Bump version: 0.8.0-beta.0 → 0.8.0

The wheels are built to `WORKDIR/target/wheels` and the step was
configured to look for them at `WORKDIR/python/target/wheels`.

.bumpversion.cfg

@@ -1,22 +0,0 @@
-[bumpversion]
-current_version = 0.4.16
-commit = True
-message = Bump version: {current_version} → {new_version}
-tag = True
-tag_name = v{new_version}
-
-[bumpversion:file:node/package.json]
-
-[bumpversion:file:nodejs/package.json]
-
-[bumpversion:file:nodejs/npm/darwin-x64/package.json]
-
-[bumpversion:file:nodejs/npm/darwin-arm64/package.json]
-
-[bumpversion:file:nodejs/npm/linux-x64-gnu/package.json]
-
-[bumpversion:file:nodejs/npm/linux-arm64-gnu/package.json]
-
-[bumpversion:file:rust/ffi/node/Cargo.toml]
-
-[bumpversion:file:rust/lancedb/Cargo.toml]

.bumpversion.toml

@@ -0,0 +1,57 @@
+[tool.bumpversion]
+current_version = "0.5.0"
+parse = """(?x)
+    (?P<major>0|[1-9]\\d*)\\.
+    (?P<minor>0|[1-9]\\d*)\\.
+    (?P<patch>0|[1-9]\\d*)
+    (?:-(?P<pre_l>[a-zA-Z-]+)\\.(?P<pre_n>0|[1-9]\\d*))?
+"""
+serialize = [
+    "{major}.{minor}.{patch}-{pre_l}.{pre_n}",
+    "{major}.{minor}.{patch}",
+]
+search = "{current_version}"
+replace = "{new_version}"
+regex = false
+ignore_missing_version = false
+ignore_missing_files = false
+tag = true
+sign_tags = false
+tag_name = "v{new_version}"
+tag_message = "Bump version: {current_version} → {new_version}"
+allow_dirty = true
+commit = true
+message = "Bump version: {current_version} → {new_version}"
+commit_args = ""
+
+[tool.bumpversion.parts.pre_l]
+values = ["beta", "final"]
+optional_value = "final"
+
+[[tool.bumpversion.files]]
+filename = "node/package.json"
+search = "\"version\": \"{current_version}\","
+replace = "\"version\": \"{new_version}\","
+
+[[tool.bumpversion.files]]
+filename = "nodejs/package.json"
+search = "\"version\": \"{current_version}\","
+replace = "\"version\": \"{new_version}\","
+
+# nodejs binary packages
+[[tool.bumpversion.files]]
+glob = "nodejs/npm/*/package.json"
+search = "\"version\": \"{current_version}\","
+replace = "\"version\": \"{new_version}\","
+
+# Cargo files
+# ------------
+[[tool.bumpversion.files]]
+filename = "rust/ffi/node/Cargo.toml"
+search = "\nversion = \"{current_version}\""
+replace = "\nversion = \"{new_version}\""
+
+[[tool.bumpversion.files]]
+filename = "rust/lancedb/Cargo.toml"
+search = "\nversion = \"{current_version}\""
+replace = "\nversion = \"{new_version}\""

.github/labeler.yml

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

.github/release_notes.json

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

.github/workflows/cargo-publish.yml

@@ -1,13 +1,20 @@
 name: Cargo Publish
 
 on:
-  release:
-    types: [ published ]
+  push:
+    tags-ignore:
+      # We don't publish pre-releases for Rust. Crates.io is just a source
+      # distribution, so we don't need to publish pre-releases.
+      - 'v*-beta*'
+      - '*-v*' # for example, python-vX.Y.Z
 
 env:
   # This env var is used by Swatinem/rust-cache@v2 for the cache
   # key, so we set it to make sure it is always consistent.
   CARGO_TERM_COLOR: always
+  # Up-to-date compilers needed for fp16kernels.
+  CC: gcc-12
+  CXX: g++-12
 
 jobs:
   build:

.github/workflows/dev.yml

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

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

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

.github/workflows/node.yml

@@ -107,6 +107,7 @@ jobs:
       AWS_ENDPOINT: http://localhost:4566
       # this one is for dynamodb
       DYNAMODB_ENDPOINT: http://localhost:4566
+      ALLOW_HTTP: true
     steps:
     - uses: actions/checkout@v4
       with:

.github/workflows/nodejs.yml

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

.github/workflows/npm-publish.yml

@@ -1,8 +1,9 @@
 name: NPM Publish
 
 on:
-  release:
-    types: [published]
+  push:
+    tags:
+      - 'v*'
 
 jobs:
   node:
@@ -274,9 +275,15 @@ jobs:
         env:
           NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
         run: |
+          # Tag beta as "preview" instead of default "latest". See lancedb 
+          # npm publish step for more info.
+          if [[ $GITHUB_REF =~ refs/tags/v(.*)-beta.* ]]; then
+            PUBLISH_ARGS="--tag preview"
+          fi
+
           mv */*.tgz .
           for filename in *.tgz; do
-            npm publish $filename
+            npm publish $PUBLISH_ARGS $filename
           done
 
   release-nodejs:
@@ -316,11 +323,23 @@ jobs:
       - name: Publish to NPM
         env:
           NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
-        run: npm publish --access public
+        # By default, things are published to the latest tag. This is what is 
+        # installed by default if the user does not specify a version. This is
+        # good for stable releases, but for pre-releases, we want to publish to
+        # the "preview" tag so they can install with `npm install lancedb@preview`.
+        # See: https://medium.com/@mbostock/prereleases-and-npm-e778fc5e2420
+        run: |
+          if [[ $GITHUB_REF =~ refs/tags/v(.*)-beta.* ]]; then
+            npm publish --access public --tag preview
+          else
+            npm publish --access public
+          fi
 
   update-package-lock:
     needs: [release]
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -331,11 +350,13 @@ jobs:
           lfs: true
       - uses: ./.github/workflows/update_package_lock
         with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          github_token: ${{ secrets.GITHUB_TOKEN }}
 
   update-package-lock-nodejs:
     needs: [release-nodejs]
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -346,4 +367,70 @@ jobs:
           lfs: true
       - uses: ./.github/workflows/update_package_lock_nodejs
         with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+  
+  gh-release:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Extract version
+        id: extract_version
+        env:
+          GITHUB_REF: ${{ github.ref }}
+        run: |
+          set -e
+          echo "Extracting tag and version from $GITHUB_REF"
+          if [[ $GITHUB_REF =~ refs/tags/v(.*) ]]; then
+            VERSION=${BASH_REMATCH[1]}
+            TAG=v$VERSION
+            echo "tag=$TAG" >> $GITHUB_OUTPUT
+            echo "version=$VERSION" >> $GITHUB_OUTPUT
+          else
+            echo "Failed to extract version from $GITHUB_REF"
+            exit 1
+          fi
+          echo "Extracted version $VERSION from $GITHUB_REF"
+          if [[ $VERSION =~ beta ]]; then
+            echo "This is a beta release"
+
+            # Get last release (that is not this one)
+            FROM_TAG=$(git tag --sort='version:refname' \
+              | grep ^v \
+              | grep -vF "$TAG" \
+              | python ci/semver_sort.py v \
+              | tail -n 1)
+          else
+            echo "This is a stable release"
+            # Get last stable tag (ignore betas)
+            FROM_TAG=$(git tag --sort='version:refname' \
+              | grep ^v \
+              | grep -vF "$TAG" \
+              | grep -v beta \
+              | python ci/semver_sort.py v \
+              | tail -n 1)
+          fi
+          echo "Found from tag $FROM_TAG"
+          echo "from_tag=$FROM_TAG" >> $GITHUB_OUTPUT
+      - name: Create Release Notes
+        id: release_notes
+        uses: mikepenz/release-changelog-builder-action@v4
+        with:
+          configuration: .github/release_notes.json
+          toTag: ${{ steps.extract_version.outputs.tag }}
+          fromTag: ${{ steps.extract_version.outputs.from_tag }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Create GH release
+        uses: softprops/action-gh-release@v2
+        with:
+          prerelease: ${{ contains('beta', github.ref) }}
+          tag_name: ${{ steps.extract_version.outputs.tag }}
+          token: ${{ secrets.GITHUB_TOKEN }}
+          generate_release_notes: false
+          name: Node/Rust LanceDB v${{ steps.extract_version.outputs.version }}
+          body: ${{ steps.release_notes.outputs.changelog }}

.github/workflows/pypi-publish.yml

@@ -1,8 +1,9 @@
 name: PyPI Publish
 
 on:
-  release:
-    types: [published]
+  push:
+    tags:
+      - 'python-v*'
 
 jobs:
   linux:
@@ -10,7 +11,6 @@ jobs:
     timeout-minutes: 60
     strategy:
       matrix:
-        python-minor-version: ["8"]
         config:
           - platform: x86_64
             manylinux: "2_17"
@@ -32,23 +32,22 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.${{ matrix.python-minor-version }}
+          python-version: 3.8
       - uses: ./.github/workflows/build_linux_wheel
         with:
-          python-minor-version: ${{ matrix.python-minor-version }}
+          python-minor-version: 8
           args: "--release --strip ${{ matrix.config.extra_args }}"
           arm-build: ${{ matrix.config.platform == 'aarch64' }}
           manylinux: ${{ matrix.config.manylinux }}
       - uses: ./.github/workflows/upload_wheel
         with:
-          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          repo: "pypi"
+          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          fury_token: ${{ secrets.FURY_TOKEN }}
   mac:
     timeout-minutes: 60
     runs-on: ${{ matrix.config.runner }}
     strategy:
       matrix:
-        python-minor-version: ["8"]
         config:
           - target: x86_64-apple-darwin
             runner: macos-13
@@ -59,7 +58,6 @@ jobs:
     steps:
       - uses: actions/checkout@v4
         with:
-          ref: ${{ inputs.ref }}
           fetch-depth: 0
           lfs: true
       - name: Set up Python
@@ -68,36 +66,95 @@ jobs:
           python-version: 3.12
       - uses: ./.github/workflows/build_mac_wheel
         with:
-          python-minor-version: ${{ matrix.python-minor-version }}
+          python-minor-version: 8
           args: "--release --strip --target ${{ matrix.config.target }} --features fp16kernels"
       - uses: ./.github/workflows/upload_wheel
         with:
-          python-minor-version: ${{ matrix.python-minor-version }}
-          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          repo: "pypi"
+          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          fury_token: ${{ secrets.FURY_TOKEN }}
   windows:
     timeout-minutes: 60
     runs-on: windows-latest
-    strategy:
-      matrix:
-        python-minor-version: ["8"]
     steps:
       - uses: actions/checkout@v4
         with:
-          ref: ${{ inputs.ref }}
           fetch-depth: 0
           lfs: true
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.${{ matrix.python-minor-version }}
+          python-version: 3.8
       - uses: ./.github/workflows/build_windows_wheel
         with:
-          python-minor-version: ${{ matrix.python-minor-version }}
+          python-minor-version: 8
           args: "--release --strip"
           vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
       - uses: ./.github/workflows/upload_wheel
         with:
-          python-minor-version: ${{ matrix.python-minor-version }}
-          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          repo: "pypi"
+          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          fury_token: ${{ secrets.FURY_TOKEN }}
+  gh-release:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Extract version
+        id: extract_version
+        env:
+          GITHUB_REF: ${{ github.ref }}
+        run: |
+          set -e
+          echo "Extracting tag and version from $GITHUB_REF"
+          if [[ $GITHUB_REF =~ refs/tags/python-v(.*) ]]; then
+            VERSION=${BASH_REMATCH[1]}
+            TAG=python-v$VERSION
+            echo "tag=$TAG" >> $GITHUB_OUTPUT
+            echo "version=$VERSION" >> $GITHUB_OUTPUT
+          else
+            echo "Failed to extract version from $GITHUB_REF"
+            exit 1
+          fi
+          echo "Extracted version $VERSION from $GITHUB_REF"
+          if [[ $VERSION =~ beta ]]; then
+            echo "This is a beta release"
+
+            # Get last release (that is not this one)
+            FROM_TAG=$(git tag --sort='version:refname' \
+              | grep ^python-v \
+              | grep -vF "$TAG" \
+              | python ci/semver_sort.py python-v \
+              | tail -n 1)
+          else
+            echo "This is a stable release"
+            # Get last stable tag (ignore betas)
+            FROM_TAG=$(git tag --sort='version:refname' \
+              | grep ^python-v \
+              | grep -vF "$TAG" \
+              | grep -v beta \
+              | python ci/semver_sort.py python-v \
+              | tail -n 1)
+          fi
+          echo "Found from tag $FROM_TAG"
+          echo "from_tag=$FROM_TAG" >> $GITHUB_OUTPUT
+      - name: Create Python Release Notes
+        id: python_release_notes
+        uses: mikepenz/release-changelog-builder-action@v4
+        with:
+          configuration: .github/release_notes.json
+          toTag: ${{ steps.extract_version.outputs.tag }}
+          fromTag: ${{ steps.extract_version.outputs.from_tag }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Create Python GH release
+        uses: softprops/action-gh-release@v2
+        with:
+          prerelease: ${{ contains('beta', github.ref) }}
+          tag_name: ${{ steps.extract_version.outputs.tag }}
+          token: ${{ secrets.GITHUB_TOKEN }}
+          generate_release_notes: false
+          name: Python LanceDB v${{ steps.extract_version.outputs.version }}
+          body: ${{ steps.python_release_notes.outputs.changelog }}

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

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

.github/workflows/python.yml

@@ -75,7 +75,7 @@ jobs:
     timeout-minutes: 30
     strategy:
       matrix:
-        python-minor-version: ["8", "11"]
+        python-minor-version: ["9", "11"]
     runs-on: "ubuntu-22.04"
     defaults:
       run:
@@ -99,6 +99,8 @@ jobs:
           workspaces: python
       - uses: ./.github/workflows/build_linux_wheel
       - uses: ./.github/workflows/run_tests
+        with:
+          integration: true
       # Make sure wheels are not included in the Rust cache
       - name: Delete wheels
         run: rm -rf target/wheels
@@ -190,4 +192,4 @@ jobs:
           pip install -e .[tests]
           pip install tantivy
       - name: Run tests
-        run: pytest -m "not slow" -x -v --durations=30 python/tests
+        run: pytest -m "not slow and not s3_test" -x -v --durations=30 python/tests

.github/workflows/run_tests/action.yml

@@ -5,6 +5,10 @@ inputs:
   python-minor-version:
     required: true
     description: "8 9 10 11 12"
+  integration:
+    required: false
+    description: "Run integration tests"
+    default: "false"
 runs:
   using: "composite"
   steps:
@@ -12,6 +16,16 @@ runs:
       shell: bash
       run: |
         pip3 install $(ls target/wheels/lancedb-*.whl)[tests,dev]
-    - name: pytest
+    - name: Setup localstack for integration tests
+      if: ${{ inputs.integration == 'true' }}
       shell: bash
+      working-directory: .
+      run: docker compose up --detach --wait
+    - name: pytest (with integration)
+      shell: bash
+      if: ${{ inputs.integration == 'true' }}
       run: pytest -m "not slow" -x -v --durations=30 python/python/tests
+    - name: pytest (no integration tests)
+      shell: bash
+      if: ${{ inputs.integration != 'true' }}
+      run: pytest -m "not slow and not s3_test" -x -v --durations=30 python/python/tests

.github/workflows/rust.yml

@@ -74,6 +74,9 @@ jobs:
       run: |
           sudo apt update
           sudo apt install -y protobuf-compiler libssl-dev
+    - name: Start S3 integration test environment
+      working-directory: .
+      run: docker compose up --detach --wait
     - name: Build
       run: cargo build --all-features
     - name: Run tests
@@ -105,7 +108,8 @@ jobs:
       - name: Build
         run: cargo build --all-features
       - name: Run tests
-        run: cargo test --all-features
+        # Run with everything except the integration tests.
+        run: cargo test --features remote,fp16kernels
   windows:
     runs-on: windows-2022
     steps:

.github/workflows/upload_wheel/action.yml

@@ -2,28 +2,43 @@ name: upload-wheel
 
 description: "Upload wheels to Pypi"
 inputs:
-  os:
-    required: true
-    description: "ubuntu-22.04 or macos-13"
-  repo:
-    required: false
-    description: "pypi or testpypi"
-    default: "pypi"
-  token:
+  pypi_token:
     required: true
     description: "release token for the repo"
+  fury_token:
+    required: true
+    description: "release token for the fury repo"
 
 runs:
   using: "composite"
   steps:
-    - name: Install dependencies
-      shell: bash
-      run: |
-        python -m pip install --upgrade pip
-        pip install twine
-    - name: Publish wheel
-      env:
-        TWINE_USERNAME: __token__
-        TWINE_PASSWORD: ${{ inputs.token }}
-      shell: bash
-      run: twine upload --repository ${{ inputs.repo }} target/wheels/lancedb-*.whl
+  - name: Install dependencies
+    shell: bash
+    run: |
+      python -m pip install --upgrade pip
+      pip install twine
+  - name: Choose repo
+    shell: bash
+    id: choose_repo
+    run: |
+      if [ ${{ github.ref }} == "*beta*" ]; then
+        echo "repo=fury" >> $GITHUB_OUTPUT
+      else
+        echo "repo=pypi" >> $GITHUB_OUTPUT
+      fi
+  - name: Publish to PyPI
+    shell: bash
+    env:
+      FURY_TOKEN: ${{ inputs.fury_token }}
+      PYPI_TOKEN: ${{ inputs.pypi_token }}
+    run: |
+      if [ ${{ steps.choose_repo.outputs.repo }} == "fury" ]; then
+        WHEEL=$(ls target/wheels/lancedb-*.whl 2> /dev/null | head -n 1)
+        echo "Uploading $WHEEL to Fury"
+        curl -f -F package=@$WHEEL https://$FURY_TOKEN@push.fury.io/lancedb/
+      else
+        twine upload --repository ${{ steps.choose_repo.outputs.repo }} \
+          --username __token__ \
+          --password $PYPI_TOKEN \
+          target/wheels/lancedb-*.whl
+      fi

.gitignore

@@ -6,7 +6,7 @@
 venv
 
 .vscode
-
+.zed
 rust/target
 rust/Cargo.lock
 

.pre-commit-config.yaml

@@ -10,9 +10,12 @@ repos:
     rev: v0.2.2
     hooks:
     - id: ruff
-- repo: https://github.com/pre-commit/mirrors-prettier
-  rev: v3.1.0
+- repo: local
   hooks:
-    - id: prettier
+    - id: local-biome-check
+      name: biome check
+      entry: npx biome check
+      language: system
+      types: [text]
       files: "nodejs/.*"
       exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*

Cargo.toml

@@ -14,22 +14,22 @@ keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
 
 [workspace.dependencies]
-lance = { "version" = "=0.10.9", "features" = ["dynamodb"] }
-lance-index = { "version" = "=0.10.9" }
-lance-linalg = { "version" = "=0.10.9" }
-lance-testing = { "version" = "=0.10.9" }
+lance = { "version" = "=0.11.0", "features" = ["dynamodb"] }
+lance-index = { "version" = "=0.11.0" }
+lance-linalg = { "version" = "=0.11.0" }
+lance-testing = { "version" = "=0.11.0" }
 # Note that this one does not include pyarrow
-arrow = { version = "50.0", optional = false }
-arrow-array = "50.0"
-arrow-data = "50.0"
-arrow-ipc = "50.0"
-arrow-ord = "50.0"
-arrow-schema = "50.0"
-arrow-arith = "50.0"
-arrow-cast = "50.0"
+arrow = { version = "51.0", optional = false }
+arrow-array = "51.0"
+arrow-data = "51.0"
+arrow-ipc = "51.0"
+arrow-ord = "51.0"
+arrow-schema = "51.0"
+arrow-arith = "51.0"
+arrow-cast = "51.0"
 async-trait = "0"
 chrono = "0.4.35"
-half = { "version" = "=2.3.1", default-features = false, features = [
+half = { "version" = "=2.4.1", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"

README.md

@@ -20,7 +20,7 @@
 
 <hr />
 
-LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
+LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.
 
 The key features of LanceDB include:
 
@@ -36,7 +36,7 @@ The key features of LanceDB include:
 
 * GPU support in building vector index(*).
 
-* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lanecdb.html), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
+* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/docs/integrations/vectorstores/lancedb/), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
 
 LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
 

ci/bump_version.sh

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

ci/check_breaking_changes.py

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

ci/semver_sort.py

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

docker-compose.yml

@@ -1,18 +1,18 @@
 version: "3.9"
 services:
   localstack:
-    image: localstack/localstack:0.14
+    image: localstack/localstack:3.3
     ports:
       - 4566:4566
     environment:
-      - SERVICES=s3,dynamodb
+      - SERVICES=s3,dynamodb,kms
       - 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" ]
+      test: [ "CMD", "curl", "-s", "http://localhost:4566/_localstack/health" ]
       interval: 5s
       retries: 3
       start_period: 10s

docs/mkdocs.yml

@@ -57,16 +57,6 @@ plugins:
             - https://arrow.apache.org/docs/objects.inv
             - https://pandas.pydata.org/docs/objects.inv
   - mkdocs-jupyter
-  - ultralytics:
-      verbose: True
-      enabled: True
-      default_image: "assets/lancedb_and_lance.png" # Default image for all pages
-      add_image: True # Automatically add meta image
-      add_keywords: True # Add page keywords in the header tag
-      add_share_buttons: True # Add social share buttons
-      add_authors: False # Display page authors
-      add_desc: False
-      add_dates: False
 
 markdown_extensions:
   - admonition
@@ -104,6 +94,14 @@ nav:
               - Overview: hybrid_search/hybrid_search.md
               - Comparing Rerankers: hybrid_search/eval.md
               - Airbnb financial data example: notebooks/hybrid_search.ipynb
+          - Reranking:
+              - Quickstart: reranking/index.md
+              - Cohere Reranker: reranking/cohere.md
+              - Linear Combination Reranker: reranking/linear_combination.md
+              - Cross Encoder Reranker: reranking/cross_encoder.md
+              - ColBERT Reranker: reranking/colbert.md
+              - OpenAI Reranker: reranking/openai.md
+              - Building Custom Rerankers: reranking/custom_reranker.md
           - Filtering: sql.md
           - Versioning & Reproducibility: notebooks/reproducibility.ipynb
           - Configuring Storage: guides/storage.md
@@ -120,9 +118,10 @@ nav:
           - Pandas and PyArrow: python/pandas_and_pyarrow.md
           - Polars: python/polars_arrow.md
           - DuckDB: python/duckdb.md
-          - LangChain 🔗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
-          - LangChain JS/TS 🔗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
-          - LlamaIndex 🦙: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
+          - LangChain:
+            - LangChain 🔗: integrations/langchain.md
+            - LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
+          - LlamaIndex 🦙: https://docs.llamaindex.ai/en/stable/examples/vector_stores/LanceDBIndexDemo/
           - Pydantic: python/pydantic.md
           - Voxel51: integrations/voxel51.md
           - PromptTools: integrations/prompttools.md
@@ -143,7 +142,6 @@ nav:
               - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
           - 🦀 Rust:
               - Overview: examples/examples_rust.md
-      - 🔧 CLI & Config: cli_config.md
       - 💭 FAQs: faq.md
       - ⚙️ API reference:
           - 🐍 Python: python/python.md
@@ -171,6 +169,14 @@ nav:
           - Overview: hybrid_search/hybrid_search.md
           - Comparing Rerankers: hybrid_search/eval.md
           - Airbnb financial data example: notebooks/hybrid_search.ipynb
+      - Reranking:
+          - Quickstart: reranking/index.md
+          - Cohere Reranker: reranking/cohere.md
+          - Linear Combination Reranker: reranking/linear_combination.md
+          - Cross Encoder Reranker: reranking/cross_encoder.md
+          - ColBERT Reranker: reranking/colbert.md
+          - OpenAI Reranker: reranking/openai.md
+          - Building Custom Rerankers: reranking/custom_reranker.md
       - Filtering: sql.md
       - Versioning & Reproducibility: notebooks/reproducibility.ipynb
       - Configuring Storage: guides/storage.md
@@ -187,8 +193,8 @@ nav:
       - Pandas and PyArrow: python/pandas_and_pyarrow.md
       - Polars: python/polars_arrow.md
       - DuckDB: python/duckdb.md
-      - LangChain 🦜️🔗↗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
-      - LangChain.js 🦜️🔗↗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
+      - LangChain 🦜️🔗↗: https://python.langchain.com/docs/integrations/vectorstores/lancedb
+      - LangChain.js 🦜️🔗↗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
       - LlamaIndex 🦙↗: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
       - Pydantic: python/pydantic.md
       - Voxel51: integrations/voxel51.md

docs/requirements.txt

@@ -2,5 +2,4 @@ mkdocs==1.5.3
 mkdocs-jupyter==0.24.1
 mkdocs-material==9.5.3
 mkdocstrings[python]==0.20.0
-pydantic
-mkdocs-ultralytics-plugin==0.0.44
+pydantic

docs/src/basic.md

@@ -44,6 +44,36 @@
 
     !!! info "Please also make sure you're using the same version of Arrow as in the [lancedb crate](https://github.com/lancedb/lancedb/blob/main/Cargo.toml)"
 
+### Preview releases
+
+Stable releases are created about every 2 weeks. For the latest features and bug
+fixes, you can install the preview release. These releases receive the same
+level of testing as stable releases, but are not guaranteed to be available for
+more than 6 months after they are released. Once your application is stable, we
+recommend switching to stable releases.
+
+=== "Python"
+
+      ```shell
+      pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
+      ```
+
+=== "Typescript"
+
+      ```shell
+      npm install vectordb@preview
+      ```
+
+=== "Rust"
+    
+    We don't push preview releases to crates.io, but you can referent the tag
+    in GitHub within your Cargo dependencies:
+
+    ```toml
+    [dependencies]
+    lancedb = { git = "https://github.com/lancedb/lancedb.git", tag = "vX.Y.Z-beta.N" }
+    ```
+
 ## Connect to a database
 
 === "Python"

docs/src/cli_config.md

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

docs/src/embeddings/default_embedding_functions.md

@@ -154,9 +154,12 @@ Allows you to set parameters when registering a `sentence-transformers` object.
 !!! note "BAAI Embeddings example"
     Here is an example that uses BAAI embedding model from the HuggingFace Hub [supported models](https://huggingface.co/models?library=sentence-transformers)
     ```python
+    import lancedb
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.embeddings import get_registry
+
     db = lancedb.connect("/tmp/db")
-    registry = EmbeddingFunctionRegistry.get_instance()
-    model = registry.get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
+    model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
 
     class Words(LanceModel):
         text: str = model.SourceField()
@@ -165,7 +168,7 @@ Allows you to set parameters when registering a `sentence-transformers` object.
     table = db.create_table("words", schema=Words)
     table.add(
         [
-            {"text": "hello world"}
+            {"text": "hello world"},
             {"text": "goodbye world"}
         ]
     )
@@ -177,6 +180,70 @@ Allows you to set parameters when registering a `sentence-transformers` object.
 Visit sentence-transformers [HuggingFace HUB](https://huggingface.co/sentence-transformers) page for more information on the available models.
 
 
+### Huggingface embedding models
+We offer support for all huggingface models (which can be loaded via [transformers](https://huggingface.co/docs/transformers/en/index) library). The default model is `colbert-ir/colbertv2.0` which also has its own special callout - `registry.get("colbert")` 
+
+Example usage - 
+```python
+import lancedb
+import pandas as pd
+
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+
+model = get_registry().get("huggingface").create(name='facebook/bart-base')
+
+class TextModel(LanceModel):
+    text: str = model.SourceField()
+    vector: Vector(model.ndims()) = model.VectorField()
+
+df = pd.DataFrame({"text": ["hi hello sayonara", "goodbye world"]})
+table = db.create_table("greets", schema=Words)
+table.add()
+query = "old greeting"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```
+
+
+### Ollama embeddings
+Generate embeddings via the [ollama](https://github.com/ollama/ollama-python) python library. More details:
+
+- [Ollama docs on embeddings](https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings)
+- [Ollama blog on embeddings](https://ollama.com/blog/embedding-models)
+
+| Parameter              | Type                       | Default Value            | Description                                                                                                                                    |
+|------------------------|----------------------------|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|
+| `name`                 | `str`                      | `nomic-embed-text`       | The name of the model.                                                                                                                         |
+| `host`                 | `str`                      | `http://localhost:11434` | The Ollama host to connect to.                                                                                                                 |
+| `options`              | `ollama.Options` or `dict` | `None`                   | Additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`. |
+| `keep_alive`           | `float` or `str`           | `"5m"`                   | Controls how long the model will stay loaded into memory following the request.                                                                |
+| `ollama_client_kwargs` | `dict`                     | `{}`                     | kwargs that can be past to the `ollama.Client`.                                                                                                |
+
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+db = lancedb.connect("/tmp/db")
+func = get_registry().get("ollama").create(name="nomic-embed-text")
+
+class Words(LanceModel):
+    text: str = func.SourceField()
+    vector: Vector(func.ndims()) = func.VectorField()
+
+table = db.create_table("words", schema=Words, mode="overwrite")
+table.add([
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+])
+
+query = "greetings"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```
+
+
 ### OpenAI embeddings
 LanceDB registers the OpenAI embeddings function in the registry by default, as `openai`. Below are the parameters that you can customize when creating the instances:
 
@@ -187,18 +254,21 @@ LanceDB registers the OpenAI embeddings function in the registry by default, as
 
 
 ```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
 db = lancedb.connect("/tmp/db")
-registry = EmbeddingFunctionRegistry.get_instance()
-func = registry.get("openai").create()
+func = get_registry().get("openai").create(name="text-embedding-ada-002")
 
 class Words(LanceModel):
     text: str = func.SourceField()
     vector: Vector(func.ndims()) = func.VectorField()
 
-table = db.create_table("words", schema=Words)
+table = db.create_table("words", schema=Words, mode="overwrite")
 table.add(
     [
-        {"text": "hello world"}
+        {"text": "hello world"},
         {"text": "goodbye world"}
     ]
     )
@@ -327,6 +397,10 @@ Supported parameters (to be passed in `create` method) are:
 Usage Example:
 
 ```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
 model = get_registry().get("bedrock-text").create()
 
 class TextModel(LanceModel):
@@ -361,10 +435,12 @@ This embedding function supports ingesting images as both bytes and urls. You ca
     LanceDB supports ingesting images directly from accessible links.
 
 ```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
 
 db = lancedb.connect(tmp_path)
-registry = EmbeddingFunctionRegistry.get_instance()
-func = registry.get("open-clip").create()
+func = get_registry.get("open-clip").create()
 
 class Images(LanceModel):
     label: str
@@ -439,9 +515,12 @@ This function is registered as `imagebind` and supports Audio, Video and Text mo
 Below is an example demonstrating how the API works:
 
 ```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
 db = lancedb.connect(tmp_path)
-registry = EmbeddingFunctionRegistry.get_instance()
-func = registry.get("imagebind").create()
+func = get_registry.get("imagebind").create()
 
 class ImageBindModel(LanceModel):
     text: str

docs/src/embeddings/embedding_functions.md

@@ -46,7 +46,7 @@ For this purpose, LanceDB introduces an **embedding functions API**, that allow
 
     ```python
     class Pets(LanceModel):
-        vector: Vector(clip.ndims) = clip.VectorField()
+        vector: Vector(clip.ndims()) = clip.VectorField()
         image_uri: str = clip.SourceField()
     ```
 
@@ -149,7 +149,7 @@ You can also use the integration for adding utility operations in the schema. Fo
 
 ```python
 class Pets(LanceModel):
-    vector: Vector(clip.ndims) = clip.VectorField()
+    vector: Vector(clip.ndims()) = clip.VectorField()
     image_uri: str = clip.SourceField()
 
     @property
@@ -166,4 +166,4 @@ rs[2].image
 ![](../assets/dog_clip_output.png)
 
 Now that you have the basic idea about LanceDB embedding functions and the embedding function registry,
-let's dive deeper into defining your own [custom functions](./custom_embedding_function.md).
+let's dive deeper into defining your own [custom functions](./custom_embedding_function.md).

docs/src/embeddings/index.md

@@ -11,4 +11,64 @@ LanceDB supports 3 methods of working with embeddings.
    that extends the default embedding functions.
 
 For python users, there is also a legacy [with_embeddings API](./legacy.md).
-It is retained for compatibility and will be removed in a future version.
+It is retained for compatibility and will be removed in a future version.
+
+## Quickstart
+
+To get started with embeddings, you can use the built-in embedding functions.
+
+### OpenAI Embedding function
+LanceDB registers the OpenAI embeddings function in the registry as `openai`. You can pass any supported model name to the `create`. By default it uses `"text-embedding-ada-002"`.
+
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+db = lancedb.connect("/tmp/db")
+func = get_registry().get("openai").create(name="text-embedding-ada-002")
+
+class Words(LanceModel):
+    text: str = func.SourceField()
+    vector: Vector(func.ndims()) = func.VectorField()
+
+table = db.create_table("words", schema=Words, mode="overwrite")
+table.add(
+    [
+        {"text": "hello world"},
+        {"text": "goodbye world"}
+    ]
+    )
+
+query = "greetings"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```
+
+### Sentence Transformers Embedding function
+LanceDB registers the Sentence Transformers embeddings function in the registry as `sentence-transformers`. You can pass any supported model name to the `create`. By default it uses `"sentence-transformers/paraphrase-MiniLM-L6-v2"`.
+
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+db = lancedb.connect("/tmp/db")
+model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
+
+class Words(LanceModel):
+    text: str = model.SourceField()
+    vector: Vector(model.ndims()) = model.VectorField()
+
+table = db.create_table("words", schema=Words)
+table.add(
+    [
+        {"text": "hello world"},
+        {"text": "goodbye world"}
+    ]
+)
+
+query = "greetings"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```

docs/src/guides/storage.md

@@ -55,18 +55,139 @@ LanceDB OSS supports object stores such as AWS S3 (and compatible stores), Azure
     const db = await lancedb.connect("az://bucket/path");
     ```
 
-In most cases, when running in the respective cloud and permissions are set up correctly, no additional configuration is required. When running outside of the respective cloud, authentication credentials must be provided using environment variables. In general, these environment variables are the same as those used by the respective cloud SDKs. The sections below describe the environment variables that can be used to configure each object store.
+In most cases, when running in the respective cloud and permissions are set up correctly, no additional configuration is required. When running outside of the respective cloud, authentication credentials must be provided. Credentials and other configuration options can be set in two ways: first, by setting environment variables. And second, by passing a `storage_options` object to the `connect` function. For example, to increase the request timeout to 60 seconds, you can set the `TIMEOUT` environment variable to `60s`:
 
-LanceDB OSS uses the [object-store](https://docs.rs/object_store/latest/object_store/) Rust crate for object store access. There are general environment variables that can be used to configure the object store, such as the request timeout and proxy configuration. See the [object_store ClientConfigKey](https://docs.rs/object_store/latest/object_store/enum.ClientConfigKey.html) doc for available configuration options. The environment variables that can be set are the snake-cased versions of these variable names. For example, to set `ProxyUrl` use the environment variable `PROXY_URL`. (Don't let the Rust docs intimidate you! We link to them so you can see an up-to-date list of the available options.)
+```bash
+export TIMEOUT=60s
+```
+
+!!! note "`storage_options` availability"
+
+    The `storage_options` parameter is only available in Python *async* API and JavaScript API.
+    It is not yet supported in the Python synchronous API.
+
+If you only want this to apply to one particular connection, you can pass the `storage_options` argument when opening the connection:
+
+=== "Python"
+
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "s3://bucket/path",
+        storage_options={"timeout": "60s"}
+    )
+    ```
+
+=== "JavaScript"
+
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect("s3://bucket/path",
+                                     {storageOptions: {timeout: "60s"}});
+    ```
+
+Getting even more specific, you can set the `timeout` for only a particular table:
+
+=== "Python"
+
+    <!-- skip-test -->
+    ```python
+    import lancedb
+    db = await lancedb.connect_async("s3://bucket/path")
+    table = await db.create_table(
+        "table",
+        [{"a": 1, "b": 2}],
+        storage_options={"timeout": "60s"}
+    )
+    ```
+
+=== "JavaScript"
+
+    <!-- skip-test -->
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect("s3://bucket/path");
+    const table = db.createTable(
+        "table",
+        [{ a: 1, b: 2}],
+        {storageOptions: {timeout: "60s"}}
+    );
+    ```
+
+!!! info "Storage option casing"
+
+    The storage option keys are case-insensitive. So `connect_timeout` and `CONNECT_TIMEOUT` are the same setting. Usually lowercase is used in the `storage_options` argument and uppercase is used for environment variables. In the `lancedb` Node package, the keys can also be provided in `camelCase` capitalization. For example, `connectTimeout` is equivalent to `connect_timeout`.
+
+### General configuration
+
+There are several options that can be set for all object stores, mostly related to network client configuration.
+
+<!-- from here: https://docs.rs/object_store/latest/object_store/enum.ClientConfigKey.html -->
+
+| Key                        | Description                                                                                      |
+|----------------------------|--------------------------------------------------------------------------------------------------|
+| `allow_http`               | Allow non-TLS, i.e. non-HTTPS connections. Default: `False`.                                      |
+| `allow_invalid_certificates`| Skip certificate validation on HTTPS connections. Default: `False`.                               |
+| `connect_timeout`          | Timeout for only the connect phase of a Client. Default: `5s`.                                    |
+| `timeout`                  | Timeout for the entire request, from connection until the response body has finished. Default: `30s`. |
+| `user_agent`               | User agent string to use in requests.                                                             |
+| `proxy_url`                | URL of a proxy server to use for requests. Default: `None`.                                       |
+| `proxy_ca_certificate`     | PEM-formatted CA certificate for proxy connections.                                                |
+| `proxy_excludes`           | List of hosts that bypass the proxy. This is a comma-separated list of domains and IP masks. Any subdomain of the provided domain will be bypassed. For example, `example.com, 192.168.1.0/24` would bypass `https://api.example.com`, `https://www.example.com`, and any IP in the range `192.168.1.0/24`. |
 
 
 ### AWS S3
 
-To configure credentials for AWS S3, you can use the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` environment variables.
+To configure credentials for AWS S3, you can use the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` keys. Region can also be set, but it is not mandatory when using AWS.
+These can be set as environment variables or passed in the `storage_options` parameter:
+
+=== "Python"
+
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "s3://bucket/path",
+        storage_options={
+            "aws_access_key_id": "my-access-key",
+            "aws_secret_access_key": "my-secret-key",
+            "aws_session_token": "my-session-token",
+        }
+    )
+    ```
+
+=== "JavaScript"
+
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect(
+        "s3://bucket/path",
+        {
+            storageOptions: {
+                awsAccessKeyId: "my-access-key",
+                awsSecretAccessKey: "my-secret-key",
+                awsSessionToken: "my-session-token",
+            }
+        }
+    );
+    ```
 
 Alternatively, if you are using AWS SSO, you can use the `AWS_PROFILE` and `AWS_DEFAULT_REGION` environment variables.
 
-You can see a full list of environment variables [here](https://docs.rs/object_store/latest/object_store/aws/struct.AmazonS3Builder.html#method.from_env).
+The following keys can be used as both environment variables or keys in the `storage_options` parameter:
+
+| Key                                | Description                                                                                          |
+|------------------------------------|------------------------------------------------------------------------------------------------------|
+| `aws_region` / `region`             | The AWS region the bucket is in. This can be automatically detected when using AWS S3, but must be specified for S3-compatible stores. |
+| `aws_access_key_id` / `access_key_id` | The AWS access key ID to use.                                                                       |
+| `aws_secret_access_key` / `secret_access_key` | The AWS secret access key to use.                                                               |
+| `aws_session_token` / `session_token` | The AWS session token to use.                                                                     |
+| `aws_endpoint` / `endpoint`         | The endpoint to use for S3-compatible stores.                                                       |
+| `aws_virtual_hosted_style_request` / `virtual_hosted_style_request` | Whether to use virtual hosted-style requests, where the bucket name is part of the endpoint. Meant to be used with `aws_endpoint`. Default: `False`. |
+| `aws_s3_express` / `s3_express`     | Whether to use S3 Express One Zone endpoints. Default: `False`. See more details below.             |
+| `aws_server_side_encryption`        | The server-side encryption algorithm to use. Must be one of `"AES256"`, `"aws:kms"`, or `"aws:kms:dsse"`. Default: `None`. |
+| `aws_sse_kms_key_id`                | The KMS key ID to use for server-side encryption. If set, `aws_server_side_encryption` must be `"aws:kms"` or `"aws:kms:dsse"`. |
+| `aws_sse_bucket_key_enabled`        | Whether to use bucket keys for server-side encryption.                                               |
+
 
 !!! tip "Automatic cleanup for failed writes"
 
@@ -146,22 +267,182 @@ For **read-only access**, LanceDB will need a policy such as:
 
 #### S3-compatible stores
 
-LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you must specify two environment variables: `AWS_ENDPOINT` and `AWS_DEFAULT_REGION`. `AWS_ENDPOINT` should be the URL of the S3-compatible store, and `AWS_DEFAULT_REGION` should be the region to use.
+LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you must specify both region and endpoint:
+
+=== "Python"
+
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "s3://bucket/path",
+        storage_options={
+            "region": "us-east-1",
+            "endpoint": "http://minio:9000",
+        }
+    )
+    ```
+
+=== "JavaScript"
+
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect(
+        "s3://bucket/path",
+        {
+            storageOptions: {
+                region: "us-east-1",
+                endpoint: "http://minio:9000",
+            }
+        }
+    );
+    ```
+
+This can also be done with the ``AWS_ENDPOINT`` and ``AWS_DEFAULT_REGION`` environment variables.
+
+!!! tip "Local servers"
+
+    For local development, the server often has a `http` endpoint rather than a
+    secure `https` endpoint. In this case, you must also set the `ALLOW_HTTP`
+    environment variable to `true` to allow non-TLS connections, or pass the
+    storage option `allow_http` as `true`. If you do not do this, you will get
+    an error like `URL scheme is not allowed`.
+
+#### S3 Express
+
+LanceDB supports [S3 Express One Zone](https://aws.amazon.com/s3/storage-classes/express-one-zone/) endpoints, but requires additional configuration. Also, S3 Express endpoints only support connecting from an EC2 instance within the same region.
+
+To configure LanceDB to use an S3 Express endpoint, you must set the storage option `s3_express`. The bucket name in your table URI should **include the suffix**.
+
+=== "Python"
+
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "s3://my-bucket--use1-az4--x-s3/path",
+        storage_options={
+            "region": "us-east-1",
+            "s3_express": "true",
+        }
+    )
+    ```
+
+=== "JavaScript"
+
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect(
+        "s3://my-bucket--use1-az4--x-s3/path",
+        {
+            storageOptions: {
+                region: "us-east-1",
+                s3Express: "true",
+            }
+        }
+    );
+    ```
 
-<!-- TODO: we should also document the use of S3 Express once we fully support it -->
 
 ### Google Cloud Storage
 
-GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environment variable to the path of a JSON file containing the service account credentials. There are several aliases for this environment variable, documented [here](https://docs.rs/object_store/latest/object_store/gcp/struct.GoogleCloudStorageBuilder.html#method.from_env).
+GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environment variable to the path of a JSON file containing the service account credentials. Alternatively, you can pass the path to the JSON file in the `storage_options`:
+
+=== "Python"
+
+    <!-- skip-test -->
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "gs://my-bucket/my-database",
+        storage_options={
+            "service_account": "path/to/service-account.json",
+        }
+    )
+    ```
+
+=== "JavaScript"
+
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect(
+        "gs://my-bucket/my-database",
+        {
+            storageOptions: {
+                serviceAccount: "path/to/service-account.json",
+            }
+        }
+    );
+    ```
 
 
 !!! info "HTTP/2 support"
 
     By default, GCS uses HTTP/1 for communication, as opposed to HTTP/2. This improves maximum throughput significantly. However, if you wish to use HTTP/2 for some reason, you can set the environment variable `HTTP1_ONLY` to `false`.
 
+
+The following keys can be used as both environment variables or keys in the `storage_options` parameter:
+<!-- source: https://docs.rs/object_store/latest/object_store/gcp/enum.GoogleConfigKey.html -->
+
+| Key                                   | Description                                  |
+|---------------------------------------|----------------------------------------------|
+| ``google_service_account`` / `service_account` | Path to the service account JSON file.       |
+| ``google_service_account_key``        | The serialized service account key.          |
+| ``google_application_credentials``    | Path to the application credentials.         |
+
+
 ### Azure Blob Storage
 
-Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_ACCOUNT_NAME` and ``AZURE_STORAGE_ACCOUNT_KEY`` environment variables. The full list of environment variables that can be set are documented [here](https://docs.rs/object_store/latest/object_store/azure/struct.MicrosoftAzureBuilder.html#method.from_env).
+Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_ACCOUNT_NAME`and `AZURE_STORAGE_ACCOUNT_KEY` environment variables. Alternatively, you can pass the account name and key in the `storage_options` parameter:
 
+=== "Python"
+
+    <!-- skip-test -->
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "az://my-container/my-database",
+        storage_options={
+            account_name: "some-account",
+            account_key: "some-key",
+        }
+    )
+    ```
+
+=== "JavaScript"
+
+    ```javascript
+    const lancedb = require("lancedb");
+    const db = await lancedb.connect(
+        "az://my-container/my-database",
+        {
+            storageOptions: {
+                accountName: "some-account",
+                accountKey: "some-key",
+            }
+        }
+    );
+    ```
+
+These keys can be used as both environment variables or keys in the `storage_options` parameter:
+
+<!-- source: https://docs.rs/object_store/latest/object_store/azure/enum.AzureConfigKey.html -->
+
+| Key                                   | Description                                                                                      |
+|---------------------------------------|--------------------------------------------------------------------------------------------------|
+| ``azure_storage_account_name``        | The name of the azure storage account.                                                           |
+| ``azure_storage_account_key``         | The serialized service account key.                                                              |
+| ``azure_client_id``                   | Service principal client id for authorizing requests.                                            |
+| ``azure_client_secret``               | Service principal client secret for authorizing requests.                                        |
+| ``azure_tenant_id``                   | Tenant id used in oauth flows.                                                                   |
+| ``azure_storage_sas_key``             | Shared access signature. The signature is expected to be percent-encoded, much like they are provided in the azure storage explorer or azure portal. |
+| ``azure_storage_token``               | Bearer token.                                                                                    |
+| ``azure_storage_use_emulator``        | Use object store with azurite storage emulator.                                                  |
+| ``azure_endpoint``                    | Override the endpoint used to communicate with blob storage.                                      |
+| ``azure_use_fabric_endpoint``         | Use object store with url scheme account.dfs.fabric.microsoft.com.                               |
+| ``azure_msi_endpoint``                | Endpoint to request a imds managed identity token.                                               |
+| ``azure_object_id``                   | Object id for use with managed identity authentication.                                          |
+| ``azure_msi_resource_id``             | Msi resource id for use with managed identity authentication.                                    |
+| ``azure_federated_token_file``        | File containing token for Azure AD workload identity federation.                                 |
+| ``azure_use_azure_cli``               | Use azure cli for acquiring access token.                                                        |
+| ``azure_disable_tagging``             | Disables tagging objects. This can be desirable if not supported by the backing store.           |
 
 <!-- TODO: demonstrate how to configure networked file systems for optimal performance -->

docs/src/integrations/index.md

@@ -13,7 +13,7 @@ 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/docs/integrations/vectorstores/lancedb) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
+| <h3>Langchain</h3>Langchain allows building applications with LLMs through composability <h3>[Lean More](https://lancedb.github.io/lancedb/integrations/langchain/) | <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/langchain.md

@@ -0,0 +1,92 @@
+# Langchain
+![Illustration](../assets/langchain.png)
+
+## Quick Start
+You can load your document data using langchain's loaders, for this example we are using `TextLoader` and `OpenAIEmbeddings` as the embedding model.
+```python
+import os
+from langchain.document_loaders import TextLoader
+from langchain.vectorstores import LanceDB
+from langchain_openai import OpenAIEmbeddings
+from langchain_text_splitters import CharacterTextSplitter
+
+os.environ["OPENAI_API_KEY"] = "sk-..."
+
+loader = TextLoader("../../modules/state_of_the_union.txt") # Replace with your data path
+documents = loader.load()
+
+documents = CharacterTextSplitter().split_documents(documents)
+embeddings = OpenAIEmbeddings()
+
+docsearch = LanceDB.from_documents(documents, embeddings)
+query = "What did the president say about Ketanji Brown Jackson"
+docs = docsearch.similarity_search(query)
+print(docs[0].page_content)
+```
+
+## Documentation
+In the above example `LanceDB` vector store class object is created using `from_documents()` method  which is a `classmethod` and returns the initialized class object. 
+You can also use `LanceDB.from_texts(texts: List[str],embedding: Embeddings)` class method.  
+
+The exhaustive list of parameters for `LanceDB` vector store are :  
+- `connection`: (Optional) `lancedb.db.LanceDBConnection` connection object to use.  If not provided, a new connection will be created.  
+- `embedding`: Langchain embedding model.  
+- `vector_key`: (Optional) Column name to use for vector's in the table. Defaults to `'vector'`.   
+- `id_key`: (Optional) Column name to use for id's in the table. Defaults to `'id'`.  
+- `text_key`: (Optional) Column name to use for text in the table. Defaults to `'text'`.  
+- `table_name`: (Optional) Name of your table in the database. Defaults to `'vectorstore'`.  
+- `api_key`: (Optional) API key to use for LanceDB cloud database. Defaults to `None`.  
+- `region`: (Optional) Region to use for LanceDB cloud database. Only for LanceDB Cloud, defaults to `None`.  
+- `mode`: (Optional) Mode to use for adding data to the table. Defaults to `'overwrite'`.  
+
+```python
+db_url = "db://lang_test" # url of db you created
+api_key = "xxxxx" # your API key
+region="us-east-1-dev"  # your selected region
+
+vector_store = LanceDB(
+    uri=db_url,
+    api_key=api_key, #(dont include for local API)
+    region=region, #(dont include for local API)
+    embedding=embeddings,
+    table_name='langchain_test' #Optional
+    )
+```
+
+### Methods 
+To add texts and store respective embeddings automatically:   
+##### add_texts()
+- `texts`: `Iterable` of strings to add to the vectorstore.
+- `metadatas`: Optional `list[dict()]` of metadatas associated with the texts.
+- `ids`: Optional `list` of ids to associate with the texts. 
+
+
+```python
+vector_store.add_texts(texts = ['test_123'], metadatas =[{'source' :'wiki'}]) 
+
+#Additionaly, to explore the table you can load it into a df or save it in a csv file:
+
+tbl = vector_store.get_table()
+print("tbl:", tbl)
+pd_df = tbl.to_pandas()
+pd_df.to_csv("docsearch.csv", index=False)
+
+# you can also create a new vector store object using an older connection object:
+vector_store = LanceDB(connection=tbl, embedding=embeddings)
+```
+For index creation make sure your table has enough data in it. An ANN index is ususally not needed for datasets ~100K vectors. For large-scale (>1M) or higher dimension vectors, it is beneficial to create an ANN index.
+##### create_index() 
+- `col_name`: `Optional[str] = None`
+- `vector_col`: `Optional[str] = None`
+- `num_partitions`: `Optional[int] = 256`
+- `num_sub_vectors`: `Optional[int] = 96`
+- `index_cache_size`: `Optional[int] = None`
+
+```python
+# for creating vector index
+vector_store.create_index(vector_col='vector', metric = 'cosine')
+
+# for creating scalar index(for non-vector columns)
+vector_store.create_index(col_name='text')
+
+```

docs/src/js/modules.md

@@ -142,6 +142,7 @@ rules are as follows:
 
 **`Example`**
 
+```ts
 import { fromTableToBuffer, makeArrowTable } from "../arrow";
 import { Field, FixedSizeList, Float16, Float32, Int32, Schema } from "apache-arrow";
 

docs/src/notebooks/youtube_transcript_search.ipynb

@@ -36,7 +36,7 @@
     }
    ],
    "source": [
-    "!pip install --quiet openai datasets \n",
+    "!pip install --quiet openai datasets\n",
     "!pip install --quiet -U lancedb"
    ]
   },
@@ -213,7 +213,7 @@
     "if \"OPENAI_API_KEY\" not in os.environ:\n",
     "    # OR set the key here as a variable\n",
     "    os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
-    "    \n",
+    "\n",
     "client = OpenAI()\n",
     "assert len(client.models.list().data) > 0"
    ]
@@ -234,9 +234,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def embed_func(c):    \n",
+    "def embed_func(c):\n",
     "    rs = client.embeddings.create(input=c, model=\"text-embedding-ada-002\")\n",
-    "    return [rs.data[0].embedding]"
+    "    return [\n",
+    "        data.embedding\n",
+    "        for data in rs.data\n",
+    "    ]"
    ]
   },
   {
@@ -514,7 +517,7 @@
     "                prompt_start +\n",
     "                \"\\n\\n---\\n\\n\".join(context.text) +\n",
     "                prompt_end\n",
-    "            )    \n",
+    "            )\n",
     "    return prompt"
    ]
   },

docs/src/python/duckdb.md

@@ -24,7 +24,8 @@ data = [
 table = db.create_table("pd_table", data=data)
 ```
 
-To query the table, first call `to_lance` to convert the table to a "dataset", which is an object that can be queried by DuckDB. Then all you need to do is reference that dataset by the same name in your SQL query.
+The `to_lance` method converts the LanceDB table to a `LanceDataset`, which is accessible to DuckDB through the Arrow compatibility layer.
+To query the resulting Lance dataset in DuckDB, all you need to do is reference the dataset by the same name in your SQL query.
 
 ```python
 import duckdb

docs/src/reranking/cohere.md

@@ -0,0 +1,75 @@
+# Cohere Reranker
+
+This re-ranker uses the [Cohere](https://cohere.ai/) API to rerank the search results. You can use this re-ranker by passing `CohereReranker()` to the `rerank()` method. Note that you'll either need to set the `COHERE_API_KEY` environment variable or pass the `api_key` argument to use this re-ranker.
+
+
+!!! note
+    Supported Query Types: Hybrid, Vector, FTS
+
+
+```python
+import numpy
+import lancedb
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.rerankers import CohereReranker
+
+embedder = get_registry().get("sentence-transformers").create()
+db = lancedb.connect("~/.lancedb")
+
+class Schema(LanceModel):
+    text: str = embedder.SourceField()
+    vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+data = [
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+    ]
+tbl = db.create_table("test", schema=Schema, mode="overwrite")
+tbl.add(data)
+reranker = CohereReranker(api_key="key")
+
+# Run vector search with a reranker
+result = tbl.search("hello").rerank(reranker=reranker).to_list() 
+
+# Run FTS search with a reranker
+result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+# Run hybrid search with a reranker
+tbl.create_fts_index("text", replace=True)
+result = tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+
+```
+
+Accepted Arguments
+----------------
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `"rerank-english-v2.0"` | The name of the reranker model to use. Available cohere models are: rerank-english-v2.0, rerank-multilingual-v2.0 |
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
+| `api_key` | `str` | `None` | The API key for the Cohere API. If not provided, the `COHERE_API_KEY` environment variable is used. |
+| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
+
+
+
+## Supported Scores for each query type
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
+
+### Vector Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
+
+### FTS Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |

docs/src/reranking/colbert.md

@@ -0,0 +1,71 @@
+# ColBERT Reranker
+
+This re-ranker uses ColBERT model to rerank the search results. You can use this re-ranker by passing `ColbertReranker()` to the `rerank()` method. 
+!!! note
+    Supported Query Types: Hybrid, Vector, FTS
+
+
+```python
+import numpy
+import lancedb
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.rerankers import ColbertReranker
+
+embedder = get_registry().get("sentence-transformers").create()
+db = lancedb.connect("~/.lancedb")
+
+class Schema(LanceModel):
+    text: str = embedder.SourceField()
+    vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+data = [
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+    ]
+tbl = db.create_table("test", schema=Schema, mode="overwrite")
+tbl.add(data)
+reranker = ColbertReranker()
+
+# Run vector search with a reranker
+result = tbl.search("hello").rerank(reranker=reranker).to_list() 
+
+# Run FTS search with a reranker
+result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+# Run hybrid search with a reranker
+tbl.create_fts_index("text", replace=True)
+result = tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+
+```
+
+Accepted Arguments
+----------------
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `"colbert-ir/colbertv2.0"` | The name of the reranker model to use.|
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". |
+| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
+
+
+## Supported Scores for each query type
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
+
+### Vector Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
+
+### FTS Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |

docs/src/reranking/cross_encoder.md

@@ -0,0 +1,70 @@
+# Cross Encoder Reranker
+
+This re-ranker uses Cross Encoder models from sentence-transformers to rerank the search results. You can use this re-ranker by passing `CrossEncoderReranker()` to the `rerank()` method. 
+!!! note
+    Supported Query Types: Hybrid, Vector, FTS
+
+
+```python
+import numpy
+import lancedb
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.rerankers import CrossEncoderReranker
+
+embedder = get_registry().get("sentence-transformers").create()
+db = lancedb.connect("~/.lancedb")
+
+class Schema(LanceModel):
+    text: str = embedder.SourceField()
+    vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+data = [
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+    ]
+tbl = db.create_table("test", schema=Schema, mode="overwrite")
+tbl.add(data)
+reranker = CrossEncoderReranker()
+
+# Run vector search with a reranker
+result = tbl.search("hello").rerank(reranker=reranker).to_list() 
+
+# Run FTS search with a reranker
+result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+# Run hybrid search with a reranker
+tbl.create_fts_index("text", replace=True)
+result = tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+
+```
+
+Accepted Arguments
+----------------
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `""cross-encoder/ms-marco-TinyBERT-L-6"` | The name of the reranker model to use.|
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". |
+| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
+
+## Supported Scores for each query type
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
+
+### Vector Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
+
+### FTS Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |

docs/src/reranking/custom_reranker.md

@@ -0,0 +1,88 @@
+## Building Custom Rerankers
+You can build your own custom reranker by subclassing the `Reranker` class and implementing the `rerank_hybrid()` method. Optionally, you can also implement the `rerank_vector()` and `rerank_fts()` methods if you want to support reranking for vector and FTS search separately.
+Here's an example of a custom reranker that combines the results of semantic and full-text search using a linear combination of the scores.
+
+The `Reranker` base interface comes with a `merge_results()` method that can be used to combine the results of semantic and full-text search. This is a vanilla merging algorithm that simply concatenates the results and removes the duplicates without taking the scores into consideration. It only keeps the first copy of the row encountered. This works well in cases that don't require the scores of semantic and full-text search to combine the results. If you want to use the scores or want to support `return_score="all"`, you'll need to implement your own merging algorithm.
+
+```python
+
+from lancedb.rerankers import Reranker
+import pyarrow as pa
+
+class MyReranker(Reranker):
+    def __init__(self, param1, param2, ..., return_score="relevance"):
+        super().__init__(return_score)
+        self.param1 = param1
+        self.param2 = param2
+
+    def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table):
+        # Use the built-in merging function
+        combined_result = self.merge_results(vector_results, fts_results)
+
+        # Do something with the combined results
+        # ...
+
+        # Return the combined results
+        return combined_result
+
+    def rerank_vector(self, query: str, vector_results: pa.Table):
+        # Do something with the vector results
+        # ...
+
+        # Return the vector results
+        return vector_results
+
+    def rerank_fts(self, query: str, fts_results: pa.Table):
+        # Do something with the FTS results
+        # ...
+
+        # Return the FTS results
+        return fts_results
+
+```
+
+### Example of a Custom Reranker
+For the sake of simplicity let's build custom reranker that just enchances the Cohere Reranker by accepting a filter query, and accept other CohereReranker params as kwags.
+
+```python
+
+from typing import List, Union
+import pandas as pd
+from lancedb.rerankers import CohereReranker
+
+class ModifiedCohereReranker(CohereReranker):
+    def __init__(self, filters: Union[str, List[str]], **kwargs):
+        super().__init__(**kwargs)
+        filters = filters if isinstance(filters, list) else [filters]
+        self.filters = filters
+
+    def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table)-> pa.Table:
+        combined_result = super().rerank_hybrid(query, vector_results, fts_results)
+        df = combined_result.to_pandas()
+        for filter in self.filters:
+            df = df.query("not text.str.contains(@filter)")
+
+        return pa.Table.from_pandas(df)
+
+    def rerank_vector(self, query: str, vector_results: pa.Table)-> pa.Table:
+        vector_results = super().rerank_vector(query, vector_results)
+        df = vector_results.to_pandas()
+        for filter in self.filters:
+            df = df.query("not text.str.contains(@filter)")
+
+        return pa.Table.from_pandas(df)
+
+    def rerank_fts(self, query: str, fts_results: pa.Table)-> pa.Table:
+        fts_results = super().rerank_fts(query, fts_results)
+        df = fts_results.to_pandas()
+        for filter in self.filters:
+            df = df.query("not text.str.contains(@filter)")
+
+        return pa.Table.from_pandas(df)
+
+```
+
+!!! tip
+    The `vector_results` and `fts_results` are pyarrow tables. Lean more about pyarrow tables [here](https://arrow.apache.org/docs/python). It can be convered to other data types like pandas dataframe, pydict, pylist etc.
+
+    For example, You can convert them to pandas dataframes using `to_pandas()` method and perform any operations you want. After you are done, you can convert the dataframe back to pyarrow table using `pa.Table.from_pandas()` method and return it.

docs/src/reranking/index.md

@@ -0,0 +1,60 @@
+Reranking is the process of reordering a list of items based on some criteria. In the context of search, reranking is used to reorder the search results returned by a search engine based on some criteria. This can be useful when the initial ranking of the search results is not satisfactory or when the user has provided additional information that can be used to improve the ranking of the search results.
+
+LanceDB comes with some built-in rerankers. Some of the rerankers that are available in LanceDB are:
+
+| Reranker | Description | Supported Query Types |
+| --- | --- | --- |
+| `LinearCombinationReranker` | Reranks search results based on a linear combination of FTS and vector search scores | Hybrid |
+| `CohereReranker` | Uses cohere Reranker API to rerank results | Vector, FTS, Hybrid |
+| `CrossEncoderReranker` | Uses a cross-encoder model to rerank search results | Vector, FTS, Hybrid |
+| `ColbertReranker` | Uses a colbert model to rerank search results | Vector, FTS, Hybrid |
+| `OpenaiReranker`(Experimental) | Uses OpenAI's chat model to rerank search results | Vector, FTS, Hybrid |
+
+
+## Using a Reranker
+Using rerankers is optional for vector and FTS. However, for hybrid search, rerankers are required. To use a reranker, you need to create an instance of the reranker and pass it to the `rerank` method of the query builder.
+
+```python
+import numpy
+import lancedb
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.rerankers import CohereReranker
+
+embedder = get_registry().get("sentence-transformers").create()
+db = lancedb.connect("~/.lancedb")
+
+class Schema(LanceModel):
+    text: str = embedder.SourceField()
+    vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+data = [
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+    ]
+tbl = db.create_table("test", data)
+reranker = CohereReranker(api_key="your_api_key")
+
+# Run vector search with a reranker
+result = tbl.query("hello").rerank(reranker).to_list() 
+
+# Run FTS search with a reranker
+result = tbl.query("hello", query_type="fts").rerank(reranker).to_list()
+
+# Run hybrid search with a reranker
+tbl.create_fts_index("text")
+result = tbl.query("hello", query_type="hybrid").rerank(reranker).to_list()
+```
+
+## Available Rerankers
+LanceDB comes with some built-in rerankers. Here are some of the rerankers that are available in LanceDB:
+
+- [Cohere Reranker](./cohere.md)
+- [Cross Encoder Reranker](./cross_encoder.md)
+- [ColBERT Reranker](./colbert.md)
+- [OpenAI Reranker](./openai.md)
+- [Linear Combination Reranker](./linear_combination.md)
+
+## Creating Custom Rerankers
+
+LanceDB also you to create custom rerankers by extending the base `Reranker` class. The custom reranker should implement the `rerank` method that takes a list of search results and returns a reranked list of search results. This is covered in more detail in the [Creating Custom Rerankers](./custom_reranker.md) section.

docs/src/reranking/linear_combination.md

@@ -0,0 +1,52 @@
+# Linear Combination Reranker
+
+This is the default re-ranker used by LanceDB hybrid search. It combines the results of semantic and full-text search using a linear combination of the scores. The weights for the linear combination can be specified. It defaults to 0.7, i.e, 70% weight for semantic search and 30% weight for full-text search.
+
+!!! note
+    Supported Query Types: Hybrid
+
+
+```python
+import numpy
+import lancedb
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.rerankers import LinearCombinationReranker
+
+embedder = get_registry().get("sentence-transformers").create()
+db = lancedb.connect("~/.lancedb")
+
+class Schema(LanceModel):
+    text: str = embedder.SourceField()
+    vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+data = [
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+    ]
+tbl = db.create_table("test", schema=Schema, mode="overwrite")
+tbl.add(data)
+reranker = LinearCombinationReranker()
+
+# Run hybrid search with a reranker
+tbl.create_fts_index("text", replace=True)
+result = tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+
+```
+
+Accepted Arguments
+----------------
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `weight` | `float` | `0.7` | The weight to use for the semantic search score. The weight for the full-text search score is `1 - weights`. |
+| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all", will return all scores from the vector and FTS search along with the relevance score. |
+
+
+## Supported Scores for each query type
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_distance`) |

docs/src/reranking/openai.md

@@ -0,0 +1,73 @@
+# OpenAI Reranker (Experimental)
+
+This re-ranker uses OpenAI chat model to rerank the search results. You can use this re-ranker by passing `OpenAI()` to the `rerank()` method. 
+!!! note
+    Supported Query Types: Hybrid, Vector, FTS
+
+!!! warning
+    This re-ranker is experimental. OpenAI doesn't have a dedicated reranking model, so we are using the chat model for reranking. 
+
+```python
+import numpy
+import lancedb
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.rerankers import OpenaiReranker
+
+embedder = get_registry().get("sentence-transformers").create()
+db = lancedb.connect("~/.lancedb")
+
+class Schema(LanceModel):
+    text: str = embedder.SourceField()
+    vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+data = [
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+    ]
+tbl = db.create_table("test", schema=Schema, mode="overwrite")
+tbl.add(data)
+reranker = OpenaiReranker()
+
+# Run vector search with a reranker
+result = tbl.search("hello").rerank(reranker=reranker).to_list() 
+
+# Run FTS search with a reranker
+result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+# Run hybrid search with a reranker
+tbl.create_fts_index("text", replace=True)
+result = tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+
+```
+
+Accepted Arguments
+----------------
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `"gpt-4-turbo-preview"` | The name of the reranker model to use.|
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
+| `api_key` | str | `None` | The API key to use. If None, will use the OPENAI_API_KEY environment variable.
+
+
+## Supported Scores for each query type
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
+
+### Vector Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
+
+### FTS Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |

docs/test/md_testing.py

@@ -1,5 +1,5 @@
 import glob
-from typing import Iterator
+from typing import Iterator, List
 from pathlib import Path
 
 glob_string = "../src/**/*.md"
@@ -8,6 +8,7 @@ excluded_globs = [
     "../src/embedding.md",
     "../src/examples/*.md",
     "../src/integrations/voxel51.md",
+    "../src/integrations/langchain.md",
     "../src/guides/tables.md",
     "../src/python/duckdb.md",
     "../src/embeddings/*.md",
@@ -15,6 +16,7 @@ excluded_globs = [
     "../src/ann_indexes.md",
     "../src/basic.md",
     "../src/hybrid_search/hybrid_search.md",
+    "../src/reranking/*.md",
 ]
 
 python_prefix = "py"
@@ -50,11 +52,24 @@ def yield_lines(lines: Iterator[str], prefix: str, suffix: str):
                 yield line[strip_length:]
 
 
+def wrap_async(lines: List[str]) -> List[str]:
+    # Indent all the lines
+    lines = ["    " + line for line in lines]
+    # Put all lines in `async def main():`
+    lines = ["async def main():\n"] + lines
+    # Put `import asyncio\n asyncio.run(main())` at the end
+    lines = lines + ["\n", "import asyncio\n", "asyncio.run(main())\n"]
+    return lines
+
+
 for file in filter(lambda file: file not in excluded_files, files):
     with open(file, "r") as f:
         lines = list(yield_lines(iter(f), "```", "```"))
 
     if len(lines) > 0:
+        if any("await" in line for line in lines):
+            lines = wrap_async(lines)
+
         print(lines)
         out_path = (
             Path(python_folder)

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.4.16",
+  "version": "0.5.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.4.16",
+      "version": "0.5.0",
       "cpu": [
         "x64",
         "arm64"
@@ -52,11 +52,11 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.4.16",
-        "@lancedb/vectordb-darwin-x64": "0.4.16",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.4.16",
-        "@lancedb/vectordb-linux-x64-gnu": "0.4.16",
-        "@lancedb/vectordb-win32-x64-msvc": "0.4.16"
+        "@lancedb/vectordb-darwin-arm64": "0.4.20",
+        "@lancedb/vectordb-darwin-x64": "0.4.20",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.4.20",
+        "@lancedb/vectordb-linux-x64-gnu": "0.4.20",
+        "@lancedb/vectordb-win32-x64-msvc": "0.4.20"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",
@@ -333,6 +333,66 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
+    "node_modules/@lancedb/vectordb-darwin-arm64": {
+      "version": "0.4.20",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.4.20.tgz",
+      "integrity": "sha512-ffP2K4sA5mQTgePyARw1y8dPN996FmpvyAYoWO+TSItaXlhcXvc+KVa5udNMCZMDYeEnEv2Xpj6k4PwW3oBz+A==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-darwin-x64": {
+      "version": "0.4.20",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.4.20.tgz",
+      "integrity": "sha512-GSYsXE20RIehDu30FjREhJdEzhnwOTV7ZsrSXagStzLY1gr7pyd7sfqxmmUtdD09di7LnQoiM71AOpPTa01YwQ==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
+      "version": "0.4.20",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.4.20.tgz",
+      "integrity": "sha512-FpNOjOsz3nJVm6EBGyNgbOW2aFhsWZ/igeY45Z8hbZaaK2YBwrg/DASoNlUzgv6IR8cUaGJ2irNVJfsKR2cG6g==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
+      "version": "0.4.20",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.4.20.tgz",
+      "integrity": "sha512-pOqWjrRZQSrLTlQPkjidRii7NZDw8Xu9pN6ouVu2JAK8n81FXaPtFCyAI+Y3v9GpnYDN0rvD4eQ36aHAVPsa2g==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
+      "version": "0.4.20",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.4.20.tgz",
+      "integrity": "sha512-5J5SsYSJ7jRCmU/sgwVHdrGz43B/7R2T9OEoFTKyVAtqTZdu75rkytXyn9SyEayXVhlUOaw76N0ASm0hAoDS/A==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "win32"
+      ]
+    },
     "node_modules/@neon-rs/cli": {
       "version": "0.0.160",
       "resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.160.tgz",

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.4.16",
+  "version": "0.5.0",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
@@ -88,10 +88,10 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-arm64": "0.4.16",
-    "@lancedb/vectordb-darwin-x64": "0.4.16",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.4.16",
-    "@lancedb/vectordb-linux-x64-gnu": "0.4.16",
-    "@lancedb/vectordb-win32-x64-msvc": "0.4.16"
+    "@lancedb/vectordb-darwin-arm64": "0.4.20",
+    "@lancedb/vectordb-darwin-x64": "0.4.20",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.4.20",
+    "@lancedb/vectordb-linux-x64-gnu": "0.4.20",
+    "@lancedb/vectordb-win32-x64-msvc": "0.4.20"
   }
 }

node/src/arrow.ts

@@ -27,23 +27,23 @@ import {
   RecordBatch,
   makeData,
   Struct,
-  Float,
+  type Float,
   DataType,
   Binary,
   Float32
-} from 'apache-arrow'
-import { type EmbeddingFunction } from './index'
-import { sanitizeSchema } from './sanitize'
+} from "apache-arrow";
+import { type EmbeddingFunction } from "./index";
+import { sanitizeSchema } from "./sanitize";
 
 /*
  * Options to control how a column should be converted to a vector array
  */
 export class VectorColumnOptions {
   /** Vector column type. */
-  type: Float = new Float32()
+  type: Float = new Float32();
 
-  constructor (values?: Partial<VectorColumnOptions>) {
-    Object.assign(this, values)
+  constructor(values?: Partial<VectorColumnOptions>) {
+    Object.assign(this, values);
   }
 }
 
@@ -60,7 +60,7 @@ export class MakeArrowTableOptions {
    * The schema must be specified if there are no records (e.g. to make
    * an empty table)
    */
-  schema?: Schema
+  schema?: Schema;
 
   /*
    * Mapping from vector column name to expected type
@@ -80,7 +80,9 @@ export class MakeArrowTableOptions {
    */
   vectorColumns: Record<string, VectorColumnOptions> = {
     vector: new VectorColumnOptions()
-  }
+  };
+
+  embeddings?: EmbeddingFunction<any>;
 
   /**
    * If true then string columns will be encoded with dictionary encoding
@@ -91,10 +93,10 @@ export class MakeArrowTableOptions {
    *
    * If `schema` is provided then this property is ignored.
    */
-  dictionaryEncodeStrings: boolean = false
+  dictionaryEncodeStrings: boolean = false;
 
-  constructor (values?: Partial<MakeArrowTableOptions>) {
-    Object.assign(this, values)
+  constructor(values?: Partial<MakeArrowTableOptions>) {
+    Object.assign(this, values);
   }
 }
 
@@ -193,59 +195,68 @@ export class MakeArrowTableOptions {
  * assert.deepEqual(table.schema, schema)
  * ```
  */
-export function makeArrowTable (
+export function makeArrowTable(
   data: Array<Record<string, any>>,
   options?: Partial<MakeArrowTableOptions>
 ): ArrowTable {
-  if (data.length === 0 && (options?.schema === undefined || options?.schema === null)) {
-    throw new Error('At least one record or a schema needs to be provided')
+  if (
+    data.length === 0 &&
+    (options?.schema === undefined || options?.schema === null)
+  ) {
+    throw new Error("At least one record or a schema needs to be provided");
   }
 
-  const opt = new MakeArrowTableOptions(options !== undefined ? options : {})
+  const opt = new MakeArrowTableOptions(options !== undefined ? options : {});
   if (opt.schema !== undefined && opt.schema !== null) {
-    opt.schema = sanitizeSchema(opt.schema)
+    opt.schema = sanitizeSchema(opt.schema);
+    opt.schema = validateSchemaEmbeddings(opt.schema, data, opt.embeddings);
   }
-  const columns: Record<string, Vector> = {}
+
+  const columns: Record<string, Vector> = {};
   // TODO: sample dataset to find missing columns
   // Prefer the field ordering of the schema, if present
-  const columnNames = ((opt.schema) != null) ? (opt.schema.names as string[]) : Object.keys(data[0])
+  const columnNames =
+    opt.schema != null ? (opt.schema.names as string[]) : Object.keys(data[0]);
   for (const colName of columnNames) {
-    if (data.length !== 0 && !Object.prototype.hasOwnProperty.call(data[0], colName)) {
+    if (
+      data.length !== 0 &&
+      !Object.prototype.hasOwnProperty.call(data[0], colName)
+    ) {
       // The field is present in the schema, but not in the data, skip it
-      continue
+      continue;
     }
     // Extract a single column from the records (transpose from row-major to col-major)
-    let values = data.map((datum) => datum[colName])
+    let values = data.map((datum) => datum[colName]);
 
     // By default (type === undefined) arrow will infer the type from the JS type
-    let type
+    let type;
     if (opt.schema !== undefined) {
       // If there is a schema provided, then use that for the type instead
-      type = opt.schema?.fields.filter((f) => f.name === colName)[0]?.type
+      type = opt.schema?.fields.filter((f) => f.name === colName)[0]?.type;
       if (DataType.isInt(type) && type.bitWidth === 64) {
         // wrap in BigInt to avoid bug: https://github.com/apache/arrow/issues/40051
         values = values.map((v) => {
           if (v === null) {
-            return v
+            return v;
           }
-          return BigInt(v)
-        })
+          return BigInt(v);
+        });
       }
     } else {
       // Otherwise, check to see if this column is one of the vector columns
       // defined by opt.vectorColumns and, if so, use the fixed size list type
-      const vectorColumnOptions = opt.vectorColumns[colName]
+      const vectorColumnOptions = opt.vectorColumns[colName];
       if (vectorColumnOptions !== undefined) {
-        type = newVectorType(values[0].length, vectorColumnOptions.type)
+        type = newVectorType(values[0].length, vectorColumnOptions.type);
       }
     }
 
     try {
       // Convert an Array of JS values to an arrow vector
-      columns[colName] = makeVector(values, type, opt.dictionaryEncodeStrings)
+      columns[colName] = makeVector(values, type, opt.dictionaryEncodeStrings);
     } catch (error: unknown) {
       // eslint-disable-next-line @typescript-eslint/restrict-template-expressions
-      throw Error(`Could not convert column "${colName}" to Arrow: ${error}`)
+      throw Error(`Could not convert column "${colName}" to Arrow: ${error}`);
     }
   }
 
@@ -260,97 +271,116 @@ export function makeArrowTable (
     // To work around this we first create a table with the wrong schema and
     // then patch the schema of the batches so we can use
     // `new ArrowTable(schema, batches)` which does not do any schema inference
-    const firstTable = new ArrowTable(columns)
-    // eslint-disable-next-line @typescript-eslint/no-non-null-assertion
-    const batchesFixed = firstTable.batches.map(batch => new RecordBatch(opt.schema!, batch.data))
-    return new ArrowTable(opt.schema, batchesFixed)
+    const firstTable = new ArrowTable(columns);
+    const batchesFixed = firstTable.batches.map(
+      // eslint-disable-next-line @typescript-eslint/no-non-null-assertion
+      (batch) => new RecordBatch(opt.schema!, batch.data)
+    );
+    return new ArrowTable(opt.schema, batchesFixed);
   } else {
-    return new ArrowTable(columns)
+    return new ArrowTable(columns);
   }
 }
 
 /**
  * Create an empty Arrow table with the provided schema
  */
-export function makeEmptyTable (schema: Schema): ArrowTable {
-  return makeArrowTable([], { schema })
+export function makeEmptyTable(schema: Schema): ArrowTable {
+  return makeArrowTable([], { schema });
 }
 
 // Helper function to convert Array<Array<any>> to a variable sized list array
-function makeListVector (lists: any[][]): Vector<any> {
+function makeListVector(lists: any[][]): Vector<any> {
   if (lists.length === 0 || lists[0].length === 0) {
-    throw Error('Cannot infer list vector from empty array or empty list')
+    throw Error("Cannot infer list vector from empty array or empty list");
   }
-  const sampleList = lists[0]
-  let inferredType
+  const sampleList = lists[0];
+  let inferredType;
   try {
-    const sampleVector = makeVector(sampleList)
-    inferredType = sampleVector.type
+    const sampleVector = makeVector(sampleList);
+    inferredType = sampleVector.type;
   } catch (error: unknown) {
     // eslint-disable-next-line @typescript-eslint/restrict-template-expressions
-    throw Error(`Cannot infer list vector.  Cannot infer inner type: ${error}`)
+    throw Error(`Cannot infer list vector.  Cannot infer inner type: ${error}`);
   }
 
   const listBuilder = makeBuilder({
-    type: new List(new Field('item', inferredType, true))
-  })
+    type: new List(new Field("item", inferredType, true))
+  });
   for (const list of lists) {
-    listBuilder.append(list)
+    listBuilder.append(list);
   }
-  return listBuilder.finish().toVector()
+  return listBuilder.finish().toVector();
 }
 
 // Helper function to convert an Array of JS values to an Arrow Vector
-function makeVector (values: any[], type?: DataType, stringAsDictionary?: boolean): Vector<any> {
+function makeVector(
+  values: any[],
+  type?: DataType,
+  stringAsDictionary?: boolean
+): Vector<any> {
   if (type !== undefined) {
     // No need for inference, let Arrow create it
-    return vectorFromArray(values, type)
+    return vectorFromArray(values, type);
   }
   if (values.length === 0) {
-    throw Error('makeVector requires at least one value or the type must be specfied')
+    throw Error(
+      "makeVector requires at least one value or the type must be specfied"
+    );
   }
-  const sampleValue = values.find(val => val !== null && val !== undefined)
+  const sampleValue = values.find((val) => val !== null && val !== undefined);
   if (sampleValue === undefined) {
-    throw Error('makeVector cannot infer the type if all values are null or undefined')
+    throw Error(
+      "makeVector cannot infer the type if all values are null or undefined"
+    );
   }
   if (Array.isArray(sampleValue)) {
     // Default Arrow inference doesn't handle list types
-    return makeListVector(values)
+    return makeListVector(values);
   } else if (Buffer.isBuffer(sampleValue)) {
     // Default Arrow inference doesn't handle Buffer
-    return vectorFromArray(values, new Binary())
-  } else if (!(stringAsDictionary ?? false) && (typeof sampleValue === 'string' || sampleValue instanceof String)) {
+    return vectorFromArray(values, new Binary());
+  } else if (
+    !(stringAsDictionary ?? false) &&
+    (typeof sampleValue === "string" || sampleValue instanceof String)
+  ) {
     // If the type is string then don't use Arrow's default inference unless dictionaries are requested
     // because it will always use dictionary encoding for strings
-    return vectorFromArray(values, new Utf8())
+    return vectorFromArray(values, new Utf8());
   } else {
     // Convert a JS array of values to an arrow vector
-    return vectorFromArray(values)
+    return vectorFromArray(values);
   }
 }
 
-async function applyEmbeddings<T> (table: ArrowTable, embeddings?: EmbeddingFunction<T>, schema?: Schema): Promise<ArrowTable> {
+async function applyEmbeddings<T>(
+  table: ArrowTable,
+  embeddings?: EmbeddingFunction<T>,
+  schema?: Schema
+): Promise<ArrowTable> {
   if (embeddings == null) {
-    return table
+    return table;
   }
   if (schema !== undefined && schema !== null) {
-    schema = sanitizeSchema(schema)
+    schema = sanitizeSchema(schema);
   }
 
   // Convert from ArrowTable to Record<String, Vector>
   const colEntries = [...Array(table.numCols).keys()].map((_, idx) => {
-    const name = table.schema.fields[idx].name
+    const name = table.schema.fields[idx].name;
     // eslint-disable-next-line @typescript-eslint/no-non-null-assertion
-    const vec = table.getChildAt(idx)!
-    return [name, vec]
-  })
-  const newColumns = Object.fromEntries(colEntries)
+    const vec = table.getChildAt(idx)!;
+    return [name, vec];
+  });
+  const newColumns = Object.fromEntries(colEntries);
 
-  const sourceColumn = newColumns[embeddings.sourceColumn]
-  const destColumn = embeddings.destColumn ?? 'vector'
-  const innerDestType = embeddings.embeddingDataType ?? new Float32()
+  const sourceColumn = newColumns[embeddings.sourceColumn];
+  const destColumn = embeddings.destColumn ?? "vector";
+  const innerDestType = embeddings.embeddingDataType ?? new Float32();
   if (sourceColumn === undefined) {
-    throw new Error(`Cannot apply embedding function because the source column '${embeddings.sourceColumn}' was not present in the data`)
+    throw new Error(
+      `Cannot apply embedding function because the source column '${embeddings.sourceColumn}' was not present in the data`
+    );
   }
 
   if (table.numRows === 0) {
@@ -358,45 +388,60 @@ async function applyEmbeddings<T> (table: ArrowTable, embeddings?: EmbeddingFunc
       // We have an empty table and it already has the embedding column so no work needs to be done
       // Note: we don't return an error like we did below because this is a common occurrence.  For example,
       // if we call convertToTable with 0 records and a schema that includes the embedding
-      return table
+      return table;
     }
     if (embeddings.embeddingDimension !== undefined) {
-      const destType = newVectorType(embeddings.embeddingDimension, innerDestType)
-      newColumns[destColumn] = makeVector([], destType)
+      const destType = newVectorType(
+        embeddings.embeddingDimension,
+        innerDestType
+      );
+      newColumns[destColumn] = makeVector([], destType);
     } else if (schema != null) {
-      const destField = schema.fields.find(f => f.name === destColumn)
+      const destField = schema.fields.find((f) => f.name === destColumn);
       if (destField != null) {
-        newColumns[destColumn] = makeVector([], destField.type)
+        newColumns[destColumn] = makeVector([], destField.type);
       } else {
-        throw new Error(`Attempt to apply embeddings to an empty table failed because schema was missing embedding column '${destColumn}'`)
+        throw new Error(
+          `Attempt to apply embeddings to an empty table failed because schema was missing embedding column '${destColumn}'`
+        );
       }
     } else {
-      throw new Error('Attempt to apply embeddings to an empty table when the embeddings function does not specify `embeddingDimension`')
+      throw new Error(
+        "Attempt to apply embeddings to an empty table when the embeddings function does not specify `embeddingDimension`"
+      );
     }
   } else {
     if (Object.prototype.hasOwnProperty.call(newColumns, destColumn)) {
-      throw new Error(`Attempt to apply embeddings to table failed because column ${destColumn} already existed`)
+      throw new Error(
+        `Attempt to apply embeddings to table failed because column ${destColumn} already existed`
+      );
     }
     if (table.batches.length > 1) {
-      throw new Error('Internal error: `makeArrowTable` unexpectedly created a table with more than one batch')
+      throw new Error(
+        "Internal error: `makeArrowTable` unexpectedly created a table with more than one batch"
+      );
     }
-    const values = sourceColumn.toArray()
-    const vectors = await embeddings.embed(values as T[])
+    const values = sourceColumn.toArray();
+    const vectors = await embeddings.embed(values as T[]);
     if (vectors.length !== values.length) {
-      throw new Error('Embedding function did not return an embedding for each input element')
+      throw new Error(
+        "Embedding function did not return an embedding for each input element"
+      );
     }
-    const destType = newVectorType(vectors[0].length, innerDestType)
-    newColumns[destColumn] = makeVector(vectors, destType)
+    const destType = newVectorType(vectors[0].length, innerDestType);
+    newColumns[destColumn] = makeVector(vectors, destType);
   }
 
-  const newTable = new ArrowTable(newColumns)
+  const newTable = new ArrowTable(newColumns);
   if (schema != null) {
-    if (schema.fields.find(f => f.name === destColumn) === undefined) {
-      throw new Error(`When using embedding functions and specifying a schema the schema should include the embedding column but the column ${destColumn} was missing`)
+    if (schema.fields.find((f) => f.name === destColumn) === undefined) {
+      throw new Error(
+        `When using embedding functions and specifying a schema the schema should include the embedding column but the column ${destColumn} was missing`
+      );
     }
-    return alignTable(newTable, schema)
+    return alignTable(newTable, schema);
   }
-  return newTable
+  return newTable;
 }
 
 /*
@@ -417,21 +462,24 @@ async function applyEmbeddings<T> (table: ArrowTable, embeddings?: EmbeddingFunc
  * embedding columns.  If no schema is provded then embedding columns will
  * be placed at the end of the table, after all of the input columns.
  */
-export async function convertToTable<T> (
+export async function convertToTable<T>(
   data: Array<Record<string, unknown>>,
   embeddings?: EmbeddingFunction<T>,
   makeTableOptions?: Partial<MakeArrowTableOptions>
 ): Promise<ArrowTable> {
-  const table = makeArrowTable(data, makeTableOptions)
-  return await applyEmbeddings(table, embeddings, makeTableOptions?.schema)
+  const table = makeArrowTable(data, makeTableOptions);
+  return await applyEmbeddings(table, embeddings, makeTableOptions?.schema);
 }
 
 // Creates the Arrow Type for a Vector column with dimension `dim`
-function newVectorType <T extends Float> (dim: number, innerType: T): FixedSizeList<T> {
+function newVectorType<T extends Float>(
+  dim: number,
+  innerType: T
+): FixedSizeList<T> {
   // Somewhere we always default to have the elements nullable, so we need to set it to true
   // otherwise we often get schema mismatches because the stored data always has schema with nullable elements
-  const children = new Field<T>('item', innerType, true)
-  return new FixedSizeList(dim, children)
+  const children = new Field<T>("item", innerType, true);
+  return new FixedSizeList(dim, children);
 }
 
 /**
@@ -441,17 +489,17 @@ function newVectorType <T extends Float> (dim: number, innerType: T): FixedSizeL
  *
  * `schema` is required if data is empty
  */
-export async function fromRecordsToBuffer<T> (
+export async function fromRecordsToBuffer<T>(
   data: Array<Record<string, unknown>>,
   embeddings?: EmbeddingFunction<T>,
   schema?: Schema
 ): Promise<Buffer> {
   if (schema !== undefined && schema !== null) {
-    schema = sanitizeSchema(schema)
+    schema = sanitizeSchema(schema);
   }
-  const table = await convertToTable(data, embeddings, { schema })
-  const writer = RecordBatchFileWriter.writeAll(table)
-  return Buffer.from(await writer.toUint8Array())
+  const table = await convertToTable(data, embeddings, { schema, embeddings });
+  const writer = RecordBatchFileWriter.writeAll(table);
+  return Buffer.from(await writer.toUint8Array());
 }
 
 /**
@@ -461,17 +509,17 @@ export async function fromRecordsToBuffer<T> (
  *
  * `schema` is required if data is empty
  */
-export async function fromRecordsToStreamBuffer<T> (
+export async function fromRecordsToStreamBuffer<T>(
   data: Array<Record<string, unknown>>,
   embeddings?: EmbeddingFunction<T>,
   schema?: Schema
 ): Promise<Buffer> {
   if (schema !== null && schema !== undefined) {
-    schema = sanitizeSchema(schema)
+    schema = sanitizeSchema(schema);
   }
-  const table = await convertToTable(data, embeddings, { schema })
-  const writer = RecordBatchStreamWriter.writeAll(table)
-  return Buffer.from(await writer.toUint8Array())
+  const table = await convertToTable(data, embeddings, { schema });
+  const writer = RecordBatchStreamWriter.writeAll(table);
+  return Buffer.from(await writer.toUint8Array());
 }
 
 /**
@@ -482,17 +530,17 @@ export async function fromRecordsToStreamBuffer<T> (
  *
  * `schema` is required if the table is empty
  */
-export async function fromTableToBuffer<T> (
+export async function fromTableToBuffer<T>(
   table: ArrowTable,
   embeddings?: EmbeddingFunction<T>,
   schema?: Schema
 ): Promise<Buffer> {
   if (schema !== null && schema !== undefined) {
-    schema = sanitizeSchema(schema)
+    schema = sanitizeSchema(schema);
   }
-  const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema)
-  const writer = RecordBatchFileWriter.writeAll(tableWithEmbeddings)
-  return Buffer.from(await writer.toUint8Array())
+  const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
+  const writer = RecordBatchFileWriter.writeAll(tableWithEmbeddings);
+  return Buffer.from(await writer.toUint8Array());
 }
 
 /**
@@ -503,49 +551,85 @@ export async function fromTableToBuffer<T> (
  *
  * `schema` is required if the table is empty
  */
-export async function fromTableToStreamBuffer<T> (
+export async function fromTableToStreamBuffer<T>(
   table: ArrowTable,
   embeddings?: EmbeddingFunction<T>,
   schema?: Schema
 ): Promise<Buffer> {
   if (schema !== null && schema !== undefined) {
-    schema = sanitizeSchema(schema)
+    schema = sanitizeSchema(schema);
   }
-  const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema)
-  const writer = RecordBatchStreamWriter.writeAll(tableWithEmbeddings)
-  return Buffer.from(await writer.toUint8Array())
+  const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
+  const writer = RecordBatchStreamWriter.writeAll(tableWithEmbeddings);
+  return Buffer.from(await writer.toUint8Array());
 }
 
-function alignBatch (batch: RecordBatch, schema: Schema): RecordBatch {
-  const alignedChildren = []
+function alignBatch(batch: RecordBatch, schema: Schema): RecordBatch {
+  const alignedChildren = [];
   for (const field of schema.fields) {
     const indexInBatch = batch.schema.fields?.findIndex(
       (f) => f.name === field.name
-    )
+    );
     if (indexInBatch < 0) {
       throw new Error(
         `The column ${field.name} was not found in the Arrow Table`
-      )
+      );
     }
-    alignedChildren.push(batch.data.children[indexInBatch])
+    alignedChildren.push(batch.data.children[indexInBatch]);
   }
   const newData = makeData({
     type: new Struct(schema.fields),
     length: batch.numRows,
     nullCount: batch.nullCount,
     children: alignedChildren
-  })
-  return new RecordBatch(schema, newData)
+  });
+  return new RecordBatch(schema, newData);
 }
 
-function alignTable (table: ArrowTable, schema: Schema): ArrowTable {
+function alignTable(table: ArrowTable, schema: Schema): ArrowTable {
   const alignedBatches = table.batches.map((batch) =>
     alignBatch(batch, schema)
-  )
-  return new ArrowTable(schema, alignedBatches)
+  );
+  return new ArrowTable(schema, alignedBatches);
 }
 
 // Creates an empty Arrow Table
-export function createEmptyTable (schema: Schema): ArrowTable {
-  return new ArrowTable(sanitizeSchema(schema))
+export function createEmptyTable(schema: Schema): ArrowTable {
+  return new ArrowTable(sanitizeSchema(schema));
+}
+
+function validateSchemaEmbeddings(
+  schema: Schema<any>,
+  data: Array<Record<string, unknown>>,
+  embeddings: EmbeddingFunction<any> | undefined
+) {
+  const fields = [];
+  const missingEmbeddingFields = [];
+
+  // First we check if the field is a `FixedSizeList`
+  // Then we check if the data contains the field
+  // if it does not, we add it to the list of missing embedding fields
+  // Finally, we check if those missing embedding fields are `this._embeddings`
+  // if they are not, we throw an error
+  for (const field of schema.fields) {
+    if (field.type instanceof FixedSizeList) {
+      if (data.length !== 0 && data?.[0]?.[field.name] === undefined) {
+        missingEmbeddingFields.push(field);
+      } else {
+        fields.push(field);
+      }
+    } else {
+      fields.push(field);
+    }
+  }
+
+  if (missingEmbeddingFields.length > 0 && embeddings === undefined) {
+    throw new Error(
+      `Table has embeddings: "${missingEmbeddingFields
+        .map((f) => f.name)
+        .join(",")}", but no embedding function was provided`
+    );
+  }
+
+  return new Schema(fields, schema.metadata);
 }

node/src/index.ts

@@ -12,19 +12,20 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { type Schema, Table as ArrowTable, tableFromIPC } from 'apache-arrow'
+import { type Schema, Table as ArrowTable, tableFromIPC } from "apache-arrow";
 import {
   createEmptyTable,
   fromRecordsToBuffer,
   fromTableToBuffer,
   makeArrowTable
-} from './arrow'
-import type { EmbeddingFunction } from './embedding/embedding_function'
-import { RemoteConnection } from './remote'
-import { Query } from './query'
-import { isEmbeddingFunction } from './embedding/embedding_function'
-import { type Literal, toSQL } from './util'
-import { type HttpMiddleware } from './middleware'
+} from "./arrow";
+import type { EmbeddingFunction } from "./embedding/embedding_function";
+import { RemoteConnection } from "./remote";
+import { Query } from "./query";
+import { isEmbeddingFunction } from "./embedding/embedding_function";
+import { type Literal, toSQL } from "./util";
+
+import { type HttpMiddleware } from "./middleware";
 
 const {
   databaseNew,
@@ -48,14 +49,18 @@ const {
   tableAlterColumns,
   tableDropColumns
   // eslint-disable-next-line @typescript-eslint/no-var-requires
-} = require('../native.js')
+} = require("../native.js");
 
-export { Query }
-export type { EmbeddingFunction }
-export { OpenAIEmbeddingFunction } from './embedding/openai'
-export { convertToTable, makeArrowTable, type MakeArrowTableOptions } from './arrow'
+export { Query };
+export type { EmbeddingFunction };
+export { OpenAIEmbeddingFunction } from "./embedding/openai";
+export {
+  convertToTable,
+  makeArrowTable,
+  type MakeArrowTableOptions
+} from "./arrow";
 
-const defaultAwsRegion = 'us-west-2'
+const defaultAwsRegion = "us-west-2";
 
 export interface AwsCredentials {
   accessKeyId: string
@@ -78,12 +83,25 @@ export interface ConnectionOptions {
   /** User provided AWS crednetials.
    *
    * If not provided, LanceDB will use the default credentials provider chain.
+   *
+   * @deprecated Pass `aws_access_key_id`, `aws_secret_access_key`, and `aws_session_token`
+   * through `storageOptions` instead.
    */
   awsCredentials?: AwsCredentials
 
-  /** AWS region to connect to. Default is {@link defaultAwsRegion}. */
+  /** AWS region to connect to. Default is {@link defaultAwsRegion}
+   *
+   * @deprecated Pass `region` through `storageOptions` instead.
+   */
   awsRegion?: string
 
+  /**
+   * User provided options for object storage. For example, S3 credentials or request timeouts.
+   *
+   * The various options are described at https://lancedb.github.io/lancedb/guides/storage/
+   */
+  storageOptions?: Record<string, string>
+
   /**
    * API key for the remote connections
    *
@@ -115,19 +133,19 @@ export interface ConnectionOptions {
   readConsistencyInterval?: number
 }
 
-function getAwsArgs (opts: ConnectionOptions): any[] {
-  const callArgs: any[] = []
-  const awsCredentials = opts.awsCredentials
+function getAwsArgs(opts: ConnectionOptions): any[] {
+  const callArgs: any[] = [];
+  const awsCredentials = opts.awsCredentials;
   if (awsCredentials !== undefined) {
-    callArgs.push(awsCredentials.accessKeyId)
-    callArgs.push(awsCredentials.secretKey)
-    callArgs.push(awsCredentials.sessionToken)
+    callArgs.push(awsCredentials.accessKeyId);
+    callArgs.push(awsCredentials.secretKey);
+    callArgs.push(awsCredentials.sessionToken);
   } else {
-    callArgs.fill(undefined, 0, 3)
+    callArgs.fill(undefined, 0, 3);
   }
 
-  callArgs.push(opts.awsRegion)
-  return callArgs
+  callArgs.push(opts.awsRegion);
+  return callArgs;
 }
 
 export interface CreateTableOptions<T> {
@@ -150,7 +168,7 @@ export interface CreateTableOptions<T> {
 /**
  * Connect to a LanceDB instance at the given URI.
  *
- * Accpeted formats:
+ * Accepted formats:
  *
  * - `/path/to/database` - local database
  * - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
@@ -160,53 +178,66 @@ export interface CreateTableOptions<T> {
  *
  * @see {@link ConnectionOptions} for more details on the URI format.
  */
-export async function connect (uri: string): Promise<Connection>
+export async function connect(uri: string): Promise<Connection>;
 /**
  * Connect to a LanceDB instance with connection options.
  *
  * @param opts The {@link ConnectionOptions} to use when connecting to the database.
  */
-export async function connect (
+export async function connect(
   opts: Partial<ConnectionOptions>
-): Promise<Connection>
-export async function connect (
+): Promise<Connection>;
+export async function connect(
   arg: string | Partial<ConnectionOptions>
 ): Promise<Connection> {
-  let opts: ConnectionOptions
-  if (typeof arg === 'string') {
-    opts = { uri: arg }
+  let opts: ConnectionOptions;
+  if (typeof arg === "string") {
+    opts = { uri: arg };
   } else {
-    // opts = { uri: arg.uri, awsCredentials = arg.awsCredentials }
-    const keys = Object.keys(arg)
-    if (keys.length === 1 && keys[0] === 'uri' && typeof arg.uri === 'string') {
-      opts = { uri: arg.uri }
+    const keys = Object.keys(arg);
+    if (keys.length === 1 && keys[0] === "uri" && typeof arg.uri === "string") {
+      opts = { uri: arg.uri };
     } else {
       opts = Object.assign(
         {
-          uri: '',
+          uri: "",
           awsCredentials: undefined,
           awsRegion: defaultAwsRegion,
           apiKey: undefined,
           region: defaultAwsRegion
         },
         arg
-      )
+      );
     }
   }
 
-  if (opts.uri.startsWith('db://')) {
+  if (opts.uri.startsWith("db://")) {
     // Remote connection
-    return new RemoteConnection(opts)
+    return new RemoteConnection(opts);
   }
+
+  const storageOptions = opts.storageOptions ?? {};
+  if (opts.awsCredentials?.accessKeyId !== undefined) {
+    storageOptions.aws_access_key_id = opts.awsCredentials.accessKeyId;
+  }
+  if (opts.awsCredentials?.secretKey !== undefined) {
+    storageOptions.aws_secret_access_key = opts.awsCredentials.secretKey;
+  }
+  if (opts.awsCredentials?.sessionToken !== undefined) {
+    storageOptions.aws_session_token = opts.awsCredentials.sessionToken;
+  }
+  if (opts.awsRegion !== undefined) {
+    storageOptions.region = opts.awsRegion;
+  }
+  // It's a pain to pass a record to Rust, so we convert it to an array of key-value pairs
+  const storageOptionsArr = Object.entries(storageOptions);
+
   const db = await databaseNew(
     opts.uri,
-    opts.awsCredentials?.accessKeyId,
-    opts.awsCredentials?.secretKey,
-    opts.awsCredentials?.sessionToken,
-    opts.awsRegion,
+    storageOptionsArr,
     opts.readConsistencyInterval
-  )
-  return new LocalConnection(db, opts)
+  );
+  return new LocalConnection(db, opts);
 }
 
 /**
@@ -507,7 +538,11 @@ export interface Table<T = number[]> {
    * @param data the new data to insert
    * @param args parameters controlling how the operation should behave
    */
-  mergeInsert: (on: string, data: Array<Record<string, unknown>> | ArrowTable, args: MergeInsertArgs) => Promise<void>
+  mergeInsert: (
+    on: string,
+    data: Array<Record<string, unknown>> | ArrowTable,
+    args: MergeInsertArgs
+  ) => Promise<void>
 
   /**
    * List the indicies on this table.
@@ -532,7 +567,9 @@ export interface Table<T = number[]> {
    *                            expressions will be evaluated for each row in the
    *                            table, and can reference existing columns in the table.
    */
-  addColumns(newColumnTransforms: Array<{ name: string, valueSql: string }>): Promise<void>
+  addColumns(
+    newColumnTransforms: Array<{ name: string, valueSql: string }>
+  ): Promise<void>
 
   /**
    * Alter the name or nullability of columns.
@@ -673,23 +710,23 @@ export interface IndexStats {
  * A connection to a LanceDB database.
  */
 export class LocalConnection implements Connection {
-  private readonly _options: () => ConnectionOptions
-  private readonly _db: any
+  private readonly _options: () => ConnectionOptions;
+  private readonly _db: any;
 
-  constructor (db: any, options: ConnectionOptions) {
-    this._options = () => options
-    this._db = db
+  constructor(db: any, options: ConnectionOptions) {
+    this._options = () => options;
+    this._db = db;
   }
 
-  get uri (): string {
-    return this._options().uri
+  get uri(): string {
+    return this._options().uri;
   }
 
   /**
    * Get the names of all tables in the database.
    */
-  async tableNames (): Promise<string[]> {
-    return databaseTableNames.call(this._db)
+  async tableNames(): Promise<string[]> {
+    return databaseTableNames.call(this._db);
   }
 
   /**
@@ -697,7 +734,7 @@ export class LocalConnection implements Connection {
    *
    * @param name The name of the table.
    */
-  async openTable (name: string): Promise<Table>
+  async openTable(name: string): Promise<Table>;
 
   /**
    * Open a table in the database.
@@ -708,24 +745,20 @@ export class LocalConnection implements Connection {
   async openTable<T>(
     name: string,
     embeddings: EmbeddingFunction<T>
-  ): Promise<Table<T>>
+  ): Promise<Table<T>>;
   async openTable<T>(
     name: string,
     embeddings?: EmbeddingFunction<T>
-  ): Promise<Table<T>>
+  ): Promise<Table<T>>;
   async openTable<T>(
     name: string,
     embeddings?: EmbeddingFunction<T>
   ): Promise<Table<T>> {
-    const tbl = await databaseOpenTable.call(
-      this._db,
-      name,
-      ...getAwsArgs(this._options())
-    )
+    const tbl = await databaseOpenTable.call(this._db, name);
     if (embeddings !== undefined) {
-      return new LocalTable(tbl, name, this._options(), embeddings)
+      return new LocalTable(tbl, name, this._options(), embeddings);
     } else {
-      return new LocalTable(tbl, name, this._options())
+      return new LocalTable(tbl, name, this._options());
     }
   }
 
@@ -735,32 +768,32 @@ export class LocalConnection implements Connection {
     optsOrEmbedding?: WriteOptions | EmbeddingFunction<T>,
     opt?: WriteOptions
   ): Promise<Table<T>> {
-    if (typeof name === 'string') {
-      let writeOptions: WriteOptions = new DefaultWriteOptions()
+    if (typeof name === "string") {
+      let writeOptions: WriteOptions = new DefaultWriteOptions();
       if (opt !== undefined && isWriteOptions(opt)) {
-        writeOptions = opt
+        writeOptions = opt;
       } else if (
         optsOrEmbedding !== undefined &&
         isWriteOptions(optsOrEmbedding)
       ) {
-        writeOptions = optsOrEmbedding
+        writeOptions = optsOrEmbedding;
       }
 
-      let embeddings: undefined | EmbeddingFunction<T>
+      let embeddings: undefined | EmbeddingFunction<T>;
       if (
         optsOrEmbedding !== undefined &&
         isEmbeddingFunction(optsOrEmbedding)
       ) {
-        embeddings = optsOrEmbedding
+        embeddings = optsOrEmbedding;
       }
       return await this.createTableImpl({
         name,
         data,
         embeddingFunction: embeddings,
         writeOptions
-      })
+      });
     }
-    return await this.createTableImpl(name)
+    return await this.createTableImpl(name);
   }
 
   private async createTableImpl<T>({
@@ -776,27 +809,27 @@ export class LocalConnection implements Connection {
     embeddingFunction?: EmbeddingFunction<T> | undefined
     writeOptions?: WriteOptions | undefined
   }): Promise<Table<T>> {
-    let buffer: Buffer
+    let buffer: Buffer;
 
-    function isEmpty (
+    function isEmpty(
       data: Array<Record<string, unknown>> | ArrowTable<any>
     ): boolean {
       if (data instanceof ArrowTable) {
-        return data.data.length === 0
+        return data.data.length === 0;
       }
-      return 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')
+        throw new Error("Either data or schema needs to defined");
       }
-      buffer = await fromTableToBuffer(createEmptyTable(schema))
+      buffer = await fromTableToBuffer(createEmptyTable(schema));
     } else if (data instanceof ArrowTable) {
-      buffer = await fromTableToBuffer(data, embeddingFunction, schema)
+      buffer = await fromTableToBuffer(data, embeddingFunction, schema);
     } else {
       // data is Array<Record<...>>
-      buffer = await fromRecordsToBuffer(data, embeddingFunction, schema)
+      buffer = await fromRecordsToBuffer(data, embeddingFunction, schema);
     }
 
     const tbl = await tableCreate.call(
@@ -805,11 +838,11 @@ export class LocalConnection implements Connection {
       buffer,
       writeOptions?.writeMode?.toString(),
       ...getAwsArgs(this._options())
-    )
+    );
     if (embeddingFunction !== undefined) {
-      return new LocalTable(tbl, name, this._options(), embeddingFunction)
+      return new LocalTable(tbl, name, this._options(), embeddingFunction);
     } else {
-      return new LocalTable(tbl, name, this._options())
+      return new LocalTable(tbl, name, this._options());
     }
   }
 
@@ -817,69 +850,69 @@ export class LocalConnection implements Connection {
    * Drop an existing table.
    * @param name The name of the table to drop.
    */
-  async dropTable (name: string): Promise<void> {
-    await databaseDropTable.call(this._db, name)
+  async dropTable(name: string): Promise<void> {
+    await databaseDropTable.call(this._db, name);
   }
 
-  withMiddleware (middleware: HttpMiddleware): Connection {
-    return this
+  withMiddleware(middleware: HttpMiddleware): Connection {
+    return this;
   }
 }
 
 export class LocalTable<T = number[]> implements Table<T> {
-  private _tbl: any
-  private readonly _name: string
-  private readonly _isElectron: boolean
-  private readonly _embeddings?: EmbeddingFunction<T>
-  private readonly _options: () => ConnectionOptions
+  private _tbl: any;
+  private readonly _name: string;
+  private readonly _isElectron: boolean;
+  private readonly _embeddings?: EmbeddingFunction<T>;
+  private readonly _options: () => ConnectionOptions;
 
-  constructor (tbl: any, name: string, options: ConnectionOptions)
+  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 (
+  constructor(
     tbl: any,
     name: string,
     options: ConnectionOptions,
     embeddings: EmbeddingFunction<T>
-  )
-  constructor (
+  );
+  constructor(
     tbl: any,
     name: string,
     options: ConnectionOptions,
     embeddings?: EmbeddingFunction<T>
   ) {
-    this._tbl = tbl
-    this._name = name
-    this._embeddings = embeddings
-    this._options = () => options
-    this._isElectron = this.checkElectron()
+    this._tbl = tbl;
+    this._name = name;
+    this._embeddings = embeddings;
+    this._options = () => options;
+    this._isElectron = this.checkElectron();
   }
 
-  get name (): string {
-    return this._name
+  get name(): string {
+    return this._name;
   }
 
   /**
    * Creates a search query to find the nearest neighbors of the given search term
    * @param query The query search term
    */
-  search (query: T): Query<T> {
-    return new Query(query, this._tbl, this._embeddings)
+  search(query: T): Query<T> {
+    return new Query(query, this._tbl, this._embeddings);
   }
 
   /**
    * Creates a filter query to find all rows matching the specified criteria
    * @param value The filter criteria (like SQL where clause syntax)
    */
-  filter (value: string): Query<T> {
-    return new Query(undefined, this._tbl, this._embeddings).filter(value)
+  filter(value: string): Query<T> {
+    return new Query(undefined, this._tbl, this._embeddings).filter(value);
   }
 
-  where = this.filter
+  where = this.filter;
 
   /**
    * Insert records into this Table.
@@ -887,16 +920,19 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @param data Records to be inserted into the Table
    * @return The number of rows added to the table
    */
-  async add (
+  async add(
     data: Array<Record<string, unknown>> | ArrowTable
   ): Promise<number> {
-    const schema = await this.schema
-    let tbl: ArrowTable
+    const schema = await this.schema;
+
+    let tbl: ArrowTable;
+
     if (data instanceof ArrowTable) {
-      tbl = data
+      tbl = data;
     } else {
-      tbl = makeArrowTable(data, { schema })
+      tbl = makeArrowTable(data, { schema, embeddings: this._embeddings });
     }
+
     return tableAdd
       .call(
         this._tbl,
@@ -905,8 +941,8 @@ export class LocalTable<T = number[]> implements Table<T> {
         ...getAwsArgs(this._options())
       )
       .then((newTable: any) => {
-        this._tbl = newTable
-      })
+        this._tbl = newTable;
+      });
   }
 
   /**
@@ -915,14 +951,14 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @param data Records to be inserted into the Table
    * @return The number of rows added to the table
    */
-  async overwrite (
+  async overwrite(
     data: Array<Record<string, unknown>> | ArrowTable
   ): Promise<number> {
-    let buffer: Buffer
+    let buffer: Buffer;
     if (data instanceof ArrowTable) {
-      buffer = await fromTableToBuffer(data, this._embeddings)
+      buffer = await fromTableToBuffer(data, this._embeddings);
     } else {
-      buffer = await fromRecordsToBuffer(data, this._embeddings)
+      buffer = await fromRecordsToBuffer(data, this._embeddings);
     }
     return tableAdd
       .call(
@@ -932,8 +968,8 @@ export class LocalTable<T = number[]> implements Table<T> {
         ...getAwsArgs(this._options())
       )
       .then((newTable: any) => {
-        this._tbl = newTable
-      })
+        this._tbl = newTable;
+      });
   }
 
   /**
@@ -941,26 +977,26 @@ export class LocalTable<T = number[]> implements Table<T> {
    *
    * @param indexParams The parameters of this Index, @see VectorIndexParams.
    */
-  async createIndex (indexParams: VectorIndexParams): Promise<any> {
+  async createIndex(indexParams: VectorIndexParams): Promise<any> {
     return tableCreateVectorIndex
       .call(this._tbl, indexParams)
       .then((newTable: any) => {
-        this._tbl = newTable
-      })
+        this._tbl = newTable;
+      });
   }
 
-  async createScalarIndex (column: string, replace?: boolean): Promise<void> {
+  async createScalarIndex(column: string, replace?: boolean): Promise<void> {
     if (replace === undefined) {
-      replace = true
+      replace = true;
     }
-    return tableCreateScalarIndex.call(this._tbl, column, replace)
+    return tableCreateScalarIndex.call(this._tbl, column, replace);
   }
 
   /**
    * Returns the number of rows in this table.
    */
-  async countRows (filter?: string): Promise<number> {
-    return tableCountRows.call(this._tbl, filter)
+  async countRows(filter?: string): Promise<number> {
+    return tableCountRows.call(this._tbl, filter);
   }
 
   /**
@@ -968,10 +1004,10 @@ 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> {
+  async delete(filter: string): Promise<void> {
     return tableDelete.call(this._tbl, filter).then((newTable: any) => {
-      this._tbl = newTable
-    })
+      this._tbl = newTable;
+    });
   }
 
   /**
@@ -981,55 +1017,65 @@ export class LocalTable<T = number[]> implements Table<T> {
    *
    * @returns
    */
-  async update (args: UpdateArgs | UpdateSqlArgs): Promise<void> {
-    let filter: string | null
-    let updates: Record<string, string>
+  async update(args: UpdateArgs | UpdateSqlArgs): Promise<void> {
+    let filter: string | null;
+    let updates: Record<string, string>;
 
-    if ('valuesSql' in args) {
-      filter = args.where ?? null
-      updates = args.valuesSql
+    if ("valuesSql" in args) {
+      filter = args.where ?? null;
+      updates = args.valuesSql;
     } else {
-      filter = args.where ?? null
-      updates = {}
+      filter = args.where ?? null;
+      updates = {};
       for (const [key, value] of Object.entries(args.values)) {
-        updates[key] = toSQL(value)
+        updates[key] = toSQL(value);
       }
     }
 
     return tableUpdate
       .call(this._tbl, filter, updates)
       .then((newTable: any) => {
-        this._tbl = newTable
-      })
+        this._tbl = newTable;
+      });
   }
 
-  async mergeInsert (on: string, data: Array<Record<string, unknown>> | ArrowTable, args: MergeInsertArgs): Promise<void> {
-    let whenMatchedUpdateAll = false
-    let whenMatchedUpdateAllFilt = null
-    if (args.whenMatchedUpdateAll !== undefined && args.whenMatchedUpdateAll !== null) {
-      whenMatchedUpdateAll = true
+  async mergeInsert(
+    on: string,
+    data: Array<Record<string, unknown>> | ArrowTable,
+    args: MergeInsertArgs
+  ): Promise<void> {
+    let whenMatchedUpdateAll = false;
+    let whenMatchedUpdateAllFilt = null;
+    if (
+      args.whenMatchedUpdateAll !== undefined &&
+      args.whenMatchedUpdateAll !== null
+    ) {
+      whenMatchedUpdateAll = true;
       if (args.whenMatchedUpdateAll !== true) {
-        whenMatchedUpdateAllFilt = args.whenMatchedUpdateAll
+        whenMatchedUpdateAllFilt = args.whenMatchedUpdateAll;
       }
     }
-    const whenNotMatchedInsertAll = args.whenNotMatchedInsertAll ?? false
-    let whenNotMatchedBySourceDelete = false
-    let whenNotMatchedBySourceDeleteFilt = null
-    if (args.whenNotMatchedBySourceDelete !== undefined && args.whenNotMatchedBySourceDelete !== null) {
-      whenNotMatchedBySourceDelete = true
+    const whenNotMatchedInsertAll = args.whenNotMatchedInsertAll ?? false;
+    let whenNotMatchedBySourceDelete = false;
+    let whenNotMatchedBySourceDeleteFilt = null;
+    if (
+      args.whenNotMatchedBySourceDelete !== undefined &&
+      args.whenNotMatchedBySourceDelete !== null
+    ) {
+      whenNotMatchedBySourceDelete = true;
       if (args.whenNotMatchedBySourceDelete !== true) {
-        whenNotMatchedBySourceDeleteFilt = args.whenNotMatchedBySourceDelete
+        whenNotMatchedBySourceDeleteFilt = args.whenNotMatchedBySourceDelete;
       }
     }
 
-    const schema = await this.schema
-    let tbl: ArrowTable
+    const schema = await this.schema;
+    let tbl: ArrowTable;
     if (data instanceof ArrowTable) {
-      tbl = data
+      tbl = data;
     } else {
-      tbl = makeArrowTable(data, { schema })
+      tbl = makeArrowTable(data, { schema });
     }
-    const buffer = await fromTableToBuffer(tbl, this._embeddings, schema)
+    const buffer = await fromTableToBuffer(tbl, this._embeddings, schema);
 
     this._tbl = await tableMergeInsert.call(
       this._tbl,
@@ -1040,7 +1086,7 @@ export class LocalTable<T = number[]> implements Table<T> {
       whenNotMatchedBySourceDelete,
       whenNotMatchedBySourceDeleteFilt,
       buffer
-    )
+    );
   }
 
   /**
@@ -1058,16 +1104,16 @@ export class LocalTable<T = number[]> implements Table<T> {
    *                 uphold this promise can lead to corrupted tables.
    * @returns
    */
-  async cleanupOldVersions (
+  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
-      })
+        this._tbl = res.newTable;
+        return res.metrics;
+      });
   }
 
   /**
@@ -1081,62 +1127,64 @@ export class LocalTable<T = number[]> implements Table<T> {
    *               for most tables.
    * @returns Metrics about the compaction operation.
    */
-  async compactFiles (options?: CompactionOptions): Promise<CompactionMetrics> {
-    const optionsArg = options ?? {}
+  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
-      })
+        this._tbl = res.newTable;
+        return res.metrics;
+      });
   }
 
-  async listIndices (): Promise<VectorIndex[]> {
-    return tableListIndices.call(this._tbl)
+  async listIndices(): Promise<VectorIndex[]> {
+    return tableListIndices.call(this._tbl);
   }
 
-  async indexStats (indexUuid: string): Promise<IndexStats> {
-    return tableIndexStats.call(this._tbl, indexUuid)
+  async indexStats(indexUuid: string): Promise<IndexStats> {
+    return tableIndexStats.call(this._tbl, indexUuid);
   }
 
-  get schema (): Promise<Schema> {
+  get schema(): Promise<Schema> {
     // empty table
-    return this.getSchema()
+    return this.getSchema();
   }
 
-  private async getSchema (): Promise<Schema> {
-    const buffer = await tableSchema.call(this._tbl, this._isElectron)
-    const table = tableFromIPC(buffer)
-    return table.schema
+  private async getSchema(): Promise<Schema> {
+    const buffer = await tableSchema.call(this._tbl, this._isElectron);
+    const table = tableFromIPC(buffer);
+    return table.schema;
   }
 
   // See https://github.com/electron/electron/issues/2288
-  private checkElectron (): boolean {
+  private checkElectron(): boolean {
     try {
       // eslint-disable-next-line no-prototype-builtins
       return (
-        Object.prototype.hasOwnProperty.call(process?.versions, 'electron') ||
-        navigator?.userAgent?.toLowerCase()?.includes(' electron')
-      )
+        Object.prototype.hasOwnProperty.call(process?.versions, "electron") ||
+        navigator?.userAgent?.toLowerCase()?.includes(" electron")
+      );
     } catch (e) {
-      return false
+      return false;
     }
   }
 
-  async addColumns (newColumnTransforms: Array<{ name: string, valueSql: string }>): Promise<void> {
-    return tableAddColumns.call(this._tbl, newColumnTransforms)
+  async addColumns(
+    newColumnTransforms: Array<{ name: string, valueSql: string }>
+  ): Promise<void> {
+    return tableAddColumns.call(this._tbl, newColumnTransforms);
   }
 
-  async alterColumns (columnAlterations: ColumnAlteration[]): Promise<void> {
-    return tableAlterColumns.call(this._tbl, columnAlterations)
+  async alterColumns(columnAlterations: ColumnAlteration[]): Promise<void> {
+    return tableAlterColumns.call(this._tbl, columnAlterations);
   }
 
-  async dropColumns (columnNames: string[]): Promise<void> {
-    return tableDropColumns.call(this._tbl, columnNames)
+  async dropColumns(columnNames: string[]): Promise<void> {
+    return tableDropColumns.call(this._tbl, columnNames);
   }
 
-  withMiddleware (middleware: HttpMiddleware): Table<T> {
-    return this
+  withMiddleware(middleware: HttpMiddleware): Table<T> {
+    return this;
   }
 }
 
@@ -1159,7 +1207,7 @@ export interface CompactionOptions {
    */
   targetRowsPerFragment?: number
   /**
-   * The maximum number of rows per group. Defaults to 1024.
+   * The maximum number of T per group. Defaults to 1024.
    */
   maxRowsPerGroup?: number
   /**
@@ -1259,21 +1307,21 @@ export interface IvfPQIndexConfig {
    */
   index_cache_size?: number
 
-  type: 'ivf_pq'
+  type: "ivf_pq"
 }
 
-export type VectorIndexParams = IvfPQIndexConfig
+export type VectorIndexParams = IvfPQIndexConfig;
 
 /**
  * Write mode for writing a table.
  */
 export enum WriteMode {
   /** Create a new {@link Table}. */
-  Create = 'create',
+  Create = "create",
   /** Overwrite the existing {@link Table} if presented. */
-  Overwrite = 'overwrite',
+  Overwrite = "overwrite",
   /** Append new data to the table. */
-  Append = 'append',
+  Append = "append",
 }
 
 /**
@@ -1285,14 +1333,14 @@ export interface WriteOptions {
 }
 
 export class DefaultWriteOptions implements WriteOptions {
-  writeMode = WriteMode.Create
+  writeMode = WriteMode.Create;
 }
 
-export function isWriteOptions (value: any): value is WriteOptions {
+export function isWriteOptions(value: any): value is WriteOptions {
   return (
     Object.keys(value).length === 1 &&
-    (value.writeMode === undefined || typeof value.writeMode === 'string')
-  )
+    (value.writeMode === undefined || typeof value.writeMode === "string")
+  );
 }
 
 /**
@@ -1302,15 +1350,15 @@ export enum MetricType {
   /**
    * Euclidean distance
    */
-  L2 = 'l2',
+  L2 = "l2",
 
   /**
    * Cosine distance
    */
-  Cosine = 'cosine',
+  Cosine = "cosine",
 
   /**
    * Dot product
    */
-  Dot = 'dot',
+  Dot = "dot",
 }

node/src/integration_test/test.ts

@@ -51,7 +51,7 @@ describe('LanceDB Mirrored Store Integration test', function () {
 
     const dir = tmpdir()
     console.log(dir)
-    const conn = await lancedb.connect(`s3://lancedb-integtest?mirroredStore=${dir}`)
+    const conn = await lancedb.connect({ uri: `s3://lancedb-integtest?mirroredStore=${dir}`, storageOptions: { allowHttp: 'true' } })
     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 }))

node/src/remote/client.ts

@@ -111,7 +111,11 @@ async function decodeErrorData(
   if (responseType === 'arraybuffer') {
       return new TextDecoder().decode(errorData)
   } else {
-      return errorData
+    if (typeof errorData === 'object') {
+      return JSON.stringify(errorData)
+    }
+
+    return errorData
   }
 }
 

node/src/remote/index.ts

@@ -38,7 +38,7 @@ import {
   fromRecordsToStreamBuffer,
   fromTableToStreamBuffer
 } from '../arrow'
-import { toSQL } from '../util'
+import { toSQL, TTLCache } from '../util'
 import { type HttpMiddleware } from '../middleware'
 
 /**
@@ -47,6 +47,7 @@ import { type HttpMiddleware } from '../middleware'
 export class RemoteConnection implements Connection {
   private _client: HttpLancedbClient
   private readonly _dbName: string
+  private readonly _tableCache = new TTLCache(300_000)
 
   constructor (opts: ConnectionOptions) {
     if (!opts.uri.startsWith('db://')) {
@@ -89,6 +90,9 @@ export class RemoteConnection implements Connection {
       page_token: pageToken
     })
     const body = await response.body()
+    for (const table of body.tables) {
+      this._tableCache.set(table, true)
+    }
     return body.tables
   }
 
@@ -101,6 +105,12 @@ export class RemoteConnection implements Connection {
     name: string,
     embeddings?: EmbeddingFunction<T>
   ): Promise<Table<T>> {
+      // check if the table exists
+      if (this._tableCache.get(name) === undefined) {
+        await this._client.post(`/v1/table/${encodeURIComponent(name)}/describe/`)
+        this._tableCache.set(name, true)
+      }
+
     if (embeddings !== undefined) {
       return new RemoteTable(this._client, name, embeddings)
     } else {
@@ -130,6 +140,9 @@ export class RemoteConnection implements Connection {
       schema = nameOrOpts.schema
       embeddings = nameOrOpts.embeddingFunction
       tableName = nameOrOpts.name
+      if (data === undefined) {
+        data = nameOrOpts.data
+      }
     }
 
     let buffer: Buffer
@@ -169,6 +182,7 @@ export class RemoteConnection implements Connection {
       )
     }
 
+    this._tableCache.set(tableName, true)
     if (embeddings === undefined) {
       return new RemoteTable(this._client, tableName)
     } else {
@@ -178,6 +192,7 @@ export class RemoteConnection implements Connection {
 
   async dropTable (name: string): Promise<void> {
     await this._client.post(`/v1/table/${encodeURIComponent(name)}/drop/`)
+    this._tableCache.delete(name)
   }
 
   withMiddleware (middleware: HttpMiddleware): Connection {

node/src/sanitize.ts

@@ -32,7 +32,7 @@ import {
   Bool,
   Date_,
   Decimal,
-  DataType,
+  type DataType,
   Dictionary,
   Binary,
   Float32,
@@ -74,12 +74,12 @@ import {
   DurationNanosecond,
   DurationMicrosecond,
   DurationMillisecond,
-  DurationSecond,
+  DurationSecond
 } from "apache-arrow";
 import type { IntBitWidth, TimeBitWidth } from "apache-arrow/type";
 
 function sanitizeMetadata(
-  metadataLike?: unknown,
+  metadataLike?: unknown
 ): Map<string, string> | undefined {
   if (metadataLike === undefined || metadataLike === null) {
     return undefined;
@@ -90,7 +90,7 @@ function sanitizeMetadata(
   for (const item of metadataLike) {
     if (!(typeof item[0] === "string" || !(typeof item[1] === "string"))) {
       throw Error(
-        "Expected metadata, if present, to be a Map<string, string> but it had non-string keys or values",
+        "Expected metadata, if present, to be a Map<string, string> but it had non-string keys or values"
       );
     }
   }
@@ -105,7 +105,7 @@ function sanitizeInt(typeLike: object) {
     typeof typeLike.isSigned !== "boolean"
   ) {
     throw Error(
-      "Expected an Int Type to have a `bitWidth` and `isSigned` property",
+      "Expected an Int Type to have a `bitWidth` and `isSigned` property"
     );
   }
   return new Int(typeLike.isSigned, typeLike.bitWidth as IntBitWidth);
@@ -128,7 +128,7 @@ function sanitizeDecimal(typeLike: object) {
     typeof typeLike.bitWidth !== "number"
   ) {
     throw Error(
-      "Expected a Decimal Type to have `scale`, `precision`, and `bitWidth` properties",
+      "Expected a Decimal Type to have `scale`, `precision`, and `bitWidth` properties"
     );
   }
   return new Decimal(typeLike.scale, typeLike.precision, typeLike.bitWidth);
@@ -149,7 +149,7 @@ function sanitizeTime(typeLike: object) {
     typeof typeLike.bitWidth !== "number"
   ) {
     throw Error(
-      "Expected a Time type to have `unit` and `bitWidth` properties",
+      "Expected a Time type to have `unit` and `bitWidth` properties"
     );
   }
   return new Time(typeLike.unit, typeLike.bitWidth as TimeBitWidth);
@@ -172,7 +172,7 @@ function sanitizeTypedTimestamp(
     | typeof TimestampNanosecond
     | typeof TimestampMicrosecond
     | typeof TimestampMillisecond
-    | typeof TimestampSecond,
+    | typeof TimestampSecond
 ) {
   let timezone = null;
   if ("timezone" in typeLike && typeof typeLike.timezone === "string") {
@@ -191,7 +191,7 @@ function sanitizeInterval(typeLike: object) {
 function sanitizeList(typeLike: object) {
   if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
     throw Error(
-      "Expected a List type to have an array-like `children` property",
+      "Expected a List type to have an array-like `children` property"
     );
   }
   if (typeLike.children.length !== 1) {
@@ -203,7 +203,7 @@ function sanitizeList(typeLike: object) {
 function sanitizeStruct(typeLike: object) {
   if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
     throw Error(
-      "Expected a Struct type to have an array-like `children` property",
+      "Expected a Struct type to have an array-like `children` property"
     );
   }
   return new Struct(typeLike.children.map((child) => sanitizeField(child)));
@@ -216,47 +216,47 @@ function sanitizeUnion(typeLike: object) {
     typeof typeLike.mode !== "number"
   ) {
     throw Error(
-      "Expected a Union type to have `typeIds` and `mode` properties",
+      "Expected a Union type to have `typeIds` and `mode` properties"
     );
   }
   if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
     throw Error(
-      "Expected a Union type to have an array-like `children` property",
+      "Expected a Union type to have an array-like `children` property"
     );
   }
 
   return new Union(
     typeLike.mode,
     typeLike.typeIds as any,
-    typeLike.children.map((child) => sanitizeField(child)),
+    typeLike.children.map((child) => sanitizeField(child))
   );
 }
 
 function sanitizeTypedUnion(
   typeLike: object,
-  UnionType: typeof DenseUnion | typeof SparseUnion,
+  UnionType: typeof DenseUnion | typeof SparseUnion
 ) {
   if (!("typeIds" in typeLike)) {
     throw Error(
-      "Expected a DenseUnion/SparseUnion type to have a `typeIds` property",
+      "Expected a DenseUnion/SparseUnion type to have a `typeIds` property"
     );
   }
   if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
     throw Error(
-      "Expected a DenseUnion/SparseUnion type to have an array-like `children` property",
+      "Expected a DenseUnion/SparseUnion type to have an array-like `children` property"
     );
   }
 
   return new UnionType(
     typeLike.typeIds as any,
-    typeLike.children.map((child) => sanitizeField(child)),
+    typeLike.children.map((child) => sanitizeField(child))
   );
 }
 
 function sanitizeFixedSizeBinary(typeLike: object) {
   if (!("byteWidth" in typeLike) || typeof typeLike.byteWidth !== "number") {
     throw Error(
-      "Expected a FixedSizeBinary type to have a `byteWidth` property",
+      "Expected a FixedSizeBinary type to have a `byteWidth` property"
     );
   }
   return new FixedSizeBinary(typeLike.byteWidth);
@@ -268,7 +268,7 @@ function sanitizeFixedSizeList(typeLike: object) {
   }
   if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
     throw Error(
-      "Expected a FixedSizeList type to have an array-like `children` property",
+      "Expected a FixedSizeList type to have an array-like `children` property"
     );
   }
   if (typeLike.children.length !== 1) {
@@ -276,14 +276,14 @@ function sanitizeFixedSizeList(typeLike: object) {
   }
   return new FixedSizeList(
     typeLike.listSize,
-    sanitizeField(typeLike.children[0]),
+    sanitizeField(typeLike.children[0])
   );
 }
 
 function sanitizeMap(typeLike: object) {
   if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
     throw Error(
-      "Expected a Map type to have an array-like `children` property",
+      "Expected a Map type to have an array-like `children` property"
     );
   }
   if (!("keysSorted" in typeLike) || typeof typeLike.keysSorted !== "boolean") {
@@ -291,7 +291,7 @@ function sanitizeMap(typeLike: object) {
   }
   return new Map_(
     typeLike.children.map((field) => sanitizeField(field)) as any,
-    typeLike.keysSorted,
+    typeLike.keysSorted
   );
 }
 
@@ -319,7 +319,7 @@ function sanitizeDictionary(typeLike: object) {
     sanitizeType(typeLike.dictionary),
     sanitizeType(typeLike.indices) as any,
     typeLike.id,
-    typeLike.isOrdered,
+    typeLike.isOrdered
   );
 }
 
@@ -454,7 +454,7 @@ function sanitizeField(fieldLike: unknown): Field {
     !("nullable" in fieldLike)
   ) {
     throw Error(
-      "The field passed in is missing a `type`/`name`/`nullable` property",
+      "The field passed in is missing a `type`/`name`/`nullable` property"
     );
   }
   const type = sanitizeType(fieldLike.type);
@@ -489,7 +489,7 @@ export function sanitizeSchema(schemaLike: unknown): Schema {
   }
   if (!("fields" in schemaLike)) {
     throw Error(
-      "The schema passed in does not appear to be a schema (no 'fields' property)",
+      "The schema passed in does not appear to be a schema (no 'fields' property)"
     );
   }
   let metadata;
@@ -498,11 +498,11 @@ export function sanitizeSchema(schemaLike: unknown): Schema {
   }
   if (!Array.isArray(schemaLike.fields)) {
     throw Error(
-      "The schema passed in had a 'fields' property but it was not an array",
+      "The schema passed in had a 'fields' property but it was not an array"
     );
   }
   const sanitizedFields = schemaLike.fields.map((field) =>
-    sanitizeField(field),
+    sanitizeField(field)
   );
   return new Schema(sanitizedFields, metadata);
 }

node/src/util.ts

@@ -42,3 +42,36 @@ export function toSQL (value: Literal): string {
   // eslint-disable-next-line @typescript-eslint/restrict-template-expressions
   throw new Error(`Unsupported value type: ${typeof value} value: (${value})`)
 }
+
+export class TTLCache {
+  private readonly cache: Map<string, { value: any, expires: number }>
+
+  /**
+   * @param ttl Time to live in milliseconds
+   */
+  constructor (private readonly ttl: number) {
+    this.cache = new Map()
+  }
+
+  get (key: string): any | undefined {
+    const entry = this.cache.get(key)
+    if (entry === undefined) {
+      return undefined
+    }
+
+    if (entry.expires < Date.now()) {
+      this.cache.delete(key)
+      return undefined
+    }
+
+    return entry.value
+  }
+
+  set (key: string, value: any): void {
+    this.cache.set(key, { value, expires: Date.now() + this.ttl })
+  }
+
+  delete (key: string): void {
+    this.cache.delete(key)
+  }
+}

nodejs/.eslintignore

@@ -1,3 +0,0 @@
-**/dist/**/*
-**/native.js
-**/native.d.ts

nodejs/.gitignore

@@ -0,0 +1 @@
+yarn.lock

nodejs/.prettierignore

@@ -1 +0,0 @@
-.eslintignore

nodejs/README.md

@@ -43,29 +43,20 @@ npm run test
 
 ### Running lint / format
 
-LanceDb uses eslint for linting. VSCode does not need any plugins to use eslint. However, it
-may need some additional configuration. Make sure that eslint.experimental.useFlatConfig is
-set to true. Also, if your vscode root folder is the repo root then you will need to set
-the eslint.workingDirectories to ["nodejs"]. To manually lint your code you can run:
+LanceDb uses [biome](https://biomejs.dev/) for linting and formatting. if you are using VSCode you will need to install the official [Biome](https://marketplace.visualstudio.com/items?itemName=biomejs.biome) extension.
+To manually lint your code you can run:
 
 ```sh
 npm run lint
 ```
 
-LanceDb uses prettier for formatting. If you are using VSCode you will need to install the
-"Prettier - Code formatter" extension. You should then configure it to be the default formatter
-for typescript and you should enable format on save. To manually check your code's format you
-can run:
+to automatically fix all fixable issues:
 
 ```sh
-npm run chkformat
+npm run lint-fix
 ```
 
-If you need to manually format your code you can run:
-
-```sh
-npx prettier --write .
-```
+If you do not have your workspace root set to the `nodejs` directory, unfortunately the extension will not work. You can still run the linting and formatting commands manually.
 
 ### Generating docs
 

nodejs/__test__/arrow.test.ts

@@ -13,32 +13,26 @@
 // limitations under the License.
 
 import {
-  convertToTable,
-  fromTableToBuffer,
-  makeArrowTable,
-  makeEmptyTable,
-} from "../dist/arrow";
-import {
-  Field,
-  FixedSizeList,
-  Float16,
-  Float32,
-  Int32,
-  tableFromIPC,
-  Schema,
-  Float64,
-  type Table,
   Binary,
   Bool,
-  Utf8,
-  Struct,
-  List,
   DataType,
   Dictionary,
-  Int64,
+  Field,
+  FixedSizeList,
   Float,
-  Precision,
+  Float16,
+  Float32,
+  Float64,
+  Int32,
+  Int64,
+  List,
   MetadataVersion,
+  Precision,
+  Schema,
+  Struct,
+  type Table,
+  Utf8,
+  tableFromIPC,
 } from "apache-arrow";
 import {
   Dictionary as OldDictionary,
@@ -46,14 +40,20 @@ import {
   FixedSizeList as OldFixedSizeList,
   Float32 as OldFloat32,
   Int32 as OldInt32,
-  Struct as OldStruct,
   Schema as OldSchema,
+  Struct as OldStruct,
   TimestampNanosecond as OldTimestampNanosecond,
   Utf8 as OldUtf8,
 } from "apache-arrow-old";
-import { type EmbeddingFunction } from "../dist/embedding/embedding_function";
+import {
+  convertToTable,
+  fromTableToBuffer,
+  makeArrowTable,
+  makeEmptyTable,
+} from "../lancedb/arrow";
+import { type EmbeddingFunction } from "../lancedb/embedding/embedding_function";
 
-// eslint-disable-next-line @typescript-eslint/no-explicit-any
+// biome-ignore lint/suspicious/noExplicitAny: skip
 function sampleRecords(): Array<Record<string, any>> {
   return [
     {
@@ -438,7 +438,7 @@ describe("when using two versions of arrow", function () {
           new OldField("ts_no_tz", new OldTimestampNanosecond(null)),
         ]),
       ),
-      // eslint-disable-next-line @typescript-eslint/no-explicit-any
+      // biome-ignore lint/suspicious/noExplicitAny: skip
     ]) as any;
     schema.metadataVersion = MetadataVersion.V5;
     const table = makeArrowTable([], { schema });

nodejs/__test__/connection.test.ts

@@ -14,11 +14,13 @@
 
 import * as tmp from "tmp";
 
-import { Connection, connect } from "../dist/index.js";
+import { Connection, connect } from "../lancedb";
 
 describe("when connecting", () => {
   let tmpDir: tmp.DirResult;
-  beforeEach(() => (tmpDir = tmp.dirSync({ unsafeCleanup: true })));
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
   afterEach(() => tmpDir.removeCallback());
 
   it("should connect", async () => {

nodejs/__test__/s3_integration.test.ts

@@ -0,0 +1,226 @@
+// Copyright 2024 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.
+
+/* eslint-disable @typescript-eslint/naming-convention */
+
+import {
+  CreateKeyCommand,
+  KMSClient,
+  ScheduleKeyDeletionCommand,
+} from "@aws-sdk/client-kms";
+import {
+  CreateBucketCommand,
+  DeleteBucketCommand,
+  DeleteObjectCommand,
+  HeadObjectCommand,
+  ListObjectsV2Command,
+  S3Client,
+} from "@aws-sdk/client-s3";
+import { connect } from "../lancedb";
+
+// Skip these tests unless the S3_TEST environment variable is set
+const maybeDescribe = process.env.S3_TEST ? describe : describe.skip;
+
+// These are all keys that are accepted by storage_options
+const CONFIG = {
+  allowHttp: "true",
+  awsAccessKeyId: "ACCESSKEY",
+  awsSecretAccessKey: "SECRETKEY",
+  awsEndpoint: "http://127.0.0.1:4566",
+  awsRegion: "us-east-1",
+};
+
+class S3Bucket {
+  name: string;
+  constructor(name: string) {
+    this.name = name;
+  }
+
+  static s3Client() {
+    return new S3Client({
+      region: CONFIG.awsRegion,
+      credentials: {
+        accessKeyId: CONFIG.awsAccessKeyId,
+        secretAccessKey: CONFIG.awsSecretAccessKey,
+      },
+      endpoint: CONFIG.awsEndpoint,
+    });
+  }
+
+  public static async create(name: string): Promise<S3Bucket> {
+    const client = this.s3Client();
+    // Delete the bucket if it already exists
+    try {
+      await this.deleteBucket(client, name);
+    } catch {
+      // It's fine if the bucket doesn't exist
+    }
+    // biome-ignore lint/style/useNamingConvention: we dont control s3's api
+    await client.send(new CreateBucketCommand({ Bucket: name }));
+    return new S3Bucket(name);
+  }
+
+  public async delete() {
+    const client = S3Bucket.s3Client();
+    await S3Bucket.deleteBucket(client, this.name);
+  }
+
+  static async deleteBucket(client: S3Client, name: string) {
+    // Must delete all objects before we can delete the bucket
+    const objects = await client.send(
+      // biome-ignore lint/style/useNamingConvention: we dont control s3's api
+      new ListObjectsV2Command({ Bucket: name }),
+    );
+    if (objects.Contents) {
+      for (const object of objects.Contents) {
+        await client.send(
+          // biome-ignore lint/style/useNamingConvention: we dont control s3's api
+          new DeleteObjectCommand({ Bucket: name, Key: object.Key }),
+        );
+      }
+    }
+
+    // biome-ignore lint/style/useNamingConvention: we dont control s3's api
+    await client.send(new DeleteBucketCommand({ Bucket: name }));
+  }
+
+  public async assertAllEncrypted(path: string, keyId: string) {
+    const client = S3Bucket.s3Client();
+    const objects = await client.send(
+      // biome-ignore lint/style/useNamingConvention: we dont control s3's api
+      new ListObjectsV2Command({ Bucket: this.name, Prefix: path }),
+    );
+    if (objects.Contents) {
+      for (const object of objects.Contents) {
+        const metadata = await client.send(
+          // biome-ignore lint/style/useNamingConvention: we dont control s3's api
+          new HeadObjectCommand({ Bucket: this.name, Key: object.Key }),
+        );
+        expect(metadata.ServerSideEncryption).toBe("aws:kms");
+        expect(metadata.SSEKMSKeyId).toContain(keyId);
+      }
+    }
+  }
+}
+
+class KmsKey {
+  keyId: string;
+  constructor(keyId: string) {
+    this.keyId = keyId;
+  }
+
+  static kmsClient() {
+    return new KMSClient({
+      region: CONFIG.awsRegion,
+      credentials: {
+        accessKeyId: CONFIG.awsAccessKeyId,
+        secretAccessKey: CONFIG.awsSecretAccessKey,
+      },
+      endpoint: CONFIG.awsEndpoint,
+    });
+  }
+
+  public static async create(): Promise<KmsKey> {
+    const client = this.kmsClient();
+    const key = await client.send(new CreateKeyCommand({}));
+    const keyId = key?.KeyMetadata?.KeyId;
+    if (!keyId) {
+      throw new Error("Failed to create KMS key");
+    }
+    return new KmsKey(keyId);
+  }
+
+  public async delete() {
+    const client = KmsKey.kmsClient();
+    // biome-ignore lint/style/useNamingConvention: we dont control s3's api
+    await client.send(new ScheduleKeyDeletionCommand({ KeyId: this.keyId }));
+  }
+}
+
+maybeDescribe("storage_options", () => {
+  let bucket: S3Bucket;
+  let kmsKey: KmsKey;
+  beforeAll(async () => {
+    bucket = await S3Bucket.create("lancedb");
+    kmsKey = await KmsKey.create();
+  });
+  afterAll(async () => {
+    await kmsKey.delete();
+    await bucket.delete();
+  });
+
+  it("can be used to configure auth and endpoints", async () => {
+    const uri = `s3://${bucket.name}/test`;
+    const db = await connect(uri, { storageOptions: CONFIG });
+
+    let table = await db.createTable("test", [{ a: 1, b: 2 }]);
+
+    let rowCount = await table.countRows();
+    expect(rowCount).toBe(1);
+
+    let tableNames = await db.tableNames();
+    expect(tableNames).toEqual(["test"]);
+
+    table = await db.openTable("test");
+    rowCount = await table.countRows();
+    expect(rowCount).toBe(1);
+
+    await table.add([
+      { a: 2, b: 3 },
+      { a: 3, b: 4 },
+    ]);
+    rowCount = await table.countRows();
+    expect(rowCount).toBe(3);
+
+    await db.dropTable("test");
+
+    tableNames = await db.tableNames();
+    expect(tableNames).toEqual([]);
+  });
+
+  it("can configure encryption at connection and table level", async () => {
+    const uri = `s3://${bucket.name}/test`;
+    let db = await connect(uri, { storageOptions: CONFIG });
+
+    let table = await db.createTable("table1", [{ a: 1, b: 2 }], {
+      storageOptions: {
+        awsServerSideEncryption: "aws:kms",
+        awsSseKmsKeyId: kmsKey.keyId,
+      },
+    });
+
+    let rowCount = await table.countRows();
+    expect(rowCount).toBe(1);
+
+    await table.add([{ a: 2, b: 3 }]);
+
+    await bucket.assertAllEncrypted("test/table1.lance", kmsKey.keyId);
+
+    // Now with encryption settings at connection level
+    db = await connect(uri, {
+      storageOptions: {
+        ...CONFIG,
+        awsServerSideEncryption: "aws:kms",
+        awsSseKmsKeyId: kmsKey.keyId,
+      },
+    });
+    table = await db.createTable("table2", [{ a: 1, b: 2 }]);
+    rowCount = await table.countRows();
+    expect(rowCount).toBe(1);
+
+    await table.add([{ a: 2, b: 3 }]);
+
+    await bucket.assertAllEncrypted("test/table2.lance", kmsKey.keyId);
+  });
+});

nodejs/__test__/table.test.ts

@@ -16,18 +16,18 @@ import * as fs from "fs";
 import * as path from "path";
 import * as tmp from "tmp";
 
-import { Table, connect } from "../dist";
 import {
-  Schema,
   Field,
-  Float32,
-  Int32,
   FixedSizeList,
-  Int64,
+  Float32,
   Float64,
+  Int32,
+  Int64,
+  Schema,
 } from "apache-arrow";
-import { makeArrowTable } from "../dist/arrow";
-import { Index } from "../dist/indices";
+import { Table, connect } from "../lancedb";
+import { makeArrowTable } from "../lancedb/arrow";
+import { Index } from "../lancedb/indices";
 
 describe("Given a table", () => {
   let tmpDir: tmp.DirResult;
@@ -419,3 +419,31 @@ describe("when dealing with versioning", () => {
     );
   });
 });
+
+describe("when optimizing a dataset", () => {
+  let tmpDir: tmp.DirResult;
+  let table: Table;
+  beforeEach(async () => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+    const con = await connect(tmpDir.name);
+    table = await con.createTable("vectors", [{ id: 1 }]);
+    await table.add([{ id: 2 }]);
+  });
+  afterEach(() => {
+    tmpDir.removeCallback();
+  });
+
+  it("compacts files", async () => {
+    const stats = await table.optimize();
+    expect(stats.compaction.filesAdded).toBe(1);
+    expect(stats.compaction.filesRemoved).toBe(2);
+    expect(stats.compaction.fragmentsAdded).toBe(1);
+    expect(stats.compaction.fragmentsRemoved).toBe(2);
+  });
+
+  it("cleanups old versions", async () => {
+    const stats = await table.optimize({ cleanupOlderThan: new Date() });
+    expect(stats.prune.bytesRemoved).toBeGreaterThan(0);
+    expect(stats.prune.oldVersionsRemoved).toBe(3);
+  });
+});

nodejs/biome.json

@@ -0,0 +1,136 @@
+{
+  "$schema": "https://biomejs.dev/schemas/1.7.3/schema.json",
+  "organizeImports": {
+    "enabled": true
+  },
+  "files": {
+    "ignore": [
+      "**/dist/**/*",
+      "**/native.js",
+      "**/native.d.ts",
+      "**/npm/**/*",
+      "**/.vscode/**"
+    ]
+  },
+  "formatter": {
+    "indentStyle": "space"
+  },
+  "linter": {
+    "enabled": true,
+    "rules": {
+      "recommended": false,
+      "complexity": {
+        "noBannedTypes": "error",
+        "noExtraBooleanCast": "error",
+        "noMultipleSpacesInRegularExpressionLiterals": "error",
+        "noUselessCatch": "error",
+        "noUselessThisAlias": "error",
+        "noUselessTypeConstraint": "error",
+        "noWith": "error"
+      },
+      "correctness": {
+        "noConstAssign": "error",
+        "noConstantCondition": "error",
+        "noEmptyCharacterClassInRegex": "error",
+        "noEmptyPattern": "error",
+        "noGlobalObjectCalls": "error",
+        "noInnerDeclarations": "error",
+        "noInvalidConstructorSuper": "error",
+        "noNewSymbol": "error",
+        "noNonoctalDecimalEscape": "error",
+        "noPrecisionLoss": "error",
+        "noSelfAssign": "error",
+        "noSetterReturn": "error",
+        "noSwitchDeclarations": "error",
+        "noUndeclaredVariables": "error",
+        "noUnreachable": "error",
+        "noUnreachableSuper": "error",
+        "noUnsafeFinally": "error",
+        "noUnsafeOptionalChaining": "error",
+        "noUnusedLabels": "error",
+        "noUnusedVariables": "error",
+        "useIsNan": "error",
+        "useValidForDirection": "error",
+        "useYield": "error"
+      },
+      "style": {
+        "noNamespace": "error",
+        "useAsConstAssertion": "error",
+        "useBlockStatements": "off",
+        "useNamingConvention": {
+          "level": "error",
+          "options": {
+            "strictCase": false
+          }
+        }
+      },
+      "suspicious": {
+        "noAssignInExpressions": "error",
+        "noAsyncPromiseExecutor": "error",
+        "noCatchAssign": "error",
+        "noClassAssign": "error",
+        "noCompareNegZero": "error",
+        "noControlCharactersInRegex": "error",
+        "noDebugger": "error",
+        "noDuplicateCase": "error",
+        "noDuplicateClassMembers": "error",
+        "noDuplicateObjectKeys": "error",
+        "noDuplicateParameters": "error",
+        "noEmptyBlockStatements": "error",
+        "noExplicitAny": "error",
+        "noExtraNonNullAssertion": "error",
+        "noFallthroughSwitchClause": "error",
+        "noFunctionAssign": "error",
+        "noGlobalAssign": "error",
+        "noImportAssign": "error",
+        "noMisleadingCharacterClass": "error",
+        "noMisleadingInstantiator": "error",
+        "noPrototypeBuiltins": "error",
+        "noRedeclare": "error",
+        "noShadowRestrictedNames": "error",
+        "noUnsafeDeclarationMerging": "error",
+        "noUnsafeNegation": "error",
+        "useGetterReturn": "error",
+        "useValidTypeof": "error"
+      }
+    },
+    "ignore": ["**/dist/**/*", "**/native.js", "**/native.d.ts"]
+  },
+  "javascript": {
+    "globals": []
+  },
+  "overrides": [
+    {
+      "include": ["**/*.ts", "**/*.tsx", "**/*.mts", "**/*.cts"],
+      "linter": {
+        "rules": {
+          "correctness": {
+            "noConstAssign": "off",
+            "noGlobalObjectCalls": "off",
+            "noInvalidConstructorSuper": "off",
+            "noNewSymbol": "off",
+            "noSetterReturn": "off",
+            "noUndeclaredVariables": "off",
+            "noUnreachable": "off",
+            "noUnreachableSuper": "off"
+          },
+          "style": {
+            "noArguments": "error",
+            "noVar": "error",
+            "useConst": "error"
+          },
+          "suspicious": {
+            "noDuplicateClassMembers": "off",
+            "noDuplicateObjectKeys": "off",
+            "noDuplicateParameters": "off",
+            "noFunctionAssign": "off",
+            "noImportAssign": "off",
+            "noRedeclare": "off",
+            "noUnsafeNegation": "off",
+            "useGetterReturn": "off"
+          }
+        }
+      }
+    }
+  ]
+}

nodejs/eslint.config.js

@@ -1,28 +0,0 @@
-/* eslint-disable @typescript-eslint/naming-convention */
-// @ts-check
-
-const eslint = require("@eslint/js");
-const tseslint = require("typescript-eslint");
-const eslintConfigPrettier = require("eslint-config-prettier");
-const jsdoc = require("eslint-plugin-jsdoc");
-
-module.exports = tseslint.config(
-  eslint.configs.recommended,
-  jsdoc.configs["flat/recommended"],
-  eslintConfigPrettier,
-  ...tseslint.configs.recommended,
-  {
-    rules: {
-      "@typescript-eslint/naming-convention": "error",
-      "jsdoc/require-returns": "off",
-      "jsdoc/require-param": "off",
-      "jsdoc/require-jsdoc": [
-        "error",
-        {
-          publicOnly: true,
-        },
-      ],
-    },
-    plugins: jsdoc,
-  },
-);

nodejs/lancedb/arrow.ts

@@ -13,25 +13,25 @@
 // limitations under the License.
 
 import {
-  Field,
-  makeBuilder,
-  RecordBatchFileWriter,
-  Utf8,
-  type Vector,
-  FixedSizeList,
-  vectorFromArray,
-  type Schema,
   Table as ArrowTable,
-  RecordBatchStreamWriter,
+  Binary,
+  DataType,
+  Field,
+  FixedSizeList,
+  type Float,
+  Float32,
   List,
   RecordBatch,
-  makeData,
+  RecordBatchFileWriter,
+  RecordBatchStreamWriter,
+  Schema,
   Struct,
-  type Float,
-  DataType,
-  Binary,
-  Float32,
+  Utf8,
+  type Vector,
+  makeBuilder,
+  makeData,
   type makeTable,
+  vectorFromArray,
 } from "apache-arrow";
 import { type EmbeddingFunction } from "./embedding/embedding_function";
 import { sanitizeSchema } from "./sanitize";
@@ -85,6 +85,7 @@ export class MakeArrowTableOptions {
   vectorColumns: Record<string, VectorColumnOptions> = {
     vector: new VectorColumnOptions(),
   };
+  embeddings?: EmbeddingFunction<unknown>;
 
   /**
    * If true then string columns will be encoded with dictionary encoding
@@ -208,6 +209,7 @@ export function makeArrowTable(
   const opt = new MakeArrowTableOptions(options !== undefined ? options : {});
   if (opt.schema !== undefined && opt.schema !== null) {
     opt.schema = sanitizeSchema(opt.schema);
+    opt.schema = validateSchemaEmbeddings(opt.schema, data, opt.embeddings);
   }
   const columns: Record<string, Vector> = {};
   // TODO: sample dataset to find missing columns
@@ -287,8 +289,8 @@ export function makeArrowTable(
     // then patch the schema of the batches so we can use
     // `new ArrowTable(schema, batches)` which does not do any schema inference
     const firstTable = new ArrowTable(columns);
-    // eslint-disable-next-line @typescript-eslint/no-non-null-assertion
     const batchesFixed = firstTable.batches.map(
+      // eslint-disable-next-line @typescript-eslint/no-non-null-assertion
       (batch) => new RecordBatch(opt.schema!, batch.data),
     );
     return new ArrowTable(opt.schema, batchesFixed);
@@ -313,7 +315,7 @@ function makeListVector(lists: unknown[][]): Vector<unknown> {
     throw Error("Cannot infer list vector from empty array or empty list");
   }
   const sampleList = lists[0];
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  // biome-ignore lint/suspicious/noExplicitAny: skip
   let inferredType: any;
   try {
     const sampleVector = makeVector(sampleList);
@@ -337,7 +339,7 @@ function makeVector(
   values: unknown[],
   type?: DataType,
   stringAsDictionary?: boolean,
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  // biome-ignore lint/suspicious/noExplicitAny: skip
 ): Vector<any> {
   if (type !== undefined) {
     // No need for inference, let Arrow create it
@@ -648,3 +650,39 @@ function alignTable(table: ArrowTable, schema: Schema): ArrowTable {
 export function createEmptyTable(schema: Schema): ArrowTable {
   return new ArrowTable(sanitizeSchema(schema));
 }
+
+function validateSchemaEmbeddings(
+  schema: Schema,
+  data: Array<Record<string, unknown>>,
+  embeddings: EmbeddingFunction<unknown> | undefined,
+) {
+  const fields = [];
+  const missingEmbeddingFields = [];
+
+  // First we check if the field is a `FixedSizeList`
+  // Then we check if the data contains the field
+  // if it does not, we add it to the list of missing embedding fields
+  // Finally, we check if those missing embedding fields are `this._embeddings`
+  // if they are not, we throw an error
+  for (const field of schema.fields) {
+    if (field.type instanceof FixedSizeList) {
+      if (data.length !== 0 && data?.[0]?.[field.name] === undefined) {
+        missingEmbeddingFields.push(field);
+      } else {
+        fields.push(field);
+      }
+    } else {
+      fields.push(field);
+    }
+  }
+
+  if (missingEmbeddingFields.length > 0 && embeddings === undefined) {
+    throw new Error(
+      `Table has embeddings: "${missingEmbeddingFields
+        .map((f) => f.name)
+        .join(",")}", but no embedding function was provided`,
+    );
+  }
+
+  return new Schema(fields, schema.metadata);
+}

nodejs/lancedb/connection.ts

@@ -12,10 +12,32 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { fromTableToBuffer, makeArrowTable, makeEmptyTable } from "./arrow";
-import { Connection as LanceDbConnection } from "./native";
-import { Table } from "./table";
 import { Table as ArrowTable, Schema } from "apache-arrow";
+import { fromTableToBuffer, makeArrowTable, makeEmptyTable } from "./arrow";
+import { ConnectionOptions, Connection as LanceDbConnection } from "./native";
+import { Table } from "./table";
+
+/**
+ * Connect to a LanceDB instance at the given URI.
+ *
+ * Accepted formats:
+ *
+ * - `/path/to/database` - local database
+ * - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
+ * - `db://host:port` - remote database (LanceDB cloud)
+ * @param {string} uri - The uri of the database. If the database uri starts
+ * with `db://` then it connects to a remote database.
+ * @see {@link ConnectionOptions} for more details on the URI format.
+ */
+export async function connect(
+  uri: string,
+  opts?: Partial<ConnectionOptions>,
+): Promise<Connection> {
+  opts = opts ?? {};
+  opts.storageOptions = cleanseStorageOptions(opts.storageOptions);
+  const nativeConn = await LanceDbConnection.new(uri, opts);
+  return new Connection(nativeConn);
+}
 
 export interface CreateTableOptions {
   /**
@@ -33,6 +55,40 @@ export interface CreateTableOptions {
    * then no error will be raised.
    */
   existOk: boolean;
+
+  /**
+   * Configuration for object storage.
+   *
+   * Options already set on the connection will be inherited by the table,
+   * but can be overridden here.
+   *
+   * The available options are described at https://lancedb.github.io/lancedb/guides/storage/
+   */
+  storageOptions?: Record<string, string>;
+}
+
+export interface OpenTableOptions {
+  /**
+   * Configuration for object storage.
+   *
+   * Options already set on the connection will be inherited by the table,
+   * but can be overridden here.
+   *
+   * The available options are described at https://lancedb.github.io/lancedb/guides/storage/
+   */
+  storageOptions?: Record<string, string>;
+  /**
+   * Set the size of the index cache, specified as a number of entries
+   *
+   * The exact meaning of an "entry" will depend on the type of index:
+   * - IVF: there is one entry for each IVF partition
+   * - BTREE: there is one entry for the entire index
+   *
+   * This cache applies to the entire opened table, across all indices.
+   * Setting this value higher will increase performance on larger datasets
+   * at the expense of more RAM
+   */
+  indexCacheSize?: number;
 }
 
 export interface TableNamesOptions {
@@ -109,8 +165,15 @@ export class Connection {
    * Open a table in the database.
    * @param {string} name - The name of the table
    */
-  async openTable(name: string): Promise<Table> {
-    const innerTable = await this.inner.openTable(name);
+  async openTable(
+    name: string,
+    options?: Partial<OpenTableOptions>,
+  ): Promise<Table> {
+    const innerTable = await this.inner.openTable(
+      name,
+      cleanseStorageOptions(options?.storageOptions),
+      options?.indexCacheSize,
+    );
     return new Table(innerTable);
   }
 
@@ -139,7 +202,12 @@ export class Connection {
       table = makeArrowTable(data);
     }
     const buf = await fromTableToBuffer(table);
-    const innerTable = await this.inner.createTable(name, buf, mode);
+    const innerTable = await this.inner.createTable(
+      name,
+      buf,
+      mode,
+      cleanseStorageOptions(options?.storageOptions),
+    );
     return new Table(innerTable);
   }
 
@@ -162,7 +230,12 @@ export class Connection {
 
     const table = makeEmptyTable(schema);
     const buf = await fromTableToBuffer(table);
-    const innerTable = await this.inner.createEmptyTable(name, buf, mode);
+    const innerTable = await this.inner.createEmptyTable(
+      name,
+      buf,
+      mode,
+      cleanseStorageOptions(options?.storageOptions),
+    );
     return new Table(innerTable);
   }
 
@@ -174,3 +247,43 @@ export class Connection {
     return this.inner.dropTable(name);
   }
 }
+
+/**
+ * Takes storage options and makes all the keys snake case.
+ */
+function cleanseStorageOptions(
+  options?: Record<string, string>,
+): Record<string, string> | undefined {
+  if (options === undefined) {
+    return undefined;
+  }
+  const result: Record<string, string> = {};
+  for (const [key, value] of Object.entries(options)) {
+    if (value !== undefined) {
+      const newKey = camelToSnakeCase(key);
+      result[newKey] = value;
+    }
+  }
+  return result;
+}
+
+/**
+ * Convert a string to snake case. It might already be snake case, in which case it is
+ * returned unchanged.
+ */
+function camelToSnakeCase(camel: string): string {
+  if (camel.includes("_")) {
+    // Assume if there is at least one underscore, it is already snake case
+    return camel;
+  }
+  if (camel.toLocaleUpperCase() === camel) {
+    // Assume if the string is all uppercase, it is already snake case
+    return camel;
+  }
+
+  let result = camel.replace(/[A-Z]/g, (letter) => `_${letter.toLowerCase()}`);
+  if (result.startsWith("_")) {
+    result = result.slice(1);
+  }
+  return result;
+}

nodejs/lancedb/embedding/openai.ts

@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { type EmbeddingFunction } from "./embedding_function";
 import type OpenAI from "openai";
+import { type EmbeddingFunction } from "./embedding_function";
 
 export class OpenAIEmbeddingFunction implements EmbeddingFunction<string> {
   private readonly _openai: OpenAI;

nodejs/lancedb/index.ts

@@ -12,12 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { Connection } from "./connection";
-import {
-  Connection as LanceDbConnection,
-  ConnectionOptions,
-} from "./native.js";
-
 export {
   WriteOptions,
   WriteMode,
@@ -32,6 +26,7 @@ export {
   VectorColumnOptions,
 } from "./arrow";
 export {
+  connect,
   Connection,
   CreateTableOptions,
   TableNamesOptions,
@@ -46,24 +41,3 @@ export {
 export { Index, IndexOptions, IvfPqOptions } from "./indices";
 export { Table, AddDataOptions, IndexConfig, UpdateOptions } from "./table";
 export * as embedding from "./embedding";
-
-/**
- * Connect to a LanceDB instance at the given URI.
- *
- * Accpeted formats:
- *
- * - `/path/to/database` - local database
- * - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
- * - `db://host:port` - remote database (LanceDB cloud)
- * @param {string} uri - The uri of the database. If the database uri starts
- * with `db://` then it connects to a remote database.
- * @see {@link ConnectionOptions} for more details on the URI format.
- */
-export async function connect(
-  uri: string,
-  opts?: Partial<ConnectionOptions>,
-): Promise<Connection> {
-  opts = opts ?? {};
-  const nativeConn = await LanceDbConnection.new(uri, opts);
-  return new Connection(nativeConn);
-}

nodejs/lancedb/query.ts

@@ -12,14 +12,14 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { RecordBatch, tableFromIPC, Table as ArrowTable } from "apache-arrow";
+import { Table as ArrowTable, RecordBatch, tableFromIPC } from "apache-arrow";
+import { type IvfPqOptions } from "./indices";
 import {
   RecordBatchIterator as NativeBatchIterator,
   Query as NativeQuery,
   Table as NativeTable,
   VectorQuery as NativeVectorQuery,
 } from "./native";
-import { type IvfPqOptions } from "./indices";
 export class RecordBatchIterator implements AsyncIterator<RecordBatch> {
   private promisedInner?: Promise<NativeBatchIterator>;
   private inner?: NativeBatchIterator;
@@ -29,7 +29,7 @@ export class RecordBatchIterator implements AsyncIterator<RecordBatch> {
     this.promisedInner = promise;
   }
 
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  // biome-ignore lint/suspicious/noExplicitAny: skip
   async next(): Promise<IteratorResult<RecordBatch<any>>> {
     if (this.inner === undefined) {
       this.inner = await this.promisedInner;
@@ -56,7 +56,9 @@ export class QueryBase<
   QueryType,
 > implements AsyncIterable<RecordBatch>
 {
-  protected constructor(protected inner: NativeQueryType) {}
+  protected constructor(protected inner: NativeQueryType) {
+    // intentionally empty
+  }
 
   /**
    * A filter statement to be applied to this query.
@@ -150,7 +152,7 @@ export class QueryBase<
     return new RecordBatchIterator(this.nativeExecute());
   }
 
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  // biome-ignore lint/suspicious/noExplicitAny: skip
   [Symbol.asyncIterator](): AsyncIterator<RecordBatch<any>> {
     const promise = this.nativeExecute();
     return new RecordBatchIterator(promise);
@@ -368,7 +370,7 @@ export class Query extends QueryBase<NativeQuery, Query> {
    * a default `limit` of 10 will be used.  @see {@link Query#limit}
    */
   nearestTo(vector: unknown): VectorQuery {
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    // biome-ignore lint/suspicious/noExplicitAny: skip
     const vectorQuery = this.inner.nearestTo(Float32Array.from(vector as any));
     return new VectorQuery(vectorQuery);
   }

nodejs/lancedb/sanitize.ts

@@ -21,60 +21,60 @@
 // and so we must sanitize the input to ensure that it is compatible.
 
 import {
-  Field,
-  Utf8,
-  FixedSizeBinary,
-  FixedSizeList,
-  Schema,
-  List,
-  Struct,
-  Float,
+  Binary,
   Bool,
+  DataType,
+  DateDay,
+  DateMillisecond,
+  type DateUnit,
   Date_,
   Decimal,
-  DataType,
+  DenseUnion,
   Dictionary,
-  Binary,
-  Float32,
-  Interval,
-  Map_,
   Duration,
-  Union,
-  Time,
-  Timestamp,
-  Type,
-  Null,
+  DurationMicrosecond,
+  DurationMillisecond,
+  DurationNanosecond,
+  DurationSecond,
+  Field,
+  FixedSizeBinary,
+  FixedSizeList,
+  Float,
+  Float16,
+  Float32,
+  Float64,
   Int,
-  type Precision,
-  type DateUnit,
   Int8,
   Int16,
   Int32,
   Int64,
+  Interval,
+  IntervalDayTime,
+  IntervalYearMonth,
+  List,
+  Map_,
+  Null,
+  type Precision,
+  Schema,
+  SparseUnion,
+  Struct,
+  Time,
+  TimeMicrosecond,
+  TimeMillisecond,
+  TimeNanosecond,
+  TimeSecond,
+  Timestamp,
+  TimestampMicrosecond,
+  TimestampMillisecond,
+  TimestampNanosecond,
+  TimestampSecond,
+  Type,
   Uint8,
   Uint16,
   Uint32,
   Uint64,
-  Float16,
-  Float64,
-  DateDay,
-  DateMillisecond,
-  DenseUnion,
-  SparseUnion,
-  TimeNanosecond,
-  TimeMicrosecond,
-  TimeMillisecond,
-  TimeSecond,
-  TimestampNanosecond,
-  TimestampMicrosecond,
-  TimestampMillisecond,
-  TimestampSecond,
-  IntervalDayTime,
-  IntervalYearMonth,
-  DurationNanosecond,
-  DurationMicrosecond,
-  DurationMillisecond,
-  DurationSecond,
+  Union,
+  Utf8,
 } from "apache-arrow";
 import type { IntBitWidth, TKeys, TimeBitWidth } from "apache-arrow/type";
 
@@ -228,7 +228,7 @@ function sanitizeUnion(typeLike: object) {
 
   return new Union(
     typeLike.mode,
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    // biome-ignore lint/suspicious/noExplicitAny: skip
     typeLike.typeIds as any,
     typeLike.children.map((child) => sanitizeField(child)),
   );
@@ -294,7 +294,7 @@ function sanitizeMap(typeLike: object) {
   }
 
   return new Map_(
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    // biome-ignore lint/suspicious/noExplicitAny: skip
     typeLike.children.map((field) => sanitizeField(field)) as any,
     typeLike.keysSorted,
   );
@@ -328,7 +328,7 @@ function sanitizeDictionary(typeLike: object) {
   );
 }
 
-// eslint-disable-next-line @typescript-eslint/no-explicit-any
+// biome-ignore lint/suspicious/noExplicitAny: skip
 function sanitizeType(typeLike: unknown): DataType<any> {
   if (typeof typeLike !== "object" || typeLike === null) {
     throw Error("Expected a Type but object was null/undefined");

nodejs/lancedb/table.ts

@@ -13,15 +13,16 @@
 // limitations under the License.
 
 import { Schema, tableFromIPC } from "apache-arrow";
+import { Data, fromDataToBuffer } from "./arrow";
+import { IndexOptions } from "./indices";
 import {
   AddColumnsSql,
   ColumnAlteration,
   IndexConfig,
+  OptimizeStats,
   Table as _NativeTable,
 } from "./native";
 import { Query, VectorQuery } from "./query";
-import { IndexOptions } from "./indices";
-import { Data, fromDataToBuffer } from "./arrow";
 
 export { IndexConfig } from "./native";
 /**
@@ -50,6 +51,23 @@ export interface UpdateOptions {
   where: string;
 }
 
+export interface OptimizeOptions {
+  /**
+   * If set then all versions older than the given date
+   * be removed.  The current version will never be removed.
+   * The default is 7 days
+   * @example
+   * // Delete all versions older than 1 day
+   * const olderThan = new Date();
+   * olderThan.setDate(olderThan.getDate() - 1));
+   * tbl.cleanupOlderVersions(olderThan);
+   *
+   * // Delete all versions except the current version
+   * tbl.cleanupOlderVersions(new Date());
+   */
+  cleanupOlderThan: Date;
+}
+
 /**
  * A Table is a collection of Records in a LanceDB Database.
  *
@@ -169,21 +187,24 @@ export class Table {
    * // If the column has a vector (fixed size list) data type then
    * // an IvfPq vector index will be created.
    * const table = await conn.openTable("my_table");
-   * await table.createIndex(["vector"]);
+   * await table.createIndex("vector");
    * @example
    * // For advanced control over vector index creation you can specify
    * // the index type and options.
    * const table = await conn.openTable("my_table");
-   * await table.createIndex(["vector"], I)
-   *   .ivf_pq({ num_partitions: 128, num_sub_vectors: 16 })
-   *   .build();
+   * await table.createIndex("vector", {
+   *   config: lancedb.Index.ivfPq({
+   *     numPartitions: 128,
+   *     numSubVectors: 16,
+   *   }),
+   * });
    * @example
    * // Or create a Scalar index
-   * await table.createIndex("my_float_col").build();
+   * await table.createIndex("my_float_col");
    */
   async createIndex(column: string, options?: Partial<IndexOptions>) {
     // Bit of a hack to get around the fact that TS has no package-scope.
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    // biome-ignore lint/suspicious/noExplicitAny: skip
     const nativeIndex = (options?.config as any)?.inner;
     await this.inner.createIndex(nativeIndex, column, options?.replace);
   }
@@ -197,8 +218,7 @@ export class Table {
    * vector similarity, sorting, and more.
    *
    * Note: By default, all columns are returned.  For best performance, you should
-   * only fetch the columns you need.  See [`Query::select_with_projection`] for
-   * more details.
+   * only fetch the columns you need.
    *
    * When appropriate, various indices and statistics based pruning will be used to
    * accelerate the query.
@@ -206,10 +226,13 @@ export class Table {
    * // SQL-style filtering
    * //
    * // This query will return up to 1000 rows whose value in the `id` column
-   * // is greater than 5.  LanceDb supports a broad set of filtering functions.
-   * for await (const batch of table.query()
-   *                          .filter("id > 1").select(["id"]).limit(20)) {
-   *  console.log(batch);
+   * // is greater than 5. LanceDb supports a broad set of filtering functions.
+   * for await (const batch of table
+   *   .query()
+   *   .where("id > 1")
+   *   .select(["id"])
+   *   .limit(20)) {
+   *   console.log(batch);
    * }
    * @example
    * // Vector Similarity Search
@@ -218,13 +241,14 @@ export class Table {
    * // closest to the query vector [1.0, 2.0, 3.0].  If an index has been created
    * // on the "vector" column then this will perform an ANN search.
    * //
-   * // The `refine_factor` and `nprobes` methods are used to control the recall /
+   * // The `refineFactor` and `nprobes` methods are used to control the recall /
    * // latency tradeoff of the search.
-   * for await (const batch of table.query()
-   *                    .nearestTo([1, 2, 3])
-   *                    .refineFactor(5).nprobe(10)
-   *                    .limit(10)) {
-   *  console.log(batch);
+   * for await (const batch of table
+   *   .query()
+   *   .where("id > 1")
+   *   .select(["id"])
+   *   .limit(20)) {
+   *   console.log(batch);
    * }
    * @example
    * // Scan the full dataset
@@ -286,43 +310,45 @@ export class Table {
     await this.inner.dropColumns(columnNames);
   }
 
-  /**
-   * Retrieve the version of the table
-   *
-   * LanceDb supports versioning.  Every operation that modifies the table increases
-   * version.  As long as a version hasn't been deleted you can `[Self::checkout]` that
-   * version to view the data at that point.  In addition, you can `[Self::restore]` the
-   * version to replace the current table with a previous version.
-   */
+  /** Retrieve the version of the table */
   async version(): Promise<number> {
     return await this.inner.version();
   }
 
   /**
-   * Checks out a specific version of the Table
+   * Checks out a specific version of the table _This is an in-place operation._
    *
-   * Any read operation on the table will now access the data at the checked out version.
-   * As a consequence, calling this method will disable any read consistency interval
-   * that was previously set.
+   * This allows viewing previous versions of the table. If you wish to
+   * keep writing to the dataset starting from an old version, then use
+   * the `restore` function.
    *
-   * This is a read-only operation that turns the table into a sort of "view"
-   * or "detached head".  Other table instances will not be affected.  To make the change
-   * permanent you can use the `[Self::restore]` method.
+   * Calling this method will set the table into time-travel mode. If you
+   * wish to return to standard mode, call `checkoutLatest`.
+   * @param {number} version The version to checkout
+   * @example
+   * ```typescript
+   * import * as lancedb from "@lancedb/lancedb"
+   * const db = await lancedb.connect("./.lancedb");
+   * const table = await db.createTable("my_table", [
+   *   { vector: [1.1, 0.9], type: "vector" },
+   * ]);
    *
-   * Any operation that modifies the table will fail while the table is in a checked
-   * out state.
-   *
-   * To return the table to a normal state use `[Self::checkout_latest]`
+   * console.log(await table.version()); // 1
+   * console.log(table.display());
+   * await table.add([{ vector: [0.5, 0.2], type: "vector" }]);
+   * await table.checkout(1);
+   * console.log(await table.version()); // 2
+   * ```
    */
   async checkout(version: number): Promise<void> {
     await this.inner.checkout(version);
   }
 
   /**
-   * Ensures the table is pointing at the latest version
+   * Checkout the latest version of the table. _This is an in-place operation._
    *
-   * This can be used to manually update a table when the read_consistency_interval is None
-   * It can also be used to undo a `[Self::checkout]` operation
+   * The table will be set back into standard mode, and will track the latest
+   * version of the table.
    */
   async checkoutLatest(): Promise<void> {
     await this.inner.checkoutLatest();
@@ -345,8 +371,48 @@ export class Table {
   }
 
   /**
-   * List all indices that have been created with Self::create_index
+   * Optimize the on-disk data and indices for better performance.
+   *
+   * Modeled after ``VACUUM`` in PostgreSQL.
+   *
+   *  Optimization covers three operations:
+   *
+   *  - Compaction: Merges small files into larger ones
+   *  - Prune: Removes old versions of the dataset
+   *  - Index: Optimizes the indices, adding new data to existing indices
+   *
+   *
+   *  Experimental API
+   *  ----------------
+   *
+   *  The optimization process is undergoing active development and may change.
+   *  Our goal with these changes is to improve the performance of optimization and
+   *  reduce the complexity.
+   *
+   *  That being said, it is essential today to run optimize if you want the best
+   *  performance.  It should be stable and safe to use in production, but it our
+   *  hope that the API may be simplified (or not even need to be called) in the
+   *  future.
+   *
+   *  The frequency an application shoudl call optimize is based on the frequency of
+   *  data modifications.  If data is frequently added, deleted, or updated then
+   *  optimize should be run frequently.  A good rule of thumb is to run optimize if
+   *  you have added or modified 100,000 or more records or run more than 20 data
+   *  modification operations.
    */
+  async optimize(options?: Partial<OptimizeOptions>): Promise<OptimizeStats> {
+    let cleanupOlderThanMs;
+    if (
+      options?.cleanupOlderThan !== undefined &&
+      options?.cleanupOlderThan !== null
+    ) {
+      cleanupOlderThanMs =
+        new Date().getTime() - options.cleanupOlderThan.getTime();
+    }
+    return await this.inner.optimize(cleanupOlderThanMs);
+  }
+
+  /** List all indices that have been created with {@link Table.createIndex} */
   async listIndices(): Promise<IndexConfig[]> {
     return await this.inner.listIndices();
   }

nodejs/npm/darwin-arm64/package.json

@@ -1,18 +1,12 @@
 {
-  "name": "@lancedb/lancedb-darwin-arm64",
-  "version": "0.4.16",
-  "os": [
-    "darwin"
-  ],
-  "cpu": [
-    "arm64"
-  ],
-  "main": "lancedb.darwin-arm64.node",
-  "files": [
-    "lancedb.darwin-arm64.node"
-  ],
-  "license": "Apache 2.0",
-  "engines": {
-    "node": ">= 18"
-  }
+	"name": "@lancedb/lancedb-darwin-arm64",
+	"version": "0.5.0",
+	"os": ["darwin"],
+	"cpu": ["arm64"],
+	"main": "lancedb.darwin-arm64.node",
+	"files": ["lancedb.darwin-arm64.node"],
+	"license": "Apache 2.0",
+	"engines": {
+		"node": ">= 18"
+	}
 }

nodejs/npm/darwin-x64/package.json

@@ -1,18 +1,12 @@
 {
-  "name": "@lancedb/lancedb-darwin-x64",
-  "version": "0.4.16",
-  "os": [
-    "darwin"
-  ],
-  "cpu": [
-    "x64"
-  ],
-  "main": "lancedb.darwin-x64.node",
-  "files": [
-    "lancedb.darwin-x64.node"
-  ],
-  "license": "Apache 2.0",
-  "engines": {
-    "node": ">= 18"
-  }
+	"name": "@lancedb/lancedb-darwin-x64",
+	"version": "0.5.0",
+	"os": ["darwin"],
+	"cpu": ["x64"],
+	"main": "lancedb.darwin-x64.node",
+	"files": ["lancedb.darwin-x64.node"],
+	"license": "Apache 2.0",
+	"engines": {
+		"node": ">= 18"
+	}
 }

nodejs/npm/linux-arm64-gnu/package.json

@@ -1,21 +1,13 @@
 {
-  "name": "@lancedb/lancedb-linux-arm64-gnu",
-  "version": "0.4.16",
-  "os": [
-    "linux"
-  ],
-  "cpu": [
-    "arm64"
-  ],
-  "main": "lancedb.linux-arm64-gnu.node",
-  "files": [
-    "lancedb.linux-arm64-gnu.node"
-  ],
-  "license": "Apache 2.0",
-  "engines": {
-    "node": ">= 18"
-  },
-  "libc": [
-    "glibc"
-  ]
+	"name": "@lancedb/lancedb-linux-arm64-gnu",
+	"version": "0.5.0",
+	"os": ["linux"],
+	"cpu": ["arm64"],
+	"main": "lancedb.linux-arm64-gnu.node",
+	"files": ["lancedb.linux-arm64-gnu.node"],
+	"license": "Apache 2.0",
+	"engines": {
+		"node": ">= 18"
+	},
+	"libc": ["glibc"]
 }

nodejs/npm/linux-x64-gnu/package.json

@@ -1,21 +1,13 @@
 {
-  "name": "@lancedb/lancedb-linux-x64-gnu",
-  "version": "0.4.16",
-  "os": [
-    "linux"
-  ],
-  "cpu": [
-    "x64"
-  ],
-  "main": "lancedb.linux-x64-gnu.node",
-  "files": [
-    "lancedb.linux-x64-gnu.node"
-  ],
-  "license": "Apache 2.0",
-  "engines": {
-    "node": ">= 18"
-  },
-  "libc": [
-    "glibc"
-  ]
+	"name": "@lancedb/lancedb-linux-x64-gnu",
+	"version": "0.5.0",
+	"os": ["linux"],
+	"cpu": ["x64"],
+	"main": "lancedb.linux-x64-gnu.node",
+	"files": ["lancedb.linux-x64-gnu.node"],
+	"license": "Apache 2.0",
+	"engines": {
+		"node": ">= 18"
+	},
+	"libc": ["glibc"]
 }

nodejs/npm/win32-x64-msvc/package.json

@@ -1,18 +1,12 @@
 {
-  "name": "@lancedb/lancedb-win32-x64-msvc",
-  "version": "0.4.14",
-  "os": [
-    "win32"
-  ],
-  "cpu": [
-    "x64"
-  ],
-  "main": "lancedb.win32-x64-msvc.node",
-  "files": [
-    "lancedb.win32-x64-msvc.node"
-  ],
-  "license": "Apache 2.0",
-  "engines": {
-    "node": ">= 18"
-  }
+	"name": "@lancedb/lancedb-win32-x64-msvc",
+	"version": "0.5.0",
+	"os": ["win32"],
+	"cpu": ["x64"],
+	"main": "lancedb.win32-x64-msvc.node",
+	"files": ["lancedb.win32-x64-msvc.node"],
+	"license": "Apache 2.0",
+	"engines": {
+		"node": ">= 18"
+	}
 }

nodejs/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.4.16",
+  "version": "0.5.0",
   "main": "./dist/index.js",
   "types": "./dist/index.d.ts",
   "napi": {
@@ -18,17 +18,16 @@
   },
   "license": "Apache 2.0",
   "devDependencies": {
+    "@aws-sdk/client-kms": "^3.33.0",
+    "@aws-sdk/client-s3": "^3.33.0",
+    "@biomejs/biome": "^1.7.3",
+    "@jest/globals": "^29.7.0",
     "@napi-rs/cli": "^2.18.0",
     "@types/jest": "^29.1.2",
     "@types/tmp": "^0.2.6",
-    "@typescript-eslint/eslint-plugin": "^6.19.0",
-    "@typescript-eslint/parser": "^6.19.0",
     "apache-arrow-old": "npm:apache-arrow@13.0.0",
     "eslint": "^8.57.0",
-    "eslint-config-prettier": "^9.1.0",
-    "eslint-plugin-jsdoc": "^48.2.1",
     "jest": "^29.7.0",
-    "prettier": "^3.1.0",
     "shx": "^0.3.4",
     "tmp": "^0.2.3",
     "ts-jest": "^29.1.2",
@@ -43,38 +42,26 @@
   "engines": {
     "node": ">= 18"
   },
-  "cpu": [
-    "x64",
-    "arm64"
-  ],
-  "os": [
-    "darwin",
-    "linux",
-    "win32"
-  ],
+  "cpu": ["x64", "arm64"],
+  "os": ["darwin", "linux", "win32"],
   "scripts": {
     "artifacts": "napi artifacts",
-    "build:debug": "napi build --platform --dts ../lancedb/native.d.ts --js ../lancedb/native.js dist/",
+    "build:debug": "napi build --platform --dts ../lancedb/native.d.ts --js ../lancedb/native.js lancedb",
     "build:release": "napi build --platform --release --dts ../lancedb/native.d.ts --js ../lancedb/native.js dist/",
-    "build": "npm run build:debug && tsc -b && shx cp lancedb/native.d.ts dist/native.d.ts",
+    "build": "npm run build:debug && tsc -b && shx cp lancedb/native.d.ts dist/native.d.ts && shx cp lancedb/*.node dist/",
     "build-release": "npm run build:release && tsc -b && shx cp lancedb/native.d.ts dist/native.d.ts",
-    "chkformat": "prettier . --check",
+    "lint-ci": "biome ci .",
     "docs": "typedoc --plugin typedoc-plugin-markdown --out ../docs/src/js lancedb/index.ts",
-    "lint": "eslint lancedb && eslint __test__",
+    "lint": "biome check . && biome format .",
+    "lint-fix": "biome check --apply-unsafe  . && biome format --write .",
     "prepublishOnly": "napi prepublish -t npm",
-    "test": "npm run build && jest --verbose",
+    "test": "jest --verbose",
+    "integration": "S3_TEST=1 npm run test",
     "universal": "napi universal",
     "version": "napi version"
   },
-  "optionalDependencies": {
-    "@lancedb/lancedb-darwin-arm64": "0.4.16",
-    "@lancedb/lancedb-darwin-x64": "0.4.16",
-    "@lancedb/lancedb-linux-arm64-gnu": "0.4.16",
-    "@lancedb/lancedb-linux-x64-gnu": "0.4.16",
-    "@lancedb/lancedb-win32-x64-msvc": "0.4.16"
-  },
   "dependencies": {
-    "openai": "^4.29.2",
-    "apache-arrow": "^15.0.0"
+    "apache-arrow": "^15.0.0",
+    "openai": "^4.29.2"
   }
 }

nodejs/src/connection.rs

@@ -12,6 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+use std::collections::HashMap;
+
 use napi::bindgen_prelude::*;
 use napi_derive::*;
 
@@ -64,6 +66,11 @@ impl Connection {
             builder =
                 builder.read_consistency_interval(std::time::Duration::from_secs_f64(interval));
         }
+        if let Some(storage_options) = options.storage_options {
+            for (key, value) in storage_options {
+                builder = builder.storage_option(key, value);
+            }
+        }
         Ok(Self::inner_new(
             builder
                 .execute()
@@ -118,14 +125,18 @@ impl Connection {
         name: String,
         buf: Buffer,
         mode: String,
+        storage_options: Option<HashMap<String, String>>,
     ) -> napi::Result<Table> {
         let batches = ipc_file_to_batches(buf.to_vec())
             .map_err(|e| napi::Error::from_reason(format!("Failed to read IPC file: {}", e)))?;
         let mode = Self::parse_create_mode_str(&mode)?;
-        let tbl = self
-            .get_inner()?
-            .create_table(&name, batches)
-            .mode(mode)
+        let mut builder = self.get_inner()?.create_table(&name, batches).mode(mode);
+        if let Some(storage_options) = storage_options {
+            for (key, value) in storage_options {
+                builder = builder.storage_option(key, value);
+            }
+        }
+        let tbl = builder
             .execute()
             .await
             .map_err(|e| napi::Error::from_reason(format!("{}", e)))?;
@@ -138,15 +149,22 @@ impl Connection {
         name: String,
         schema_buf: Buffer,
         mode: String,
+        storage_options: Option<HashMap<String, String>>,
     ) -> napi::Result<Table> {
         let schema = ipc_file_to_schema(schema_buf.to_vec()).map_err(|e| {
             napi::Error::from_reason(format!("Failed to marshal schema from JS to Rust: {}", e))
         })?;
         let mode = Self::parse_create_mode_str(&mode)?;
-        let tbl = self
+        let mut builder = self
             .get_inner()?
             .create_empty_table(&name, schema)
-            .mode(mode)
+            .mode(mode);
+        if let Some(storage_options) = storage_options {
+            for (key, value) in storage_options {
+                builder = builder.storage_option(key, value);
+            }
+        }
+        let tbl = builder
             .execute()
             .await
             .map_err(|e| napi::Error::from_reason(format!("{}", e)))?;
@@ -154,10 +172,22 @@ impl Connection {
     }
 
     #[napi]
-    pub async fn open_table(&self, name: String) -> napi::Result<Table> {
-        let tbl = self
-            .get_inner()?
-            .open_table(&name)
+    pub async fn open_table(
+        &self,
+        name: String,
+        storage_options: Option<HashMap<String, String>>,
+        index_cache_size: Option<u32>,
+    ) -> napi::Result<Table> {
+        let mut builder = self.get_inner()?.open_table(&name);
+        if let Some(storage_options) = storage_options {
+            for (key, value) in storage_options {
+                builder = builder.storage_option(key, value);
+            }
+        }
+        if let Some(index_cache_size) = index_cache_size {
+            builder = builder.index_cache_size(index_cache_size);
+        }
+        let tbl = builder
             .execute()
             .await
             .map_err(|e| napi::Error::from_reason(format!("{}", e)))?;

nodejs/src/lib.rs

@@ -12,7 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use connection::Connection;
+use std::collections::HashMap;
+
 use napi_derive::*;
 
 mod connection;
@@ -38,6 +39,10 @@ pub struct ConnectionOptions {
     /// Note: this consistency only applies to read operations. Write operations are
     /// always consistent.
     pub read_consistency_interval: Option<f64>,
+    /// (For LanceDB OSS only): configuration for object storage.
+    ///
+    /// The available options are described at https://lancedb.github.io/lancedb/guides/storage/
+    pub storage_options: Option<HashMap<String, String>>,
 }
 
 /// Write mode for writing a table.
@@ -54,7 +59,7 @@ pub struct WriteOptions {
     pub mode: Option<WriteMode>,
 }
 
-#[napi]
-pub async fn connect(uri: String, options: ConnectionOptions) -> napi::Result<Connection> {
-    Connection::new(uri, options).await
+#[napi(object)]
+pub struct OpenTableOptions {
+    pub storage_options: Option<HashMap<String, String>>,
 }

nodejs/src/table.rs

@@ -15,8 +15,8 @@
 use arrow_ipc::writer::FileWriter;
 use lancedb::ipc::ipc_file_to_batches;
 use lancedb::table::{
-    AddDataMode, ColumnAlteration as LanceColumnAlteration, NewColumnTransform,
-    Table as LanceDbTable,
+    AddDataMode, ColumnAlteration as LanceColumnAlteration, Duration, NewColumnTransform,
+    OptimizeAction, OptimizeOptions, Table as LanceDbTable,
 };
 use napi::bindgen_prelude::*;
 use napi_derive::napi;
@@ -263,6 +263,60 @@ impl Table {
         self.inner_ref()?.restore().await.default_error()
     }
 
+    #[napi]
+    pub async fn optimize(&self, older_than_ms: Option<i64>) -> napi::Result<OptimizeStats> {
+        let inner = self.inner_ref()?;
+
+        let older_than = if let Some(ms) = older_than_ms {
+            if ms == i64::MIN {
+                return Err(napi::Error::from_reason(format!(
+                    "older_than_ms can not be {}",
+                    i32::MIN,
+                )));
+            }
+            Duration::try_milliseconds(ms)
+        } else {
+            None
+        };
+
+        let compaction_stats = inner
+            .optimize(OptimizeAction::Compact {
+                options: lancedb::table::CompactionOptions::default(),
+                remap_options: None,
+            })
+            .await
+            .default_error()?
+            .compaction
+            .unwrap();
+        let prune_stats = inner
+            .optimize(OptimizeAction::Prune {
+                older_than,
+                delete_unverified: None,
+            })
+            .await
+            .default_error()?
+            .prune
+            .unwrap();
+        inner
+            .optimize(lancedb::table::OptimizeAction::Index(
+                OptimizeOptions::default(),
+            ))
+            .await
+            .default_error()?;
+        Ok(OptimizeStats {
+            compaction: CompactionStats {
+                files_added: compaction_stats.files_added as i64,
+                files_removed: compaction_stats.files_removed as i64,
+                fragments_added: compaction_stats.fragments_added as i64,
+                fragments_removed: compaction_stats.fragments_removed as i64,
+            },
+            prune: RemovalStats {
+                bytes_removed: prune_stats.bytes_removed as i64,
+                old_versions_removed: prune_stats.old_versions as i64,
+            },
+        })
+    }
+
     #[napi]
     pub async fn list_indices(&self) -> napi::Result<Vec<IndexConfig>> {
         Ok(self
@@ -298,6 +352,40 @@ impl From<lancedb::index::IndexConfig> for IndexConfig {
     }
 }
 
+/// Statistics about a compaction operation.
+#[napi(object)]
+#[derive(Clone, Debug)]
+pub struct CompactionStats {
+    /// The number of fragments removed
+    pub fragments_removed: i64,
+    /// The number of new, compacted fragments added
+    pub fragments_added: i64,
+    /// The number of data files removed
+    pub files_removed: i64,
+    /// The number of new, compacted data files added
+    pub files_added: i64,
+}
+
+/// Statistics about a cleanup operation
+#[napi(object)]
+#[derive(Clone, Debug)]
+pub struct RemovalStats {
+    /// The number of bytes removed
+    pub bytes_removed: i64,
+    /// The number of old versions removed
+    pub old_versions_removed: i64,
+}
+
+/// Statistics about an optimize operation
+#[napi(object)]
+#[derive(Clone, Debug)]
+pub struct OptimizeStats {
+    /// Statistics about the compaction operation
+    pub compaction: CompactionStats,
+    /// Statistics about the removal operation
+    pub prune: RemovalStats,
+}
+
 ///  A definition of a column alteration. The alteration changes the column at
 /// `path` to have the new name `name`, to be nullable if `nullable` is true,
 /// and to have the data type `data_type`. At least one of `rename` or `nullable`

python/.bumpversion.cfg

@@ -1,8 +0,0 @@
-[bumpversion]
-current_version = 0.6.7
-commit = True
-message = [python] Bump version: {current_version} → {new_version}
-tag = True
-tag_name = python-v{new_version}
-
-[bumpversion:file:pyproject.toml]

python/.bumpversion.toml

@@ -0,0 +1,34 @@
+[tool.bumpversion]
+current_version = "0.8.0"
+parse = """(?x)
+    (?P<major>0|[1-9]\\d*)\\.
+    (?P<minor>0|[1-9]\\d*)\\.
+    (?P<patch>0|[1-9]\\d*)
+    (?:-(?P<pre_l>[a-zA-Z-]+)\\.(?P<pre_n>0|[1-9]\\d*))?
+"""
+serialize = [
+    "{major}.{minor}.{patch}-{pre_l}.{pre_n}",
+    "{major}.{minor}.{patch}",
+]
+search = "{current_version}"
+replace = "{new_version}"
+regex = false
+ignore_missing_version = false
+ignore_missing_files = false
+tag = true
+sign_tags = false
+tag_name = "python-v{new_version}"
+tag_message = "Bump version: {current_version} → {new_version}"
+allow_dirty = true
+commit = true
+message = "Bump version: {current_version} → {new_version}"
+commit_args = ""
+
+[tool.bumpversion.parts.pre_l]
+values = ["beta", "final"]
+optional_value = "final"
+
+[[tool.bumpversion.files]]
+filename = "Cargo.toml"
+search = "\nversion = \"{current_version}\""
+replace = "\nversion = \"{new_version}\""

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.4.10"
+version = "0.8.0"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
@@ -14,7 +14,7 @@ name = "_lancedb"
 crate-type = ["cdylib"]
 
 [dependencies]
-arrow = { version = "50.0.0", features = ["pyarrow"] }
+arrow = { version = "51.0.0", features = ["pyarrow"] }
 lancedb = { path = "../rust/lancedb" }
 env_logger = "0.10"
 pyo3 = { version = "0.20", features = ["extension-module", "abi3-py38"] }

python/README.md

@@ -41,7 +41,7 @@ To build the python package you can use maturin:
 ```bash
 # This will build the rust bindings and place them in the appropriate place
 # in your venv or conda environment
-matruin develop
+maturin develop
 ```
 
 To run the unit tests:

python/pyproject.toml

@@ -1,19 +1,17 @@
 [project]
 name = "lancedb"
-version = "0.6.7"
+# version in Cargo.toml
 dependencies = [
     "deprecation",
-    "pylance==0.10.9",
+    "pylance==0.11.0",
     "ratelimiter~=1.0",
+    "requests>=2.31.0",
     "retry>=0.9.2",
     "tqdm>=4.27.0",
     "pydantic>=1.10",
     "attrs>=21.3.0",
-    "semver>=3.0",
+    "semver",
     "cachetools",
-    "pyyaml>=6.0",
-    "click>=8.1.7",
-    "requests>=2.31.0",
     "overrides>=0.7",
 ]
 description = "lancedb"
@@ -51,6 +49,7 @@ repository = "https://github.com/lancedb/lancedb"
 [project.optional-dependencies]
 tests = [
     "aiohttp",
+    "boto3",
     "pandas>=1.4",
     "pytest",
     "pytest-mock",
@@ -58,6 +57,7 @@ tests = [
     "duckdb",
     "pytz",
     "polars>=0.19",
+    "tantivy"
 ]
 dev = ["ruff", "pre-commit"]
 docs = [
@@ -65,7 +65,6 @@ docs = [
     "mkdocs-jupyter",
     "mkdocs-material",
     "mkdocstrings[python]",
-    "mkdocs-ultralytics-plugin==0.0.44",
 ]
 clip = ["torch", "pillow", "open-clip"]
 embeddings = [
@@ -81,6 +80,7 @@ embeddings = [
     "boto3>=1.28.57",
     "awscli>=1.29.57",
     "botocore>=1.31.57",
+    "ollama",
 ]
 azure = ["adlfs>=2024.2.0"]
 
@@ -88,19 +88,17 @@ azure = ["adlfs>=2024.2.0"]
 python-source = "python"
 module-name = "lancedb._lancedb"
 
-[project.scripts]
-lancedb = "lancedb.cli.cli:cli"
-
 [build-system]
 requires = ["maturin>=1.4"]
 build-backend = "maturin"
 
 [tool.ruff.lint]
-select = ["F", "E", "W", "I", "G", "TCH", "PERF"]
+select = ["F", "E", "W", "G", "TCH", "PERF"]
 
 [tool.pytest.ini_options]
 addopts = "--strict-markers --ignore-glob=lancedb/embeddings/*.py"
 markers = [
     "slow: marks tests as slow (deselect with '-m \"not slow\"')",
     "asyncio",
+    "s3_test"
 ]

python/python/lancedb/__init__.py

@@ -15,7 +15,7 @@ import importlib.metadata
 import os
 from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
-from typing import Optional, Union
+from typing import Dict, Optional, Union
 
 __version__ = importlib.metadata.version("lancedb")
 
@@ -25,7 +25,6 @@ from .db import AsyncConnection, DBConnection, LanceDBConnection
 from .remote.db import RemoteDBConnection
 from .schema import vector
 from .table import AsyncTable
-from .utils import sentry_log
 
 
 def connect(
@@ -84,7 +83,7 @@ def connect(
 
     >>> db = lancedb.connect("s3://my-bucket/lancedb")
 
-    Connect to LancdDB cloud:
+    Connect to LanceDB cloud:
 
     >>> db = lancedb.connect("db://my_database", api_key="ldb_...")
 
@@ -108,6 +107,9 @@ def connect(
             request_thread_pool=request_thread_pool,
             **kwargs,
         )
+
+    if kwargs:
+        raise ValueError(f"Unknown keyword arguments: {kwargs}")
     return LanceDBConnection(uri, read_consistency_interval=read_consistency_interval)
 
 
@@ -119,6 +121,7 @@ async def connect_async(
     host_override: Optional[str] = None,
     read_consistency_interval: Optional[timedelta] = None,
     request_thread_pool: Optional[Union[int, ThreadPoolExecutor]] = None,
+    storage_options: Optional[Dict[str, str]] = None,
 ) -> AsyncConnection:
     """Connect to a LanceDB database.
 
@@ -145,6 +148,9 @@ async def connect_async(
         the last check, then the table will be checked for updates. Note: this
         consistency only applies to read operations. Write operations are
         always consistent.
+    storage_options: dict, optional
+        Additional options for the storage backend. See available options at
+        https://lancedb.github.io/lancedb/guides/storage/
 
     Examples
     --------
@@ -173,6 +179,7 @@ async def connect_async(
             region,
             host_override,
             read_consistency_interval_secs,
+            storage_options,
         )
     )
 
@@ -184,7 +191,6 @@ __all__ = [
     "AsyncTable",
     "URI",
     "sanitize_uri",
-    "sentry_log",
     "vector",
     "DBConnection",
     "LanceDBConnection",

python/python/lancedb/_lancedb.pyi

@@ -19,10 +19,18 @@ class Connection(object):
         self, start_after: Optional[str], limit: Optional[int]
     ) -> list[str]: ...
     async def create_table(
-        self, name: str, mode: str, data: pa.RecordBatchReader
+        self,
+        name: str,
+        mode: str,
+        data: pa.RecordBatchReader,
+        storage_options: Optional[Dict[str, str]] = None,
     ) -> Table: ...
     async def create_empty_table(
-        self, name: str, mode: str, schema: pa.Schema
+        self,
+        name: str,
+        mode: str,
+        schema: pa.Schema,
+        storage_options: Optional[Dict[str, str]] = None,
     ) -> Table: ...
 
 class Table:
@@ -78,3 +86,17 @@ class VectorQuery:
     def refine_factor(self, refine_factor: int): ...
     def nprobes(self, nprobes: int): ...
     def bypass_vector_index(self): ...
+
+class CompactionStats:
+    fragments_removed: int
+    fragments_added: int
+    files_removed: int
+    files_added: int
+
+class RemovalStats:
+    bytes_removed: int
+    old_versions_removed: int
+
+class OptimizeStats:
+    compaction: CompactionStats
+    prune: RemovalStats

python/python/lancedb/cli/__init__.py

@@ -1,12 +0,0 @@
-#  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/python/lancedb/cli/cli.py

@@ -1,47 +0,0 @@
-#  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/python/lancedb/db.py

@@ -18,14 +18,13 @@ import inspect
 import os
 from abc import abstractmethod
 from pathlib import Path
-from typing import TYPE_CHECKING, Iterable, List, Literal, Optional, Union
+from typing import TYPE_CHECKING, Dict, Iterable, List, Literal, Optional, Union
 
 import pyarrow as pa
 from overrides import EnforceOverrides, override
 from pyarrow import fs
 
 from lancedb.common import data_to_reader, validate_schema
-from lancedb.utils.events import register_event
 
 from ._lancedb import connect as lancedb_connect
 from .pydantic import LanceModel
@@ -225,13 +224,23 @@ class DBConnection(EnforceOverrides):
     def __getitem__(self, name: str) -> LanceTable:
         return self.open_table(name)
 
-    def open_table(self, name: str) -> Table:
+    def open_table(self, name: str, *, index_cache_size: Optional[int] = None) -> Table:
         """Open a Lance Table in the database.
 
         Parameters
         ----------
         name: str
             The name of the table.
+        index_cache_size: int, default 256
+            Set the size of the index cache, specified as a number of entries
+
+            The exact meaning of an "entry" will depend on the type of index:
+            * IVF - there is one entry for each IVF partition
+            * BTREE - there is one entry for the entire index
+
+            This cache applies to the entire opened table, across all indices.
+            Setting this value higher will increase performance on larger datasets
+            at the expense of more RAM
 
         Returns
         -------
@@ -249,6 +258,18 @@ class DBConnection(EnforceOverrides):
         """
         raise NotImplementedError
 
+    def rename_table(self, cur_name: str, new_name: str):
+        """Rename a table in the database.
+
+        Parameters
+        ----------
+        cur_name: str
+            The current name of the table.
+        new_name: str
+            The new name of the table.
+        """
+        raise NotImplementedError
+
     def drop_database(self):
         """
         Drop database
@@ -408,7 +429,9 @@ class LanceDBConnection(DBConnection):
         return tbl
 
     @override
-    def open_table(self, name: str) -> LanceTable:
+    def open_table(
+        self, name: str, *, index_cache_size: Optional[int] = None
+    ) -> LanceTable:
         """Open a table in the database.
 
         Parameters
@@ -420,7 +443,7 @@ class LanceDBConnection(DBConnection):
         -------
         A LanceTable object representing the table.
         """
-        return LanceTable.open(self, name)
+        return LanceTable.open(self, name, index_cache_size=index_cache_size)
 
     @override
     def drop_table(self, name: str, ignore_missing: bool = False):
@@ -534,6 +557,7 @@ class AsyncConnection(object):
         exist_ok: Optional[bool] = None,
         on_bad_vectors: Optional[str] = None,
         fill_value: Optional[float] = None,
+        storage_options: Optional[Dict[str, str]] = None,
     ) -> AsyncTable:
         """Create an [AsyncTable][lancedb.table.AsyncTable] in the database.
 
@@ -571,6 +595,12 @@ class AsyncConnection(object):
             One of "error", "drop", "fill".
         fill_value: float
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
+        storage_options: dict, optional
+            Additional options for the storage backend. Options already set on the
+            connection will be inherited by the table, but can be overridden here.
+            See available options at
+            https://lancedb.github.io/lancedb/guides/storage/
+
 
         Returns
         -------
@@ -730,32 +760,53 @@ class AsyncConnection(object):
             mode = "exist_ok"
 
         if data is None:
-            new_table = await self._inner.create_empty_table(name, mode, schema)
+            new_table = await self._inner.create_empty_table(
+                name, mode, schema, storage_options=storage_options
+            )
         else:
             data = data_to_reader(data, schema)
             new_table = await self._inner.create_table(
                 name,
                 mode,
                 data,
+                storage_options=storage_options,
             )
 
-        register_event("create_table")
         return AsyncTable(new_table)
 
-    async def open_table(self, name: str) -> Table:
+    async def open_table(
+        self,
+        name: str,
+        storage_options: Optional[Dict[str, str]] = None,
+        index_cache_size: Optional[int] = None,
+    ) -> Table:
         """Open a Lance Table in the database.
 
         Parameters
         ----------
         name: str
             The name of the table.
+        storage_options: dict, optional
+            Additional options for the storage backend. Options already set on the
+            connection will be inherited by the table, but can be overridden here.
+            See available options at
+            https://lancedb.github.io/lancedb/guides/storage/
+        index_cache_size: int, default 256
+            Set the size of the index cache, specified as a number of entries
+
+            The exact meaning of an "entry" will depend on the type of index:
+            * IVF - there is one entry for each IVF partition
+            * BTREE - there is one entry for the entire index
+
+            This cache applies to the entire opened table, across all indices.
+            Setting this value higher will increase performance on larger datasets
+            at the expense of more RAM
 
         Returns
         -------
         A LanceTable object representing the table.
         """
-        table = await self._inner.open_table(name)
-        register_event("open_table")
+        table = await self._inner.open_table(name, storage_options, index_cache_size)
         return AsyncTable(table)
 
     async def drop_table(self, name: str):

python/python/lancedb/embeddings/__init__.py

@@ -10,15 +10,18 @@
 #  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.
-
 # ruff: noqa: F401
 from .base import EmbeddingFunction, EmbeddingFunctionConfig, TextEmbeddingFunction
 from .bedrock import BedRockText
 from .cohere import CohereEmbeddingFunction
 from .gemini_text import GeminiText
 from .instructor import InstructorEmbeddingFunction
+from .ollama import OllamaEmbeddings
 from .open_clip import OpenClipEmbeddings
 from .openai import OpenAIEmbeddings
 from .registry import EmbeddingFunctionRegistry, get_registry
 from .sentence_transformers import SentenceTransformerEmbeddings
+from .gte import GteEmbeddings
+from .transformers import TransformersEmbeddingFunction, ColbertEmbeddings
+from .imagebind import ImageBindEmbeddings
 from .utils import with_embeddings

python/python/lancedb/embeddings/bedrock.py

@@ -78,6 +78,9 @@ class BedRockText(TextEmbeddingFunction):
 
         class Config:
             keep_untouched = (cached_property,)
+    else:
+        model_config = dict()
+        model_config["ignored_types"] = (cached_property,)
 
     def ndims(self):
         # return len(self._generate_embedding("test"))

python/python/lancedb/embeddings/gemini_text.py

@@ -94,6 +94,9 @@ class GeminiText(TextEmbeddingFunction):
 
         class Config:
             keep_untouched = (cached_property,)
+    else:
+        model_config = dict()
+        model_config["ignored_types"] = (cached_property,)
 
     def ndims(self):
         # TODO: fix hardcoding

python/python/lancedb/embeddings/imagebind.py

@@ -22,6 +22,8 @@ from .base import EmbeddingFunction
 from .registry import register
 from .utils import AUDIO, IMAGES, TEXT
 
+from lancedb.pydantic import PYDANTIC_VERSION
+
 
 @register("imagebind")
 class ImageBindEmbeddings(EmbeddingFunction):
@@ -38,6 +40,14 @@ class ImageBindEmbeddings(EmbeddingFunction):
     device: str = "cpu"
     normalize: bool = False
 
+    if PYDANTIC_VERSION < (2, 0):  # Pydantic 1.x compat
+
+        class Config:
+            keep_untouched = (cached_property,)
+    else:
+        model_config = dict()
+        model_config["ignored_types"] = (cached_property,)
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._ndims = 1024

python/python/lancedb/embeddings/ollama.py

@@ -0,0 +1,69 @@
+#  Copyright (c) 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.
+from functools import cached_property
+from typing import TYPE_CHECKING, List, Optional, Union
+
+from ..util import attempt_import_or_raise
+from .base import TextEmbeddingFunction
+from .registry import register
+
+if TYPE_CHECKING:
+    import numpy as np
+
+
+@register("ollama")
+class OllamaEmbeddings(TextEmbeddingFunction):
+    """
+    An embedding function that uses Ollama
+
+    https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings
+    https://ollama.com/blog/embedding-models
+    """
+
+    name: str = "nomic-embed-text"
+    host: str = "http://localhost:11434"
+    options: Optional[dict] = None  # type = ollama.Options
+    keep_alive: Optional[Union[float, str]] = None
+    ollama_client_kwargs: Optional[dict] = {}
+
+    def ndims(self):
+        return len(self.generate_embeddings(["foo"])[0])
+
+    def _compute_embedding(self, text):
+        return self._ollama_client.embeddings(
+            model=self.name,
+            prompt=text,
+            options=self.options,
+            keep_alive=self.keep_alive,
+        )["embedding"]
+
+    def generate_embeddings(
+        self, texts: Union[List[str], "np.ndarray"]
+    ) -> List["np.array"]:
+        """
+        Get the embeddings for the given texts
+
+        Parameters
+        ----------
+        texts: list[str] or np.ndarray (of str)
+            The texts to embed
+        """
+        # TODO retry, rate limit, token limit
+        embeddings = [self._compute_embedding(text) for text in texts]
+        return embeddings
+
+    @cached_property
+    def _ollama_client(self):
+        ollama = attempt_import_or_raise("ollama")
+        # ToDo explore ollama.AsyncClient
+        return ollama.Client(host=self.host, **self.ollama_client_kwargs)

python/python/lancedb/embeddings/transformers.py

@@ -0,0 +1,106 @@
+#  Copyright (c) 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.
+
+from functools import cached_property
+from typing import List, Any
+
+import numpy as np
+
+from pydantic import PrivateAttr
+from lancedb.pydantic import PYDANTIC_VERSION
+
+from ..util import attempt_import_or_raise
+from .base import EmbeddingFunction
+from .registry import register
+from .utils import TEXT
+
+
+@register("huggingface")
+class TransformersEmbeddingFunction(EmbeddingFunction):
+    """
+    An embedding function that can use any model from the transformers library.
+
+    Parameters:
+    ----------
+    name : str
+        The name of the model to use. This should be a model name that can be loaded
+        by transformers.AutoModel.from_pretrained. For example, "bert-base-uncased".
+        default: "colbert-ir/colbertv2.0""
+
+    to download package, run :
+        `pip install transformers`
+    you may need to install pytorch as well - `https://pytorch.org/get-started/locally/`
+
+    """
+
+    name: str = "colbert-ir/colbertv2.0"
+    _tokenizer: Any = PrivateAttr()
+    _model: Any = PrivateAttr()
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._ndims = None
+        transformers = attempt_import_or_raise("transformers")
+        self._tokenizer = transformers.AutoTokenizer.from_pretrained(self.name)
+        self._model = transformers.AutoModel.from_pretrained(self.name)
+
+    if PYDANTIC_VERSION < (2, 0):  # Pydantic 1.x compat
+
+        class Config:
+            keep_untouched = (cached_property,)
+    else:
+        model_config = dict()
+        model_config["ignored_types"] = (cached_property,)
+
+    def ndims(self):
+        self._ndims = self._model.config.hidden_size
+        return self._ndims
+
+    def compute_query_embeddings(self, query: str, *args, **kwargs) -> List[np.array]:
+        return self.compute_source_embeddings(query)
+
+    def compute_source_embeddings(self, texts: TEXT, *args, **kwargs) -> List[np.array]:
+        texts = self.sanitize_input(texts)
+        embedding = []
+        for text in texts:
+            encoding = self._tokenizer(
+                text, return_tensors="pt", padding=True, truncation=True
+            )
+            emb = self._model(**encoding).last_hidden_state.mean(dim=1).squeeze()
+            embedding.append(emb.detach().numpy())
+
+        return embedding
+
+
+@register("colbert")
+class ColbertEmbeddings(TransformersEmbeddingFunction):
+    """
+    An embedding function that uses the colbert model from the huggingface library.
+
+    Parameters:
+    ----------
+    name : str
+        The name of the model to use. This should be a model name that can be loaded
+        by transformers.AutoModel.from_pretrained. For example, "bert-base-uncased".
+        default: "colbert-ir/colbertv2.0""
+
+    to download package, run :
+        `pip install transformers`
+    you may need to install pytorch as well - `https://pytorch.org/get-started/locally/`
+
+    """
+
+    name: str = "colbert-ir/colbertv2.0"
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)

python/python/lancedb/embeddings/utils.py

@@ -19,15 +19,14 @@ import sys
 import time
 import urllib.error
 import weakref
+import logging
 from typing import Callable, List, Union
-
 import numpy as np
 import pyarrow as pa
 from lance.vector import vec_to_table
 from retry import retry
 
 from ..util import deprecated, safe_import_pandas
-from ..utils.general import LOGGER
 
 pd = safe_import_pandas()
 
@@ -256,7 +255,13 @@ def retry_with_exponential_backoff(
                     )
 
                 delay *= exponential_base * (1 + jitter * random.random())
-                LOGGER.info(f"Retrying in {delay:.2f} seconds due to {e}")
+                logging.warning(
+                    "Error occurred: %s \n Retrying in %s seconds (retry %s of %s) \n",
+                    e,
+                    delay,
+                    num_retries,
+                    max_retries,
+                )
                 time.sleep(delay)
 
     return wrapper
@@ -277,5 +282,5 @@ def url_retrieve(url: str):
 
 
 def api_key_not_found_help(provider):
-    LOGGER.error(f"Could not find API key for {provider}.")
+    logging.error("Could not find API key for %s", provider)
     raise ValueError(f"Please set the {provider.upper()}_API_KEY environment variable.")

python/python/lancedb/pydantic.py

@@ -37,7 +37,7 @@ import pyarrow as pa
 import pydantic
 import semver
 
-PYDANTIC_VERSION = semver.Version.parse(pydantic.__version__)
+PYDANTIC_VERSION = semver.parse_version_info(pydantic.__version__)
 try:
     from pydantic_core import CoreSchema, core_schema
 except ImportError: