[python] Bump version: 0.3.0 → 0.3.1

#488

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.10
+current_version = 0.3.0
 commit = True
 message = Bump version: {current_version} → {new_version}
 tag = True

.github/workflows/docs.yml

@@ -39,6 +39,28 @@ jobs:
         run: |
           python -m pip install -e .
           python -m pip install -r ../docs/requirements.txt
+      - name: Set up node
+        uses: actions/setup-node@v3
+        with:
+          node-version: ${{ matrix.node-version }}
+          cache: 'npm'
+          cache-dependency-path: node/package-lock.json
+      - uses: Swatinem/rust-cache@v2
+      - name: Install node dependencies
+        working-directory: node
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+      - name: Build node
+        working-directory: node
+        run: |
+          npm ci
+          npm run build
+          npm run tsc
+      - name: Create markdown files
+        working-directory: node
+        run: |
+          npx typedoc --plugin typedoc-plugin-markdown --out ../docs/src/javascript src/index.ts
       - name: Build docs
         run: |
           PYTHONPATH=. mkdocs build -f docs/mkdocs.yml
@@ -50,4 +72,4 @@ jobs:
           path: "docs/site"
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v1
+        uses: actions/deploy-pages@v1

.github/workflows/docs_test.yml

@@ -81,7 +81,7 @@ jobs:
       run: |
         cd docs/test/node_modules/vectordb 
         npm ci
-        npm run build
+        npm run build-release
         npm run tsc
     - name: Create test files
       run: |

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

@@ -25,31 +25,35 @@ jobs:
   bump-version:
     runs-on: ubuntu-latest
     steps:
-    - name: Check out main
-      uses: actions/checkout@v3
-      with:
-        ref: main
-        persist-credentials: false
-        fetch-depth: 0
-        lfs: true
-    - name: Set git configs for bumpversion
-      shell: bash
-      run: |
-        git config user.name 'Lance Release'
-        git config user.email 'lance-dev@lancedb.com'
-    - name: Set up Python 3.10
-      uses: actions/setup-python@v4
-      with:
-        python-version: "3.10"
-    - name: Bump version, create tag and commit
-      run: |
-        pip install bump2version
-        bumpversion --verbose ${{ inputs.part }}
-    - name: Push new version and tag
-      if: ${{ inputs.dry_run }} == "false"
-      uses: ad-m/github-push-action@master
-      with:
-        github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
-        branch: main
-        tags: true
+      - name: Check out main
+        uses: actions/checkout@v3
+        with:
+          ref: main
+          persist-credentials: false
+          fetch-depth: 0
+          lfs: true
+      - name: Set git configs for bumpversion
+        shell: bash
+        run: |
+          git config user.name 'Lance Release'
+          git config user.email 'lance-dev@lancedb.com'
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Bump version, create tag and commit
+        run: |
+          pip install bump2version
+          bumpversion --verbose ${{ inputs.part }}
+      - name: Push new version and tag
+        if: ${{ inputs.dry_run }} == "false"
+        uses: ad-m/github-push-action@master
+        with:
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          branch: main
+          tags: true
+      - uses: ./.github/workflows/update_package_lock
+        if: ${{ inputs.dry_run }} == "false"
+        with:
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
 

.github/workflows/node.yml

@@ -9,6 +9,7 @@ on:
       - node/**
       - rust/ffi/node/**
       - .github/workflows/node.yml
+      - docker-compose.yml
 
 env:
   # Disable full debug symbol generation to speed up CI build and keep memory down
@@ -67,8 +68,12 @@ jobs:
     - name: Build
       run: |
         npm ci
-        npm run build
         npm run tsc
+        npm run build
+        npm run pack-build
+        npm install --no-save ./dist/lancedb-vectordb-*.tgz
+        # Remove index.node to test with dependency installed
+        rm index.node
     - name: Test
       run: npm run test
   macos:
@@ -94,8 +99,65 @@ jobs:
     - name: Build
       run: |
         npm ci
-        npm run build
         npm run tsc
+        npm run build
+        npm run pack-build
+        npm install --no-save ./dist/lancedb-vectordb-*.tgz
+        # Remove index.node to test with dependency installed
+        rm index.node
     - name: Test
       run: |
         npm run test
+  aws-integtest:
+    timeout-minutes: 45
+    runs-on: "ubuntu-22.04"
+    defaults:
+      run:
+        shell: bash
+        working-directory: node
+    env:
+      AWS_ACCESS_KEY_ID: ACCESSKEY
+      AWS_SECRET_ACCESS_KEY: SECRETKEY
+      AWS_DEFAULT_REGION: us-west-2
+      # this one is for s3
+      AWS_ENDPOINT: http://localhost:4566
+      # this one is for dynamodb
+      DYNAMODB_ENDPOINT: http://localhost:4566
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        lfs: true
+    - uses: actions/setup-node@v3
+      with:
+        node-version: 18
+        cache: 'npm'
+        cache-dependency-path: node/package-lock.json
+    - name: start local stack
+      run: docker compose -f ../docker-compose.yml up -d --wait
+    - name: create s3
+      run: aws s3 mb s3://lancedb-integtest --endpoint $AWS_ENDPOINT
+    - name: create ddb
+      run: |
+        aws dynamodb create-table \
+          --table-name lancedb-integtest \
+          --attribute-definitions '[{"AttributeName": "base_uri", "AttributeType": "S"}, {"AttributeName": "version", "AttributeType": "N"}]' \
+          --key-schema '[{"AttributeName": "base_uri", "KeyType": "HASH"}, {"AttributeName": "version", "KeyType": "RANGE"}]' \
+          --provisioned-throughput '{"ReadCapacityUnits": 10, "WriteCapacityUnits": 10}' \
+          --endpoint-url $DYNAMODB_ENDPOINT
+    - uses: Swatinem/rust-cache@v2
+    - name: Install dependencies
+      run: |
+        sudo apt update
+        sudo apt install -y protobuf-compiler libssl-dev
+    - name: Build
+      run: |
+        npm ci
+        npm run tsc
+        npm run build
+        npm run pack-build
+        npm install --no-save ./dist/lancedb-vectordb-*.tgz
+        # Remove index.node to test with dependency installed
+        rm index.node
+    - name: Test
+      run: npm run integration-test

.github/workflows/npm-publish.yml

@@ -0,0 +1,163 @@
+name: NPM Publish
+
+on:
+  release:
+    types: [ published ]
+
+jobs:
+  node:
+    runs-on: ubuntu-latest
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
+    defaults:
+      run:
+        shell: bash
+        working-directory: node
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - uses: actions/setup-node@v3
+        with:
+          node-version: 20
+          cache: 'npm'
+          cache-dependency-path: node/package-lock.json
+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+      - name: Build
+        run: |
+          npm ci
+          npm run tsc
+          npm pack
+      - name: Upload Linux Artifacts
+        uses: actions/upload-artifact@v3
+        with:
+          name: node-package
+          path: |
+            node/vectordb-*.tgz
+
+  node-macos:
+    runs-on: macos-12
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
+    strategy:
+      fail-fast: false
+      matrix:
+        target: [x86_64-apple-darwin, aarch64-apple-darwin]
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Install system dependencies
+        run: brew install protobuf
+      - name: Install npm dependencies
+        run: |
+          cd node
+          npm ci
+      - name: Install rustup target
+        if: ${{ matrix.target == 'aarch64-apple-darwin' }}
+        run: rustup target add aarch64-apple-darwin
+      - name: Build MacOS native node modules
+        run: bash ci/build_macos_artifacts.sh ${{ matrix.target }}
+      - name: Upload Darwin Artifacts
+        uses: actions/upload-artifact@v3
+        with:
+          name: native-darwin
+          path: |
+            node/dist/lancedb-vectordb-darwin*.tgz
+
+  node-linux:
+    name: node-linux (${{ 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')
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - arch: x86_64
+            runner: ubuntu-latest
+          - arch: aarch64
+            runner: buildjet-4vcpu-ubuntu-2204-arm
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Build Linux Artifacts
+        run: |
+          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }}
+      - name: Upload Linux Artifacts
+        uses: actions/upload-artifact@v3
+        with:
+          name: native-linux
+          path: |
+            node/dist/lancedb-vectordb-linux*.tgz
+
+  node-windows:
+    runs-on: windows-2022
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
+    strategy:
+      fail-fast: false
+      matrix:
+        target: [x86_64-pc-windows-msvc]
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Install Protoc v21.12
+        working-directory: C:\
+        run: |
+          New-Item -Path 'C:\protoc' -ItemType Directory
+          Set-Location C:\protoc
+          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+          7z x protoc.zip
+          Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+        shell: powershell
+      - name: Install npm dependencies
+        run: |
+          cd node
+          npm ci
+      - name: Build Windows native node modules
+        run: .\ci\build_windows_artifacts.ps1 ${{ matrix.target }}
+      - name: Upload Windows Artifacts
+        uses: actions/upload-artifact@v3
+        with:
+          name: native-windows
+          path: |
+            node/dist/lancedb-vectordb-win32*.tgz
+
+  release:
+    needs: [node, node-macos, node-linux, node-windows]
+    runs-on: ubuntu-latest
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
+    steps:
+      - uses: actions/download-artifact@v3
+      - name: Display structure of downloaded files
+        run: ls -R
+      - uses: actions/setup-node@v3
+        with:
+          node-version: 20
+          registry-url: 'https://registry.npmjs.org'
+      - name: Publish to NPM
+        env:
+          NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
+        run: |
+          mv */*.tgz .
+          for filename in *.tgz; do
+            npm publish $filename
+          done
+
+  update-package-lock:
+    needs: [release]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+        with:
+          ref: main
+          persist-credentials: false
+          fetch-depth: 0
+          lfs: true
+      - uses: ./.github/workflows/update_package_lock
+        with:
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}

.github/workflows/python.yml

@@ -30,7 +30,7 @@ jobs:
         python-version: 3.${{ matrix.python-minor-version }}
     - name: Install lancedb
       run: |
-        pip install -e .
+        pip install -e .[tests]
         pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
         pip install pytest pytest-mock black isort
     - name: Black
@@ -38,7 +38,7 @@ jobs:
     - name: isort
       run: isort --check --diff --quiet .
     - name: Run tests
-      run: pytest -x -v --durations=30 tests
+      run: pytest -m "not slow" -x -v --durations=30 tests
     - name: doctest
       run: pytest --doctest-modules lancedb
   mac:
@@ -59,8 +59,40 @@ jobs:
         python-version: "3.11"
     - name: Install lancedb
       run: |
-        pip install -e .
+        pip install -e .[tests]
         pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
-        pip install pytest pytest-mock
+        pip install pytest pytest-mock black
+    - name: Black
+      run: black --check --diff --no-color --quiet .
     - name: Run tests
-      run: pytest -x -v --durations=30 tests
+      run: pytest -m "not slow" -x -v --durations=30 tests
+  pydantic1x:
+    timeout-minutes: 30
+    runs-on: "ubuntu-22.04"
+    defaults:
+      run:
+        shell: bash
+        working-directory: python
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        lfs: true
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.9
+    - name: Install lancedb
+      run: |
+        pip install "pydantic<2"
+        pip install -e .[tests]
+        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+        pip install pytest pytest-mock black isort
+    - name: Black
+      run: black --check --diff --no-color --quiet .
+    - name: isort
+      run: isort --check --diff --quiet .
+    - name: Run tests
+      run: pytest -m "not slow" -x -v --durations=30 tests
+    - name: doctest
+      run: pytest --doctest-modules lancedb

.github/workflows/rust.yml

@@ -6,6 +6,7 @@ on:
       - main
   pull_request:
     paths:
+      - Cargo.toml
       - rust/**
       - .github/workflows/rust.yml
 
@@ -65,3 +66,24 @@ jobs:
         run: cargo build --all-features
       - name: Run tests
         run: cargo test --all-features
+  windows:
+    runs-on: windows-2022
+    steps:
+      - uses: actions/checkout@v3
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: rust
+      - name: Install Protoc v21.12
+        working-directory: C:\
+        run: |
+          New-Item -Path 'C:\protoc' -ItemType Directory
+          Set-Location C:\protoc
+          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+          7z x protoc.zip
+          Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+        shell: powershell
+      - name: Run tests
+        run: |
+          $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
+          cargo build
+          cargo test

.github/workflows/trigger-vectordb-recipes.yml

@@ -0,0 +1,26 @@
+name: Trigger vectordb-recipers workflow
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    paths:
+      - .github/workflows/trigger-vectordb-recipes.yml
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Trigger vectordb-recipes workflow
+        uses: actions/github-script@v6
+        with:
+          github-token: ${{ secrets.VECTORDB_RECIPES_ACTION_TOKEN }}
+          script: |
+            const result = await github.rest.actions.createWorkflowDispatch({
+                owner: 'lancedb',
+                repo: 'vectordb-recipes',
+                workflow_id: 'examples-test.yml',
+                ref: 'main'
+            });
+            console.log(result);

.github/workflows/update_package_lock/action.yml

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

.github/workflows/update_package_lock_run.yml

@@ -0,0 +1,19 @@
+name: Update package-lock.json
+
+on:
+  workflow_dispatch:
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+        with:
+          ref: main
+          persist-credentials: false
+          fetch-depth: 0
+          lfs: true
+      - uses: ./.github/workflows/update_package_lock
+        with:
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}

.gitignore

@@ -5,6 +5,8 @@
 .DS_Store
 venv
 
+.vscode
+
 rust/target
 rust/Cargo.lock
 

Cargo.toml

@@ -1,14 +1,27 @@
 [workspace]
-members = [
-    "rust/vectordb",
-    "rust/ffi/node"
-]
+members = ["rust/ffi/node", "rust/vectordb"]
+# Python package needs to be built by maturin.
+exclude = ["python"]
 resolver = "2"
 
 [workspace.dependencies]
-lance = "0.5.3"
-arrow-array = "40.0"
-arrow-data = "40.0"
-arrow-schema = "40.0"
-arrow-ipc = "40.0"
+lance = { "version" = "=0.8.3", "features" = ["dynamodb"] }
+lance-linalg = { "version" = "=0.8.3" }
+lance-testing = { "version" = "=0.8.3" }
+# Note that this one does not include pyarrow
+arrow = { version = "43.0.0", optional = false }
+arrow-array = "43.0"
+arrow-data = "43.0"
+arrow-ipc = "43.0"
+arrow-ord = "43.0"
+arrow-schema = "43.0"
+arrow-arith = "43.0"
+arrow-cast = "43.0"
+chrono = "0.4.23"
+half = { "version" = "=2.2.1", default-features = false, features = [
+    "num-traits"
+] }
+log = "0.4"
 object_store = "0.6.1"
+snafu = "0.7.4"
+url = "2"

README.md

@@ -1,78 +1,79 @@
-<div align="center">
-<p align="center">
-
-<img width="275" alt="LanceDB Logo" src="https://user-images.githubusercontent.com/917119/226205734-6063d87a-1ecc-45fe-85be-1dea6383a3d8.png">
-
-**Developer-friendly, serverless vector database for AI applications**
-
-<a href="https://lancedb.github.io/lancedb/">Documentation</a> •
-<a href="https://blog.lancedb.com/">Blog</a> •
-<a href="https://discord.gg/zMM32dvNtd">Discord</a> •
-<a href="https://twitter.com/lancedb">Twitter</a>
-
-</p>
-
-<img max-width="750px" alt="LanceDB Multimodal Search" src="https://github.com/lancedb/lancedb/assets/917119/09c5afc5-7816-4687-bae4-f2ca194426ec">
-
-</p>
-</div>
-
-<hr />
-
-LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
-
-The key features of LanceDB include:
-
-* Production-scale vector search with no servers to manage.
-
-* Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
-
-* Support for vector similarity search, full-text search and SQL.
-
-* Native Python and Javascript/Typescript support.
-
-* Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
-
-* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lanecdb.html), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
-
-LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
-
-## Quick Start
-
-**Javascript**
-```shell
-npm install vectordb
-```
-
-```javascript
-const lancedb = require('vectordb');
-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 }])
-
-const query = table.search([0.1, 0.3]);
-query.limit = 20;
-const results = await query.execute();
-```
-
-**Python**
-```shell
-pip install lancedb
-```
-
-```python
-import lancedb
-
-uri = "data/sample-lancedb"
-db = lancedb.connect(uri)
-table = db.create_table("my_table",
-                         data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-                               {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
-result = table.search([100, 100]).limit(2).to_df()
-```
-
-## Blogs, Tutorials & Videos
-* 📈 <a href="https://blog.eto.ai/benchmarking-random-access-in-lance-ed690757a826">2000x better performance with Lance over Parquet</a>
-* 🤖 <a href="https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb">Build a question and answer bot with LanceDB</a>
+<div align="center">
+<p align="center">
+
+<img width="275" alt="LanceDB Logo" src="https://user-images.githubusercontent.com/917119/226205734-6063d87a-1ecc-45fe-85be-1dea6383a3d8.png">
+
+**Developer-friendly, serverless vector database for AI applications**
+
+<a href="https://lancedb.github.io/lancedb/">Documentation</a> •
+<a href="https://blog.lancedb.com/">Blog</a> •
+<a href="https://discord.gg/zMM32dvNtd">Discord</a> •
+<a href="https://twitter.com/lancedb">Twitter</a>
+
+</p>
+
+<img max-width="750px" alt="LanceDB Multimodal Search" src="https://github.com/lancedb/lancedb/assets/917119/09c5afc5-7816-4687-bae4-f2ca194426ec">
+
+</p>
+</div>
+
+<hr />
+
+LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
+
+The key features of LanceDB include:
+
+* Production-scale vector search with no servers to manage.
+
+* Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
+
+* Support for vector similarity search, full-text search and SQL.
+
+* Native Python and Javascript/Typescript support.
+
+* Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
+
+* GPU support in building vector index(*).
+
+* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lanecdb.html), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
+
+LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
+
+## Quick Start
+
+**Javascript**
+```shell
+npm install vectordb
+```
+
+```javascript
+const lancedb = require('vectordb');
+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 }])
+
+const query = table.search([0.1, 0.3]).limit(2);
+const results = await query.execute();
+```
+
+**Python**
+```shell
+pip install lancedb
+```
+
+```python
+import lancedb
+
+uri = "data/sample-lancedb"
+db = lancedb.connect(uri)
+table = db.create_table("my_table",
+                         data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                               {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+result = table.search([100, 100]).limit(2).to_pandas()
+```
+
+## Blogs, Tutorials & Videos
+* 📈 <a href="https://blog.eto.ai/benchmarking-random-access-in-lance-ed690757a826">2000x better performance with Lance over Parquet</a>
+* 🤖 <a href="https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb">Build a question and answer bot with LanceDB</a>

ci/build_linux_artifacts.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+set -e
+ARCH=${1:-x86_64}
+
+# We pass down the current user so that when we later mount the local files 
+# into the container, the files are accessible by the current user.
+pushd ci/manylinux_node
+docker build \
+    -t lancedb-node-manylinux \
+    --build-arg="ARCH=$ARCH" \
+    --build-arg="DOCKER_USER=$(id -u)" \
+    --progress=plain \
+    .
+popd
+
+docker run \
+    -v $(pwd):/io -w /io \
+    lancedb-node-manylinux \
+    bash ci/manylinux_node/build.sh $ARCH

ci/build_macos_artifacts.sh

@@ -0,0 +1,33 @@
+# Builds the macOS artifacts (node binaries).
+# Usage: ./ci/build_macos_artifacts.sh [target]
+# Targets supported: x86_64-apple-darwin aarch64-apple-darwin
+
+prebuild_rust() {
+    # Building here for the sake of easier debugging.
+    pushd rust/ffi/node
+    echo "Building rust library for $1"
+    export RUST_BACKTRACE=1
+    cargo build --release --target $1
+    popd
+}
+
+build_node_binaries() {
+    pushd node
+    echo "Building node library for $1"
+    npm run build-release -- --target $1
+    npm run pack-build -- --target $1
+    popd
+}
+
+if [ -n "$1" ]; then
+    targets=$1
+else
+    targets="x86_64-apple-darwin aarch64-apple-darwin"
+fi
+
+echo "Building artifacts for targets: $targets"
+for target in $targets
+    do
+    prebuild_rust $target
+    build_node_binaries $target
+done

ci/build_windows_artifacts.ps1

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

ci/manylinux_node/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}

ci/manylinux_node/build.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Builds the node module for manylinux. Invoked by ci/build_linux_artifacts.sh.
+set -e
+ARCH=${1:-x86_64}
+
+if [ "$ARCH" = "x86_64" ]; then
+    export OPENSSL_LIB_DIR=/usr/local/lib64/
+else 
+    export OPENSSL_LIB_DIR=/usr/local/lib/
+fi
+export OPENSSL_STATIC=1
+export OPENSSL_INCLUDE_DIR=/usr/local/include/openssl
+
+source $HOME/.bashrc
+
+cd node
+npm ci
+npm run build-release
+npm run pack-build

ci/manylinux_node/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

ci/manylinux_node/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

ci/manylinux_node/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

docker-compose.yml

@@ -0,0 +1,18 @@
+version: "3.9"
+services:
+  localstack:
+    image: localstack/localstack:0.14
+    ports:
+      - 4566:4566
+    environment:
+      - SERVICES=s3,dynamodb
+      - DEBUG=1
+      - LS_LOG=trace
+      - DOCKER_HOST=unix:///var/run/docker.sock
+      - AWS_ACCESS_KEY_ID=ACCESSKEY
+      - AWS_SECRET_ACCESS_KEY=SECRETKEY
+    healthcheck:
+      test: [ "CMD", "curl", "-f", "http://localhost:4566/health" ]
+      interval: 5s
+      retries: 3
+      start_period: 10s

docs/mkdocs.yml

@@ -1,5 +1,6 @@
 site_name: LanceDB Docs
 repo_url: https://github.com/lancedb/lancedb
+edit_uri: https://github.com/lancedb/lancedb/tree/main/docs/src
 repo_name: lancedb/lancedb
 docs_dir: src
 
@@ -10,6 +11,17 @@ theme:
   features:
     - content.code.copy
     - content.tabs.link
+    - content.action.edit
+    - toc.follow
+    - toc.integrate
+    - navigation.top
+    - navigation.tabs
+    - navigation.tabs.sticky
+    - navigation.footer
+    - navigation.tracking
+    - navigation.instant
+    - navigation.indexes
+    - navigation.expand
   icon:
     repo: fontawesome/brands/github
   custom_dir: overrides
@@ -50,28 +62,82 @@ markdown_extensions:
 - pymdownx.superfences
 - pymdownx.tabbed:
     alternate_style: true
+- md_in_html
 
 nav:
-- Home: index.md
+- Home:
+  - 🏢 Home: index.md
+  - 💡 Basics: basic.md
+  - 📚 Guides:
+    - Create Ingest Update Delete: guides/tables.md
+    - Vector Search: search.md
+    - SQL filters: sql.md
+    - Indexing: ann_indexes.md
+  - 🧬 Embeddings: embedding.md
+  - 🔍 Python full-text search: fts.md
+  - 🔌 Integrations:
+    - integrations/index.md
+    - Pandas and PyArrow: python/arrow.md
+    - DuckDB: python/duckdb.md
+    - LangChain 🔗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
+    - LangChain JS/TS 🔗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
+    - LlamaIndex 🦙: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
+    - Pydantic: python/pydantic.md
+    - Voxel51: integrations/voxel51.md
+    - PromptTools: integrations/prompttools.md
+  - 🐍 Python examples:
+    - examples/index.md
+    - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+    - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+    - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+    - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+    - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
+  - 🌐 Javascript examples:
+    - Examples: examples/index_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
+  - ⚙️ CLI & Config: cli_config.md
+
 - Basics: basic.md
+- Guides:
+  - Create Ingest Update Delete: guides/tables.md
+  - Vector Search: search.md
+  - SQL filters: sql.md
+  - Indexing: ann_indexes.md
 - Embeddings: embedding.md
 - Python full-text search: fts.md
-- Python integrations: integrations.md
+- Integrations:
+  - integrations/index.md
+  - Pandas and PyArrow: python/arrow.md
+  - DuckDB: python/duckdb.md
+  - LangChain 🦜️🔗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
+  - LangChain JS/TS 🦜️🔗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
+  - LlamaIndex 🦙: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
+  - Pydantic: python/pydantic.md
+  - Voxel51: integrations/voxel51.md
+  - PromptTools: integrations/prompttools.md
 - Python examples:
+  - examples/index.md
   - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
   - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
   - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
   - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
   - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
 - Javascript examples:
+  - examples/index_js.md
   - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
-- References:
-  - Vector Search: search.md
-  - SQL filters: sql.md
-  - Indexing: ann_indexes.md
+  - Serverless Chatbot from any website: examples/serverless_website_chatbot.md
+  - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
 - API references:
   - Python API: python/python.md
   - Javascript API: javascript/modules.md
+- LanceDB Cloud↗: https://noteforms.com/forms/lancedb-mailing-list-cloud-kty1o5?notionforms=1&utm_source=notionforms
 
 extra_css:
   - styles/global.css
+
+extra:
+  analytics:
+    provider: google
+    property: G-B7NFM40W74

docs/src/ann_indexes.md

@@ -1,16 +1,27 @@
 # ANN (Approximate Nearest Neighbor) Indexes
 
 You can create an index over your vector data to make search faster.
-Vector indexes are faster but less accurate than exhaustive search.
+Vector indexes are faster but less accurate than exhaustive search (KNN or Flat Search).
 LanceDB provides many parameters to fine-tune the index's size, the speed of queries, and the accuracy of results.
 
 Currently, LanceDB does *not* automatically create the ANN index.
 LanceDB has optimized code for KNN as well. For many use-cases, datasets under 100K vectors won't require index creation at all.
-If you can live with <100ms latency, skipping index creation is a simpler workflow while guaranteeing 100% recall.
+If you can live with < 100ms latency, skipping index creation is a simpler workflow while guaranteeing 100% recall.
 
 In the future we will look to automatically create and configure the ANN index.
 
-## Creating an ANN Index
+## Types of Index
+
+Lance can support multiple index types, the most widely used one is `IVF_PQ`.
+
+* `IVF_PQ`: use **Inverted File Index (IVF)** to first divide the dataset into `N` partitions,
+   and then use **Product Quantization** to compress vectors in each partition.
+* `DISKANN` (**Experimental**): organize the vector as a on-disk graph, where the vertices approximately
+   represent the nearest neighbors of each vector.
+
+## Creating an IVF_PQ Index
+
+Lance supports `IVF_PQ` index type by default.
 
 === "Python"
      Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
@@ -45,15 +56,24 @@ In the future we will look to automatically create and configure the ANN index.
      await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 256, num_sub_vectors: 96 })
      ```
 
-Since `create_index` has a training step, it can take a few minutes to finish for large tables. You can control the index
-creation by providing the following parameters:
+- **metric** (default: "L2"): The distance metric to use. By default it uses euclidean distance "`L2`".
+We also support "cosine" and "dot" distance as well.
+- **num_partitions** (default: 256): The number of partitions of the index.
+- **num_sub_vectors** (default: 96): The number of sub-vectors (M) that will be created during Product Quantization (PQ).
+For D dimensional vector, it will be divided into `M` of `D/M` sub-vectors, each of which is presented by
+a single PQ code.
 
-- **metric** (default: "L2"): The distance metric to use. By default we use euclidean distance. We also support "cosine" distance.
-- **num_partitions** (default: 256): The number of partitions of the index. The number of partitions should be configured so each partition has 3-5K vectors. For example, a table
-with ~1M vectors should use 256 partitions. You can specify arbitrary number of partitions but powers of 2 is most conventional.
-A higher number leads to faster queries, but it makes index generation slower.
-- **num_sub_vectors** (default: 96): The number of subvectors (M) that will be created during Product Quantization (PQ). A larger number makes
-search more accurate, but also makes the index larger and slower to build.
+<figure markdown>
+  ![IVF PQ](./assets/ivf_pq.png)
+  <figcaption>IVF_PQ index with <code>num_partitions=2, num_sub_vectors=4</code></figcaption>
+</figure>
+
+### Use GPU to build vector index
+
+Lance Python SDK has experimental GPU support for creating IVF index.
+You can specify the GPU device to train IVF partitions via
+
+- **accelerator**: Specify to `"cuda"`` to enable GPU training.
 
 ## Querying an ANN Index
 
@@ -77,10 +97,10 @@ There are a couple of parameters that can be used to fine-tune the search:
          .limit(2) \
          .nprobes(20) \
          .refine_factor(10) \
-         .to_df()
+         .to_pandas()
      ```
      ```
-                                              vector       item       score
+                                              vector       item       _distance
      0  [0.44949695, 0.8444449, 0.06281311, 0.23338133...  item 1141  103.575333
      1  [0.48587373, 0.269207, 0.15095535, 0.65531915,...  item 3953  108.393867
      ```
@@ -95,9 +115,8 @@ There are a couple of parameters that can be used to fine-tune the search:
          .execute()
      ```
 
-The search will return the data requested in addition to the score of each item.
+The search will return the data requested in addition to the distance of each item.
 
-**Note:** The score is the distance between the query vector and the element. A lower number means that the result is more relevant.
 
 ### Filtering (where clause)
 
@@ -105,7 +124,7 @@ 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_df()
+     tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
      ```
 
 === "Javascript"
@@ -122,10 +141,10 @@ You can select the columns returned by the query using a select clause.
 
 === "Python"
      ```python
-     tbl.search(np.random.random((1536))).select(["vector"]).to_df()
+     tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
      ```
      ```
-     vector      score
+        vector                                             _distance
      0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
      1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
      ...
@@ -138,3 +157,31 @@ You can select the columns returned by the query using a select clause.
          .select(["id"])
          .execute()
      ```
+
+## FAQ
+
+### When is it necessary to create an ANN vector index?
+
+`LanceDB` has manually-tuned SIMD code for computing vector distances.
+In our benchmarks, computing 100K pairs of 1K dimension vectors takes **less than 20ms**.
+For small datasets (< 100K rows) or applications that can accept 100ms latency, vector indices are usually not necessary.
+
+For large-scale or higher dimension vectors, it is beneficial to create vector index.
+
+### How big is my index, and how many memory will it take?
+
+In LanceDB, all vector indices are **disk-based**, meaning that when responding to a vector query, only the relevant pages from the index file are loaded from disk and cached in memory. Additionally, each sub-vector is usually encoded into 1 byte PQ code.
+
+For example, with a 1024-dimension dataset, if we choose `num_sub_vectors=64`, each sub-vector has `1024 / 64 = 16` float32 numbers.
+Product quantization can lead to approximately `16 * sizeof(float32) / 1 = 64` times of space reduction.
+
+### How to choose `num_partitions` and `num_sub_vectors` for `IVF_PQ` index?
+
+`num_partitions` is used to decide how many partitions the first level `IVF` index uses.
+Higher number of partitions could lead to more efficient I/O during queries and better accuracy, but it takes much more time to train.
+On `SIFT-1M` dataset, our benchmark shows that keeping each partition 1K-4K rows lead to a good latency / recall.
+
+`num_sub_vectors` specifies how many Product Quantization (PQ) short codes to generate on each vector. Because
+PQ is a lossy compression of the original vector, a higher `num_sub_vectors` usually results in
+less space distortion, and thus yields better accuracy. However, a higher `num_sub_vectors` also causes heavier I/O and
+more PQ computation, and thus, higher latency. `dimension / num_sub_vectors` should be a multiple of 8 for optimum SIMD efficiency.

docs/src/basic.md

@@ -79,6 +79,18 @@ We'll cover the basics of using LanceDB on your local machine in this section.
 
 ??? info "Under the hood, LanceDB is converting the input data into an Apache Arrow table and persisting it to disk in [Lance format](https://www.github.com/lancedb/lance)."
 
+### Creating an empty table
+
+Sometimes you may not have the data to insert into the table at creation time.
+In this case, you can create an empty table and specify the schema.
+
+=== "Python"
+      ```python
+      import pyarrow as pa
+      schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
+      tbl = db.create_table("empty_table", schema=schema)
+      ```
+
 ## How to open an existing table
 
 Once created, you can open a table using the following code:
@@ -111,9 +123,15 @@ After a table has been created, you can always add more data to it using
 
 === "Python"
       ```python
-      df = pd.DataFrame([{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
-                        {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}])
-      tbl.add(df)
+
+      # Option 1: Add a list of dicts to a table
+      data = [{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
+            {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}]
+      tbl.add(data)
+
+      # Option 2: Add a pandas DataFrame to a table
+      df = pd.DataFrame(data)
+      tbl.add(data)
       ```
 
 === "Javascript"
@@ -128,7 +146,7 @@ Once you've embedded the query, you can find its nearest neighbors using the fol
 
 === "Python"
       ```python
-      tbl.search([100, 100]).limit(2).to_df()
+      tbl.search([100, 100]).limit(2).to_pandas()
       ```
 
       This returns a pandas DataFrame with the results.
@@ -138,8 +156,63 @@ Once you've embedded the query, you can find its nearest neighbors using the fol
       const query = await tbl.search([100, 100]).limit(2).execute();
       ```
 
+## How to delete rows from a table
+
+Use the `delete()` method on tables to delete rows from a table. To choose
+which rows to delete, provide a filter that matches on the metadata columns.
+This can delete any number of rows that match the filter.
+
+=== "Python"
+      ```python
+      tbl.delete('item = "fizz"')
+      ```
+
+=== "Javascript"
+      ```javascript
+      await tbl.delete('item = "fizz"')
+      ```
+
+The deletion predicate is a SQL expression that supports the same expressions
+as the `where()` clause on a search. They can be as simple or complex as needed.
+To see what expressions are supported, see the [SQL filters](sql.md) section.
+
+
+=== "Python"
+
+      Read more: [lancedb.table.Table.delete][]
+
+=== "Javascript"
+
+      Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)
+
+## How to remove a table
+
+Use the `drop_table()` method on the database to remove a table.
+
+=== "Python"
+      ```python
+      db.drop_table("my_table")
+      ```
+
+This permanently removes the table and is not recoverable, unlike deleting rows.
+By default, if the table does not exist an exception is raised. To suppress this,
+you can pass in `ignore_missing=True`.
+
+
 ## What's next
 
 This section covered the very basics of the LanceDB API.
 LanceDB supports many additional features when creating indices to speed up search and options for search.
 These are contained in the next section of the documentation.
+
+## 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 on Vercel.
+```javascript
+/** @type {import('next').NextConfig} */
+module.exports = ({
+  webpack(config) {
+    config.externals.push({ vectordb: 'vectordb' })
+    return config;
+  }
+})
+```

docs/src/cli_config.md

@@ -0,0 +1,37 @@
+
+## LanceDB CLI
+Once lanceDB is installed, you can access the CLI using `lancedb` command on the console
+```
+lancedb
+```
+This lists out all the various command-line options available. You can get the usage or help for a particular command
+```
+lancedb {command} --help
+```
+
+## LanceDB config
+LanceDB uses a global config file to store certain settings. These settings are configurable using the lanceDB cli.
+To view your config settings, you can use:
+```
+lancedb config
+```
+These config parameters can be tuned using the cli.
+```
+lancedb {config_name} --{argument}
+```
+
+## LanceDB Opt-in Diagnostics
+When enabled, LanceDB will send anonymous events to help us improve LanceDB. These diagnostics are used only for error reporting and no data is collected. Error & stats allow us to automate certain aspects of bug reporting, prioritization of fixes and feature requests.
+These diagnostics are opt-in and can be enabled or disabled using the `lancedb diagnostics` command. These are enabled by default.
+Get usage help.
+```
+lancedb diagnostics --help
+```
+Disable diagnostics
+```
+lancedb diagnostics --disabled
+```
+Enable diagnostics
+```
+lancedb diagnostics --enabled
+```

docs/src/embedding.md

@@ -46,7 +46,7 @@ You can also use an external API like OpenAI to generate embeddings
 
         def embed_func(c):
             rs = openai.Embedding.create(input=c, engine="text-embedding-ada-002")
-        return [record["embedding"] for record in rs["data"]]
+            return [record["embedding"] for record in rs["data"]]
       ```
 
 === "Javascript"
@@ -66,7 +66,7 @@ You can also use an external API like OpenAI to generate embeddings
     to generate embeddings for each row.
 
     Say if you have a pandas DataFrame with a `text` column that you want to be embedded,
-    you can use the [with_embeddings](https://lancedb.github.io/lancedb/python/#lancedb.embeddings.with_embeddings)
+    you can use the [with_embeddings](https://lancedb.github.io/lancedb/python/python/#lancedb.embeddings.with_embeddings)
     function to generate embeddings and add create a combined pyarrow table:
 
 
@@ -118,7 +118,7 @@ belong in the same latent space and your results will be nonsensical.
      ```python
      query = "What's the best pizza topping?"
      query_vector = embed_func([query])[0]
-     tbl.search(query_vector).limit(10).to_df()
+     tbl.search(query_vector).limit(10).to_pandas()
      ```
 
      The above snippet returns a pandas DataFrame with the 10 closest vectors to the query.
@@ -126,7 +126,7 @@ belong in the same latent space and your results will be nonsensical.
 === "Javascript"
      ```javascript
       const results = await table
-        .search('What's the best pizza topping?')
+        .search("What's the best pizza topping?")
         .limit(10)
         .execute()
      ```

docs/src/examples/index.md

@@ -0,0 +1,23 @@
+# Examples
+
+Here are some of the examples, projects and applications using LanceDB python library. Some examples are covered in detail in the next sections. You can find more on [VectorDB Recipes](https://github.com/lancedb/vectordb-recipes)
+
+| Example | Interactive Envs | Scripts  |
+|-------- | ---------------- | ------   |
+| | | |
+| [Youtube transcript search bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/youtube_bot/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/main.py)|
+| [Langchain: Code Docs QA bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/main.py) |
+| [AI Agents: Reducing Hallucination](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/main.py)|
+| [Multimodal CLIP: DiffusionDB](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_clip/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_clip/main.py)  |
+| [Multimodal CLIP: Youtube videos](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_video_search/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_video_search/main.py)  |
+| [Movie Recommender](https://github.com/lancedb/vectordb-recipes/tree/main/examples/movie-recommender/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/movie-recommender/main.py)  |
+| [Audio Search](https://github.com/lancedb/vectordb-recipes/tree/main/examples/audio_search/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/audio_search/main.py)  |
+| [Multimodal Image + Text Search](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_search/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_search/main.py)  |
+| [Evaluating Prompts with Prompttools](https://github.com/lancedb/vectordb-recipes/tree/main/examples/prompttools-eval-prompts/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |  |
+
+## Projects & Applications powered by LanceDB
+
+| Project Name                                        | Description                                                                                                          | Screenshot                                |
+|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
+| [YOLOExplorer](https://github.com/lancedb/yoloexplorer) | Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds                  | ![YOLOExplorer](https://github.com/lancedb/vectordb-recipes/assets/15766192/ae513a29-8f15-4e0b-99a1-ccd8272b6131) |
+| [Website Chatbot (Deployable Vercel Template)](https://github.com/lancedb/lancedb-vercel-chatbot) | Create a chatbot from the sitemap of any website/docs of your choice. Built using vectorDB serverless native javascript package. | ![Chatbot](../assets/vercel-template.gif)    |

docs/src/examples/index_js.md

@@ -0,0 +1,19 @@
+# Examples
+
+Here are some of the examples, projects and applications using vectordb native javascript library.
+Some examples are covered in detail in the next sections. You can find more on [VectorDB Recipes](https://github.com/lancedb/vectordb-recipes)
+
+| Example | Scripts  |
+|-------- | ------   |
+| | |
+| [Youtube transcript search bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/) | [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/index.js)|
+| [Langchain: Code Docs QA bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/) | [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/index.js)|
+| [AI Agents: Reducing Hallucination](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/) | [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/index.js)|
+| [TransformersJS Embedding example](https://github.com/lancedb/vectordb-recipes/tree/main/examples/js-transformers/) | [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/js-transformers/index.js)  |
+
+## Projects & Applications
+
+| Project Name                                        | Description                                                                                                          | Screenshot                                |
+|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
+| [YOLOExplorer](https://github.com/lancedb/yoloexplorer) | Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds                  | ![YOLOExplorer](https://github.com/lancedb/vectordb-recipes/assets/15766192/ae513a29-8f15-4e0b-99a1-ccd8272b6131) |
+| [Website Chatbot (Deployable Vercel Template)](https://github.com/lancedb/lancedb-vercel-chatbot) | Create a chatbot from the sitemap of any website/docs of your choice. Built using vectorDB serverless native javascript package. | ![Chatbot](../assets/vercel-template.gif)    |

docs/src/examples/serverless_lancedb_with_s3_and_lambda.md

@@ -80,14 +80,14 @@ def handler(event, context):
     # Shape of SIFT is (128,1M), d=float32
     query_vector = np.array(event['query_vector'], dtype=np.float32)
 
-    rs = table.search(query_vector).limit(2).to_df()
+    rs = table.search(query_vector).limit(2).to_list()
 
     return {
         "statusCode": status_code,
         "headers": {
             "Content-Type": "application/json"
         },
-        "body": rs.to_json()
+        "body": json.dumps(rs)
     }
 ``` 
 

docs/src/examples/serverless_website_chatbot.md

@@ -0,0 +1,61 @@
+# LanceDB Chatbot - Vercel Next.js Template
+Use an AI chatbot with website context retrieved from a vector store like LanceDB. LanceDB is lightweight and can be embedded directly into Next.js, with data stored on-prem.
+
+## One click deploy on Vercel
+[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Flancedb%2Flancedb-vercel-chatbot&env=OPENAI_API_KEY&envDescription=OpenAI%20API%20Key%20for%20chat%20completion.&project-name=lancedb-vercel-chatbot&repository-name=lancedb-vercel-chatbot&demo-title=LanceDB%20Chatbot%20Demo&demo-description=Demo%20website%20chatbot%20with%20LanceDB.&demo-url=https%3A%2F%2Flancedb.vercel.app&demo-image=https%3A%2F%2Fi.imgur.com%2FazVJtvr.png)
+
+![Demo website landing page](../assets/vercel-template.gif)
+
+## Development
+
+First, rename `.env.example` to `.env.local`, and fill out `OPENAI_API_KEY` with your OpenAI API key. You can get one [here](https://openai.com/blog/openai-api).
+
+Run the development server:
+
+```bash
+npm run dev
+# or
+yarn dev
+# or
+pnpm dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
+
+This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
+
+## Learn More
+
+To learn more about LanceDB or Next.js, take a look at the following resources:
+
+- [LanceDB Documentation](https://lancedb.github.io/lancedb/) - learn about LanceDB, the developer-friendly serverless vector database.
+- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
+- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
+
+## LanceDB on Next.js and Vercel
+
+FYI: these configurations have been pre-implemented in this template.
+
+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 on Vercel.
+```js
+/** @type {import('next').NextConfig} */
+module.exports = ({
+  webpack(config) {
+    config.externals.push({ vectordb: 'vectordb' })
+    return config;
+  }
+})
+```
+
+To deploy on Vercel, we need to make sure that the NodeJS runtime static file analysis for Vercel can find the binary, since LanceDB uses dynamic imports by default. We can do this by modifying `package.json` in the `scripts` section.
+```json
+{
+  ...
+  "scripts": {
+    ...
+    "vercel-build": "sed -i 's/nativeLib = require(`@lancedb\\/vectordb-\\${currentTarget()}`);/nativeLib = require(`@lancedb\\/vectordb-linux-x64-gnu`);/' node_modules/vectordb/native.js && next build",
+    ...
+  },
+  ...
+}
+```

docs/src/examples/transformerjs_embedding_search_nodejs.md

@@ -0,0 +1,121 @@
+# Vector embedding search using TransformersJS
+
+## Embed and query data from LanceDB using TransformersJS
+
+<img id="splash" width="400" alt="transformersjs" src="https://github.com/lancedb/lancedb/assets/43097991/88a31e30-3d6f-4eef-9216-4b7c688f1b4f">
+
+This example shows how to use the [transformers.js](https://github.com/xenova/transformers.js) library to perform vector embedding search using LanceDB's Javascript API.
+
+
+### Setting up
+First, install the dependencies:
+```bash
+npm install vectordb
+npm i @xenova/transformers
+```
+
+We will also be using the [all-MiniLM-L6-v2](https://huggingface.co/Xenova/all-MiniLM-L6-v2) model to make it compatible with Transformers.js
+
+Within our `index.js` file we will import the necessary libraries and define our model and database:
+
+```javascript
+const lancedb = require('vectordb')
+const { pipeline } = await import('@xenova/transformers')
+const pipe = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+```
+
+### Creating the embedding function
+
+Next, we will create a function that will take in a string and return the vector embedding of that string. We will use the `pipe` function we defined earlier to get the vector embedding of the string.
+
+```javascript
+// Define the function. `sourceColumn` is required for LanceDB to know
+// which column to use as input.
+const embed_fun = {}
+embed_fun.sourceColumn = 'text'
+embed_fun.embed = async function (batch) {
+    let result = []
+    // Given a batch of strings, we will use the `pipe` function to get
+    // the vector embedding of each string.
+    for (let text of batch) {
+        // 'mean' pooling and normalizing allows the embeddings to share the
+        // same length.
+        const res = await pipe(text, { pooling: 'mean', normalize: true })
+        result.push(Array.from(res['data']))
+    }
+    return (result)
+}
+```
+
+### Creating the database
+
+Now, we will create the LanceDB database and add the embedding function we defined earlier.
+
+```javascript
+// Link a folder and create a table with data
+const db = await lancedb.connect('data/sample-lancedb')
+
+// You can also import any other data, but make sure that you have a column
+// for the embedding function to use.
+const data = [
+    { id: 1, text: 'Cherry', type: 'fruit' },
+    { id: 2, text: 'Carrot', type: 'vegetable' },
+    { id: 3, text: 'Potato', type: 'vegetable' },
+    { id: 4, text: 'Apple', type: 'fruit' },
+    { id: 5, text: 'Banana', type: 'fruit' }
+]
+
+// Create the table with the embedding function
+const table = await db.createTable('food_table', data, "create", embed_fun)
+```
+
+### Performing the search
+
+Now, we can perform the search using the `search` function. LanceDB automatically uses the embedding function we defined earlier to get the vector embedding of the query string.
+
+```javascript
+// Query the table
+const results = await table
+    .search("a sweet fruit to eat")
+    .metricType("cosine")
+    .limit(2)
+    .execute()
+console.log(results.map(r => r.text))
+```
+```bash
+[ 'Banana', 'Cherry' ]
+```
+
+Output of `results`:
+```bash
+[
+  {
+    vector: Float32Array(384) [
+      -0.057455405592918396,
+      0.03617725893855095,
+      -0.0367760956287384,
+      ... 381 more items
+    ],
+    id: 5,
+    text: 'Banana',
+    type: 'fruit',
+    _distance: 0.4919965863227844
+  },
+  {
+    vector: Float32Array(384) [
+      0.0009714411571621895,
+      0.008223623037338257,
+      0.009571489877998829,
+      ... 381 more items
+    ],
+    id: 1,
+    text: 'Cherry',
+    type: 'fruit',
+    _distance: 0.5540297031402588
+  }
+]
+```
+
+### Wrapping it up
+
+In this example, we showed how to use the `transformers.js` library to perform vector embedding search using LanceDB's Javascript API. You can find the full code for this example on [Github](https://github.com/lancedb/lancedb/blob/main/node/examples/js-transformers/index.js)!

docs/src/examples/youtube_transcript_bot.md

@@ -4,4 +4,10 @@
 
 <img id="splash" width="400" alt="youtube transcript search" src="https://user-images.githubusercontent.com/917119/236965568-def7394d-171c-45f2-939d-8edfeaadd88c.png">
 
+
+<a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/youtube_bot/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
+
+Scripts - [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipesexamples/youtube_bot/main.py)  [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](https://github.com/lancedb/vectordb-recipes/examples/youtube_bot/index.js)
+
+
 This example is in a [notebook](https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb)

docs/src/fts.md

@@ -6,17 +6,19 @@ to make this available for JS as well.
 
 ## Installation
 
-To use full text search, you must install optional dependency tantivy-py:
+To use full text search, you must install the dependency `tantivy-py`:
 
-# tantivy 0.19.2
-pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+# tantivy 0.20.1
+```sh
+pip install tantivy==0.20.1
+```
 
 
 ## Quickstart
 
 Assume:
 1. `table` is a LanceDB Table
-2. `text` is the name of the Table column that we want to index
+2. `text` is the name of the `Table` column that we want to index
 
 For example,
 
@@ -41,7 +43,13 @@ table.create_fts_index("text")
 To search:
 
 ```python
-df = table.search("puppy").limit(10).select(["text"]).to_df()
+table.search("puppy").limit(10).select(["text"]).to_list()
+```
+
+Which returns a list of dictionaries:
+
+```python
+[{'text': 'Frodo was a happy puppy', 'score': 0.6931471824645996}]
 ```
 
 LanceDB automatically looks for an FTS index if the input is str.

docs/src/guides/tables.md

@@ -0,0 +1,411 @@
+ <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/tables_guide.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
+A Table is a collection of Records in a LanceDB Database. You can follow along on colab!
+
+## Creating a LanceDB Table
+
+=== "Python"
+    ### LanceDB Connection
+
+    ```python
+    import lancedb
+    db = lancedb.connect("./.lancedb")
+    ```
+
+    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.
+
+    ### From list of tuples or dictionaries
+
+    ```python
+    import lancedb
+
+    db = lancedb.connect("./.lancedb")
+
+    data = [{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
+            {"vector": [0.2, 1.8], "lat": 40.1, "long": -74.1}]
+
+    db.create_table("my_table", data)
+
+    db["my_table"].head()
+    ```
+
+    !!! info "Note"
+        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.
+
+        ```python
+        db.create_table("name", data, mode="overwrite")
+        ```
+
+
+    ### From 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("table2", data)
+
+    db["table2"].head()
+    ```
+    !!! info "Note"
+        Data is converted to Arrow before being written to disk. For maximum control over how data is saved, either provide the PyArrow schema to convert to or else provide a PyArrow Table directly.
+
+    ```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("table3", data, schema=custom_schema)
+    ```
+
+    ### From PyArrow Tables
+    You can also create LanceDB tables directly from pyarrow tables
+
+    ```python
+    table = pa.Table.from_arrays(
+            [
+                pa.array([[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
+                        pa.list_(pa.float32(), 4)),
+                pa.array(["foo", "bar"]),
+                pa.array([10.0, 20.0]),
+            ],
+            ["vector", "item", "price"],
+        )
+
+    db = lancedb.connect("db")
+
+    tbl = db.create_table("test1", table)
+    ```
+
+    ### From Pydantic Models
+    When you create an empty table without data, you must specify the table schema.
+    LanceDB supports creating tables by specifying a pyarrow schema or a specialized
+    pydantic model called `LanceModel`.
+
+    For example, the following Content model specifies a table with 5 columns:
+    movie_id, vector, genres, title, and imdb_id. When you create a table, you can
+    pass the class as the value of the `schema` parameter to `create_table`.
+    The `vector` column is a `Vector` type, which is a specialized pydantic type that
+    can be configured with the vector dimensions. It is also important to note that
+    LanceDB only understands subclasses of `lancedb.pydantic.LanceModel`
+    (which itself derives from `pydantic.BaseModel`).
+
+    ```python
+    from lancedb.pydantic import Vector, LanceModel
+
+    class Content(LanceModel):
+        movie_id: int
+        vector: Vector(128)
+        genres: str
+        title: str
+        imdb_id: int
+
+        @property
+        def imdb_url(self) -> str:
+            return f"https://www.imdb.com/title/tt{self.imdb_id}"
+
+    import pyarrow as pa
+    db = lancedb.connect("~/.lancedb")
+    table_name = "movielens_small"
+    table = db.create_table(table_name, schema=Content)
+    ```
+
+    ### Using Iterators / Writing Large Datasets
+
+    It is recommended to use itertators to add large datasets in batches when creating your table in one go. This does not create multiple versions of your dataset unlike manually adding batches using `table.add()`
+
+    LanceDB additionally supports pyarrow's `RecordBatch` Iterators or other generators producing supported data types.
+
+    Here's an example using using `RecordBatch` iterator for creating tables.
+
+    ```python
+    import pyarrow as pa
+
+    def make_batches():
+        for i in range(5):
+            yield pa.RecordBatch.from_arrays(
+                [
+                    pa.array([[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
+                            pa.list_(pa.float32(), 4)),
+                    pa.array(["foo", "bar"]),
+                    pa.array([10.0, 20.0]),
+                ],
+                ["vector", "item", "price"],
+            )
+
+    schema = pa.schema([
+        pa.field("vector", pa.list_(pa.float32(), 4)),
+        pa.field("item", pa.utf8()),
+        pa.field("price", pa.float32()),
+    ])
+
+    db.create_table("table4", make_batches(), schema=schema)
+    ```
+
+    You can also use iterators of other types like Pandas dataframe or Pylists directly in the above example.
+
+    ## Creating Empty Table
+    You can also create empty tables in python. Initialize it with schema and later ingest data into it.
+
+    ```python
+    import lancedb
+    import pyarrow as pa
+
+    schema = pa.schema(
+      [
+          pa.field("vector", pa.list_(pa.float32(), 2)),
+          pa.field("item", pa.string()),
+          pa.field("price", pa.float32()),
+      ])
+    tbl = db.create_table("table5", schema=schema)
+    data = [
+        {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+        {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
+    ]
+    tbl.add(data=data)
+    ```
+
+    You can also use Pydantic to specify the schema
+
+    ```python
+    import lancedb
+    from lancedb.pydantic import LanceModel, vector
+
+    class Model(LanceModel):
+          vector: Vector(2)
+
+    tbl = db.create_table("table5", schema=Model.to_arrow_schema())
+    ```
+
+=== "Javascript/Typescript"
+
+    ### VectorDB Connection
+
+    ```javascript
+    const lancedb = require("vectordb");
+
+    const uri = "data/sample-lancedb";
+    const db = await lancedb.connect(uri);
+    ```
+
+    ### Creating a Table
+
+    You can create a LanceDB table in javascript using an array of records.
+
+    ```javascript
+    data
+    const tb = await db.createTable("my_table",
+                  data=[{"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 })
+    ```
+
+## Open existing tables
+
+If you forget the name of your table, you can always get a listing of all table names:
+
+
+=== "Python"
+    ### Get a list of existing Tables
+
+    ```python
+    print(db.table_names())
+    ```
+=== "Javascript/Typescript"
+
+    ```javascript
+    console.log(await db.tableNames());
+    ```
+
+Then, you can open any existing tables
+
+=== "Python"
+
+    ```python
+    tbl = db.open_table("my_table")
+    ```
+=== "Javascript/Typescript"
+
+    ```javascript
+    const tbl = await db.openTable("my_table");
+    ```
+
+## Adding to a Table
+After a table has been created, you can always add more data to it using
+
+=== "Python"
+    You can add any of the valid data structures accepted by LanceDB table, i.e, `dict`, `list[dict]`, `pd.DataFrame`, or a `Iterator[pa.RecordBatch]`. Here are some examples.
+
+    ### Adding Pandas DataFrame
+
+    ```python
+    df = pd.DataFrame([{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
+                  {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}])
+    tbl.add(df)
+    ```
+
+    You can also add a large dataset batch in one go using Iterator of any supported data types.
+
+    ### Adding to table using Iterator
+
+    ```python
+    import pandas as pd
+
+    def make_batches():
+        for i in range(5):
+            yield pd.DataFrame(
+                {
+                    "vector": [[3.1, 4.1], [1, 1]],
+                    "item": ["foo", "bar"],
+                    "price": [10.0, 20.0],
+                })
+
+    tbl.add(make_batches())
+    ```
+
+    The other arguments accepted:
+
+    | Name | Type | Description | Default |
+    |---|---|---|---|
+    | data | DATA | The data to insert into the table. | required |
+    | mode | str | The mode to use when writing the data. Valid values are "append" and "overwrite". | append |
+    | on_bad_vectors | str | What to do if any of the vectors are not the same size or contains NaNs. One of "error", "drop", "fill". | drop |
+    | fill value | float | The value to use when filling vectors: Only used if on_bad_vectors="fill". | 0.0 |
+
+
+=== "Javascript/Typescript"
+
+    ```javascript
+    await tbl.add([{vector: [1.3, 1.4], item: "fizz", price: 100.0},
+        {vector: [9.5, 56.2], item: "buzz", price: 200.0}])
+    ```
+
+## Deleting from a Table
+
+Use the `delete()` method on tables to delete rows from a table. To choose which rows to delete, provide a filter that matches on the metadata columns. This can delete any number of rows that match the filter.
+
+=== "Python"
+
+    ```python
+    tbl.delete('item = "fizz"')
+    ```
+
+    ### Deleting row with specific column value
+
+    ```python
+    import lancedb
+    import pandas as pd
+
+    data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
+    db = lancedb.connect("./.lancedb")
+    table = db.create_table("my_table", data)
+    table.to_pandas()
+    #   x      vector
+    # 0  1  [1.0, 2.0]
+    # 1  2  [3.0, 4.0]
+    # 2  3  [5.0, 6.0]
+
+    table.delete("x = 2")
+    table.to_pandas()
+    #   x      vector
+    # 0  1  [1.0, 2.0]
+    # 1  3  [5.0, 6.0]
+    ```
+
+    ### Delete from a list of values
+
+    ```python
+    to_remove = [1, 5]
+    to_remove = ", ".join(str(v) for v in to_remove)
+
+    table.delete(f"x IN ({to_remove})")
+    table.to_pandas()
+    #   x      vector
+    # 0  3  [5.0, 6.0]
+    ```
+
+=== "Javascript/Typescript"
+
+    ```javascript
+    await tbl.delete('item = "fizz"')
+    ```
+
+    ### Deleting row with specific column value
+
+    ```javascript
+    const con = await lancedb.connect("./.lancedb")
+    const data = [
+      {id: 1, vector: [1, 2]},
+      {id: 2, vector: [3, 4]},
+      {id: 3, vector: [5, 6]},
+    ];
+    const tbl = await con.createTable("my_table", data)
+    await tbl.delete("id = 2")
+    await tbl.countRows() // Returns 2
+    ```
+
+    ### Delete from a list of values
+
+    ```javascript
+    const to_remove = [1, 5];
+    await tbl.delete(`id IN (${to_remove.join(",")})`)
+    await tbl.countRows() // Returns 1
+    ```
+
+### Updating a Table [Experimental]
+EXPERIMENTAL: Update rows in the table (not threadsafe).
+
+This can be used to update zero to all rows depending on how many rows match the where clause.
+
+| Parameter | Type | Description |
+|---|---|---|
+| `where` | `str` | The SQL where clause to use when updating rows. For example, `'x = 2'` or `'x IN (1, 2, 3)'`. The filter must not be empty, or it will error. |
+| `values` | `dict` | The values to update. The keys are the column names and the values are the values to set. |
+
+
+=== "Python"
+
+    ```python
+    import lancedb
+    import pandas as pd
+
+    # Create a lancedb connection
+    db = lancedb.connect("./.lancedb")
+
+    # Create a table from a pandas DataFrame
+    data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
+    table = db.create_table("my_table", data)
+
+    # Update the table where x = 2
+    table.update(where="x = 2", values={"vector": [10, 10]})
+
+    # Get the updated table as a pandas DataFrame
+    df = table.to_pandas()
+
+    # Print the DataFrame
+    print(df)
+    ```
+
+    Output
+    ```shell
+        x  vector
+    0  1  [1.0, 2.0]
+    1  3  [5.0, 6.0]
+    2  2  [10.0, 10.0]
+    ```
+
+## What's Next?
+
+Learn how to Query your tables and create indices

docs/src/index.md

@@ -1,20 +1,23 @@
-# Welcome to LanceDB's Documentation
+# LanceDB
 
-LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
+LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.
+
+![Illustration](/lancedb/assets/ecosystem-illustration.png)
 
 The key features of LanceDB include:
 
-* Production-scale vector search with no servers to manage.
-
 * Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
 
-* Support for vector similarity search, full-text search and SQL.
+* Support for production-scale vector similarity search, full-text search and SQL, with no servers to manage.
 
 * Native Python and Javascript/Typescript support.
 
 * Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
 
-* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
+* Persisted on HDD, allowing scalability without breaking the bank.
+
+* Ingest your favorite data formats directly, like pandas DataFrames, Pydantic objects and more.
+
 
 LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
 
@@ -33,7 +36,7 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
       table = db.create_table("my_table",
                               data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
                                     {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
-      result = table.search([100, 100]).limit(2).to_df()
+      result = table.search([100, 100]).limit(2).to_list()
       ```
 
 === "Javascript"
@@ -46,7 +49,7 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
 
       const uri = "data/sample-lancedb";
       const db = await lancedb.connect(uri);
-      const table = await db.createTable("my_table", 
+      const table = await db.createTable("my_table",
             [{ id: 1, vector: [3.1, 4.1], item: "foo", price: 10.0 },
             { id: 2, vector: [5.9, 26.5], item: "bar", price: 20.0 }])
       const results = await table.search([100, 100]).limit(2).execute();
@@ -67,6 +70,6 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
 * [`Embedding Functions`](embedding.md) - functions for working with embeddings.
 * [`Indexing`](ann_indexes.md) - create vector indexes to speed up queries.
 * [`Full text search`](fts.md) - [EXPERIMENTAL] full-text search API
-* [`Ecosystem Integrations`](integrations.md) - integrating LanceDB with python data tooling ecosystem.
+* [`Ecosystem Integrations`](python/integration.md) - integrating LanceDB with python data tooling ecosystem.
 * [`Python API Reference`](python/python.md) - detailed documentation for the LanceDB Python SDK.
-* [`Node API Reference`](javascript/modules.md) - detailed documentation for the LanceDB Python SDK.
+* [`Node API Reference`](javascript/modules.md) - detailed documentation for the LanceDB Node SDK.

docs/src/integrations.md

@@ -1,116 +0,0 @@
-# Integrations
-
-Built on top of Apache Arrow, `LanceDB` is easy to integrate with the Python ecosystem, including Pandas, PyArrow and DuckDB.
-
-## Pandas and PyArrow
-
-First, we need to connect to a `LanceDB` database.
-
-```py
-
-import lancedb
-
-db = lancedb.connect("data/sample-lancedb")
-```
-
-And write a `Pandas DataFrame` to LanceDB directly.
-
-```py
-import pandas as pd
-
-data = pd.DataFrame({
-    "vector": [[3.1, 4.1], [5.9, 26.5]],
-    "item": ["foo", "bar"],
-    "price": [10.0, 20.0]
-})
-table = db.create_table("pd_table", data=data)
-```
-
-You will find detailed instructions of creating dataset and index in [Basic Operations](basic.md) and [Indexing](ann_indexes.md)
-sections.
-
-
-We can now perform similarity searches via `LanceDB`.
-
-```py
-# Open the table previously created.
-table = db.open_table("pd_table")
-
-query_vector = [100, 100]
-# Pandas DataFrame
-df = table.search(query_vector).limit(1).to_df()
-print(df)
-```
-
-```
-    vector     item  price        score
-0  [5.9, 26.5]  bar   20.0  14257.05957
-```
-
-If you have a simple filter, it's faster to provide a where clause to `LanceDB`'s search query.
-If you have more complex criteria, you can always apply the filter to the resulting pandas `DataFrame` from the search query.
-
-```python
-
-# Apply the filter via LanceDB
-results = table.search([100, 100]).where("price < 15").to_df()
-assert len(results) == 1
-assert results["item"].iloc[0] == "foo"
-
-# Apply the filter via Pandas
-df = results = table.search([100, 100]).to_df()
-results = df[df.price < 15]
-assert len(results) == 1
-assert results["item"].iloc[0] == "foo"
-```
-
-## DuckDB
-
-`LanceDB` works with `DuckDB` via [PyArrow integration](https://duckdb.org/docs/guides/python/sql_on_arrow).
-
-Let us start with installing `duckdb` and `lancedb`.
-
-```shell
-pip install duckdb lancedb
-```
-
-We will re-use the dataset created previously
-
-```python
-import lancedb
-
-db = lancedb.connect("data/sample-lancedb")
-table = db.open_table("pd_table")
-arrow_table = table.to_arrow()
-```
-
-`DuckDB` can directly query the `arrow_table`:
-
-```python
-import duckdb 
-
-duckdb.query("SELECT * FROM arrow_table")
-```
-
-```
-┌─────────────┬─────────┬────────┐
-│   vector    │  item   │ price  │
-│   float[]   │ varchar │ double │
-├─────────────┼─────────┼────────┤
-│ [3.1, 4.1]  │ foo     │   10.0 │
-│ [5.9, 26.5] │ bar     │   20.0 │
-└─────────────┴─────────┴────────┘
-```
-```python
-duckdb.query("SELECT mean(price) FROM arrow_table")
-```
-
-```
-Out[16]:
-┌─────────────┐
-│ mean(price) │
-│   double    │
-├─────────────┤
-│        15.0 │
-└─────────────┘
-```

docs/src/integrations/index.md

@@ -0,0 +1,21 @@
+# Integrations
+
+## Data Formats
+
+LanceDB supports ingesting from your favorite data tools.
+
+![Illustration](/lancedb/assets/ecosystem-illustration.png)
+
+
+## Tools
+
+LanceDB is integrated with most of the popular AI tools, with more coming soon.
+Get started using these examples and quick links.
+
+| Integrations | |
+|---|---:|
+| <h3> LlamaIndex </h3>LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. Llama index integrates with LanceDB as the serverless VectorDB. <h3>[Lean More](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html) </h3> |<img src="../assets/llama-index.jpg" alt="image" width="150" height="auto">|
+| <h3>Langchain</h3>Langchain allows building applications with LLMs through composability <h3>[Lean More](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
+| <h3>Langchain TS</h3> Javascript bindings for Langchain. It integrates with LanceDB's serverless vectordb allowing you to build powerful AI applications through composibility using only serverless functions. <h3>[Learn More]( https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
+| <h3>Voxel51</h3>  It is an open source toolkit that enables you to build better computer vision workflows by improving the quality of your datasets and delivering insights about your models.<h3>[Learn More](./voxel51.md) | <img src="../assets/voxel.gif" alt="image" width="150" height="auto">|
+| <h3>PromptTools</h3>  Offers a set of free, open-source tools for testing and experimenting with models, prompts, and configurations. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks. You can use it to experiment with different configurations of LanceDB, and test how LanceDB integrates with the LLM of your choice.<h3>[Learn More](./prompttools.md) | <img src="../assets/prompttools.jpeg" alt="image" width="150" height="auto">|

docs/src/integrations/prompttools.md

@@ -0,0 +1,7 @@
+
+[PromptTools](https://github.com/hegelai/prompttools) offers a set of free, open-source tools for testing and experimenting with models, prompts, and configurations. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks. You can use it to experiment with different configurations of LanceDB, and test how LanceDB integrates with the LLM of your choice.
+
+[Evaluating Prompts with PromptTools](./examples/prompttools-eval-prompts/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+
+![Alt text](https://prompttools.readthedocs.io/en/latest/_images/demo.gif "a title")
+

docs/src/integrations/voxel51.md

@@ -0,0 +1,71 @@
+![example](/assets/voxel.gif)
+
+Basic recipe
+____________
+
+The basic workflow to use LanceDB to create a similarity index on your FiftyOne
+datasets and use this to query your data is as follows:
+
+1)  Load a dataset into FiftyOne
+
+2)  Compute embedding vectors for samples or patches in your dataset, or select
+    a model to use to generate embeddings
+
+3)  Use the `compute_similarity()`
+    method to generate a LanceDB table for the samples or object
+    patches embeddings in a dataset by setting the parameter `backend="lancedb"` and
+    specifying a `brain_key` of your choice
+
+4)  Use this LanceDB table to query your data with
+    `sort_by_similarity()`
+
+5) If desired, delete the table
+
+The example below demonstrates this workflow.
+
+!!! Note
+
+    You must install the LanceDB Python client to run this
+    ```
+    pip install lancedb
+    ```
+
+```python
+
+import fiftyone as fo
+import fiftyone.brain as fob
+import fiftyone.zoo as foz
+
+# Step 1: Load your data into FiftyOne
+dataset = foz.load_zoo_dataset("quickstart")
+
+# Steps 2 and 3: Compute embeddings and create a similarity index
+lancedb_index = fob.compute_similarity(
+    dataset, 
+    model="clip-vit-base32-torch",
+    brain_key="lancedb_index",
+    backend="lancedb",
+)
+```
+Once the similarity index has been generated, we can query our data in FiftyOne
+by specifying the `brain_key`:
+
+```python
+# Step 4: Query your data
+query = dataset.first().id  # query by sample ID
+view = dataset.sort_by_similarity(
+    query, 
+    brain_key="lancedb_index",
+    k=10,  # limit to 10 most similar samples
+)
+
+# Step 5 (optional): Cleanup
+
+# Delete the LanceDB table
+lancedb_index.cleanup()
+
+# Delete run record from FiftyOne
+dataset.delete_brain_run("lancedb_index")
+```
+
+More in depth walkthrough of the integration, visit the LanceDB guide on Voxel51 - [LaceDB x Voxel51](https://docs.voxel51.com/integrations/lancedb.html)

docs/src/javascript/README.md

@@ -10,6 +10,10 @@ A JavaScript / Node.js library for [LanceDB](https://github.com/lancedb/lancedb)
 npm install vectordb
 ```
 
+This will download the appropriate native library for your platform. We currently
+support x86_64 Linux, aarch64 Linux, Intel MacOS, and ARM (M1/M2) MacOS. We do not
+yet support Windows or musl-based Linux (such as Alpine Linux).
+
 ## Usage
 
 ### Basic Example
@@ -28,12 +32,34 @@ The [examples](./examples) folder contains complete examples.
 
 ## Development
 
-Run the tests with
+To build everything fresh:
+
+```bash
+npm install
+npm run tsc
+npm run build
+```
+
+Then you should be able to run the tests with:
 
 ```bash
 npm test
 ```
 
+### Rebuilding Rust library
+
+```bash
+npm run build
+```
+
+### Rebuilding Typescript
+
+```bash
+npm run tsc
+```
+
+### Fix lints
+
 To run the linter and have it automatically fix all errors
 
 ```bash

docs/src/javascript/classes/LocalConnection.md

@@ -17,7 +17,7 @@ A connection to a LanceDB database.
 ### Properties
 
 - [\_db](LocalConnection.md#_db)
-- [\_uri](LocalConnection.md#_uri)
+- [\_options](LocalConnection.md#_options)
 
 ### Accessors
 
@@ -35,18 +35,18 @@ A connection to a LanceDB database.
 
 ### constructor
 
-• **new LocalConnection**(`db`, `uri`)
+• **new LocalConnection**(`db`, `options`)
 
 #### Parameters
 
 | Name | Type |
 | :------ | :------ |
 | `db` | `any` |
-| `uri` | `string` |
+| `options` | [`ConnectionOptions`](../interfaces/ConnectionOptions.md) |
 
 #### Defined in
 
-[index.ts:132](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L132)
+[index.ts:184](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L184)
 
 ## Properties
 
@@ -56,17 +56,17 @@ A connection to a LanceDB database.
 
 #### Defined in
 
-[index.ts:130](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L130)
+[index.ts:182](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L182)
 
 ___
 
-### \_uri
+### \_options
 
-• `Private` `Readonly` **\_uri**: `string`
+• `Private` `Readonly` **\_options**: [`ConnectionOptions`](../interfaces/ConnectionOptions.md)
 
 #### Defined in
 
-[index.ts:129](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L129)
+[index.ts:181](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L181)
 
 ## Accessors
 
@@ -84,7 +84,7 @@ ___
 
 #### Defined in
 
-[index.ts:137](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L137)
+[index.ts:189](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L189)
 
 ## Methods
 
@@ -112,7 +112,7 @@ Creates a new Table and initialize it with new data.
 
 #### Defined in
 
-[index.ts:177](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L177)
+[index.ts:230](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L230)
 
 ▸ **createTable**(`name`, `data`, `mode`): `Promise`<[`Table`](../interfaces/Table.md)<`number`[]\>\>
 
@@ -134,7 +134,7 @@ Connection.createTable
 
 #### Defined in
 
-[index.ts:178](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L178)
+[index.ts:231](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L231)
 
 ▸ **createTable**<`T`\>(`name`, `data`, `mode`, `embeddings`): `Promise`<[`Table`](../interfaces/Table.md)<`T`\>\>
 
@@ -165,7 +165,36 @@ Connection.createTable
 
 #### Defined in
 
-[index.ts:188](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L188)
+[index.ts:241](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L241)
+
+▸ **createTable**<`T`\>(`name`, `data`, `mode`, `embeddings?`): `Promise`<[`Table`](../interfaces/Table.md)<`T`\>\>
+
+#### Type parameters
+
+| Name |
+| :------ |
+| `T` |
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `name` | `string` |
+| `data` | `Record`<`string`, `unknown`\>[] |
+| `mode` | [`WriteMode`](../enums/WriteMode.md) |
+| `embeddings?` | [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)<`T`\> |
+
+#### Returns
+
+`Promise`<[`Table`](../interfaces/Table.md)<`T`\>\>
+
+#### Implementation of
+
+Connection.createTable
+
+#### Defined in
+
+[index.ts:242](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L242)
 
 ___
 
@@ -190,7 +219,7 @@ ___
 
 #### Defined in
 
-[index.ts:201](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L201)
+[index.ts:266](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L266)
 
 ___
 
@@ -216,7 +245,7 @@ Drop an existing table.
 
 #### Defined in
 
-[index.ts:211](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L211)
+[index.ts:276](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L276)
 
 ___
 
@@ -242,7 +271,7 @@ Open a table in the database.
 
 #### Defined in
 
-[index.ts:153](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L153)
+[index.ts:205](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L205)
 
 ▸ **openTable**<`T`\>(`name`, `embeddings`): `Promise`<[`Table`](../interfaces/Table.md)<`T`\>\>
 
@@ -271,7 +300,34 @@ Connection.openTable
 
 #### Defined in
 
-[index.ts:160](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L160)
+[index.ts:212](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L212)
+
+▸ **openTable**<`T`\>(`name`, `embeddings?`): `Promise`<[`Table`](../interfaces/Table.md)<`T`\>\>
+
+#### Type parameters
+
+| Name |
+| :------ |
+| `T` |
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `name` | `string` |
+| `embeddings?` | [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)<`T`\> |
+
+#### Returns
+
+`Promise`<[`Table`](../interfaces/Table.md)<`T`\>\>
+
+#### Implementation of
+
+Connection.openTable
+
+#### Defined in
+
+[index.ts:213](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L213)
 
 ___
 
@@ -291,4 +347,4 @@ Get the names of all tables in the database.
 
 #### Defined in
 
-[index.ts:144](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L144)
+[index.ts:196](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L196)

docs/src/javascript/classes/LocalTable.md

@@ -24,6 +24,7 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 
 - [\_embeddings](LocalTable.md#_embeddings)
 - [\_name](LocalTable.md#_name)
+- [\_options](LocalTable.md#_options)
 - [\_tbl](LocalTable.md#_tbl)
 
 ### Accessors
@@ -43,7 +44,7 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 
 ### constructor
 
-• **new LocalTable**<`T`\>(`tbl`, `name`)
+• **new LocalTable**<`T`\>(`tbl`, `name`, `options`)
 
 #### Type parameters
 
@@ -57,12 +58,13 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 | :------ | :------ |
 | `tbl` | `any` |
 | `name` | `string` |
+| `options` | [`ConnectionOptions`](../interfaces/ConnectionOptions.md) |
 
 #### Defined in
 
-[index.ts:221](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L221)
+[index.ts:287](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L287)
 
-• **new LocalTable**<`T`\>(`tbl`, `name`, `embeddings`)
+• **new LocalTable**<`T`\>(`tbl`, `name`, `options`, `embeddings`)
 
 #### Type parameters
 
@@ -76,11 +78,12 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 | :------ | :------ | :------ |
 | `tbl` | `any` |  |
 | `name` | `string` |  |
+| `options` | [`ConnectionOptions`](../interfaces/ConnectionOptions.md) |  |
 | `embeddings` | [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)<`T`\> | An embedding function to use when interacting with this table |
 
 #### Defined in
 
-[index.ts:227](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L227)
+[index.ts:294](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L294)
 
 ## Properties
 
@@ -90,7 +93,7 @@ A LanceDB Table is the collection of Records. Each Record has one or more vector
 
 #### Defined in
 
-[index.ts:219](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L219)
+[index.ts:284](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L284)
 
 ___
 
@@ -100,7 +103,17 @@ ___
 
 #### Defined in
 
-[index.ts:218](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L218)
+[index.ts:283](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L283)
+
+___
+
+### \_options
+
+• `Private` `Readonly` **\_options**: [`ConnectionOptions`](../interfaces/ConnectionOptions.md)
+
+#### Defined in
+
+[index.ts:285](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L285)
 
 ___
 
@@ -110,7 +123,7 @@ ___
 
 #### Defined in
 
-[index.ts:217](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L217)
+[index.ts:282](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L282)
 
 ## Accessors
 
@@ -128,7 +141,7 @@ ___
 
 #### Defined in
 
-[index.ts:234](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L234)
+[index.ts:302](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L302)
 
 ## Methods
 
@@ -156,7 +169,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:252](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L252)
+[index.ts:320](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L320)
 
 ___
 
@@ -176,7 +189,7 @@ Returns the number of rows in this table.
 
 #### Defined in
 
-[index.ts:278](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L278)
+[index.ts:362](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L362)
 
 ___
 
@@ -194,7 +207,7 @@ VectorIndexParams.
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `indexParams` | `IvfPQIndexConfig` | The parameters of this Index, |
+| `indexParams` | [`IvfPQIndexConfig`](../interfaces/IvfPQIndexConfig.md) | The parameters of this Index, |
 
 #### Returns
 
@@ -206,7 +219,7 @@ VectorIndexParams.
 
 #### Defined in
 
-[index.ts:271](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L271)
+[index.ts:355](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L355)
 
 ___
 
@@ -232,7 +245,7 @@ Delete rows from this table.
 
 #### Defined in
 
-[index.ts:287](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L287)
+[index.ts:371](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L371)
 
 ___
 
@@ -260,7 +273,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:262](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L262)
+[index.ts:338](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L338)
 
 ___
 
@@ -286,4 +299,4 @@ Creates a search query to find the nearest neighbors of the given search term
 
 #### Defined in
 
-[index.ts:242](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L242)
+[index.ts:310](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L310)

docs/src/javascript/classes/OpenAIEmbeddingFunction.md

@@ -40,7 +40,7 @@ An embedding function that automatically creates vector representation for a giv
 
 #### Defined in
 
-[embedding/openai.ts:21](https://github.com/lancedb/lancedb/blob/7247834/node/src/embedding/openai.ts#L21)
+[embedding/openai.ts:21](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/embedding/openai.ts#L21)
 
 ## Properties
 
@@ -50,7 +50,7 @@ An embedding function that automatically creates vector representation for a giv
 
 #### Defined in
 
-[embedding/openai.ts:19](https://github.com/lancedb/lancedb/blob/7247834/node/src/embedding/openai.ts#L19)
+[embedding/openai.ts:19](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/embedding/openai.ts#L19)
 
 ___
 
@@ -60,7 +60,7 @@ ___
 
 #### Defined in
 
-[embedding/openai.ts:18](https://github.com/lancedb/lancedb/blob/7247834/node/src/embedding/openai.ts#L18)
+[embedding/openai.ts:18](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/embedding/openai.ts#L18)
 
 ___
 
@@ -76,7 +76,7 @@ The name of the column that will be used as input for the Embedding Function.
 
 #### Defined in
 
-[embedding/openai.ts:50](https://github.com/lancedb/lancedb/blob/7247834/node/src/embedding/openai.ts#L50)
+[embedding/openai.ts:50](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/embedding/openai.ts#L50)
 
 ## Methods
 
@@ -102,4 +102,4 @@ Creates a vector representation for the given values.
 
 #### Defined in
 
-[embedding/openai.ts:38](https://github.com/lancedb/lancedb/blob/7247834/node/src/embedding/openai.ts#L38)
+[embedding/openai.ts:38](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/embedding/openai.ts#L38)

docs/src/javascript/classes/Query.md

@@ -62,7 +62,7 @@ A builder for nearest neighbor queries for LanceDB.
 
 #### Defined in
 
-[index.ts:362](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L362)
+[index.ts:448](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L448)
 
 ## Properties
 
@@ -72,7 +72,7 @@ A builder for nearest neighbor queries for LanceDB.
 
 #### Defined in
 
-[index.ts:360](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L360)
+[index.ts:446](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L446)
 
 ___
 
@@ -82,7 +82,7 @@ ___
 
 #### Defined in
 
-[index.ts:358](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L358)
+[index.ts:444](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L444)
 
 ___
 
@@ -92,7 +92,7 @@ ___
 
 #### Defined in
 
-[index.ts:354](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L354)
+[index.ts:440](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L440)
 
 ___
 
@@ -102,7 +102,7 @@ ___
 
 #### Defined in
 
-[index.ts:359](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L359)
+[index.ts:445](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L445)
 
 ___
 
@@ -112,7 +112,7 @@ ___
 
 #### Defined in
 
-[index.ts:356](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L356)
+[index.ts:442](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L442)
 
 ___
 
@@ -122,7 +122,7 @@ ___
 
 #### Defined in
 
-[index.ts:352](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L352)
+[index.ts:438](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L438)
 
 ___
 
@@ -132,7 +132,7 @@ ___
 
 #### Defined in
 
-[index.ts:353](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L353)
+[index.ts:439](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L439)
 
 ___
 
@@ -142,7 +142,7 @@ ___
 
 #### Defined in
 
-[index.ts:355](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L355)
+[index.ts:441](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L441)
 
 ___
 
@@ -152,7 +152,7 @@ ___
 
 #### Defined in
 
-[index.ts:357](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L357)
+[index.ts:443](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L443)
 
 ___
 
@@ -162,7 +162,7 @@ ___
 
 #### Defined in
 
-[index.ts:351](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L351)
+[index.ts:437](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L437)
 
 ___
 
@@ -188,7 +188,7 @@ A filter statement to be applied to this query.
 
 #### Defined in
 
-[index.ts:410](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L410)
+[index.ts:496](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L496)
 
 ## Methods
 
@@ -210,7 +210,7 @@ Execute the query and return the results as an Array of Objects
 
 #### Defined in
 
-[index.ts:433](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L433)
+[index.ts:519](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L519)
 
 ___
 
@@ -232,7 +232,7 @@ A filter statement to be applied to this query.
 
 #### Defined in
 
-[index.ts:405](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L405)
+[index.ts:491](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L491)
 
 ___
 
@@ -254,7 +254,7 @@ Sets the number of results that will be returned
 
 #### Defined in
 
-[index.ts:378](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L378)
+[index.ts:464](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L464)
 
 ___
 
@@ -280,7 +280,7 @@ MetricType for the different options
 
 #### Defined in
 
-[index.ts:425](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L425)
+[index.ts:511](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L511)
 
 ___
 
@@ -302,7 +302,7 @@ The number of probes used. A higher number makes search more accurate but also s
 
 #### Defined in
 
-[index.ts:396](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L396)
+[index.ts:482](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L482)
 
 ___
 
@@ -324,7 +324,7 @@ Refine the results by reading extra elements and re-ranking them in memory.
 
 #### Defined in
 
-[index.ts:387](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L387)
+[index.ts:473](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L473)
 
 ___
 
@@ -346,4 +346,4 @@ Return only the specified columns.
 
 #### Defined in
 
-[index.ts:416](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L416)
+[index.ts:502](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L502)

docs/src/javascript/enums/MetricType.md

@@ -22,7 +22,7 @@ Cosine distance
 
 #### Defined in
 
-[index.ts:481](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L481)
+[index.ts:567](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L567)
 
 ___
 
@@ -34,7 +34,7 @@ Dot product
 
 #### Defined in
 
-[index.ts:486](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L486)
+[index.ts:572](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L572)
 
 ___
 
@@ -46,4 +46,4 @@ Euclidean distance
 
 #### Defined in
 
-[index.ts:476](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L476)
+[index.ts:562](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L562)

docs/src/javascript/enums/WriteMode.md

@@ -22,7 +22,7 @@ Append new data to the table.
 
 #### Defined in
 
-[index.ts:466](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L466)
+[index.ts:552](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L552)
 
 ___
 
@@ -34,7 +34,7 @@ Create a new [Table](../interfaces/Table.md).
 
 #### Defined in
 
-[index.ts:462](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L462)
+[index.ts:548](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L548)
 
 ___
 
@@ -46,4 +46,4 @@ Overwrite the existing [Table](../interfaces/Table.md) if presented.
 
 #### Defined in
 
-[index.ts:464](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L464)
+[index.ts:550](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L550)

docs/src/javascript/interfaces/AwsCredentials.md

@@ -0,0 +1,41 @@
+[vectordb](../README.md) / [Exports](../modules.md) / AwsCredentials
+
+# Interface: AwsCredentials
+
+## Table of contents
+
+### Properties
+
+- [accessKeyId](AwsCredentials.md#accesskeyid)
+- [secretKey](AwsCredentials.md#secretkey)
+- [sessionToken](AwsCredentials.md#sessiontoken)
+
+## Properties
+
+### accessKeyId
+
+• **accessKeyId**: `string`
+
+#### Defined in
+
+[index.ts:31](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L31)
+
+___
+
+### secretKey
+
+• **secretKey**: `string`
+
+#### Defined in
+
+[index.ts:33](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L33)
+
+___
+
+### sessionToken
+
+• `Optional` **sessionToken**: `string`
+
+#### Defined in
+
+[index.ts:35](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L35)

docs/src/javascript/interfaces/Connection.md

@@ -32,7 +32,7 @@ Connection could be local against filesystem or remote against a server.
 
 #### Defined in
 
-[index.ts:45](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L45)
+[index.ts:70](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L70)
 
 ## Methods
 
@@ -63,7 +63,7 @@ Creates a new Table and initialize it with new data.
 
 #### Defined in
 
-[index.ts:65](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L65)
+[index.ts:90](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L90)
 
 ___
 
@@ -84,7 +84,7 @@ ___
 
 #### Defined in
 
-[index.ts:67](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L67)
+[index.ts:92](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L92)
 
 ___
 
@@ -106,7 +106,7 @@ Drop an existing table.
 
 #### Defined in
 
-[index.ts:73](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L73)
+[index.ts:98](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L98)
 
 ___
 
@@ -135,7 +135,7 @@ Open a table in the database.
 
 #### Defined in
 
-[index.ts:55](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L55)
+[index.ts:80](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L80)
 
 ___
 
@@ -149,4 +149,4 @@ ___
 
 #### Defined in
 
-[index.ts:47](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L47)
+[index.ts:72](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L72)

docs/src/javascript/interfaces/ConnectionOptions.md

@@ -0,0 +1,30 @@
+[vectordb](../README.md) / [Exports](../modules.md) / ConnectionOptions
+
+# Interface: ConnectionOptions
+
+## Table of contents
+
+### Properties
+
+- [awsCredentials](ConnectionOptions.md#awscredentials)
+- [uri](ConnectionOptions.md#uri)
+
+## Properties
+
+### awsCredentials
+
+• `Optional` **awsCredentials**: [`AwsCredentials`](AwsCredentials.md)
+
+#### Defined in
+
+[index.ts:40](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L40)
+
+___
+
+### uri
+
+• **uri**: `string`
+
+#### Defined in
+
+[index.ts:39](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L39)

docs/src/javascript/interfaces/EmbeddingFunction.md

@@ -45,7 +45,7 @@ Creates a vector representation for the given values.
 
 #### Defined in
 
-[embedding/embedding_function.ts:27](https://github.com/lancedb/lancedb/blob/7247834/node/src/embedding/embedding_function.ts#L27)
+[embedding/embedding_function.ts:27](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/embedding/embedding_function.ts#L27)
 
 ___
 
@@ -57,4 +57,4 @@ The name of the column that will be used as input for the Embedding Function.
 
 #### Defined in
 
-[embedding/embedding_function.ts:22](https://github.com/lancedb/lancedb/blob/7247834/node/src/embedding/embedding_function.ts#L22)
+[embedding/embedding_function.ts:22](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/embedding/embedding_function.ts#L22)

docs/src/javascript/interfaces/IvfPQIndexConfig.md

@@ -0,0 +1,149 @@
+[vectordb](../README.md) / [Exports](../modules.md) / IvfPQIndexConfig
+
+# Interface: IvfPQIndexConfig
+
+## Table of contents
+
+### Properties
+
+- [column](IvfPQIndexConfig.md#column)
+- [index\_name](IvfPQIndexConfig.md#index_name)
+- [max\_iters](IvfPQIndexConfig.md#max_iters)
+- [max\_opq\_iters](IvfPQIndexConfig.md#max_opq_iters)
+- [metric\_type](IvfPQIndexConfig.md#metric_type)
+- [num\_bits](IvfPQIndexConfig.md#num_bits)
+- [num\_partitions](IvfPQIndexConfig.md#num_partitions)
+- [num\_sub\_vectors](IvfPQIndexConfig.md#num_sub_vectors)
+- [replace](IvfPQIndexConfig.md#replace)
+- [type](IvfPQIndexConfig.md#type)
+- [use\_opq](IvfPQIndexConfig.md#use_opq)
+
+## Properties
+
+### column
+
+• `Optional` **column**: `string`
+
+The column to be indexed
+
+#### Defined in
+
+[index.ts:382](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L382)
+
+___
+
+### index\_name
+
+• `Optional` **index\_name**: `string`
+
+A unique name for the index
+
+#### Defined in
+
+[index.ts:387](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L387)
+
+___
+
+### max\_iters
+
+• `Optional` **max\_iters**: `number`
+
+The max number of iterations for kmeans training.
+
+#### Defined in
+
+[index.ts:402](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L402)
+
+___
+
+### max\_opq\_iters
+
+• `Optional` **max\_opq\_iters**: `number`
+
+Max number of iterations to train OPQ, if `use_opq` is true.
+
+#### Defined in
+
+[index.ts:421](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L421)
+
+___
+
+### metric\_type
+
+• `Optional` **metric\_type**: [`MetricType`](../enums/MetricType.md)
+
+Metric type, L2 or Cosine
+
+#### Defined in
+
+[index.ts:392](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L392)
+
+___
+
+### num\_bits
+
+• `Optional` **num\_bits**: `number`
+
+The number of bits to present one PQ centroid.
+
+#### Defined in
+
+[index.ts:416](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L416)
+
+___
+
+### num\_partitions
+
+• `Optional` **num\_partitions**: `number`
+
+The number of partitions this index
+
+#### Defined in
+
+[index.ts:397](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L397)
+
+___
+
+### num\_sub\_vectors
+
+• `Optional` **num\_sub\_vectors**: `number`
+
+Number of subvectors to build PQ code
+
+#### Defined in
+
+[index.ts:412](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L412)
+
+___
+
+### replace
+
+• `Optional` **replace**: `boolean`
+
+Replace an existing index with the same name if it exists.
+
+#### Defined in
+
+[index.ts:426](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L426)
+
+___
+
+### type
+
+• **type**: ``"ivf_pq"``
+
+#### Defined in
+
+[index.ts:428](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L428)
+
+___
+
+### use\_opq
+
+• `Optional` **use\_opq**: `boolean`
+
+Train as optimized product quantization.
+
+#### Defined in
+
+[index.ts:407](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L407)

docs/src/javascript/interfaces/Table.md

@@ -52,7 +52,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:95](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L95)
+[index.ts:120](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L120)
 
 ___
 
@@ -72,13 +72,13 @@ Returns the number of rows in this table.
 
 #### Defined in
 
-[index.ts:115](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L115)
+[index.ts:140](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L140)
 
 ___
 
 ### createIndex
 
-• **createIndex**: (`indexParams`: `IvfPQIndexConfig`) => `Promise`<`any`\>
+• **createIndex**: (`indexParams`: [`IvfPQIndexConfig`](IvfPQIndexConfig.md)) => `Promise`<`any`\>
 
 #### Type declaration
 
@@ -94,7 +94,7 @@ VectorIndexParams.
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `indexParams` | `IvfPQIndexConfig` | The parameters of this Index, |
+| `indexParams` | [`IvfPQIndexConfig`](IvfPQIndexConfig.md) | The parameters of this Index, |
 
 ##### Returns
 
@@ -102,7 +102,7 @@ VectorIndexParams.
 
 #### Defined in
 
-[index.ts:110](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L110)
+[index.ts:135](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L135)
 
 ___
 
@@ -116,11 +116,37 @@ ___
 
 Delete rows from this table.
 
+This can be used to delete a single row, many rows, all rows, or
+sometimes no rows (if your predicate matches nothing).
+
+**`Examples`**
+
+```ts
+const con = await lancedb.connect("./.lancedb")
+const data = [
+   {id: 1, vector: [1, 2]},
+   {id: 2, vector: [3, 4]},
+   {id: 3, vector: [5, 6]},
+];
+const tbl = await con.createTable("my_table", data)
+await tbl.delete("id = 2")
+await tbl.countRows() // Returns 2
+```
+
+If you have a list of values to delete, you can combine them into a
+stringified list and use the `IN` operator:
+
+```ts
+const to_remove = [1, 5];
+await tbl.delete(`id IN (${to_remove.join(",")})`)
+await tbl.countRows() // Returns 1
+```
+
 ##### Parameters
 
 | Name | Type | Description |
 | :------ | :------ | :------ |
-| `filter` | `string` | A filter in the same format used by a sql WHERE clause. |
+| `filter` | `string` | A filter in the same format used by a sql WHERE clause. The filter must not be empty. |
 
 ##### Returns
 
@@ -128,7 +154,7 @@ Delete rows from this table.
 
 #### Defined in
 
-[index.ts:122](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L122)
+[index.ts:174](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L174)
 
 ___
 
@@ -138,7 +164,7 @@ ___
 
 #### Defined in
 
-[index.ts:81](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L81)
+[index.ts:106](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L106)
 
 ___
 
@@ -166,7 +192,7 @@ The number of rows added to the table
 
 #### Defined in
 
-[index.ts:103](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L103)
+[index.ts:128](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L128)
 
 ___
 
@@ -192,4 +218,4 @@ Creates a search query to find the nearest neighbors of the given search term
 
 #### Defined in
 
-[index.ts:87](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L87)
+[index.ts:112](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L112)

docs/src/javascript/modules.md

@@ -18,8 +18,11 @@
 
 ### Interfaces
 
+- [AwsCredentials](interfaces/AwsCredentials.md)
 - [Connection](interfaces/Connection.md)
+- [ConnectionOptions](interfaces/ConnectionOptions.md)
 - [EmbeddingFunction](interfaces/EmbeddingFunction.md)
+- [IvfPQIndexConfig](interfaces/IvfPQIndexConfig.md)
 - [Table](interfaces/Table.md)
 
 ### Type Aliases
@@ -34,11 +37,11 @@
 
 ### VectorIndexParams
 
-Ƭ **VectorIndexParams**: `IvfPQIndexConfig`
+Ƭ **VectorIndexParams**: [`IvfPQIndexConfig`](interfaces/IvfPQIndexConfig.md)
 
 #### Defined in
 
-[index.ts:345](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L345)
+[index.ts:431](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L431)
 
 ## Functions
 
@@ -60,4 +63,20 @@ Connect to a LanceDB instance at the given URI
 
 #### Defined in
 
-[index.ts:34](https://github.com/lancedb/lancedb/blob/7247834/node/src/index.ts#L34)
+[index.ts:47](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L47)
+
+▸ **connect**(`opts`): `Promise`<[`Connection`](interfaces/Connection.md)\>
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `opts` | `Partial`<[`ConnectionOptions`](interfaces/ConnectionOptions.md)\> |
+
+#### Returns
+
+`Promise`<[`Connection`](interfaces/Connection.md)\>
+
+#### Defined in
+
+[index.ts:48](https://github.com/lancedb/lancedb/blob/b1eeb90/node/src/index.ts#L48)

docs/src/notebooks/code_qa_bot.ipynb

@@ -10,7 +10,11 @@
     "\n",
     "This Q&A bot will allow you to query your own documentation easily using questions. We'll also demonstrate the use of LangChain and LanceDB using the OpenAI API. \n",
     "\n",
-    "In this example we'll use Pandas 2.0 documentation, but, this could be replaced for your own docs as well"
+    "In this example we'll use Pandas 2.0 documentation, but, this could be replaced for your own docs as well\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot/main.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+    "\n",
+    "Scripts -  [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Code-Documentation-QA-Bot/main.py)  [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/Code-Documentation-QA-Bot/index.js)"
    ]
   },
   {
@@ -140,7 +144,7 @@
    "source": [
     "# Pre-processing and loading the documentation\n",
     "\n",
-    "Next, let's pre-process and load the documentation. To make sure we don't need to do this repeatedly if we were updating code, we're caching it using pickle so we can retrieve it again (this could take a few minutes to run the first time yyou do it). We'll also add some more metadata to the docs here such as the title and version of the code:"
+    "Next, let's pre-process and load the documentation. To make sure we don't need to do this repeatedly if we were updating code, we're caching it using pickle so we can retrieve it again (this could take a few minutes to run the first time you do it). We'll also add some more metadata to the docs here such as the title and version of the code:"
    ]
   },
   {
@@ -181,7 +185,7 @@
    "id": "c3852dd3",
    "metadata": {},
    "source": [
-    "# Generating emebeddings from our docs\n",
+    "# Generating embeddings from our docs\n",
     "\n",
     "Now that we have our raw documents loaded, we need to pre-process them to generate embeddings:"
    ]
@@ -251,7 +255,7 @@
    "id": "28d93b85",
    "metadata": {},
    "source": [
-    "And thats it! We're all setup. The next step is to run some queries, let's try a few:"
+    "And that's it! We're all set up. The next step is to run some queries, let's try a few:"
    ]
   },
   {

docs/src/notebooks/multimodal_search.ipynb

@@ -1,5 +1,14 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![example](https://github.com/lancedb/vectordb-recipes/assets/15766192/799f94a1-a01d-4a5b-a627-2a733bbb4227)\n",
+    "\n",
+    " <a href=\"https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip/main.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_clip/main.py)  |"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -10,11 +19,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.1.2\u001B[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n",
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.1.2\u001B[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n"
      ]
     }
    ],
@@ -30,6 +39,7 @@
    "outputs": [],
    "source": [
     "import io\n",
+    "\n",
     "import PIL\n",
     "import duckdb\n",
     "import lancedb"
@@ -42,6 +52,19 @@
     "## First run setup: Download data and pre-process"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Get dataset\n",
+    "\n",
+    "!wget https://eto-public.s3.us-west-2.amazonaws.com/datasets/diffusiondb_lance.tar.gz\n",
+    "!tar -xvf diffusiondb_lance.tar.gz\n",
+    "!mv diffusiondb_test rawdata.lance\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 30,
@@ -136,18 +159,18 @@
     "        \"db = lancedb.connect('~/datasets/demo')\\n\"\n",
     "        \"tbl = db.open_table('diffusiondb')\\n\\n\"\n",
     "        f\"embedding = embed_func('{query}')\\n\"\n",
-    "        \"tbl.search(embedding).limit(9).to_df()\"\n",
+    "        \"tbl.search(embedding).limit(9).to_pandas()\"\n",
     "    )\n",
-    "    return (_extract(tbl.search(emb).limit(9).to_df()), code)\n",
+    "    return (_extract(tbl.search(emb).limit(9).to_pandas()), code)\n",
     "\n",
     "def find_image_keywords(query):\n",
     "    code = (\n",
     "        \"import lancedb\\n\"\n",
     "        \"db = lancedb.connect('~/datasets/demo')\\n\"\n",
     "        \"tbl = db.open_table('diffusiondb')\\n\\n\"\n",
-    "        f\"tbl.search('{query}').limit(9).to_df()\"\n",
+    "        f\"tbl.search('{query}').limit(9).to_pandas()\"\n",
     "    )\n",
-    "    return (_extract(tbl.search(query).limit(9).to_df()), code)\n",
+    "    return (_extract(tbl.search(query).limit(9).to_pandas()), code)\n",
     "\n",
     "def find_image_sql(query):\n",
     "    code = (\n",
@@ -247,7 +270,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.11.4 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -261,7 +284,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.11.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+   }
   }
  },
  "nbformat": 4,

docs/src/notebooks/tables_guide.ipynb

@@ -0,0 +1,831 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d24eb4c6-e246-44ca-ba7c-6eae7923bd4c",
+   "metadata": {},
+   "source": [
+    "## LanceDB Tables\n",
+    "A Table is a collection of Records in a LanceDB Database.\n",
+    "\n",
+    "![illustration](../assets/ecosystem-illustration.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c1b4e34b-a49c-471d-a343-a5940bb5138a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install lancedb -qq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4e5a8d07-d9a1-48c1-913a-8e0629289579",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lancedb\n",
+    "db = lancedb.connect(\"./.lancedb\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66fb93d5-3551-406b-99b2-488442d61d06",
+   "metadata": {},
+   "source": [
+    "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.\n",
+    "\n",
+    " ### From list of tuples or dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5df12f66-8d99-43ad-8d0b-22189ec0a6b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyarrow.Table\n",
+       "vector: fixed_size_list<item: float>[2]\n",
+       "  child 0, item: float\n",
+       "lat: double\n",
+       "long: double\n",
+       "----\n",
+       "vector: [[[1.1,1.2],[0.2,1.8]]]\n",
+       "lat: [[45.5,40.1]]\n",
+       "long: [[-122.7,-74.1]]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import lancedb\n",
+    "\n",
+    "db = lancedb.connect(\"./.lancedb\")\n",
+    "\n",
+    "data = [{\"vector\": [1.1, 1.2], \"lat\": 45.5, \"long\": -122.7},\n",
+    "        {\"vector\": [0.2, 1.8], \"lat\": 40.1, \"long\": -74.1}]\n",
+    "\n",
+    "db.create_table(\"my_table\", data)\n",
+    "\n",
+    "db[\"my_table\"].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10ce802f-1a10-49ee-8ee3-a9bfb302d86c",
+   "metadata": {},
+   "source": [
+    "## From pandas DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f4d87ae9-0ccb-48eb-b31d-bb8f2370e47e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyarrow.Table\n",
+       "vector: fixed_size_list<item: float>[2]\n",
+       "  child 0, item: float\n",
+       "lat: double\n",
+       "long: double\n",
+       "----\n",
+       "vector: [[[1.1,1.2],[0.2,1.8]]]\n",
+       "lat: [[45.5,40.1]]\n",
+       "long: [[-122.7,-74.1]]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "data = pd.DataFrame({\n",
+    "    \"vector\": [[1.1, 1.2], [0.2, 1.8]],\n",
+    "    \"lat\": [45.5, 40.1],\n",
+    "    \"long\": [-122.7, -74.1]\n",
+    "})\n",
+    "\n",
+    "db.create_table(\"table2\", data)\n",
+    "\n",
+    "db[\"table2\"].head() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4be81469-5b57-4f78-9c72-3938c0378d9d",
+   "metadata": {},
+   "source": [
+    "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.\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "25f34bcf-fca0-4431-8601-eac95d1bd347",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vector: fixed_size_list<item: float>[2]\n",
+       "  child 0, item: float\n",
+       "lat: float\n",
+       "long: float"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pyarrow as pa\n",
+    "\n",
+    "custom_schema = pa.schema([\n",
+    "pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n",
+    "pa.field(\"lat\", pa.float32()),\n",
+    "pa.field(\"long\", pa.float32())\n",
+    "])\n",
+    "\n",
+    "table = db.create_table(\"table3\", data, schema=custom_schema, mode=\"overwrite\")\n",
+    "table.schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4df51925-7ca2-4005-9c72-38b3d26240c6",
+   "metadata": {},
+   "source": [
+    "### From PyArrow Tables\n",
+    "\n",
+    "You can also create LanceDB tables directly from pyarrow tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "90a880f6-be43-4c9d-ba65-0b05197c0f6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vector: fixed_size_list<item: float>[2]\n",
+       "  child 0, item: float\n",
+       "item: string\n",
+       "price: double"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = pa.Table.from_arrays(\n",
+    "        [\n",
+    "            pa.array([[3.1, 4.1], [5.9, 26.5]],\n",
+    "                    pa.list_(pa.float32(), 2)),\n",
+    "            pa.array([\"foo\", \"bar\"]),\n",
+    "            pa.array([10.0, 20.0]),\n",
+    "        ],\n",
+    "        [\"vector\", \"item\", \"price\"],\n",
+    "    )\n",
+    "\n",
+    "db = lancedb.connect(\"db\")\n",
+    "\n",
+    "tbl = db.create_table(\"test1\", table, mode=\"overwrite\")\n",
+    "tbl.schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f36c51c-d902-449d-8292-700e53990c32",
+   "metadata": {},
+   "source": [
+    "### From Pydantic Models\n",
+    "\n",
+    "LanceDB supports to create Apache Arrow Schema from a Pydantic BaseModel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d81121d7-e4b7-447c-a48c-974b6ebb464a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "movie_id: int64 not null\n",
+       "vector: fixed_size_list<item: float>[128] not null\n",
+       "  child 0, item: float\n",
+       "genres: string not null\n",
+       "title: string not null\n",
+       "imdb_id: int64 not null"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from lancedb.pydantic import Vector, LanceModel\n",
+    "\n",
+    "class Content(LanceModel):\n",
+    "    movie_id: int\n",
+    "    vector: Vector(128)\n",
+    "    genres: str\n",
+    "    title: str\n",
+    "    imdb_id: int\n",
+    "        \n",
+    "    @property\n",
+    "    def imdb_url(self) -> str:\n",
+    "        return f\"https://www.imdb.com/title/tt{self.imdb_id}\"\n",
+    "\n",
+    "import pyarrow as pa\n",
+    "db = lancedb.connect(\"~/.lancedb\")\n",
+    "table_name = \"movielens_small\"\n",
+    "table = db.create_table(table_name, schema=Content)\n",
+    "table.schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "860e1f77-e860-46a9-98b7-b2979092ccd6",
+   "metadata": {},
+   "source": [
+    "### Using Iterators / Writing Large Datasets\n",
+    "\n",
+    "It is recommended to use itertators to add large datasets in batches when creating your table in one go. This does not create multiple versions of your dataset unlike manually adding batches using `table.add()`\n",
+    "\n",
+    "LanceDB additionally supports pyarrow's `RecordBatch` Iterators or other generators producing supported data types.\n",
+    "\n",
+    "## Here's an example using using `RecordBatch` iterator for creating tables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bc247142-4e3c-41a2-b94c-8e00d2c2a508",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanceTable(table4)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pyarrow as pa\n",
+    "\n",
+    "def make_batches():\n",
+    "    for i in range(5):\n",
+    "        yield pa.RecordBatch.from_arrays(\n",
+    "            [\n",
+    "                pa.array([[3.1, 4.1], [5.9, 26.5]],\n",
+    "                        pa.list_(pa.float32(), 2)),\n",
+    "                pa.array([\"foo\", \"bar\"]),\n",
+    "                pa.array([10.0, 20.0]),\n",
+    "            ],\n",
+    "            [\"vector\", \"item\", \"price\"],\n",
+    "        )\n",
+    "\n",
+    "schema = pa.schema([\n",
+    "    pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n",
+    "    pa.field(\"item\", pa.utf8()),\n",
+    "    pa.field(\"price\", pa.float32()),\n",
+    "])\n",
+    "\n",
+    "db.create_table(\"table4\", make_batches(), schema=schema)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94f7dd2b-bae4-4bdf-8534-201437c31027",
+   "metadata": {},
+   "source": [
+    "### Using pandas `DataFrame` Iterator and Pydantic Schema\n",
+    "\n",
+    "You can set the schema via pyarrow schema object or using Pydantic object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "25ad3523-e0c9-4c28-b3df-38189c4e0e5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vector: fixed_size_list<item: float>[2] not null\n",
+       "  child 0, item: float\n",
+       "item: string not null\n",
+       "price: double not null"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pyarrow as pa\n",
+    "import pandas as pd\n",
+    "\n",
+    "class PydanticSchema(LanceModel):\n",
+    "    vector: Vector(2)\n",
+    "    item: str\n",
+    "    price: float\n",
+    "\n",
+    "def make_batches():\n",
+    "    for i in range(5):\n",
+    "        yield pd.DataFrame(\n",
+    "                    {\n",
+    "                        \"vector\": [[3.1, 4.1], [1, 1]],\n",
+    "                        \"item\": [\"foo\", \"bar\"],\n",
+    "                        \"price\": [10.0, 20.0],\n",
+    "                    })\n",
+    "\n",
+    "tbl = db.create_table(\"table5\", make_batches(), schema=PydanticSchema)\n",
+    "tbl.schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4aa955e9-fcd0-4c99-b644-f218f3bb3f1a",
+   "metadata": {},
+   "source": [
+    "## Creating Empty Table\n",
+    "\n",
+    "You can create an empty table by just passing the schema and later add to it using `table.add()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2814173a-eacc-4dd8-a64d-6312b44582cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lancedb\n",
+    "from lancedb.pydantic import LanceModel, Vector\n",
+    "\n",
+    "class Model(LanceModel):\n",
+    "      vector: Vector(2)\n",
+    "\n",
+    "tbl = db.create_table(\"table6\", schema=Model.to_arrow_schema())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d1b0f5c-a1d9-459f-8614-8376b6f577e1",
+   "metadata": {},
+   "source": [
+    "## Open Existing Tables\n",
+    "\n",
+    "If you forget the name of your table, you can always get a listing of all table names:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "df9e13c0-41f6-437f-9dfa-2fd71d3d9c45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['table6', 'table4', 'table5', 'movielens_small']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db.table_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9343f5ad-6024-42ee-ac2f-6c1471df8679",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>vector</th>\n",
+       "      <th>item</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[3.1, 4.1]</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[5.9, 26.5]</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[3.1, 4.1]</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[5.9, 26.5]</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[3.1, 4.1]</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[5.9, 26.5]</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[3.1, 4.1]</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>[5.9, 26.5]</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>[3.1, 4.1]</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[5.9, 26.5]</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        vector item  price\n",
+       "0   [3.1, 4.1]  foo   10.0\n",
+       "1  [5.9, 26.5]  bar   20.0\n",
+       "2   [3.1, 4.1]  foo   10.0\n",
+       "3  [5.9, 26.5]  bar   20.0\n",
+       "4   [3.1, 4.1]  foo   10.0\n",
+       "5  [5.9, 26.5]  bar   20.0\n",
+       "6   [3.1, 4.1]  foo   10.0\n",
+       "7  [5.9, 26.5]  bar   20.0\n",
+       "8   [3.1, 4.1]  foo   10.0\n",
+       "9  [5.9, 26.5]  bar   20.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tbl = db.open_table(\"table4\")\n",
+    "tbl.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5019246f-12e3-4f78-88a8-9f4939802c76",
+   "metadata": {},
+   "source": [
+    "## Adding to table\n",
+    "After a table has been created, you can always add more data to it using\n",
+    "\n",
+    "You can add any of the valid data structures accepted by LanceDB table, i.e, `dict`, `list[dict]`, `pd.DataFrame`, or a `Iterator[pa.RecordBatch]`. Here are some examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8a56250f-73a1-4c26-a6ad-5c7a0ce3a9ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame([{\"vector\": [1.3, 1.4], \"item\": \"fizz\", \"price\": 100.0},\n",
+    "              {\"vector\": [9.5, 56.2], \"item\": \"buzz\", \"price\": 200.0}])\n",
+    "tbl.add(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9985f6ee-67e1-45a9-b233-94e3d121ecbf",
+   "metadata": {},
+   "source": [
+    "You can also add a large dataset batch in one go using Iterator of supported data types\n",
+    "\n",
+    "### Adding via Iterator\n",
+    "\n",
+    "here, we'll use pandas DataFrame Iterator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "030c7057-b98e-4e2f-be14-b8c1f927f83c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "def make_batches():\n",
+    "    for i in range(5):\n",
+    "        yield pd.DataFrame(\n",
+    "                {\n",
+    "                    \"vector\": [[3.1, 4.1], [1, 1]],\n",
+    "                    \"item\": [\"foo\", \"bar\"],\n",
+    "                    \"price\": [10.0, 20.0],\n",
+    "                })\n",
+    "tbl.add(make_batches())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8316d5d-0a23-4675-b0ee-178711db873a",
+   "metadata": {},
+   "source": [
+    "## Deleting from a Table\n",
+    "\n",
+    "Use the `delete()` method on tables to delete rows from a table. To choose which rows to delete, provide a filter that matches on the metadata columns. This can delete any number of rows that match the filter, like:\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "tbl.delete('item = \"fizz\"')\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e7a17de2-08d2-41b7-bd05-f63d1045ab1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "32\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(len(tbl))\n",
+    "      \n",
+    "tbl.delete(\"price = 20.0\")\n",
+    "      \n",
+    "len(tbl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74ac180b-5432-4c14-b1a8-22c35ac83af8",
+   "metadata": {},
+   "source": [
+    "### Delete from a list of values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "fe3310bd-08f4-4a22-a63b-b3127d22f9f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         vector  item  price\n",
+      "0    [3.1, 4.1]   foo   10.0\n",
+      "1    [3.1, 4.1]   foo   10.0\n",
+      "2    [3.1, 4.1]   foo   10.0\n",
+      "3    [3.1, 4.1]   foo   10.0\n",
+      "4    [3.1, 4.1]   foo   10.0\n",
+      "5    [1.3, 1.4]  fizz  100.0\n",
+      "6   [9.5, 56.2]  buzz  200.0\n",
+      "7    [3.1, 4.1]   foo   10.0\n",
+      "8    [3.1, 4.1]   foo   10.0\n",
+      "9    [3.1, 4.1]   foo   10.0\n",
+      "10   [3.1, 4.1]   foo   10.0\n",
+      "11   [3.1, 4.1]   foo   10.0\n",
+      "12   [3.1, 4.1]   foo   10.0\n",
+      "13   [3.1, 4.1]   foo   10.0\n",
+      "14   [3.1, 4.1]   foo   10.0\n",
+      "15   [3.1, 4.1]   foo   10.0\n",
+      "16   [3.1, 4.1]   foo   10.0\n"
+     ]
+    },
+    {
+     "ename": "OSError",
+     "evalue": "LanceError(IO): Error during planning: column foo does not exist",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[30], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m to_remove \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mstr\u001b[39m(v) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m to_remove)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(tbl\u001b[38;5;241m.\u001b[39mto_pandas())\n\u001b[0;32m----> 4\u001b[0m \u001b[43mtbl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mitem IN (\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mto_remove\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m tbl\u001b[38;5;241m.\u001b[39mto_pandas()\n",
+      "File \u001b[0;32m~/Documents/lancedb/lancedb/python/lancedb/table.py:610\u001b[0m, in \u001b[0;36mLanceTable.delete\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdelete\u001b[39m(\u001b[38;5;28mself\u001b[39m, where: \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m--> 610\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/lancedb/lancedb/env/lib/python3.11/site-packages/lance/dataset.py:489\u001b[0m, in \u001b[0;36mLanceDataset.delete\u001b[0;34m(self, predicate)\u001b[0m\n\u001b[1;32m    487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(predicate, pa\u001b[38;5;241m.\u001b[39mcompute\u001b[38;5;241m.\u001b[39mExpression):\n\u001b[1;32m    488\u001b[0m     predicate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(predicate)\n\u001b[0;32m--> 489\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredicate\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mOSError\u001b[0m: LanceError(IO): Error during planning: column foo does not exist"
+     ]
+    }
+   ],
+   "source": [
+    "to_remove = [\"foo\", \"buzz\"]\n",
+    "to_remove = \", \".join(str(v) for v in to_remove)\n",
+    "print(tbl.to_pandas())\n",
+    "tbl.delete(f\"item IN ({to_remove})\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "87d5bc21-847f-4c81-b56e-f6dbe5d05aac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(\n",
+    "                    {\n",
+    "                        \"vector\": [[3.1, 4.1], [1, 1]],\n",
+    "                        \"item\": [\"foo\", \"bar\"],\n",
+    "                        \"price\": [10.0, 20.0],\n",
+    "                    })\n",
+    "\n",
+    "tbl = db.create_table(\"table7\", data=df, mode=\"overwrite\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "9cba4519-eb3a-4941-ab7e-873d762e750f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "to_remove = [10.0, 20.0]\n",
+    "to_remove = \", \".join(str(v) for v in to_remove)\n",
+    "\n",
+    "tbl.delete(f\"price IN ({to_remove})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "5bdc9801-d5ed-4871-92d0-88b27108e788",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>vector</th>\n",
+       "      <th>item</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [vector, item, price]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tbl.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "752d33d4-ce1c-48e5-90d2-c85f0982182d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/src/notebooks/youtube_transcript_search.ipynb

@@ -8,7 +8,12 @@
    "source": [
     "# Youtube Transcript Search QA Bot\n",
     "\n",
-    "This Q&A bot will allow you to search through youtube transcripts using natural language! By going through this notebook, we'll introduce how you can use LanceDB to store and manage your data easily."
+    "This Q&A bot will allow you to search through youtube transcripts using natural language! By going through this notebook, we'll introduce how you can use LanceDB to store and manage your data easily.\n",
+    "\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/youtube_bot/main.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\">\n",
+    "\n",
+    "Scripts - [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/youtube_bot/main.py)  [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/youtube_bot/index.js)\n"
    ]
   },
   {
@@ -22,11 +27,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.1.1\u001B[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n",
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.1.1\u001B[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n"
      ]
     }
    ],
@@ -179,7 +184,7 @@
     "df = (contextualize(data.to_pandas())\n",
     "      .groupby(\"title\").text_col(\"text\")\n",
     "      .window(20).stride(4)\n",
-    "      .to_df())\n",
+    "      .to_pandas())\n",
     "df.head(1)"
    ]
   },
@@ -598,7 +603,7 @@
    "outputs": [],
    "source": [
     "# Use LanceDB to get top 3 most relevant context\n",
-    "context = tbl.search(emb).limit(3).to_df()"
+    "context = tbl.search(emb).limit(3).to_pandas()"
    ]
   },
   {

docs/src/python/arrow.md

@@ -0,0 +1,101 @@
+# Pandas and PyArrow
+
+
+Built on top of [Apache Arrow](https://arrow.apache.org/),
+`LanceDB` is easy to integrate with the Python ecosystem, including [Pandas](https://pandas.pydata.org/)
+and PyArrow.
+
+## Create dataset
+
+First, we need to connect to a `LanceDB` database.
+
+```py
+
+import lancedb
+
+db = lancedb.connect("data/sample-lancedb")
+```
+
+Afterwards, we write a `Pandas DataFrame` to LanceDB directly.
+
+```py
+import pandas as pd
+
+data = pd.DataFrame({
+    "vector": [[3.1, 4.1], [5.9, 26.5]],
+    "item": ["foo", "bar"],
+    "price": [10.0, 20.0]
+})
+table = db.create_table("pd_table", data=data)
+```
+
+Similar to [`pyarrow.write_dataset()`](https://arrow.apache.org/docs/python/generated/pyarrow.dataset.write_dataset.html),
+[db.create_table()](../python/#lancedb.db.DBConnection.create_table) accepts a wide-range of forms of data.
+
+For example, if you have a dataset that is larger than memory size, you can create table with `Iterator[pyarrow.RecordBatch]`,
+to lazily generate data:
+
+```py
+
+from typing import Iterable
+import pyarrow as pa
+import lancedb
+
+def make_batches() -> Iterable[pa.RecordBatch]:
+    for i in range(5):
+        yield pa.RecordBatch.from_arrays(
+            [
+                pa.array([[3.1, 4.1], [5.9, 26.5]]),
+                pa.array(["foo", "bar"]),
+                pa.array([10.0, 20.0]),
+            ],
+            ["vector", "item", "price"])
+
+schema=pa.schema([
+    pa.field("vector", pa.list_(pa.float32())),
+    pa.field("item", pa.utf8()),
+    pa.field("price", pa.float32()),
+])
+
+table = db.create_table("iterable_table", data=make_batches(), schema=schema)
+```
+
+You will find detailed instructions of creating dataset in
+[Basic Operations](../basic.md) and [API](../python/#lancedb.db.DBConnection.create_table)
+sections.
+
+## Vector Search
+
+We can now perform similarity search via `LanceDB` Python API.
+
+```py
+# Open the table previously created.
+table = db.open_table("pd_table")
+
+query_vector = [100, 100]
+# Pandas DataFrame
+df = table.search(query_vector).limit(1).to_pandas()
+print(df)
+```
+
+```
+    vector     item  price    _distance
+0  [5.9, 26.5]  bar   20.0  14257.05957
+```
+
+If you have a simple filter, it's faster to provide a `where clause` to `LanceDB`'s search query.
+If you have more complex criteria, you can always apply the filter to the resulting Pandas `DataFrame`.
+
+```python
+
+# Apply the filter via LanceDB
+results = table.search([100, 100]).where("price < 15").to_pandas()
+assert len(results) == 1
+assert results["item"].iloc[0] == "foo"
+
+# Apply the filter via Pandas
+df = results = table.search([100, 100]).to_pandas()
+results = df[df.price < 15]
+assert len(results) == 1
+assert results["item"].iloc[0] == "foo"
+```

docs/src/python/duckdb.md

@@ -0,0 +1,56 @@
+# DuckDB
+
+`LanceDB` works with `DuckDB` via [PyArrow integration](https://duckdb.org/docs/guides/python/sql_on_arrow).
+
+Let us start with installing `duckdb` and `lancedb`.
+
+```shell
+pip install duckdb lancedb
+```
+
+We will re-use [the dataset created previously](./arrow.md):
+
+```python
+import pandas as pd
+import lancedb
+
+db = lancedb.connect("data/sample-lancedb")
+data = pd.DataFrame({
+    "vector": [[3.1, 4.1], [5.9, 26.5]],
+    "item": ["foo", "bar"],
+    "price": [10.0, 20.0]
+})
+table = db.create_table("pd_table", data=data)
+arrow_table = table.to_arrow()
+```
+
+`DuckDB` can directly query the `arrow_table`:
+
+```python
+import duckdb
+
+duckdb.query("SELECT * FROM arrow_table")
+```
+
+```
+┌─────────────┬─────────┬────────┐
+│   vector    │  item   │ price  │
+│   float[]   │ varchar │ double │
+├─────────────┼─────────┼────────┤
+│ [3.1, 4.1]  │ foo     │   10.0 │
+│ [5.9, 26.5] │ bar     │   20.0 │
+└─────────────┴─────────┴────────┘
+```
+
+```py
+duckdb.query("SELECT mean(price) FROM arrow_table")
+```
+
+```
+┌─────────────┐
+│ mean(price) │
+│   double    │
+├─────────────┤
+│        15.0 │
+└─────────────┘
+```

docs/src/python/integration.md

@@ -0,0 +1,7 @@
+# Integration
+
+Built on top of [Apache Arrow](https://arrow.apache.org/),
+`LanceDB` is very easy to be integrate with Python ecosystems.
+
+* [Pandas and Arrow Integration](./arrow.md)
+* [DuckDB Integration](./duckdb.md)

docs/src/python/pydantic.md

@@ -0,0 +1,36 @@
+# Pydantic
+
+[Pydantic](https://docs.pydantic.dev/latest/) is a data validation library in Python.
+LanceDB integrates with Pydantic for schema inference, data ingestion, and query result casting.
+
+## Schema
+
+LanceDB supports to create Apache Arrow Schema from a
+[Pydantic BaseModel](https://docs.pydantic.dev/latest/api/main/#pydantic.main.BaseModel)
+via [pydantic_to_schema()](python.md##lancedb.pydantic.pydantic_to_schema) method.
+
+::: lancedb.pydantic.pydantic_to_schema
+
+## Vector Field
+
+LanceDB provides a [`Vector(dim)`](python.md#lancedb.pydantic.Vector) method to define a
+vector Field in a Pydantic Model.
+
+::: lancedb.pydantic.Vector
+
+## Type Conversion
+
+LanceDB automatically convert Pydantic fields to
+[Apache Arrow DataType](https://arrow.apache.org/docs/python/generated/pyarrow.DataType.html#pyarrow.DataType).
+
+Current supported type conversions:
+
+| Pydantic Field Type | PyArrow Data Type |
+| ------------------- | ----------------- |
+| `int`               | `pyarrow.int64`   |
+| `float`              | `pyarrow.float64`  |
+| `bool`              | `pyarrow.bool`    |
+| `str`               | `pyarrow.utf8()`    |
+| `list`              | `pyarrow.List`    |
+| `BaseModel`         | `pyarrow.Struct`    |
+| `Vector(n)`         | `pyarrow.FixedSizeList(float32, n)` |

docs/src/python/python.md

@@ -10,23 +10,35 @@ pip install lancedb
 
 ::: lancedb.connect
 
-::: lancedb.LanceDBConnection
+::: lancedb.db.DBConnection
 
 ## Table
 
-::: lancedb.table.LanceTable
+::: lancedb.table.Table
 
 ## Querying
 
+::: lancedb.query.Query
+
 ::: lancedb.query.LanceQueryBuilder
 
 ::: lancedb.query.LanceFtsQueryBuilder
 
 ## Embeddings
 
-::: lancedb.embeddings.with_embeddings
+::: lancedb.embeddings.functions.EmbeddingFunctionRegistry
 
-::: lancedb.embeddings.EmbeddingFunction
+::: lancedb.embeddings.functions.EmbeddingFunction
+
+::: lancedb.embeddings.functions.TextEmbeddingFunction
+
+::: lancedb.embeddings.functions.SentenceTransformerEmbeddings
+
+::: lancedb.embeddings.functions.OpenAIEmbeddings
+
+::: lancedb.embeddings.functions.OpenClipEmbeddings
+
+::: lancedb.embeddings.with_embeddings
 
 ## Context
 
@@ -41,3 +53,17 @@ pip install lancedb
 ::: lancedb.fts.populate_index
 
 ::: lancedb.fts.search_index
+
+## Utilities
+
+::: lancedb.vector
+
+## Integrations
+
+### Pydantic
+
+::: lancedb.pydantic.pydantic_to_schema
+
+::: lancedb.pydantic.vector
+
+::: lancedb.pydantic.LanceModel

docs/src/search.md

@@ -25,9 +25,9 @@ Currently, we support the following metrics:
 
 ### Flat Search
 
+If you do not create a vector index, LanceDB would need to exhaustively scan the entire vector column (via `Flat Search`)
+and compute the distance for *every* vector in order to find the closest matches. This is effectively a KNN search.
 
-If there is no [vector index is created](ann_indexes.md), LanceDB will just brute-force scan
-the vector column and compute the distance.
 
 <!-- Setup Code
 ```python
@@ -67,7 +67,7 @@ await db_setup.createTable('my_vectors', data)
 
     df = tbl.search(np.random.random((1536))) \
         .limit(10) \
-        .to_df()
+        .to_list()
     ```
 
 === "JavaScript"
@@ -79,39 +79,43 @@ await db_setup.createTable('my_vectors', data)
     const tbl = await db.openTable("my_vectors")
 
     const results_1 = await tbl.search(Array(1536).fill(1.2))
-        .limit(20)
+        .limit(10)
         .execute()
     ```
 
-
-<!-- Commenting out for now since metricType fails for JS on Ubuntu 22.04.
-
 By default, `l2` will be used as `Metric` type. You can customize the metric type
 as well.
--->
 
-<!--
 === "Python"
--->
-<!--    ```python
+
+    ```python
     df = tbl.search(np.random.random((1536))) \
         .metric("cosine") \
         .limit(10) \
-        .to_df()
+        .to_list()
     ```
--->
-<!--
-=== "JavaScript"
--->
 
-<!--   ```javascript
+
+=== "JavaScript"
+
+    ```javascript
     const results_2 = await tbl.search(Array(1536).fill(1.2))
         .metricType("cosine")
-        .limit(20)
+        .limit(10)
         .execute()
     ```
--->
 
-### Search with Vector Index.
+
+### Approximate Nearest Neighbor (ANN) Search with Vector Index.
+
+To accelerate vector retrievals, it is common to build vector indices.
+A vector index is a data structure specifically designed to efficiently organize and
+search vector data based on their similarity via the chosen distance metric.
+By constructing a vector index, you can reduce the search space and avoid the need
+for brute-force scanning of the entire vector column.
+
+However, fast vector search using indices often entails making a trade-off with accuracy to some extent.
+This is why it is often called **Approximate Nearest Neighbors (ANN)** search, while the Flat Search (KNN)
+always returns 100% recall.
 
 See [ANN Index](ann_indexes.md) for more details.

docs/src/styles/global.css

@@ -3,4 +3,13 @@
     --md-primary-fg-color--dark:  #4338ca;
     --md-text-font: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
     --md-code-font: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
-}
+}
+
+.md-nav__item, .md-tabs__item {
+    font-size: large;
+}
+
+/* Maximum space for text block */
+.md-grid {
+    max-width: 90%;
+  }

docs/test/md_testing.js

@@ -2,17 +2,17 @@ const glob = require("glob");
 const fs = require("fs");
 const path = require("path");
 
-const excludedFiles = [
+const globString = "../src/**/*.md";
+const excludedGlobs = [
   "../src/fts.md",
   "../src/embedding.md",
-  "../src/examples/serverless_lancedb_with_s3_and_lambda.md",
-  "../src/examples/serverless_qa_bot_with_modal_and_langchain.md",
-  "../src/examples/youtube_transcript_bot_with_nodejs.md",
+  "../src/examples/*.md",
+  "../src/guides/tables.md",
 ];
+
 const nodePrefix = "javascript";
 const nodeFile = ".js";
 const nodeFolder = "node";
-const globString = "../src/**/*.md";
 const asyncPrefix = "(async () => {\n";
 const asyncSuffix = "})();";
 
@@ -31,6 +31,7 @@ function* yieldLines(lines, prefix, suffix) {
 }
 
 const files = glob.sync(globString, { recursive: true });
+const excludedFiles = glob.sync(excludedGlobs, { recursive: true });
 
 for (const file of files.filter((file) => !excludedFiles.includes(file))) {
   const lines = [];

docs/test/md_testing.py

@@ -2,18 +2,22 @@ import glob
 from typing import Iterator
 from pathlib import Path
 
-excluded_files = [
+glob_string = "../src/**/*.md"
+excluded_globs = [
     "../src/fts.md",
     "../src/embedding.md",
-    "../src/examples/serverless_lancedb_with_s3_and_lambda.md",
-    "../src/examples/serverless_qa_bot_with_modal_and_langchain.md",
-    "../src/examples/youtube_transcript_bot_with_nodejs.md"
+    "../src/examples/*.md",
+    "../src/integrations/voxel51.md",
+    "../src/guides/tables.md",
+    "../src/python/duckdb.md",
 ]
 
 python_prefix = "py"
 python_file = ".py"
 python_folder = "python"
-glob_string = "../src/**/*.md"
+
+files = glob.glob(glob_string, recursive=True)
+excluded_files = [f for excluded_glob in excluded_globs for f in glob.glob(excluded_glob, recursive=True)]
 
 def yield_lines(lines: Iterator[str], prefix: str, suffix: str):
     in_code_block = False
@@ -29,7 +33,7 @@ def yield_lines(lines: Iterator[str], prefix: str, suffix: str):
         elif in_code_block:
             yield line[strip_length:]
 
-for file in filter(lambda file: file not in excluded_files, glob.glob(glob_string, recursive=True)):
+for file in filter(lambda file: file not in excluded_files, files):
     with open(file, "r") as f:
         lines = list(yield_lines(iter(f), "```", "```"))
 
@@ -38,4 +42,4 @@ for file in filter(lambda file: file not in excluded_files, glob.glob(glob_strin
         print(out_path)
         out_path.parent.mkdir(exist_ok=True, parents=True)
         with open(out_path, "w") as out:
-            out.writelines(lines)
+            out.writelines(lines)

docs/test/requirements.txt

@@ -1,5 +1,8 @@
-lancedb @ git+https://github.com/lancedb/lancedb.git#egg=subdir&subdirectory=python
+-e ../../python
 numpy
 pandas
 pylance
-duckdb
+duckdb
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch
+

node/.npmignore

@@ -0,0 +1,4 @@
+gen_test_data.py
+index.node
+dist/lancedb*.tgz
+vectordb*.tgz

node/README.md

@@ -8,6 +8,10 @@ A JavaScript / Node.js library for [LanceDB](https://github.com/lancedb/lancedb)
 npm install vectordb
 ```
 
+This will download the appropriate native library for your platform. We currently
+support x86_64 Linux, aarch64 Linux, Intel MacOS, and ARM (M1/M2) MacOS. We do not
+yet support Windows or musl-based Linux (such as Alpine Linux).
+
 ## Usage
 
 ### Basic Example
@@ -26,12 +30,34 @@ The [examples](./examples) folder contains complete examples.
 
 ## Development
 
-Run the tests with
+To build everything fresh:
+
+```bash
+npm install
+npm run tsc
+npm run build
+```
+
+Then you should be able to run the tests with:
 
 ```bash
 npm test
 ```
 
+### Rebuilding Rust library
+
+```bash
+npm run build
+```
+
+### Rebuilding Typescript
+
+```bash
+npm run tsc
+```
+
+### Fix lints
+
 To run the linter and have it automatically fix all errors
 
 ```bash

node/examples/js-transformers/index.js

@@ -0,0 +1,66 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+'use strict'
+
+
+async function example() {
+
+    const lancedb = require('vectordb')
+
+    // Import transformers and the all-MiniLM-L6-v2 model (https://huggingface.co/Xenova/all-MiniLM-L6-v2)
+    const { pipeline } = await import('@xenova/transformers')
+    const pipe = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+
+
+    // Create embedding function from pipeline which returns a list of vectors from batch
+    // sourceColumn is the name of the column in the data to be embedded
+    //
+    // Output of pipe is a Tensor { data: Float32Array(384) }, so filter for the vector
+    const embed_fun = {}
+    embed_fun.sourceColumn = 'text'
+    embed_fun.embed = async function (batch) {
+        let result = []
+        for (let text of batch) {
+            const res = await pipe(text, { pooling: 'mean', normalize: true })
+            result.push(Array.from(res['data']))
+        }
+        return (result)
+    }
+
+    // Link a folder and create a table with data
+    const db = await lancedb.connect('data/sample-lancedb')
+
+    const data = [
+        { id: 1, text: 'Cherry', type: 'fruit' },
+        { id: 2, text: 'Carrot', type: 'vegetable' },
+        { id: 3, text: 'Potato', type: 'vegetable' },
+        { id: 4, text: 'Apple', type: 'fruit' },
+        { id: 5, text: 'Banana', type: 'fruit' }
+    ]
+
+    const table = await db.createTable('food_table', data, embed_fun)
+
+
+    // Query the table
+    const results = await table
+        .search("a sweet fruit to eat")
+        .metricType("cosine")
+        .limit(2)
+        .execute()
+    console.log(results.map(r => r.text))
+
+}
+
+example().then(_ => { console.log("Done!") })

node/examples/js-transformers/package.json

@@ -0,0 +1,16 @@
+{
+  "name": "vectordb-example-js-transformers",
+  "version": "1.0.0",
+  "description": "Example for using transformers.js with lancedb",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "author": "Lance Devs",
+  "license": "Apache-2.0",
+  "dependencies": {
+    "@xenova/transformers": "^2.4.1",
+    "vectordb": "file:../.."
+  }
+
+}

node/native.js

@@ -12,29 +12,25 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-let nativeLib;
+const { currentTarget } = require('@neon-rs/load')
 
-function getPlatformLibrary() {
-    if (process.platform === "darwin" && process.arch == "arm64") {
-        return require('./aarch64-apple-darwin.node');
-    } else if (process.platform === "darwin" && process.arch == "x64") {
-        return require('./x86_64-apple-darwin.node');
-    } else if (process.platform === "linux" && process.arch == "x64") {
-        return require('./x86_64-unknown-linux-gnu.node');
-    } else {
-        throw new Error(`vectordb: unsupported platform ${process.platform}_${process.arch}. Please file a bug report at https://github.com/lancedb/lancedb/issues`)
-    }
-}
+let nativeLib
 
 try {
-    nativeLib = require('./index.node')
-} catch (e) {
-    if (e.code === "MODULE_NOT_FOUND") {
-        nativeLib = getPlatformLibrary();
-    } else {
-        throw new Error('vectordb: failed to load native library. Please file a bug report at https://github.com/lancedb/lancedb/issues');
-    }
+  // When developing locally, give preference to the local built library
+  nativeLib = require('./index.node')
+} catch {
+  try {
+    nativeLib = require(`@lancedb/vectordb-${currentTarget()}`)
+  } catch (e) {
+    throw new Error(`vectordb: failed to load native library.
+  You may need to run \`npm install @lancedb/vectordb-${currentTarget()}\`.
+
+  If that does not work, please file a bug report at https://github.com/lancedb/lancedb/issues
+      
+  Source error: ${e}`)
+  }
 }
 
+// Dynamic require for runtime.
 module.exports = nativeLib
-

node/package-lock.json

@@ -1,24 +1,37 @@
 {
   "name": "vectordb",
-  "version": "0.1.9",
+  "version": "0.3.0",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.1.9",
+      "version": "0.3.0",
+      "cpu": [
+        "x64",
+        "arm64"
+      ],
       "license": "Apache-2.0",
+      "os": [
+        "darwin",
+        "linux",
+        "win32"
+      ],
       "dependencies": {
         "@apache-arrow/ts": "^12.0.0",
-        "apache-arrow": "^12.0.0"
+        "@neon-rs/load": "^0.0.74",
+        "apache-arrow": "^12.0.0",
+        "axios": "^1.4.0"
       },
       "devDependencies": {
+        "@neon-rs/cli": "^0.0.160",
         "@types/chai": "^4.3.4",
         "@types/chai-as-promised": "^7.1.5",
         "@types/mocha": "^10.0.1",
         "@types/node": "^18.16.2",
         "@types/sinon": "^10.0.15",
         "@types/temp": "^0.9.1",
+        "@types/uuid": "^9.0.3",
         "@typescript-eslint/eslint-plugin": "^5.59.1",
         "cargo-cp-artifact": "^0.1",
         "chai": "^4.3.7",
@@ -36,7 +49,15 @@
         "ts-node-dev": "^2.0.0",
         "typedoc": "^0.24.7",
         "typedoc-plugin-markdown": "^3.15.3",
-        "typescript": "*"
+        "typescript": "*",
+        "uuid": "^9.0.0"
+      },
+      "optionalDependencies": {
+        "@lancedb/vectordb-darwin-arm64": "0.3.0",
+        "@lancedb/vectordb-darwin-x64": "0.3.0",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.3.0",
+        "@lancedb/vectordb-linux-x64-gnu": "0.3.0",
+        "@lancedb/vectordb-win32-x64-msvc": "0.3.0"
       }
     },
     "node_modules/@apache-arrow/ts": {
@@ -66,6 +87,97 @@
       "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.5.0.tgz",
       "integrity": "sha512-336iVw3rtn2BUK7ORdIAHTyxHGRIHVReokCR3XjbckJMK7ms8FysBfhLR8IXnAgy7T0PTPNBWKiH514FOW/WSg=="
     },
+    "node_modules/@cargo-messages/android-arm-eabi": {
+      "version": "0.0.160",
+      "resolved": "https://registry.npmjs.org/@cargo-messages/android-arm-eabi/-/android-arm-eabi-0.0.160.tgz",
+      "integrity": "sha512-PTgCEmBHEPKJbxwlHVXB3aGES+NqpeBvn6hJNYWIkET3ZQCSJnScMlIDQXEkWndK7J+hW3Or3H32a93B/MbbfQ==",
+      "cpu": [
+        "arm"
+      ],
+      "dev": true,
+      "optional": true,
+      "os": [
+        "android"
+      ]
+    },
+    "node_modules/@cargo-messages/darwin-arm64": {
+      "version": "0.0.160",
+      "resolved": "https://registry.npmjs.org/@cargo-messages/darwin-arm64/-/darwin-arm64-0.0.160.tgz",
+      "integrity": "sha512-YSVUuc8TUTi/XmZVg9KrH0bDywKLqC1zeTyZYAYDDmqVDZW9KeTnbBUECKRs56iyHeO+kuEkVW7MKf7j2zb/FA==",
+      "cpu": [
+        "arm64"
+      ],
+      "dev": true,
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@cargo-messages/darwin-x64": {
+      "version": "0.0.160",
+      "resolved": "https://registry.npmjs.org/@cargo-messages/darwin-x64/-/darwin-x64-0.0.160.tgz",
+      "integrity": "sha512-U+YlAR+9tKpBljnNPWMop5YhvtwfIPQSAaUYN2llteC7ZNU5/cv8CGT1vm7uFNxr2LeGuAtRbzIh2gUmTV8mng==",
+      "cpu": [
+        "x64"
+      ],
+      "dev": true,
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@cargo-messages/linux-arm-gnueabihf": {
+      "version": "0.0.160",
+      "resolved": "https://registry.npmjs.org/@cargo-messages/linux-arm-gnueabihf/-/linux-arm-gnueabihf-0.0.160.tgz",
+      "integrity": "sha512-wqAelTzVv1E7Ls4aviqUbem5xjzCaJQxQtVnLhv6pf1k0UyEHCS2WdufFFmWcojGe7QglI4uve3KTe01MKYj0A==",
+      "cpu": [
+        "arm"
+      ],
+      "dev": true,
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@cargo-messages/linux-x64-gnu": {
+      "version": "0.0.160",
+      "resolved": "https://registry.npmjs.org/@cargo-messages/linux-x64-gnu/-/linux-x64-gnu-0.0.160.tgz",
+      "integrity": "sha512-LQ6e7O7YYkWfDNIi/53q2QG/+lZok72LOG+NKDVCrrY4TYUcrTqWAybOV6IlkVntKPnpx8YB95umSQGeVuvhpQ==",
+      "cpu": [
+        "x64"
+      ],
+      "dev": true,
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@cargo-messages/win32-arm64-msvc": {
+      "version": "0.0.160",
+      "resolved": "https://registry.npmjs.org/@cargo-messages/win32-arm64-msvc/-/win32-arm64-msvc-0.0.160.tgz",
+      "integrity": "sha512-VDMBhyun02gIDwmEhkYP1W9Z0tYqn4drgY5Iua1qV2tYOU58RVkWhzUYxM9rzYbnwKZlltgM46J/j5QZ3VaFrA==",
+      "cpu": [
+        "arm64"
+      ],
+      "dev": true,
+      "optional": true,
+      "os": [
+        "win32"
+      ]
+    },
+    "node_modules/@cargo-messages/win32-x64-msvc": {
+      "version": "0.0.160",
+      "resolved": "https://registry.npmjs.org/@cargo-messages/win32-x64-msvc/-/win32-x64-msvc-0.0.160.tgz",
+      "integrity": "sha512-vnoglDxF6zj0W/Co9D0H/bgnrhUuO5EumIf9v3ujLtBH94rAX11JsXh/FgC/8wQnQSsLyWSq70YxNS2wdETxjA==",
+      "cpu": [
+        "x64"
+      ],
+      "dev": true,
+      "optional": true,
+      "os": [
+        "win32"
+      ]
+    },
     "node_modules/@cspotcode/source-map-support": {
       "version": "0.8.1",
       "resolved": "https://registry.npmjs.org/@cspotcode/source-map-support/-/source-map-support-0.8.1.tgz",
@@ -204,6 +316,89 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
+    "node_modules/@lancedb/vectordb-darwin-arm64": {
+      "version": "0.3.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.3.0.tgz",
+      "integrity": "sha512-Fg+k/cSnqmNQlSWyDp0PpaAJ67kAISfZAD+zZ3mcE8/3ml2I/wM/GVjPy2zeiQX9aR93lG1mZXFSNTDUc74tWQ==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-darwin-x64": {
+      "version": "0.3.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.3.0.tgz",
+      "integrity": "sha512-CXp4b/brMbnBPZuGzKIOskd9uD90R73rWubaJ0du/Kt6fcyQX1dM1wEhWTLxI6eKf8IDL/R9QLL2cIahm1J86w==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
+      "version": "0.3.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.3.0.tgz",
+      "integrity": "sha512-1bjaRzYcDsWIRUbO2K/f+ohNmNvCgKcrrOhmiXSHVlYY8kH1LUMFZj+BhqBC0Ea0Stt7/1rsRLMRXRtaeVOEHw==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
+      "version": "0.3.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.3.0.tgz",
+      "integrity": "sha512-BEDIJ6ReGAi+tLTS/RzxIw621yo1UUUiVNTzPGV2didyiJCr1chIGbES+39d/wiFQM43Xs3CBZLNzp+jKkv0/w==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
+      "version": "0.3.0",
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         "asynckit": "^0.4.0",
         "combined-stream": "^1.0.8",
@@ -6619,14 +6938,12 @@
     "mime-db": {
       "version": "1.52.0",
       "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz",
-      "integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==",
-      "dev": true
+      "integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg=="
     },
     "mime-types": {
       "version": "2.1.35",
       "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz",
       "integrity": "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==",
-      "dev": true,
       "requires": {
         "mime-db": "1.52.0"
       }
@@ -6852,6 +7169,17 @@
       "requires": {
         "axios": "^0.26.0",
         "form-data": "^4.0.0"
+      },
+      "dependencies": {
+        "axios": {
+          "version": "0.26.1",
+          "resolved": "https://registry.npmjs.org/axios/-/axios-0.26.1.tgz",
+          "integrity": "sha512-fPwcX4EvnSHuInCMItEhAGnaSEXRBjtzh9fOtsE6E1G6p7vl7edEeZe11QHf18+6+9gR5PbKV/sGKNaD8YaMeA==",
+          "dev": true,
+          "requires": {
+            "follow-redirects": "^1.14.8"
+          }
+        }
       }
     },
     "optionator": {
@@ -6960,6 +7288,11 @@
       "integrity": "sha512-vkcDPrRZo1QZLbn5RLGPpg/WmIQ65qoWWhcGKf/b5eplkkarX0m9z8ppCat4mlOqUsWpyNuYgO3VRyrYHSzX5g==",
       "dev": true
     },
+    "proxy-from-env": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/proxy-from-env/-/proxy-from-env-1.1.0.tgz",
+      "integrity": "sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg=="
+    },
     "punycode": {
       "version": "2.3.0",
       "resolved": "https://registry.npmjs.org/punycode/-/punycode-2.3.0.tgz",
@@ -7534,6 +7867,12 @@
         "punycode": "^2.1.0"
       }
     },
+    "uuid": {
+      "version": "9.0.0",
+      "resolved": "https://registry.npmjs.org/uuid/-/uuid-9.0.0.tgz",
+      "integrity": "sha512-MXcSTerfPa4uqyzStbRoTgt5XIe3x5+42+q1sDuy3R5MDk66URdLMOZe5aPX/SQd+kuYAh0FdP/pO28IkQyTeg==",
+      "dev": true
+    },
     "v8-compile-cache-lib": {
       "version": "3.0.1",
       "resolved": "https://registry.npmjs.org/v8-compile-cache-lib/-/v8-compile-cache-lib-3.0.1.tgz",

node/package.json

@@ -1,16 +1,19 @@
 {
   "name": "vectordb",
-  "version": "0.1.10",
+  "version": "0.3.0",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
   "scripts": {
     "tsc": "tsc -b",
-    "build": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json-render-diagnostics",
+    "build": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json",
     "build-release": "npm run build -- --release",
-    "test": "npm run tsc; mocha -recursive dist/test",
-    "lint": "eslint src --ext .js,.ts",
-    "clean": "rm -rf node_modules *.node dist/"
+    "test": "npm run tsc && mocha -recursive dist/test",
+    "integration-test": "npm run tsc && mocha -recursive dist/integration_test",
+    "lint": "eslint native.js src --ext .js,.ts",
+    "clean": "rm -rf node_modules *.node dist/",
+    "pack-build": "neon pack-build",
+    "check-npm": "printenv && which node && which npm && npm --version"
   },
   "repository": {
     "type": "git",
@@ -25,12 +28,14 @@
   "author": "Lance Devs",
   "license": "Apache-2.0",
   "devDependencies": {
+    "@neon-rs/cli": "^0.0.160",
     "@types/chai": "^4.3.4",
     "@types/chai-as-promised": "^7.1.5",
     "@types/mocha": "^10.0.1",
     "@types/node": "^18.16.2",
     "@types/sinon": "^10.0.15",
     "@types/temp": "^0.9.1",
+    "@types/uuid": "^9.0.3",
     "@typescript-eslint/eslint-plugin": "^5.59.1",
     "cargo-cp-artifact": "^0.1",
     "chai": "^4.3.7",
@@ -48,10 +53,38 @@
     "ts-node-dev": "^2.0.0",
     "typedoc": "^0.24.7",
     "typedoc-plugin-markdown": "^3.15.3",
-    "typescript": "*"
+    "typescript": "*",
+    "uuid": "^9.0.0"
   },
   "dependencies": {
     "@apache-arrow/ts": "^12.0.0",
-    "apache-arrow": "^12.0.0"
+    "@neon-rs/load": "^0.0.74",
+    "apache-arrow": "^12.0.0",
+    "axios": "^1.4.0"
+  },
+  "os": [
+    "darwin",
+    "linux",
+    "win32"
+  ],
+  "cpu": [
+    "x64",
+    "arm64"
+  ],
+  "neon": {
+    "targets": {
+      "x86_64-apple-darwin": "@lancedb/vectordb-darwin-x64",
+      "aarch64-apple-darwin": "@lancedb/vectordb-darwin-arm64",
+      "x86_64-unknown-linux-gnu": "@lancedb/vectordb-linux-x64-gnu",
+      "aarch64-unknown-linux-gnu": "@lancedb/vectordb-linux-arm64-gnu",
+      "x86_64-pc-windows-msvc": "@lancedb/vectordb-win32-x64-msvc"
+    }
+  },
+  "optionalDependencies": {
+    "@lancedb/vectordb-darwin-arm64": "0.3.0",
+    "@lancedb/vectordb-darwin-x64": "0.3.0",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.3.0",
+    "@lancedb/vectordb-linux-x64-gnu": "0.3.0",
+    "@lancedb/vectordb-win32-x64-msvc": "0.3.0"
   }
 }

node/src/arrow.ts

@@ -13,18 +13,19 @@
 // limitations under the License.
 
 import {
-  Field,
+  Field, type FixedSizeListBuilder,
   Float32,
-  List, type ListBuilder,
   makeBuilder,
   RecordBatchFileWriter,
-  Table, Utf8,
+  Utf8,
   type Vector,
-  vectorFromArray
+  FixedSizeList,
+  vectorFromArray, type Schema, Table as ArrowTable
 } from 'apache-arrow'
 import { type EmbeddingFunction } from './index'
 
-export async function convertToTable<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Table> {
+// Converts an Array of records into an Arrow Table, optionally applying an embeddings function to it.
+export async function convertToTable<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<ArrowTable> {
   if (data.length === 0) {
     throw new Error('At least one record needs to be provided')
   }
@@ -34,8 +35,8 @@ export async function convertToTable<T> (data: Array<Record<string, unknown>>, e
 
   for (const columnsKey of columns) {
     if (columnsKey === 'vector') {
-      const listBuilder = newVectorListBuilder()
       const vectorSize = (data[0].vector as any[]).length
+      const listBuilder = newVectorBuilder(vectorSize)
       for (const datum of data) {
         if ((datum[columnsKey] as any[]).length !== vectorSize) {
           throw new Error(`Invalid vector size, expected ${vectorSize}`)
@@ -52,9 +53,7 @@ export async function convertToTable<T> (data: Array<Record<string, unknown>>, e
 
       if (columnsKey === embeddings?.sourceColumn) {
         const vectors = await embeddings.embed(values as T[])
-        const listBuilder = newVectorListBuilder()
-        vectors.map(v => listBuilder.append(v))
-        records.vector = listBuilder.finish().toVector()
+        records.vector = vectorFromArray(vectors, newVectorType(vectors[0].length))
       }
 
       if (typeof values[0] === 'string') {
@@ -66,20 +65,47 @@ export async function convertToTable<T> (data: Array<Record<string, unknown>>, e
     }
   }
 
-  return new Table(records)
+  return new ArrowTable(records)
 }
 
 // Creates a new Arrow ListBuilder that stores a Vector column
-function newVectorListBuilder (): ListBuilder<Float32, any> {
-  const children = new Field<Float32>('item', new Float32())
-  const list = new List(children)
+function newVectorBuilder (dim: number): FixedSizeListBuilder<Float32> {
   return makeBuilder({
-    type: list
+    type: newVectorType(dim)
   })
 }
 
+// Creates the Arrow Type for a Vector column with dimension `dim`
+function newVectorType (dim: number): FixedSizeList<Float32> {
+  const children = new Field<Float32>('item', new Float32())
+  return new FixedSizeList(dim, children)
+}
+
+// Converts an Array of records into Arrow IPC format
 export async function fromRecordsToBuffer<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Buffer> {
   const table = await convertToTable(data, embeddings)
   const writer = RecordBatchFileWriter.writeAll(table)
   return Buffer.from(await writer.toUint8Array())
 }
+
+// Converts an Arrow Table into Arrow IPC format
+export async function fromTableToBuffer<T> (table: ArrowTable, embeddings?: EmbeddingFunction<T>): Promise<Buffer> {
+  if (embeddings !== undefined) {
+    const source = table.getChild(embeddings.sourceColumn)
+
+    if (source === null) {
+      throw new Error(`The embedding source column ${embeddings.sourceColumn} was not found in the Arrow Table`)
+    }
+
+    const vectors = await embeddings.embed(source.toArray() as T[])
+    const column = vectorFromArray(vectors, newVectorType(vectors[0].length))
+    table = table.assign(new ArrowTable({ vector: column }))
+  }
+  const writer = RecordBatchFileWriter.writeAll(table)
+  return Buffer.from(await writer.toUint8Array())
+}
+
+// Creates an empty Arrow Table
+export function createEmptyTable (schema: Schema): ArrowTable {
+  return new ArrowTable(schema)
+}

node/src/embedding/embedding_function.ts

@@ -26,3 +26,8 @@ export interface EmbeddingFunction<T> {
      */
   embed: (data: T[]) => Promise<number[][]>
 }
+
+export function isEmbeddingFunction<T> (value: any): value is EmbeddingFunction<T> {
+  return typeof value.sourceColumn === 'string' &&
+      typeof value.embed === 'function'
+}

node/src/index.ts

@@ -13,27 +13,106 @@
 // limitations under the License.
 
 import {
-  RecordBatchFileWriter,
-  type Table as ArrowTable,
-  tableFromIPC,
-  Vector
+  type Schema,
+  Table as ArrowTable
 } from 'apache-arrow'
-import { fromRecordsToBuffer } from './arrow'
+import { createEmptyTable, fromRecordsToBuffer, fromTableToBuffer } from './arrow'
 import type { EmbeddingFunction } from './embedding/embedding_function'
+import { RemoteConnection } from './remote'
+import { Query } from './query'
+import { isEmbeddingFunction } from './embedding/embedding_function'
 
 // eslint-disable-next-line @typescript-eslint/no-var-requires
-const { databaseNew, databaseTableNames, databaseOpenTable, databaseDropTable, tableCreate, tableSearch, tableAdd, tableCreateVectorIndex, tableCountRows, tableDelete } = require('../native.js')
+const { databaseNew, databaseTableNames, databaseOpenTable, databaseDropTable, tableCreate, tableAdd, tableCreateVectorIndex, tableCountRows, tableDelete, tableCleanupOldVersions, tableCompactFiles } = require('../native.js')
 
+export { Query }
 export type { EmbeddingFunction }
 export { OpenAIEmbeddingFunction } from './embedding/openai'
 
+export interface AwsCredentials {
+  accessKeyId: string
+
+  secretKey: string
+
+  sessionToken?: string
+}
+
+export interface ConnectionOptions {
+  uri: string
+
+  awsCredentials?: AwsCredentials
+
+  awsRegion?: string
+
+  // API key for the remote connections
+  apiKey?: string
+  // Region to connect
+  region?: string
+
+  // override the host for the remote connections
+  hostOverride?: string
+}
+
+function getAwsArgs (opts: ConnectionOptions): any[] {
+  const callArgs = []
+  const awsCredentials = opts.awsCredentials
+  if (awsCredentials !== undefined) {
+    callArgs.push(awsCredentials.accessKeyId)
+    callArgs.push(awsCredentials.secretKey)
+    callArgs.push(awsCredentials.sessionToken)
+  } else {
+    callArgs.push(undefined)
+    callArgs.push(undefined)
+    callArgs.push(undefined)
+  }
+
+  callArgs.push(opts.awsRegion)
+  return callArgs
+}
+
+export interface CreateTableOptions<T> {
+  // Name of Table
+  name: string
+
+  // Data to insert into the Table
+  data?: Array<Record<string, unknown>> | ArrowTable | undefined
+
+  // Optional Arrow Schema for this table
+  schema?: Schema | undefined
+
+  // Optional embedding function used to create embeddings
+  embeddingFunction?: EmbeddingFunction<T> | undefined
+
+  // WriteOptions for this operation
+  writeOptions?: WriteOptions | undefined
+}
+
 /**
  * Connect to a LanceDB instance at the given URI
  * @param uri The uri of the database.
  */
-export async function connect (uri: string): Promise<Connection> {
-  const db = await databaseNew(uri)
-  return new LocalConnection(db, uri)
+export async function connect (uri: string): Promise<Connection>
+export async function connect (opts: Partial<ConnectionOptions>): Promise<Connection>
+export async function connect (arg: string | Partial<ConnectionOptions>): Promise<Connection> {
+  let opts: ConnectionOptions
+  if (typeof arg === 'string') {
+    opts = { uri: arg }
+  } else {
+    // opts = { uri: arg.uri, awsCredentials = arg.awsCredentials }
+    opts = Object.assign({
+      uri: '',
+      awsCredentials: undefined,
+      apiKey: undefined,
+      region: 'us-west-2'
+    }, arg)
+  }
+
+  if (opts.uri.startsWith('db://')) {
+    // Remote connection
+    return new RemoteConnection(opts)
+  }
+  const db = await databaseNew(opts.uri)
+  return new LocalConnection(db, opts)
 }
 
 /**
@@ -54,17 +133,51 @@ export interface Connection {
    */
   openTable<T>(name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
 
+  /**
+   * Creates a new Table, optionally initializing it with new data.
+   *
+   * @param {string} name - The name of the table.
+   * @param data - Array of Records to be inserted into the table
+   * @param schema - An Arrow Schema that describe this table columns
+   * @param {EmbeddingFunction} embeddings - An embedding function to use on this table
+   * @param {WriteOptions} writeOptions - The write options to use when creating the table.
+   */
+  createTable<T> ({ name, data, schema, embeddingFunction, writeOptions }: CreateTableOptions<T>): Promise<Table<T>>
+
+  /**
+   * Creates a new Table and initialize it with new data.
+   *
+   * @param {string} name - The name of the table.
+   * @param data - Non-empty Array of Records to be inserted into the table
+   */
+  createTable (name: string, data: Array<Record<string, unknown>>): Promise<Table>
+
+  /**
+   * Creates a new Table and initialize it with new data.
+   *
+   * @param {string} name - The name of the table.
+   * @param data - Non-empty Array of Records to be inserted into the table
+   * @param {WriteOptions} options - The write options to use when creating the table.
+   */
+  createTable (name: string, data: Array<Record<string, unknown>>, options: WriteOptions): Promise<Table>
+
   /**
    * Creates a new Table and initialize it with new data.
    *
    * @param {string} name - The name of the table.
    * @param data - Non-empty Array of Records to be inserted into the table
-   * @param {WriteMode} mode - The write mode to use when creating the table.
    * @param {EmbeddingFunction} embeddings - An embedding function to use on this table
    */
-  createTable<T>(name: string, data: Array<Record<string, unknown>>, mode?: WriteMode, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
-
-  createTableArrow(name: string, table: ArrowTable): Promise<Table>
+  createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
+  /**
+   * Creates a new Table and initialize it with new data.
+   *
+   * @param {string} name - The name of the table.
+   * @param data - Non-empty Array of Records to be inserted into the table
+   * @param {EmbeddingFunction} embeddings - An embedding function to use on this table
+   * @param {WriteOptions} options - The write options to use when creating the table.
+   */
+  createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>, options: WriteOptions): Promise<Table<T>>
 
   /**
    * Drop an existing table.
@@ -117,7 +230,34 @@ export interface Table<T = number[]> {
   /**
    * Delete rows from this table.
    *
-   * @param filter  A filter in the same format used by a sql WHERE clause.
+   * This can be used to delete a single row, many rows, all rows, or
+   * sometimes no rows (if your predicate matches nothing).
+   *
+   * @param filter  A filter in the same format used by a sql WHERE clause. The
+   *                filter must not be empty.
+   *
+   * @examples
+   *
+   * ```ts
+   * const con = await lancedb.connect("./.lancedb")
+   * const data = [
+   *    {id: 1, vector: [1, 2]},
+   *    {id: 2, vector: [3, 4]},
+   *    {id: 3, vector: [5, 6]},
+   * ];
+   * const tbl = await con.createTable("my_table", data)
+   * await tbl.delete("id = 2")
+   * await tbl.countRows() // Returns 2
+   * ```
+   *
+   * If you have a list of values to delete, you can combine them into a
+   * stringified list and use the `IN` operator:
+   *
+   * ```ts
+   * const to_remove = [1, 5];
+   * await tbl.delete(`id IN (${to_remove.join(",")})`)
+   * await tbl.countRows() // Returns 1
+   * ```
    */
   delete: (filter: string) => Promise<void>
 }
@@ -126,21 +266,21 @@ export interface Table<T = number[]> {
  * A connection to a LanceDB database.
  */
 export class LocalConnection implements Connection {
-  private readonly _uri: string
+  private readonly _options: () => ConnectionOptions
   private readonly _db: any
 
-  constructor (db: any, uri: string) {
-    this._uri = uri
+  constructor (db: any, options: ConnectionOptions) {
+    this._options = () => options
     this._db = db
   }
 
   get uri (): string {
-    return this._uri
+    return this._options().uri
   }
 
   /**
-     * Get the names of all tables in the database.
-     */
+   * Get the names of all tables in the database.
+   */
   async tableNames (): Promise<string[]> {
     return databaseTableNames.call(this._db)
   }
@@ -151,6 +291,7 @@ export class LocalConnection implements Connection {
    * @param name The name of the table.
    */
   async openTable (name: string): Promise<Table>
+
   /**
    * Open a table in the database.
    *
@@ -158,50 +299,68 @@ export class LocalConnection implements Connection {
    * @param embeddings An embedding function to use on this Table
    */
   async openTable<T> (name: string, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
+  async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
   async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
-    const tbl = await databaseOpenTable.call(this._db, name)
+    const tbl = await databaseOpenTable.call(this._db, name, ...getAwsArgs(this._options()))
     if (embeddings !== undefined) {
-      return new LocalTable(tbl, name, embeddings)
+      return new LocalTable(tbl, name, this._options(), embeddings)
     } else {
-      return new LocalTable(tbl, name)
+      return new LocalTable(tbl, name, this._options())
     }
   }
 
-  /**
-   * Creates a new Table and initialize it with new data.
-   *
-   * @param name The name of the table.
-   * @param data Non-empty Array of Records to be inserted into the Table
-   * @param mode The write mode to use when creating the table.
-   */
-  async createTable (name: string, data: Array<Record<string, unknown>>, mode?: WriteMode): Promise<Table>
-  async createTable (name: string, data: Array<Record<string, unknown>>, mode: WriteMode): Promise<Table>
+  async createTable<T> (name: string | CreateTableOptions<T>, data?: Array<Record<string, unknown>>, optsOrEmbedding?: WriteOptions | EmbeddingFunction<T>, opt?: WriteOptions): Promise<Table<T>> {
+    if (typeof name === 'string') {
+      let writeOptions: WriteOptions = new DefaultWriteOptions()
+      if (opt !== undefined && isWriteOptions(opt)) {
+        writeOptions = opt
+      } else if (optsOrEmbedding !== undefined && isWriteOptions(optsOrEmbedding)) {
+        writeOptions = optsOrEmbedding
+      }
 
-  /**
-   * Creates a new Table and initialize it with new data.
-   *
-   * @param name The name of the table.
-   * @param data Non-empty Array of Records to be inserted into the Table
-   * @param mode The write mode to use when creating the table.
-   * @param embeddings An embedding function to use on this Table
-   */
-  async createTable<T> (name: string, data: Array<Record<string, unknown>>, mode: WriteMode, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
-  async createTable<T> (name: string, data: Array<Record<string, unknown>>, mode: WriteMode, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
-    if (mode === undefined) {
-      mode = WriteMode.Create
-    }
-    const tbl = await tableCreate.call(this._db, name, await fromRecordsToBuffer(data, embeddings), mode.toLowerCase())
-    if (embeddings !== undefined) {
-      return new LocalTable(tbl, name, embeddings)
-    } else {
-      return new LocalTable(tbl, name)
+      let embeddings: undefined | EmbeddingFunction<T>
+      if (optsOrEmbedding !== undefined && isEmbeddingFunction(optsOrEmbedding)) {
+        embeddings = optsOrEmbedding
+      }
+      return await this.createTableImpl({ name, data, embeddingFunction: embeddings, writeOptions })
     }
+    return await this.createTableImpl(name)
   }
 
-  async createTableArrow (name: string, table: ArrowTable): Promise<Table> {
-    const writer = RecordBatchFileWriter.writeAll(table)
-    await tableCreate.call(this._db, name, Buffer.from(await writer.toUint8Array()))
-    return await this.openTable(name)
+  private async createTableImpl<T> ({ name, data, schema, embeddingFunction, writeOptions = new DefaultWriteOptions() }: {
+    name: string
+    data?: Array<Record<string, unknown>> | ArrowTable | undefined
+    schema?: Schema | undefined
+    embeddingFunction?: EmbeddingFunction<T> | undefined
+    writeOptions?: WriteOptions | undefined
+  }): Promise<Table<T>> {
+    let buffer: Buffer
+
+    function isEmpty (data: Array<Record<string, unknown>> | ArrowTable<any>): boolean {
+      if (data instanceof ArrowTable) {
+        return data.data.length === 0
+      }
+      return data.length === 0
+    }
+
+    if ((data === undefined) || isEmpty(data)) {
+      if (schema === undefined) {
+        throw new Error('Either data or schema needs to defined')
+      }
+      buffer = await fromTableToBuffer(createEmptyTable(schema))
+    } else if (data instanceof ArrowTable) {
+      buffer = await fromTableToBuffer(data, embeddingFunction)
+    } else {
+      // data is Array<Record<...>>
+      buffer = await fromRecordsToBuffer(data, embeddingFunction)
+    }
+
+    const tbl = await tableCreate.call(this._db, name, buffer, writeOptions?.writeMode?.toString(), ...getAwsArgs(this._options()))
+    if (embeddingFunction !== undefined) {
+      return new LocalTable(tbl, name, this._options(), embeddingFunction)
+    } else {
+      return new LocalTable(tbl, name, this._options())
+    }
   }
 
   /**
@@ -214,21 +373,24 @@ export class LocalConnection implements Connection {
 }
 
 export class LocalTable<T = number[]> implements Table<T> {
-  private readonly _tbl: any
+  private _tbl: any
   private readonly _name: string
   private readonly _embeddings?: EmbeddingFunction<T>
+  private readonly _options: () => ConnectionOptions
 
-  constructor (tbl: any, name: string)
+  constructor (tbl: any, name: string, options: ConnectionOptions)
   /**
    * @param tbl
    * @param name
+   * @param options
    * @param embeddings An embedding function to use when interacting with this table
    */
-  constructor (tbl: any, name: string, embeddings: EmbeddingFunction<T>)
-  constructor (tbl: any, name: string, embeddings?: EmbeddingFunction<T>) {
+  constructor (tbl: any, name: string, options: ConnectionOptions, embeddings: EmbeddingFunction<T>)
+  constructor (tbl: any, name: string, options: ConnectionOptions, embeddings?: EmbeddingFunction<T>) {
     this._tbl = tbl
     this._name = name
     this._embeddings = embeddings
+    this._options = () => options
   }
 
   get name (): string {
@@ -240,7 +402,7 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @param query The query search term
    */
   search (query: T): Query<T> {
-    return new Query(this._tbl, query, this._embeddings)
+    return new Query(query, this._tbl, this._embeddings)
   }
 
   /**
@@ -250,7 +412,12 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @return The number of rows added to the table
    */
   async add (data: Array<Record<string, unknown>>): Promise<number> {
-    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Append.toString())
+    return tableAdd.call(
+      this._tbl,
+      await fromRecordsToBuffer(data, this._embeddings),
+      WriteMode.Append.toString(),
+      ...getAwsArgs(this._options())
+    ).then((newTable: any) => { this._tbl = newTable })
   }
 
   /**
@@ -260,7 +427,12 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @return The number of rows added to the table
    */
   async overwrite (data: Array<Record<string, unknown>>): Promise<number> {
-    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Overwrite.toString())
+    return tableAdd.call(
+      this._tbl,
+      await fromRecordsToBuffer(data, this._embeddings),
+      WriteMode.Overwrite.toString(),
+      ...getAwsArgs(this._options())
+    ).then((newTable: any) => { this._tbl = newTable })
   }
 
   /**
@@ -269,7 +441,7 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @param indexParams The parameters of this Index, @see VectorIndexParams.
    */
   async createIndex (indexParams: VectorIndexParams): Promise<any> {
-    return tableCreateVectorIndex.call(this._tbl, indexParams)
+    return tableCreateVectorIndex.call(this._tbl, indexParams).then((newTable: any) => { this._tbl = newTable })
   }
 
   /**
@@ -285,8 +457,113 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @param filter A filter in the same format used by a sql WHERE clause.
    */
   async delete (filter: string): Promise<void> {
-    return tableDelete.call(this._tbl, filter)
+    return tableDelete.call(this._tbl, filter).then((newTable: any) => { this._tbl = newTable })
   }
+
+  /**
+   * Clean up old versions of the table, freeing disk space.
+   *
+   * @param olderThan The minimum age in minutes of the versions to delete. If not
+   *                  provided, defaults to two weeks.
+   * @param deleteUnverified Because they may be part of an in-progress
+   *                  transaction, uncommitted files newer than 7 days old are
+   *                  not deleted by default. This means that failed transactions
+   *                  can leave around data that takes up disk space for up to
+   *                  7 days. You can override this safety mechanism by setting
+   *                 this option to `true`, only if you promise there are no
+   *                 in progress writes while you run this operation. Failure to
+   *                 uphold this promise can lead to corrupted tables.
+   * @returns
+   */
+  async cleanupOldVersions (olderThan?: number, deleteUnverified?: boolean): Promise<CleanupStats> {
+    return tableCleanupOldVersions.call(this._tbl, olderThan, deleteUnverified)
+      .then((res: { newTable: any, metrics: CleanupStats }) => {
+        this._tbl = res.newTable
+        return res.metrics
+      })
+  }
+
+  /**
+   * Run the compaction process on the table.
+   *
+   * This can be run after making several small appends to optimize the table
+   * for faster reads.
+   *
+   * @param options Advanced options configuring compaction. In most cases, you
+   *               can omit this arguments, as the default options are sensible
+   *               for most tables.
+   * @returns Metrics about the compaction operation.
+   */
+  async compactFiles (options?: CompactionOptions): Promise<CompactionMetrics> {
+    const optionsArg = options ?? {}
+    return tableCompactFiles.call(this._tbl, optionsArg)
+      .then((res: { newTable: any, metrics: CompactionMetrics }) => {
+        this._tbl = res.newTable
+        return res.metrics
+      })
+  }
+}
+
+export interface CleanupStats {
+  /**
+   * The number of bytes removed from disk.
+   */
+  bytesRemoved: number
+  /**
+   * The number of old table versions removed.
+   */
+  oldVersions: number
+}
+
+export interface CompactionOptions {
+  /**
+   * The number of rows per fragment to target. Fragments that have fewer rows
+   * will be compacted into adjacent fragments to produce larger fragments.
+   * Defaults to 1024 * 1024.
+   */
+  targetRowsPerFragment?: number
+  /**
+   * The maximum number of rows per group. Defaults to 1024.
+   */
+  maxRowsPerGroup?: number
+  /**
+   * If true, fragments that have rows that are deleted may be compacted to
+   * remove the deleted rows. This can improve the performance of queries.
+   * Default is true.
+   */
+  materializeDeletions?: boolean
+  /**
+   * A number between 0 and 1, representing the proportion of rows that must be
+   * marked deleted before a fragment is a candidate for compaction to remove
+   * the deleted rows. Default is 10%.
+   */
+  materializeDeletionsThreshold?: number
+  /**
+   * The number of threads to use for compaction. If not provided, defaults to
+   * the number of cores on the machine.
+   */
+  numThreads?: number
+}
+
+export interface CompactionMetrics {
+  /**
+   * The number of fragments that were removed.
+   */
+  fragmentsRemoved: number
+  /**
+   * The number of new fragments that were created.
+   */
+  fragmentsAdded: number
+  /**
+   * The number of files that were removed. Each fragment may have more than one
+   * file.
+   */
+  filesRemoved: number
+  /**
+   * The number of files added. This is typically equal to the number of
+   * fragments added.
+   */
+  filesAdded: number
 }
 
 /// Config to build IVF_PQ index.
@@ -346,116 +623,6 @@ export interface IvfPQIndexConfig {
 
 export type VectorIndexParams = IvfPQIndexConfig
 
-/**
- * A builder for nearest neighbor queries for LanceDB.
- */
-export class Query<T = number[]> {
-  private readonly _tbl: any
-  private readonly _query: T
-  private _queryVector?: number[]
-  private _limit: number
-  private _refineFactor?: number
-  private _nprobes: number
-  private _select?: string[]
-  private _filter?: string
-  private _metricType?: MetricType
-  private readonly _embeddings?: EmbeddingFunction<T>
-
-  constructor (tbl: any, query: T, embeddings?: EmbeddingFunction<T>) {
-    this._tbl = tbl
-    this._query = query
-    this._limit = 10
-    this._nprobes = 20
-    this._refineFactor = undefined
-    this._select = undefined
-    this._filter = undefined
-    this._metricType = undefined
-    this._embeddings = embeddings
-  }
-
-  /***
-   * Sets the number of results that will be returned
-   * @param value number of results
-   */
-  limit (value: number): Query<T> {
-    this._limit = value
-    return this
-  }
-
-  /**
-   * Refine the results by reading extra elements and re-ranking them in memory.
-   * @param value refine factor to use in this query.
-   */
-  refineFactor (value: number): Query<T> {
-    this._refineFactor = value
-    return this
-  }
-
-  /**
-   * The number of probes used. A higher number makes search more accurate but also slower.
-   * @param value The number of probes used.
-   */
-  nprobes (value: number): Query<T> {
-    this._nprobes = value
-    return this
-  }
-
-  /**
-   * A filter statement to be applied to this query.
-   * @param value A filter in the same format used by a sql WHERE clause.
-   */
-  filter (value: string): Query<T> {
-    this._filter = value
-    return this
-  }
-
-  where = this.filter
-
-  /** Return only the specified columns.
-   *
-   * @param value Only select the specified columns. If not specified, all columns will be returned.
-   */
-  select (value: string[]): Query<T> {
-    this._select = value
-    return this
-  }
-
-  /**
-   * The MetricType used for this Query.
-   * @param value The metric to the. @see MetricType for the different options
-   */
-  metricType (value: MetricType): Query<T> {
-    this._metricType = value
-    return this
-  }
-
-  /**
-   * Execute the query and return the results as an Array of Objects
-   */
-  async execute<T = Record<string, unknown>> (): Promise<T[]> {
-    if (this._embeddings !== undefined) {
-      this._queryVector = (await this._embeddings.embed([this._query]))[0]
-    } else {
-      this._queryVector = this._query as number[]
-    }
-
-    const buffer = await tableSearch.call(this._tbl, this)
-    const data = tableFromIPC(buffer)
-
-    return data.toArray().map((entry: Record<string, unknown>) => {
-      const newObject: Record<string, unknown> = {}
-      Object.keys(entry).forEach((key: string) => {
-        if (entry[key] instanceof Vector) {
-          newObject[key] = (entry[key] as Vector).toArray()
-        } else {
-          newObject[key] = entry[key]
-        }
-      })
-      return newObject as unknown as T
-    })
-  }
-}
-
 /**
  * Write mode for writing a table.
  */
@@ -468,6 +635,23 @@ export enum WriteMode {
   Append = 'append'
 }
 
+/**
+ * Write options when creating a Table.
+ */
+export interface WriteOptions {
+  /** A {@link WriteMode} to use on this operation */
+  writeMode?: WriteMode
+}
+
+export class DefaultWriteOptions implements WriteOptions {
+  writeMode = WriteMode.Create
+}
+
+export function isWriteOptions (value: any): value is WriteOptions {
+  return Object.keys(value).length === 1 &&
+      (value.writeMode === undefined || typeof value.writeMode === 'string')
+}
+
 /**
  * Distance metrics type.
  */

node/src/integration_test/test.ts

@@ -0,0 +1,173 @@
+// Copyright 2023 LanceDB Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { describe } from 'mocha'
+import * as chai from 'chai'
+import * as chaiAsPromised from 'chai-as-promised'
+import { v4 as uuidv4 } from 'uuid'
+
+import * as lancedb from '../index'
+import { tmpdir } from 'os'
+import * as fs from 'fs'
+import * as path from 'path'
+
+const assert = chai.assert
+chai.use(chaiAsPromised)
+
+describe('LanceDB AWS Integration test', function () {
+  it('s3+ddb schema is processed correctly', async function () {
+    this.timeout(15000)
+
+    // WARNING: specifying engine is NOT a publicly supported feature in lancedb yet
+    // THE API WILL CHANGE
+    const conn = await lancedb.connect('s3://lancedb-integtest?engine=ddb&ddbTableName=lancedb-integtest')
+    const data = [{ vector: Array(128).fill(1.0) }]
+
+    const tableName = uuidv4()
+    let table = await conn.createTable(tableName, data, { writeMode: lancedb.WriteMode.Overwrite })
+
+    const futs = [table.add(data), table.add(data), table.add(data), table.add(data), table.add(data)]
+    await Promise.allSettled(futs)
+
+    table = await conn.openTable(tableName)
+    assert.equal(await table.countRows(), 6)
+  })
+})
+
+describe('LanceDB Mirrored Store Integration test', function () {
+  it('s3://...?mirroredStore=... param is processed correctly', async function () {
+    this.timeout(600000)
+
+    const dir = tmpdir()
+    console.log(dir)
+    const conn = await lancedb.connect(`s3://lancedb-integtest?mirroredStore=${dir}`)
+    const data = Array(200).fill({ vector: Array(128).fill(1.0), id: 0 })
+    data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 1 }))
+    data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 2 }))
+    data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 3 }))
+
+    const tableName = uuidv4()
+
+    // try create table and check if it's mirrored
+    const t = await conn.createTable(tableName, data, { writeMode: lancedb.WriteMode.Overwrite })
+
+    const mirroredPath = path.join(dir, `${tableName}.lance`)
+    fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
+      if (err != null) throw err
+      // there should be two dirs
+      assert.equal(files.length, 2)
+      assert.isTrue(files[0].isDirectory())
+      assert.isTrue(files[1].isDirectory())
+
+      fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.txn'))
+      })
+
+      fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.lance'))
+      })
+    })
+
+    // try create index and check if it's mirrored
+    await t.createIndex({ column: 'vector', type: 'ivf_pq' })
+
+    fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
+      if (err != null) throw err
+      // there should be two dirs
+      assert.equal(files.length, 3)
+      assert.isTrue(files[0].isDirectory())
+      assert.isTrue(files[1].isDirectory())
+      assert.isTrue(files[2].isDirectory())
+
+      // Two TXs now
+      fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 2)
+        assert.isTrue(files[0].name.endsWith('.txn'))
+        assert.isTrue(files[1].name.endsWith('.txn'))
+      })
+
+      fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.lance'))
+      })
+
+      fs.readdir(path.join(mirroredPath, '_indices'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].isDirectory())
+
+        fs.readdir(path.join(mirroredPath, '_indices', files[0].name), { withFileTypes: true }, (err, files) => {
+          if (err != null) throw err
+
+          assert.equal(files.length, 1)
+          assert.isTrue(files[0].isFile())
+          assert.isTrue(files[0].name.endsWith('.idx'))
+        })
+      })
+    })
+
+    // try delete and check if it's mirrored
+    await t.delete('id = 0')
+
+    fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
+      if (err != null) throw err
+      // there should be two dirs
+      assert.equal(files.length, 4)
+      assert.isTrue(files[0].isDirectory())
+      assert.isTrue(files[1].isDirectory())
+      assert.isTrue(files[2].isDirectory())
+      assert.isTrue(files[3].isDirectory())
+
+      // Three TXs now
+      fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 3)
+        assert.isTrue(files[0].name.endsWith('.txn'))
+        assert.isTrue(files[1].name.endsWith('.txn'))
+      })
+
+      fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.lance'))
+      })
+
+      fs.readdir(path.join(mirroredPath, '_indices'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].isDirectory())
+
+        fs.readdir(path.join(mirroredPath, '_indices', files[0].name), { withFileTypes: true }, (err, files) => {
+          if (err != null) throw err
+
+          assert.equal(files.length, 1)
+          assert.isTrue(files[0].isFile())
+          assert.isTrue(files[0].name.endsWith('.idx'))
+        })
+      })
+
+      fs.readdir(path.join(mirroredPath, '_deletions'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.arrow'))
+      })
+    })
+  })
+})

node/src/query.ts

@@ -0,0 +1,141 @@
+// Copyright 2023 LanceDB Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { Vector, tableFromIPC } from 'apache-arrow'
+import { type EmbeddingFunction } from './embedding/embedding_function'
+import { type MetricType } from '.'
+
+// eslint-disable-next-line @typescript-eslint/no-var-requires
+const { tableSearch } = require('../native.js')
+
+/**
+ * A builder for nearest neighbor queries for LanceDB.
+ */
+export class Query<T = number[]> {
+  private readonly _query: T
+  private readonly _tbl?: any
+  private _queryVector?: number[]
+  private _limit: number
+  private _refineFactor?: number
+  private _nprobes: number
+  private _select?: string[]
+  private _filter?: string
+  private _metricType?: MetricType
+  protected readonly _embeddings?: EmbeddingFunction<T>
+
+  constructor (query: T, tbl?: any, embeddings?: EmbeddingFunction<T>) {
+    this._tbl = tbl
+    this._query = query
+    this._limit = 10
+    this._nprobes = 20
+    this._refineFactor = undefined
+    this._select = undefined
+    this._filter = undefined
+    this._metricType = undefined
+    this._embeddings = embeddings
+  }
+
+  /***
+     * Sets the number of results that will be returned
+     * @param value number of results
+     */
+  limit (value: number): Query<T> {
+    this._limit = value
+    return this
+  }
+
+  /**
+     * Refine the results by reading extra elements and re-ranking them in memory.
+     * @param value refine factor to use in this query.
+     */
+  refineFactor (value: number): Query<T> {
+    this._refineFactor = value
+    return this
+  }
+
+  /**
+     * The number of probes used. A higher number makes search more accurate but also slower.
+     * @param value The number of probes used.
+     */
+  nprobes (value: number): Query<T> {
+    this._nprobes = value
+    return this
+  }
+
+  /**
+     * A filter statement to be applied to this query.
+     * @param value A filter in the same format used by a sql WHERE clause.
+     */
+  filter (value: string): Query<T> {
+    this._filter = value
+    return this
+  }
+
+  where = this.filter
+
+  /** Return only the specified columns.
+     *
+     * @param value Only select the specified columns. If not specified, all columns will be returned.
+     */
+  select (value: string[]): Query<T> {
+    this._select = value
+    return this
+  }
+
+  /**
+     * The MetricType used for this Query.
+     * @param value The metric to the. @see MetricType for the different options
+     */
+  metricType (value: MetricType): Query<T> {
+    this._metricType = value
+    return this
+  }
+
+  /**
+     * Execute the query and return the results as an Array of Objects
+     */
+  async execute<T = Record<string, unknown>> (): Promise<T[]> {
+    if (this._embeddings !== undefined) {
+      this._queryVector = (await this._embeddings.embed([this._query]))[0]
+    } else {
+      this._queryVector = this._query as number[]
+    }
+
+    const isElectron = this.isElectron()
+    const buffer = await tableSearch.call(this._tbl, this, isElectron)
+    const data = tableFromIPC(buffer)
+
+    return data.toArray().map((entry: Record<string, unknown>) => {
+      const newObject: Record<string, unknown> = {}
+      Object.keys(entry).forEach((key: string) => {
+        if (entry[key] instanceof Vector) {
+          newObject[key] = (entry[key] as Vector).toArray()
+        } else {
+          newObject[key] = entry[key]
+        }
+      })
+      return newObject as unknown as T
+    })
+  }
+
+  // See https://github.com/electron/electron/issues/2288
+  private isElectron (): boolean {
+    try {
+      // eslint-disable-next-line no-prototype-builtins
+      return (process?.versions?.hasOwnProperty('electron') || navigator?.userAgent?.toLowerCase()?.includes(' electron'))
+    } catch (e) {
+      return false
+    }
+  }
+}

node/src/remote/client.ts

@@ -0,0 +1,137 @@
+// Copyright 2023 LanceDB Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import axios, { type AxiosResponse } from 'axios'
+
+import { tableFromIPC, type Table as ArrowTable } from 'apache-arrow'
+
+export class HttpLancedbClient {
+  private readonly _url: string
+  private readonly _apiKey: () => string
+
+  public constructor (
+    url: string,
+    apiKey: string,
+    private readonly _dbName?: string
+  ) {
+    this._url = url
+    this._apiKey = () => apiKey
+  }
+
+  get uri (): string {
+    return this._url
+  }
+
+  public async search (
+    tableName: string,
+    vector: number[],
+    k: number,
+    nprobes: number,
+    refineFactor?: number,
+    columns?: string[],
+    filter?: string
+  ): Promise<ArrowTable<any>> {
+    const response = await axios.post(
+              `${this._url}/v1/table/${tableName}/query/`,
+              {
+                vector,
+                k,
+                nprobes,
+                refineFactor,
+                columns,
+                filter
+              },
+              {
+                headers: {
+                  'Content-Type': 'application/json',
+                  'x-api-key': this._apiKey(),
+                  ...(this._dbName !== undefined ? { 'x-lancedb-database': this._dbName } : {})
+                },
+                responseType: 'arraybuffer',
+                timeout: 10000
+              }
+    ).catch((err) => {
+      console.error('error: ', err)
+      return err.response
+    })
+    if (response.status !== 200) {
+      const errorData = new TextDecoder().decode(response.data)
+      throw new Error(
+        `Server Error, status: ${response.status as number}, ` +
+        `message: ${response.statusText as string}: ${errorData}`
+      )
+    }
+
+    const table = tableFromIPC(response.data)
+    return table
+  }
+
+  /**
+   * Sent GET request.
+   */
+  public async get (path: string, params?: Record<string, string | number>): Promise<AxiosResponse> {
+    const response = await axios.get(
+      `${this._url}${path}`,
+      {
+        headers: {
+          'Content-Type': 'application/json',
+          'x-api-key': this._apiKey()
+        },
+        params,
+        timeout: 10000
+      }
+    ).catch((err) => {
+      console.error('error: ', err)
+      return err.response
+    })
+    if (response.status !== 200) {
+      const errorData = new TextDecoder().decode(response.data)
+      throw new Error(
+        `Server Error, status: ${response.status as number}, ` +
+        `message: ${response.statusText as string}: ${errorData}`
+      )
+    }
+    return response
+  }
+
+  /**
+   * Sent POST request.
+   */
+  public async post (path: string, data?: any, params?: Record<string, string | number>): Promise<AxiosResponse> {
+    const response = await axios.post(
+        `${this._url}${path}`,
+        data,
+        {
+          headers: {
+            'Content-Type': 'application/json',
+            'x-api-key': this._apiKey(),
+            ...(this._dbName !== undefined ? { 'x-lancedb-database': this._dbName } : {})
+          },
+          params,
+          timeout: 30000
+        }
+    ).catch((err) => {
+      console.error('error: ', err)
+      return err.response
+    })
+    if (response.status !== 200) {
+      const errorData = new TextDecoder().decode(response.data)
+      throw new Error(
+          `Server Error, status: ${response.status as number}, ` +
+          `message: ${response.statusText as string}: ${errorData}`
+      )
+    }
+    return response
+  }
+}

node/src/remote/index.ts

@@ -0,0 +1,162 @@
+// Copyright 2023 LanceDB Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import {
+  type EmbeddingFunction, type Table, type VectorIndexParams, type Connection,
+  type ConnectionOptions, type CreateTableOptions, type WriteOptions
+} from '../index'
+import { Query } from '../query'
+
+import { Vector } from 'apache-arrow'
+import { HttpLancedbClient } from './client'
+
+/**
+ * Remote connection.
+ */
+export class RemoteConnection implements Connection {
+  private readonly _client: HttpLancedbClient
+  private readonly _dbName: string
+
+  constructor (opts: ConnectionOptions) {
+    if (!opts.uri.startsWith('db://')) {
+      throw new Error(`Invalid remote DB URI: ${opts.uri}`)
+    }
+    if (opts.apiKey === undefined || opts.region === undefined) {
+      throw new Error('API key and region are not supported for remote connections')
+    }
+
+    this._dbName = opts.uri.slice('db://'.length)
+    let server: string
+    if (opts.hostOverride === undefined) {
+      server = `https://${this._dbName}.${opts.region}.api.lancedb.com`
+    } else {
+      server = opts.hostOverride
+    }
+    this._client = new HttpLancedbClient(server, opts.apiKey, opts.hostOverride === undefined ? undefined : this._dbName)
+  }
+
+  get uri (): string {
+    // add the lancedb+ prefix back
+    return 'db://' + this._client.uri
+  }
+
+  async tableNames (): Promise<string[]> {
+    const response = await this._client.get('/v1/table/')
+    return response.data.tables
+  }
+
+  async openTable (name: string): Promise<Table>
+  async openTable<T> (name: string, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
+  async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
+    if (embeddings !== undefined) {
+      return new RemoteTable(this._client, name, embeddings)
+    } else {
+      return new RemoteTable(this._client, name)
+    }
+  }
+
+  async createTable<T> (name: string | CreateTableOptions<T>, data?: Array<Record<string, unknown>>, optsOrEmbedding?: WriteOptions | EmbeddingFunction<T>, opt?: WriteOptions): Promise<Table<T>> {
+    throw new Error('Not implemented')
+  }
+
+  async dropTable (name: string): Promise<void> {
+    await this._client.post(`/v1/table/${name}/drop/`)
+  }
+}
+
+export class RemoteQuery<T = number[]> extends Query<T> {
+  constructor (query: T, private readonly _client: HttpLancedbClient,
+    private readonly _name: string, embeddings?: EmbeddingFunction<T>) {
+    super(query, undefined, embeddings)
+  }
+
+  // TODO: refactor this to a base class + queryImpl pattern
+  async execute<T = Record<string, unknown>>(): Promise<T[]> {
+    const embeddings = this._embeddings
+    const query = (this as any)._query
+    let queryVector: number[]
+
+    if (embeddings !== undefined) {
+      queryVector = (await embeddings.embed([query]))[0]
+    } else {
+      queryVector = query as number[]
+    }
+
+    const data = await this._client.search(
+      this._name,
+      queryVector,
+      (this as any)._limit,
+      (this as any)._nprobes,
+      (this as any)._refineFactor,
+      (this as any)._select,
+      (this as any)._filter
+    )
+
+    return data.toArray().map((entry: Record<string, unknown>) => {
+      const newObject: Record<string, unknown> = {}
+      Object.keys(entry).forEach((key: string) => {
+        if (entry[key] instanceof Vector) {
+          newObject[key] = (entry[key] as Vector).toArray()
+        } else {
+          newObject[key] = entry[key]
+        }
+      })
+      return newObject as unknown as T
+    })
+  }
+}
+
+// we are using extend until we have next next version release
+// Table and Connection has both been refactored to interfaces
+export class RemoteTable<T = number[]> implements Table<T> {
+  private readonly _client: HttpLancedbClient
+  private readonly _embeddings?: EmbeddingFunction<T>
+  private readonly _name: string
+
+  constructor (client: HttpLancedbClient, name: string)
+  constructor (client: HttpLancedbClient, name: string, embeddings: EmbeddingFunction<T>)
+  constructor (client: HttpLancedbClient, name: string, embeddings?: EmbeddingFunction<T>) {
+    this._client = client
+    this._name = name
+    this._embeddings = embeddings
+  }
+
+  get name (): string {
+    return this._name
+  }
+
+  search (query: T): Query<T> {
+    return new RemoteQuery(query, this._client, this._name)//, this._embeddings_new)
+  }
+
+  async add (data: Array<Record<string, unknown>>): Promise<number> {
+    throw new Error('Not implemented')
+  }
+
+  async overwrite (data: Array<Record<string, unknown>>): Promise<number> {
+    throw new Error('Not implemented')
+  }
+
+  async createIndex (indexParams: VectorIndexParams): Promise<any> {
+    throw new Error('Not implemented')
+  }
+
+  async countRows (): Promise<number> {
+    throw new Error('Not implemented')
+  }
+
+  async delete (filter: string): Promise<void> {
+    throw new Error('Not implemented')
+  }
+}

node/src/test/embedding/openai.ts

@@ -16,6 +16,7 @@ import { describe } from 'mocha'
 import { assert } from 'chai'
 
 import { OpenAIEmbeddingFunction } from '../../embedding/openai'
+import { isEmbeddingFunction } from '../../embedding/embedding_function'
 
 // eslint-disable-next-line @typescript-eslint/no-var-requires
 const { OpenAIApi } = require('openai')
@@ -47,4 +48,10 @@ describe('OpenAPIEmbeddings', function () {
       assert.deepEqual(vectors[1], stubValue.data.data[1].embedding)
     })
   })
+
+  describe('isEmbeddingFunction', function () {
+    it('should match the isEmbeddingFunction guard', function () {
+      assert.isTrue(isEmbeddingFunction(new OpenAIEmbeddingFunction('text', 'sk-key')))
+    })
+  })
 })

node/src/test/io.ts

@@ -18,26 +18,50 @@ import { describe } from 'mocha'
 import { assert } from 'chai'
 
 import * as lancedb from '../index'
+import { type ConnectionOptions } from '../index'
 
 describe('LanceDB S3 client', function () {
   if (process.env.TEST_S3_BASE_URL != null) {
     const baseUri = process.env.TEST_S3_BASE_URL
     it('should have a valid url', async function () {
-      const uri = `${baseUri}/valid_url`
-      const table = await createTestDB(uri, 2, 20)
-      const con = await lancedb.connect(uri)
-      assert.equal(con.uri, uri)
+      const opts = { uri: `${baseUri}/valid_url` }
+      const table = await createTestDB(opts, 2, 20)
+      const con = await lancedb.connect(opts)
+      assert.equal(con.uri, opts.uri)
 
       const results = await table.search([0.1, 0.3]).limit(5).execute()
       assert.equal(results.length, 5)
-    })
+    }).timeout(10_000)
+  } else {
+    describe.skip('Skip S3 test', function () {})
+  }
+
+  if (process.env.TEST_S3_BASE_URL != null && process.env.TEST_AWS_ACCESS_KEY_ID != null && process.env.TEST_AWS_SECRET_ACCESS_KEY != null) {
+    const baseUri = process.env.TEST_S3_BASE_URL
+    it('use custom credentials', async function () {
+      const opts: ConnectionOptions = {
+        uri: `${baseUri}/custom_credentials`,
+        awsCredentials: {
+          accessKeyId: process.env.TEST_AWS_ACCESS_KEY_ID as string,
+          secretKey: process.env.TEST_AWS_SECRET_ACCESS_KEY as string
+        }
+      }
+      const table = await createTestDB(opts, 2, 20)
+      console.log(table)
+      const con = await lancedb.connect(opts)
+      console.log(con)
+      assert.equal(con.uri, opts.uri)
+
+      const results = await table.search([0.1, 0.3]).limit(5).execute()
+      assert.equal(results.length, 5)
+    }).timeout(10_000)
   } else {
     describe.skip('Skip S3 test', function () {})
   }
 })
 
-async function createTestDB (uri: string, numDimensions: number = 2, numRows: number = 2): Promise<lancedb.Table> {
-  const con = await lancedb.connect(uri)
+async function createTestDB (opts: ConnectionOptions, numDimensions: number = 2, numRows: number = 2): Promise<lancedb.Table> {
+  const con = await lancedb.connect(opts)
 
   const data = []
   for (let i = 0; i < numRows; i++) {
@@ -48,5 +72,5 @@ async function createTestDB (uri: string, numDimensions: number = 2, numRows: nu
     data.push({ id: i + 1, name: `name_${i}`, price: i + 10, is_active: (i % 2 === 0), vector })
   }
 
-  return await con.createTable('vectors', data)
+  return await con.createTable('vectors_2', data)
 }

node/src/test/test.ts

@@ -18,7 +18,8 @@ import * as chai from 'chai'
 import * as chaiAsPromised from 'chai-as-promised'
 
 import * as lancedb from '../index'
-import { type EmbeddingFunction, MetricType, Query, WriteMode } from '../index'
+import { type AwsCredentials, type EmbeddingFunction, MetricType, Query, WriteMode, DefaultWriteOptions, isWriteOptions, type LocalTable } from '../index'
+import { FixedSizeList, Field, Int32, makeVector, Schema, Utf8, Table as ArrowTable, vectorFromArray, Float32 } from 'apache-arrow'
 
 const expect = chai.expect
 const assert = chai.assert
@@ -32,6 +33,22 @@ describe('LanceDB client', function () {
       assert.equal(con.uri, uri)
     })
 
+    it('should accept an options object', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect({ uri })
+      assert.equal(con.uri, uri)
+    })
+
+    it('should accept custom aws credentials', async function () {
+      const uri = await createTestDB()
+      const awsCredentials: AwsCredentials = {
+        accessKeyId: '',
+        secretKey: ''
+      }
+      const con = await lancedb.connect({ uri, awsCredentials })
+      assert.equal(con.uri, uri)
+    })
+
     it('should return the existing table names', async function () {
       const uri = await createTestDB()
       const con = await lancedb.connect(uri)
@@ -91,9 +108,9 @@ describe('LanceDB client', function () {
       const table = await con.openTable('vectors')
       const results = await table.search([0.1, 0.1]).select(['is_active']).execute()
       assert.equal(results.length, 2)
-      // vector and score are always returned
+      // vector and _distance are always returned
       assert.isDefined(results[0].vector)
-      assert.isDefined(results[0].score)
+      assert.isDefined(results[0]._distance)
       assert.isDefined(results[0].is_active)
 
       assert.isUndefined(results[0].id)
@@ -103,6 +120,45 @@ describe('LanceDB client', function () {
   })
 
   describe('when creating a new dataset', function () {
+    it('create an empty table', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('id', new Int32()), new Field('name', new Utf8())]
+      )
+      const table = await con.createTable({ name: 'vectors', schema })
+      assert.equal(table.name, 'vectors')
+      assert.deepEqual(await con.tableNames(), ['vectors'])
+    })
+
+    it('create a table with a empty data array', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('id', new Int32()), new Field('name', new Utf8())]
+      )
+      const table = await con.createTable({ name: 'vectors', schema, data: [] })
+      assert.equal(table.name, 'vectors')
+      assert.deepEqual(await con.tableNames(), ['vectors'])
+    })
+
+    it('create a table from an Arrow Table', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const i32s = new Int32Array(new Array<number>(10))
+      const i32 = makeVector(i32s)
+
+      const data = new ArrowTable({ vector: i32 })
+
+      const table = await con.createTable({ name: 'vectors', data })
+      assert.equal(table.name, 'vectors')
+      assert.equal(await table.countRows(), 10)
+      assert.deepEqual(await con.tableNames(), ['vectors'])
+    })
+
     it('creates a new table from javascript objects', async function () {
       const dir = await track().mkdir('lancejs')
       const con = await lancedb.connect(dir)
@@ -118,6 +174,18 @@ describe('LanceDB client', function () {
       assert.equal(await table.countRows(), 2)
     })
 
+    it('fails to create a new table when the vector column is missing', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const data = [
+        { id: 1, price: 10 }
+      ]
+
+      const create = con.createTable('missing_vector', data)
+      await expect(create).to.be.rejectedWith(Error, 'column \'vector\' is missing')
+    })
+
     it('use overwrite flag to overwrite existing table', async function () {
       const dir = await track().mkdir('lancejs')
       const con = await lancedb.connect(dir)
@@ -128,7 +196,7 @@ describe('LanceDB client', function () {
       ]
 
       const tableName = 'overwrite'
-      await con.createTable(tableName, data, WriteMode.Create)
+      await con.createTable(tableName, data, { writeMode: WriteMode.Create })
 
       const newData = [
         { id: 1, vector: [0.1, 0.2], price: 10 },
@@ -138,7 +206,7 @@ describe('LanceDB client', function () {
 
       await expect(con.createTable(tableName, newData)).to.be.rejectedWith(Error, 'already exists')
 
-      const table = await con.createTable(tableName, newData, WriteMode.Overwrite)
+      const table = await con.createTable(tableName, newData, { writeMode: WriteMode.Overwrite })
       assert.equal(table.name, tableName)
       assert.equal(await table.countRows(), 3)
     })
@@ -190,6 +258,36 @@ describe('LanceDB client', function () {
     })
   })
 
+  describe('when searching an empty dataset', function () {
+    it('should not fail', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('vector', new FixedSizeList(128, new Field('float32', new Float32())))]
+      )
+      const table = await con.createTable({ name: 'vectors', schema })
+      const result = await table.search(Array(128).fill(0.1)).execute()
+      assert.isEmpty(result)
+    })
+  })
+
+  describe('when searching an empty-after-delete dataset', function () {
+    it('should not fail', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('vector', new FixedSizeList(128, new Field('float32', new Float32())))]
+      )
+      const table = await con.createTable({ name: 'vectors', schema })
+      await table.add([{ vector: Array(128).fill(0.1) }])
+      await table.delete('vector IS NOT NULL')
+      const result = await table.search(Array(128).fill(0.1)).execute()
+      assert.isEmpty(result)
+    })
+  })
+
   describe('when creating a vector index', function () {
     it('overwrite all records in a table', async function () {
       const uri = await createTestDB(32, 300)
@@ -214,6 +312,22 @@ describe('LanceDB client', function () {
       // Default replace = true
       await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
     }).timeout(50_000)
+
+    it('it should fail when the column is not a vector', async function () {
+      const uri = await createTestDB(32, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      const createIndex = table.createIndex({ type: 'ivf_pq', column: 'name', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
+      await expect(createIndex).to.be.rejectedWith(/VectorIndex requires the column data type to be fixed size list of float32s/)
+    })
+
+    it('it should fail when the column is not a vector', async function () {
+      const uri = await createTestDB(32, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      const createIndex = table.createIndex({ type: 'ivf_pq', column: 'name', num_partitions: -1, max_iters: 2, num_sub_vectors: 2 })
+      await expect(createIndex).to.be.rejectedWith('num_partitions: must be > 0')
+    })
   })
 
   describe('when using a custom embedding function', function () {
@@ -243,7 +357,21 @@ describe('LanceDB client', function () {
         { price: 10, name: 'foo' },
         { price: 50, name: 'bar' }
       ]
-      const table = await con.createTable('vectors', data, WriteMode.Create, embeddings)
+      const table = await con.createTable('vectors', data, embeddings, { writeMode: WriteMode.Create })
+      const results = await table.search('foo').execute()
+      assert.equal(results.length, 2)
+    })
+
+    it('should create embeddings for Arrow Table', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+      const embeddingFunction = new TextEmbedding('name')
+
+      const names = vectorFromArray(['foo', 'bar'], new Utf8())
+      const data = new ArrowTable({ name: names })
+
+      const table = await con.createTable({ name: 'vectors', data, embeddingFunction })
+      assert.equal(table.name, 'vectors')
       const results = await table.search('foo').execute()
       assert.equal(results.length, 2)
     })
@@ -252,7 +380,7 @@ describe('LanceDB client', function () {
 
 describe('Query object', function () {
   it('sets custom parameters', async function () {
-    const query = new Query(undefined, [0.1, 0.3])
+    const query = new Query([0.1, 0.3])
       .limit(1)
       .metricType(MetricType.Cosine)
       .refineFactor(100)
@@ -301,3 +429,62 @@ describe('Drop table', function () {
     assert.deepEqual(await con.tableNames(), ['t2'])
   })
 })
+
+describe('WriteOptions', function () {
+  context('#isWriteOptions', function () {
+    it('should not match empty object', function () {
+      assert.equal(isWriteOptions({}), false)
+    })
+    it('should match write options', function () {
+      assert.equal(isWriteOptions({ writeMode: WriteMode.Create }), true)
+    })
+    it('should match undefined write mode', function () {
+      assert.equal(isWriteOptions({ writeMode: undefined }), true)
+    })
+    it('should match default write options', function () {
+      assert.equal(isWriteOptions(new DefaultWriteOptions()), true)
+    })
+  })
+})
+
+describe('Compact and cleanup', function () {
+  it('can cleanup after compaction', async function () {
+    const dir = await track().mkdir('lancejs')
+    const con = await lancedb.connect(dir)
+
+    const data = [
+      { price: 10, name: 'foo', vector: [1, 2, 3] },
+      { price: 50, name: 'bar', vector: [4, 5, 6] }
+    ]
+    const table = await con.createTable('t1', data) as LocalTable
+
+    const newData = [
+      { price: 30, name: 'baz', vector: [7, 8, 9] }
+    ]
+    await table.add(newData)
+
+    const compactionMetrics = await table.compactFiles({
+      numThreads: 2
+    })
+    assert.equal(compactionMetrics.fragmentsRemoved, 2)
+    assert.equal(compactionMetrics.fragmentsAdded, 1)
+    assert.equal(await table.countRows(), 3)
+
+    await table.cleanupOldVersions()
+    assert.equal(await table.countRows(), 3)
+
+    // should have no effect, but this validates the arguments are parsed.
+    await table.compactFiles({
+      targetRowsPerFragment: 1024 * 10,
+      maxRowsPerGroup: 1024,
+      materializeDeletions: true,
+      materializeDeletionsThreshold: 0.5,
+      numThreads: 2
+    })
+
+    const cleanupMetrics = await table.cleanupOldVersions(0, true)
+    assert.isAtLeast(cleanupMetrics.bytesRemoved, 1)
+    assert.isAtLeast(cleanupMetrics.oldVersions, 1)
+    assert.equal(await table.countRows(), 3)
+  })
+})

python/.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.8
+current_version = 0.3.1
 commit = True
 message = [python] Bump version: {current_version} → {new_version}
 tag = True

python/README.md

@@ -16,7 +16,7 @@ pip install lancedb
 import lancedb
 db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>')
 table = db.open_table('my_table')
-results = table.search([0.1, 0.3]).limit(20).to_df()
+results = table.search([0.1, 0.3]).limit(20).to_list()
 print(results)
 ```
 

python/lancedb/__init__.py

@@ -11,16 +11,37 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-from .db import URI, LanceDBConnection
+import importlib.metadata
+from typing import Optional
+
+__version__ = importlib.metadata.version("lancedb")
+
+from .db import URI, DBConnection, LanceDBConnection
+from .remote.db import RemoteDBConnection
+from .schema import vector
+from .utils import sentry_log
 
 
-def connect(uri: URI) -> LanceDBConnection:
-    """Connect to a LanceDB instance at the given URI
+def connect(
+    uri: URI,
+    *,
+    api_key: Optional[str] = None,
+    region: str = "us-west-2",
+    host_override: Optional[str] = None,
+) -> DBConnection:
+    """Connect to a LanceDB database.
 
     Parameters
     ----------
     uri: str or Path
         The uri of the database.
+    api_key: str, optional
+        If presented, connect to LanceDB cloud.
+        Otherwise, connect to a database on file system or cloud storage.
+    region: str, default "us-west-2"
+        The region to use for LanceDB Cloud.
+    host_override: str, optional
+        The override url for LanceDB Cloud.
 
     Examples
     --------
@@ -34,9 +55,17 @@ def connect(uri: URI) -> LanceDBConnection:
 
     >>> db = lancedb.connect("s3://my-bucket/lancedb")
 
+    Connect to LancdDB cloud:
+
+    >>> db = lancedb.connect("db://my_database", api_key="ldb_...")
+
     Returns
     -------
-    conn : LanceDBConnection
+    conn : DBConnection
         A connection to a LanceDB database.
     """
+    if isinstance(uri, str) and uri.startswith("db://"):
+        if api_key is None:
+            raise ValueError(f"api_key is required to connected LanceDB cloud: {uri}")
+        return RemoteDBConnection(uri, api_key, region, host_override)
     return LanceDBConnection(uri)

python/lancedb/cli/__init__.py

@@ -0,0 +1,12 @@
+#  Copyright 2023 LanceDB Developers
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.

python/lancedb/cli/cli.py

@@ -0,0 +1,46 @@
+#  Copyright 2023 LanceDB Developers
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import click
+
+from lancedb.utils import CONFIG
+
+
+@click.group()
+@click.version_option(help="LanceDB command line interface entry point")
+def cli():
+    "LanceDB command line interface"
+
+
+diagnostics_help = """
+Enable or disable LanceDB diagnostics. When enabled, LanceDB will send anonymous events to help us improve LanceDB.
+These diagnostics are used only for error reporting and no data is collected. You can find more about diagnosis on
+our docs: https://lancedb.github.io/lancedb/cli_config/
+"""
+
+
+@cli.command(help=diagnostics_help)
+@click.option("--enabled/--disabled", default=True)
+def diagnostics(enabled):
+    CONFIG.update({"diagnostics": True if enabled else False})
+    click.echo("LanceDB diagnostics is %s" % ("enabled" if enabled else "disabled"))
+
+
+@cli.command(help="Show current LanceDB configuration")
+def config():
+    # TODO: pretty print as table with colors and formatting
+    click.echo("Current LanceDB configuration:")
+    cfg = CONFIG.copy()
+    cfg.pop("uuid")  # Don't show uuid as it is not configurable
+    for item, amount in cfg.items():
+        click.echo("{} ({})".format(item, amount))

python/lancedb/common.py

@@ -11,15 +11,26 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 from pathlib import Path
-from typing import List, Union
+from typing import Iterable, List, Union
 
 import numpy as np
-import pandas as pd
 import pyarrow as pa
 
+from .util import safe_import_pandas
+
+pd = safe_import_pandas()
+
+DATA = Union[List[dict], dict, "pd.DataFrame", pa.Table, Iterable[pa.RecordBatch]]
 VEC = Union[list, np.ndarray, pa.Array, pa.ChunkedArray]
 URI = Union[str, Path]
-
-# TODO support generator
-DATA = Union[List[dict], dict, pd.DataFrame]
 VECTOR_COLUMN_NAME = "vector"
+
+
+class Credential(str):
+    """Credential field"""
+
+    def __repr__(self) -> str:
+        return "********"
+
+    def __str__(self) -> str:
+        return "********"

python/lancedb/conftest.py

@@ -1,10 +1,11 @@
-import builtins
 import os
 
+import numpy as np
 import pytest
 
+from .embeddings import EmbeddingFunctionRegistry, TextEmbeddingFunction
+
 # import lancedb so we don't have to in every example
-import lancedb
 
 
 @pytest.fixture(autouse=True)
@@ -16,3 +17,24 @@ def doctest_setup(monkeypatch, tmpdir):
     monkeypatch.setitem(os.environ, "COLUMNS", "80")
     # Work in a temporary directory
     monkeypatch.chdir(tmpdir)
+
+
+registry = EmbeddingFunctionRegistry.get_instance()
+
+
+@registry.register("test")
+class MockTextEmbeddingFunction(TextEmbeddingFunction):
+    """
+    Return the hash of the first 10 characters
+    """
+
+    def generate_embeddings(self, texts):
+        return [self._compute_one_embedding(row) for row in texts]
+
+    def _compute_one_embedding(self, row):
+        emb = np.array([float(hash(c)) for c in row[:10]])
+        emb /= np.linalg.norm(emb)
+        return emb
+
+    def ndims(self):
+        return 10