixedSizeList, Int32, Float16 } from "apache-arrow";
 // --8<-- [end:import]
 import * as fs from "fs";
 import { Table as ArrowTable, Utf8 } from "apache-arrow";
@@ -28,33 +20,9 @@ const example = async () => {
      { vector: [3.1, 4.1], item: "foo", price: 10.0 },
      { vector: [5.9, 26.5], item: "bar", price: 20.0 },
    ],
-    { writeMode: lancedb.WriteMode.Overwrite },
+    { writeMode: lancedb.WriteMode.Overwrite }
  );
  // --8<-- [end:create_table]
-  {
-    // --8<-- [start:create_table_with_schema]
-    const schema = new arrow.Schema([
-      new arrow.Field(
-        "vector",
-        new arrow.FixedSizeList(
-          2,
-          new arrow.Field("item", new arrow.Float32(), true),
-        ),
-      ),
-      new arrow.Field("item", new arrow.Utf8(), true),
-      new arrow.Field("price", new arrow.Float32(), true),
-    ]);
-    const data = [
-      { vector: [3.1, 4.1], item: "foo", price: 10.0 },
-      { vector: [5.9, 26.5], item: "bar", price: 20.0 },
-    ];
-    const tbl = await db.createTable({
-      name: "myTableWithSchema",
-      data,
-      schema,
-    });
-    // --8<-- [end:create_table_with_schema]
-  }

  // --8<-- [start:add]
  const newData = Array.from({ length: 500 }, (_, i) => ({
@@ -74,35 +42,33 @@ const example = async () => {
  // --8<-- [end:create_index]

  // --8<-- [start:create_empty_table]
-  const schema = new arrow.Schema([
-    new arrow.Field("id", new arrow.Int32()),
-    new arrow.Field("name", new arrow.Utf8()),
+  const schema = new Schema([
+    new Field("id", new Int32()),
+    new Field("name", new Utf8()),
  ]);
-
  const empty_tbl = await db.createTable({ name: "empty_table", schema });
  // --8<-- [end:create_empty_table]
-  {
-    // --8<-- [start:create_f16_table]
-    const dim = 16;
-    const total = 10;
-    const schema = new Schema([
-      new Field("id", new Int32()),
+
+  // --8<-- [start:create_f16_table]
+  const dim = 16
+  const total = 10
+  const f16_schema = new Schema([
+      new Field('id', new Int32()),
      new Field(
-        "vector",
-        new FixedSizeList(dim, new Field("item", new Float16(), true)),
-        false,
-      ),
-    ]);
-    const data = lancedb.makeArrowTable(
+        'vector',
+        new FixedSizeList(dim, new Field('item', new Float16(), true)),
+        false
+      )
+    ])
+  const data = lancedb.makeArrowTable(
      Array.from(Array(total), (_, i) => ({
        id: i,
-        vector: Array.from(Array(dim), Math.random),
+        vector: Array.from(Array(dim), Math.random)
      })),
-      { schema },
-    );
-    const table = await db.createTable("f16_tbl", data);
-    // --8<-- [end:create_f16_table]
-  }
+      { f16_schema }
+    )
+  const table = await db.createTable('f16_tbl', data)
+  // --8<-- [end:create_f16_table]

  // --8<-- [start:search]
  const query = await tbl.search([100, 100]).limit(2).execute();
--- a/docs/src/cloud/rest.md
+++ b/docs/src/cloud/rest.md
@@ -1 +0,0 @@
-!!swagger ../../openapi.yml!!
--- a/docs/src/embeddings/custom_embedding_function.md
+++ b/docs/src/embeddings/custom_embedding_function.md
@@ -15,226 +15,198 @@ There is another optional layer of abstraction available: `TextEmbeddingFunction

 Let's implement `SentenceTransformerEmbeddings` class. All you need to do is implement the `generate_embeddings()` and `ndims` function to handle the input types you expect and register the class in the global `EmbeddingFunctionRegistry`

+```python
+from lancedb.embeddings import register
+from lancedb.util import attempt_import_or_raise

-=== "Python"
+@register("sentence-transformers")
+class SentenceTransformerEmbeddings(TextEmbeddingFunction):
+    name: str = "all-MiniLM-L6-v2"
+    # set more default instance vars like device, etc.

-    ```python
-    from lancedb.embeddings import register
-    from lancedb.util import attempt_import_or_raise
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._ndims = None
+    
+    def generate_embeddings(self, texts):
+        return self._embedding_model().encode(list(texts), ...).tolist()

-    @register("sentence-transformers")
-    class SentenceTransformerEmbeddings(TextEmbeddingFunction):
-        name: str = "all-MiniLM-L6-v2"
-        # set more default instance vars like device, etc.
+    def ndims(self):
+        if self._ndims is None:
+            self._ndims = len(self.generate_embeddings("foo")[0])
+        return self._ndims

-        def __init__(self, **kwargs):
-            super().__init__(**kwargs)
-            self._ndims = None
+    @cached(cache={}) 
+    def _embedding_model(self):
+        return sentence_transformers.SentenceTransformer(name)
+```

-        def generate_embeddings(self, texts):
-            return self._embedding_model().encode(list(texts), ...).tolist()
-
-        def ndims(self):
-            if self._ndims is None:
-                self._ndims = len(self.generate_embeddings("foo")[0])
-            return self._ndims
-
-        @cached(cache={})
-        def _embedding_model(self):
-            return sentence_transformers.SentenceTransformer(name)
-    ```
-
-=== "TypeScript"
-
-    ```ts
-    --8<--- "nodejs/examples/custom_embedding_function.ts:imports"
-
-    --8<--- "nodejs/examples/custom_embedding_function.ts:embedding_impl"
-    ```
-
-
-This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and default settings.
+This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and defaul settings.

 Now you can use this embedding function to create your table schema and that's it! you can then ingest data and run queries without manually vectorizing the inputs.

-=== "Python"
+```python
+from lancedb.pydantic import LanceModel, Vector

-    ```python
-    from lancedb.pydantic import LanceModel, Vector
+registry = EmbeddingFunctionRegistry.get_instance()
+stransformer = registry.get("sentence-transformers").create()

-    registry = EmbeddingFunctionRegistry.get_instance()
-    stransformer = registry.get("sentence-transformers").create()
+class TextModelSchema(LanceModel):
+    vector: Vector(stransformer.ndims) = stransformer.VectorField()
+    text: str = stransformer.SourceField()

-    class TextModelSchema(LanceModel):
-        vector: Vector(stransformer.ndims) = stransformer.VectorField()
-        text: str = stransformer.SourceField()
+tbl = db.create_table("table", schema=TextModelSchema)

-    tbl = db.create_table("table", schema=TextModelSchema)
+tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
+result = tbl.search("world").limit(5)
+```

-    tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
-    result = tbl.search("world").limit(5)
-    ```
+NOTE:

-=== "TypeScript"
-
-    ```ts
-    --8<--- "nodejs/examples/custom_embedding_function.ts:call_custom_function"
-    ```
-
-!!! note
-
-    You can always implement the `EmbeddingFunction` interface directly if you want or need to, `TextEmbeddingFunction` just makes it much simpler and faster for you to do so, by setting up the boiler plat for text-specific use case
+You can always implement the `EmbeddingFunction` interface directly if you want or need to, `TextEmbeddingFunction` just makes it much simpler and faster for you to do so, by setting up the boiler plat for text-specific use case

 ## Multi-modal embedding function example
-You can also use the `EmbeddingFunction` interface to implement more complex workflows such as multi-modal embedding function support.
+You can also use the `EmbeddingFunction` interface to implement more complex workflows such as multi-modal embedding function support. LanceDB implements `OpenClipEmeddingFunction` class that suppports multi-modal seach. Here's the implementation that you can use as a reference to build your own multi-modal embedding functions.

-=== "Python"
+```python
+@register("open-clip")
+class OpenClipEmbeddings(EmbeddingFunction):
+    name: str = "ViT-B-32"
+    pretrained: str = "laion2b_s34b_b79k"
+    device: str = "cpu"
+    batch_size: int = 64
+    normalize: bool = True
+    _model = PrivateAttr()
+    _preprocess = PrivateAttr()
+    _tokenizer = PrivateAttr()

-    LanceDB implements `OpenClipEmeddingFunction` class that suppports multi-modal seach. Here's the implementation that you can use as a reference to build your own multi-modal embedding functions.
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        open_clip = attempt_import_or_raise("open_clip", "open-clip") # EmbeddingFunction util to import external libs and raise if not found
+        model, _, preprocess = open_clip.create_model_and_transforms(
+            self.name, pretrained=self.pretrained
+        )
+        model.to(self.device)
+        self._model, self._preprocess = model, preprocess
+        self._tokenizer = open_clip.get_tokenizer(self.name)
+        self._ndims = None

-    ```python
-    @register("open-clip")
-    class OpenClipEmbeddings(EmbeddingFunction):
-        name: str = "ViT-B-32"
-        pretrained: str = "laion2b_s34b_b79k"
-        device: str = "cpu"
-        batch_size: int = 64
-        normalize: bool = True
-        _model = PrivateAttr()
-        _preprocess = PrivateAttr()
-        _tokenizer = PrivateAttr()
+    def ndims(self):
+        if self._ndims is None:
+            self._ndims = self.generate_text_embeddings("foo").shape[0]
+        return self._ndims

-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-            open_clip = attempt_import_or_raise("open_clip", "open-clip") # EmbeddingFunction util to import external libs and raise if not found
-            model, _, preprocess = open_clip.create_model_and_transforms(
-                self.name, pretrained=self.pretrained
-            )
-            model.to(self.device)
-            self._model, self._preprocess = model, preprocess
-            self._tokenizer = open_clip.get_tokenizer(self.name)
-            self._ndims = None
+    def compute_query_embeddings(
+        self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
+    ) -> List[np.ndarray]:
+        """
+        Compute the embeddings for a given user query

-        def ndims(self):
-            if self._ndims is None:
-                self._ndims = self.generate_text_embeddings("foo").shape[0]
-            return self._ndims
-
-        def compute_query_embeddings(
-            self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
-        ) -> List[np.ndarray]:
-            """
-            Compute the embeddings for a given user query
-
-            Parameters
-            ----------
-            query : Union[str, PIL.Image.Image]
-                The query to embed. A query can be either text or an image.
-            """
-            if isinstance(query, str):
-                return [self.generate_text_embeddings(query)]
-            else:
-                PIL = attempt_import_or_raise("PIL", "pillow")
-                if isinstance(query, PIL.Image.Image):
-                    return [self.generate_image_embedding(query)]
-                else:
-                    raise TypeError("OpenClip supports str or PIL Image as query")
-
-        def generate_text_embeddings(self, text: str) -> np.ndarray:
-            torch = attempt_import_or_raise("torch")
-            text = self.sanitize_input(text)
-            text = self._tokenizer(text)
-            text.to(self.device)
-            with torch.no_grad():
-                text_features = self._model.encode_text(text.to(self.device))
-                if self.normalize:
-                    text_features /= text_features.norm(dim=-1, keepdim=True)
-                return text_features.cpu().numpy().squeeze()
-
-        def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
-            """
-            Sanitize the input to the embedding function.
-            """
-            if isinstance(images, (str, bytes)):
-                images = [images]
-            elif isinstance(images, pa.Array):
-                images = images.to_pylist()
-            elif isinstance(images, pa.ChunkedArray):
-                images = images.combine_chunks().to_pylist()
-            return images
-
-        def compute_source_embeddings(
-            self, images: IMAGES, *args, **kwargs
-        ) -> List[np.array]:
-            """
-            Get the embeddings for the given images
-            """
-            images = self.sanitize_input(images)
-            embeddings = []
-            for i in range(0, len(images), self.batch_size):
-                j = min(i + self.batch_size, len(images))
-                batch = images[i:j]
-                embeddings.extend(self._parallel_get(batch))
-            return embeddings
-
-        def _parallel_get(self, images: Union[List[str], List[bytes]]) -> List[np.ndarray]:
-            """
-            Issue concurrent requests to retrieve the image data
-            """
-            with concurrent.futures.ThreadPoolExecutor() as executor:
-                futures = [
-                    executor.submit(self.generate_image_embedding, image)
-                    for image in images
-                ]
-                return [future.result() for future in futures]
-
-        def generate_image_embedding(
-            self, image: Union[str, bytes, "PIL.Image.Image"]
-        ) -> np.ndarray:
-            """
-            Generate the embedding for a single image
-
-            Parameters
-            ----------
-            image : Union[str, bytes, PIL.Image.Image]
-                The image to embed. If the image is a str, it is treated as a uri.
-                If the image is bytes, it is treated as the raw image bytes.
-            """
-            torch = attempt_import_or_raise("torch")
-            # TODO handle retry and errors for https
-            image = self._to_pil(image)
-            image = self._preprocess(image).unsqueeze(0)
-            with torch.no_grad():
-                return self._encode_and_normalize_image(image)
-
-        def _to_pil(self, image: Union[str, bytes]):
+        Parameters
+        ----------
+        query : Union[str, PIL.Image.Image]
+            The query to embed. A query can be either text or an image.
+        """
+        if isinstance(query, str):
+            return [self.generate_text_embeddings(query)]
+        else:
            PIL = attempt_import_or_raise("PIL", "pillow")
-            if isinstance(image, bytes):
-                return PIL.Image.open(io.BytesIO(image))
-            if isinstance(image, PIL.Image.Image):
-                return image
-            elif isinstance(image, str):
-                parsed = urlparse.urlparse(image)
-                # TODO handle drive letter on windows.
-                if parsed.scheme == "file":
-                    return PIL.Image.open(parsed.path)
-                elif parsed.scheme == "":
-                    return PIL.Image.open(image if os.name == "nt" else parsed.path)
-                elif parsed.scheme.startswith("http"):
-                    return PIL.Image.open(io.BytesIO(url_retrieve(image)))
-                else:
-                    raise NotImplementedError("Only local and http(s) urls are supported")
+            if isinstance(query, PIL.Image.Image):
+                return [self.generate_image_embedding(query)]
+            else:
+                raise TypeError("OpenClip supports str or PIL Image as query")

-        def _encode_and_normalize_image(self, image_tensor: "torch.Tensor"):
-            """
-            encode a single image tensor and optionally normalize the output
-            """
-            image_features = self._model.encode_image(image_tensor)
+    def generate_text_embeddings(self, text: str) -> np.ndarray:
+        torch = attempt_import_or_raise("torch")
+        text = self.sanitize_input(text)
+        text = self._tokenizer(text)
+        text.to(self.device)
+        with torch.no_grad():
+            text_features = self._model.encode_text(text.to(self.device))
            if self.normalize:
-                image_features /= image_features.norm(dim=-1, keepdim=True)
-            return image_features.cpu().numpy().squeeze()
-    ```
+                text_features /= text_features.norm(dim=-1, keepdim=True)
+            return text_features.cpu().numpy().squeeze()

-=== "TypeScript"
+    def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
+        """
+        Sanitize the input to the embedding function.
+        """
+        if isinstance(images, (str, bytes)):
+            images = [images]
+        elif isinstance(images, pa.Array):
+            images = images.to_pylist()
+        elif isinstance(images, pa.ChunkedArray):
+            images = images.combine_chunks().to_pylist()
+        return images

-    Coming Soon! See this [issue](https://github.com/lancedb/lancedb/issues/1482) to track the status!
+    def compute_source_embeddings(
+        self, images: IMAGES, *args, **kwargs
+    ) -> List[np.array]:
+        """
+        Get the embeddings for the given images
+        """
+        images = self.sanitize_input(images)
+        embeddings = []
+        for i in range(0, len(images), self.batch_size):
+            j = min(i + self.batch_size, len(images))
+            batch = images[i:j]
+            embeddings.extend(self._parallel_get(batch))
+        return embeddings
+
+    def _parallel_get(self, images: Union[List[str], List[bytes]]) -> List[np.ndarray]:
+        """
+        Issue concurrent requests to retrieve the image data
+        """
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            futures = [
+                executor.submit(self.generate_image_embedding, image)
+                for image in images
+            ]
+            return [future.result() for future in futures]
+
+    def generate_image_embedding(
+        self, image: Union[str, bytes, "PIL.Image.Image"]
+    ) -> np.ndarray:
+        """
+        Generate the embedding for a single image
+
+        Parameters
+        ----------
+        image : Union[str, bytes, PIL.Image.Image]
+            The image to embed. If the image is a str, it is treated as a uri.
+            If the image is bytes, it is treated as the raw image bytes.
+        """
+        torch = attempt_import_or_raise("torch")
+        # TODO handle retry and errors for https
+        image = self._to_pil(image)
+        image = self._preprocess(image).unsqueeze(0)
+        with torch.no_grad():
+            return self._encode_and_normalize_image(image)
+
+    def _to_pil(self, image: Union[str, bytes]):
+        PIL = attempt_import_or_raise("PIL", "pillow")
+        if isinstance(image, bytes):
+            return PIL.Image.open(io.BytesIO(image))
+        if isinstance(image, PIL.Image.Image):
+            return image
+        elif isinstance(image, str):
+            parsed = urlparse.urlparse(image)
+            # TODO handle drive letter on windows.
+            if parsed.scheme == "file":
+                return PIL.Image.open(parsed.path)
+            elif parsed.scheme == "":
+                return PIL.Image.open(image if os.name == "nt" else parsed.path)
+            elif parsed.scheme.startswith("http"):
+                return PIL.Image.open(io.BytesIO(url_retrieve(image)))
+            else:
+                raise NotImplementedError("Only local and http(s) urls are supported")
+
+    def _encode_and_normalize_image(self, image_tensor: "torch.Tensor"):
+        """
+        encode a single image tensor and optionally normalize the output
+        """
+        image_features = self._model.encode_image(image_tensor)
+        if self.normalize:
+            image_features /= image_features.norm(dim=-1, keepdim=True)
+        return image_features.cpu().numpy().squeeze()
+```
--- a/docs/src/embeddings/default_embedding_functions.md
+++ b/docs/src/embeddings/default_embedding_functions.md
@@ -17,7 +17,6 @@ Allows you to set parameters when registering a `sentence-transformers` object.
 | `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!"
@@ -194,13 +193,13 @@ from lancedb.pydantic import LanceModel, Vector

 model = get_registry().get("huggingface").create(name='facebook/bart-base')

-class Words(LanceModel):
+class TextModel(LanceModel):
    text: str = model.SourceField()
    vector: Vector(model.ndims()) = model.VectorField()

 df = pd.DataFrame({"text": ["hi hello sayonara", "goodbye world"]})
 table = db.create_table("greets", schema=Words)
-table.add(df)
+table.add()
 query = "old greeting"
 actual = table.search(query).limit(1).to_pydantic(Words)[0]
 print(actual.text)
@@ -390,7 +389,6 @@ Supported parameters (to be passed in `create` method) are:
 | `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|
@@ -429,45 +427,6 @@ Usage Example:
        tbl.add(data)
    ```

-### 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)
-```
-
 ### 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:
@@ -518,82 +477,6 @@ tbl.add(df)
 rs = tbl.search("hello").limit(1).to_pandas()
 ```

-# 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)
-```
-
 ## Multi-modal embedding functions
 Multi-modal embedding functions allow you to query your table using both images and text.

@@ -641,7 +524,7 @@ uris = [
 # 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})
+    [{"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
@@ -747,54 +630,3 @@ 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()
-
-
-    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})
-    )
-```
--- a/docs/src/embeddings/embedding_functions.md
+++ b/docs/src/embeddings/embedding_functions.md
@@ -2,12 +2,12 @@ Representing multi-modal data as vector embeddings is becoming a standard practi

 For this purpose, LanceDB introduces an **embedding functions API**, that allow you simply set up once, during the configuration stage of your project. After this, the table remembers it, effectively making the embedding functions *disappear in the background* so you don't have to worry about manually passing callables, and instead, simply focus on the rest of your data engineering pipeline.

-!!! Note "Embedding functions on LanceDB cloud"
-    When using embedding functions with LanceDB cloud, the embeddings will be generated on the source device and sent to the cloud. This means that the source device must have the necessary resources to generate the embeddings.
+!!! 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.

 !!! warning
-    Using the embedding function registry means that you don't have to explicitly generate the embeddings yourself.
-    However, if your embedding function changes, you'll have to re-configure your table with the new embedding function
+    Using the embedding function registry means that you don't have to explicitly generate the embeddings yourself. 
+    However, if your embedding function changes, you'll have to re-configure your table with the new embedding function 
    and regenerate the embeddings. In the future, we plan to support the ability to change the embedding function via
    table metadata and have LanceDB automatically take care of regenerating the embeddings.

@@ -16,7 +16,7 @@ For this purpose, LanceDB introduces an **embedding functions API**, that allow

 === "Python"
    In the LanceDB python SDK, we define a global embedding function registry with
-    many different embedding models and even more coming soon.
+    many different embedding models and even more coming soon. 
    Here's let's an implementation of CLIP as example.

    ```python
@@ -26,35 +26,20 @@ For this purpose, LanceDB introduces an **embedding functions API**, that allow
    clip = registry.get("open-clip").create()
    ```

-    You can also define your own embedding function by implementing the `EmbeddingFunction`
+    You can also define your own embedding function by implementing the `EmbeddingFunction` 
    abstract base interface. It subclasses Pydantic Model which can be utilized to write complex schemas simply as we'll see next!

-=== "TypeScript"
+=== "JavaScript""
    In the TypeScript SDK, the choices are more limited. For now, only the OpenAI
    embedding function is available.

    ```javascript
-    import * as lancedb from '@lancedb/lancedb'
-    import { getRegistry } from '@lancedb/lancedb/embeddings'
+    const lancedb = require("vectordb");

    // You need to provide an OpenAI API key
    const apiKey = "sk-..."
    // The embedding function will create embeddings for the 'text' column
-    const func = getRegistry().get("openai").create({apiKey})
-    ```
-=== "Rust"
-    In the Rust SDK, the choices are more limited. For now, only the OpenAI
-    embedding function is available. But unlike the Python and TypeScript SDKs, you need manually register the OpenAI embedding function.
-
-    ```toml
-    // Make sure to include the `openai` feature
-    [dependencies]
-    lancedb = {version = "*", features = ["openai"]}
-    ```
-
-    ```rust
-    --8<-- "rust/lancedb/examples/openai.rs:imports"
-    --8<-- "rust/lancedb/examples/openai.rs:openai_embeddings"
+    const embedding = new lancedb.OpenAIEmbeddingFunction('text', apiKey)
    ```

 ## 2. Define the data model or schema
@@ -70,14 +55,14 @@ For this purpose, LanceDB introduces an **embedding functions API**, that allow

    `VectorField` tells LanceDB to use the clip embedding function to generate query embeddings for the `vector` column and `SourceField` ensures that when adding data, we automatically use the specified embedding function to encode `image_uri`.

-=== "TypeScript"
+=== "JavaScript"

    For the TypeScript SDK, a schema can be inferred from input data, or an explicit
    Arrow schema can be provided.

 ## 3. Create table and add data

-Now that we have chosen/defined our embedding function and the schema,
+Now that we have chosen/defined our embedding function and the schema, 
 we can create the table and ingest data without needing to explicitly generate
 the embeddings at all:

@@ -89,26 +74,17 @@ the embeddings at all:
    table.add([{"image_uri": u} for u in uris])
    ```

-=== "TypeScript"
+=== "JavaScript"

-    === "@lancedb/lancedb"
+    ```javascript
+    const db = await lancedb.connect("data/sample-lancedb");
+    const data = [
+    { text: "pepperoni"},
+    { text: "pineapple"}
+    ]

-        ```ts
-        --8<-- "nodejs/examples/embedding.ts:imports"
-        --8<-- "nodejs/examples/embedding.ts:embedding_function"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```ts
-        const db = await lancedb.connect("data/sample-lancedb");
-        const data = [
-            { text: "pepperoni"},
-            { text: "pineapple"}
-        ]
-
-        const table = await db.createTable("vectors", data, embedding)
-        ```
+    const table = await db.createTable("vectors", data, embedding)
+    ```

 ## 4. Querying your table
 Not only can you forget about the embeddings during ingestion, you also don't
@@ -121,8 +97,8 @@ need to worry about it when you query the table:
    ```python
    results = (
        table.search("dog")
-            .limit(10)
-            .to_pandas()
+        .limit(10)
+        .to_pandas()
    )
    ```

@@ -133,32 +109,22 @@ need to worry about it when you query the table:
    query_image = Image.open(p)
    results = (
        table.search(query_image)
-            .limit(10)
-            .to_pandas()
+        .limit(10)
+        .to_pandas()
    )
    ```

    Both of the above snippet returns a pandas DataFrame with the 10 closest vectors to the query.

-=== "TypeScript"
-
-    === "@lancedb/lancedb"
-
-        ```ts
-        const results = await table.search("What's the best pizza topping?")
-            .limit(10)
-            .toArray()
-        ```
-
-    === "vectordb (deprecated)
-
-        ```ts
-        const results = await table
-            .search("What's the best pizza topping?")
-            .limit(10)
-            .execute()
-        ```
+=== "JavaScript"

+    ```javascript
+    const results = await table
+    .search("What's the best pizza topping?")
+    .limit(10)
+    .execute()
+    ```    
+    
    The above snippet returns an array of records with the top 10 nearest neighbors to the query.

 ---
--- a/docs/src/embeddings/index.md
+++ b/docs/src/embeddings/index.md
@@ -1,13 +1,13 @@
-Due to the nature of vector embeddings, they can be used to represent any kind of data, from text to images to audio.
-This makes them a very powerful tool for machine learning practitioners.
-However, there's no one-size-fits-all solution for generating embeddings - there are many different libraries and APIs
+Due to the nature of vector embeddings, they can be used to represent any kind of data, from text to images to audio. 
+This makes them a very powerful tool for machine learning practitioners. 
+However, there's no one-size-fits-all solution for generating embeddings - there are many different libraries and APIs 
 (both commercial and open source) that can be used to generate embeddings from structured/unstructured data.

 LanceDB supports 3 methods of working with embeddings.

 1. You can manually generate embeddings for the data and queries. This is done outside of LanceDB.
 2. You can use the built-in [embedding functions](./embedding_functions.md) to embed the data and queries in the background.
-3. You can define your own [custom embedding function](./custom_embedding_function.md)
+3. For python users, you can define your own [custom embedding function](./custom_embedding_function.md)
   that extends the default embedding functions.

 For python users, there is also a legacy [with_embeddings API](./legacy.md).
@@ -18,109 +18,21 @@ It is retained for compatibility and will be removed in a future version.
 To get started with embeddings, you can use the built-in embedding functions.

 ### OpenAI Embedding function
-
 LanceDB registers the OpenAI embeddings function in the registry as `openai`. You can pass any supported model name to the `create`. By default it uses `"text-embedding-ada-002"`.

-=== "Python"
-
-    ```python
-    import lancedb
-    from lancedb.pydantic import LanceModel, Vector
-    from lancedb.embeddings import get_registry
-
-    db = lancedb.connect("/tmp/db")
-    func = get_registry().get("openai").create(name="text-embedding-ada-002")
-
-    class Words(LanceModel):
-        text: str = func.SourceField()
-        vector: Vector(func.ndims()) = func.VectorField()
-
-    table = db.create_table("words", schema=Words, mode="overwrite")
-    table.add(
-        [
-            {"text": "hello world"},
-            {"text": "goodbye world"}
-        ]
-        )
-
-    query = "greetings"
-    actual = table.search(query).limit(1).to_pydantic(Words)[0]
-    print(actual.text)
-    ```
-
-=== "TypeScript"
-
-    ```typescript
-    --8<--- "nodejs/examples/embedding.ts:imports"
-    --8<--- "nodejs/examples/embedding.ts:openai_embeddings"
-    ```
-
-=== "Rust"
-
-    ```rust
-    --8<--- "rust/lancedb/examples/openai.rs:imports"
-    --8<--- "rust/lancedb/examples/openai.rs:openai_embeddings"
-    ```
-
-### Sentence Transformers Embedding function
-LanceDB registers the Sentence Transformers embeddings function in the registry as `sentence-transformers`. You can pass any supported model name to the `create`. By default it uses `"sentence-transformers/paraphrase-MiniLM-L6-v2"`.
-
-=== "Python"
-    ```python
-    import lancedb
-    from lancedb.pydantic import LanceModel, Vector
-    from lancedb.embeddings import get_registry
-
-    db = lancedb.connect("/tmp/db")
-    model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
-
-    class Words(LanceModel):
-        text: str = model.SourceField()
-        vector: Vector(model.ndims()) = model.VectorField()
-
-    table = db.create_table("words", schema=Words)
-    table.add(
-        [
-            {"text": "hello world"},
-            {"text": "goodbye world"}
-        ]
-    )
-
-    query = "greetings"
-    actual = table.search(query).limit(1).to_pydantic(Words)[0]
-    print(actual.text)
-    ```
-
-=== "TypeScript"
-
-    Coming Soon!
-
-=== "Rust"
-
-    Coming Soon!
-
-### Embedding function with LanceDB cloud
-Embedding functions are now supported on LanceDB cloud. The embeddings will be generated on the source device and sent to the cloud. This means that the source device must have the necessary resources to generate the embeddings. Here's an example using the OpenAI embedding function:
-
 ```python
-import os
 import lancedb
 from lancedb.pydantic import LanceModel, Vector
 from lancedb.embeddings import get_registry
-os.environ['OPENAI_API_KEY'] = "..."

-db = lancedb.connect(
-  uri="db://....",
-  api_key="sk_...",
-  region="us-east-1"
-)
-func = get_registry().get("openai").create()
+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)
+table = db.create_table("words", schema=Words, mode="overwrite")
 table.add(
    [
        {"text": "hello world"},
@@ -132,3 +44,31 @@ query = "greetings"
 actual = table.search(query).limit(1).to_pydantic(Words)[0]
 print(actual.text)
 ```
+
+### Sentence Transformers Embedding function
+LanceDB registers the Sentence Transformers embeddings function in the registry as `sentence-transformers`. You can pass any supported model name to the `create`. By default it uses `"sentence-transformers/paraphrase-MiniLM-L6-v2"`.
+
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+db = lancedb.connect("/tmp/db")
+model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
+
+class Words(LanceModel):
+    text: str = model.SourceField()
+    vector: Vector(model.ndims()) = model.VectorField()
+
+table = db.create_table("words", schema=Words)
+table.add(
+    [
+        {"text": "hello world"},
+        {"text": "goodbye world"}
+    ]
+)
+
+query = "greetings"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```
--- a/docs/src/examples/index.md
+++ b/docs/src/examples/index.md
@@ -10,7 +10,7 @@ LanceDB provides language APIs, allowing you to embed a database in your languag

 ## Applications powered by LanceDB

-| Project Name | Description |
-| --- | --- |
-| **Ultralytics Explorer 🚀**<br>[![Ultralytics](https://img.shields.io/badge/Ultralytics-Docs-green?labelColor=0f3bc4&style=flat-square&logo=https://cdn.prod.website-files.com/646dd1f1a3703e451ba81ecc/64994922cf2a6385a4bf4489_UltralyticsYOLO_mark_blue.svg&link=https://docs.ultralytics.com/datasets/explorer/)](https://docs.ultralytics.com/datasets/explorer/)<br>[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb) |  - 🔍 **Explore CV Datasets**: Semantic search, SQL queries, vector similarity, natural language.<br>- 🖥️ **GUI & Python API**: Seamless dataset interaction.<br>- ⚡ **Efficient & Scalable**: Leverages LanceDB for large datasets.<br>- 📊 **Detailed Analysis**: Easily analyze data patterns.<br>- 🌐 **Browser GUI Demo**: Create embeddings, search images, run queries. |
-| **Website Chatbot🤖**<br>[![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/lancedb-vercel-chatbot)<br>[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Flancedb%2Flancedb-vercel-chatbot&amp;env=OPENAI_API_KEY&amp;envDescription=OpenAI%20API%20Key%20for%20chat%20completion.&amp;project-name=lancedb-vercel-chatbot&amp;repository-name=lancedb-vercel-chatbot&amp;demo-title=LanceDB%20Chatbot%20Demo&amp;demo-description=Demo%20website%20chatbot%20with%20LanceDB.&amp;demo-url=https%3A%2F%2Flancedb.vercel.app&amp;demo-image=https%3A%2F%2Fi.imgur.com%2FazVJtvr.png) | - 🌐 **Chatbot from Sitemap/Docs**: Create a chatbot using site or document context.<br>- 🚀 **Embed LanceDB in Next.js**: Lightweight, on-prem storage.<br>- 🧠 **AI-Powered Context Retrieval**: Efficiently access relevant data.<br>- 🔧 **Serverless & Native JS**: Seamless integration with Next.js.<br>- ⚡ **One-Click Deploy on Vercel**: Quick and easy setup.. |
+| Project Name                                        | Description                                                                                                          | Screenshot                                |
+|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
+| [YOLOExplorer](https://github.com/lancedb/yoloexplorer) | Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds                  | ![YOLOExplorer](https://github.com/lancedb/vectordb-recipes/assets/15766192/ae513a29-8f15-4e0b-99a1-ccd8272b6131) |
+| [Website Chatbot (Deployable Vercel Template)](https://github.com/lancedb/lancedb-vercel-chatbot) | Create a chatbot from the sitemap of any website/docs of your choice. Built using vectorDB serverless native javascript package. | ![Chatbot](../assets/vercel-template.gif)    |
--- a/docs/src/examples/python_examples/aiagent.md
+++ b/docs/src/examples/python_examples/aiagent.md
@@ -1,27 +0,0 @@
-# AI Agents: Intelligent Collaboration🤖
-
-Think of a platform💻 where AI Agents🤖 can seamlessly exchange information, coordinate over tasks, and achieve shared targets with great efficiency📈🚀.
-
-## Vector-Based Coordination: The Technical Advantage
-Leveraging LanceDB's vector-based capabilities, our coordination application enables 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
-
-
--- a/docs/src/examples/python_examples/build_from_scratch.md
+++ b/docs/src/examples/python_examples/build_from_scratch.md
@@ -1,13 +0,0 @@
-# **Build from Scratch with LanceDB 🛠️🚀**
-
-Start building your GenAI applications from the ground up using LanceDB's efficient vector-based document retrieval capabilities! 📑
-
-**Get Started in Minutes ⏱️**
-
-These examples provide a solid foundation for building your own GenAI applications using LanceDB. Jump from idea to proof of concept quickly with applied examples. Get started and see what you can create! 💻
-
-| **Build From Scratch**                     | **Description**                                                                                              | **Links**                                                                                                                                                                                                                                                                                                                                                                                                                |
-|:-------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| **Build RAG from Scratch🚀💻**             | 📝 Create a **Retrieval-Augmented Generation** (RAG) model from scratch using LanceDB.                       | [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/RAG-from-Scratch)<br>[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)]()                                                                                                                                  |
-| **Local RAG from Scratch with Llama3🔥💡** | 🐫 Build a local RAG model using **Llama3** and **LanceDB** for fast and efficient text generation.          | [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Local-RAG-from-Scratch)<br>[![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Local-RAG-from-Scratch/rag.py)    |
-| **Multi-Head RAG from Scratch📚💻**        | 🤯 Develop a **Multi-Head RAG model** from scratch, enabling generation of text based on multiple documents. | [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Multi-Head-RAG-from-Scratch)<br>[![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Multi-Head-RAG-from-Scratch) |
--- a/docs/src/examples/python_examples/chatbot.md
+++ b/docs/src/examples/python_examples/chatbot.md
@@ -1,41 +0,0 @@
-**Chatbot Application with LanceDB 🤖**
-====================================================================
-
- Create an innovative chatbot application 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 ⚡️** | Unlock magical conversations with the Hogwarts chatbot, powered by Open-source RAG, DBRX, LanceDB, LLama-index, and Hugging Face Embeddings, delivering enchanting user experiences and spellbinding interactions ✨ | [![GitHub](../../assets/github.svg)][databricks_github] <br>[![Python](../../assets/python.svg)][databricks_python] |
-| **CLI SDK Manual Chatbot Locally 💻** | CLI chatbot for SDK/hardware documents, powered by Local RAG, LLama3, Ollama, LanceDB, and Openhermes Embeddings, built with Phidata Assistant and Knowledge Base for instant technical support 🤖 | [![GitHub](../../assets/github.svg)][clisdk_github] <br>[![Python](../../assets/python.svg)][clisdk_python] |
-| **Youtube Transcript Search QA Bot 📹** | Unlock the power of YouTube transcripts with a Q&A bot, leveraging natural language search and LanceDB for effortless data management and instant answers 💬 | [![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 🤖** | Revolutionize code documentation with a Q&A bot, powered by LangChain and LanceDB, allowing effortless querying of documentation using natural language, 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 🤖** | Experience the future of conversational AI with a context-aware chatbot, powered by Llama 2, LanceDB, and LangChain, enabling 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 📊** | Revolutionize data interaction with a chat application that harnesses LanceDB's hybrid search capabilities to converse with CSV and Excel files, enabling efficient and scalable data exploration and analysis 🚀 | [![GitHub](../../assets/github.svg)][csv_github] <br>[![Open In Collab](../../assets/colab.svg)][csv_colab] <br>[![Ghost](../../assets/ghost.svg)][csv_ghost] |
-
-
-[databricks_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/databricks_DBRX_website_bot
-[databricks_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/databricks_DBRX_website_bot/main.py
-
-[clisdk_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/CLI-SDK-Manual-Chatbot-Locally
-[clisdk_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/CLI-SDK-Manual-Chatbot-Locally/assistant.py
-
-[youtube_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Youtube-Search-QA-Bot
-[youtube_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Youtube-Search-QA-Bot/main.ipynb
-[youtube_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Youtube-Search-QA-Bot/main.py
-
-[docs_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot
-[docs_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot/main.ipynb
-[docs_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot/main.py
-
-[aware_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB
-[aware_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb
-[aware_ghost]: https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755
-
-[csv_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Chat_with_csv_file
-[csv_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Chat_with_csv_file/main.ipynb
-[csv_ghost]: https://blog.lancedb.com/p/d8c71df4-e55f-479a-819e-cde13354a6a3/
--- a/docs/src/examples/python_examples/evaluations.md
+++ b/docs/src/examples/python_examples/evaluations.md
@@ -1,23 +0,0 @@
-**Evaluation: Assessing Text Performance with Precision 📊💡**
-====================================================================
-
-**Evaluation Fundamentals 📊**
-
-Evaluation is a comprehensive tool designed to measure the performance of text-based inputs, enabling data-driven optimization and improvement 📈. 
-
-**Text Evaluation 101 📚**
-
-By leveraging cutting-edge technologies, this provides a robust framework for evaluating reference and candidate texts across various metrics 📊, ensuring high-quality text outputs that 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
--- a/docs/src/examples/python_examples/multimodal.md
+++ b/docs/src/examples/python_examples/multimodal.md
@@ -1,28 +0,0 @@
-# **Multimodal Search with LanceDB 🤹‍♂️🔍**
-
-Experience the future of search with 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 🌐💥**    | Revolutionize search with Multimodal CLIP and DiffusionDB, combining text and image understanding for a new dimension of discovery! 🔓 | [![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 📸🔍**               | Discover relevant documents and images with a single query, using LanceDB's multimodal search capabilities to bridge the gap between text and visuals! 🌉 | [![GitHub](../../assets/github.svg)](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search) <br>[![Open In Collab](../../assets/colab.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.ipynb) <br> [![Python](../../assets/python.svg)](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.py)<br> [![Ghost](../../assets/ghost.svg)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/) |
-| **Cambrian-1: Vision-Centric Image Exploration 🔍👀** | Dive into vision-centric exploration of images with Cambrian-1, powered by LanceDB's multimodal search to uncover new insights! 🔎                        | [![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/
--- a/docs/src/examples/python_examples/rag.md
+++ b/docs/src/examples/python_examples/rag.md
@@ -1,84 +0,0 @@
-
-**RAG: Revolutionize Information Retrieval with LanceDB 🔓🧐**
-====================================================================
-
-Unlock the full potential of Retrieval-Augmented Generation (RAG) with LanceDB, a solution for efficient vector-based information retrieval 📊. 
-
-**Experience the Future of Search 🔄**
-
-RAG integrates large language models (LLMs) with scalable knowledge bases, enabling efficient information retrieval and answer generation 🤖. By applying RAG to industry-specific use cases, developers can optimize query processing 📊, reduce response latency ⏱️, and improve resource utilization 💻. LanceDB provides a robust framework for integrating LLMs with external knowledge sources, facilitating accurate and informative responses 📝.
-
-| **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 Forward-Looking Active REtrieval 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** ⚡🌐 | Revolutionize search with RAG Fusion, utilizing the **RRF algorithm** to rerank documents based on user queries, and leveraging LanceDB and OPENAI Embeddings for efficient information retrieval ⚡🌐                                                                          | [![Github](../../assets/github.svg)][fusion_github] <br>[![Open In Collab](../../assets/colab.svg)][fusion_colab] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
-| **Agentic RAG** 🤖📚 | Unlock autonomous information retrieval with **Agentic RAG**, a framework of **intelligent agents** that collaborate to synthesize, summarize, and compare data across sources, enabling proactive and informed decision-making 🤖📚                                                              | [![Github](../../assets/github.svg)][agentic_github] <br>[![Open In Collab](../../assets/colab.svg)][agentic_colab] |
-
-
-
-
-
-
-
-
-
-
-
-
-[matryoshka_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex
-[matryoshka_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex/RAG_with_MatryoshkaEmbedding_and_Llamaindex.ipynb
-
-[rag_reranking_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/RAG_Reranking
-[rag_reranking_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/RAG_Reranking/main.ipynb
-[rag_reranking_ghost]: https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544
-
-
-[instruct_multitask_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/instruct-multitask
-[instruct_multitask_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/instruct-multitask/main.ipynb
-[instruct_multitask_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/instruct-multitask/main.py
-[instruct_multitask_ghost]: https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543
-
-[hyde_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Advance-RAG-with-HyDE
-[hyde_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance-RAG-with-HyDE/main.ipynb
-[hyde_ghost]: https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb
-
-[lotr_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Advance_RAG_LOTR
-[lotr_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance_RAG_LOTR/main.ipynb
-[lotr_ghost]: https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35
-
-[parent_doc_retriever_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/parent_document_retriever
-[parent_doc_retriever_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/parent_document_retriever/main.ipynb
-[parent_doc_retriever_ghost]: https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6
-
-[corrective_rag_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph
-[corrective_rag_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb
-[corrective_rag_ghost]: https://blog.lancedb.com/implementing-corrective-rag-in-the-easiest-way-2/
-
-[compression_rag_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG
-[compression_rag_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb
-[compression_rag_ghost]: https://blog.lancedb.com/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301/
-
-[flare_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/better-rag-FLAIR
-[flare_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/better-rag-FLAIR/main.ipynb
-[flare_ghost]: https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/
-
-[query_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/QueryExpansion&Reranker
-[query_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/QueryExpansion&Reranker/main.ipynb
-
-
-[fusion_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/RAG_Fusion
-[fusion_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/RAG_Fusion/main.ipynb
-
-[agentic_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG
-[agentic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG/main.ipynb
-
-
--- a/docs/src/examples/python_examples/vector_search.md
+++ b/docs/src/examples/python_examples/vector_search.md
@@ -1,80 +0,0 @@
-**Vector Search: Unlock Efficient Document Retrieval 🔓👀**
-====================================================================
-
-Unlock the power of vector search with LanceDB, a cutting-edge solution for efficient vector-based document retrieval 📊. 
-
-**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 🔄** | Combine the power of traditional search algorithms with LanceDB's vector-based search for a robust and efficient search experience 📊 | [![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 💡** | 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 🔎** | Unlock contextual understanding with Named Entity Recognition (NER) methods: Dictionary-Based, Rule-Based, and Deep Learning-Based, to accurately identify and extract 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 ⚖️** | Unlock powerful semantic search capabilities by performing 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, leveraging LanceDB API for seamless image search and retrieval experiences 📸 | [![Github](../../assets/github.svg)][imagebind_github] <br> [![Open in Spaces](../../assets/open_hf_space.svg)][imagebind_huggingface] |                                                                                                                                                                                                                                                                                                    
-| **Search Engine using SAM & CLIP 🔍** | Build a search engine within an image using SAM and CLIP models, enabling object-level search and retrieval, with LanceDB indexing and search capabilities to find the closest match between image embeddings and user queries 📸 | [![Github](../../assets/github.svg)][swi_github] <br>[![Open In Collab](../../assets/colab.svg)][swi_colab] <br>[![Ghost](../../assets/ghost.svg)][swi_ghost] |                                                                                                                                                                                                                                                                                                    
-| **Zero Shot Object Localization and Detection with CLIP 🔎** | Perform object detection on images using OpenAI's CLIP, enabling zero-shot localization and detection of objects, with capabilities to split images into patches, parse with CLIP, and plot bounding boxes 📊 | [![Github](../../assets/github.svg)][zsod_github] <br>[![Open In Collab](../../assets/colab.svg)][zsod_colab] |                                                                                                                                                                                                                                                                                                    
-| **Accelerate Vector Search with OpenVINO 🚀** | Boost vector search applications using OpenVINO, achieving significant speedups with CLIP for text-to-image and image-to-image searching, through PyTorch model optimization, FP16 and INT8 format conversion, and quantization with OpenVINO NNCF 📈 | [![Github](../../assets/github.svg)][openvino_github] <br>[![Open In Collab](../../assets/colab.svg)][openvino_colab] <br>[![Ghost](../../assets/ghost.svg)][openvino_ghost] |                                                                                                                                                                                                                                                                                                    
-| **Zero-Shot Image Classification with CLIP and LanceDB 📸** | Achieve zero-shot image classification using CLIP and LanceDB, enabling models to classify images without prior training on specific use cases, unlocking flexible and adaptable image classification capabilities 🔓 | [![Github](../../assets/github.svg)][zsic_github] <br>[![Open In Collab](../../assets/colab.svg)][zsic_colab] <br>[![Ghost](../../assets/ghost.svg)][zsic_ghost] |                                                                                                                                                                                                                                                                                                    
-
-
-
-
-[inbuilt_hybrid_search_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Inbuilt-Hybrid-Search
-[inbuilt_hybrid_search_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Inbuilt-Hybrid-Search/Inbuilt_Hybrid_Search_with_LanceDB.ipynb
-
-[BM25_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Hybrid_search_bm25_lancedb
-[BM25_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Hybrid_search_bm25_lancedb/main.ipynb
-[BM25_ghost]: https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6
-
-[NER_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search
-[NER_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb
-[NER_ghost]: https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493
-
-[audio_search_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/audio_search
-[audio_search_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.ipynb
-[audio_search_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.py
-
-[mls_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa
-[mls_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa/main.ipynb
-[mls_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa/main.py
-
-[fr_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/facial_recognition
-[fr_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/facial_recognition/main.ipynb
-
-[sentiment_analysis_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews
-[sentiment_analysis_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb
-[sentiment_analysis_ghost]: https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6
-
-[arithmetic_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Vector-Arithmetic-with-LanceDB
-[arithmetic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Vector-Arithmetic-with-LanceDB/main.ipynb
-[arithmetic_ghost]: https://blog.lancedb.com/vector-arithmetic-with-lancedb-an-intro-to-vector-embeddings/
-
-[imagebind_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/imagebind_demo
-[imagebind_huggingface]: https://huggingface.co/spaces/raghavd99/imagebind2
-
-[swi_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip
-[swi_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb
-[swi_ghost]: https://blog.lancedb.com/search-within-an-image-331b54e4285e
-
-[zsod_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/zero-shot-object-detection-CLIP
-[zsod_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/zero-shot-object-detection-CLIP/zero_shot_object_detection_clip.ipynb
-
-[openvino_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO
-[openvino_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb
-[openvino_ghost]: https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-lancedb/
-
-[zsic_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/zero-shot-image-classification
-[zsic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/zero-shot-image-classification/main.ipynb
-[zsic_ghost]: https://blog.lancedb.com/zero-shot-image-classification-with-vector-search/
-
-
-
-
-
--- a/docs/src/fts.md
+++ b/docs/src/fts.md
@@ -1,14 +1,9 @@
 # Full-text search

-LanceDB provides support for full-text search via Lance (before via [Tantivy](https://github.com/quickwit-oss/tantivy) (Python only)), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
-
-Currently, the Lance full text search is missing some features that are in the Tantivy full text search. This includes phrase queries, re-ranking, and customizing the tokenizer. Thus, in Python, Tantivy is still the default way to do full text search and many of the instructions below apply just to Tantivy-based indices.
+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)


-## Installation (Only for Tantivy-based FTS)
-
-!!! note
-    No need to install the tantivy dependency if using native FTS
+## Installation

 To use full-text search, install the dependency [`tantivy-py`](https://github.com/quickwit-oss/tantivy-py):

@@ -19,83 +14,42 @@ pip install tantivy==0.20.1

 ## Example

-Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search, the FTS index must be created before you can search via keywords.
+Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search.

-=== "Python"
+```python
+import lancedb

-    ```python
-    import lancedb
+uri = "data/sample-lancedb"
+db = lancedb.connect(uri)

-    uri = "data/sample-lancedb"
-    db = lancedb.connect(uri)
+table = db.create_table(
+    "my_table",
+    data=[
+        {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
+        {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
+    ],
+)
+```

-    table = db.create_table(
-        "my_table",
-        data=[
-            {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
-            {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
-        ],
-    )
+## Create FTS index on single column

-    # passing `use_tantivy=False` to use lance FTS index
-    # `use_tantivy=True` by default
-    table.create_fts_index("text")
-    table.search("puppy").limit(10).select(["text"]).to_list()
-    # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
-    # ...
-    ```
+The FTS index must be created before you can search via keywords.

-=== "TypeScript"
+```python
+table.create_fts_index("text")
+```

-    ```typescript
-    import * as lancedb from "@lancedb/lancedb";
-    const uri = "data/sample-lancedb"
-    const db = await lancedb.connect(uri);
+To search an FTS index via keywords, LanceDB's `table.search` accepts a string as input:

-    const data = [
-    { vector: [3.1, 4.1], text: "Frodo was a happy puppy" },
-    { vector: [5.9, 26.5], text: "There are several kittens playing" },
-    ];
-    const tbl = await db.createTable("my_table", data, { mode: "overwrite" });
-    await tbl.createIndex("text", {
-        config: lancedb.Index.fts(),
-    });
+```python
+table.search("puppy").limit(10).select(["text"]).to_list()
+```

-    await tbl
-        .search("puppy")
-        .select(["text"])
-        .limit(10)
-        .toArray();
-    ```
+This returns the result as a list of dictionaries as follows.

-=== "Rust"
-
-    ```rust
-    let uri = "data/sample-lancedb";
-    let db = connect(uri).execute().await?;
-    let initial_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
-    let tbl = db
-        .create_table("my_table", initial_data)
-        .execute()
-        .await?;
-    tbl
-        .create_index(&["text"], Index::FTS(FtsIndexBuilder::default()))
-        .execute()
-        .await?;
-
-    tbl
-        .query()
-        .full_text_search(FullTextSearchQuery::new("puppy".to_owned()))
-        .select(lancedb::query::Select::Columns(vec!["text".to_owned()]))
-        .limit(10)
-        .execute()
-        .await?;
-    ```
-
-It would search on all indexed columns by default, so it's useful when there are multiple indexed columns.
-For now, this is supported in tantivy way only.
-
-Passing `fts_columns="text"` if you want to specify the columns to search, but it's not available for Tantivy-based full text search.
+```python
+[{'text': 'Frodo was a happy puppy', 'score': 0.6931471824645996}]
+```

 !!! note
    LanceDB automatically searches on the existing FTS index if the input to the search is of type `str`. If you provide a vector as input, LanceDB will search the ANN index instead.
@@ -103,33 +57,20 @@ Passing `fts_columns="text"` if you want to specify the columns to search, but i
 ## Tokenization
 By default the text is tokenized by splitting on punctuation and whitespaces and then removing tokens that are longer than 40 chars. For more language specific tokenization then provide the argument tokenizer_name with the 2 letter language code followed by "_stem". So for english it would be "en_stem".

-For now, only the Tantivy-based FTS index supports to specify the tokenizer, so it's only available in Python with `use_tantivy=True`.
+```python
+table.create_fts_index("text", tokenizer_name="en_stem")
+```

-=== "use_tantivy=True"
+The following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.

-    ```python
-    table.create_fts_index("text", use_tantivy=True, tokenizer_name="en_stem")
-    ```
-
-=== "use_tantivy=False"
-
-    [**Not supported yet**](https://github.com/lancedb/lance/issues/1195)
-
-the following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.

 ## Index multiple columns

 If you have multiple string columns to index, there's no need to combine them manually -- simply pass them all as a list to `create_fts_index`:

-=== "use_tantivy=True"
-
-    ```python
-    table.create_fts_index(["text1", "text2"])
-    ```
-
-=== "use_tantivy=False"
-
-    [**Not supported yet**](https://github.com/lancedb/lance/issues/1195)
+```python
+table.create_fts_index(["text1", "text2"])
+```

 Note that the search API call does not change - you can search over all indexed columns at once.

@@ -139,48 +80,19 @@ Currently the LanceDB full text search feature supports *post-filtering*, meanin
 applied on top of the full text search results. This can be invoked via the familiar
 `where` syntax:

-=== "Python"
-
-    ```python
-    table.search("puppy").limit(10).where("meta='foo'").to_list()
-    ```
-
-=== "TypeScript"
-
-    ```typescript
-    await tbl
-    .search("apple")
-    .select(["id", "doc"])
-    .limit(10)
-    .where("meta='foo'")
-    .toArray();
-    ```
-
-=== "Rust"
-
-    ```rust
-    table
-        .query()
-        .full_text_search(FullTextSearchQuery::new(words[0].to_owned()))
-        .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
-        .limit(10)
-        .only_if("meta='foo'")
-        .execute()
-        .await?;
-    ```
+```python
+table.search("puppy").limit(10).where("meta='foo'").to_list()
+```

 ## Sorting

-!!! warning "Warn"
-    Sorting is available for only Tantivy-based FTS
-
 You can pre-sort the documents by specifying `ordering_field_names` when
 creating the full-text search index. Once pre-sorted, you can then specify
 `ordering_field_name` while searching to return results sorted by the given
-field. For example,
+field. For example, 

-```python
-table.create_fts_index(["text_field"], use_tantivy=True, ordering_field_names=["sort_by_field"])
+```
+table.create_fts_index(["text_field"], ordering_field_names=["sort_by_field"])

 (table.search("terms", ordering_field_name="sort_by_field")
 .limit(20)
@@ -193,8 +105,8 @@ table.create_fts_index(["text_field"], use_tantivy=True, ordering_field_names=["
    error will be raised that looks like `ValueError: The field does not exist: xxx`

 !!! note
-    The fields to sort on must be of typed unsigned integer, or else you will see
-    an error during indexing that looks like
+    The fields to sort on must be of typed unsigned integer, or else you will see 
+    an error during indexing that looks like 
    `TypeError: argument 'value': 'float' object cannot be interpreted as an integer`.

 !!! note
@@ -204,9 +116,6 @@ table.create_fts_index(["text_field"], use_tantivy=True, ordering_field_names=["

 ## Phrase queries vs. terms queries

-!!! warning "Warn"
-    Phrase queries are available for only Tantivy-based FTS
-
 For full-text search you can specify either a **phrase** query like `"the old man and the sea"`,
 or a **terms** search query like `"(Old AND Man) AND Sea"`. For more details on the terms
 query syntax, see Tantivy's [query parser rules](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html).
@@ -233,7 +142,7 @@ enforce it in one of two ways:

 1. Place the double-quoted query inside single quotes. For example, `table.search('"they could have been dogs OR cats"')` is treated as
 a phrase query.
-1. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
+2. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
 itself contains double quotes. For example, `table.search('the cats OR dogs were not really "pets" at all').phrase_query()`
 is treated as a phrase query.

@@ -241,7 +150,7 @@ In general, a query that's declared as a phrase query will be wrapped in double
 double quotes replaced by single quotes.


-## Configurations (Only for Tantivy-based FTS)
+## Configurations

 By default, LanceDB configures a 1GB heap size limit for creating the index. You can
 reduce this if running on a smaller node, or increase this for faster performance while
@@ -255,8 +164,6 @@ table.create_fts_index(["text1", "text2"], writer_heap_size=heap, replace=True)

 ## Current limitations

-For that Tantivy-based FTS:
-
 1. Currently we do not yet support incremental writes.
   If you add data after FTS index creation, it won't be reflected
   in search results until you do a full reindex.
--- a/docs/src/guides/scalar_index.md
+++ b/docs/src/guides/scalar_index.md
@@ -1,108 +0,0 @@
-# Building Scalar Index
-
-Similar to many SQL databases, LanceDB supports several types of Scalar indices to accelerate search
-over scalar columns.
-
- `BTREE`: The most common type is BTREE. This index is inspired by the btree data structure
-  although only the first few layers of the btree are cached in memory.
-  It will perform well on columns with a large number of unique values and few rows per value.
- `BITMAP`: this index stores a bitmap for each unique value in the column.
-  This index is useful for columns with a finite number of unique values and many rows per value.
-  For example, columns that represent "categories", "labels", or "tags"
- `LABEL_LIST`: a special index that is used to index list columns whose values have a finite set of possibilities.
-  For example, a column that contains lists of tags (e.g. `["tag1", "tag2", "tag3"]`) can be indexed with a `LABEL_LIST` index.
-
-| Data Type                                                       | Filter                                    | Index Type   |
-| --------------------------------------------------------------- | ----------------------------------------- | ------------ |
-| Numeric, String, Temporal                                       | `<`, `=`, `>`, `in`, `between`, `is null` | `BTREE`      |
-| Boolean, numbers or strings with fewer than 1,000 unique values | `<`, `=`, `>`, `in`, `between`, `is null` | `BITMAP`     |
-| List of low cardinality of numbers or strings                   | `array_has_any`, `array_has_all`          | `LABEL_LIST` |
-
-=== "Python"
-
-    ```python
-    import lancedb
-    books = [
-      {"book_id": 1, "publisher": "plenty of books", "tags": ["fantasy", "adventure"]},
-      {"book_id": 2, "publisher": "book town", "tags": ["non-fiction"]},
-      {"book_id": 3, "publisher": "oreilly", "tags": ["textbook"]}
-    ]
-
-    db = lancedb.connect("./db")
-    table = db.create_table("books", books)
-    table.create_scalar_index("book_id")  # BTree by default
-    table.create_scalar_index("publisher", index_type="BITMAP")
-    ```
-
-=== "Typescript"
-
-    === "@lancedb/lancedb"
-
-        ```js
-        const db = await lancedb.connect("data");
-        const tbl = await db.openTable("my_vectors");
-
-        await tbl.create_index("book_id");
-        await tlb.create_index("publisher", { config: lancedb.Index.bitmap() })
-        ```
-
-For example, the following scan will be faster if the column `my_col` has a scalar index:
-
-=== "Python"
-
-    ```python
-    import lancedb
-
-    table = db.open_table("books")
-    my_df = table.search().where("book_id = 2").to_pandas()
-    ```
-
-=== "Typescript"
-
-    === "@lancedb/lancedb"
-
-        ```js
-        const db = await lancedb.connect("data");
-        const tbl = await db.openTable("books");
-
-        await tbl
-          .query()
-          .where("book_id = 2")
-          .limit(10)
-          .toArray();
-        ```
-
-Scalar indices can also speed up scans containing a vector search or full text search, and a prefilter:
-
-=== "Python"
-
-    ```python
-    import lancedb
-
-    data = [
-      {"book_id": 1, "vector": [1, 2]},
-      {"book_id": 2, "vector": [3, 4]},
-      {"book_id": 3, "vector": [5, 6]}
-    ]
-    table = db.create_table("book_with_embeddings", data)
-
-    (
-        table.search([1, 2])
-        .where("book_id != 3", prefilter=True)
-        .to_pandas()
-    )
-    ```
-
-=== "Typescript"
-
-    === "@lancedb/lancedb"
-
-        ```js
-        const db = await lancedb.connect("data/lance");
-        const tbl = await db.openTable("book_with_embeddings");
-
-        await tbl.search(Array(1536).fill(1.2))
-          .where("book_id != 3")  // prefilter is default behavior.
-          .limit(10)
-          .toArray();
-        ```
--- a/docs/src/guides/storage.md
+++ b/docs/src/guides/storage.md
@@ -32,54 +32,28 @@ LanceDB OSS supports object stores such as AWS S3 (and compatible stores), Azure
    db = lancedb.connect("az://bucket/path")
    ```

-=== "TypeScript"
+=== "JavaScript"

-    === "@lancedb/lancedb"
+    AWS S3:

-        AWS S3:
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect("s3://bucket/path");
+    ```

-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect("s3://bucket/path");
-        ```
+    Google Cloud Storage:

-        Google Cloud Storage:
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect("gs://bucket/path");
+    ```

-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect("gs://bucket/path");
-        ```
+    Azure Blob Storage:

-        Azure Blob Storage:
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect("az://bucket/path");
-        ```
-
-
-    === "vectordb (deprecated)"
-
-        AWS S3:
-
-        ```ts
-        const lancedb = require("lancedb");
-        const db = await lancedb.connect("s3://bucket/path");
-        ```
-
-        Google Cloud Storage:
-
-        ```ts
-        const lancedb = require("lancedb");
-        const db = await lancedb.connect("gs://bucket/path");
-        ```
-
-        Azure Blob Storage:
-
-        ```ts
-        const lancedb = require("lancedb");
-        const db = await lancedb.connect("az://bucket/path");
-        ```
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect("az://bucket/path");
+    ```

 In most cases, when running in the respective cloud and permissions are set up correctly, no additional configuration is required. When running outside of the respective cloud, authentication credentials must be provided. Credentials and other configuration options can be set in two ways: first, by setting environment variables. And second, by passing a `storage_options` object to the `connect` function. For example, to increase the request timeout to 60 seconds, you can set the `TIMEOUT` environment variable to `60s`:

@@ -104,26 +78,13 @@ If you only want this to apply to one particular connection, you can pass the `s
    )
    ```

-=== "TypeScript"
+=== "JavaScript"

-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-
-        const db = await lancedb.connect("s3://bucket/path", {
-            storageOptions: {timeout: "60s"}
-        });
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```ts
-        const lancedb = require("lancedb");
-        const db = await lancedb.connect("s3://bucket/path", {
-            storageOptions: {timeout: "60s"}
-        });
-        ```
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect("s3://bucket/path",
+                                     {storageOptions: {timeout: "60s"}});
+    ```

 Getting even more specific, you can set the `timeout` for only a particular table:

@@ -140,33 +101,18 @@ Getting even more specific, you can set the `timeout` for only a particular tabl
    )
    ```

-=== "TypeScript"
+=== "JavaScript"

-    === "@lancedb/lancedb"
-
-        <!-- skip-test -->
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect("s3://bucket/path");
-        const table = db.createTable(
-            "table",
-            [{ a: 1, b: 2}],
-            {storageOptions: {timeout: "60s"}}
-        );
-        ```
-
-    === "vectordb (deprecated)"
-
-        <!-- skip-test -->
-        ```ts
-        const lancedb = require("lancedb");
-        const db = await lancedb.connect("s3://bucket/path");
-        const table = db.createTable(
-            "table",
-            [{ a: 1, b: 2}],
-            {storageOptions: {timeout: "60s"}}
-        );
-        ```
+    <!-- skip-test -->
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect("s3://bucket/path");
+    const table = db.createTable(
+        "table",
+        [{ a: 1, b: 2}],
+        {storageOptions: {timeout: "60s"}}
+    );
+    ```

 !!! info "Storage option casing"

@@ -189,6 +135,7 @@ There are several options that can be set for all object stores, mostly related
 | `proxy_ca_certificate`     | PEM-formatted CA certificate for proxy connections.                                                |
 | `proxy_excludes`           | List of hosts that bypass the proxy. This is a comma-separated list of domains and IP masks. Any subdomain of the provided domain will be bypassed. For example, `example.com, 192.168.1.0/24` would bypass `https://api.example.com`, `https://www.example.com`, and any IP in the range `192.168.1.0/24`. |

+
 ### AWS S3

 To configure credentials for AWS S3, you can use the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` keys. Region can also be set, but it is not mandatory when using AWS.
@@ -208,39 +155,21 @@ These can be set as environment variables or passed in the `storage_options` par
    )
    ```

-=== "TypeScript"
+=== "JavaScript"

-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect(
-            "s3://bucket/path",
-            {
-                storageOptions: {
-                    awsAccessKeyId: "my-access-key",
-                    awsSecretAccessKey: "my-secret-key",
-                    awsSessionToken: "my-session-token",
-                }
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect(
+        "s3://bucket/path",
+        {
+            storageOptions: {
+                awsAccessKeyId: "my-access-key",
+                awsSecretAccessKey: "my-secret-key",
+                awsSessionToken: "my-session-token",
            }
-        );
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```ts
-        const lancedb = require("lancedb");
-        const db = await lancedb.connect(
-            "s3://bucket/path",
-            {
-                storageOptions: {
-                    awsAccessKeyId: "my-access-key",
-                    awsSecretAccessKey: "my-secret-key",
-                    awsSessionToken: "my-session-token",
-                }
-            }
-        );
-        ```
+        }
+    );
+    ```

 Alternatively, if you are using AWS SSO, you can use the `AWS_PROFILE` and `AWS_DEFAULT_REGION` environment variables.

@@ -259,6 +188,7 @@ The following keys can be used as both environment variables or keys in the `sto
 | `aws_sse_kms_key_id`                | The KMS key ID to use for server-side encryption. If set, `aws_server_side_encryption` must be `"aws:kms"` or `"aws:kms:dsse"`. |
 | `aws_sse_bucket_key_enabled`        | Whether to use bucket keys for server-side encryption.                                               |

+
 !!! tip "Automatic cleanup for failed writes"

    LanceDB uses [multi-part uploads](https://docs.aws.amazon.com/AmazonS3/latest/userguide/mpuoverview.html) when writing data to S3 in order to maximize write speed. LanceDB will abort these uploads when it shuts down gracefully, such as when cancelled by keyboard interrupt. However, in the rare case that LanceDB crashes, it is possible that some data will be left lingering in your account. To cleanup this data, we recommend (as AWS themselves do) that you setup a lifecycle rule to delete in-progress uploads after 7 days. See the AWS guide:
@@ -335,108 +265,6 @@ For **read-only access**, LanceDB will need a policy such as:
 }
 ```

-#### DynamoDB Commit Store for concurrent writes
-
-By default, S3 does not support concurrent writes. Having two or more processes
-writing to the same table at the same time can lead to data corruption. This is
-because S3, unlike other object stores, does not have any atomic put or copy
-operation.
-
-To enable concurrent writes, you can configure LanceDB to use a DynamoDB table
-as a commit store. This table will be used to coordinate writes between
-different processes. To enable this feature, you must modify your connection
-URI to use the `s3+ddb` scheme and add a query parameter `ddbTableName` with the
-name of the table to use.
-
-=== "Python"
-
-    ```python
-    import lancedb
-    db = await lancedb.connect_async(
-        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-    )
-    ```
-
-=== "JavaScript"
-
-    ```javascript
-    const lancedb = require("lancedb");
-
-    const db = await lancedb.connect(
-        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-    );
-    ```
-
-The DynamoDB table must be created with the following schema:
-
- Hash key: `base_uri` (string)
- Range key: `version` (number)
-
-You can create this programmatically with:
-
-=== "Python"
-
-    <!-- skip-test -->
-    ```python
-    import boto3
-
-    dynamodb = boto3.client("dynamodb")
-    table = dynamodb.create_table(
-        TableName=table_name,
-        KeySchema=[
-            {"AttributeName": "base_uri", "KeyType": "HASH"},
-            {"AttributeName": "version", "KeyType": "RANGE"},
-        ],
-        AttributeDefinitions=[
-            {"AttributeName": "base_uri", "AttributeType": "S"},
-            {"AttributeName": "version", "AttributeType": "N"},
-        ],
-        ProvisionedThroughput={"ReadCapacityUnits": 1, "WriteCapacityUnits": 1},
-    )
-    ```
-
-=== "JavaScript"
-
-    <!-- skip-test -->
-    ```javascript
-    import {
-      CreateTableCommand,
-      DynamoDBClient,
-    } from "@aws-sdk/client-dynamodb";
-
-    const dynamodb = new DynamoDBClient({
-      region: CONFIG.awsRegion,
-      credentials: {
-        accessKeyId: CONFIG.awsAccessKeyId,
-        secretAccessKey: CONFIG.awsSecretAccessKey,
-      },
-      endpoint: CONFIG.awsEndpoint,
-    });
-    const command = new CreateTableCommand({
-      TableName: table_name,
-      AttributeDefinitions: [
-        {
-          AttributeName: "base_uri",
-          AttributeType: "S",
-        },
-        {
-          AttributeName: "version",
-          AttributeType: "N",
-        },
-      ],
-      KeySchema: [
-        { AttributeName: "base_uri", KeyType: "HASH" },
-        { AttributeName: "version", KeyType: "RANGE" },
-      ],
-      ProvisionedThroughput: {
-        ReadCapacityUnits: 1,
-        WriteCapacityUnits: 1,
-      },
-    });
-    await client.send(command);
-    ```
-
-
 #### S3-compatible stores

 LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you must specify both region and endpoint:
@@ -454,37 +282,20 @@ LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you m
    )
    ```

-=== "TypeScript"
+=== "JavaScript"

-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect(
-            "s3://bucket/path",
-            {
-                storageOptions: {
-                    region: "us-east-1",
-                    endpoint: "http://minio:9000",
-                }
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect(
+        "s3://bucket/path",
+        {
+            storageOptions: {
+                region: "us-east-1",
+                endpoint: "http://minio:9000",
            }
-        );
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```ts
-        const lancedb = require("lancedb");
-        const db = await lancedb.connect(
-            "s3://bucket/path",
-            {
-                storageOptions: {
-                    region: "us-east-1",
-                    endpoint: "http://minio:9000",
-                }
-            }
-        );
-        ```
+        }
+    );
+    ```

 This can also be done with the ``AWS_ENDPOINT`` and ``AWS_DEFAULT_REGION`` environment variables.

@@ -515,37 +326,21 @@ To configure LanceDB to use an S3 Express endpoint, you must set the storage opt
    )
    ```

-=== "TypeScript"
+=== "JavaScript"

-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect(
-            "s3://my-bucket--use1-az4--x-s3/path",
-            {
-                storageOptions: {
-                    region: "us-east-1",
-                    s3Express: "true",
-                }
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect(
+        "s3://my-bucket--use1-az4--x-s3/path",
+        {
+            storageOptions: {
+                region: "us-east-1",
+                s3Express: "true",
            }
-        );
-        ```
+        }
+    );
+    ```

-    === "vectordb (deprecated)"
-
-        ```ts
-        const lancedb = require("lancedb");
-        const db = await lancedb.connect(
-            "s3://my-bucket--use1-az4--x-s3/path",
-            {
-                storageOptions: {
-                    region: "us-east-1",
-                    s3Express: "true",
-                }
-            }
-        );
-        ```

 ### Google Cloud Storage

@@ -564,40 +359,26 @@ GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environme
    )
    ```

-=== "TypeScript"
+=== "JavaScript"

-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect(
-            "gs://my-bucket/my-database",
-            {
-                storageOptions: {
-                    serviceAccount: "path/to/service-account.json",
-                }
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect(
+        "gs://my-bucket/my-database",
+        {
+            storageOptions: {
+                serviceAccount: "path/to/service-account.json",
            }
-        );
-        ```
+        }
+    );
+    ```

-    === "vectordb (deprecated)"
-
-        ```ts
-        const lancedb = require("lancedb");
-        const db = await lancedb.connect(
-            "gs://my-bucket/my-database",
-            {
-                storageOptions: {
-                    serviceAccount: "path/to/service-account.json",
-                }
-            }
-        );
-        ```

 !!! info "HTTP/2 support"

    By default, GCS uses HTTP/1 for communication, as opposed to HTTP/2. This improves maximum throughput significantly. However, if you wish to use HTTP/2 for some reason, you can set the environment variable `HTTP1_ONLY` to `false`.

+
 The following keys can be used as both environment variables or keys in the `storage_options` parameter:
 <!-- source: https://docs.rs/object_store/latest/object_store/gcp/enum.GoogleConfigKey.html -->

@@ -607,6 +388,7 @@ The following keys can be used as both environment variables or keys in the `sto
 | ``google_service_account_key``        | The serialized service account key.          |
 | ``google_application_credentials``    | Path to the application credentials.         |

+
 ### Azure Blob Storage

 Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_ACCOUNT_NAME`and `AZURE_STORAGE_ACCOUNT_KEY` environment variables. Alternatively, you can pass the account name and key in the `storage_options` parameter:
@@ -625,37 +407,20 @@ Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_A
    )
    ```

-=== "TypeScript"
+=== "JavaScript"

-    === "@lancedb/lancedb"
-
-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
-        const db = await lancedb.connect(
-            "az://my-container/my-database",
-            {
-                storageOptions: {
-                    accountName: "some-account",
-                    accountKey: "some-key",
-                }
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect(
+        "az://my-container/my-database",
+        {
+            storageOptions: {
+                accountName: "some-account",
+                accountKey: "some-key",
            }
-        );
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```ts
-        const lancedb = require("lancedb");
-        const db = await lancedb.connect(
-            "az://my-container/my-database",
-            {
-                storageOptions: {
-                    accountName: "some-account",
-                    accountKey: "some-key",
-                }
-            }
-        );
-        ```
+        }
+    );
+    ```

 These keys can be used as both environment variables or keys in the `storage_options` parameter:

@@ -680,4 +445,4 @@ These keys can be used as both environment variables or keys in the `storage_opt
 | ``azure_use_azure_cli``               | Use azure cli for acquiring access token.                                                        |
 | ``azure_disable_tagging``             | Disables tagging objects. This can be desirable if not supported by the backing store.           |

-<!-- TODO: demonstrate how to configure networked file systems for optimal performance -->
+<!-- TODO: demonstrate how to configure networked file systems for optimal performance -->
--- a/docs/src/guides/tables.md
+++ b/docs/src/guides/tables.md
@@ -3,46 +3,32 @@

 A Table is a collection of Records in a LanceDB Database. Tables in Lance have a schema that defines the columns and their types. These schemas can include nested columns and can evolve over time.

-This guide will show how to create tables, insert data into them, and update the data.
+This guide will show how to create tables, insert data into them, and update the data.  


 ## Creating a LanceDB Table

-Initialize a LanceDB connection and create a table
-
 === "Python"
+    Initialize a LanceDB connection and create a table using one of the many methods listed below.

    ```python
    import lancedb
    db = lancedb.connect("./.lancedb")
    ```

+=== "Javascript"
+
+    Initialize a VectorDB connection and create a table using one of the many methods listed below.
+
+    ```javascript
+    const lancedb = require("vectordb");
+
+    const uri = "data/sample-lancedb";
+    const db = await lancedb.connect(uri);
+    ```
+
    LanceDB allows ingesting data from various sources - `dict`, `list[dict]`, `pd.DataFrame`, `pa.Table` or a `Iterator[pa.RecordBatch]`. Let's take a look at some of the these.

-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        import * as lancedb from "@lancedb/lancedb";
-        import * as arrow from "apache-arrow";
-
-        const uri = "data/sample-lancedb";
-        const db = await lancedb.connect(uri);
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        const lancedb = require("vectordb");
-        const arrow = require("apache-arrow");
-
-        const uri = "data/sample-lancedb";
-        const db = await lancedb.connect(uri);
-        ```
-
-
-
 ### From list of tuples or dictionaries

 === "Python"
@@ -59,150 +45,102 @@ Initialize a LanceDB connection and create a table

    db["my_table"].head()
    ```
-
    !!! info "Note"
-        If the table already exists, LanceDB will raise an error by default.
+        If the table already exists, LanceDB will raise an error by default. 

        `create_table` supports an optional `exist_ok` parameter. When set to True
        and the table exists, then it simply opens the existing table. The data you
        passed in will NOT be appended to the table in that case.

-    ```python
-    db.create_table("name", data, exist_ok=True)
+        ```python
+        db.create_table("name", data, exist_ok=True)
+        ```
+        
+        Sometimes you want to make sure that you start fresh. If you want to 
+        overwrite the table, you can pass in mode="overwrite" to the createTable function.
+
+        ```python
+        db.create_table("name", data, mode="overwrite")
+        ```
+
+=== "Javascript"
+    You can create a LanceDB table in JavaScript using an array of JSON records as follows.
+
+    ```javascript
+    const tb = await db.createTable("my_table", [{
+        "vector": [3.1, 4.1],
+        "item": "foo",
+        "price": 10.0
+    }, {
+        "vector": [5.9, 26.5],
+        "item": "bar",
+        "price": 20.0
+    }]);
+    ```
+    !!! info "Note"
+    If the table already exists, LanceDB will raise an error by default. If you want to overwrite the table, you need to specify the `WriteMode` in the createTable function.
+
+    ```javascript
+    const table = await con.createTable(tableName, data, { writeMode: WriteMode.Overwrite })
    ```

-    Sometimes you want to make sure that you start fresh. If you want to
-    overwrite the table, you can pass in mode="overwrite" to the createTable function.
+    ### From a Pandas DataFrame

    ```python
-    db.create_table("name", data, mode="overwrite")
+    import pandas as pd
+
+    data = pd.DataFrame({
+        "vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],
+        "lat": [45.5, 40.1],
+        "long": [-122.7, -74.1]
+    })
+
+    db.create_table("my_table", data)
+
+    db["my_table"].head()
    ```
-
-=== "Typescript[^1]"
-    You can create a LanceDB table in JavaScript using an array of records as follows.
-
-    === "@lancedb/lancedb"
-
-
-        ```ts
-        --8<-- "nodejs/examples/basic.ts:create_table"
-        ```
-
-        This will infer the schema from the provided data. If you want to explicitly provide a schema, you can use `apache-arrow` to declare a schema
-
-        ```ts
-        --8<-- "nodejs/examples/basic.ts:create_table_with_schema"
-        ```
-
-        !!! info "Note"
-            `createTable` supports an optional `existsOk` parameter. When set to true
-            and the table exists, then it simply opens the existing table. The data you
-            passed in will NOT be appended to the table in that case.
-
-        ```ts
-        --8<-- "nodejs/examples/basic.ts:create_table_exists_ok"
-        ```
-
-        Sometimes you want to make sure that you start fresh. If you want to
-        overwrite the table, you can pass in mode: "overwrite" to the createTable function.
-
-        ```ts
-        --8<-- "nodejs/examples/basic.ts:create_table_overwrite"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```ts
-        --8<-- "docs/src/basic_legacy.ts:create_table"
-        ```
-
-        This will infer the schema from the provided data. If you want to explicitly provide a schema, you can use apache-arrow to declare a schema
-
-
-
-        ```ts
-        --8<-- "docs/src/basic_legacy.ts:create_table_with_schema"
-        ```
-
-        !!! warning
-            `existsOk` is not available in `vectordb`
-
-
-
-            If the table already exists, vectordb will raise an error by default.
-            You can use `writeMode: WriteMode.Overwrite` to overwrite the table.
-            But this will delete the existing table and create a new one with the same name.
-
-
-        Sometimes you want to make sure that you start fresh.
-
-        If you want to overwrite the table, you can pass in `writeMode: lancedb.WriteMode.Overwrite` to the createTable function.
-
-        ```ts
-        const table = await con.createTable(tableName, data, {
-            writeMode: WriteMode.Overwrite
-        })
-        ```
-
-### From a Pandas DataFrame
-
-```python
-import pandas as pd
-
-data = pd.DataFrame({
-    "vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],
-    "lat": [45.5, 40.1],
-    "long": [-122.7, -74.1]
-})
-
-db.create_table("my_table", data)
-
-db["my_table"].head()
-```
-
-!!! info "Note"
+    !!! info "Note"
    Data is converted to Arrow before being written to disk. For maximum control over how data is saved, either provide the PyArrow schema to convert to or else provide a PyArrow Table directly.

-The **`vector`** column needs to be a [Vector](../python/pydantic.md#vector-field) (defined as [pyarrow.FixedSizeList](https://arrow.apache.org/docs/python/generated/pyarrow.list_.html)) type.
+    The **`vector`** column needs to be a [Vector](../python/pydantic.md#vector-field) (defined as [pyarrow.FixedSizeList](https://arrow.apache.org/docs/python/generated/pyarrow.list_.html)) type. 

-```python
-custom_schema = pa.schema([
-pa.field("vector", pa.list_(pa.float32(), 4)),
-pa.field("lat", pa.float32()),
-pa.field("long", pa.float32())
-])
+    ```python
+    custom_schema = pa.schema([
+    pa.field("vector", pa.list_(pa.float32(), 4)),
+    pa.field("lat", pa.float32()),
+    pa.field("long", pa.float32())
+    ])

-table = db.create_table("my_table", data, schema=custom_schema)
-```
+    table = db.create_table("my_table", data, schema=custom_schema)
+    ```

 ### From a Polars DataFrame

-LanceDB supports [Polars](https://pola.rs/), a modern, fast DataFrame library
-written in Rust. Just like in Pandas, the Polars integration is enabled by PyArrow
-under the hood. A deeper integration between LanceDB Tables and Polars DataFrames
-is on the way.
+    LanceDB supports [Polars](https://pola.rs/), a modern, fast DataFrame library
+    written in Rust. Just like in Pandas, the Polars integration is enabled by PyArrow
+    under the hood. A deeper integration between LanceDB Tables and Polars DataFrames
+    is on the way.

-```python
-import polars as pl
+    ```python
+    import polars as pl

-data = pl.DataFrame({
-    "vector": [[3.1, 4.1], [5.9, 26.5]],
-    "item": ["foo", "bar"],
-    "price": [10.0, 20.0]
-})
-table = db.create_table("pl_table", data=data)
-```
+    data = pl.DataFrame({
+        "vector": [[3.1, 4.1], [5.9, 26.5]],
+        "item": ["foo", "bar"],
+        "price": [10.0, 20.0]
+    })
+    table = db.create_table("pl_table", data=data)
+    ```

 ### From an Arrow Table
-You can also create LanceDB tables directly from Arrow tables.
-LanceDB supports float16 data type!
-
 === "Python"
+    You can also create LanceDB tables directly from Arrow tables. 
+    LanceDB supports float16 data type!

    ```python
    import pyarrows as pa
    import numpy as np
-
+    
    dim = 16
    total = 2
    schema = pa.schema(
@@ -222,19 +160,13 @@ LanceDB supports float16 data type!
    tbl = db.create_table("f16_tbl", data, schema=schema)
    ```

-=== "Typescript[^1]"
+=== "Javascript"
+    You can also create LanceDB tables directly from Arrow tables. 
+    LanceDB supports Float16 data type!

-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_f16_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:create_f16_table"
-        ```
+    ```javascript
+    --8<-- "docs/src/basic_legacy.ts:create_f16_table"
+    ```

 ### From Pydantic Models

@@ -293,7 +225,7 @@ class NestedSchema(LanceModel):
 tbl = db.create_table("nested_table", schema=NestedSchema, mode="overwrite")
 ```

-This creates a struct column called "document" that has two subfields
+This creates a struct column called "document" that has two subfields 
 called "content" and "source":

 ```
@@ -304,7 +236,7 @@ vector: fixed_size_list<item: float>[1536] not null
    child 0, item: float
 document: struct<content: string not null, source: string not null> not null
    child 0, content: string not null
-    child 1, source: string not null
+    child 1, source: string not null    
 ```

 #### Validators
@@ -329,7 +261,7 @@ class TestModel(LanceModel):
    @classmethod
    def tz_must_match(cls, dt: datetime) -> datetime:
        assert dt.tzinfo == tz
-        return dt
+        return dt        

 ok = TestModel(dt_with_tz=datetime.now(tz))

@@ -397,24 +329,23 @@ You can also use iterators of other types like Pandas DataFrame or Pylists direc
    tbl = db.open_table("my_table")
    ```

-=== "Typescript[^1]"
-
+=== "JavaScript"
    If you forget the name of your table, you can always get a listing of all table names.

-    ```typescript
+    ```javascript
    console.log(await db.tableNames());
    ```

    Then, you can open any existing tables.

-    ```typescript
+    ```javascript
    const tbl = await db.openTable("my_table");
    ```

 ## Creating empty table
-You can create an empty table for scenarios where you want to add data to the table later. An example would be when you want to collect data from a stream/external file and then add it to a table in batches.

 === "Python"
+    In Python, you can create an empty table for scenarios where you want to add data to the table later. An example would be when you want to collect data from a stream/external file and then add it to a table in batches.

    ```python

@@ -433,8 +364,8 @@ You can create an empty table for scenarios where you want to add data to the ta
    tbl = db.create_table("empty_table_add", schema=schema)
    ```

-    Alternatively, you can also use Pydantic to specify the schema for the empty table. Note that we do not
-    directly import `pydantic` but instead use `lancedb.pydantic` which is a subclass of `pydantic.BaseModel`
+    Alternatively, you can also use Pydantic to specify the schema for the empty table. Note that we do not 
+    directly import `pydantic` but instead use `lancedb.pydantic` which is a subclass of `pydantic.BaseModel` 
    that has been extended to support LanceDB specific types like `Vector`.

    ```python
@@ -451,23 +382,9 @@ You can create an empty table for scenarios where you want to add data to the ta

    Once the empty table has been created, you can add data to it via the various methods listed in the [Adding to a table](#adding-to-a-table) section.

-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.ts:create_empty_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:create_empty_table"
-        ```
-
 ## Adding to a table

-After a table has been created, you can always add more data to it usind the `add` method
+After a table has been created, you can always add more data to it using the various methods available.

 === "Python"
    You can add any of the valid data structures accepted by LanceDB table, i.e, `dict`, `list[dict]`, `pd.DataFrame`, or `Iterator[pa.RecordBatch]`. Below are some examples.
@@ -555,7 +472,9 @@ After a table has been created, you can always add more data to it usind the `ad
        tbl.add(models)
        ```

-=== "Typescript[^1]"
+
+
+=== "JavaScript"

    ```javascript
    await tbl.add(
@@ -611,15 +530,15 @@ Use the `delete()` method on tables to delete rows from a table. To choose which
    # 0  3  [5.0, 6.0]
    ```

-=== "Typescript[^1]"
+=== "JavaScript"

-    ```ts
+    ```javascript
    await tbl.delete('item = "fizz"')
    ```

    ### Deleting row with specific column value

-    ```ts
+    ```javascript
    const con = await lancedb.connect("./.lancedb")
    const data = [
      {id: 1, vector: [1, 2]},
@@ -633,7 +552,7 @@ Use the `delete()` method on tables to delete rows from a table. To choose which

    ### Delete from a list of values

-    ```ts
+    ```javascript
    const to_remove = [1, 5];
    await tbl.delete(`id IN (${to_remove.join(",")})`)
    await tbl.countRows() // Returns 1
@@ -690,49 +609,26 @@ This can be used to update zero to all rows depending on how many rows match the
    2  2  [10.0, 10.0]
    ```

-=== "Typescript[^1]"
+=== "JavaScript/Typescript"

-    === "@lancedb/lancedb"
+    API Reference: [vectordb.Table.update](../javascript/interfaces/Table.md/#update)

-        API Reference: [lancedb.Table.update](../js/classes/Table.md/#update)
+    ```javascript
+    const lancedb = require("vectordb");

-        ```ts
-        import * as lancedb from "@lancedb/lancedb";
+    const db = await lancedb.connect("./.lancedb");

-        const db = await lancedb.connect("./.lancedb");
+    const data = [
+      {x: 1, vector: [1, 2]},
+      {x: 2, vector: [3, 4]},
+      {x: 3, vector: [5, 6]},
+    ];
+    const tbl = await db.createTable("my_table", data)

-        const data = [
-            {x: 1, vector: [1, 2]},
-            {x: 2, vector: [3, 4]},
-            {x: 3, vector: [5, 6]},
-        ];
-        const tbl = await db.createTable("my_table", data)
+    await tbl.update({ where: "x = 2", values: {vector: [10, 10]} })
+    ```

-        await tbl.update({vector: [10, 10]}, { where: "x = 2"})
-        ```
-
-    === "vectordb (deprecated)"
-
-        API Reference: [vectordb.Table.update](../javascript/interfaces/Table.md/#update)
-
-        ```ts
-        const lancedb = require("vectordb");
-
-        const db = await lancedb.connect("./.lancedb");
-
-        const data = [
-            {x: 1, vector: [1, 2]},
-            {x: 2, vector: [3, 4]},
-            {x: 3, vector: [5, 6]},
-        ];
-        const tbl = await db.createTable("my_table", data)
-
-        await tbl.update({ where: "x = 2", values: {vector: [10, 10]} })
-        ```
-
-#### Updating using a sql query
-
-  The `values` parameter is used to provide the new values for the columns as literal values. You can also use the `values_sql` / `valuesSql` parameter to provide SQL expressions for the new values. For example, you can use `values_sql="x + 1"` to increment the value of the `x` column by 1.
+The `values` parameter is used to provide the new values for the columns as literal values. You can also use the `values_sql` / `valuesSql` parameter to provide SQL expressions for the new values. For example, you can use `values_sql="x + 1"` to increment the value of the `x` column by 1.

 === "Python"

@@ -751,17 +647,11 @@ This can be used to update zero to all rows depending on how many rows match the
    2  3  [10.0, 10.0]
    ```

-=== "Typescript[^1]"
+=== "JavaScript/Typescript"

-    === "@lancedb/lancedb"
-
-        Coming Soon!
-
-    === "vectordb (deprecated)"
-
-        ```ts
-        await tbl.update({ valuesSql: { x: "x + 1" } })
-        ```
+    ```javascript
+    await tbl.update({ valuesSql: { x: "x + 1" } })
+    ```

 !!! info "Note"

@@ -782,7 +672,7 @@ Use the `drop_table()` method on the database to remove a table.
      By default, if the table does not exist an exception is raised. To suppress this,
      you can pass in `ignore_missing=True`.

-=== "TypeScript"
+=== "Javascript/Typescript"

      ```typescript
      --8<-- "docs/src/basic_legacy.ts:drop_table"
@@ -807,7 +697,7 @@ There are three possible settings for `read_consistency_interval`:
    This is only tune-able in LanceDB OSS. In LanceDB Cloud, readers are always eventually consistent.

 === "Python"
-
+    
    To set strong consistency, use `timedelta(0)`:

    ```python
@@ -829,35 +719,33 @@ There are three possible settings for `read_consistency_interval`:
    ```python
    db = lancedb.connect("./.lancedb")
    table = db.open_table("my_table")
-
+    
    # (Other writes happen to my_table from another process)

    # Check for updates
    table.checkout_latest()
    ```

-=== "Typescript[^1]"
+=== "JavaScript/Typescript"

    To set strong consistency, use `0`:

-    ```ts
+    ```javascript
    const db = await lancedb.connect({ uri: "./.lancedb", readConsistencyInterval: 0 });
    const table = await db.openTable("my_table");
    ```

    For eventual consistency, specify the update interval as seconds:

-    ```ts
+    ```javascript
    const db = await lancedb.connect({ uri: "./.lancedb", readConsistencyInterval: 5 });
    const table = await db.openTable("my_table");
    ```

-<!-- Node doesn't yet support the version time travel: https://github.com/lancedb/lancedb/issues/1007
+<!-- Node doesn't yet support the version time travel: https://github.com/lancedb/lancedb/issues/1007 
    Once it does, we can show manual consistency check for Node as well.
 -->

 ## What's next?

-Learn the best practices on creating an ANN index and getting the most out of it.
-
-[^1]: The `vectordb` package is a legacy package that is  deprecated in favor of `@lancedb/lancedb`.  The `vectordb` package will continue to receive bug fixes and security updates until September 2024.  We recommend all new projects use `@lancedb/lancedb`.  See the [migration guide](migration.md) for more information.
+Learn the best practices on creating an ANN index and getting the most out of it.
--- a/docs/src/guides/tuning_retrievers/1_query_types.md
+++ b/docs/src/guides/tuning_retrievers/1_query_types.md
@@ -1,7 +1,4 @@
 ## Improving retriever performance
-
-Try it yourself - <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/lancedb_reranking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
-
 VectorDBs are used as retreivers in recommender or chatbot-based systems for retrieving relevant data based on user queries. For example, retriever is a critical component of Retrieval Augmented Generation (RAG) acrhitectures. In this section, we will discuss how to improve the performance of retrievers.

 There are serveral ways to improve the performance of retrievers. Some of the common techniques are:
--- a/docs/src/guides/tuning_retrievers/2_reranking.md
+++ b/docs/src/guides/tuning_retrievers/2_reranking.md
@@ -1,6 +1,4 @@
-Continuing from the previous section, we can now rerank the results using more complex rerankers.
-
-Try it yourself - <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/lancedb_reranking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
+Continuing from the previous example, we can now rerank the results using more complex rerankers.

 ## Reranking search results
 You can rerank any search results using a reranker. The syntax for reranking is as follows:
--- a/docs/src/guides/tuning_retrievers/3_embed_tuning.md
+++ b/docs/src/guides/tuning_retrievers/3_embed_tuning.md
@@ -1,82 +0,0 @@
-## Finetuning the Embedding Model
-Try it yourself - <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/embedding_tuner.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
-
-Another way to improve retriever performance is to fine-tune the embedding model itself. Fine-tuning the embedding model can help in learning better representations for the documents and queries in the dataset. This can be particularly useful when the dataset is very different from the pre-trained data used to train the embedding model.
-
-We'll use the same dataset as in the previous sections. Start off by splitting the dataset into training and validation sets:
-```python
-from sklearn.model_selection import train_test_split
-
-train_df, validation_df = train_test_split("data_qa.csv", test_size=0.2, random_state=42)
-
-train_df.to_csv("data_train.csv", index=False)
-validation_df.to_csv("data_val.csv", index=False)
-```
-
-You can use any tuning API to fine-tune embedding models. In this example, we'll utilise Llama-index as it also comes with utilities for synthetic data generation and training the model. 
-
-
-Then parse the dataset as llama-index text nodes and generate synthetic QA pairs from each node.
-```python
-from llama_index.core.node_parser import SentenceSplitter
-from llama_index.readers.file import PagedCSVReader
-from llama_index.finetuning import generate_qa_embedding_pairs
-from llama_index.core.evaluation import EmbeddingQAFinetuneDataset
-
-def load_corpus(file):
-    loader = PagedCSVReader(encoding="utf-8")
-    docs = loader.load_data(file=Path(file))
-
-    parser = SentenceSplitter()
-    nodes = parser.get_nodes_from_documents(docs)
-
-    return nodes
-
-from llama_index.llms.openai import OpenAI
-
-
-train_dataset = generate_qa_embedding_pairs(
-    llm=OpenAI(model="gpt-3.5-turbo"), nodes=train_nodes, verbose=False
-)
-val_dataset = generate_qa_embedding_pairs(
-    llm=OpenAI(model="gpt-3.5-turbo"), nodes=val_nodes, verbose=False
-)
-```
-
-Now we'll use `SentenceTransformersFinetuneEngine` engine to fine-tune the model. You can also use `sentence-transformers` or `transformers` library to fine-tune the model. 
-
-```python
-from llama_index.finetuning import SentenceTransformersFinetuneEngine
-
-finetune_engine = SentenceTransformersFinetuneEngine(
-    train_dataset,
-    model_id="BAAI/bge-small-en-v1.5",
-    model_output_path="tuned_model",
-    val_dataset=val_dataset,
-)
-finetune_engine.finetune()
-embed_model = finetune_engine.get_finetuned_model()
-```
-This saves the fine tuned embedding model in `tuned_model` folder. This al
-
-# Evaluation results
-In order to eval the retriever, you can either use this model to ingest the data into LanceDB directly or llama-index's LanceDB integration to create a `VectorStoreIndex` and use it as a retriever. 
-On performing the same hit-rate evaluation as before, we see a significant improvement in the hit-rate across all query types.
-
-### Baseline
-| Query Type | Hit-rate@5 |
-| --- | --- |
-| Vector Search | 0.640 |
-| Full-text Search | 0.595 |
-| Reranked Vector Search | 0.677 |
-| Reranked Full-text Search | 0.672 |
-| Hybrid Search (w/ CohereReranker) | 0.759|
-
-### Fine-tuned model ( 2 iterations )
-| Query Type | Hit-rate@5 |
-| --- | --- |
-| Vector Search | 0.672 |
-| Full-text Search | 0.595 |
-| Reranked Vector Search | 0.754 |
-| Reranked Full-text Search | 0.672|
-| Hybrid Search (w/ CohereReranker) | 0.768 |
--- a/docs/src/integrations/dlt.md
+++ b/docs/src/integrations/dlt.md
@@ -1,142 +0,0 @@
-# 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).
--- a/docs/src/integrations/langchain.md
+++ b/docs/src/integrations/langchain.md
@@ -2,7 +2,7 @@
 ![Illustration](../assets/langchain.png)

 ## 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)
+You can load your document data using langchain's loaders, for this example we are using `TextLoader` and `OpenAIEmbeddings` as the embedding model.
 ```python
 import os
 from langchain.document_loaders import TextLoader
@@ -38,8 +38,6 @@ The exhaustive list of parameters for `LanceDB` vector store are :
 - `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`. 

 ```python
 db_url = "db://lang_test" # url of db you created
@@ -56,14 +54,12 @@ vector_store = LanceDB(
 ```

 ### Methods 
-
+To add texts and store respective embeddings automatically:   
 ##### 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.

 ```python
 vector_store.add_texts(texts = ['test_123'], metadatas =[{'source' :'wiki'}]) 
@@ -78,6 +74,7 @@ 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)
 ```
+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() 
 - `col_name`: `Optional[str] = None`
 - `vector_col`: `Optional[str] = None`
@@ -85,8 +82,6 @@ vector_store = LanceDB(connection=tbl, embedding=embeddings)
 - `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.
-
 ```python
 # for creating vector index
 vector_store.create_index(vector_col='vector', metric = 'cosine')
@@ -94,108 +89,4 @@ vector_store.create_index(vector_col='vector', metric = 'cosine')
 # for creating scalar index(for non-vector columns)
 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
-
-```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.
-
-```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`.
-
-```python
-docs = docsearch.similarity_search_with_relevance_scores(query)
-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 
-
-```python
-docs = docsearch.similarity_search_by_vector_with_relevance_scores(query_embedding)
-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).
-Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
-
-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
-result = docsearch.max_marginal_relevance_search(
-        query="text"
-    )
-result_texts = [doc.page_content for doc in result]
-print(result_texts)
-
-## search by vector :
-result = docsearch.max_marginal_relevance_search_by_vector(
-        embeddings.embed_query("text")
-    )
-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.
-
-```python
-vec_store.add_images(uris=image_uris) 
-# here image_uris are local fs paths to the images.
-```
-
-
+```
--- a/docs/src/integrations/llamaIndex.md
+++ b/docs/src/integrations/llamaIndex.md
@@ -2,8 +2,7 @@
 ![Illustration](../assets/llama-index.jpg)

 ## Quick start
-You would need to install the integration via `pip install llama-index-vector-stores-lancedb` in order to use it. 
-You can run the below script to try it out :
+You would need to install the integration via `pip install llama-index-vector-stores-lancedb` in order to use it. You can run the below script to try it out :
 ```python
 import logging
 import sys
@@ -44,8 +43,6 @@ retriever = index.as_retriever(vector_store_kwargs={"where": lance_filter})
 response = retriever.retrieve("What did the author do growing up?")
 ```

-Checkout Complete example here - [LlamaIndex demo](../notebooks/LlamaIndex_example.ipynb)
-
 ### Filtering
 For metadata filtering, you can use a Lance SQL-like string filter as demonstrated in the example above. Additionally, you can also filter using the `MetadataFilters` class from LlamaIndex:
 ```python
--- a/docs/src/js/README.md
+++ b/docs/src/js/README.md
@@ -1,6 +1,4 @@
-**@lancedb/lancedb** • [**Docs**](globals.md)
-
-***
+@lancedb/lancedb / [Exports](modules.md)

 # LanceDB JavaScript SDK

@@ -47,20 +45,29 @@ npm run test

 ### Running lint / format

-LanceDb uses [biome](https://biomejs.dev/) for linting and formatting. if you are using VSCode you will need to install the official [Biome](https://marketplace.visualstudio.com/items?itemName=biomejs.biome) extension.
-To manually lint your code you can run:
+LanceDb uses eslint for linting. VSCode does not need any plugins to use eslint. However, it
+may need some additional configuration. Make sure that eslint.experimental.useFlatConfig is
+set to true. Also, if your vscode root folder is the repo root then you will need to set
+the eslint.workingDirectories to ["nodejs"]. To manually lint your code you can run:

 ```sh
 npm run lint
 ```

-to automatically fix all fixable issues:
+LanceDb uses prettier for formatting. If you are using VSCode you will need to install the
+"Prettier - Code formatter" extension. You should then configure it to be the default formatter
+for typescript and you should enable format on save. To manually check your code's format you
+can run:

 ```sh
-npm run lint-fix
+npm run chkformat
 ```

-If you do not have your workspace root set to the `nodejs` directory, unfortunately the extension will not work. You can still run the linting and formatting commands manually.
+If you need to manually format your code you can run:
+
+```sh
+npx prettier --write .
+```

 ### Generating docs

--- a/docs/src/js/classes/Connection.md
+++ b/docs/src/js/classes/Connection.md
@@ -1,10 +1,6 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / Connection

-***
-
-[@lancedb/lancedb](../globals.md) / Connection
-
-# Class: `abstract` Connection
+# Class: Connection

 A LanceDB Connection that allows you to open tables and create new ones.

@@ -23,21 +19,62 @@ be closed when they are garbage collected.
 Any created tables are independent and will continue to work even if
 the underlying connection has been closed.

+## Table of contents
+
+### Constructors
+
+- [constructor](Connection.md#constructor)
+
+### Properties
+
+- [inner](Connection.md#inner)
+
+### Methods
+
+- [close](Connection.md#close)
+- [createEmptyTable](Connection.md#createemptytable)
+- [createTable](Connection.md#createtable)
+- [display](Connection.md#display)
+- [dropTable](Connection.md#droptable)
+- [isOpen](Connection.md#isopen)
+- [openTable](Connection.md#opentable)
+- [tableNames](Connection.md#tablenames)
+
 ## Constructors

-### new Connection()
+### constructor

-> **new Connection**(): [`Connection`](Connection.md)
+• **new Connection**(`inner`): [`Connection`](Connection.md)
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `inner` | `Connection` |

 #### Returns

 [`Connection`](Connection.md)

+#### Defined in
+
+[connection.ts:72](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L72)
+
+## Properties
+
+### inner
+
+• `Readonly` **inner**: `Connection`
+
+#### Defined in
+
+[connection.ts:70](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L70)
+
 ## Methods

-### close()
+### close

-> `abstract` **close**(): `void`
+▸ **close**(): `void`

 Close the connection, releasing any underlying resources.

@@ -49,78 +86,63 @@ Any attempt to use the connection after it is closed will result in an error.

 `void`

-***
+#### Defined in

-### createEmptyTable()
+[connection.ts:88](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L88)

-> `abstract` **createEmptyTable**(`name`, `schema`, `options`?): `Promise`&lt;[`Table`](Table.md)&gt;
+___
+
+### createEmptyTable
+
+▸ **createEmptyTable**(`name`, `schema`, `options?`): `Promise`\<[`Table`](Table.md)\>

 Creates a new empty Table

 #### Parameters

-• **name**: `string`
-
-The name of the table.
-
-• **schema**: `SchemaLike`
-
-The schema of the table
-
-• **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `name` | `string` | The name of the table. |
+| `schema` | `Schema`\<`any`\> | The schema of the table |
+| `options?` | `Partial`\<[`CreateTableOptions`](../interfaces/CreateTableOptions.md)\> | - |

 #### Returns

-`Promise`&lt;[`Table`](Table.md)&gt;
+`Promise`\<[`Table`](Table.md)\>

-***
+#### Defined in

-### createTable()
+[connection.ts:151](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L151)

-#### createTable(options)
+___

-> `abstract` **createTable**(`options`): `Promise`&lt;[`Table`](Table.md)&gt;
+### createTable
+
+▸ **createTable**(`name`, `data`, `options?`): `Promise`\<[`Table`](Table.md)\>

 Creates a new Table and initialize it with new data.

-##### Parameters
+#### Parameters

-• **options**: `object` & `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `name` | `string` | The name of the table. |
+| `data` | `Table`\<`any`\> \| `Record`\<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the table |
+| `options?` | `Partial`\<[`CreateTableOptions`](../interfaces/CreateTableOptions.md)\> | - |

-The options object.
+#### Returns

-##### Returns
+`Promise`\<[`Table`](Table.md)\>

-`Promise`&lt;[`Table`](Table.md)&gt;
+#### Defined in

-#### createTable(name, data, options)
+[connection.ts:123](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L123)

-> `abstract` **createTable**(`name`, `data`, `options`?): `Promise`&lt;[`Table`](Table.md)&gt;
+___

-Creates a new Table and initialize it with new data.
+### display

-##### Parameters
-
-• **name**: `string`
-
-The name of the table.
-
-• **data**: `TableLike` \| `Record`&lt;`string`, `unknown`&gt;[]
-
-Non-empty Array of Records
-to be inserted into the table
-
-• **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
-
-##### Returns
-
-`Promise`&lt;[`Table`](Table.md)&gt;
-
-***
-
-### display()
-
-> `abstract` **display**(): `string`
+▸ **display**(): `string`

 Return a brief description of the connection

@@ -128,29 +150,37 @@ Return a brief description of the connection

 `string`

-***
+#### Defined in

-### dropTable()
+[connection.ts:93](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L93)

-> `abstract` **dropTable**(`name`): `Promise`&lt;`void`&gt;
+___
+
+### dropTable
+
+▸ **dropTable**(`name`): `Promise`\<`void`\>

 Drop an existing table.

 #### Parameters

-• **name**: `string`
-
-The name of the table to drop.
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `name` | `string` | The name of the table to drop. |

 #### Returns

-`Promise`&lt;`void`&gt;
+`Promise`\<`void`\>

-***
+#### Defined in

-### isOpen()
+[connection.ts:173](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L173)

-> `abstract` **isOpen**(): `boolean`
+___
+
+### isOpen
+
+▸ **isOpen**(): `boolean`

 Return true if the connection has not been closed

@@ -158,31 +188,37 @@ Return true if the connection has not been closed

 `boolean`

-***
+#### Defined in

-### openTable()
+[connection.ts:77](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L77)

-> `abstract` **openTable**(`name`, `options`?): `Promise`&lt;[`Table`](Table.md)&gt;
+___
+
+### openTable
+
+▸ **openTable**(`name`): `Promise`\<[`Table`](Table.md)\>

 Open a table in the database.

 #### Parameters

-• **name**: `string`
-
-The name of the table
-
-• **options?**: `Partial`&lt;`OpenTableOptions`&gt;
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `name` | `string` | The name of the table |

 #### Returns

-`Promise`&lt;[`Table`](Table.md)&gt;
+`Promise`\<[`Table`](Table.md)\>

-***
+#### Defined in

-### tableNames()
+[connection.ts:112](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L112)

-> `abstract` **tableNames**(`options`?): `Promise`&lt;`string`[]&gt;
+___
+
+### tableNames
+
+▸ **tableNames**(`options?`): `Promise`\<`string`[]\>

 List all the table names in this database.

@@ -190,11 +226,14 @@ Tables will be returned in lexicographical order.

 #### Parameters

-• **options?**: `Partial`&lt;[`TableNamesOptions`](../interfaces/TableNamesOptions.md)&gt;
-
-options to control the
-paging / start point
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `options?` | `Partial`\<[`TableNamesOptions`](../interfaces/TableNamesOptions.md)\> | options to control the paging / start point |

 #### Returns

-`Promise`&lt;`string`[]&gt;
+`Promise`\<`string`[]\>
+
+#### Defined in
+
+[connection.ts:104](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L104)
--- a/docs/src/js/classes/Index.md
+++ b/docs/src/js/classes/Index.md
@@ -1,16 +1,57 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / Index
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / Index

 # Class: Index

+## Table of contents
+
+### Constructors
+
+- [constructor](Index.md#constructor)
+
+### Properties
+
+- [inner](Index.md#inner)
+
+### Methods
+
+- [btree](Index.md#btree)
+- [ivfPq](Index.md#ivfpq)
+
+## Constructors
+
+### constructor
+
+• **new Index**(`inner`): [`Index`](Index.md)
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `inner` | `Index` |
+
+#### Returns
+
+[`Index`](Index.md)
+
+#### Defined in
+
+[indices.ts:118](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L118)
+
+## Properties
+
+### inner
+
+• `Private` `Readonly` **inner**: `Index`
+
+#### Defined in
+
+[indices.ts:117](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L117)
+
 ## Methods

-### btree()
+### btree

-> `static` **btree**(): [`Index`](Index.md)
+▸ **btree**(): [`Index`](Index.md)

 Create a btree index

@@ -34,11 +75,15 @@ block size may be added in the future.

 [`Index`](Index.md)

-***
+#### Defined in

-### ivfPq()
+[indices.ts:175](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L175)

-> `static` **ivfPq**(`options`?): [`Index`](Index.md)
+___
+
+### ivfPq
+
+▸ **ivfPq**(`options?`): [`Index`](Index.md)

 Create an IvfPq index

@@ -63,8 +108,14 @@ currently is also a memory intensive operation.

 #### Parameters

-• **options?**: `Partial`&lt;[`IvfPqOptions`](../interfaces/IvfPqOptions.md)&gt;
+| Name | Type |
+| :------ | :------ |
+| `options?` | `Partial`\<[`IvfPqOptions`](../interfaces/IvfPqOptions.md)\> |

 #### Returns

 [`Index`](Index.md)
+
+#### Defined in
+
+[indices.ts:144](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L144)
--- a/docs/src/js/classes/MakeArrowTableOptions.md
+++ b/docs/src/js/classes/MakeArrowTableOptions.md
@@ -1,32 +1,46 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / MakeArrowTableOptions
+[@lancedb/lancedb](../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)
+- [schema](MakeArrowTableOptions.md#schema)
+- [vectorColumns](MakeArrowTableOptions.md#vectorcolumns)
+
 ## Constructors

-### new MakeArrowTableOptions()
+### constructor

-> **new MakeArrowTableOptions**(`values`?): [`MakeArrowTableOptions`](MakeArrowTableOptions.md)
+• **new MakeArrowTableOptions**(`values?`): [`MakeArrowTableOptions`](MakeArrowTableOptions.md)

 #### Parameters

-• **values?**: `Partial`&lt;[`MakeArrowTableOptions`](MakeArrowTableOptions.md)&gt;
+| Name | Type |
+| :------ | :------ |
+| `values?` | `Partial`\<[`MakeArrowTableOptions`](MakeArrowTableOptions.md)\> |

 #### Returns

 [`MakeArrowTableOptions`](MakeArrowTableOptions.md)

+#### Defined in
+
+[arrow.ts:100](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/arrow.ts#L100)
+
 ## Properties

 ### dictionaryEncodeStrings

-> **dictionaryEncodeStrings**: `boolean` = `false`
+• **dictionaryEncodeStrings**: `boolean` = `false`

 If true then string columns will be encoded with dictionary encoding

@@ -36,26 +50,26 @@ data type for individual columns.

 If `schema` is provided then this property is ignored.

-***
+#### Defined in

-### embeddingFunction?
+[arrow.ts:98](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/arrow.ts#L98)

-> `optional` **embeddingFunction**: [`EmbeddingFunctionConfig`](../namespaces/embedding/interfaces/EmbeddingFunctionConfig.md)
+___

-***
+### schema

-### embeddings?
+• `Optional` **schema**: `Schema`\<`any`\>

-> `optional` **embeddings**: [`EmbeddingFunction`](../namespaces/embedding/classes/EmbeddingFunction.md)&lt;`unknown`, `FunctionOptions`&gt;
+#### Defined in

-***
+[arrow.ts:67](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/arrow.ts#L67)

-### schema?
-
-> `optional` **schema**: `SchemaLike`
-
-***
+___

 ### vectorColumns

-> **vectorColumns**: `Record`&lt;`string`, [`VectorColumnOptions`](VectorColumnOptions.md)&gt;
+• **vectorColumns**: `Record`\<`string`, [`VectorColumnOptions`](VectorColumnOptions.md)\>
+
+#### Defined in
+
+[arrow.ts:85](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/arrow.ts#L85)
--- a/docs/src/js/classes/Query.md
+++ b/docs/src/js/classes/Query.md
@@ -1,26 +1,48 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / Query
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / Query

 # Class: Query

 A builder for LanceDB queries.

-## Extends
+## Hierarchy

- [`QueryBase`](QueryBase.md)&lt;`NativeQuery`&gt;
+- [`QueryBase`](QueryBase.md)\<`NativeQuery`, [`Query`](Query.md)\>
+
+  ↳ **`Query`**
+
+## Table of contents
+
+### Constructors
+
+- [constructor](Query.md#constructor)
+
+### Properties
+
+- [inner](Query.md#inner)
+
+### Methods
+
+- [[asyncIterator]](Query.md#[asynciterator])
+- [execute](Query.md#execute)
+- [limit](Query.md#limit)
+- [nativeExecute](Query.md#nativeexecute)
+- [nearestTo](Query.md#nearestto)
+- [select](Query.md#select)
+- [toArray](Query.md#toarray)
+- [toArrow](Query.md#toarrow)
+- [where](Query.md#where)

 ## Constructors

-### new Query()
+### constructor

-> **new Query**(`tbl`): [`Query`](Query.md)
+• **new Query**(`tbl`): [`Query`](Query.md)

 #### Parameters

-• **tbl**: `Table`
+| Name | Type |
+| :------ | :------ |
+| `tbl` | `Table` |

 #### Returns

@@ -28,67 +50,57 @@ A builder for LanceDB queries.

 #### Overrides

-[`QueryBase`](QueryBase.md).[`constructor`](QueryBase.md#constructors)
+[QueryBase](QueryBase.md).[constructor](QueryBase.md#constructor)
+
+#### Defined in
+
+[query.ts:329](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L329)

 ## Properties

 ### inner

-> `protected` **inner**: `Query` \| `Promise`&lt;`Query`&gt;
+• `Protected` **inner**: `Query`

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`inner`](QueryBase.md#inner)
+[QueryBase](QueryBase.md).[inner](QueryBase.md#inner)
+
+#### Defined in
+
+[query.ts:59](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L59)

 ## Methods

-### \[asyncIterator\]()
+### [asyncIterator]

-> **\[asyncIterator\]**(): `AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;
+▸ **[asyncIterator]**(): `AsyncIterator`\<`RecordBatch`\<`any`\>, `any`, `undefined`\>

 #### Returns

-`AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;
+`AsyncIterator`\<`RecordBatch`\<`any`\>, `any`, `undefined`\>

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`[asyncIterator]`](QueryBase.md#%5Basynciterator%5D)
+[QueryBase](QueryBase.md).[[asyncIterator]](QueryBase.md#[asynciterator])

-***
+#### Defined in

-### doCall()
+[query.ts:154](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L154)

-> `protected` **doCall**(`fn`): `void`
+___

-#### Parameters
+### execute

-• **fn**
-
-#### Returns
-
-`void`
-
-#### Inherited from
-
-[`QueryBase`](QueryBase.md).[`doCall`](QueryBase.md#docall)
-
-***
-
-### execute()
-
-> `protected` **execute**(`options`?): [`RecordBatchIterator`](RecordBatchIterator.md)
+▸ **execute**(): [`RecordBatchIterator`](RecordBatchIterator.md)

 Execute the query and return the results as an

-#### Parameters
-
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
-
 #### Returns

 [`RecordBatchIterator`](RecordBatchIterator.md)

-#### See
+**`See`**

 - AsyncIterator
 of
@@ -102,76 +114,17 @@ single query)

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`execute`](QueryBase.md#execute)
+[QueryBase](QueryBase.md).[execute](QueryBase.md#execute)

-***
+#### Defined in

-### explainPlan()
+[query.ts:149](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L149)

-> **explainPlan**(`verbose`): `Promise`&lt;`string`&gt;
+___

-Generates an explanation of the query execution plan.
+### limit

-#### Parameters
-
-• **verbose**: `boolean` = `false`
-
-If true, provides a more detailed explanation. Defaults to false.
-
-#### Returns
-
-`Promise`&lt;`string`&gt;
-
-A Promise that resolves to a string containing the query execution plan explanation.
-
-#### Example
-
-```ts
-import * as lancedb from "@lancedb/lancedb"
-const db = await lancedb.connect("./.lancedb");
-const table = await db.createTable("my_table", [
-  { vector: [1.1, 0.9], id: "1" },
-]);
-const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
-```
-
-#### Inherited from
-
-[`QueryBase`](QueryBase.md).[`explainPlan`](QueryBase.md#explainplan)
-
-***
-
-### ~~filter()~~
-
-> **filter**(`predicate`): `this`
-
-A filter statement to be applied to this query.
-
-#### Parameters
-
-• **predicate**: `string`
-
-#### Returns
-
-`this`
-
-#### Alias
-
-where
-
-#### Deprecated
-
-Use `where` instead
-
-#### Inherited from
-
-[`QueryBase`](QueryBase.md).[`filter`](QueryBase.md#filter)
-
-***
-
-### limit()
-
-> **limit**(`limit`): `this`
+▸ **limit**(`limit`): [`Query`](Query.md)

 Set the maximum number of results to return.

@@ -180,39 +133,45 @@ called then every valid row from the table will be returned.

 #### Parameters

-• **limit**: `number`
+| Name | Type |
+| :------ | :------ |
+| `limit` | `number` |

 #### Returns

-`this`
+[`Query`](Query.md)

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`limit`](QueryBase.md#limit)
+[QueryBase](QueryBase.md).[limit](QueryBase.md#limit)

-***
+#### Defined in

-### nativeExecute()
+[query.ts:129](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L129)

-> `protected` **nativeExecute**(`options`?): `Promise`&lt;`RecordBatchIterator`&gt;
+___

-#### Parameters
+### nativeExecute

-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+▸ **nativeExecute**(): `Promise`\<`RecordBatchIterator`\>

 #### Returns

-`Promise`&lt;`RecordBatchIterator`&gt;
+`Promise`\<`RecordBatchIterator`\>

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`nativeExecute`](QueryBase.md#nativeexecute)
+[QueryBase](QueryBase.md).[nativeExecute](QueryBase.md#nativeexecute)

-***
+#### Defined in

-### nearestTo()
+[query.ts:134](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L134)

-> **nearestTo**(`vector`): [`VectorQuery`](VectorQuery.md)
+___
+
+### nearestTo
+
+▸ **nearestTo**(`vector`): [`VectorQuery`](VectorQuery.md)

 Find the nearest vectors to the given query vector.

@@ -232,13 +191,15 @@ If there is more than one vector column you must use

 #### Parameters

-• **vector**: `IntoVector`
+| Name | Type |
+| :------ | :------ |
+| `vector` | `unknown` |

 #### Returns

 [`VectorQuery`](VectorQuery.md)

-#### See
+**`See`**

 - [VectorQuery#column](VectorQuery.md#column)  to specify which column you would like
 to compare with.
@@ -262,11 +223,15 @@ Vector searches always have a `limit`.  If `limit` has not been called then
 a default `limit` of 10 will be used.
 - [Query#limit](Query.md#limit)

-***
+#### Defined in

-### select()
+[query.ts:370](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L370)

-> **select**(`columns`): `this`
+___
+
+### select
+
+▸ **select**(`columns`): [`Query`](Query.md)

 Return only the specified columns.

@@ -290,13 +255,15 @@ input to this method would be:

 #### Parameters

-• **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+| Name | Type |
+| :------ | :------ |
+| `columns` | `string`[] \| `Record`\<`string`, `string`\> \| `Map`\<`string`, `string`\> |

 #### Returns

-`this`
+[`Query`](Query.md)

-#### Example
+**`Example`**

 ```ts
 new Map([["combined", "a + b"], ["c", "c"]])
@@ -311,57 +278,61 @@ object insertion order is easy to get wrong and `Map` is more foolproof.

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`select`](QueryBase.md#select)
+[QueryBase](QueryBase.md).[select](QueryBase.md#select)

-***
+#### Defined in

-### toArray()
+[query.ts:108](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L108)

-> **toArray**(`options`?): `Promise`&lt;`any`[]&gt;
+___
+
+### toArray
+
+▸ **toArray**(): `Promise`\<`unknown`[]\>

 Collect the results as an array of objects.

-#### Parameters
-
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
-
 #### Returns

-`Promise`&lt;`any`[]&gt;
+`Promise`\<`unknown`[]\>

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`toArray`](QueryBase.md#toarray)
+[QueryBase](QueryBase.md).[toArray](QueryBase.md#toarray)

-***
+#### Defined in

-### toArrow()
+[query.ts:169](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L169)

-> **toArrow**(`options`?): `Promise`&lt;`Table`&lt;`any`&gt;&gt;
+___
+
+### toArrow
+
+▸ **toArrow**(): `Promise`\<`Table`\<`any`\>\>

 Collect the results as an Arrow

-#### Parameters
-
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
-
 #### Returns

-`Promise`&lt;`Table`&lt;`any`&gt;&gt;
+`Promise`\<`Table`\<`any`\>\>

-#### See
+**`See`**

 ArrowTable.

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`toArrow`](QueryBase.md#toarrow)
+[QueryBase](QueryBase.md).[toArrow](QueryBase.md#toarrow)

-***
+#### Defined in

-### where()
+[query.ts:160](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L160)

-> **where**(`predicate`): `this`
+___
+
+### where
+
+▸ **where**(`predicate`): [`Query`](Query.md)

 A filter statement to be applied to this query.

@@ -369,13 +340,15 @@ The filter should be supplied as an SQL query string.  For example:

 #### Parameters

-• **predicate**: `string`
+| Name | Type |
+| :------ | :------ |
+| `predicate` | `string` |

 #### Returns

-`this`
+[`Query`](Query.md)

-#### Example
+**`Example`**

 ```ts
 x > 10
@@ -388,4 +361,8 @@ on the filter column(s).

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`where`](QueryBase.md#where)
+[QueryBase](QueryBase.md).[where](QueryBase.md#where)
+
+#### Defined in
+
+[query.ts:73](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L73)
--- a/docs/src/js/classes/QueryBase.md
+++ b/docs/src/js/classes/QueryBase.md
@@ -1,91 +1,117 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / QueryBase

-***
-
-[@lancedb/lancedb](../globals.md) / QueryBase
-
-# Class: QueryBase&lt;NativeQueryType&gt;
+# Class: QueryBase\<NativeQueryType, QueryType\>

 Common methods supported by all query types

-## Extended by
+## Type parameters

- [`Query`](Query.md)
- [`VectorQuery`](VectorQuery.md)
+| Name | Type |
+| :------ | :------ |
+| `NativeQueryType` | extends `NativeQuery` \| `NativeVectorQuery` |
+| `QueryType` | `QueryType` |

-## Type Parameters
+## Hierarchy

-• **NativeQueryType** *extends* `NativeQuery` \| `NativeVectorQuery`
+- **`QueryBase`**
+
+  ↳ [`Query`](Query.md)
+
+  ↳ [`VectorQuery`](VectorQuery.md)

 ## Implements

- `AsyncIterable`&lt;`RecordBatch`&gt;
+- `AsyncIterable`\<`RecordBatch`\>
+
+## Table of contents
+
+### Constructors
+
+- [constructor](QueryBase.md#constructor)
+
+### Properties
+
+- [inner](QueryBase.md#inner)
+
+### Methods
+
+- [[asyncIterator]](QueryBase.md#[asynciterator])
+- [execute](QueryBase.md#execute)
+- [limit](QueryBase.md#limit)
+- [nativeExecute](QueryBase.md#nativeexecute)
+- [select](QueryBase.md#select)
+- [toArray](QueryBase.md#toarray)
+- [toArrow](QueryBase.md#toarrow)
+- [where](QueryBase.md#where)

 ## Constructors

-### new QueryBase()
+### constructor

-> `protected` **new QueryBase**&lt;`NativeQueryType`&gt;(`inner`): [`QueryBase`](QueryBase.md)&lt;`NativeQueryType`&gt;
+• **new QueryBase**\<`NativeQueryType`, `QueryType`\>(`inner`): [`QueryBase`](QueryBase.md)\<`NativeQueryType`, `QueryType`\>
+
+#### Type parameters
+
+| Name | Type |
+| :------ | :------ |
+| `NativeQueryType` | extends `Query` \| `VectorQuery` |
+| `QueryType` | `QueryType` |

 #### Parameters

-• **inner**: `NativeQueryType` \| `Promise`&lt;`NativeQueryType`&gt;
+| Name | Type |
+| :------ | :------ |
+| `inner` | `NativeQueryType` |

 #### Returns

-[`QueryBase`](QueryBase.md)&lt;`NativeQueryType`&gt;
+[`QueryBase`](QueryBase.md)\<`NativeQueryType`, `QueryType`\>
+
+#### Defined in
+
+[query.ts:59](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L59)

 ## Properties

 ### inner

-> `protected` **inner**: `NativeQueryType` \| `Promise`&lt;`NativeQueryType`&gt;
+• `Protected` **inner**: `NativeQueryType`
+
+#### Defined in
+
+[query.ts:59](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L59)

 ## Methods

-### \[asyncIterator\]()
+### [asyncIterator]

-> **\[asyncIterator\]**(): `AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;
+▸ **[asyncIterator]**(): `AsyncIterator`\<`RecordBatch`\<`any`\>, `any`, `undefined`\>

 #### Returns

-`AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;
+`AsyncIterator`\<`RecordBatch`\<`any`\>, `any`, `undefined`\>

 #### Implementation of

-`AsyncIterable.[asyncIterator]`
+AsyncIterable.[asyncIterator]

-***
+#### Defined in

-### doCall()
+[query.ts:154](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L154)

-> `protected` **doCall**(`fn`): `void`
+___

-#### Parameters
+### execute

-• **fn**
-
-#### Returns
-
-`void`
-
-***
-
-### execute()
-
-> `protected` **execute**(`options`?): [`RecordBatchIterator`](RecordBatchIterator.md)
+▸ **execute**(): [`RecordBatchIterator`](RecordBatchIterator.md)

 Execute the query and return the results as an

-#### Parameters
-
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
-
 #### Returns

 [`RecordBatchIterator`](RecordBatchIterator.md)

-#### See
+**`See`**

 - AsyncIterator
 of
@@ -97,66 +123,15 @@ This readahead is limited however and backpressure will be applied if this
 stream is consumed slowly (this constrains the maximum memory used by a
 single query)

-***
+#### Defined in

-### explainPlan()
+[query.ts:149](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L149)

-> **explainPlan**(`verbose`): `Promise`&lt;`string`&gt;
+___

-Generates an explanation of the query execution plan.
+### limit

-#### Parameters
-
-• **verbose**: `boolean` = `false`
-
-If true, provides a more detailed explanation. Defaults to false.
-
-#### Returns
-
-`Promise`&lt;`string`&gt;
-
-A Promise that resolves to a string containing the query execution plan explanation.
-
-#### Example
-
-```ts
-import * as lancedb from "@lancedb/lancedb"
-const db = await lancedb.connect("./.lancedb");
-const table = await db.createTable("my_table", [
-  { vector: [1.1, 0.9], id: "1" },
-]);
-const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
-```
-
-***
-
-### ~~filter()~~
-
-> **filter**(`predicate`): `this`
-
-A filter statement to be applied to this query.
-
-#### Parameters
-
-• **predicate**: `string`
-
-#### Returns
-
-`this`
-
-#### Alias
-
-where
-
-#### Deprecated
-
-Use `where` instead
-
-***
-
-### limit()
-
-> **limit**(`limit`): `this`
+▸ **limit**(`limit`): `QueryType`

 Set the maximum number of results to return.

@@ -165,31 +140,37 @@ called then every valid row from the table will be returned.

 #### Parameters

-• **limit**: `number`
+| Name | Type |
+| :------ | :------ |
+| `limit` | `number` |

 #### Returns

-`this`
+`QueryType`

-***
+#### Defined in

-### nativeExecute()
+[query.ts:129](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L129)

-> `protected` **nativeExecute**(`options`?): `Promise`&lt;`RecordBatchIterator`&gt;
+___

-#### Parameters
+### nativeExecute

-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+▸ **nativeExecute**(): `Promise`\<`RecordBatchIterator`\>

 #### Returns

-`Promise`&lt;`RecordBatchIterator`&gt;
+`Promise`\<`RecordBatchIterator`\>

-***
+#### Defined in

-### select()
+[query.ts:134](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L134)

-> **select**(`columns`): `this`
+___
+
+### select
+
+▸ **select**(`columns`): `QueryType`

 Return only the specified columns.

@@ -213,13 +194,15 @@ input to this method would be:

 #### Parameters

-• **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+| Name | Type |
+| :------ | :------ |
+| `columns` | `string`[] \| `Record`\<`string`, `string`\> \| `Map`\<`string`, `string`\> |

 #### Returns

-`this`
+`QueryType`

-#### Example
+**`Example`**

 ```ts
 new Map([["combined", "a + b"], ["c", "c"]])
@@ -232,47 +215,51 @@ uses `Object.entries` which should preserve the insertion order of the object.
 object insertion order is easy to get wrong and `Map` is more foolproof.
 ```

-***
+#### Defined in

-### toArray()
+[query.ts:108](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L108)

-> **toArray**(`options`?): `Promise`&lt;`any`[]&gt;
+___
+
+### toArray
+
+▸ **toArray**(): `Promise`\<`unknown`[]\>

 Collect the results as an array of objects.

-#### Parameters
-
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
-
 #### Returns

-`Promise`&lt;`any`[]&gt;
+`Promise`\<`unknown`[]\>

-***
+#### Defined in

-### toArrow()
+[query.ts:169](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L169)

-> **toArrow**(`options`?): `Promise`&lt;`Table`&lt;`any`&gt;&gt;
+___
+
+### toArrow
+
+▸ **toArrow**(): `Promise`\<`Table`\<`any`\>\>

 Collect the results as an Arrow

-#### Parameters
-
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
-
 #### Returns

-`Promise`&lt;`Table`&lt;`any`&gt;&gt;
+`Promise`\<`Table`\<`any`\>\>

-#### See
+**`See`**

 ArrowTable.

-***
+#### Defined in

-### where()
+[query.ts:160](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L160)

-> **where**(`predicate`): `this`
+___
+
+### where
+
+▸ **where**(`predicate`): `QueryType`

 A filter statement to be applied to this query.

@@ -280,13 +267,15 @@ The filter should be supplied as an SQL query string.  For example:

 #### Parameters

-• **predicate**: `string`
+| Name | Type |
+| :------ | :------ |
+| `predicate` | `string` |

 #### Returns

-`this`
+`QueryType`

-#### Example
+**`Example`**

 ```ts
 x > 10
@@ -296,3 +285,7 @@ x > 5 OR y = 'test'
 Filtering performance can often be improved by creating a scalar index
 on the filter column(s).
 ```
+
+#### Defined in
+
+[query.ts:73](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L73)
--- a/docs/src/js/classes/RecordBatchIterator.md
+++ b/docs/src/js/classes/RecordBatchIterator.md
@@ -1,39 +1,80 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / RecordBatchIterator
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / RecordBatchIterator

 # Class: RecordBatchIterator

 ## Implements

- `AsyncIterator`&lt;`RecordBatch`&gt;
+- `AsyncIterator`\<`RecordBatch`\>
+
+## Table of contents
+
+### Constructors
+
+- [constructor](RecordBatchIterator.md#constructor)
+
+### Properties
+
+- [inner](RecordBatchIterator.md#inner)
+- [promisedInner](RecordBatchIterator.md#promisedinner)
+
+### Methods
+
+- [next](RecordBatchIterator.md#next)

 ## Constructors

-### new RecordBatchIterator()
+### constructor

-> **new RecordBatchIterator**(`promise`?): [`RecordBatchIterator`](RecordBatchIterator.md)
+• **new RecordBatchIterator**(`promise?`): [`RecordBatchIterator`](RecordBatchIterator.md)

 #### Parameters

-• **promise?**: `Promise`&lt;`RecordBatchIterator`&gt;
+| Name | Type |
+| :------ | :------ |
+| `promise?` | `Promise`\<`RecordBatchIterator`\> |

 #### Returns

 [`RecordBatchIterator`](RecordBatchIterator.md)

+#### Defined in
+
+[query.ts:27](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L27)
+
+## Properties
+
+### inner
+
+• `Private` `Optional` **inner**: `RecordBatchIterator`
+
+#### Defined in
+
+[query.ts:25](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L25)
+
+___
+
+### promisedInner
+
+• `Private` `Optional` **promisedInner**: `Promise`\<`RecordBatchIterator`\>
+
+#### Defined in
+
+[query.ts:24](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L24)
+
 ## Methods

-### next()
+### next

-> **next**(): `Promise`&lt;`IteratorResult`&lt;`RecordBatch`&lt;`any`&gt;, `any`&gt;&gt;
+▸ **next**(): `Promise`\<`IteratorResult`\<`RecordBatch`\<`any`\>, `any`\>\>

 #### Returns

-`Promise`&lt;`IteratorResult`&lt;`RecordBatch`&lt;`any`&gt;, `any`&gt;&gt;
+`Promise`\<`IteratorResult`\<`RecordBatch`\<`any`\>, `any`\>\>

 #### Implementation of

-`AsyncIterator.next`
+AsyncIterator.next
+
+#### Defined in
+
+[query.ts:33](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L33)
--- a/docs/src/js/classes/Table.md
+++ b/docs/src/js/classes/Table.md
@@ -1,10 +1,6 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / Table

-***
-
-[@lancedb/lancedb](../globals.md) / Table
-
-# Class: `abstract` Table
+# Class: Table

 A Table is a collection of Records in a LanceDB Database.

@@ -17,149 +13,196 @@ further operations.
 Closing a table is optional.  It not closed, it will be closed when it is garbage
 collected.

+## Table of contents
+
+### Constructors
+
+- [constructor](Table.md#constructor)
+
+### Properties
+
+- [inner](Table.md#inner)
+
+### Methods
+
+- [add](Table.md#add)
+- [addColumns](Table.md#addcolumns)
+- [alterColumns](Table.md#altercolumns)
+- [checkout](Table.md#checkout)
+- [checkoutLatest](Table.md#checkoutlatest)
+- [close](Table.md#close)
+- [countRows](Table.md#countrows)
+- [createIndex](Table.md#createindex)
+- [delete](Table.md#delete)
+- [display](Table.md#display)
+- [dropColumns](Table.md#dropcolumns)
+- [isOpen](Table.md#isopen)
+- [listIndices](Table.md#listindices)
+- [query](Table.md#query)
+- [restore](Table.md#restore)
+- [schema](Table.md#schema)
+- [update](Table.md#update)
+- [vectorSearch](Table.md#vectorsearch)
+- [version](Table.md#version)
+
 ## Constructors

-### new Table()
+### constructor

-> **new Table**(): [`Table`](Table.md)
+• **new Table**(`inner`): [`Table`](Table.md)
+
+Construct a Table. Internal use only.
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `inner` | `Table` |

 #### Returns

 [`Table`](Table.md)

-## Accessors
+#### Defined in

-### name
+[table.ts:69](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L69)

-> `get` `abstract` **name**(): `string`
+## Properties

-Returns the name of the table
+### inner

-#### Returns
+• `Private` `Readonly` **inner**: `Table`

-`string`
+#### Defined in
+
+[table.ts:66](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L66)

 ## Methods

-### add()
+### add

-> `abstract` **add**(`data`, `options`?): `Promise`&lt;`void`&gt;
+▸ **add**(`data`, `options?`): `Promise`\<`void`\>

 Insert records into this Table.

 #### Parameters

-• **data**: [`Data`](../type-aliases/Data.md)
-
-Records to be inserted into the Table
-
-• **options?**: `Partial`&lt;[`AddDataOptions`](../interfaces/AddDataOptions.md)&gt;
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `data` | [`Data`](../modules.md#data) | Records to be inserted into the Table |
+| `options?` | `Partial`\<[`AddDataOptions`](../interfaces/AddDataOptions.md)\> | - |

 #### Returns

-`Promise`&lt;`void`&gt;
+`Promise`\<`void`\>

-***
+#### Defined in

-### addColumns()
+[table.ts:105](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L105)

-> `abstract` **addColumns**(`newColumnTransforms`): `Promise`&lt;`void`&gt;
+___
+
+### addColumns
+
+▸ **addColumns**(`newColumnTransforms`): `Promise`\<`void`\>

 Add new columns with defined values.

 #### Parameters

-• **newColumnTransforms**: [`AddColumnsSql`](../interfaces/AddColumnsSql.md)[]
-
-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.
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `newColumnTransforms` | [`AddColumnsSql`](../interfaces/AddColumnsSql.md)[] | 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`&lt;`void`&gt;
+`Promise`\<`void`\>

-***
+#### Defined in

-### alterColumns()
+[table.ts:261](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L261)

-> `abstract` **alterColumns**(`columnAlterations`): `Promise`&lt;`void`&gt;
+___
+
+### alterColumns
+
+▸ **alterColumns**(`columnAlterations`): `Promise`\<`void`\>

 Alter the name or nullability of columns.

 #### Parameters

-• **columnAlterations**: [`ColumnAlteration`](../interfaces/ColumnAlteration.md)[]
-
-One or more alterations to
-apply to columns.
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `columnAlterations` | [`ColumnAlteration`](../interfaces/ColumnAlteration.md)[] | One or more alterations to apply to columns. |

 #### Returns

-`Promise`&lt;`void`&gt;
+`Promise`\<`void`\>

-***
+#### Defined in

-### checkout()
+[table.ts:270](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L270)

-> `abstract` **checkout**(`version`): `Promise`&lt;`void`&gt;
+___

-Checks out a specific version of the table _This is an in-place operation._
+### checkout

-This allows viewing previous versions of the table. If you wish to
-keep writing to the dataset starting from an old version, then use
-the `restore` function.
+▸ **checkout**(`version`): `Promise`\<`void`\>

-Calling this method will set the table into time-travel mode. If you
-wish to return to standard mode, call `checkoutLatest`.
+Checks out a specific version of the Table
+
+Any read operation on the table will now access the data at the checked out version.
+As a consequence, calling this method will disable any read consistency interval
+that was previously set.
+
+This is a read-only operation that turns the table into a sort of "view"
+or "detached head".  Other table instances will not be affected.  To make the change
+permanent you can use the `[Self::restore]` method.
+
+Any operation that modifies the table will fail while the table is in a checked
+out state.
+
+To return the table to a normal state use `[Self::checkout_latest]`

 #### Parameters

-• **version**: `number`
-
-The version to checkout
+| Name | Type |
+| :------ | :------ |
+| `version` | `number` |

 #### Returns

-`Promise`&lt;`void`&gt;
+`Promise`\<`void`\>

-#### Example
+#### Defined in

-```typescript
-import * as lancedb from "@lancedb/lancedb"
-const db = await lancedb.connect("./.lancedb");
-const table = await db.createTable("my_table", [
-  { vector: [1.1, 0.9], type: "vector" },
-]);
+[table.ts:317](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L317)

-console.log(await table.version()); // 1
-console.log(table.display());
-await table.add([{ vector: [0.5, 0.2], type: "vector" }]);
-await table.checkout(1);
-console.log(await table.version()); // 2
-```
+___

-***
+### checkoutLatest

-### checkoutLatest()
+▸ **checkoutLatest**(): `Promise`\<`void`\>

-> `abstract` **checkoutLatest**(): `Promise`&lt;`void`&gt;
+Ensures the table is pointing at the latest version

-Checkout the latest version of the table. _This is an in-place operation._
-
-The table will be set back into standard mode, and will track the latest
-version of the table.
+This can be used to manually update a table when the read_consistency_interval is None
+It can also be used to undo a `[Self::checkout]` operation

 #### Returns

-`Promise`&lt;`void`&gt;
+`Promise`\<`void`\>

-***
+#### Defined in

-### close()
+[table.ts:327](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L327)

-> `abstract` **close**(): `void`
+___
+
+### close
+
+▸ **close**(): `void`

 Close the table, releasing any underlying resources.

@@ -171,27 +214,37 @@ Any attempt to use the table after it is closed will result in an error.

 `void`

-***
+#### Defined in

-### countRows()
+[table.ts:85](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L85)

-> `abstract` **countRows**(`filter`?): `Promise`&lt;`number`&gt;
+___
+
+### countRows
+
+▸ **countRows**(`filter?`): `Promise`\<`number`\>

 Count the total number of rows in the dataset.

 #### Parameters

-• **filter?**: `string`
+| Name | Type |
+| :------ | :------ |
+| `filter?` | `string` |

 #### Returns

-`Promise`&lt;`number`&gt;
+`Promise`\<`number`\>

-***
+#### Defined in

-### createIndex()
+[table.ts:152](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L152)

-> `abstract` **createIndex**(`column`, `options`?): `Promise`&lt;`void`&gt;
+___
+
+### createIndex
+
+▸ **createIndex**(`column`, `options?`): `Promise`\<`void`\>

 Create an index to speed up queries.

@@ -202,66 +255,73 @@ vector and non-vector searches)

 #### Parameters

-• **column**: `string`
-
-• **options?**: `Partial`&lt;[`IndexOptions`](../interfaces/IndexOptions.md)&gt;
+| Name | Type |
+| :------ | :------ |
+| `column` | `string` |
+| `options?` | `Partial`\<[`IndexOptions`](../interfaces/IndexOptions.md)\> |

 #### Returns

-`Promise`&lt;`void`&gt;
+`Promise`\<`void`\>

-#### Note
-
-We currently don't support custom named indexes,
-The index name will always be `${column}_idx`
-
-#### Examples
+**`Example`**

 ```ts
 // If the column has a vector (fixed size list) data type then
 // an IvfPq vector index will be created.
 const table = await conn.openTable("my_table");
-await table.createIndex("vector");
+await table.createIndex(["vector"]);
 ```

+**`Example`**
+
 ```ts
 // For advanced control over vector index creation you can specify
 // the index type and options.
 const table = await conn.openTable("my_table");
-await table.createIndex("vector", {
-  config: lancedb.Index.ivfPq({
-    numPartitions: 128,
-    numSubVectors: 16,
-  }),
-});
+await table.createIndex(["vector"], I)
+  .ivf_pq({ num_partitions: 128, num_sub_vectors: 16 })
+  .build();
 ```

+**`Example`**
+
 ```ts
 // Or create a Scalar index
-await table.createIndex("my_float_col");
+await table.createIndex("my_float_col").build();
 ```

-***
+#### Defined in

-### delete()
+[table.ts:184](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L184)

-> `abstract` **delete**(`predicate`): `Promise`&lt;`void`&gt;
+___
+
+### delete
+
+▸ **delete**(`predicate`): `Promise`\<`void`\>

 Delete the rows that satisfy the predicate.

 #### Parameters

-• **predicate**: `string`
+| Name | Type |
+| :------ | :------ |
+| `predicate` | `string` |

 #### Returns

-`Promise`&lt;`void`&gt;
+`Promise`\<`void`\>

-***
+#### Defined in

-### display()
+[table.ts:157](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L157)

-> `abstract` **display**(): `string`
+___
+
+### display
+
+▸ **display**(): `string`

 Return a brief description of the table

@@ -269,11 +329,15 @@ Return a brief description of the table

 `string`

-***
+#### Defined in

-### dropColumns()
+[table.ts:90](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L90)

-> `abstract` **dropColumns**(`columnNames`): `Promise`&lt;`void`&gt;
+___
+
+### dropColumns
+
+▸ **dropColumns**(`columnNames`): `Promise`\<`void`\>

 Drop one or more columns from the dataset

@@ -284,41 +348,23 @@ then call ``cleanup_files`` to remove the old files.

 #### Parameters

-• **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").
+| 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`&lt;`void`&gt;
+`Promise`\<`void`\>

-***
+#### Defined in

-### indexStats()
+[table.ts:285](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L285)

-> `abstract` **indexStats**(`name`): `Promise`&lt;`undefined` \| [`IndexStatistics`](../interfaces/IndexStatistics.md)&gt;
+___

-List all the stats of a specified index
+### isOpen

-#### Parameters
-
-• **name**: `string`
-
-The name of the index.
-
-#### Returns
-
-`Promise`&lt;`undefined` \| [`IndexStatistics`](../interfaces/IndexStatistics.md)&gt;
-
-The stats of the index. If the index does not exist, it will return undefined
-
-***
-
-### isOpen()
-
-> `abstract` **isOpen**(): `boolean`
+▸ **isOpen**(): `boolean`

 Return true if the table has not been closed

@@ -326,79 +372,31 @@ Return true if the table has not been closed

 `boolean`

-***
+#### Defined in

-### listIndices()
+[table.ts:74](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L74)

-> `abstract` **listIndices**(): `Promise`&lt;[`IndexConfig`](../interfaces/IndexConfig.md)[]&gt;
+___

-List all indices that have been created with [Table.createIndex](Table.md#createindex)
+### listIndices
+
+▸ **listIndices**(): `Promise`\<[`IndexConfig`](../interfaces/IndexConfig.md)[]\>
+
+List all indices that have been created with Self::create_index

 #### Returns

-`Promise`&lt;[`IndexConfig`](../interfaces/IndexConfig.md)[]&gt;
+`Promise`\<[`IndexConfig`](../interfaces/IndexConfig.md)[]\>

-***
+#### Defined in

-### mergeInsert()
+[table.ts:350](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L350)

-> `abstract` **mergeInsert**(`on`): `MergeInsertBuilder`
+___

-#### Parameters
+### query

-• **on**: `string` \| `string`[]
-
-#### Returns
-
-`MergeInsertBuilder`
-
-***
-
-### optimize()
-
-> `abstract` **optimize**(`options`?): `Promise`&lt;`OptimizeStats`&gt;
-
-Optimize the on-disk data and indices for better performance.
-
-Modeled after ``VACUUM`` in PostgreSQL.
-
- Optimization covers three operations:
-
- - Compaction: Merges small files into larger ones
- - Prune: Removes old versions of the dataset
- - Index: Optimizes the indices, adding new data to existing indices
-
- Experimental API
- ----------------
-
- The optimization process is undergoing active development and may change.
- Our goal with these changes is to improve the performance of optimization and
- reduce the complexity.
-
- That being said, it is essential today to run optimize if you want the best
- performance.  It should be stable and safe to use in production, but it our
- hope that the API may be simplified (or not even need to be called) in the
- future.
-
- The frequency an application shoudl call optimize is based on the frequency of
- data modifications.  If data is frequently added, deleted, or updated then
- optimize should be run frequently.  A good rule of thumb is to run optimize if
- you have added or modified 100,000 or more records or run more than 20 data
- modification operations.
-
-#### Parameters
-
-• **options?**: `Partial`&lt;`OptimizeOptions`&gt;
-
-#### Returns
-
-`Promise`&lt;`OptimizeStats`&gt;
-
-***
-
-### query()
-
-> `abstract` **query**(): [`Query`](Query.md)
+▸ **query**(): [`Query`](Query.md)

 Create a [Query](Query.md) Builder.

@@ -408,7 +406,8 @@ returned by this method can be used to control the query using filtering,
 vector similarity, sorting, and more.

 Note: By default, all columns are returned.  For best performance, you should
-only fetch the columns you need.
+only fetch the columns you need.  See [`Query::select_with_projection`] for
+more details.

 When appropriate, various indices and statistics based pruning will be used to
 accelerate the query.
@@ -419,22 +418,21 @@ accelerate the query.

 A builder that can be used to parameterize the query

-#### Examples
+**`Example`**

 ```ts
 // SQL-style filtering
 //
 // This query will return up to 1000 rows whose value in the `id` column
-// is greater than 5. LanceDb supports a broad set of filtering functions.
-for await (const batch of table
-  .query()
-  .where("id > 1")
-  .select(["id"])
-  .limit(20)) {
-  console.log(batch);
+// is greater than 5.  LanceDb supports a broad set of filtering functions.
+for await (const batch of table.query()
+                         .filter("id > 1").select(["id"]).limit(20)) {
+ console.log(batch);
 }
 ```

+**`Example`**
+
 ```ts
 // Vector Similarity Search
 //
@@ -442,17 +440,18 @@ for await (const batch of table
 // closest to the query vector [1.0, 2.0, 3.0].  If an index has been created
 // on the "vector" column then this will perform an ANN search.
 //
-// The `refineFactor` and `nprobes` methods are used to control the recall /
+// The `refine_factor` and `nprobes` methods are used to control the recall /
 // latency tradeoff of the search.
-for await (const batch of table
-  .query()
-  .where("id > 1")
-  .select(["id"])
-  .limit(20)) {
-  console.log(batch);
+for await (const batch of table.query()
+                   .nearestTo([1, 2, 3])
+                   .refineFactor(5).nprobe(10)
+                   .limit(10)) {
+ console.log(batch);
 }
 ```

+**`Example`**
+
 ```ts
 // Scan the full dataset
 //
@@ -462,11 +461,15 @@ for await (const batch of table.query()) {
 }
 ```

-***
+#### Defined in

-### restore()
+[table.ts:238](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L238)

-> `abstract` **restore**(): `Promise`&lt;`void`&gt;
+___
+
+### restore
+
+▸ **restore**(): `Promise`\<`void`\>

 Restore the table to the currently checked out version

@@ -481,121 +484,33 @@ out state and the read_consistency_interval, if any, will apply.

 #### Returns

-`Promise`&lt;`void`&gt;
+`Promise`\<`void`\>

-***
+#### Defined in

-### schema()
+[table.ts:343](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L343)

-> `abstract` **schema**(): `Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+___
+
+### schema
+
+▸ **schema**(): `Promise`\<`Schema`\<`any`\>\>

 Get the schema of the table.

 #### Returns

-`Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+`Promise`\<`Schema`\<`any`\>\>

-***
+#### Defined in

-### search()
+[table.ts:95](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L95)

-#### search(query)
+___

-> `abstract` **search**(`query`): [`VectorQuery`](VectorQuery.md)
+### update

-Create a search query to find the nearest neighbors
-of the given query vector
-
-##### Parameters
-
-• **query**: `string`
-
-the query. This will be converted to a vector using the table's provided embedding function
-
-##### Returns
-
-[`VectorQuery`](VectorQuery.md)
-
-##### Note
-
-If no embedding functions are defined in the table, this will error when collecting the results.
-
-#### search(query)
-
-> `abstract` **search**(`query`): [`VectorQuery`](VectorQuery.md)
-
-Create a search query to find the nearest neighbors
-of the given query vector
-
-##### Parameters
-
-• **query**: `IntoVector`
-
-the query vector
-
-##### Returns
-
-[`VectorQuery`](VectorQuery.md)
-
-***
-
-### toArrow()
-
-> `abstract` **toArrow**(): `Promise`&lt;`Table`&lt;`any`&gt;&gt;
-
-Return the table as an arrow table
-
-#### Returns
-
-`Promise`&lt;`Table`&lt;`any`&gt;&gt;
-
-***
-
-### update()
-
-#### update(opts)
-
-> `abstract` **update**(`opts`): `Promise`&lt;`void`&gt;
-
-Update existing records in the Table
-
-##### Parameters
-
-• **opts**: `object` & `Partial`&lt;[`UpdateOptions`](../interfaces/UpdateOptions.md)&gt;
-
-##### Returns
-
-`Promise`&lt;`void`&gt;
-
-##### Example
-
-```ts
-table.update({where:"x = 2", values:{"vector": [10, 10]}})
-```
-
-#### update(opts)
-
-> `abstract` **update**(`opts`): `Promise`&lt;`void`&gt;
-
-Update existing records in the Table
-
-##### Parameters
-
-• **opts**: `object` & `Partial`&lt;[`UpdateOptions`](../interfaces/UpdateOptions.md)&gt;
-
-##### Returns
-
-`Promise`&lt;`void`&gt;
-
-##### Example
-
-```ts
-table.update({where:"x = 2", valuesSql:{"x": "x + 1"}})
-```
-
-#### update(updates, options)
-
-> `abstract` **update**(`updates`, `options`?): `Promise`&lt;`void`&gt;
+▸ **update**(`updates`, `options?`): `Promise`\<`void`\>

 Update existing records in the Table

@@ -612,32 +527,26 @@ you are updating many rows (with different ids) then you will get
 better performance with a single [`merge_insert`] call instead of
 repeatedly calilng this method.

-##### Parameters
+#### Parameters

-• **updates**: `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `updates` | `Record`\<`string`, `string`\> \| `Map`\<`string`, `string`\> | the columns to update Keys in the map should specify the name of the column to update. Values in the map provide the new value of the column. These can be SQL literal strings (e.g. "7" or "'foo'") or they can be expressions based on the row being updated (e.g. "my_col + 1") |
+| `options?` | `Partial`\<[`UpdateOptions`](../interfaces/UpdateOptions.md)\> | additional options to control the update behavior |

-the
-columns to update
+#### Returns

-Keys in the map should specify the name of the column to update.
-Values in the map provide the new value of the column.  These can
-be SQL literal strings (e.g. "7" or "'foo'") or they can be expressions
-based on the row being updated (e.g. "my_col + 1")
+`Promise`\<`void`\>

-• **options?**: `Partial`&lt;[`UpdateOptions`](../interfaces/UpdateOptions.md)&gt;
+#### Defined in

-additional options to control
-the update behavior
+[table.ts:137](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L137)

-##### Returns
+___

-`Promise`&lt;`void`&gt;
+### vectorSearch

-***
-
-### vectorSearch()
-
-> `abstract` **vectorSearch**(`vector`): [`VectorQuery`](VectorQuery.md)
+▸ **vectorSearch**(`vector`): [`VectorQuery`](VectorQuery.md)

 Search the table with a given query vector.

@@ -647,50 +556,39 @@ by `query`.

 #### Parameters

-• **vector**: `IntoVector`
+| Name | Type |
+| :------ | :------ |
+| `vector` | `unknown` |

 #### Returns

 [`VectorQuery`](VectorQuery.md)

-#### See
+**`See`**

 [Query#nearestTo](Query.md#nearestto) for more details.

-***
+#### Defined in

-### version()
+[table.ts:249](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L249)

-> `abstract` **version**(): `Promise`&lt;`number`&gt;
+___
+
+### version
+
+▸ **version**(): `Promise`\<`number`\>

 Retrieve the version of the table

-#### Returns
-
-`Promise`&lt;`number`&gt;
-
-***
-
-### parseTableData()
-
-> `static` **parseTableData**(`data`, `options`?, `streaming`?): `Promise`&lt;`object`&gt;
-
-#### Parameters
-
-• **data**: `TableLike` \| `Record`&lt;`string`, `unknown`&gt;[]
-
-• **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
-
-• **streaming?**: `boolean` = `false`
+LanceDb supports versioning.  Every operation that modifies the table increases
+version.  As long as a version hasn't been deleted you can `[Self::checkout]` that
+version to view the data at that point.  In addition, you can `[Self::restore]` the
+version to replace the current table with a previous version.

 #### Returns

-`Promise`&lt;`object`&gt;
+`Promise`\<`number`\>

-##### buf
+#### Defined in

-> **buf**: `Buffer`
-
-##### mode
-
-> **mode**: `string`
+[table.ts:297](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L297)
--- a/docs/src/js/classes/VectorColumnOptions.md
+++ b/docs/src/js/classes/VectorColumnOptions.md
@@ -1,29 +1,45 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / VectorColumnOptions
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / VectorColumnOptions

 # Class: VectorColumnOptions

+## Table of contents
+
+### Constructors
+
+- [constructor](VectorColumnOptions.md#constructor)
+
+### Properties
+
+- [type](VectorColumnOptions.md#type)
+
 ## Constructors

-### new VectorColumnOptions()
+### constructor

-> **new VectorColumnOptions**(`values`?): [`VectorColumnOptions`](VectorColumnOptions.md)
+• **new VectorColumnOptions**(`values?`): [`VectorColumnOptions`](VectorColumnOptions.md)

 #### Parameters

-• **values?**: `Partial`&lt;[`VectorColumnOptions`](VectorColumnOptions.md)&gt;
+| Name | Type |
+| :------ | :------ |
+| `values?` | `Partial`\<[`VectorColumnOptions`](VectorColumnOptions.md)\> |

 #### Returns

 [`VectorColumnOptions`](VectorColumnOptions.md)

+#### Defined in
+
+[arrow.ts:49](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/arrow.ts#L49)
+
 ## Properties

 ### type

-> **type**: `Float`&lt;`Floats`&gt;
+• **type**: `Float`\<`Floats`\>

 Vector column type.
+
+#### Defined in
+
+[arrow.ts:47](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/arrow.ts#L47)
--- a/docs/src/js/classes/VectorQuery.md
+++ b/docs/src/js/classes/VectorQuery.md
@@ -1,8 +1,4 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / VectorQuery
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / VectorQuery

 # Class: VectorQuery

@@ -10,19 +6,50 @@ A builder used to construct a vector search

 This builder can be reused to execute the query many times.

-## Extends
+## Hierarchy

- [`QueryBase`](QueryBase.md)&lt;`NativeVectorQuery`&gt;
+- [`QueryBase`](QueryBase.md)\<`NativeVectorQuery`, [`VectorQuery`](VectorQuery.md)\>
+
+  ↳ **`VectorQuery`**
+
+## Table of contents
+
+### Constructors
+
+- [constructor](VectorQuery.md#constructor)
+
+### Properties
+
+- [inner](VectorQuery.md#inner)
+
+### Methods
+
+- [[asyncIterator]](VectorQuery.md#[asynciterator])
+- [bypassVectorIndex](VectorQuery.md#bypassvectorindex)
+- [column](VectorQuery.md#column)
+- [distanceType](VectorQuery.md#distancetype)
+- [execute](VectorQuery.md#execute)
+- [limit](VectorQuery.md#limit)
+- [nativeExecute](VectorQuery.md#nativeexecute)
+- [nprobes](VectorQuery.md#nprobes)
+- [postfilter](VectorQuery.md#postfilter)
+- [refineFactor](VectorQuery.md#refinefactor)
+- [select](VectorQuery.md#select)
+- [toArray](VectorQuery.md#toarray)
+- [toArrow](VectorQuery.md#toarrow)
+- [where](VectorQuery.md#where)

 ## Constructors

-### new VectorQuery()
+### constructor

-> **new VectorQuery**(`inner`): [`VectorQuery`](VectorQuery.md)
+• **new VectorQuery**(`inner`): [`VectorQuery`](VectorQuery.md)

 #### Parameters

-• **inner**: `VectorQuery` \| `Promise`&lt;`VectorQuery`&gt;
+| Name | Type |
+| :------ | :------ |
+| `inner` | `VectorQuery` |

 #### Returns

@@ -30,37 +57,49 @@ This builder can be reused to execute the query many times.

 #### Overrides

-[`QueryBase`](QueryBase.md).[`constructor`](QueryBase.md#constructors)
+[QueryBase](QueryBase.md).[constructor](QueryBase.md#constructor)
+
+#### Defined in
+
+[query.ts:189](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L189)

 ## Properties

 ### inner

-> `protected` **inner**: `VectorQuery` \| `Promise`&lt;`VectorQuery`&gt;
+• `Protected` **inner**: `VectorQuery`

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`inner`](QueryBase.md#inner)
+[QueryBase](QueryBase.md).[inner](QueryBase.md#inner)
+
+#### Defined in
+
+[query.ts:59](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L59)

 ## Methods

-### \[asyncIterator\]()
+### [asyncIterator]

-> **\[asyncIterator\]**(): `AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;
+▸ **[asyncIterator]**(): `AsyncIterator`\<`RecordBatch`\<`any`\>, `any`, `undefined`\>

 #### Returns

-`AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;
+`AsyncIterator`\<`RecordBatch`\<`any`\>, `any`, `undefined`\>

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`[asyncIterator]`](QueryBase.md#%5Basynciterator%5D)
+[QueryBase](QueryBase.md).[[asyncIterator]](QueryBase.md#[asynciterator])

-***
+#### Defined in

-### bypassVectorIndex()
+[query.ts:154](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L154)

-> **bypassVectorIndex**(): [`VectorQuery`](VectorQuery.md)
+___
+
+### bypassVectorIndex
+
+▸ **bypassVectorIndex**(): [`VectorQuery`](VectorQuery.md)

 If this is called then any vector index is skipped

@@ -74,11 +113,15 @@ calculate your recall to select an appropriate value for nprobes.

 [`VectorQuery`](VectorQuery.md)

-***
+#### Defined in

-### column()
+[query.ts:321](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L321)

-> **column**(`column`): [`VectorQuery`](VectorQuery.md)
+___
+
+### column
+
+▸ **column**(`column`): [`VectorQuery`](VectorQuery.md)

 Set the vector column to query

@@ -87,24 +130,30 @@ the call to

 #### Parameters

-• **column**: `string`
+| Name | Type |
+| :------ | :------ |
+| `column` | `string` |

 #### Returns

 [`VectorQuery`](VectorQuery.md)

-#### See
+**`See`**

 [Query#nearestTo](Query.md#nearestto)

 This parameter must be specified if the table has more than one column
 whose data type is a fixed-size-list of floats.

-***
+#### Defined in

-### distanceType()
+[query.ts:229](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L229)

-> **distanceType**(`distanceType`): [`VectorQuery`](VectorQuery.md)
+___
+
+### distanceType
+
+▸ **distanceType**(`distanceType`): [`VectorQuery`](VectorQuery.md)

 Set the distance metric to use

@@ -114,13 +163,15 @@ use.  See

 #### Parameters

-• **distanceType**: `"l2"` \| `"cosine"` \| `"dot"`
+| Name | Type |
+| :------ | :------ |
+| `distanceType` | `string` |

 #### Returns

 [`VectorQuery`](VectorQuery.md)

-#### See
+**`See`**

 [IvfPqOptions.distanceType](../interfaces/IvfPqOptions.md#distancetype) for more details on the different
 distance metrics available.
@@ -131,41 +182,23 @@ invalid.

 By default "l2" is used.

-***
+#### Defined in

-### doCall()
+[query.ts:248](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L248)

-> `protected` **doCall**(`fn`): `void`
+___

-#### Parameters
+### execute

-• **fn**
-
-#### Returns
-
-`void`
-
-#### Inherited from
-
-[`QueryBase`](QueryBase.md).[`doCall`](QueryBase.md#docall)
-
-***
-
-### execute()
-
-> `protected` **execute**(`options`?): [`RecordBatchIterator`](RecordBatchIterator.md)
+▸ **execute**(): [`RecordBatchIterator`](RecordBatchIterator.md)

 Execute the query and return the results as an

-#### Parameters
-
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
-
 #### Returns

 [`RecordBatchIterator`](RecordBatchIterator.md)

-#### See
+**`See`**

 - AsyncIterator
 of
@@ -179,76 +212,17 @@ single query)

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`execute`](QueryBase.md#execute)
+[QueryBase](QueryBase.md).[execute](QueryBase.md#execute)

-***
+#### Defined in

-### explainPlan()
+[query.ts:149](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L149)

-> **explainPlan**(`verbose`): `Promise`&lt;`string`&gt;
+___

-Generates an explanation of the query execution plan.
+### limit

-#### Parameters
-
-• **verbose**: `boolean` = `false`
-
-If true, provides a more detailed explanation. Defaults to false.
-
-#### Returns
-
-`Promise`&lt;`string`&gt;
-
-A Promise that resolves to a string containing the query execution plan explanation.
-
-#### Example
-
-```ts
-import * as lancedb from "@lancedb/lancedb"
-const db = await lancedb.connect("./.lancedb");
-const table = await db.createTable("my_table", [
-  { vector: [1.1, 0.9], id: "1" },
-]);
-const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
-```
-
-#### Inherited from
-
-[`QueryBase`](QueryBase.md).[`explainPlan`](QueryBase.md#explainplan)
-
-***
-
-### ~~filter()~~
-
-> **filter**(`predicate`): `this`
-
-A filter statement to be applied to this query.
-
-#### Parameters
-
-• **predicate**: `string`
-
-#### Returns
-
-`this`
-
-#### Alias
-
-where
-
-#### Deprecated
-
-Use `where` instead
-
-#### Inherited from
-
-[`QueryBase`](QueryBase.md).[`filter`](QueryBase.md#filter)
-
-***
-
-### limit()
-
-> **limit**(`limit`): `this`
+▸ **limit**(`limit`): [`VectorQuery`](VectorQuery.md)

 Set the maximum number of results to return.

@@ -257,39 +231,45 @@ called then every valid row from the table will be returned.

 #### Parameters

-• **limit**: `number`
+| Name | Type |
+| :------ | :------ |
+| `limit` | `number` |

 #### Returns

-`this`
+[`VectorQuery`](VectorQuery.md)

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`limit`](QueryBase.md#limit)
+[QueryBase](QueryBase.md).[limit](QueryBase.md#limit)

-***
+#### Defined in

-### nativeExecute()
+[query.ts:129](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L129)

-> `protected` **nativeExecute**(`options`?): `Promise`&lt;`RecordBatchIterator`&gt;
+___

-#### Parameters
+### nativeExecute

-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+▸ **nativeExecute**(): `Promise`\<`RecordBatchIterator`\>

 #### Returns

-`Promise`&lt;`RecordBatchIterator`&gt;
+`Promise`\<`RecordBatchIterator`\>

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`nativeExecute`](QueryBase.md#nativeexecute)
+[QueryBase](QueryBase.md).[nativeExecute](QueryBase.md#nativeexecute)

-***
+#### Defined in

-### nprobes()
+[query.ts:134](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L134)

-> **nprobes**(`nprobes`): [`VectorQuery`](VectorQuery.md)
+___
+
+### nprobes
+
+▸ **nprobes**(`nprobes`): [`VectorQuery`](VectorQuery.md)

 Set the number of partitions to search (probe)

@@ -314,17 +294,23 @@ you the desired recall.

 #### Parameters

-• **nprobes**: `number`
+| Name | Type |
+| :------ | :------ |
+| `nprobes` | `number` |

 #### Returns

 [`VectorQuery`](VectorQuery.md)

-***
+#### Defined in

-### postfilter()
+[query.ts:215](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L215)

-> **postfilter**(): [`VectorQuery`](VectorQuery.md)
+___
+
+### postfilter
+
+▸ **postfilter**(): [`VectorQuery`](VectorQuery.md)

 If this is called then filtering will happen after the vector search instead of
 before.
@@ -347,16 +333,20 @@ Post filtering happens during the "refine stage" (described in more detail in

 [`VectorQuery`](VectorQuery.md)

-#### See
+**`See`**

 [VectorQuery#refineFactor](VectorQuery.md#refinefactor)).  This means that setting a higher refine
 factor can often help restore some of the results lost by post filtering.

-***
+#### Defined in

-### refineFactor()
+[query.ts:307](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L307)

-> **refineFactor**(`refineFactor`): [`VectorQuery`](VectorQuery.md)
+___
+
+### refineFactor
+
+▸ **refineFactor**(`refineFactor`): [`VectorQuery`](VectorQuery.md)

 A multiplier to control how many additional rows are taken during the refine step

@@ -388,17 +378,23 @@ distance between the query vector and the actual uncompressed vector.

 #### Parameters

-• **refineFactor**: `number`
+| Name | Type |
+| :------ | :------ |
+| `refineFactor` | `number` |

 #### Returns

 [`VectorQuery`](VectorQuery.md)

-***
+#### Defined in

-### select()
+[query.ts:282](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L282)

-> **select**(`columns`): `this`
+___
+
+### select
+
+▸ **select**(`columns`): [`VectorQuery`](VectorQuery.md)

 Return only the specified columns.

@@ -422,13 +418,15 @@ input to this method would be:

 #### Parameters

-• **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+| Name | Type |
+| :------ | :------ |
+| `columns` | `string`[] \| `Record`\<`string`, `string`\> \| `Map`\<`string`, `string`\> |

 #### Returns

-`this`
+[`VectorQuery`](VectorQuery.md)

-#### Example
+**`Example`**

 ```ts
 new Map([["combined", "a + b"], ["c", "c"]])
@@ -443,57 +441,61 @@ object insertion order is easy to get wrong and `Map` is more foolproof.

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`select`](QueryBase.md#select)
+[QueryBase](QueryBase.md).[select](QueryBase.md#select)

-***
+#### Defined in

-### toArray()
+[query.ts:108](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L108)

-> **toArray**(`options`?): `Promise`&lt;`any`[]&gt;
+___
+
+### toArray
+
+▸ **toArray**(): `Promise`\<`unknown`[]\>

 Collect the results as an array of objects.

-#### Parameters
-
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
-
 #### Returns

-`Promise`&lt;`any`[]&gt;
+`Promise`\<`unknown`[]\>

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`toArray`](QueryBase.md#toarray)
+[QueryBase](QueryBase.md).[toArray](QueryBase.md#toarray)

-***
+#### Defined in

-### toArrow()
+[query.ts:169](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L169)

-> **toArrow**(`options`?): `Promise`&lt;`Table`&lt;`any`&gt;&gt;
+___
+
+### toArrow
+
+▸ **toArrow**(): `Promise`\<`Table`\<`any`\>\>

 Collect the results as an Arrow

-#### Parameters
-
-• **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
-
 #### Returns

-`Promise`&lt;`Table`&lt;`any`&gt;&gt;
+`Promise`\<`Table`\<`any`\>\>

-#### See
+**`See`**

 ArrowTable.

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`toArrow`](QueryBase.md#toarrow)
+[QueryBase](QueryBase.md).[toArrow](QueryBase.md#toarrow)

-***
+#### Defined in

-### where()
+[query.ts:160](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L160)

-> **where**(`predicate`): `this`
+___
+
+### where
+
+▸ **where**(`predicate`): [`VectorQuery`](VectorQuery.md)

 A filter statement to be applied to this query.

@@ -501,13 +503,15 @@ The filter should be supplied as an SQL query string.  For example:

 #### Parameters

-• **predicate**: `string`
+| Name | Type |
+| :------ | :------ |
+| `predicate` | `string` |

 #### Returns

-`this`
+[`VectorQuery`](VectorQuery.md)

-#### Example
+**`Example`**

 ```ts
 x > 10
@@ -520,4 +524,8 @@ on the filter column(s).

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`where`](QueryBase.md#where)
+[QueryBase](QueryBase.md).[where](QueryBase.md#where)
+
+#### Defined in
+
+[query.ts:73](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/query.ts#L73)
--- a/docs/src/js/classes/embedding.OpenAIEmbeddingFunction.md
+++ b/docs/src/js/classes/embedding.OpenAIEmbeddingFunction.md
@@ -0,0 +1,111 @@
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / [embedding](../modules/embedding.md) / OpenAIEmbeddingFunction
+
+# Class: OpenAIEmbeddingFunction
+
+[embedding](../modules/embedding.md).OpenAIEmbeddingFunction
+
+An embedding function that automatically creates vector representation for a given column.
+
+## Implements
+
+- [`EmbeddingFunction`](../interfaces/embedding.EmbeddingFunction.md)\<`string`\>
+
+## Table of contents
+
+### Constructors
+
+- [constructor](embedding.OpenAIEmbeddingFunction.md#constructor)
+
+### Properties
+
+- [\_modelName](embedding.OpenAIEmbeddingFunction.md#_modelname)
+- [\_openai](embedding.OpenAIEmbeddingFunction.md#_openai)
+- [sourceColumn](embedding.OpenAIEmbeddingFunction.md#sourcecolumn)
+
+### Methods
+
+- [embed](embedding.OpenAIEmbeddingFunction.md#embed)
+
+## Constructors
+
+### constructor
+
+• **new OpenAIEmbeddingFunction**(`sourceColumn`, `openAIKey`, `modelName?`): [`OpenAIEmbeddingFunction`](embedding.OpenAIEmbeddingFunction.md)
+
+#### Parameters
+
+| Name | Type | Default value |
+| :------ | :------ | :------ |
+| `sourceColumn` | `string` | `undefined` |
+| `openAIKey` | `string` | `undefined` |
+| `modelName` | `string` | `"text-embedding-ada-002"` |
+
+#### Returns
+
+[`OpenAIEmbeddingFunction`](embedding.OpenAIEmbeddingFunction.md)
+
+#### Defined in
+
+[embedding/openai.ts:22](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/embedding/openai.ts#L22)
+
+## Properties
+
+### \_modelName
+
+• `Private` `Readonly` **\_modelName**: `string`
+
+#### Defined in
+
+[embedding/openai.ts:20](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/embedding/openai.ts#L20)
+
+___
+
+### \_openai
+
+• `Private` `Readonly` **\_openai**: `OpenAI`
+
+#### Defined in
+
+[embedding/openai.ts:19](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/embedding/openai.ts#L19)
+
+___
+
+### sourceColumn
+
+• **sourceColumn**: `string`
+
+The name of the column that will be used as input for the Embedding Function.
+
+#### Implementation of
+
+[EmbeddingFunction](../interfaces/embedding.EmbeddingFunction.md).[sourceColumn](../interfaces/embedding.EmbeddingFunction.md#sourcecolumn)
+
+#### Defined in
+
+[embedding/openai.ts:61](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/embedding/openai.ts#L61)
+
+## Methods
+
+### embed
+
+▸ **embed**(`data`): `Promise`\<`number`[][]\>
+
+Creates a vector representation for the given values.
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `data` | `string`[] |
+
+#### Returns
+
+`Promise`\<`number`[][]\>
+
+#### Implementation of
+
+[EmbeddingFunction](../interfaces/embedding.EmbeddingFunction.md).[embed](../interfaces/embedding.EmbeddingFunction.md#embed)
+
+#### Defined in
+
+[embedding/openai.ts:48](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/embedding/openai.ts#L48)
--- a/docs/src/js/enumerations/WriteMode.md
+++ b/docs/src/js/enumerations/WriteMode.md
@@ -1,27 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / WriteMode
-
-# Enumeration: WriteMode
-
-Write mode for writing a table.
-
-## Enumeration Members
-
-### Append
-
-> **Append**: `"Append"`
-
-***
-
-### Create
-
-> **Create**: `"Create"`
-
-***
-
-### Overwrite
-
-> **Overwrite**: `"Overwrite"`
--- a/docs/src/js/enums/WriteMode.md
+++ b/docs/src/js/enums/WriteMode.md
@@ -0,0 +1,43 @@
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / WriteMode
+
+# Enumeration: WriteMode
+
+Write mode for writing a table.
+
+## Table of contents
+
+### Enumeration Members
+
+- [Append](WriteMode.md#append)
+- [Create](WriteMode.md#create)
+- [Overwrite](WriteMode.md#overwrite)
+
+## Enumeration Members
+
+### Append
+
+• **Append** = ``"Append"``
+
+#### Defined in
+
+native.d.ts:69
+
+___
+
+### Create
+
+• **Create** = ``"Create"``
+
+#### Defined in
+
+native.d.ts:68
+
+___
+
+### Overwrite
+
+• **Overwrite** = ``"Overwrite"``
+
+#### Defined in
+
+native.d.ts:70
--- a/docs/src/js/functions/connect.md
+++ b/docs/src/js/functions/connect.md
@@ -1,82 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / connect
-
-# Function: connect()
-
-## connect(uri, opts)
-
-> **connect**(`uri`, `opts`?): `Promise`&lt;[`Connection`](../classes/Connection.md)&gt;
-
-Connect to a LanceDB instance at the given URI.
-
-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 (LanceDB cloud)
-
-### Parameters
-
-• **uri**: `string`
-
-The uri of the database. If the database uri starts
-with `db://` then it connects to a remote database.
-
-• **opts?**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md) \| `RemoteConnectionOptions`&gt;
-
-### Returns
-
-`Promise`&lt;[`Connection`](../classes/Connection.md)&gt;
-
-### See
-
-[ConnectionOptions](../interfaces/ConnectionOptions.md) for more details on the URI format.
-
-### Examples
-
-```ts
-const conn = await connect("/path/to/database");
-```
-
-```ts
-const conn = await connect(
-  "s3://bucket/path/to/database",
-  {storageOptions: {timeout: "60s"}
-});
-```
-
-## connect(opts)
-
-> **connect**(`opts`): `Promise`&lt;[`Connection`](../classes/Connection.md)&gt;
-
-Connect to a LanceDB instance at the given URI.
-
-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 (LanceDB cloud)
-
-### Parameters
-
-• **opts**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md) \| `RemoteConnectionOptions`&gt; & `object`
-
-### Returns
-
-`Promise`&lt;[`Connection`](../classes/Connection.md)&gt;
-
-### See
-
-[ConnectionOptions](../interfaces/ConnectionOptions.md) for more details on the URI format.
-
-### Example
-
-```ts
-const conn = await connect({
-  uri: "/path/to/database",
-  storageOptions: {timeout: "60s"}
-});
-```
--- a/docs/src/js/globals.md
+++ b/docs/src/js/globals.md
@@ -1,51 +0,0 @@
-[**@lancedb/lancedb**](README.md) • **Docs**
-
-***
-
-# @lancedb/lancedb
-
-## Namespaces
-
- [embedding](namespaces/embedding/README.md)
-
-## Enumerations
-
- [WriteMode](enumerations/WriteMode.md)
-
-## Classes
-
- [Connection](classes/Connection.md)
- [Index](classes/Index.md)
- [MakeArrowTableOptions](classes/MakeArrowTableOptions.md)
- [Query](classes/Query.md)
- [QueryBase](classes/QueryBase.md)
- [RecordBatchIterator](classes/RecordBatchIterator.md)
- [Table](classes/Table.md)
- [VectorColumnOptions](classes/VectorColumnOptions.md)
- [VectorQuery](classes/VectorQuery.md)
-
-## Interfaces
-
- [AddColumnsSql](interfaces/AddColumnsSql.md)
- [AddDataOptions](interfaces/AddDataOptions.md)
- [ColumnAlteration](interfaces/ColumnAlteration.md)
- [ConnectionOptions](interfaces/ConnectionOptions.md)
- [CreateTableOptions](interfaces/CreateTableOptions.md)
- [ExecutableQuery](interfaces/ExecutableQuery.md)
- [IndexConfig](interfaces/IndexConfig.md)
- [IndexMetadata](interfaces/IndexMetadata.md)
- [IndexOptions](interfaces/IndexOptions.md)
- [IndexStatistics](interfaces/IndexStatistics.md)
- [IvfPqOptions](interfaces/IvfPqOptions.md)
- [TableNamesOptions](interfaces/TableNamesOptions.md)
- [UpdateOptions](interfaces/UpdateOptions.md)
- [WriteOptions](interfaces/WriteOptions.md)
-
-## Type Aliases
-
- [Data](type-aliases/Data.md)
-
-## Functions
-
- [connect](functions/connect.md)
- [makeArrowTable](functions/makeArrowTable.md)
--- a/docs/src/js/interfaces/AddColumnsSql.md
+++ b/docs/src/js/interfaces/AddColumnsSql.md
@@ -1,26 +1,37 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / AddColumnsSql
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / AddColumnsSql

 # Interface: AddColumnsSql

 A definition of a new column to add to a table.

+## Table of contents
+
+### Properties
+
+- [name](AddColumnsSql.md#name)
+- [valueSql](AddColumnsSql.md#valuesql)
+
 ## Properties

 ### name

-> **name**: `string`
+• **name**: `string`

 The name of the new column.

-***
+#### Defined in
+
+native.d.ts:43
+
+___

 ### valueSql

-> **valueSql**: `string`
+• **valueSql**: `string`

 The values to populate the new column with, as a SQL expression.
 The expression can reference other columns in the table.
+
+#### Defined in
+
+native.d.ts:48
--- a/docs/src/js/interfaces/AddDataOptions.md
+++ b/docs/src/js/interfaces/AddDataOptions.md
@@ -1,19 +1,25 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / AddDataOptions
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / AddDataOptions

 # Interface: AddDataOptions

 Options for adding data to a table.

+## Table of contents
+
+### Properties
+
+- [mode](AddDataOptions.md#mode)
+
 ## Properties

 ### mode

-> **mode**: `"append"` \| `"overwrite"`
+• **mode**: ``"append"`` \| ``"overwrite"``

 If "append" (the default) then the new data will be added to the table

 If "overwrite" then the new data will replace the existing data in the table.
+
+#### Defined in
+
+[table.ts:36](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L36)
--- a/docs/src/js/interfaces/ColumnAlteration.md
+++ b/docs/src/js/interfaces/ColumnAlteration.md
@@ -1,8 +1,4 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / ColumnAlteration
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / ColumnAlteration

 # Interface: ColumnAlteration

@@ -11,30 +7,50 @@ A definition of a column alteration. The alteration changes the column at
 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?
+### nullable

-> `optional` **nullable**: `boolean`
+• `Optional` **nullable**: `boolean`

 Set the new nullability. Note that a nullable column cannot be made non-nullable.

-***
+#### Defined in
+
+native.d.ts:38
+
+___

 ### path

-> **path**: `string`
+• **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

-### rename?
+native.d.ts:31

-> `optional` **rename**: `string`
+___
+
+### rename
+
+• `Optional` **rename**: `string`

 The new name of the column. If not provided then the name will not be changed.
 This must be distinct from the names of all other columns in the table.
+
+#### Defined in
+
+native.d.ts:36
--- a/docs/src/js/interfaces/ConnectionOptions.md
+++ b/docs/src/js/interfaces/ConnectionOptions.md
@@ -1,16 +1,40 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / ConnectionOptions
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / ConnectionOptions

 # Interface: ConnectionOptions

+## Table of contents
+
+### Properties
+
+- [apiKey](ConnectionOptions.md#apikey)
+- [hostOverride](ConnectionOptions.md#hostoverride)
+- [readConsistencyInterval](ConnectionOptions.md#readconsistencyinterval)
+
 ## Properties

-### readConsistencyInterval?
+### apiKey

-> `optional` **readConsistencyInterval**: `number`
+• `Optional` **apiKey**: `string`
+
+#### Defined in
+
+native.d.ts:51
+
+___
+
+### hostOverride
+
+• `Optional` **hostOverride**: `string`
+
+#### Defined in
+
+native.d.ts:52
+
+___
+
+### 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
@@ -22,12 +46,6 @@ 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

-### storageOptions?
-
-> `optional` **storageOptions**: `Record`&lt;`string`, `string`&gt;
-
-(For LanceDB OSS only): configuration for object storage.
-
-The available options are described at https://lancedb.github.io/lancedb/guides/storage/
+native.d.ts:64
--- a/docs/src/js/interfaces/CreateTableOptions.md
+++ b/docs/src/js/interfaces/CreateTableOptions.md
@@ -1,31 +1,32 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / CreateTableOptions
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / CreateTableOptions

 # Interface: CreateTableOptions

+## Table of contents
+
+### Properties
+
+- [existOk](CreateTableOptions.md#existok)
+- [mode](CreateTableOptions.md#mode)
+
 ## Properties

-### embeddingFunction?
-
-> `optional` **embeddingFunction**: [`EmbeddingFunctionConfig`](../namespaces/embedding/interfaces/EmbeddingFunctionConfig.md)
-
-***
-
 ### existOk

-> **existOk**: `boolean`
+• **existOk**: `boolean`

 If this is true and the table already exists and the mode is "create"
 then no error will be raised.

-***
+#### Defined in
+
+[connection.ts:35](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L35)
+
+___

 ### mode

-> **mode**: `"overwrite"` \| `"create"`
+• **mode**: ``"overwrite"`` \| ``"create"``

 The mode to use when creating the table.

@@ -35,31 +36,6 @@ happen.  Any provided data will be ignored.

 If this is set to "overwrite" then any existing table will be replaced.

-***
+#### Defined in

-### schema?
-
-> `optional` **schema**: `SchemaLike`
-
-***
-
-### storageOptions?
-
-> `optional` **storageOptions**: `Record`&lt;`string`, `string`&gt;
-
-Configuration for object storage.
-
-Options already set on the connection will be inherited by the table,
-but can be overridden here.
-
-The available options are described at https://lancedb.github.io/lancedb/guides/storage/
-
-***
-
-### useLegacyFormat?
-
-> `optional` **useLegacyFormat**: `boolean`
-
-If true then data files will be written with the legacy format
-
-The default is true while the new format is in beta
+[connection.ts:30](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L30)
--- a/docs/src/js/interfaces/ExecutableQuery.md
+++ b/docs/src/js/interfaces/ExecutableQuery.md
@@ -1,8 +1,4 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / ExecutableQuery
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / ExecutableQuery

 # Interface: ExecutableQuery

--- a/docs/src/js/interfaces/IndexConfig.md
+++ b/docs/src/js/interfaces/IndexConfig.md
@@ -1,36 +1,39 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / IndexConfig
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / IndexConfig

 # Interface: IndexConfig

 A description of an index currently configured on a column

+## Table of contents
+
+### Properties
+
+- [columns](IndexConfig.md#columns)
+- [indexType](IndexConfig.md#indextype)
+
 ## Properties

 ### columns

-> **columns**: `string`[]
+• **columns**: `string`[]

 The columns in the index

-Currently this is always an array of size 1. In the future there may
+Currently this is always an array of size 1.  In the future there may
 be more columns to represent composite indices.

-***
+#### Defined in
+
+native.d.ts:16
+
+___

 ### indexType

-> **indexType**: `string`
+• **indexType**: `string`

 The type of the index

-***
+#### Defined in

-### name
-
-> **name**: `string`
-
-The name of the index
+native.d.ts:9
--- a/docs/src/js/interfaces/IndexMetadata.md
+++ b/docs/src/js/interfaces/IndexMetadata.md
@@ -1,19 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / IndexMetadata
-
-# Interface: IndexMetadata
-
-## Properties
-
-### indexType?
-
-> `optional` **indexType**: `string`
-
-***
-
-### metricType?
-
-> `optional` **metricType**: `string`
--- a/docs/src/js/interfaces/IndexOptions.md
+++ b/docs/src/js/interfaces/IndexOptions.md
@@ -1,16 +1,19 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / IndexOptions
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / IndexOptions

 # Interface: IndexOptions

+## Table of contents
+
+### Properties
+
+- [config](IndexOptions.md#config)
+- [replace](IndexOptions.md#replace)
+
 ## Properties

-### config?
+### config

-> `optional` **config**: [`Index`](../classes/Index.md)
+• `Optional` **config**: [`Index`](../classes/Index.md)

 Advanced index configuration

@@ -22,11 +25,15 @@ See the static methods on Index for details on the various index types.
 If this is not supplied then column data type(s) and column statistics
 will be used to determine the most useful kind of index to create.

-***
+#### Defined in

-### replace?
+[indices.ts:192](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L192)

-> `optional` **replace**: `boolean`
+___
+
+### replace
+
+• `Optional` **replace**: `boolean`

 Whether to replace the existing index

@@ -35,3 +42,7 @@ and the same name, then an error will be returned.  This is true even if
 that index is out of date.

 The default is true
+
+#### Defined in
+
+[indices.ts:202](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L202)
--- a/docs/src/js/interfaces/IndexStatistics.md
+++ b/docs/src/js/interfaces/IndexStatistics.md
@@ -1,39 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / IndexStatistics
-
-# Interface: IndexStatistics
-
-## Properties
-
-### indexType?
-
-> `optional` **indexType**: `string`
-
-The type of the index
-
-***
-
-### indices
-
-> **indices**: [`IndexMetadata`](IndexMetadata.md)[]
-
-The metadata for each index
-
-***
-
-### numIndexedRows
-
-> **numIndexedRows**: `number`
-
-The number of rows indexed by the index
-
-***
-
-### numUnindexedRows
-
-> **numUnindexedRows**: `number`
-
-The number of rows not indexed
--- a/docs/src/js/interfaces/IvfPqOptions.md
+++ b/docs/src/js/interfaces/IvfPqOptions.md
@@ -1,18 +1,24 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / IvfPqOptions
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / IvfPqOptions

 # Interface: IvfPqOptions

 Options to create an `IVF_PQ` index

+## Table of contents
+
+### Properties
+
+- [distanceType](IvfPqOptions.md#distancetype)
+- [maxIterations](IvfPqOptions.md#maxiterations)
+- [numPartitions](IvfPqOptions.md#numpartitions)
+- [numSubVectors](IvfPqOptions.md#numsubvectors)
+- [sampleRate](IvfPqOptions.md#samplerate)
+
 ## Properties

-### distanceType?
+### distanceType

-> `optional` **distanceType**: `"l2"` \| `"cosine"` \| `"dot"`
+• `Optional` **distanceType**: ``"l2"`` \| ``"cosine"`` \| ``"dot"``

 Distance type to use to build the index.

@@ -46,11 +52,15 @@ never be returned from a vector search.
 distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their
 L2 norm is 1), then dot distance is equivalent to the cosine distance.

-***
+#### Defined in

-### maxIterations?
+[indices.ts:83](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L83)

-> `optional` **maxIterations**: `number`
+___
+
+### maxIterations
+
+• `Optional` **maxIterations**: `number`

 Max iteration to train IVF kmeans.

@@ -62,11 +72,15 @@ iterations have diminishing returns.

 The default value is 50.

-***
+#### Defined in

-### numPartitions?
+[indices.ts:96](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L96)

-> `optional` **numPartitions**: `number`
+___
+
+### numPartitions
+
+• `Optional` **numPartitions**: `number`

 The number of IVF partitions to create.

@@ -78,11 +92,15 @@ If this value is too large then the first part of the search (picking the
 right partition) will be slow.  If this value is too small then the second
 part of the search (searching within a partition) will be slow.

-***
+#### Defined in

-### numSubVectors?
+[indices.ts:32](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L32)

-> `optional` **numSubVectors**: `number`
+___
+
+### numSubVectors
+
+• `Optional` **numSubVectors**: `number`

 Number of sub-vectors of PQ.

@@ -97,11 +115,15 @@ us to use efficient SIMD instructions.
 If the dimension is not visible by 8 then we use 1 subvector.  This is not ideal and
 will likely result in poor performance.

-***
+#### Defined in

-### sampleRate?
+[indices.ts:48](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L48)

-> `optional` **sampleRate**: `number`
+___
+
+### sampleRate
+
+• `Optional` **sampleRate**: `number`

 The number of vectors, per partition, to sample when training IVF kmeans.

@@ -116,3 +138,7 @@ Increasing this value might improve the quality of the index but in most cases t
 default should be sufficient.

 The default value is 256.
+
+#### Defined in
+
+[indices.ts:113](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/indices.ts#L113)
--- a/docs/src/js/interfaces/TableNamesOptions.md
+++ b/docs/src/js/interfaces/TableNamesOptions.md
@@ -1,27 +1,38 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / TableNamesOptions
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / TableNamesOptions

 # Interface: TableNamesOptions

+## Table of contents
+
+### Properties
+
+- [limit](TableNamesOptions.md#limit)
+- [startAfter](TableNamesOptions.md#startafter)
+
 ## Properties

-### limit?
+### limit

-> `optional` **limit**: `number`
+• `Optional` **limit**: `number`

 An optional limit to the number of results to return.

-***
+#### Defined in

-### startAfter?
+[connection.ts:48](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L48)

-> `optional` **startAfter**: `string`
+___
+
+### startAfter
+
+• `Optional` **startAfter**: `string`

 If present, only return names that come lexicographically after the
 supplied value.

 This can be combined with limit to implement pagination by setting this to
 the last table name from the previous page.
+
+#### Defined in
+
+[connection.ts:46](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/connection.ts#L46)
--- a/docs/src/js/interfaces/UpdateOptions.md
+++ b/docs/src/js/interfaces/UpdateOptions.md
@@ -1,16 +1,18 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / UpdateOptions
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / UpdateOptions

 # Interface: UpdateOptions

+## Table of contents
+
+### Properties
+
+- [where](UpdateOptions.md#where)
+
 ## Properties

 ### where

-> **where**: `string`
+• **where**: `string`

 A filter that limits the scope of the update.

@@ -20,3 +22,7 @@ Only rows that satisfy the expression will be updated.

 For example, this could be 'my_col == 0' to replace all instances
 of 0 in a column with some other default value.
+
+#### Defined in
+
+[table.ts:50](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/table.ts#L50)
--- a/docs/src/js/interfaces/WriteOptions.md
+++ b/docs/src/js/interfaces/WriteOptions.md
@@ -1,17 +1,21 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / WriteOptions
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / WriteOptions

 # Interface: WriteOptions

 Write options when creating a Table.

+## Table of contents
+
+### Properties
+
+- [mode](WriteOptions.md#mode)
+
 ## Properties

-### mode?
+### mode

-> `optional` **mode**: [`WriteMode`](../enumerations/WriteMode.md)
+• `Optional` **mode**: [`WriteMode`](../enums/WriteMode.md)

-Write mode for writing to a table.
+#### Defined in
+
+native.d.ts:74
--- a/docs/src/js/interfaces/embedding.EmbeddingFunction.md
+++ b/docs/src/js/interfaces/embedding.EmbeddingFunction.md
@@ -0,0 +1,129 @@
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / [embedding](../modules/embedding.md) / EmbeddingFunction
+
+# Interface: EmbeddingFunction\<T\>
+
+[embedding](../modules/embedding.md).EmbeddingFunction
+
+An embedding function that automatically creates vector representation for a given column.
+
+## Type parameters
+
+| Name |
+| :------ |
+| `T` |
+
+## Implemented by
+
+- [`OpenAIEmbeddingFunction`](../classes/embedding.OpenAIEmbeddingFunction.md)
+
+## Table of contents
+
+### Properties
+
+- [destColumn](embedding.EmbeddingFunction.md#destcolumn)
+- [embed](embedding.EmbeddingFunction.md#embed)
+- [embeddingDataType](embedding.EmbeddingFunction.md#embeddingdatatype)
+- [embeddingDimension](embedding.EmbeddingFunction.md#embeddingdimension)
+- [excludeSource](embedding.EmbeddingFunction.md#excludesource)
+- [sourceColumn](embedding.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/9d178c7/nodejs/lancedb/embedding/embedding_function.ts#L49)
+
+___
+
+### embed
+
+• **embed**: (`data`: `T`[]) => `Promise`\<`number`[][]\>
+
+Creates a vector representation for the given values.
+
+#### Type declaration
+
+▸ (`data`): `Promise`\<`number`[][]\>
+
+Creates a vector representation for the given values.
+
+##### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `data` | `T`[] |
+
+##### Returns
+
+`Promise`\<`number`[][]\>
+
+#### Defined in
+
+[embedding/embedding_function.ts:62](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/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/9d178c7/nodejs/lancedb/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/9d178c7/nodejs/lancedb/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/9d178c7/nodejs/lancedb/embedding/embedding_function.ts#L57)
+
+___
+
+### sourceColumn
+
+• **sourceColumn**: `string`
+
+The name of the column that will be used as input for the Embedding Function.
+
+#### Defined in
+
+[embedding/embedding_function.ts:24](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/embedding/embedding_function.ts#L24)
--- a/docs/src/js/functions/makeArrowTable.md
+++ b/docs/src/js/functions/makeArrowTable.md
@@ -1,12 +1,103 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
+[@lancedb/lancedb](README.md) / Exports

-***
+# @lancedb/lancedb

-[@lancedb/lancedb](../globals.md) / makeArrowTable
+## Table of contents

-# Function: makeArrowTable()
+### Namespaces

-> **makeArrowTable**(`data`, `options`?, `metadata`?): `ArrowTable`
+- [embedding](modules/embedding.md)
+
+### Enumerations
+
+- [WriteMode](enums/WriteMode.md)
+
+### Classes
+
+- [Connection](classes/Connection.md)
+- [Index](classes/Index.md)
+- [MakeArrowTableOptions](classes/MakeArrowTableOptions.md)
+- [Query](classes/Query.md)
+- [QueryBase](classes/QueryBase.md)
+- [RecordBatchIterator](classes/RecordBatchIterator.md)
+- [Table](classes/Table.md)
+- [VectorColumnOptions](classes/VectorColumnOptions.md)
+- [VectorQuery](classes/VectorQuery.md)
+
+### Interfaces
+
+- [AddColumnsSql](interfaces/AddColumnsSql.md)
+- [AddDataOptions](interfaces/AddDataOptions.md)
+- [ColumnAlteration](interfaces/ColumnAlteration.md)
+- [ConnectionOptions](interfaces/ConnectionOptions.md)
+- [CreateTableOptions](interfaces/CreateTableOptions.md)
+- [ExecutableQuery](interfaces/ExecutableQuery.md)
+- [IndexConfig](interfaces/IndexConfig.md)
+- [IndexOptions](interfaces/IndexOptions.md)
+- [IvfPqOptions](interfaces/IvfPqOptions.md)
+- [TableNamesOptions](interfaces/TableNamesOptions.md)
+- [UpdateOptions](interfaces/UpdateOptions.md)
+- [WriteOptions](interfaces/WriteOptions.md)
+
+### Type Aliases
+
+- [Data](modules.md#data)
+
+### Functions
+
+- [connect](modules.md#connect)
+- [makeArrowTable](modules.md#makearrowtable)
+
+## Type Aliases
+
+### Data
+
+Ƭ **Data**: `Record`\<`string`, `unknown`\>[] \| `ArrowTable`
+
+Data type accepted by NodeJS SDK
+
+#### Defined in
+
+[arrow.ts:40](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/arrow.ts#L40)
+
+## Functions
+
+### connect
+
+▸ **connect**(`uri`, `opts?`): `Promise`\<[`Connection`](classes/Connection.md)\>
+
+Connect to a LanceDB instance at the given URI.
+
+Accpeted formats:
+
+- `/path/to/database` - local database
+- `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
+- `db://host:port` - remote database (LanceDB cloud)
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `uri` | `string` | The uri of the database. If the database uri starts with `db://` then it connects to a remote database. |
+| `opts?` | `Partial`\<[`ConnectionOptions`](interfaces/ConnectionOptions.md)\> | - |
+
+#### Returns
+
+`Promise`\<[`Connection`](classes/Connection.md)\>
+
+**`See`**
+
+[ConnectionOptions](interfaces/ConnectionOptions.md) for more details on the URI format.
+
+#### Defined in
+
+[index.ts:62](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/index.ts#L62)
+
+___
+
+### makeArrowTable
+
+▸ **makeArrowTable**(`data`, `options?`): `ArrowTable`

 An enhanced version of the makeTable function from Apache Arrow
 that supports nested fields and embeddings columns.
@@ -38,20 +129,20 @@ rules are as follows:
 - Record<String, any> => Struct
 - Array<any> => List

-## Parameters
+#### Parameters

-• **data**: `Record`&lt;`string`, `unknown`&gt;[]
+| Name | Type |
+| :------ | :------ |
+| `data` | `Record`\<`string`, `unknown`\>[] |
+| `options?` | `Partial`\<[`MakeArrowTableOptions`](classes/MakeArrowTableOptions.md)\> |

-• **options?**: `Partial`&lt;[`MakeArrowTableOptions`](../classes/MakeArrowTableOptions.md)&gt;
-
-• **metadata?**: `Map`&lt;`string`, `string`&gt;
-
-## Returns
+#### Returns

 `ArrowTable`

-## Example
+**`Example`**

+```ts
 import { fromTableToBuffer, makeArrowTable } from "../arrow";
 import { Field, FixedSizeList, Float16, Float32, Int32, Schema } from "apache-arrow";

@@ -112,3 +203,7 @@ const table = makeArrowTable([
 }
 assert.deepEqual(table.schema, schema)
 ```
+
+#### Defined in
+
+[arrow.ts:197](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/arrow.ts#L197)
--- a/docs/src/js/modules/embedding.md
+++ b/docs/src/js/modules/embedding.md
@@ -0,0 +1,45 @@
+[@lancedb/lancedb](../README.md) / [Exports](../modules.md) / embedding
+
+# Namespace: embedding
+
+## Table of contents
+
+### Classes
+
+- [OpenAIEmbeddingFunction](../classes/embedding.OpenAIEmbeddingFunction.md)
+
+### Interfaces
+
+- [EmbeddingFunction](../interfaces/embedding.EmbeddingFunction.md)
+
+### Functions
+
+- [isEmbeddingFunction](embedding.md#isembeddingfunction)
+
+## Functions
+
+### isEmbeddingFunction
+
+▸ **isEmbeddingFunction**\<`T`\>(`value`): value is EmbeddingFunction\<T\>
+
+Test if the input seems to be an embedding function
+
+#### Type parameters
+
+| Name |
+| :------ |
+| `T` |
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `value` | `unknown` |
+
+#### Returns
+
+value is EmbeddingFunction\<T\>
+
+#### Defined in
+
+[embedding/embedding_function.ts:66](https://github.com/lancedb/lancedb/blob/9d178c7/nodejs/lancedb/embedding/embedding_function.ts#L66)
--- a/docs/src/js/namespaces/embedding/README.md
+++ b/docs/src/js/namespaces/embedding/README.md
@@ -1,29 +0,0 @@
-[**@lancedb/lancedb**](../../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../../globals.md) / embedding
-
-# embedding
-
-## Index
-
-### Classes
-
- [EmbeddingFunction](classes/EmbeddingFunction.md)
- [EmbeddingFunctionRegistry](classes/EmbeddingFunctionRegistry.md)
- [OpenAIEmbeddingFunction](classes/OpenAIEmbeddingFunction.md)
-
-### Interfaces
-
- [EmbeddingFunctionConfig](interfaces/EmbeddingFunctionConfig.md)
-
-### Type Aliases
-
- [OpenAIOptions](type-aliases/OpenAIOptions.md)
-
-### Functions
-
- [LanceSchema](functions/LanceSchema.md)
- [getRegistry](functions/getRegistry.md)
- [register](functions/register.md)
--- a/docs/src/js/namespaces/embedding/classes/EmbeddingFunction.md
+++ b/docs/src/js/namespaces/embedding/classes/EmbeddingFunction.md
@@ -1,162 +0,0 @@
-[**@lancedb/lancedb**](../../../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / EmbeddingFunction
-
-# Class: `abstract` EmbeddingFunction&lt;T, M&gt;
-
-An embedding function that automatically creates vector representation for a given column.
-
-## Extended by
-
- [`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)
-
-## Type Parameters
-
-• **T** = `any`
-
-• **M** *extends* `FunctionOptions` = `FunctionOptions`
-
-## Constructors
-
-### new EmbeddingFunction()
-
-> **new EmbeddingFunction**&lt;`T`, `M`&gt;(): [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`T`, `M`&gt;
-
-#### Returns
-
-[`EmbeddingFunction`](EmbeddingFunction.md)&lt;`T`, `M`&gt;
-
-## Methods
-
-### computeQueryEmbeddings()
-
-> **computeQueryEmbeddings**(`data`): `Promise`&lt;`number`[] \| `Float32Array` \| `Float64Array`&gt;
-
-Compute the embeddings for a single query
-
-#### Parameters
-
-• **data**: `T`
-
-#### Returns
-
-`Promise`&lt;`number`[] \| `Float32Array` \| `Float64Array`&gt;
-
-***
-
-### computeSourceEmbeddings()
-
-> `abstract` **computeSourceEmbeddings**(`data`): `Promise`&lt;`number`[][] \| `Float32Array`[] \| `Float64Array`[]&gt;
-
-Creates a vector representation for the given values.
-
-#### Parameters
-
-• **data**: `T`[]
-
-#### Returns
-
-`Promise`&lt;`number`[][] \| `Float32Array`[] \| `Float64Array`[]&gt;
-
-***
-
-### embeddingDataType()
-
-> `abstract` **embeddingDataType**(): `Float`&lt;`Floats`&gt;
-
-The datatype of the embeddings
-
-#### Returns
-
-`Float`&lt;`Floats`&gt;
-
-***
-
-### ndims()
-
-> **ndims**(): `undefined` \| `number`
-
-The number of dimensions of the embeddings
-
-#### Returns
-
-`undefined` \| `number`
-
-***
-
-### sourceField()
-
-> **sourceField**(`optionsOrDatatype`): [`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
-
-sourceField is used in combination with `LanceSchema` to provide a declarative data model
-
-#### Parameters
-
-• **optionsOrDatatype**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
-
-The options for the field or the datatype
-
-#### Returns
-
-[`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
-
-#### See
-
-lancedb.LanceSchema
-
-***
-
-### toJSON()
-
-> `abstract` **toJSON**(): `Partial`&lt;`M`&gt;
-
-Convert the embedding function to a JSON object
-It is used to serialize the embedding function to the schema
-It's important that any object returned by this method contains all the necessary
-information to recreate the embedding function
-
-It should return the same object that was passed to the constructor
-If it does not, the embedding function will not be able to be recreated, or could be recreated incorrectly
-
-#### Returns
-
-`Partial`&lt;`M`&gt;
-
-#### Example
-
-```ts
-class MyEmbeddingFunction extends EmbeddingFunction {
-  constructor(options: {model: string, timeout: number}) {
-    super();
-    this.model = options.model;
-    this.timeout = options.timeout;
-  }
-  toJSON() {
-    return {
-      model: this.model,
-      timeout: this.timeout,
-    };
-}
-```
-
-***
-
-### vectorField()
-
-> **vectorField**(`optionsOrDatatype`?): [`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
-
-vectorField is used in combination with `LanceSchema` to provide a declarative data model
-
-#### Parameters
-
-• **optionsOrDatatype?**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
-
-#### Returns
-
-[`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
-
-#### See
-
-lancedb.LanceSchema
--- a/docs/src/js/namespaces/embedding/classes/EmbeddingFunctionRegistry.md
+++ b/docs/src/js/namespaces/embedding/classes/EmbeddingFunctionRegistry.md
@@ -1,124 +0,0 @@
-[**@lancedb/lancedb**](../../../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / EmbeddingFunctionRegistry
-
-# Class: EmbeddingFunctionRegistry
-
-This is a singleton class used to register embedding functions
-and fetch them by name. It also handles serializing and deserializing.
-You can implement your own embedding function by subclassing EmbeddingFunction
-or TextEmbeddingFunction and registering it with the registry
-
-## Constructors
-
-### new EmbeddingFunctionRegistry()
-
-> **new EmbeddingFunctionRegistry**(): [`EmbeddingFunctionRegistry`](EmbeddingFunctionRegistry.md)
-
-#### Returns
-
-[`EmbeddingFunctionRegistry`](EmbeddingFunctionRegistry.md)
-
-## Methods
-
-### functionToMetadata()
-
-> **functionToMetadata**(`conf`): `Record`&lt;`string`, `any`&gt;
-
-#### Parameters
-
-• **conf**: [`EmbeddingFunctionConfig`](../interfaces/EmbeddingFunctionConfig.md)
-
-#### Returns
-
-`Record`&lt;`string`, `any`&gt;
-
-***
-
-### get()
-
-> **get**&lt;`T`, `Name`&gt;(`name`): `Name` *extends* `"openai"` ? `EmbeddingFunctionCreate`&lt;[`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)&gt; : `undefined` \| `EmbeddingFunctionCreate`&lt;`T`&gt;
-
-Fetch an embedding function by name
-
-#### Type Parameters
-
-• **T** *extends* [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`unknown`, `FunctionOptions`&gt;
-
-• **Name** *extends* `string` = `""`
-
-#### Parameters
-
-• **name**: `Name` *extends* `"openai"` ? `"openai"` : `string`
-
-The name of the function
-
-#### Returns
-
-`Name` *extends* `"openai"` ? `EmbeddingFunctionCreate`&lt;[`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)&gt; : `undefined` \| `EmbeddingFunctionCreate`&lt;`T`&gt;
-
-***
-
-### getTableMetadata()
-
-> **getTableMetadata**(`functions`): `Map`&lt;`string`, `string`&gt;
-
-#### Parameters
-
-• **functions**: [`EmbeddingFunctionConfig`](../interfaces/EmbeddingFunctionConfig.md)[]
-
-#### Returns
-
-`Map`&lt;`string`, `string`&gt;
-
-***
-
-### register()
-
-> **register**&lt;`T`&gt;(`this`, `alias`?): (`ctor`) => `any`
-
-Register an embedding function
-
-#### Type Parameters
-
-• **T** *extends* `EmbeddingFunctionConstructor`&lt;[`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt; = `EmbeddingFunctionConstructor`&lt;[`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;
-
-#### Parameters
-
-• **this**: [`EmbeddingFunctionRegistry`](EmbeddingFunctionRegistry.md)
-
-• **alias?**: `string`
-
-#### Returns
-
-`Function`
-
-##### Parameters
-
-• **ctor**: `T`
-
-##### Returns
-
-`any`
-
-#### Throws
-
-Error if the function is already registered
-
-***
-
-### reset()
-
-> **reset**(`this`): `void`
-
-reset the registry to the initial state
-
-#### Parameters
-
-• **this**: [`EmbeddingFunctionRegistry`](EmbeddingFunctionRegistry.md)
-
-#### Returns
-
-`void`
--- a/docs/src/js/namespaces/embedding/classes/OpenAIEmbeddingFunction.md
+++ b/docs/src/js/namespaces/embedding/classes/OpenAIEmbeddingFunction.md
@@ -1,196 +0,0 @@
-[**@lancedb/lancedb**](../../../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / OpenAIEmbeddingFunction
-
-# Class: OpenAIEmbeddingFunction
-
-An embedding function that automatically creates vector representation for a given column.
-
-## Extends
-
- [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`string`, `Partial`&lt;[`OpenAIOptions`](../type-aliases/OpenAIOptions.md)&gt;&gt;
-
-## Constructors
-
-### new OpenAIEmbeddingFunction()
-
-> **new OpenAIEmbeddingFunction**(`options`): [`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)
-
-#### Parameters
-
-• **options**: `Partial`&lt;[`OpenAIOptions`](../type-aliases/OpenAIOptions.md)&gt; = `...`
-
-#### Returns
-
-[`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)
-
-#### Overrides
-
-[`EmbeddingFunction`](EmbeddingFunction.md).[`constructor`](EmbeddingFunction.md#constructors)
-
-## Methods
-
-### computeQueryEmbeddings()
-
-> **computeQueryEmbeddings**(`data`): `Promise`&lt;`number`[]&gt;
-
-Compute the embeddings for a single query
-
-#### Parameters
-
-• **data**: `string`
-
-#### Returns
-
-`Promise`&lt;`number`[]&gt;
-
-#### Overrides
-
-[`EmbeddingFunction`](EmbeddingFunction.md).[`computeQueryEmbeddings`](EmbeddingFunction.md#computequeryembeddings)
-
-***
-
-### computeSourceEmbeddings()
-
-> **computeSourceEmbeddings**(`data`): `Promise`&lt;`number`[][]&gt;
-
-Creates a vector representation for the given values.
-
-#### Parameters
-
-• **data**: `string`[]
-
-#### Returns
-
-`Promise`&lt;`number`[][]&gt;
-
-#### Overrides
-
-[`EmbeddingFunction`](EmbeddingFunction.md).[`computeSourceEmbeddings`](EmbeddingFunction.md#computesourceembeddings)
-
-***
-
-### embeddingDataType()
-
-> **embeddingDataType**(): `Float`&lt;`Floats`&gt;
-
-The datatype of the embeddings
-
-#### Returns
-
-`Float`&lt;`Floats`&gt;
-
-#### Overrides
-
-[`EmbeddingFunction`](EmbeddingFunction.md).[`embeddingDataType`](EmbeddingFunction.md#embeddingdatatype)
-
-***
-
-### ndims()
-
-> **ndims**(): `number`
-
-The number of dimensions of the embeddings
-
-#### Returns
-
-`number`
-
-#### Overrides
-
-[`EmbeddingFunction`](EmbeddingFunction.md).[`ndims`](EmbeddingFunction.md#ndims)
-
-***
-
-### sourceField()
-
-> **sourceField**(`optionsOrDatatype`): [`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
-
-sourceField is used in combination with `LanceSchema` to provide a declarative data model
-
-#### Parameters
-
-• **optionsOrDatatype**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
-
-The options for the field or the datatype
-
-#### Returns
-
-[`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
-
-#### See
-
-lancedb.LanceSchema
-
-#### Inherited from
-
-[`EmbeddingFunction`](EmbeddingFunction.md).[`sourceField`](EmbeddingFunction.md#sourcefield)
-
-***
-
-### toJSON()
-
-> **toJSON**(): `object`
-
-Convert the embedding function to a JSON object
-It is used to serialize the embedding function to the schema
-It's important that any object returned by this method contains all the necessary
-information to recreate the embedding function
-
-It should return the same object that was passed to the constructor
-If it does not, the embedding function will not be able to be recreated, or could be recreated incorrectly
-
-#### Returns
-
-`object`
-
-##### model
-
-> **model**: `string` & `object` \| `"text-embedding-ada-002"` \| `"text-embedding-3-small"` \| `"text-embedding-3-large"`
-
-#### Example
-
-```ts
-class MyEmbeddingFunction extends EmbeddingFunction {
-  constructor(options: {model: string, timeout: number}) {
-    super();
-    this.model = options.model;
-    this.timeout = options.timeout;
-  }
-  toJSON() {
-    return {
-      model: this.model,
-      timeout: this.timeout,
-    };
-}
-```
-
-#### Overrides
-
-[`EmbeddingFunction`](EmbeddingFunction.md).[`toJSON`](EmbeddingFunction.md#tojson)
-
-***
-
-### vectorField()
-
-> **vectorField**(`optionsOrDatatype`?): [`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
-
-vectorField is used in combination with `LanceSchema` to provide a declarative data model
-
-#### Parameters
-
-• **optionsOrDatatype?**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
-
-#### Returns
-
-[`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
-
-#### See
-
-lancedb.LanceSchema
-
-#### Inherited from
-
-[`EmbeddingFunction`](EmbeddingFunction.md).[`vectorField`](EmbeddingFunction.md#vectorfield)
--- a/docs/src/js/namespaces/embedding/functions/LanceSchema.md
+++ b/docs/src/js/namespaces/embedding/functions/LanceSchema.md
@@ -1,39 +0,0 @@
-[**@lancedb/lancedb**](../../../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / LanceSchema
-
-# Function: LanceSchema()
-
-> **LanceSchema**(`fields`): `Schema`
-
-Create a schema with embedding functions.
-
-## Parameters
-
-• **fields**: `Record`&lt;`string`, `object` \| [`object`, `Map`&lt;`string`, [`EmbeddingFunction`](../classes/EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]&gt;
-
-## Returns
-
-`Schema`
-
-Schema
-
-## Example
-
-```ts
-class MyEmbeddingFunction extends EmbeddingFunction {
-// ...
-}
-const func = new MyEmbeddingFunction();
-const schema = LanceSchema({
-  id: new Int32(),
-  text: func.sourceField(new Utf8()),
-  vector: func.vectorField(),
-  // optional: specify the datatype and/or dimensions
-  vector2: func.vectorField({ datatype: new Float32(), dims: 3}),
-});
-
-const table = await db.createTable("my_table", data, { schema });
-```
--- a/docs/src/js/namespaces/embedding/functions/getRegistry.md
+++ b/docs/src/js/namespaces/embedding/functions/getRegistry.md
@@ -1,23 +0,0 @@
-[**@lancedb/lancedb**](../../../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / getRegistry
-
-# Function: getRegistry()
-
-> **getRegistry**(): [`EmbeddingFunctionRegistry`](../classes/EmbeddingFunctionRegistry.md)
-
-Utility function to get the global instance of the registry
-
-## Returns
-
-[`EmbeddingFunctionRegistry`](../classes/EmbeddingFunctionRegistry.md)
-
-`EmbeddingFunctionRegistry` The global instance of the registry
-
-## Example
-
-```ts
-const registry = getRegistry();
-const openai = registry.get("openai").create();
--- a/docs/src/js/namespaces/embedding/functions/register.md
+++ b/docs/src/js/namespaces/embedding/functions/register.md
@@ -1,25 +0,0 @@
-[**@lancedb/lancedb**](../../../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / register
-
-# Function: register()
-
-> **register**(`name`?): (`ctor`) => `any`
-
-## Parameters
-
-• **name?**: `string`
-
-## Returns
-
-`Function`
-
-### Parameters
-
-• **ctor**: `EmbeddingFunctionConstructor`&lt;[`EmbeddingFunction`](../classes/EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;
-
-### Returns
-
-`any`
--- a/docs/src/js/namespaces/embedding/interfaces/EmbeddingFunctionConfig.md
+++ b/docs/src/js/namespaces/embedding/interfaces/EmbeddingFunctionConfig.md
@@ -1,25 +0,0 @@
-[**@lancedb/lancedb**](../../../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / EmbeddingFunctionConfig
-
-# Interface: EmbeddingFunctionConfig
-
-## Properties
-
-### function
-
-> **function**: [`EmbeddingFunction`](../classes/EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;
-
-***
-
-### sourceColumn
-
-> **sourceColumn**: `string`
-
-***
-
-### vectorColumn?
-
-> `optional` **vectorColumn**: `string`
--- a/docs/src/js/namespaces/embedding/type-aliases/OpenAIOptions.md
+++ b/docs/src/js/namespaces/embedding/type-aliases/OpenAIOptions.md
@@ -1,19 +0,0 @@
-[**@lancedb/lancedb**](../../../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / OpenAIOptions
-
-# Type Alias: OpenAIOptions
-
-> **OpenAIOptions**: `object`
-
-## Type declaration
-
-### apiKey
-
-> **apiKey**: `string`
-
-### model
-
-> **model**: `EmbeddingCreateParams`\[`"model"`\]
--- a/docs/src/js/type-aliases/Data.md
+++ b/docs/src/js/type-aliases/Data.md
@@ -1,11 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / Data
-
-# Type Alias: Data
-
-> **Data**: `Record`&lt;`string`, `unknown`&gt;[] \| `TableLike`
-
-Data type accepted by NodeJS SDK
--- a/docs/src/migration.md
+++ b/docs/src/migration.md
@@ -9,8 +9,7 @@ around the asynchronous client.
 This guide describes the differences between the two APIs and will hopefully assist users
 that would like to migrate to the new API.

-## Python
-### Closeable Connections
+## Closeable Connections

 The Connection now has a `close` method. You can call this when
 you are done with the connection to eagerly free resources. Currently
@@ -33,20 +32,20 @@ async def my_async_fn():
 It is not mandatory to call the `close` method. If you do not call it
 then the connection will be closed when the object is garbage collected.

-### Closeable Table
+## Closeable Table

 The Table now also has a `close` method, similar to the connection. This
 can be used to eagerly free the cache used by a Table object. Similar to
 the connection, it can be used as a context manager and it is not mandatory
 to call the `close` method.

-#### Changes to Table APIs
+### Changes to Table APIs

 - Previously `Table.schema` was a property. Now it is an async method.
 - The method `Table.__len__` was removed and `len(table)` will no longer
  work. Use `Table.count_rows` instead.

-#### Creating Indices
+### Creating Indices

 The `Table.create_index` method is now used for creating both vector indices
 and scalar indices. It currently requires a column name to be specified (the
@@ -56,12 +55,12 @@ the size of the data.
 To specify index configuration details you will need to specify which kind of
 index you are using.

-#### Querying
+### Querying

 The `Table.search` method has been renamed to `AsyncTable.vector_search` for
 clarity.

-### Features not yet supported
+## Features not yet supported

 The following features are not yet supported by the asynchronous API. However,
 we plan to support them soon.
@@ -75,117 +74,3 @@ we plan to support them soon.
  search
 - Remote connections to LanceDb Cloud are not yet supported.
 - The method Table.head is not yet supported.
-
-## TypeScript/JavaScript
-
-For JS/TS users, we offer a brand new SDK [@lancedb/lancedb](https://www.npmjs.com/package/@lancedb/lancedb)
-
-We tried to keep the API as similar as possible to the previous version, but there are a few small changes. Here are the most important ones:
-
-### Creating Tables
-
-[CreateTableOptions.writeOptions.writeMode](./javascript/interfaces/WriteOptions.md#writemode) has been replaced with [CreateTableOptions.mode](./js/interfaces/CreateTableOptions.md#mode)
-
-=== "vectordb (deprecated)"
-
-    ```ts
-    db.createTable(tableName, data, { writeMode: lancedb.WriteMode.Overwrite });
-    ```
-
-=== "@lancedb/lancedb"
-
-    ```ts
-    db.createTable(tableName, data, { mode: "overwrite" })
-    ```
-
-### Changes to Table APIs
-
-Previously `Table.schema` was a property. Now it is an async method.
-
-#### Creating Indices
-
-The `Table.createIndex` method is now used for creating both vector indices
-and scalar indices. It currently requires a column name to be specified (the
-column to index). Vector index defaults are now smarter and scale better with
-the size of the data.
-
-=== "vectordb (deprecated)"
-
-    ```ts
-    await tbl.createIndex({
-      column: "vector", // default
-      type: "ivf_pq",
-      num_partitions: 2,
-      num_sub_vectors: 2,
-    });
-    ```
-
-=== "@lancedb/lancedb"
-
-    ```ts
-    await table.createIndex("vector", {
-      config: lancedb.Index.ivfPq({
-        numPartitions: 2,
-        numSubVectors: 2,
-      }),
-    });
-    ```
-
-### Embedding Functions
-
-The embedding API has been completely reworked, and it now more closely resembles the Python API, including the new [embedding registry](./js/classes/embedding.EmbeddingFunctionRegistry.md)
-
-=== "vectordb (deprecated)"
-
-    ```ts
-
-    const embeddingFunction = new lancedb.OpenAIEmbeddingFunction('text', API_KEY)
-    const data = [
-        { id: 1, text: 'Black T-Shirt', price: 10 },
-        { id: 2, text: 'Leather Jacket', price: 50 }
-    ]
-    const table = await db.createTable('vectors', data, embeddingFunction)
-    ```
-
-=== "@lancedb/lancedb"
-
-    ```ts
-    import * as lancedb from "@lancedb/lancedb";
-    import * as arrow from "apache-arrow";
-    import { LanceSchema, getRegistry } from "@lancedb/lancedb/embedding";
-
-    const func = getRegistry().get("openai").create({apiKey: API_KEY});
-
-    const data = [
-        { id: 1, text: 'Black T-Shirt', price: 10 },
-        { id: 2, text: 'Leather Jacket', price: 50 }
-    ]
-
-    const table = await db.createTable('vectors', data, {
-        embeddingFunction: {
-            sourceColumn: "text",
-            function: func,
-        }
-    })
-
-    ```
-
-You can also use a schema driven approach, which parallels the Pydantic integration in our Python SDK:
-
-```ts
-const func = getRegistry().get("openai").create({apiKey: API_KEY});
-
-const data = [
-    { id: 1, text: 'Black T-Shirt', price: 10 },
-    { id: 2, text: 'Leather Jacket', price: 50 }
-]
-const schema = LanceSchema({
-    id: new arrow.Int32(),
-    text: func.sourceField(new arrow.Utf8()),
-    price: new arrow.Float64(),
-    vector: func.vectorField()
-})
-
-const table = await db.createTable('vectors', data, {schema})
-
-```
--- a/docs/src/notebooks/LlamaIndex_example.ipynb
+++ b/docs/src/notebooks/LlamaIndex_example.ipynb
@@ -1,538 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "2db56c9b",
-   "metadata": {},
-   "source": [
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/LanceDBIndexDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "db0855d0",
-   "metadata": {},
-   "source": [
-    "# LanceDB Vector Store\n",
-    "In this notebook we are going to show how to use [LanceDB](https://www.lancedb.com) to perform vector searches in LlamaIndex"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f44170b2",
-   "metadata": {},
-   "source": [
-    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6c84199c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install llama-index llama-index-vector-stores-lancedb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1a90ce34",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install lancedb==0.6.13 #Only required if the above cell installs an older version of lancedb (pypi package may not be released yet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "39c62671",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Refresh vector store URI if restarting or re-using the same notebook\n",
-    "! rm -rf ./lancedb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59b54276",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import sys\n",
-    "\n",
-    "# Uncomment to see debug logs\n",
-    "# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n",
-    "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
-    "\n",
-    "\n",
-    "from llama_index.core import SimpleDirectoryReader, Document, StorageContext\n",
-    "from llama_index.core import VectorStoreIndex\n",
-    "from llama_index.vector_stores.lancedb import LanceDBVectorStore\n",
-    "import textwrap"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "26c71b6d",
-   "metadata": {},
-   "source": [
-    "### Setup OpenAI\n",
-    "The first step is to configure the openai key. It will be used to created embeddings for the documents loaded into the index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "67b86621",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import openai\n",
-    "\n",
-    "openai.api_key = \"sk-\""
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "073f0a68",
-   "metadata": {},
-   "source": [
-    "Download Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eef1b911",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2024-06-11 16:42:37--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n",
-      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...\n",
-      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 75042 (73K) [text/plain]\n",
-      "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n",
-      "\n",
-      "data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.02s   \n",
-      "\n",
-      "2024-06-11 16:42:37 (3.97 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!mkdir -p 'data/paul_graham/'\n",
-    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
-   "metadata": {},
-   "source": [
-    "### Loading documents\n",
-    "Load the documents stored in the `data/paul_graham/` using the SimpleDirectoryReader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c154dd4b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Document ID: cac1ba78-5007-4cf8-89ba-280264790115 Document Hash: fe2d4d3ef3a860780f6c2599808caa587c8be6516fe0ba4ca53cf117044ba953\n"
-     ]
-    }
-   ],
-   "source": [
-    "documents = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()\n",
-    "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].hash)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c0232fd1",
-   "metadata": {},
-   "source": [
-    "### Create the index\n",
-    "Here we create an index backed by LanceDB using the documents loaded previously. LanceDBVectorStore takes a few arguments.\n",
-    "- uri (str, required): Location where LanceDB will store its files.\n",
-    "- table_name (str, optional): The table name where the embeddings will be stored. Defaults to \"vectors\".\n",
-    "- nprobes (int, optional): The number of probes used. A higher number makes search more accurate but also slower. Defaults to 20.\n",
-    "- refine_factor: (int, optional): Refine the results by reading extra elements and re-ranking them in memory. Defaults to None\n",
-    "\n",
-    "- More details can be found at [LanceDB docs](https://lancedb.github.io/lancedb/ann_indexes)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1f2e20ef",
-   "metadata": {},
-   "source": [
-    "##### For LanceDB cloud :\n",
-    "```python\n",
-    "vector_store = LanceDBVectorStore( \n",
-    "    uri=\"db://db_name\", # your remote DB URI\n",
-    "    api_key=\"sk_..\", # lancedb cloud api key\n",
-    "    region=\"your-region\" # the region you configured\n",
-    "    ...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8731da62",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vector_store = LanceDBVectorStore(\n",
-    "    uri=\"./lancedb\", mode=\"overwrite\", query_type=\"hybrid\"\n",
-    ")\n",
-    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
-    "\n",
-    "index = VectorStoreIndex.from_documents(\n",
-    "    documents, storage_context=storage_context\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
-   "metadata": {},
-   "source": [
-    "### Query the index\n",
-    "We can now ask questions using our index. We can use filtering via `MetadataFilters` or use native lance `where` clause."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5eb6419b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core.vector_stores import (\n",
-    "    MetadataFilters,\n",
-    "    FilterOperator,\n",
-    "    FilterCondition,\n",
-    "    MetadataFilter,\n",
-    ")\n",
-    "\n",
-    "from datetime import datetime\n",
-    "\n",
-    "\n",
-    "query_filters = MetadataFilters(\n",
-    "    filters=[\n",
-    "        MetadataFilter(\n",
-    "            key=\"creation_date\",\n",
-    "            operator=FilterOperator.EQ,\n",
-    "            value=datetime.now().strftime(\"%Y-%m-%d\"),\n",
-    "        ),\n",
-    "        MetadataFilter(\n",
-    "            key=\"file_size\", value=75040, operator=FilterOperator.GT\n",
-    "        ),\n",
-    "    ],\n",
-    "    condition=FilterCondition.AND,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee201930",
-   "metadata": {},
-   "source": [
-    "### Hybrid Search\n",
-    "\n",
-    "LanceDB offers hybrid search with reranking capabilities. For complete documentation, refer [here](https://lancedb.github.io/lancedb/hybrid_search/hybrid_search/).\n",
-    "\n",
-    "This example uses the `colbert` reranker. The following cell installs the necessary dependencies for `colbert`. If you choose a different reranker, make sure to adjust the dependencies accordingly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e12d1454",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! pip install -U torch transformers tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c742cb07",
-   "metadata": {},
-   "source": [
-    "if you want to add a reranker at vector store initialization, you can pass it in the arguments like below :\n",
-    "```\n",
-    "from lancedb.rerankers import ColbertReranker\n",
-    "reranker = ColbertReranker()\n",
-    "vector_store = LanceDBVectorStore(uri=\"./lancedb\", reranker=reranker, mode=\"overwrite\")\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "27ea047b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import lancedb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8414517f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from lancedb.rerankers import ColbertReranker\n",
-    "\n",
-    "reranker = ColbertReranker()\n",
-    "vector_store._add_reranker(reranker)\n",
-    "\n",
-    "query_engine = index.as_query_engine(\n",
-    "    filters=query_filters,\n",
-    "    # vector_store_kwargs={\n",
-    "    #     \"query_type\": \"fts\",\n",
-    "    # },\n",
-    ")\n",
-    "\n",
-    "response = query_engine.query(\"How much did Viaweb charge per month?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc6ccb7a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Viaweb charged $100 a month for a small store and $300 a month for a big one.\n",
-      "metadata - {'65ed5f07-5b8a-4143-a939-e8764884828e': {'file_path': '/Users/raghavdixit/Desktop/open_source/llama_index_lance/docs/docs/examples/vector_stores/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2024-06-11', 'last_modified_date': '2024-06-11'}, 'be231827-20b8-4988-ac75-94fa79b3c22e': {'file_path': '/Users/raghavdixit/Desktop/open_source/llama_index_lance/docs/docs/examples/vector_stores/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2024-06-11', 'last_modified_date': '2024-06-11'}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(response)\n",
-    "print(\"metadata -\", response.metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c1c6c73",
-   "metadata": {},
-   "source": [
-    "##### lance filters(SQL like) directly via the `where` clause :"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0a2bcc07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lance_filter = \"metadata.file_name = 'paul_graham_essay.txt' \"\n",
-    "retriever = index.as_retriever(vector_store_kwargs={\"where\": lance_filter})\n",
-    "response = retriever.retrieve(\"What did the author do growing up?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7ac47cf9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "What I Worked On\n",
-      "\n",
-      "February 2021\n",
-      "\n",
-      "Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\n",
-      "\n",
-      "The first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\n",
-      "\n",
-      "The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.\n",
-      "\n",
-      "I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear.\n",
-      "\n",
-      "With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]\n",
-      "\n",
-      "The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.\n",
-      "\n",
-      "Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter.\n",
-      "\n",
-      "Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.\n",
-      "\n",
-      "I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.\n",
-      "\n",
-      "AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world.\n",
-      "metadata - {'file_path': '/Users/raghavdixit/Desktop/open_source/llama_index_lance/docs/docs/examples/vector_stores/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2024-06-11', 'last_modified_date': '2024-06-11'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(response[0].get_content())\n",
-    "print(\"metadata -\", response[0].metadata)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "6afc84ac",
-   "metadata": {},
-   "source": [
-    "### Appending data\n",
-    "You can also add data to an existing index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "759a532e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nodes = [node.node for node in response]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "069fc099",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del index\n",
-    "\n",
-    "index = VectorStoreIndex.from_documents(\n",
-    "    [Document(text=\"The sky is purple in Portland, Maine\")],\n",
-    "    uri=\"/tmp/new_dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a64ed441",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "index.insert_nodes(nodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b5cffcfe",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Portland, Maine\n"
-     ]
-    }
-   ],
-   "source": [
-    "query_engine = index.as_query_engine()\n",
-    "response = query_engine.query(\"Where is the sky purple?\")\n",
-    "print(textwrap.fill(str(response), 100))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec548a02",
-   "metadata": {},
-   "source": [
-    "You can also create an index from an existing table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc99404d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del index\n",
-    "\n",
-    "vec_store = LanceDBVectorStore.from_table(vector_store._table)\n",
-    "index = VectorStoreIndex.from_vector_store(vec_store)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7b2e8cca",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The author started Viaweb and Aspra.\n"
-     ]
-    }
-   ],
-   "source": [
-    "query_engine = index.as_query_engine()\n",
-    "response = query_engine.query(\"What companies did the author start?\")\n",
-    "print(textwrap.fill(str(response), 100))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Lance Release	7a3ef68306	Bump version: 0.9.0-beta.3 → 0.9.0-beta.4	2024-12-20 16:02:53 +00:00
Ryan Green	43952e01d7	bump version	2024-12-20 09:44:46 -06:00
Ryan Green	495c335831	Fix fast_search	2024-12-20 09:43:39 -06:00
Ryan Green	77707db543	Backport fast_search and empty query builder for remote table	2024-12-20 09:21:05 -06:00
Ryan Green	d6d7ad3b06	bump version	2024-12-18 10:21:04 -06:00
Ryan Green	e58d64c286	Remove unsupported Retry params	2024-12-18 10:08:38 -06:00
Ryan Green	76cbd18c46	bump version	2024-12-18 09:38:36 -06:00
Ryan Green	4abb38ac70	bump version	2024-12-18 09:37:58 -06:00
Ryan Green	cc7bc5011d	Merge remote-tracking branch 'origin/python-v0.9.0-patch' into python-v0.9.0-patch # Conflicts: # python/pyproject.toml	2024-12-18 08:59:35 -06:00
Ryan Green	8193183304	override urllib3 version	2024-12-18 08:59:24 -06:00
Ryan Green	cf28b58b7d	override urllib3 version	2024-12-18 08:58:41 -06:00
Lance Release	e3b7ee47b9	Bump version: 0.9.0 → 0.9.0-final.1	2024-12-13 01:16:24 +00:00
Lu Qiu	97c9c906e4	Fix version test	2024-12-12 17:10:07 -08:00
Lu Qiu	358f86b9c6	fix	2024-12-12 16:44:24 -08:00
Lu Qiu	5489e215a3	Support storage options and folder prefix	2024-12-12 16:17:34 -08:00
				`@@ -1 +0,0 @@`
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