[python] Bump version: 0.6.5 → 0.6.6

<img width="1034" alt="image"
src="https://github.com/lancedb/lancedb/assets/5846846/5b8aa53c-4d93-4c0e-bed4-80c238b319ba">

.bumpversion.cfg

@@ -0,0 +1,22 @@
+[bumpversion]
+current_version = 0.4.15
+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

@@ -1,57 +0,0 @@
-[tool.bumpversion]
-current_version = "0.5.2"
-parse = """(?x)
-    (?P<major>0|[1-9]\\d*)\\.
-    (?P<minor>0|[1-9]\\d*)\\.
-    (?P<patch>0|[1-9]\\d*)
-    (?:-(?P<pre_l>[a-zA-Z-]+)\\.(?P<pre_n>0|[1-9]\\d*))?
-"""
-serialize = [
-    "{major}.{minor}.{patch}-{pre_l}.{pre_n}",
-    "{major}.{minor}.{patch}",
-]
-search = "{current_version}"
-replace = "{new_version}"
-regex = false
-ignore_missing_version = false
-ignore_missing_files = false
-tag = true
-sign_tags = false
-tag_name = "v{new_version}"
-tag_message = "Bump version: {current_version} → {new_version}"
-allow_dirty = true
-commit = true
-message = "Bump version: {current_version} → {new_version}"
-commit_args = ""
-
-[tool.bumpversion.parts.pre_l]
-values = ["beta", "final"]
-optional_value = "final"
-
-[[tool.bumpversion.files]]
-filename = "node/package.json"
-search = "\"version\": \"{current_version}\","
-replace = "\"version\": \"{new_version}\","
-
-[[tool.bumpversion.files]]
-filename = "nodejs/package.json"
-search = "\"version\": \"{current_version}\","
-replace = "\"version\": \"{new_version}\","
-
-# nodejs binary packages
-[[tool.bumpversion.files]]
-glob = "nodejs/npm/*/package.json"
-search = "\"version\": \"{current_version}\","
-replace = "\"version\": \"{new_version}\","
-
-# Cargo files
-# ------------
-[[tool.bumpversion.files]]
-filename = "rust/ffi/node/Cargo.toml"
-search = "\nversion = \"{current_version}\""
-replace = "\nversion = \"{new_version}\""
-
-[[tool.bumpversion.files]]
-filename = "rust/lancedb/Cargo.toml"
-search = "\nversion = \"{current_version}\""
-replace = "\nversion = \"{new_version}\""

.github/labeler.yml

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

.github/release_notes.json

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

.github/workflows/build_linux_wheel/action.yml

@@ -14,10 +14,6 @@ inputs:
     # Note: this does *not* mean the host is arm64, since we might be cross-compiling.
     required: false
     default: "false"
-  manylinux:
-    description: "The manylinux version to build for"
-    required: false
-    default: "2_17"
 runs:
   using: "composite"
   steps:
@@ -32,7 +28,7 @@ runs:
         command: build
         working-directory: python
         target: x86_64-unknown-linux-gnu
-        manylinux: ${{ inputs.manylinux }}
+        manylinux: "2_17"
         args: ${{ inputs.args }}
         before-script-linux: |
           set -e
@@ -46,9 +42,8 @@ runs:
       with:
         command: build
         working-directory: python
-        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         target: aarch64-unknown-linux-gnu
-        manylinux: ${{ inputs.manylinux }}
+        manylinux: "2_24"
         args: ${{ inputs.args }}
         before-script-linux: |
           set -e

.github/workflows/build_mac_wheel/action.yml

@@ -21,6 +21,5 @@ runs:
       with:
         command: build
         args: ${{ inputs.args }}
-        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         working-directory: python
         interpreter: 3.${{ inputs.python-minor-version }}

.github/workflows/build_windows_wheel/action.yml

@@ -26,7 +26,6 @@ runs:
       with:
         command: build
         args: ${{ inputs.args }}
-        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         working-directory: python
     - uses: actions/upload-artifact@v3
       with:

.github/workflows/cargo-publish.yml

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

.github/workflows/dev.yml

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

.github/workflows/java.yml

@@ -1,85 +0,0 @@
-name: Build and Run Java JNI Tests
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    paths:
-      - java/**
-      - rust/**
-      - .github/workflows/java.yml
-env:
-  # This env var is used by Swatinem/rust-cache@v2 for the cache
-  # key, so we set it to make sure it is always consistent.
-  CARGO_TERM_COLOR: always
-  # Disable full debug symbol generation to speed up CI build and keep memory down
-  # "1" means line tables only, which is useful for panic tracebacks.
-  RUSTFLAGS: "-C debuginfo=1"
-  RUST_BACKTRACE: "1"
-  # according to: https://matklad.github.io/2021/09/04/fast-rust-builds.html
-  # CI builds are faster with incremental disabled.
-  CARGO_INCREMENTAL: "0"
-  CARGO_BUILD_JOBS: "1"
-jobs:
-  linux-build:
-    runs-on: ubuntu-22.04
-    name: ubuntu-22.04 + Java 11 & 17
-    defaults:
-      run:
-        working-directory: ./java
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      - uses: Swatinem/rust-cache@v2
-        with:
-          workspaces: java/core/lancedb-jni
-      - name: Run cargo fmt
-        run: cargo fmt --check
-        working-directory: ./java/core/lancedb-jni
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler libssl-dev
-      - name: Install Java 17
-        uses: actions/setup-java@v4
-        with:
-          distribution: temurin
-          java-version: 17
-          cache: "maven"
-      - run: echo "JAVA_17=$JAVA_HOME" >> $GITHUB_ENV
-      - name: Install Java 11
-        uses: actions/setup-java@v4
-        with:
-          distribution: temurin
-          java-version: 11
-          cache: "maven"
-      - name: Java Style Check
-        run: mvn checkstyle:check
-      # Disable because of issues in lancedb rust core code
-      # - name: Rust Clippy
-      #   working-directory: java/core/lancedb-jni
-      #   run: cargo clippy --all-targets -- -D warnings
-      - name: Running tests with Java 11
-        run: mvn clean test
-      - name: Running tests with Java 17
-        run: |
-          export JAVA_TOOL_OPTIONS="$JAVA_TOOL_OPTIONS \
-          -XX:+IgnoreUnrecognizedVMOptions \
-          --add-opens=java.base/java.lang=ALL-UNNAMED \
-          --add-opens=java.base/java.lang.invoke=ALL-UNNAMED \
-          --add-opens=java.base/java.lang.reflect=ALL-UNNAMED \
-          --add-opens=java.base/java.io=ALL-UNNAMED \
-          --add-opens=java.base/java.net=ALL-UNNAMED \
-          --add-opens=java.base/java.nio=ALL-UNNAMED \
-          --add-opens=java.base/java.util=ALL-UNNAMED \
-          --add-opens=java.base/java.util.concurrent=ALL-UNNAMED \
-          --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED \
-          --add-opens=java.base/jdk.internal.ref=ALL-UNNAMED \
-          --add-opens=java.base/sun.nio.ch=ALL-UNNAMED \
-          --add-opens=java.base/sun.nio.cs=ALL-UNNAMED \
-          --add-opens=java.base/sun.security.action=ALL-UNNAMED \
-          --add-opens=java.base/sun.util.calendar=ALL-UNNAMED \
-          --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED \
-          -Djdk.reflect.useDirectMethodHandle=false \
-          -Dio.netty.tryReflectionSetAccessible=true"
-          JAVA_HOME=$JAVA_17 mvn clean test

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

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

.github/workflows/node.yml

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

.github/workflows/nodejs.yml

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

.github/workflows/npm-publish.yml

@@ -1,9 +1,8 @@
 name: NPM Publish
 
 on:
-  push:
-    tags:
-      - "v*"
+  release:
+    types: [published]
 
 jobs:
   node:
@@ -111,11 +110,12 @@ jobs:
             runner: ubuntu-latest
           - arch: aarch64
             # For successful fat LTO builds, we need a large runner to avoid OOM errors.
-            runner: warp-ubuntu-latest-arm64-4x
+            runner: buildjet-16vcpu-ubuntu-2204-arm
     steps:
       - name: Checkout
         uses: actions/checkout@v4
-      # To avoid OOM errors on ARM, we create a swap file.
+      # Buildjet aarch64 runners have only 1.5 GB RAM per core, vs 3.5 GB per core for
+      # x86_64 runners. To avoid OOM errors on ARM, we create a swap file.
       - name: Configure aarch64 build
         if: ${{ matrix.config.arch == 'aarch64' }}
         run: |
@@ -274,15 +274,9 @@ 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 $PUBLISH_ARGS $filename
+            npm publish $filename
           done
 
   release-nodejs:
@@ -322,23 +316,11 @@ jobs:
       - name: Publish to NPM
         env:
           NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
-        # By default, things are published to the latest tag. This is what is
-        # installed by default if the user does not specify a version. This is
-        # good for stable releases, but for pre-releases, we want to publish to
-        # the "preview" tag so they can install with `npm install lancedb@preview`.
-        # See: https://medium.com/@mbostock/prereleases-and-npm-e778fc5e2420
-        run: |
-          if [[ $GITHUB_REF =~ refs/tags/v(.*)-beta.* ]]; then
-            npm publish --access public --tag preview
-          else
-            npm publish --access public
-          fi
+        run: npm publish --access public
 
   update-package-lock:
     needs: [release]
     runs-on: ubuntu-latest
-    permissions:
-      contents: write
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -349,13 +331,11 @@ jobs:
           lfs: true
       - uses: ./.github/workflows/update_package_lock
         with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
 
   update-package-lock-nodejs:
     needs: [release-nodejs]
     runs-on: ubuntu-latest
-    permissions:
-      contents: write
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -366,70 +346,4 @@ jobs:
           lfs: true
       - uses: ./.github/workflows/update_package_lock_nodejs
         with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-
-  gh-release:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Extract version
-        id: extract_version
-        env:
-          GITHUB_REF: ${{ github.ref }}
-        run: |
-          set -e
-          echo "Extracting tag and version from $GITHUB_REF"
-          if [[ $GITHUB_REF =~ refs/tags/v(.*) ]]; then
-            VERSION=${BASH_REMATCH[1]}
-            TAG=v$VERSION
-            echo "tag=$TAG" >> $GITHUB_OUTPUT
-            echo "version=$VERSION" >> $GITHUB_OUTPUT
-          else
-            echo "Failed to extract version from $GITHUB_REF"
-            exit 1
-          fi
-          echo "Extracted version $VERSION from $GITHUB_REF"
-          if [[ $VERSION =~ beta ]]; then
-            echo "This is a beta release"
-
-            # Get last release (that is not this one)
-            FROM_TAG=$(git tag --sort='version:refname' \
-              | grep ^v \
-              | grep -vF "$TAG" \
-              | python ci/semver_sort.py v \
-              | tail -n 1)
-          else
-            echo "This is a stable release"
-            # Get last stable tag (ignore betas)
-            FROM_TAG=$(git tag --sort='version:refname' \
-              | grep ^v \
-              | grep -vF "$TAG" \
-              | grep -v beta \
-              | python ci/semver_sort.py v \
-              | tail -n 1)
-          fi
-          echo "Found from tag $FROM_TAG"
-          echo "from_tag=$FROM_TAG" >> $GITHUB_OUTPUT
-      - name: Create Release Notes
-        id: release_notes
-        uses: mikepenz/release-changelog-builder-action@v4
-        with:
-          configuration: .github/release_notes.json
-          toTag: ${{ steps.extract_version.outputs.tag }}
-          fromTag: ${{ steps.extract_version.outputs.from_tag }}
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      - name: Create GH release
-        uses: softprops/action-gh-release@v2
-        with:
-          prerelease: ${{ contains('beta', github.ref) }}
-          tag_name: ${{ steps.extract_version.outputs.tag }}
-          token: ${{ secrets.GITHUB_TOKEN }}
-          generate_release_notes: false
-          name: Node/Rust LanceDB v${{ steps.extract_version.outputs.version }}
-          body: ${{ steps.release_notes.outputs.changelog }}
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}

.github/workflows/pypi-publish.yml

@@ -1,28 +1,18 @@
 name: PyPI Publish
 
 on:
-  push:
-    tags:
-      - 'python-v*'
+  release:
+    types: [published]
 
 jobs:
   linux:
-    name: Python ${{ matrix.config.platform }} manylinux${{ matrix.config.manylinux }}
     timeout-minutes: 60
     strategy:
       matrix:
-        config:
-          - platform: x86_64
-            manylinux: "2_17"
-            extra_args: ""
-          - platform: x86_64
-            manylinux: "2_28"
-            extra_args: "--features fp16kernels"
-          - platform: aarch64
-            manylinux: "2_24"
-            extra_args: ""
-          # We don't build fp16 kernels for aarch64, because it uses 
-          # cross compilation image, which doesn't have a new enough compiler.
+        python-minor-version: ["8"]
+        platform:
+          - x86_64
+          - aarch64
     runs-on: "ubuntu-22.04"
     steps:
       - uses: actions/checkout@v4
@@ -32,22 +22,22 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.${{ matrix.python-minor-version }}
       - uses: ./.github/workflows/build_linux_wheel
         with:
-          python-minor-version: 8
-          args: "--release --strip ${{ matrix.config.extra_args }}"
-          arm-build: ${{ matrix.config.platform == 'aarch64' }}
-          manylinux: ${{ matrix.config.manylinux }}
+          python-minor-version: ${{ matrix.python-minor-version }}
+          args: "--release --strip"
+          arm-build: ${{ matrix.platform == 'aarch64' }}
       - uses: ./.github/workflows/upload_wheel
         with:
-          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          fury_token: ${{ secrets.FURY_TOKEN }}
+          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          repo: "pypi"
   mac:
     timeout-minutes: 60
     runs-on: ${{ matrix.config.runner }}
     strategy:
       matrix:
+        python-minor-version: ["8"]
         config:
           - target: x86_64-apple-darwin
             runner: macos-13
@@ -58,6 +48,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
         with:
+          ref: ${{ inputs.ref }}
           fetch-depth: 0
           lfs: true
       - name: Set up Python
@@ -66,95 +57,36 @@ jobs:
           python-version: 3.12
       - uses: ./.github/workflows/build_mac_wheel
         with:
-          python-minor-version: 8
-          args: "--release --strip --target ${{ matrix.config.target }} --features fp16kernels"
+          python-minor-version: ${{ matrix.python-minor-version }}
+          args: "--release --strip --target ${{ matrix.config.target }}"
       - uses: ./.github/workflows/upload_wheel
         with:
-          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          fury_token: ${{ secrets.FURY_TOKEN }}
+          python-minor-version: ${{ matrix.python-minor-version }}
+          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          repo: "pypi"
   windows:
     timeout-minutes: 60
     runs-on: windows-latest
+    strategy:
+      matrix:
+        python-minor-version: ["8"]
     steps:
       - uses: actions/checkout@v4
         with:
+          ref: ${{ inputs.ref }}
           fetch-depth: 0
           lfs: true
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.${{ matrix.python-minor-version }}
       - uses: ./.github/workflows/build_windows_wheel
         with:
-          python-minor-version: 8
+          python-minor-version: ${{ matrix.python-minor-version }}
           args: "--release --strip"
           vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
       - uses: ./.github/workflows/upload_wheel
         with:
-          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          fury_token: ${{ secrets.FURY_TOKEN }}
-  gh-release:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Extract version
-        id: extract_version
-        env:
-          GITHUB_REF: ${{ github.ref }}
-        run: |
-          set -e
-          echo "Extracting tag and version from $GITHUB_REF"
-          if [[ $GITHUB_REF =~ refs/tags/python-v(.*) ]]; then
-            VERSION=${BASH_REMATCH[1]}
-            TAG=python-v$VERSION
-            echo "tag=$TAG" >> $GITHUB_OUTPUT
-            echo "version=$VERSION" >> $GITHUB_OUTPUT
-          else
-            echo "Failed to extract version from $GITHUB_REF"
-            exit 1
-          fi
-          echo "Extracted version $VERSION from $GITHUB_REF"
-          if [[ $VERSION =~ beta ]]; then
-            echo "This is a beta release"
-
-            # Get last release (that is not this one)
-            FROM_TAG=$(git tag --sort='version:refname' \
-              | grep ^python-v \
-              | grep -vF "$TAG" \
-              | python ci/semver_sort.py python-v \
-              | tail -n 1)
-          else
-            echo "This is a stable release"
-            # Get last stable tag (ignore betas)
-            FROM_TAG=$(git tag --sort='version:refname' \
-              | grep ^python-v \
-              | grep -vF "$TAG" \
-              | grep -v beta \
-              | python ci/semver_sort.py python-v \
-              | tail -n 1)
-          fi
-          echo "Found from tag $FROM_TAG"
-          echo "from_tag=$FROM_TAG" >> $GITHUB_OUTPUT
-      - name: Create Python Release Notes
-        id: python_release_notes
-        uses: mikepenz/release-changelog-builder-action@v4
-        with:
-          configuration: .github/release_notes.json
-          toTag: ${{ steps.extract_version.outputs.tag }}
-          fromTag: ${{ steps.extract_version.outputs.from_tag }}
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      - name: Create Python GH release
-        uses: softprops/action-gh-release@v2
-        with:
-          prerelease: ${{ contains('beta', github.ref) }}
-          tag_name: ${{ steps.extract_version.outputs.tag }}
-          token: ${{ secrets.GITHUB_TOKEN }}
-          generate_release_notes: false
-          name: Python LanceDB v${{ steps.extract_version.outputs.version }}
-          body: ${{ steps.python_release_notes.outputs.changelog }}
+          python-minor-version: ${{ matrix.python-minor-version }}
+          token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          repo: "pypi"

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

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

.github/workflows/python.yml

@@ -65,7 +65,7 @@ jobs:
           workspaces: python
       - name: Install
         run: |
-          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests,dev,embeddings]
+          pip install -e .[tests,dev,embeddings]
           pip install tantivy
           pip install mlx
       - name: Doctest
@@ -75,7 +75,7 @@ jobs:
     timeout-minutes: 30
     strategy:
       matrix:
-        python-minor-version: ["9", "11"]
+        python-minor-version: ["8", "11"]
     runs-on: "ubuntu-22.04"
     defaults:
       run:
@@ -99,8 +99,6 @@ jobs:
           workspaces: python
       - uses: ./.github/workflows/build_linux_wheel
       - uses: ./.github/workflows/run_tests
-        with:
-          integration: true
       # Make sure wheels are not included in the Rust cache
       - name: Delete wheels
         run: rm -rf target/wheels
@@ -189,7 +187,7 @@ jobs:
       - name: Install lancedb
         run: |
           pip install "pydantic<2"
-          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests]
+          pip install -e .[tests]
           pip install tantivy
       - name: Run tests
-        run: pytest -m "not slow and not s3_test" -x -v --durations=30 python/tests
+        run: pytest -m "not slow" -x -v --durations=30 python/tests

.github/workflows/run_tests/action.yml

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

.github/workflows/rust.yml

@@ -31,10 +31,6 @@ jobs:
       run:
         shell: bash
         working-directory: rust
-    env:
-      # Need up-to-date compilers for kernels
-      CC: gcc-12
-      CXX: g++-12
     steps:
     - uses: actions/checkout@v4
       with:
@@ -58,10 +54,6 @@ jobs:
       run:
         shell: bash
         working-directory: rust
-    env:
-      # Need up-to-date compilers for kernels
-      CC: gcc-12
-      CXX: g++-12
     steps:
     - uses: actions/checkout@v4
       with:
@@ -74,9 +66,6 @@ jobs:
       run: |
           sudo apt update
           sudo apt install -y protobuf-compiler libssl-dev
-    - name: Start S3 integration test environment
-      working-directory: .
-      run: docker compose up --detach --wait
     - name: Build
       run: cargo build --all-features
     - name: Run tests
@@ -108,8 +97,7 @@ jobs:
       - name: Build
         run: cargo build --all-features
       - name: Run tests
-        # Run with everything except the integration tests.
-        run: cargo test --features remote,fp16kernels
+        run: cargo test --all-features
   windows:
     runs-on: windows-2022
     steps:

.github/workflows/upload_wheel/action.yml

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

.gitignore

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

.pre-commit-config.yaml

@@ -10,12 +10,9 @@ repos:
     rev: v0.2.2
     hooks:
     - id: ruff
-- repo: local
+- repo: https://github.com/pre-commit/mirrors-prettier
+  rev: v3.1.0
   hooks:
-    - id: local-biome-check
-      name: biome check
-      entry: npx @biomejs/biome@1.7.3 check --config-path nodejs/biome.json nodejs/
-      language: system
-      types: [text]
+    - id: prettier
       files: "nodejs/.*"
       exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*

Cargo.toml

@@ -1,11 +1,5 @@
 [workspace]
-members = [
-    "rust/ffi/node",
-    "rust/lancedb",
-    "nodejs",
-    "python",
-    "java/core/lancedb-jni",
-]
+members = ["rust/ffi/node", "rust/lancedb", "nodejs", "python"]
 # Python package needs to be built by maturin.
 exclude = ["python"]
 resolver = "2"
@@ -20,24 +14,22 @@ keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
 
 [workspace.dependencies]
-lance = { "version" = "=0.13.0", "features" = ["dynamodb"] }
-lance-index = { "version" = "=0.13.0" }
-lance-linalg = { "version" = "=0.13.0" }
-lance-testing = { "version" = "=0.13.0" }
-lance-datafusion = { "version" = "=0.13.0" }
+lance = { "version" = "=0.10.6", "features" = ["dynamodb"] }
+lance-index = { "version" = "=0.10.6" }
+lance-linalg = { "version" = "=0.10.6" }
+lance-testing = { "version" = "=0.10.6" }
 # Note that this one does not include pyarrow
-arrow = { version = "51.0", optional = false }
-arrow-array = "51.0"
-arrow-data = "51.0"
-arrow-ipc = "51.0"
-arrow-ord = "51.0"
-arrow-schema = "51.0"
-arrow-arith = "51.0"
-arrow-cast = "51.0"
+arrow = { version = "50.0", optional = false }
+arrow-array = "50.0"
+arrow-data = "50.0"
+arrow-ipc = "50.0"
+arrow-ord = "50.0"
+arrow-schema = "50.0"
+arrow-arith = "50.0"
+arrow-cast = "50.0"
 async-trait = "0"
 chrono = "0.4.35"
-datafusion-physical-plan = "37.1"
-half = { "version" = "=2.4.1", default-features = false, features = [
+half = { "version" = "=2.3.1", 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 retrieval, filtering and management of embeddings.
+LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
 
 The key features of LanceDB include:
 
@@ -36,7 +36,7 @@ The key features of LanceDB include:
 
 * GPU support in building vector index(*).
 
-* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/docs/integrations/vectorstores/lancedb/), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
+* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lanecdb.html), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
 
 LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
 
@@ -83,5 +83,5 @@ result = table.search([100, 100]).limit(2).to_pandas()
 ```
 
 ## Blogs, Tutorials & Videos
-* 📈 <a href="https://blog.lancedb.com/benchmarking-random-access-in-lance/">2000x better performance with Lance over Parquet</a>
+* 📈 <a href="https://blog.eto.ai/benchmarking-random-access-in-lance-ed690757a826">2000x better performance with Lance over Parquet</a>
 * 🤖 <a href="https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb">Build a question and answer bot with LanceDB</a>

ci/bump_version.sh

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

ci/check_breaking_changes.py

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

ci/semver_sort.py

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

docker-compose.yml

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

docs/mkdocs.yml

@@ -57,6 +57,16 @@ 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
@@ -94,21 +104,10 @@ 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
           - Sync -> Async Migration Guide: migration.md
-          - Tuning retrieval performance:
-              - Choosing right query type: guides/tuning_retrievers/1_query_types.md
-              - Reranking: guides/tuning_retrievers/2_reranking.md
       - 🧬 Managing embeddings:
           - Overview: embeddings/index.md
           - Embedding functions: embeddings/embedding_functions.md
@@ -121,12 +120,9 @@ nav:
           - Pandas and PyArrow: python/pandas_and_pyarrow.md
           - Polars: python/polars_arrow.md
           - DuckDB: python/duckdb.md
-          - LangChain:
-            - LangChain 🔗: integrations/langchain.md
-            - LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
-          - LlamaIndex 🦙:
-            - LlamaIndex docs: integrations/llamaIndex.md
-            - LlamaIndex demo:  https://docs.llamaindex.ai/en/stable/examples/vector_stores/LanceDBIndexDemo/
+          - LangChain 🔗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
+          - LangChain JS/TS 🔗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
+          - LlamaIndex 🦙: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
           - Pydantic: python/pydantic.md
           - Voxel51: integrations/voxel51.md
           - PromptTools: integrations/prompttools.md
@@ -147,6 +143,7 @@ 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
@@ -157,7 +154,7 @@ nav:
           - Overview: cloud/index.md
           - API reference:
               - 🐍 Python: python/saas-python.md
-              - 👾 JavaScript: javascript/modules.md
+              - 👾 JavaScript: javascript/saas-modules.md
 
   - Quick start: basic.md
   - Concepts:
@@ -174,21 +171,10 @@ 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
       - Sync -> Async Migration Guide: migration.md
-      - Tuning retrieval performance:
-          - Choosing right query type: guides/tuning_retrievers/1_query_types.md
-          - Reranking: guides/tuning_retrievers/2_reranking.md
   - Managing Embeddings:
       - Overview: embeddings/index.md
       - Embedding functions: embeddings/embedding_functions.md
@@ -201,8 +187,8 @@ nav:
       - Pandas and PyArrow: python/pandas_and_pyarrow.md
       - Polars: python/polars_arrow.md
       - DuckDB: python/duckdb.md
-      - LangChain 🦜️🔗↗: https://python.langchain.com/docs/integrations/vectorstores/lancedb
-      - LangChain.js 🦜️🔗↗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
+      - LangChain 🦜️🔗↗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
+      - LangChain.js 🦜️🔗↗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
       - LlamaIndex 🦙↗: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
       - Pydantic: python/pydantic.md
       - Voxel51: integrations/voxel51.md
@@ -227,7 +213,7 @@ nav:
       - Overview: cloud/index.md
       - API reference:
           - 🐍 Python: python/saas-python.md
-          - 👾 JavaScript: javascript/modules.md
+          - 👾 JavaScript: javascript/saas-modules.md
 
 extra_css:
   - styles/global.css
@@ -240,10 +226,3 @@ extra:
   analytics:
     provider: google
     property: G-B7NFM40W74
-  social:
-    - icon: fontawesome/brands/github
-      link: https://github.com/lancedb/lancedb
-    - icon: fontawesome/brands/x-twitter
-      link: https://twitter.com/lancedb
-    - icon: fontawesome/brands/linkedin
-      link: https://www.linkedin.com/company/lancedb

docs/requirements.txt

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

docs/src/basic.md

@@ -44,36 +44,6 @@
 
     !!! 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"
@@ -180,9 +150,6 @@ table.
 
 !!! info "Under the hood, LanceDB reads in the Apache Arrow data and persists it to disk using the [Lance format](https://www.github.com/lancedb/lance)."
 
-!!! info "Automatic embedding generation with Embedding API"
-    When working with embedding models, it is recommended to use the LanceDB embedding API to automatically create vector representation of the data and queries in the background. See the [quickstart example](#using-the-embedding-api) or the embedding API [guide](./embeddings/)
-
 ### Create an empty table
 
 Sometimes you may not have the data to insert into the table at creation time.
@@ -197,9 +164,6 @@ similar to a `CREATE TABLE` statement in SQL.
       --8<-- "python/python/tests/docs/test_basic.py:create_empty_table_async"
       ```
 
-    !!! note "You can define schema in Pydantic"
-        LanceDB comes with Pydantic support, which allows you to define the schema of your data using Pydantic models. This makes it easy to work with LanceDB tables and data. Learn more about all supported types in [tables guide](./guides/tables.md).
-
 === "Typescript"
 
     ```typescript
@@ -430,19 +394,6 @@ Use the `drop_table()` method on the database to remove a table.
     })
     ```
 
-## Using the Embedding API
-You can use the embedding API when working with embedding models. It automatically vectorizes the data at ingestion and query time and comes with built-in integrations with popular embedding models like Openai, Hugging Face, Sentence Transformers, CLIP and more.
-
-=== "Python"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_embeddings_optional.py:imports"
-    --8<-- "python/python/tests/docs/test_embeddings_optional.py:openai_embeddings"
-    ```
-
-Learn about using the existing integrations and creating custom embedding functions in the [embedding API guide](./embeddings/).
-
-
 ## What's next
 
 This section covered the very basics of using LanceDB. If you're learning about vector databases for the first time, you may want to read the page on [indexing](concepts/index_ivfpq.md) to get familiar with the concepts.

docs/src/cli_config.md

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

docs/src/embeddings/default_embedding_functions.md

@@ -154,12 +154,9 @@ 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")
-    model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
+    registry = EmbeddingFunctionRegistry.get_instance()
+    model = registry.get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
 
     class Words(LanceModel):
         text: str = model.SourceField()
@@ -168,7 +165,7 @@ 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"}
         ]
     )
@@ -180,70 +177,6 @@ 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 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:
 
@@ -254,21 +187,18 @@ 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")
-func = get_registry().get("openai").create(name="text-embedding-ada-002")
+registry = EmbeddingFunctionRegistry.get_instance()
+func = registry.get("openai").create()
 
 class Words(LanceModel):
     text: str = func.SourceField()
     vector: Vector(func.ndims()) = func.VectorField()
 
-table = db.create_table("words", schema=Words, mode="overwrite")
+table = db.create_table("words", schema=Words)
 table.add(
     [
-        {"text": "hello world"},
+        {"text": "hello world"}
         {"text": "goodbye world"}
     ]
     )
@@ -365,68 +295,6 @@ tbl.add(df)
 rs = tbl.search("hello").limit(1).to_pandas()
 ```
 
-### Cohere Embeddings
-Using cohere API requires cohere package, which can be installed using `pip install cohere`. Cohere embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
-You also need to set the `COHERE_API_KEY` environment variable to use the Cohere API.
-
-Supported models are:
-```
-    * embed-english-v3.0
-    * embed-multilingual-v3.0
-    * embed-english-light-v3.0
-    * embed-multilingual-light-v3.0
-    * embed-english-v2.0
-    * embed-english-light-v2.0
-    * embed-multilingual-v2.0
-```
-
-Supported parameters (to be passed in `create` method) are:
-
-| Parameter | Type | Default Value | Description |
-|---|---|---|---|
-| `name` | `str` | `"embed-english-v2.0"` | The model ID of the cohere model to use. Supported base models for Text Embeddings: embed-english-v3.0, embed-multilingual-v3.0, embed-english-light-v3.0, embed-multilingual-light-v3.0, embed-english-v2.0, embed-english-light-v2.0, embed-multilingual-v2.0 |
-| `source_input_type` | `str` | `"search_document"` | The type of input data to be used for the source column. |
-| `query_input_type` | `str` | `"search_query"` | The type of input data to be used for the query. |
-
-Cohere supports following input types:
-| Input Type               | Description                          |
-|-------------------------|---------------------------------------|
-| "`search_document`"     | Used for embeddings stored in a vector|
-|                         | database for search use-cases.        |
-| "`search_query`"        | Used for embeddings of search queries |
-|                         | run against a vector DB               |
-| "`semantic_similarity`" | Specifies the given text will be used |
-|                         | for Semantic Textual Similarity (STS) |
-| "`classification`"      | Used for embeddings passed through a  |
-|                         | text classifier.                      |
-| "`clustering`"          | Used for the embeddings run through a |
-|                         | clustering algorithm                  |
-
-Usage Example:
-    
-    ```python
-        import lancedb
-        from lancedb.pydantic import LanceModel, Vector
-        from lancedb.embeddings import EmbeddingFunctionRegistry
-
-        cohere = EmbeddingFunctionRegistry
-            .get_instance()
-            .get("cohere")
-            .create(name="embed-multilingual-v2.0")
-
-        class TextModel(LanceModel):
-            text: str = cohere.SourceField()
-            vector: Vector(cohere.ndims()) =  cohere.VectorField()
-
-        data = [ { "text": "hello world" },
-                { "text": "goodbye world" }]
-
-        db = lancedb.connect("~/.lancedb")
-        tbl = db.create_table("test", schema=TextModel, mode="overwrite")
-
-        tbl.add(data)
-    ```
-
 ### AWS Bedrock Text Embedding Functions
 AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function.
 You can do so by using `awscli` and also add your session_token:
@@ -459,10 +327,6 @@ 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):
@@ -497,12 +361,10 @@ 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)
-func = get_registry.get("open-clip").create()
+registry = EmbeddingFunctionRegistry.get_instance()
+func = registry.get("open-clip").create()
 
 class Images(LanceModel):
     label: str
@@ -577,12 +439,9 @@ 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)
-func = get_registry.get("imagebind").create()
+registry = EmbeddingFunctionRegistry.get_instance()
+func = registry.get("imagebind").create()
 
 class ImageBindModel(LanceModel):
     text: str

docs/src/embeddings/embedding_functions.md

@@ -2,9 +2,6 @@ Representing multi-modal data as vector embeddings is becoming a standard practi
 
 For this purpose, LanceDB introduces an **embedding functions API**, that allow you simply set up once, during the configuration stage of your project. After this, the table remembers it, effectively making the embedding functions *disappear in the background* so you don't have to worry about manually passing callables, and instead, simply focus on the rest of your data engineering pipeline.
 
-!!! Note "LanceDB cloud doesn't support embedding functions yet"
-    LanceDB Cloud does not support embedding functions yet. You need to generate embeddings before ingesting into the table or querying.
-
 !!! warning
     Using the embedding function registry means that you don't have to explicitly generate the embeddings yourself. 
     However, if your embedding function changes, you'll have to re-configure your table with the new embedding function 
@@ -49,7 +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()
     ```
 
@@ -152,7 +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
@@ -169,4 +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

@@ -11,64 +11,4 @@ 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.
-
-## 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)
-```
+It is retained for compatibility and will be removed in a future version.

docs/src/fts.md

@@ -2,6 +2,7 @@
 
 LanceDB provides support for full-text search via [Tantivy](https://github.com/quickwit-oss/tantivy) (currently Python only), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions. Our goal is to push the FTS integration down to the Rust level in the future, so that it's available for Rust and JavaScript users as well.  Follow along at [this Github issue](https://github.com/lancedb/lance/issues/1195)
 
+A hybrid search solution combining vector and full-text search is also on the way.
 
 ## Installation
 
@@ -54,16 +55,6 @@ This returns the result as a list of dictionaries as follows.
 !!! note
     LanceDB automatically searches on the existing FTS index if the input to the search is of type `str`. If you provide a vector as input, LanceDB will search the ANN index instead.
 
-## Tokenization
-By default the text is tokenized by splitting on punctuation and whitespaces and then removing tokens that are longer than 40 chars. For more language specific tokenization then provide the argument tokenizer_name with the 2 letter language code followed by "_stem". So for english it would be "en_stem".
-
-```python
-table.create_fts_index("text", tokenizer_name="en_stem")
-```
-
-The following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.
-
-
 ## Index multiple columns
 
 If you have multiple string columns to index, there's no need to combine them manually -- simply pass them all as a list to `create_fts_index`:
@@ -149,7 +140,6 @@ is treated as a phrase query.
 In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested
 double quotes replaced by single quotes.
 
-
 ## Configurations
 
 By default, LanceDB configures a 1GB heap size limit for creating the index. You can

docs/src/guides/storage.md

@@ -55,139 +55,18 @@ 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. 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`:
+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.
 
-```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`. |
+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.)
 
 
 ### AWS S3
 
-To configure credentials for AWS S3, you can use the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` keys. Region can also be set, but it is not mandatory when using AWS.
-These can be set as environment variables or passed in the `storage_options` parameter:
-
-=== "Python"
-
-    ```python
-    import lancedb
-    db = await lancedb.connect_async(
-        "s3://bucket/path",
-        storage_options={
-            "aws_access_key_id": "my-access-key",
-            "aws_secret_access_key": "my-secret-key",
-            "aws_session_token": "my-session-token",
-        }
-    )
-    ```
-
-=== "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",
-            }
-        }
-    );
-    ```
+To configure credentials for AWS S3, you can use the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` environment variables.
 
 Alternatively, if you are using AWS SSO, you can use the `AWS_PROFILE` and `AWS_DEFAULT_REGION` environment variables.
 
-The following keys can be used as both environment variables or keys in the `storage_options` parameter:
-
-| Key                                | Description                                                                                          |
-|------------------------------------|------------------------------------------------------------------------------------------------------|
-| `aws_region` / `region`             | The AWS region the bucket is in. This can be automatically detected when using AWS S3, but must be specified for S3-compatible stores. |
-| `aws_access_key_id` / `access_key_id` | The AWS access key ID to use.                                                                       |
-| `aws_secret_access_key` / `secret_access_key` | The AWS secret access key to use.                                                               |
-| `aws_session_token` / `session_token` | The AWS session token to use.                                                                     |
-| `aws_endpoint` / `endpoint`         | The endpoint to use for S3-compatible stores.                                                       |
-| `aws_virtual_hosted_style_request` / `virtual_hosted_style_request` | Whether to use virtual hosted-style requests, where the bucket name is part of the endpoint. Meant to be used with `aws_endpoint`. Default: `False`. |
-| `aws_s3_express` / `s3_express`     | Whether to use S3 Express One Zone endpoints. Default: `False`. See more details below.             |
-| `aws_server_side_encryption`        | The server-side encryption algorithm to use. Must be one of `"AES256"`, `"aws:kms"`, or `"aws:kms:dsse"`. Default: `None`. |
-| `aws_sse_kms_key_id`                | The KMS key ID to use for server-side encryption. If set, `aws_server_side_encryption` must be `"aws:kms"` or `"aws:kms:dsse"`. |
-| `aws_sse_bucket_key_enabled`        | Whether to use bucket keys for server-side encryption.                                               |
-
+You can see a full list of environment variables [here](https://docs.rs/object_store/latest/object_store/aws/struct.AmazonS3Builder.html#method.from_env).
 
 !!! tip "Automatic cleanup for failed writes"
 
@@ -267,182 +146,22 @@ 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 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",
-            }
-        }
-    );
-    ```
+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.
 
+<!-- TODO: we should also document the use of S3 Express once we fully support it -->
 
 ### Google Cloud Storage
 
-GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environment variable to the path of a JSON file containing the service account credentials. Alternatively, you can pass the path to the JSON file in the `storage_options`:
-
-=== "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",
-            }
-        }
-    );
-    ```
+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).
 
 
 !!! info "HTTP/2 support"
 
     By default, GCS uses HTTP/1 for communication, as opposed to HTTP/2. This improves maximum throughput significantly. However, if you wish to use HTTP/2 for some reason, you can set the environment variable `HTTP1_ONLY` to `false`.
 
-
-The following keys can be used as both environment variables or keys in the `storage_options` parameter:
-<!-- source: https://docs.rs/object_store/latest/object_store/gcp/enum.GoogleConfigKey.html -->
-
-| Key                                   | Description                                  |
-|---------------------------------------|----------------------------------------------|
-| ``google_service_account`` / `service_account` | Path to the service account JSON file.       |
-| ``google_service_account_key``        | The serialized service account key.          |
-| ``google_application_credentials``    | Path to the application credentials.         |
-
-
 ### Azure Blob Storage
 
-Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_ACCOUNT_NAME`and `AZURE_STORAGE_ACCOUNT_KEY` environment variables. Alternatively, you can pass the account name and key in the `storage_options` parameter:
+Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_ACCOUNT_NAME` and ``AZURE_STORAGE_ACCOUNT_KEY`` environment variables. The full list of environment variables that can be set are documented [here](https://docs.rs/object_store/latest/object_store/azure/struct.MicrosoftAzureBuilder.html#method.from_env).
 
-=== "Python"
-
-    <!-- skip-test -->
-    ```python
-    import lancedb
-    db = await lancedb.connect_async(
-        "az://my-container/my-database",
-        storage_options={
-            account_name: "some-account",
-            account_key: "some-key",
-        }
-    )
-    ```
-
-=== "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/guides/tables.md

@@ -452,27 +452,6 @@ After a table has been created, you can always add more data to it using the var
     tbl.add(pydantic_model_items)
     ```
 
-    ??? "Ingesting Pydantic models with LanceDB embedding API"
-        When using LanceDB's embedding API, you can add Pydantic models directly to the table. LanceDB will automatically convert the `vector` field to a vector before adding it to the table. You need to specify the default value of `vector` feild as None to allow LanceDB to automatically vectorize the data.
-
-        ```python
-        import lancedb
-        from lancedb.pydantic import LanceModel, Vector
-        from lancedb.embeddings import get_registry
-
-        db = lancedb.connect("~/tmp")
-        embed_fcn = get_registry().get("huggingface").create(name="BAAI/bge-small-en-v1.5")
-
-        class Schema(LanceModel):
-            text: str = embed_fcn.SourceField()
-            vector: Vector(embed_fcn.ndims()) = embed_fcn.VectorField(default=None)
-
-        tbl = db.create_table("my_table", schema=Schema, mode="overwrite")
-        models = [Schema(text="hello"), Schema(text="world")]
-        tbl.add(models)
-        ```
-
-
 
 === "JavaScript"
 
@@ -657,31 +636,6 @@ The `values` parameter is used to provide the new values for the columns as lite
 
     When rows are updated, they are moved out of the index. The row will still show up in ANN queries, but the query will not be as fast as it would be if the row was in the index. If you update a large proportion of rows, consider rebuilding the index afterwards.
 
-## Drop a table
-
-Use the `drop_table()` method on the database to remove a table.
-
-=== "Python"
-
-      ```python
-      --8<-- "python/python/tests/docs/test_basic.py:drop_table"
-      --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
-      ```
-
-      This permanently removes the table and is not recoverable, unlike deleting rows.
-      By default, if the table does not exist an exception is raised. To suppress this,
-      you can pass in `ignore_missing=True`.
-
-=== "Javascript/Typescript"
-
-      ```typescript
-      --8<-- "docs/src/basic_legacy.ts:drop_table"
-      ```
-
-      This permanently removes the table and is not recoverable, unlike deleting rows.
-      If the table does not exist an exception is raised.
-
-
 ## Consistency
 
 In LanceDB OSS, users can set the `read_consistency_interval` parameter on connections to achieve different levels of read consistency. This parameter determines how frequently the database synchronizes with the underlying storage system to check for updates made by other processes. If another process updates a table, the database will not see the changes until the next synchronization.

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

@@ -1,128 +0,0 @@
-## Improving retriever performance
-VectorDBs are used as retreivers in recommender or chatbot-based systems for retrieving relevant data based on user queries. For example, retriever is a critical component of Retrieval Augmented Generation (RAG) acrhitectures. In this section, we will discuss how to improve the performance of retrievers.
-
-There are serveral ways to improve the performance of retrievers. Some of the common techniques are:
-
-* Using different query types
-* Using hybrid search
-* Fine-tuning the embedding models
-* Using different embedding models
-
-Using different embedding models is something that's very specific to the use case and the data. So we will not discuss it here. In this section, we will discuss the first three techniques.
-
-
-!!! note "Note"
-    We'll be using a simple metric called "hit-rate" for evaluating the performance of the retriever across this guide. Hit-rate is the percentage of queries for which the retriever returned the correct answer in the top-k results. For example, if the retriever returned the correct answer in the top-3 results for 70% of the queries, then the hit-rate@3 is 0.7.
-
-
-## The dataset
-We'll be using a QA dataset generated using a LLama2 review paper. The dataset contains 221 query, context and answer triplets. The queries and answers are generated using GPT-4 based on a given query. Full script used to generate the dataset can be found on this [repo](https://github.com/lancedb/ragged). It can be downloaded from [here](https://github.com/AyushExel/assets/blob/main/data_qa.csv)
-
-### Using different query types
-Let's setup the embeddings and the dataset first. We'll use the LanceDB's `huggingface` embeddings integration for this guide. 
-
-```python
-import lancedb
-import pandas as pd
-from lancedb.embeddings import get_registry
-from lancedb.pydantic import Vector, LanceModel
-
-db = lancedb.connect("~/lancedb/query_types")
-df = pd.read_csv("data_qa.csv")
-
-embed_fcn = get_registry().get("huggingface").create(name="BAAI/bge-small-en-v1.")
-
-class Schema(LanceModel):
-    context: str = embed_fcn.SourceField()
-    vector: Vector(embed_fcn.ndims()) = embed_fcn.VectorField()
-
-table = db.create_table("qa", schema=Schema)
-table.add(df[["context"]].to_dict(orient="records"))
-
-queries = df["query"].tolist()
-```
-
-Now that we have the dataset and embeddings table set up, here's how you can run different query types on the dataset.
-
-* <b> Vector Search: </b>
-
-    ```python
-    table.search(quries[0], query_type="vector").limit(5).to_pandas()
-    ```
-    By default, LanceDB uses vector search query type for searching and it automatically converts the input query to a vector before searching when using embedding API. So, the following statement is equivalent to the above statement.
-
-    ```python
-    table.search(quries[0]).limit(5).to_pandas()
-    ```
-
-    Vector or semantic search is useful when you want to find documents that are similar to the query in terms of meaning.
-
----
-
-* <b> Full-text Search: </b>
-    
-    FTS requires creating an index on the column you want to search on. `replace=True` will replace the existing index if it exists.
-    Once the index is created, you can search using the `fts` query type.
-    ```python
-    table.create_fts_index("context", replace=True)
-    table.search(quries[0], query_type="fts").limit(5).to_pandas()
-    ```
-
-    Full-text search is useful when you want to find documents that contain the query terms.
-
----
-
-* <b> Hybrid Search: </b>
-
-    Hybrid search is a combination of vector and full-text search. Here's how you can run a hybrid search query on the dataset.
-    ```python
-    table.search(quries[0], query_type="hybrid").limit(5).to_pandas()
-    ```
-    Hybrid search requires a reranker to combine and rank the results from vector and full-text search. We'll cover reranking as a concept in the next section.
-
-    Hybrid search is useful when you want to combine the benefits of both vector and full-text search.
-
-    !!! note "Note"
-        By default, it uses `LinearCombinationReranker` that combines the scores from vector and full-text search using a weighted linear combination. It is the simplest reranker implementation available in LanceDB. You can also use other rerankers like `CrossEncoderReranker` or `CohereReranker` for reranking the results.
-        Learn more about rerankers [here](https://lancedb.github.io/lancedb/reranking/)
-
-    
-
-### Hit rate evaluation results
-
-Now that we have seen how to run different query types on the dataset, let's evaluate the hit-rate of each query type on the dataset.
-For brevity, the entire evaluation script is not shown here. You can find the complete evaluation and benchmarking utility scripts [here](https://github.com/lancedb/ragged).
-
-Here are the hit-rate results for the dataset:
-
-| Query Type | Hit-rate@5 |
-| --- | --- |
-| Vector Search | 0.640 |
-| Full-text Search | 0.595 |
-| Hybrid Search (w/ LinearCombinationReranker) | 0.645 |
-
-**Choosing query type** is very specific to the use case and the data. This synthetic dataset has been generated to be semantically challenging, i.e, the queries don't have a lot of keywords in common with the context. So, vector search performs better than full-text search. However, in real-world scenarios, full-text search might perform better than vector search. Hybrid search is a good choice when you want to combine the benefits of both vector and full-text search.
-
-### Evaluation results on other datasets
-
-The hit-rate results can vary based on the dataset and the query type. Here are the hit-rate results for the other datasets using the same embedding function.
-
-* <b> SQuAD Dataset: </b>
-
-    | Query Type | Hit-rate@5 |
-    | --- | --- |
-    | Vector Search | 0.822 |
-    | Full-text Search | 0.835 |
-    | Hybrid Search (w/ LinearCombinationReranker) | 0.8874 |
-
-* <b> Uber10K sec filing Dataset: </b>
-
-    | Query Type | Hit-rate@5 |
-    | --- | --- |
-    | Vector Search | 0.608 |
-    | Full-text Search | 0.82 |
-    | Hybrid Search (w/ LinearCombinationReranker) | 0.80 |
-
-In these standard datasets, FTS seems to perform much better than vector search because the queries have a lot of keywords in common with the context. So, in general choosing the query type is very specific to the use case and the data.
-
-

docs/src/guides/tuning_retrievers/2_reranking.md

@@ -1,78 +0,0 @@
-Continuing from the previous example, we can now rerank the results using more complex rerankers.
-
-## Reranking search results
-You can rerank any search results using a reranker. The syntax for reranking is as follows:
-
-```python
-from lancedb.rerankers import LinearCombinationReranker
-
-reranker = LinearCombinationReranker()
-table.search(quries[0], query_type="hybrid").rerank(reranker=reranker).limit(5).to_pandas()
-```
-Based on the `query_type`, the `rerank()` function can accept other arguments as well. For example, hybrid search accepts a `normalize` param to determine the score normalization method.
-
-!!! note "Note"
-    LanceDB provides a `Reranker` base class that can be extended to implement custom rerankers. Each reranker must implement the `rerank_hybrid` method. `rerank_vector` and `rerank_fts` methods are optional. For example, the `LinearCombinationReranker` only implements the `rerank_hybrid` method and so it can only be used for reranking hybrid search results.
-
-## Choosing a Reranker
-There are many rerankers available in LanceDB like `CrossEncoderReranker`, `CohereReranker`, and `ColBERT`. The choice of reranker depends on the dataset and the application. You can even implement you own custom reranker by extending the `Reranker` class. For more details about each available reranker and performance comparison, refer to the [rerankers](https://lancedb.github.io/lancedb/reranking/) documentation.
-
-In this example, we'll use the `CohereReranker` to rerank the search results. It requires  `cohere` to be installed and `COHERE_API_KEY` to be set in the environment. To get your API key, sign up on [Cohere](https://cohere.ai/).
-
-```python
-from lancedb.rerankers import CohereReranker
-
-# use Cohere reranker v3
-reranker = CohereReranker(model_name="rerank-english-v3.0") # default model is "rerank-english-v2.0"
-```
-
-### Reranking search results
-Now we can rerank all query type results using the `CohereReranker`:
-
-```python
-
-# rerank hybrid search results
-table.search(quries[0], query_type="hybrid").rerank(reranker=reranker).limit(5).to_pandas()
-
-# rerank vector search results
-table.search(quries[0], query_type="vector").rerank(reranker=reranker).limit(5).to_pandas()
-
-# rerank fts search results
-table.search(quries[0], query_type="fts").rerank(reranker=reranker).limit(5).to_pandas()
-```
-
-Each reranker can accept additional arguments. For example, `CohereReranker` accepts `top_k` and `batch_size` params to control the number of documents to rerank and the batch size for reranking respectively. Similarly, a custom reranker can accept any number of arguments based on the implementation. For example, a reranker can accept a `filter` that implements some custom logic to filter out documents before reranking.
-
-## Results
-
-Let us take a look at the same datasets from the previous sections, using the same embedding table but with Cohere reranker applied to all query types.
-
-!!! note "Note"
-    When reranking fts or vector search results, the search results are over-fetched by a factor of 2 and then reranked. From the reranked set, `top_k` (5 in this case) results are taken. This is done because reranking will have no effect on the hit-rate if we only fetch the `top_k` results.
-
-### Synthetic LLama2 paper dataset
-
-| Query Type | Hit-rate@5 |
-| --- | --- |
-| Vector |  0.640 |
-| FTS   |  0.595  |
-| Reranked vector | 0.677    |
-| Reranked fts  | 0.672    |
-| Hybrid | 0.759 |
-
-### SQuAD Dataset
-
-
-### Uber10K sec filing Dataset
-
-| Query Type | Hit-rate@5 |
-| --- | --- |
-| Vector |  0.608 |
-| FTS   |  0.824  |
-| Reranked vector | 0.671    |
-| Reranked fts  | 0.843    |
-| Hybrid | 0.849 |
-
-
-
-

docs/src/hybrid_search/eval.md

@@ -5,9 +5,7 @@ Hybrid Search is a broad (often misused) term. It can mean anything from combini
 ## The challenge of (re)ranking search results
 Once you have a group of the most relevant search results from multiple search sources, you'd likely standardize the score and rank them accordingly. This process can also be seen as another independent step - reranking.
 There are two approaches for reranking search results from multiple sources.
-
 * <b>Score-based</b>: Calculate final relevance scores based on a weighted linear combination of individual search algorithm scores. Example - Weighted linear combination of semantic search & keyword-based search results.
-
 * <b>Relevance-based</b>: Discards the existing scores and calculates the relevance of each search result - query pair. Example - Cross Encoder models
 
 Even though there are many strategies for reranking search results, none works for all cases. Moreover, evaluating them itself is a challenge. Also, reranking can be dataset, application specific so it's hard to generalize.

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://lancedb.github.io/lancedb/integrations/langchain/) | <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://python.langchain.com/docs/integrations/vectorstores/lancedb) | <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

@@ -1,92 +0,0 @@
-# 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/integrations/llamaIndex.md

@@ -1,139 +0,0 @@
-# Llama-Index
-![Illustration](../assets/llama-index.jpg)
-
-## Quick start
-You would need to install the integration via `pip install llama-index-vector-stores-lancedb` in order to use it. You can run the below script to try it out :
-```python
-import logging
-import sys
-
-# Uncomment to see debug logs
-# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
-
-from llama_index.core import SimpleDirectoryReader, Document, StorageContext
-from llama_index.core import VectorStoreIndex
-from llama_index.vector_stores.lancedb import LanceDBVectorStore
-import textwrap
-import openai
-
-openai.api_key = "sk-..."
-
-documents = SimpleDirectoryReader("./data/your-data-dir/").load_data()
-print("Document ID:", documents[0].doc_id, "Document Hash:", documents[0].hash)
-
-## For LanceDB cloud :
-# vector_store = LanceDBVectorStore( 
-#     uri="db://db_name", # your remote DB URI
-#     api_key="sk_..", # lancedb cloud api key
-#     region="your-region" # the region you configured
-#     ...
-# )
-
-vector_store = LanceDBVectorStore(
-    uri="./lancedb", mode="overwrite", query_type="vector"
-)
-storage_context = StorageContext.from_defaults(vector_store=vector_store)
-
-index = VectorStoreIndex.from_documents(
-    documents, storage_context=storage_context
-)
-lance_filter = "metadata.file_name = 'paul_graham_essay.txt' "
-retriever = index.as_retriever(vector_store_kwargs={"where": lance_filter})
-response = retriever.retrieve("What did the author do growing up?")
-```
-
-### Filtering
-For metadata filtering, you can use a Lance SQL-like string filter as demonstrated in the example above. Additionally, you can also filter using the `MetadataFilters` class from LlamaIndex:
-```python
-from llama_index.core.vector_stores import (
-    MetadataFilters,
-    FilterOperator,
-    FilterCondition,
-    MetadataFilter,
-)
-
-query_filters = MetadataFilters(
-    filters=[
-        MetadataFilter(
-            key="creation_date", operator=FilterOperator.EQ, value="2024-05-23"
-        ),
-        MetadataFilter(
-            key="file_size", value=75040, operator=FilterOperator.GT
-        ),
-    ],
-    condition=FilterCondition.AND,
-)
-```
-
-### Hybrid Search
-For complete documentation, refer [here](https://lancedb.github.io/lancedb/hybrid_search/hybrid_search/). This example uses the `colbert` reranker. Make sure to install necessary dependencies for the reranker you choose.
-```python
-from lancedb.rerankers import ColbertReranker
-
-reranker = ColbertReranker()
-vector_store._add_reranker(reranker)
-
-query_engine = index.as_query_engine(
-    filters=query_filters,
-    vector_store_kwargs={
-        "query_type": "hybrid",
-    }
-)
-
-response = query_engine.query("How much did Viaweb charge per month?")
-```
-
-In the above snippet, you can change/specify query_type again when creating the engine/retriever.
-
-## API reference
-The exhaustive list of parameters for `LanceDBVectorStore` vector store are :  
-- `connection`: Optional, `lancedb.db.LanceDBConnection` connection object to use. If not provided, a new connection will be created.
-- `uri`: Optional[str], the uri of your database. Defaults to `"/tmp/lancedb"`.
-- `table_name` : Optional[str], Name of your table in the database. Defaults to `"vectors"`.
-- `table`: Optional[Any], `lancedb.db.LanceTable` object to be passed. Defaults to `None`. 
-- `vector_column_name`: Optional[Any], Column name to use for vector's in the table. Defaults to `'vector'`.   
-- `doc_id_key`: Optional[str], Column name to use for document id's in the table. Defaults to `'doc_id'`.  
-- `text_key`: Optional[str], Column name to use for text in the table. Defaults to `'text'`.  
-- `api_key`: Optional[str], API key to use for LanceDB cloud database. Defaults to `None`.  
-- `region`: Optional[str], Region to use for LanceDB cloud database. Only for LanceDB Cloud, defaults to `None`.  
-- `nprobes` : Optional[int], Set the number of probes to use. Only applicable if ANN index is created on the table else its ignored. Defaults to `20`.
-- `refine_factor` : Optional[int], Refine the results by reading extra elements and re-ranking them in memory. Defaults to `None`.
-- `reranker`: Optional[Any], The reranker to use for LanceDB.
-        Defaults to `None`.
-- `overfetch_factor`: Optional[int], The factor by which to fetch more results.
-        Defaults to `1`.
-- `mode`: Optional[str], The mode to use for LanceDB.
-            Defaults to `"overwrite"`.
-- `query_type`:Optional[str], The type of query to use for LanceDB.
-            Defaults to `"vector"`.
-
-
-### Methods 
-
-- __from_table(cls, table: lancedb.db.LanceTable) -> `LanceDBVectorStore`__ : (class method) Creates instance from lancedb table. 
-
-- **_add_reranker(self, reranker: lancedb.rerankers.Reranker) -> `None`** : Add a reranker to an existing vector store. 
-    - Usage :
-        ```python
-        from lancedb.rerankers import ColbertReranker
-        reranker = ColbertReranker()
-        vector_store._add_reranker(reranker)
-        ```               
-- **_table_exists(self, tbl_name: `Optional[str]` = `None`) -> `bool`** : Returns `True` if `tbl_name` exists in database.
-- __create_index(  
-  self, scalar: `Optional[bool]` = False, col_name: `Optional[str]` = None, num_partitions: `Optional[int]` = 256, num_sub_vectors: `Optional[int]` = 96, index_cache_size: `Optional[int]` = None, metric: `Optional[str]` = "L2",  
-) -> `None`__ : Creates a scalar(for non-vector cols) or a vector index on a table.
-        Make sure your vector column has enough data before creating an index on it.
-
-- __add(self, nodes: `List[BaseNode]`, **add_kwargs: `Any`, ) -> `List[str]`__ :
-adds Nodes to the table
-
-- **delete(self, ref_doc_id: `str`) -> `None`**: Delete nodes using with node_ids.
-- **delete_nodes(self, node_ids: `List[str]`) -> `None`** : Delete nodes using with node_ids.
-- __query(
-        self,
-        query: `VectorStoreQuery`,
-        **kwargs: `Any`,
-    ) -> `VectorStoreQueryResult`__:
-        Query index(`VectorStoreIndex`) for top k most similar nodes. Accepts llamaIndex `VectorStoreQuery` object.

docs/src/js/modules.md

@@ -142,7 +142,6 @@ 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"
    ]
@@ -234,12 +234,9 @@
    "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 [\n",
-    "        data.embedding\n",
-    "        for data in rs.data\n",
-    "    ]"
+    "    return [rs.data[0].embedding]"
    ]
   },
   {
@@ -517,7 +514,7 @@
     "                prompt_start +\n",
     "                \"\\n\\n---\\n\\n\".join(context.text) +\n",
     "                prompt_end\n",
-    "            )\n",
+    "            )    \n",
     "    return prompt"
    ]
   },

docs/src/python/duckdb.md

@@ -24,8 +24,7 @@ data = [
 table = db.create_table("pd_table", data=data)
 ```
 
-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.
+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.
 
 ```python
 import duckdb

docs/src/reranking/cohere.md

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

@@ -1,71 +0,0 @@
-# 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

@@ -1,70 +0,0 @@
-# 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

@@ -1,88 +0,0 @@
-## 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

@@ -1,60 +0,0 @@
-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

@@ -1,52 +0,0 @@
-# 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

@@ -1,73 +0,0 @@
-# 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/src/sql.md

@@ -66,7 +66,6 @@ Currently, Lance supports a growing list of SQL expressions.
 - `LIKE`, `NOT LIKE`
 - `CAST`
 - `regexp_match(column, pattern)`
-- [DataFusion Functions](https://arrow.apache.org/datafusion/user-guide/sql/scalar_functions.html)
 
 For example, the following filter string is acceptable:
 

docs/test/md_testing.py

@@ -1,5 +1,5 @@
 import glob
-from typing import Iterator, List
+from typing import Iterator
 from pathlib import Path
 
 glob_string = "../src/**/*.md"
@@ -7,7 +7,7 @@ excluded_globs = [
     "../src/fts.md",
     "../src/embedding.md",
     "../src/examples/*.md",
-    "../src/integrations/*.md",
+    "../src/integrations/voxel51.md",
     "../src/guides/tables.md",
     "../src/python/duckdb.md",
     "../src/embeddings/*.md",
@@ -15,8 +15,6 @@ excluded_globs = [
     "../src/ann_indexes.md",
     "../src/basic.md",
     "../src/hybrid_search/hybrid_search.md",
-    "../src/reranking/*.md",
-    "../src/guides/tuning_retrievers/*.md",
 ]
 
 python_prefix = "py"
@@ -52,24 +50,11 @@ 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)

java/core/lancedb-jni/Cargo.toml

@@ -1,27 +0,0 @@
-[package]
-name = "lancedb-jni"
-description = "JNI bindings for LanceDB"
-# TODO modify lancedb/Cargo.toml for version and dependencies
-version = "0.4.18"
-edition.workspace = true
-repository.workspace = true
-readme.workspace = true
-license.workspace = true
-keywords.workspace = true
-categories.workspace = true
-publish = false
-
-[lib]
-crate-type = ["cdylib"]
-
-[dependencies]
-lancedb = { path = "../../../rust/lancedb" }
-lance = { workspace = true }
-arrow = { workspace = true, features = ["ffi"] }
-arrow-schema.workspace = true
-tokio = "1.23"
-jni = "0.21.1"
-snafu.workspace = true
-lazy_static.workspace = true
-serde = { version = "^1" }
-serde_json = { version = "1" }

java/core/lancedb-jni/src/connection.rs

@@ -1,130 +0,0 @@
-use crate::ffi::JNIEnvExt;
-use crate::traits::IntoJava;
-use crate::{Error, RT};
-use jni::objects::{JObject, JString, JValue};
-use jni::JNIEnv;
-pub const NATIVE_CONNECTION: &str = "nativeConnectionHandle";
-use crate::Result;
-use lancedb::connection::{connect, Connection};
-
-#[derive(Clone)]
-pub struct BlockingConnection {
-    pub(crate) inner: Connection,
-}
-
-impl BlockingConnection {
-    pub fn create(dataset_uri: &str) -> Result<Self> {
-        let inner = RT.block_on(connect(dataset_uri).execute())?;
-        Ok(Self { inner })
-    }
-
-    pub fn table_names(
-        &self,
-        start_after: Option<String>,
-        limit: Option<i32>,
-    ) -> Result<Vec<String>> {
-        let mut op = self.inner.table_names();
-        if let Some(start_after) = start_after {
-            op = op.start_after(start_after);
-        }
-        if let Some(limit) = limit {
-            op = op.limit(limit as u32);
-        }
-        Ok(RT.block_on(op.execute())?)
-    }
-}
-
-impl IntoJava for BlockingConnection {
-    fn into_java<'a>(self, env: &mut JNIEnv<'a>) -> JObject<'a> {
-        attach_native_connection(env, self)
-    }
-}
-
-fn attach_native_connection<'local>(
-    env: &mut JNIEnv<'local>,
-    connection: BlockingConnection,
-) -> JObject<'local> {
-    let j_connection = create_java_connection_object(env);
-    // This block sets a native Rust object (Connection) as a field in the Java object (j_Connection).
-    // Caution: This creates a potential for memory leaks. The Rust object (Connection) is not
-    // automatically garbage-collected by Java, and its memory will not be freed unless
-    // explicitly handled.
-    //
-    // To prevent memory leaks, ensure the following:
-    // 1. The Java object (`j_Connection`) should implement the `java.io.Closeable` interface.
-    // 2. Users of this Java object should be instructed to always use it within a try-with-resources
-    //    statement (or manually call the `close()` method) to ensure that `self.close()` is invoked.
-    match unsafe { env.set_rust_field(&j_connection, NATIVE_CONNECTION, connection) } {
-        Ok(_) => j_connection,
-        Err(err) => {
-            env.throw_new(
-                "java/lang/RuntimeException",
-                format!("Failed to set native handle for Connection: {}", err),
-            )
-            .expect("Error throwing exception");
-            JObject::null()
-        }
-    }
-}
-
-fn create_java_connection_object<'a>(env: &mut JNIEnv<'a>) -> JObject<'a> {
-    env.new_object("com/lancedb/lancedb/Connection", "()V", &[])
-        .expect("Failed to create Java Lance Connection instance")
-}
-
-#[no_mangle]
-pub extern "system" fn Java_com_lancedb_lancedb_Connection_releaseNativeConnection(
-    mut env: JNIEnv,
-    j_connection: JObject,
-) {
-    let _: BlockingConnection = unsafe {
-        env.take_rust_field(j_connection, NATIVE_CONNECTION)
-            .expect("Failed to take native Connection handle")
-    };
-}
-
-#[no_mangle]
-pub extern "system" fn Java_com_lancedb_lancedb_Connection_connect<'local>(
-    mut env: JNIEnv<'local>,
-    _obj: JObject,
-    dataset_uri_object: JString,
-) -> JObject<'local> {
-    let dataset_uri: String = ok_or_throw!(env, env.get_string(&dataset_uri_object)).into();
-    let blocking_connection = ok_or_throw!(env, BlockingConnection::create(&dataset_uri));
-    blocking_connection.into_java(&mut env)
-}
-
-#[no_mangle]
-pub extern "system" fn Java_com_lancedb_lancedb_Connection_tableNames<'local>(
-    mut env: JNIEnv<'local>,
-    j_connection: JObject,
-    start_after_obj: JObject, // Optional<String>
-    limit_obj: JObject,       // Optional<Integer>
-) -> JObject<'local> {
-    ok_or_throw!(
-        env,
-        inner_table_names(&mut env, j_connection, start_after_obj, limit_obj)
-    )
-}
-
-fn inner_table_names<'local>(
-    env: &mut JNIEnv<'local>,
-    j_connection: JObject,
-    start_after_obj: JObject, // Optional<String>
-    limit_obj: JObject,       // Optional<Integer>
-) -> Result<JObject<'local>> {
-    let start_after = env.get_string_opt(&start_after_obj)?;
-    let limit = env.get_int_opt(&limit_obj)?;
-    let conn =
-        unsafe { env.get_rust_field::<_, _, BlockingConnection>(j_connection, NATIVE_CONNECTION) }?;
-    let table_names = conn.table_names(start_after, limit)?;
-    drop(conn);
-    let j_names = env.new_object("java/util/ArrayList", "()V", &[])?;
-    for item in table_names {
-        let jstr_item = env.new_string(item)?;
-        let item_jobj = JObject::from(jstr_item);
-        let item_gen = JValue::Object(&item_jobj);
-        env.call_method(&j_names, "add", "(Ljava/lang/Object;)Z", &[item_gen])?;
-    }
-    Ok(j_names)
-}

java/core/lancedb-jni/src/error.rs

@@ -1,225 +0,0 @@
-// 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.
-
-use std::str::Utf8Error;
-
-use arrow_schema::ArrowError;
-use jni::errors::Error as JniError;
-use serde_json::Error as JsonError;
-use snafu::{Location, Snafu};
-
-type BoxedError = Box<dyn std::error::Error + Send + Sync + 'static>;
-
-/// Java Exception types
-pub enum JavaException {
-    IllegalArgumentException,
-    IOException,
-    RuntimeException,
-}
-
-impl JavaException {
-    pub fn as_str(&self) -> &str {
-        match self {
-            Self::IllegalArgumentException => "java/lang/IllegalArgumentException",
-            Self::IOException => "java/io/IOException",
-            Self::RuntimeException => "java/lang/RuntimeException",
-        }
-    }
-}
-/// TODO(lu) change to lancedb-jni
-#[derive(Debug, Snafu)]
-#[snafu(visibility(pub))]
-pub enum Error {
-    #[snafu(display("JNI error: {message}, {location}"))]
-    Jni { message: String, location: Location },
-    #[snafu(display("Invalid argument: {message}, {location}"))]
-    InvalidArgument { message: String, location: Location },
-    #[snafu(display("IO error: {source}, {location}"))]
-    IO {
-        source: BoxedError,
-        location: Location,
-    },
-    #[snafu(display("Arrow error: {message}, {location}"))]
-    Arrow { message: String, location: Location },
-    #[snafu(display("Index error: {message}, {location}"))]
-    Index { message: String, location: Location },
-    #[snafu(display("JSON error: {message}, {location}"))]
-    JSON { message: String, location: Location },
-    #[snafu(display("Dataset at path {path} was not found, {location}"))]
-    DatasetNotFound { path: String, location: Location },
-    #[snafu(display("Dataset already exists: {uri}, {location}"))]
-    DatasetAlreadyExists { uri: String, location: Location },
-    #[snafu(display("Table '{name}' already exists"))]
-    TableAlreadyExists { name: String },
-    #[snafu(display("Table '{name}' was not found"))]
-    TableNotFound { name: String },
-    #[snafu(display("Invalid table name '{name}': {reason}"))]
-    InvalidTableName { name: String, reason: String },
-    #[snafu(display("Embedding function '{name}' was not found: {reason}, {location}"))]
-    EmbeddingFunctionNotFound {
-        name: String,
-        reason: String,
-        location: Location,
-    },
-    #[snafu(display("Other Lance error: {message}, {location}"))]
-    OtherLance { message: String, location: Location },
-    #[snafu(display("Other LanceDB error: {message}, {location}"))]
-    OtherLanceDB { message: String, location: Location },
-}
-
-impl Error {
-    /// Throw as Java Exception
-    pub fn throw(&self, env: &mut jni::JNIEnv) {
-        match self {
-            Self::InvalidArgument { .. }
-            | Self::DatasetNotFound { .. }
-            | Self::DatasetAlreadyExists { .. }
-            | Self::TableAlreadyExists { .. }
-            | Self::TableNotFound { .. }
-            | Self::InvalidTableName { .. }
-            | Self::EmbeddingFunctionNotFound { .. } => {
-                self.throw_as(env, JavaException::IllegalArgumentException)
-            }
-            Self::IO { .. } | Self::Index { .. } => self.throw_as(env, JavaException::IOException),
-            Self::Arrow { .. }
-            | Self::JSON { .. }
-            | Self::OtherLance { .. }
-            | Self::OtherLanceDB { .. }
-            | Self::Jni { .. } => self.throw_as(env, JavaException::RuntimeException),
-        }
-    }
-
-    /// Throw as an concrete Java Exception
-    pub fn throw_as(&self, env: &mut jni::JNIEnv, exception: JavaException) {
-        let message = &format!(
-            "Error when throwing Java exception: {}:{}",
-            exception.as_str(),
-            self
-        );
-        env.throw_new(exception.as_str(), self.to_string())
-            .expect(message);
-    }
-}
-
-pub type Result<T> = std::result::Result<T, Error>;
-
-trait ToSnafuLocation {
-    fn to_snafu_location(&'static self) -> snafu::Location;
-}
-
-impl ToSnafuLocation for std::panic::Location<'static> {
-    fn to_snafu_location(&'static self) -> snafu::Location {
-        snafu::Location::new(self.file(), self.line(), self.column())
-    }
-}
-
-impl From<JniError> for Error {
-    #[track_caller]
-    fn from(source: JniError) -> Self {
-        Self::Jni {
-            message: source.to_string(),
-            location: std::panic::Location::caller().to_snafu_location(),
-        }
-    }
-}
-
-impl From<Utf8Error> for Error {
-    #[track_caller]
-    fn from(source: Utf8Error) -> Self {
-        Self::InvalidArgument {
-            message: source.to_string(),
-            location: std::panic::Location::caller().to_snafu_location(),
-        }
-    }
-}
-
-impl From<ArrowError> for Error {
-    #[track_caller]
-    fn from(source: ArrowError) -> Self {
-        Self::Arrow {
-            message: source.to_string(),
-            location: std::panic::Location::caller().to_snafu_location(),
-        }
-    }
-}
-
-impl From<JsonError> for Error {
-    #[track_caller]
-    fn from(source: JsonError) -> Self {
-        Self::JSON {
-            message: source.to_string(),
-            location: std::panic::Location::caller().to_snafu_location(),
-        }
-    }
-}
-
-impl From<lance::Error> for Error {
-    #[track_caller]
-    fn from(source: lance::Error) -> Self {
-        match source {
-            lance::Error::DatasetNotFound {
-                path,
-                source: _,
-                location,
-            } => Self::DatasetNotFound { path, location },
-            lance::Error::DatasetAlreadyExists { uri, location } => {
-                Self::DatasetAlreadyExists { uri, location }
-            }
-            lance::Error::IO { source, location } => Self::IO { source, location },
-            lance::Error::Arrow { message, location } => Self::Arrow { message, location },
-            lance::Error::Index { message, location } => Self::Index { message, location },
-            lance::Error::InvalidInput { source, location } => Self::InvalidArgument {
-                message: source.to_string(),
-                location,
-            },
-            _ => Self::OtherLance {
-                message: source.to_string(),
-                location: std::panic::Location::caller().to_snafu_location(),
-            },
-        }
-    }
-}
-
-impl From<lancedb::Error> for Error {
-    #[track_caller]
-    fn from(source: lancedb::Error) -> Self {
-        match source {
-            lancedb::Error::InvalidTableName { name, reason } => {
-                Self::InvalidTableName { name, reason }
-            }
-            lancedb::Error::InvalidInput { message } => Self::InvalidArgument {
-                message,
-                location: std::panic::Location::caller().to_snafu_location(),
-            },
-            lancedb::Error::TableNotFound { name } => Self::TableNotFound { name },
-            lancedb::Error::TableAlreadyExists { name } => Self::TableAlreadyExists { name },
-            lancedb::Error::EmbeddingFunctionNotFound { name, reason } => {
-                Self::EmbeddingFunctionNotFound {
-                    name,
-                    reason,
-                    location: std::panic::Location::caller().to_snafu_location(),
-                }
-            }
-            lancedb::Error::Arrow { source } => Self::Arrow {
-                message: source.to_string(),
-                location: std::panic::Location::caller().to_snafu_location(),
-            },
-            lancedb::Error::Lance { source } => Self::from(source),
-            _ => Self::OtherLanceDB {
-                message: source.to_string(),
-                location: std::panic::Location::caller().to_snafu_location(),
-            },
-        }
-    }
-}

java/core/lancedb-jni/src/ffi.rs

@@ -1,204 +0,0 @@
-// 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.
-
-use core::slice;
-
-use jni::objects::{JByteBuffer, JObjectArray, JString};
-use jni::sys::jobjectArray;
-use jni::{objects::JObject, JNIEnv};
-
-use crate::error::{Error, Result};
-
-/// TODO(lu) import from lance-jni without duplicate
-/// Extend JNIEnv with helper functions.
-pub trait JNIEnvExt {
-    /// Get integers from Java List<Integer> object.
-    fn get_integers(&mut self, obj: &JObject) -> Result<Vec<i32>>;
-
-    /// Get strings from Java List<String> object.
-    fn get_strings(&mut self, obj: &JObject) -> Result<Vec<String>>;
-
-    /// Get strings from Java String[] object.
-    /// Note that get Option<Vec<String>> from Java Optional<String[]> just doesn't work.
-    #[allow(unused)]
-    fn get_strings_array(&mut self, obj: jobjectArray) -> Result<Vec<String>>;
-
-    /// Get Option<String> from Java Optional<String>.
-    fn get_string_opt(&mut self, obj: &JObject) -> Result<Option<String>>;
-
-    /// Get Option<Vec<String>> from Java Optional<List<String>>.
-    #[allow(unused)]
-    fn get_strings_opt(&mut self, obj: &JObject) -> Result<Option<Vec<String>>>;
-
-    /// Get Option<i32> from Java Optional<Integer>.
-    fn get_int_opt(&mut self, obj: &JObject) -> Result<Option<i32>>;
-
-    /// Get Option<Vec<i32>> from Java Optional<List<Integer>>.
-    fn get_ints_opt(&mut self, obj: &JObject) -> Result<Option<Vec<i32>>>;
-
-    /// Get Option<i64> from Java Optional<Long>.
-    #[allow(unused)]
-    fn get_long_opt(&mut self, obj: &JObject) -> Result<Option<i64>>;
-
-    /// Get Option<u64> from Java Optional<Long>.
-    #[allow(unused)]
-    fn get_u64_opt(&mut self, obj: &JObject) -> Result<Option<u64>>;
-
-    /// Get Option<&[u8]> from Java Optional<ByteBuffer>.
-    #[allow(unused)]
-    fn get_bytes_opt(&mut self, obj: &JObject) -> Result<Option<&[u8]>>;
-
-    fn get_optional<T, F>(&mut self, obj: &JObject, f: F) -> Result<Option<T>>
-    where
-        F: FnOnce(&mut JNIEnv, &JObject) -> Result<T>;
-}
-
-impl JNIEnvExt for JNIEnv<'_> {
-    fn get_integers(&mut self, obj: &JObject) -> Result<Vec<i32>> {
-        let list = self.get_list(obj)?;
-        let mut iter = list.iter(self)?;
-        let mut results = Vec::with_capacity(list.size(self)? as usize);
-        while let Some(elem) = iter.next(self)? {
-            let int_obj = self.call_method(elem, "intValue", "()I", &[])?;
-            let int_value = int_obj.i()?;
-            results.push(int_value);
-        }
-        Ok(results)
-    }
-
-    fn get_strings(&mut self, obj: &JObject) -> Result<Vec<String>> {
-        let list = self.get_list(obj)?;
-        let mut iter = list.iter(self)?;
-        let mut results = Vec::with_capacity(list.size(self)? as usize);
-        while let Some(elem) = iter.next(self)? {
-            let jstr = JString::from(elem);
-            let val = self.get_string(&jstr)?;
-            results.push(val.to_str()?.to_string())
-        }
-        Ok(results)
-    }
-
-    fn get_strings_array(&mut self, obj: jobjectArray) -> Result<Vec<String>> {
-        let jobject_array = unsafe { JObjectArray::from_raw(obj) };
-        let array_len = self.get_array_length(&jobject_array)?;
-        let mut res: Vec<String> = Vec::new();
-        for i in 0..array_len {
-            let item: JString = self.get_object_array_element(&jobject_array, i)?.into();
-            res.push(self.get_string(&item)?.into());
-        }
-        Ok(res)
-    }
-
-    fn get_string_opt(&mut self, obj: &JObject) -> Result<Option<String>> {
-        self.get_optional(obj, |env, inner_obj| {
-            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
-            let java_string_obj = java_obj_gen.l()?;
-            let jstr = JString::from(java_string_obj);
-            let val = env.get_string(&jstr)?;
-            Ok(val.to_str()?.to_string())
-        })
-    }
-
-    fn get_strings_opt(&mut self, obj: &JObject) -> Result<Option<Vec<String>>> {
-        self.get_optional(obj, |env, inner_obj| {
-            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
-            let java_list_obj = java_obj_gen.l()?;
-            env.get_strings(&java_list_obj)
-        })
-    }
-
-    fn get_int_opt(&mut self, obj: &JObject) -> Result<Option<i32>> {
-        self.get_optional(obj, |env, inner_obj| {
-            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
-            let java_int_obj = java_obj_gen.l()?;
-            let int_obj = env.call_method(java_int_obj, "intValue", "()I", &[])?;
-            let int_value = int_obj.i()?;
-            Ok(int_value)
-        })
-    }
-
-    fn get_ints_opt(&mut self, obj: &JObject) -> Result<Option<Vec<i32>>> {
-        self.get_optional(obj, |env, inner_obj| {
-            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
-            let java_list_obj = java_obj_gen.l()?;
-            env.get_integers(&java_list_obj)
-        })
-    }
-
-    fn get_long_opt(&mut self, obj: &JObject) -> Result<Option<i64>> {
-        self.get_optional(obj, |env, inner_obj| {
-            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
-            let java_long_obj = java_obj_gen.l()?;
-            let long_obj = env.call_method(java_long_obj, "longValue", "()J", &[])?;
-            let long_value = long_obj.j()?;
-            Ok(long_value)
-        })
-    }
-
-    fn get_u64_opt(&mut self, obj: &JObject) -> Result<Option<u64>> {
-        self.get_optional(obj, |env, inner_obj| {
-            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
-            let java_long_obj = java_obj_gen.l()?;
-            let long_obj = env.call_method(java_long_obj, "longValue", "()J", &[])?;
-            let long_value = long_obj.j()?;
-            Ok(long_value as u64)
-        })
-    }
-
-    fn get_bytes_opt(&mut self, obj: &JObject) -> Result<Option<&[u8]>> {
-        self.get_optional(obj, |env, inner_obj| {
-            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
-            let java_byte_buffer_obj = java_obj_gen.l()?;
-            let j_byte_buffer = JByteBuffer::from(java_byte_buffer_obj);
-            let raw_data = env.get_direct_buffer_address(&j_byte_buffer)?;
-            let capacity = env.get_direct_buffer_capacity(&j_byte_buffer)?;
-            let data = unsafe { slice::from_raw_parts(raw_data, capacity) };
-            Ok(data)
-        })
-    }
-
-    fn get_optional<T, F>(&mut self, obj: &JObject, f: F) -> Result<Option<T>>
-    where
-        F: FnOnce(&mut JNIEnv, &JObject) -> Result<T>,
-    {
-        if obj.is_null() {
-            return Ok(None);
-        }
-        let is_present = self.call_method(obj, "isPresent", "()Z", &[])?;
-        if !is_present.z()? {
-            // TODO(lu): put get java object into here cuz can only get java Object
-            Ok(None)
-        } else {
-            f(self, obj).map(Some)
-        }
-    }
-}
-
-#[no_mangle]
-pub extern "system" fn Java_com_lancedb_lance_test_JniTestHelper_parseInts(
-    mut env: JNIEnv,
-    _obj: JObject,
-    list_obj: JObject, // List<Integer>
-) {
-    ok_or_throw_without_return!(env, env.get_integers(&list_obj));
-}
-
-#[no_mangle]
-pub extern "system" fn Java_com_lancedb_lance_test_JniTestHelper_parseIntsOpt(
-    mut env: JNIEnv,
-    _obj: JObject,
-    list_obj: JObject, // Optional<List<Integer>>
-) {
-    ok_or_throw_without_return!(env, env.get_ints_opt(&list_obj));
-}

java/core/lancedb-jni/src/lib.rs

@@ -1,68 +0,0 @@
-// 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.
-
-use lazy_static::lazy_static;
-
-// TODO import from lance-jni without duplicate
-#[macro_export]
-macro_rules! ok_or_throw {
-    ($env:expr, $result:expr) => {
-        match $result {
-            Ok(value) => value,
-            Err(err) => {
-                Error::from(err).throw(&mut $env);
-                return JObject::null();
-            }
-        }
-    };
-}
-
-macro_rules! ok_or_throw_without_return {
-    ($env:expr, $result:expr) => {
-        match $result {
-            Ok(value) => value,
-            Err(err) => {
-                Error::from(err).throw(&mut $env);
-                return;
-            }
-        }
-    };
-}
-
-#[macro_export]
-macro_rules! ok_or_throw_with_return {
-    ($env:expr, $result:expr, $ret:expr) => {
-        match $result {
-            Ok(value) => value,
-            Err(err) => {
-                Error::from(err).throw(&mut $env);
-                return $ret;
-            }
-        }
-    };
-}
-
-mod connection;
-pub mod error;
-mod ffi;
-mod traits;
-
-pub use error::{Error, Result};
-
-lazy_static! {
-    static ref RT: tokio::runtime::Runtime = tokio::runtime::Builder::new_multi_thread()
-        .enable_all()
-        .build()
-        .expect("Failed to create tokio runtime");
-}

java/core/lancedb-jni/src/traits.rs

@@ -1,122 +0,0 @@
-// 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.
-
-use jni::objects::{JMap, JObject, JString, JValue};
-use jni::JNIEnv;
-
-use crate::Result;
-
-pub trait FromJObject<T> {
-    fn extract(&self) -> Result<T>;
-}
-
-/// Convert a Rust type into a Java Object.
-pub trait IntoJava {
-    fn into_java<'a>(self, env: &mut JNIEnv<'a>) -> JObject<'a>;
-}
-
-impl FromJObject<i32> for JObject<'_> {
-    fn extract(&self) -> Result<i32> {
-        Ok(JValue::from(self).i()?)
-    }
-}
-
-impl FromJObject<i64> for JObject<'_> {
-    fn extract(&self) -> Result<i64> {
-        Ok(JValue::from(self).j()?)
-    }
-}
-
-impl FromJObject<f32> for JObject<'_> {
-    fn extract(&self) -> Result<f32> {
-        Ok(JValue::from(self).f()?)
-    }
-}
-
-impl FromJObject<f64> for JObject<'_> {
-    fn extract(&self) -> Result<f64> {
-        Ok(JValue::from(self).d()?)
-    }
-}
-
-pub trait FromJString {
-    fn extract(&self, env: &mut JNIEnv) -> Result<String>;
-}
-
-impl FromJString for JString<'_> {
-    fn extract(&self, env: &mut JNIEnv) -> Result<String> {
-        Ok(env.get_string(self)?.into())
-    }
-}
-
-pub trait JMapExt {
-    #[allow(dead_code)]
-    fn get_string(&self, env: &mut JNIEnv, key: &str) -> Result<Option<String>>;
-
-    #[allow(dead_code)]
-    fn get_i32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i32>>;
-
-    #[allow(dead_code)]
-    fn get_i64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i64>>;
-
-    #[allow(dead_code)]
-    fn get_f32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f32>>;
-
-    #[allow(dead_code)]
-    fn get_f64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f64>>;
-}
-
-fn get_map_value<T>(env: &mut JNIEnv, map: &JMap, key: &str) -> Result<Option<T>>
-where
-    for<'a> JObject<'a>: FromJObject<T>,
-{
-    let key_obj: JObject = env.new_string(key)?.into();
-    if let Some(value) = map.get(env, &key_obj)? {
-        if value.is_null() {
-            Ok(None)
-        } else {
-            Ok(Some(value.extract()?))
-        }
-    } else {
-        Ok(None)
-    }
-}
-
-impl JMapExt for JMap<'_, '_, '_> {
-    fn get_string(&self, env: &mut JNIEnv, key: &str) -> Result<Option<String>> {
-        let key_obj: JObject = env.new_string(key)?.into();
-        if let Some(value) = self.get(env, &key_obj)? {
-            let value_str: JString = value.into();
-            Ok(Some(value_str.extract(env)?))
-        } else {
-            Ok(None)
-        }
-    }
-
-    fn get_i32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i32>> {
-        get_map_value(env, self, key)
-    }
-
-    fn get_i64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i64>> {
-        get_map_value(env, self, key)
-    }
-
-    fn get_f32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f32>> {
-        get_map_value(env, self, key)
-    }
-
-    fn get_f64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f64>> {
-        get_map_value(env, self, key)
-    }
-}

java/core/pom.xml

@@ -1,94 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-
-<project xmlns="http://maven.apache.org/POM/4.0.0"
-    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-
-    <parent>
-        <groupId>com.lancedb</groupId>
-        <artifactId>lancedb-parent</artifactId>
-        <version>0.1-SNAPSHOT</version>
-        <relativePath>../pom.xml</relativePath>
-    </parent>
-
-    <artifactId>lancedb-core</artifactId>
-    <name>LanceDB Core</name>
-    <packaging>jar</packaging>
-
-    <dependencies>
-        <dependency>
-            <groupId>org.apache.arrow</groupId>
-            <artifactId>arrow-vector</artifactId>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.arrow</groupId>
-            <artifactId>arrow-memory-netty</artifactId>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.arrow</groupId>
-            <artifactId>arrow-c-data</artifactId>
-        </dependency>
-        <dependency>
-            <groupId>org.apache.arrow</groupId>
-            <artifactId>arrow-dataset</artifactId>
-        </dependency>
-        <dependency>
-            <groupId>org.json</groupId>
-            <artifactId>json</artifactId>
-        </dependency>
-        <dependency>
-            <groupId>org.questdb</groupId>
-            <artifactId>jar-jni</artifactId>
-        </dependency>
-        <dependency>
-            <groupId>org.junit.jupiter</groupId>
-            <artifactId>junit-jupiter</artifactId>
-             <scope>test</scope>
-        </dependency>
-    </dependencies>
-
-    <profiles>
-        <profile>
-            <id>build-jni</id>
-            <activation>
-                <activeByDefault>true</activeByDefault>
-            </activation>
-            <build>
-                <plugins>
-                    <plugin>
-                        <groupId>org.questdb</groupId>
-                        <artifactId>rust-maven-plugin</artifactId>
-                        <version>1.1.1</version>
-                        <executions>
-                            <execution>
-                                <id>lancedb-jni</id>
-                                <goals>
-                                    <goal>build</goal>
-                                </goals>
-                                <configuration>
-                                    <path>lancedb-jni</path>
-                                    <!--<release>true</release>-->
-                                    <!-- Copy native libraries to target/classes for runtime access -->
-                                    <copyTo>${project.build.directory}/classes/nativelib</copyTo>
-                                    <copyWithPlatformDir>true</copyWithPlatformDir>
-                                </configuration>
-                            </execution>
-                            <execution>
-                                <id>lancedb-jni-test</id>
-                                <goals>
-                                    <goal>test</goal>
-                                </goals>
-                                <configuration>
-                                    <path>lancedb-jni</path>
-                                    <release>false</release>
-                                    <verbosity>-v</verbosity>
-                                </configuration>
-                            </execution>
-                        </executions>
-                    </plugin>
-                </plugins>
-            </build>
-        </profile>
-    </profiles>
-</project>

java/core/src/main/java/com/lancedb/lancedb/Connection.java

@@ -1,120 +0,0 @@
-/*
- * 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.
- */
-
-package com.lancedb.lancedb;
-
-import io.questdb.jar.jni.JarJniLoader;
-import java.io.Closeable;
-import java.util.List;
-import java.util.Optional;
-
-/**
- * Represents LanceDB database.
- */
-public class Connection implements Closeable {
-  static {
-    JarJniLoader.loadLib(Connection.class, "/nativelib", "lancedb_jni");
-  }
-
-  private long nativeConnectionHandle;
-
-  /**
-   * Connect to a LanceDB instance.
-   */
-  public static native Connection connect(String uri);
-
-  /**
-   * Get the names of all tables in the database. The names are sorted in
-   * ascending order.
-   *
-   * @return the table names
-   */
-  public List<String> tableNames() {
-    return tableNames(Optional.empty(), Optional.empty());
-  }
-
-  /**
-   * Get the names of filtered tables in the database. The names are sorted in
-   * ascending order.
-   *
-   * @param limit The number of results to return.
-   * @return the table names
-   */
-  public List<String> tableNames(int limit) {
-    return tableNames(Optional.empty(), Optional.of(limit));
-  }
-
-  /**
-   * Get the names of filtered tables in the database. The names are sorted in
-   * ascending order.
-   *
-   * @param startAfter If present, only return names that come lexicographically after the supplied
-   *                   value. This can be combined with limit to implement pagination
-   *                   by setting this to the last table name from the previous page.
-   * @return the table names
-   */
-  public List<String> tableNames(String startAfter) {
-    return tableNames(Optional.of(startAfter), Optional.empty());
-  }
-
-  /**
-   * Get the names of filtered tables in the database. The names are sorted in
-   * ascending order.
-   *
-   * @param startAfter If present, only return names that come lexicographically after the supplied
-   *                   value. This can be combined with limit to implement pagination
-   *                   by setting this to the last table name from the previous page.
-   * @param limit The number of results to return.
-   * @return the table names
-   */
-  public List<String> tableNames(String startAfter, int limit) {
-    return tableNames(Optional.of(startAfter), Optional.of(limit));
-  }
-
-  /**
-   * Get the names of filtered tables in the database. The names are sorted in
-   * ascending order.
-   *
-   * @param startAfter If present, only return names that come lexicographically after the supplied
-   *                   value. This can be combined with limit to implement pagination
-   *                   by setting this to the last table name from the previous page.
-   * @param limit The number of results to return.
-   * @return the table names
-   */
-  public native List<String> tableNames(
-      Optional<String> startAfter, Optional<Integer> limit);
-
-  /**
-   * Closes this connection and releases any system resources associated with it. If
-   * the connection is
-   * already closed, then invoking this method has no effect.
-   */
-  @Override
-  public void close() {
-    if (nativeConnectionHandle != 0) {
-      releaseNativeConnection(nativeConnectionHandle);
-      nativeConnectionHandle = 0;
-    }
-  }
-
-  /**
-   * Native method to release the Lance connection resources associated with the
-   * given handle.
-   *
-   * @param handle The native handle to the connection resource.
-   */
-  private native void releaseNativeConnection(long handle);
-
-  private Connection() {}
-}

java/core/src/test/java/com/lancedb/lancedb/ConnectionTest.java

@@ -1,135 +0,0 @@
-/*
- * 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.
- */
-package com.lancedb.lancedb;
-
-import static org.junit.jupiter.api.Assertions.assertEquals;
-import static org.junit.jupiter.api.Assertions.assertTrue;
-
-import java.nio.file.Path;
-import java.util.List;
-import java.net.URL;
-import org.junit.jupiter.api.BeforeAll;
-import org.junit.jupiter.api.Test;
-import org.junit.jupiter.api.io.TempDir;
-
-public class ConnectionTest {
-  private static final String[] TABLE_NAMES = {
-      "dataset_version",
-      "new_empty_dataset",
-      "test",
-      "write_stream"
-  };
-
-  @TempDir
-  static Path tempDir; // Temporary directory for the tests
-  private static URL lanceDbURL;
-
-  @BeforeAll
-  static void setUp() {
-    ClassLoader classLoader = ConnectionTest.class.getClassLoader();
-    lanceDbURL = classLoader.getResource("example_db");
-  }
-
-  @Test
-  void emptyDB() {
-    String databaseUri = tempDir.resolve("emptyDB").toString();
-    try (Connection conn = Connection.connect(databaseUri)) {
-      List<String> tableNames = conn.tableNames();
-      assertTrue(tableNames.isEmpty());
-    }
-  }
-
-  @Test
-  void tableNames() {
-    try (Connection conn = Connection.connect(lanceDbURL.toString())) {
-      List<String> tableNames = conn.tableNames();
-      assertEquals(4, tableNames.size());
-      for (int i = 0; i < TABLE_NAMES.length; i++) {
-        assertEquals(TABLE_NAMES[i], tableNames.get(i));
-      }
-    }
-  }
-
-  @Test
-  void tableNamesStartAfter() {
-    try (Connection conn = Connection.connect(lanceDbURL.toString())) {
-      assertTableNamesStartAfter(conn, TABLE_NAMES[0], 3, TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
-      assertTableNamesStartAfter(conn, TABLE_NAMES[1], 2, TABLE_NAMES[2], TABLE_NAMES[3]);
-      assertTableNamesStartAfter(conn, TABLE_NAMES[2], 1, TABLE_NAMES[3]);
-      assertTableNamesStartAfter(conn, TABLE_NAMES[3], 0);
-      assertTableNamesStartAfter(conn, "a_dataset", 4, TABLE_NAMES[0], TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
-      assertTableNamesStartAfter(conn, "o_dataset", 2, TABLE_NAMES[2], TABLE_NAMES[3]);
-      assertTableNamesStartAfter(conn, "v_dataset", 1, TABLE_NAMES[3]);
-      assertTableNamesStartAfter(conn, "z_dataset", 0);
-    }
-  }
-
-  private void assertTableNamesStartAfter(Connection conn, String startAfter, int expectedSize, String... expectedNames) {
-    List<String> tableNames = conn.tableNames(startAfter);
-    assertEquals(expectedSize, tableNames.size());
-    for (int i = 0; i < expectedNames.length; i++) {
-      assertEquals(expectedNames[i], tableNames.get(i));
-    }
-  }
-
-  @Test
-  void tableNamesLimit() {
-      try (Connection conn = Connection.connect(lanceDbURL.toString())) {
-      for (int i = 0; i <= TABLE_NAMES.length; i++) {
-        List<String> tableNames = conn.tableNames(i);
-        assertEquals(i, tableNames.size());
-        for (int j = 0; j < i; j++) {
-          assertEquals(TABLE_NAMES[j], tableNames.get(j));
-        }
-      }
-    }
-  }
-
-  @Test
-  void tableNamesStartAfterLimit() {
-    try (Connection conn = Connection.connect(lanceDbURL.toString())) {
-      List<String> tableNames = conn.tableNames(TABLE_NAMES[0], 2);
-      assertEquals(2, tableNames.size());
-      assertEquals(TABLE_NAMES[1], tableNames.get(0));
-      assertEquals(TABLE_NAMES[2], tableNames.get(1));
-      tableNames = conn.tableNames(TABLE_NAMES[1], 1);
-      assertEquals(1, tableNames.size());
-      assertEquals(TABLE_NAMES[2], tableNames.get(0));
-      tableNames = conn.tableNames(TABLE_NAMES[2], 2);
-      assertEquals(1, tableNames.size());
-      assertEquals(TABLE_NAMES[3], tableNames.get(0));
-      tableNames = conn.tableNames(TABLE_NAMES[3], 2);
-      assertEquals(0, tableNames.size());
-      tableNames = conn.tableNames(TABLE_NAMES[0], 0);
-      assertEquals(0, tableNames.size());
-
-      // Limit larger than the number of remaining tables
-      tableNames = conn.tableNames(TABLE_NAMES[0], 10);
-      assertEquals(3, tableNames.size());
-      assertEquals(TABLE_NAMES[1], tableNames.get(0));
-      assertEquals(TABLE_NAMES[2], tableNames.get(1));
-      assertEquals(TABLE_NAMES[3], tableNames.get(2));
-
-      // Start after a value not in the list
-      tableNames = conn.tableNames("non_existent_table", 2);
-      assertEquals(2, tableNames.size());
-      assertEquals(TABLE_NAMES[2], tableNames.get(0));
-      assertEquals(TABLE_NAMES[3], tableNames.get(1));
-
-      // Start after the last table with a limit
-      tableNames = conn.tableNames(TABLE_NAMES[3], 1);
-      assertEquals(0, tableNames.size());
-    }
-  }
-}

java/core/src/test/resources/example_db/dataset_version.lance/_transactions/0-d51afd07-e3cd-4c76-9b9b-787e13fd55b0.txn

@@ -1 +0,0 @@
-$d51afd07-e3cd-4c76-9b9b-787e13fd55b0<62>=id <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*int320
8
name
 <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*string0
8

java/core/src/test/resources/example_db/new_empty_dataset.lance/_transactions/0-15648e72-076f-4ef1-8b90-10d305b95b3b.txn

@@ -1 +0,0 @@
-$15648e72-076f-4ef1-8b90-10d305b95b3b<33>=id <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*int320
8
name
 <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*string0
8

java/core/src/test/resources/example_db/test.lance/_transactions/0-a3689caf-4f6b-4afc-a3c7-97af75661843.txn

@@ -1 +0,0 @@
-$a3689caf-4f6b-4afc-a3c7-97af75661843<34>oitem <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*string8price
 <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*double8
0vector <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*fixed_size_list:float:28

java/pom.xml

@@ -1,129 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project xmlns="http://maven.apache.org/POM/4.0.0"
-    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
-    <modelVersion>4.0.0</modelVersion>
-
-    <groupId>com.lancedb</groupId>
-    <artifactId>lancedb-parent</artifactId>
-    <version>0.1-SNAPSHOT</version>
-    <packaging>pom</packaging>
-
-    <name>Lance Parent</name>
-
-    <properties>
-        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <maven.compiler.source>11</maven.compiler.source>
-        <maven.compiler.target>11</maven.compiler.target>
-        <arrow.version>15.0.0</arrow.version>
-    </properties>
-
-    <modules>
-        <module>core</module>
-    </modules>
-
-    <dependencyManagement>
-        <dependencies>
-            <dependency>
-                <groupId>org.apache.arrow</groupId>
-                <artifactId>arrow-vector</artifactId>
-                <version>${arrow.version}</version>
-            </dependency>
-            <dependency>
-                <groupId>org.apache.arrow</groupId>
-                <artifactId>arrow-memory-netty</artifactId>
-                <version>${arrow.version}</version>
-            </dependency>
-            <dependency>
-                <groupId>org.apache.arrow</groupId>
-                <artifactId>arrow-c-data</artifactId>
-                <version>${arrow.version}</version>
-            </dependency>
-            <dependency>
-                <groupId>org.apache.arrow</groupId>
-                <artifactId>arrow-dataset</artifactId>
-                <version>${arrow.version}</version>
-            </dependency>
-            <dependency>
-                <groupId>org.questdb</groupId>
-                <artifactId>jar-jni</artifactId>
-                <version>1.1.1</version>
-            </dependency>
-            <dependency>
-                <groupId>org.junit.jupiter</groupId>
-                <artifactId>junit-jupiter</artifactId>
-                <version>5.10.1</version>
-            </dependency>
-            <dependency>
-                <groupId>org.json</groupId>
-                <artifactId>json</artifactId>
-                <version>20210307</version>
-            </dependency>
-        </dependencies>
-    </dependencyManagement>
-
-     <build>
-        <plugins>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-checkstyle-plugin</artifactId>
-                <version>3.3.1</version>
-                <configuration>
-                    <configLocation>google_checks.xml</configLocation>
-                    <consoleOutput>true</consoleOutput>
-                    <failsOnError>true</failsOnError>
-                    <violationSeverity>warning</violationSeverity>
-                    <linkXRef>false</linkXRef>
-                </configuration>
-                <executions>
-                    <execution>
-                        <id>validate</id>
-                        <phase>validate</phase>
-                        <goals>
-                            <goal>check</goal>
-                        </goals>
-                    </execution>
-                </executions>
-            </plugin>
-        </plugins>
-        <pluginManagement>
-            <plugins>
-                <plugin>
-                    <artifactId>maven-clean-plugin</artifactId>
-                    <version>3.1.0</version>
-                </plugin>
-                <plugin>
-                    <artifactId>maven-resources-plugin</artifactId>
-                    <version>3.0.2</version>
-                </plugin>
-                <plugin>
-                    <artifactId>maven-compiler-plugin</artifactId>
-                    <version>3.8.1</version>
-                    <configuration>
-                        <compilerArgs>
-                            <arg>-h</arg>
-                            <arg>target/headers</arg>
-                        </compilerArgs>
-                    </configuration>
-                </plugin>
-                <plugin>
-                    <artifactId>maven-surefire-plugin</artifactId>
-                    <version>3.2.5</version>
-                    <configuration>
-                        <argLine>--add-opens=java.base/java.nio=ALL-UNNAMED</argLine>
-                        <forkNode implementation="org.apache.maven.plugin.surefire.extensions.SurefireForkNodeFactory"/>
-                        <useSystemClassLoader>false</useSystemClassLoader>
-                    </configuration>
-                </plugin>
-                <plugin>
-                    <artifactId>maven-jar-plugin</artifactId>
-                    <version>3.0.2</version>
-                </plugin>
-                <plugin>
-                    <artifactId>maven-install-plugin</artifactId>
-                    <version>2.5.2</version>
-                </plugin>
-            </plugins>
-        </pluginManagement>
-    </build>
-</project>

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.5.2",
+  "version": "0.4.15",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.5.2",
+      "version": "0.4.15",
       "cpu": [
         "x64",
         "arm64"
@@ -52,11 +52,11 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@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"
+        "@lancedb/vectordb-darwin-arm64": "0.4.15",
+        "@lancedb/vectordb-darwin-x64": "0.4.15",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.4.15",
+        "@lancedb/vectordb-linux-x64-gnu": "0.4.15",
+        "@lancedb/vectordb-win32-x64-msvc": "0.4.15"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",
@@ -333,66 +333,6 @@
         "@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

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.5.2",
+  "version": "0.4.15",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
   "scripts": {
     "tsc": "tsc -b",
-    "build": "npm run tsc && cargo-cp-artifact --artifact cdylib lancedb_node index.node -- cargo build --message-format=json",
+    "build": "npm run tsc && cargo-cp-artifact --artifact cdylib lancedb-node index.node -- cargo build --message-format=json",
     "build-release": "npm run build -- --release",
     "test": "npm run tsc && mocha -recursive dist/test",
     "integration-test": "npm run tsc && mocha -recursive dist/integration_test",
@@ -88,10 +88,10 @@
     }
   },
   "optionalDependencies": {
-    "@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"
+    "@lancedb/vectordb-darwin-arm64": "0.4.15",
+    "@lancedb/vectordb-darwin-x64": "0.4.15",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.4.15",
+    "@lancedb/vectordb-linux-x64-gnu": "0.4.15",
+    "@lancedb/vectordb-win32-x64-msvc": "0.4.15"
   }
 }

node/src/arrow.ts

@@ -27,23 +27,23 @@ import {
   RecordBatch,
   makeData,
   Struct,
-  type Float,
+  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
@@ -80,9 +80,7 @@ export class MakeArrowTableOptions {
    */
   vectorColumns: Record<string, VectorColumnOptions> = {
     vector: new VectorColumnOptions()
-  };
-
-  embeddings?: EmbeddingFunction<any>;
+  }
 
   /**
    * If true then string columns will be encoded with dictionary encoding
@@ -93,10 +91,10 @@ 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)
   }
 }
 
@@ -195,68 +193,59 @@ 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 = validateSchemaEmbeddings(opt.schema, data, opt.embeddings);
+    opt.schema = sanitizeSchema(opt.schema)
   }
-
-  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}`)
     }
   }
 
@@ -271,116 +260,97 @@ 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);
-    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);
+    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)
   } 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) {
@@ -388,60 +358,45 @@ async function applyEmbeddings<T>(
       // 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
 }
 
 /*
@@ -462,24 +417,21 @@ async function applyEmbeddings<T>(
  * 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)
 }
 
 /**
@@ -489,17 +441,17 @@ function newVectorType<T extends Float>(
  *
  * `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, embeddings });
-  const writer = RecordBatchFileWriter.writeAll(table);
-  return Buffer.from(await writer.toUint8Array());
+  const table = await convertToTable(data, embeddings, { schema })
+  const writer = RecordBatchFileWriter.writeAll(table)
+  return Buffer.from(await writer.toUint8Array())
 }
 
 /**
@@ -509,17 +461,17 @@ 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())
 }
 
 /**
@@ -530,17 +482,17 @@ 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())
 }
 
 /**
@@ -551,85 +503,49 @@ 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));
-}
-
-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);
+export function createEmptyTable (schema: Schema): ArrowTable {
+  return new ArrowTable(sanitizeSchema(schema))
 }

node/src/index.ts

@@ -12,20 +12,19 @@
 // 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,
@@ -49,18 +48,14 @@ 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
@@ -83,25 +78,12 @@ 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}
-   *
-   * @deprecated Pass `region` through `storageOptions` instead.
-   */
+  /** AWS region to connect to. Default is {@link defaultAwsRegion}. */
   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
    *
@@ -133,19 +115,19 @@ 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> {
@@ -168,7 +150,7 @@ export interface CreateTableOptions<T> {
 /**
  * Connect to a LanceDB instance at the given URI.
  *
- * Accepted formats:
+ * Accpeted formats:
  *
  * - `/path/to/database` - local database
  * - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
@@ -178,66 +160,53 @@ 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 {
-    const keys = Object.keys(arg);
-    if (keys.length === 1 && keys[0] === "uri" && typeof arg.uri === "string") {
-      opts = { uri: arg.uri };
+    // 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 }
     } 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,
-    storageOptionsArr,
+    opts.awsCredentials?.accessKeyId,
+    opts.awsCredentials?.secretKey,
+    opts.awsCredentials?.sessionToken,
+    opts.awsRegion,
     opts.readConsistencyInterval
-  );
-  return new LocalConnection(db, opts);
+  )
+  return new LocalConnection(db, opts)
 }
 
 /**
@@ -538,11 +507,7 @@ 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.
@@ -567,9 +532,7 @@ 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.
@@ -695,49 +658,38 @@ export interface MergeInsertArgs {
   whenNotMatchedBySourceDelete?: string | boolean
 }
 
-export enum IndexStatus {
-  Pending = "pending",
-  Indexing = "indexing",
-  Done = "done",
-  Failed = "failed"
-}
-
 export interface VectorIndex {
   columns: string[]
   name: string
   uuid: string
-  status: IndexStatus
 }
 
 export interface IndexStats {
   numIndexedRows: number | null
   numUnindexedRows: number | null
-  indexType: string | null
-  distanceType: string | null
-  completedAt: string | null
 }
 
 /**
  * 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)
   }
 
   /**
@@ -745,7 +697,7 @@ 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.
@@ -756,20 +708,24 @@ 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);
+    const tbl = await databaseOpenTable.call(
+      this._db,
+      name,
+      ...getAwsArgs(this._options())
+    )
     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())
     }
   }
 
@@ -779,32 +735,32 @@ 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>({
@@ -820,27 +776,27 @@ 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(
@@ -849,11 +805,11 @@ 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())
     }
   }
 
@@ -861,69 +817,69 @@ 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.
@@ -931,19 +887,16 @@ 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, embeddings: this._embeddings });
+      tbl = makeArrowTable(data, { schema })
     }
-
     return tableAdd
       .call(
         this._tbl,
@@ -952,8 +905,8 @@ export class LocalTable<T = number[]> implements Table<T> {
         ...getAwsArgs(this._options())
       )
       .then((newTable: any) => {
-        this._tbl = newTable;
-      });
+        this._tbl = newTable
+      })
   }
 
   /**
@@ -962,14 +915,14 @@ 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(
@@ -979,8 +932,8 @@ export class LocalTable<T = number[]> implements Table<T> {
         ...getAwsArgs(this._options())
       )
       .then((newTable: any) => {
-        this._tbl = newTable;
-      });
+        this._tbl = newTable
+      })
   }
 
   /**
@@ -988,26 +941,26 @@ 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)
   }
 
   /**
@@ -1015,10 +968,10 @@ 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
+    })
   }
 
   /**
@@ -1028,65 +981,55 @@ 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,
@@ -1097,7 +1040,7 @@ export class LocalTable<T = number[]> implements Table<T> {
       whenNotMatchedBySourceDelete,
       whenNotMatchedBySourceDeleteFilt,
       buffer
-    );
+    )
   }
 
   /**
@@ -1115,16 +1058,16 @@ 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
+      })
   }
 
   /**
@@ -1138,64 +1081,62 @@ 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
   }
 }
 
@@ -1218,7 +1159,7 @@ export interface CompactionOptions {
    */
   targetRowsPerFragment?: number
   /**
-   * The maximum number of T per group. Defaults to 1024.
+   * The maximum number of rows per group. Defaults to 1024.
    */
   maxRowsPerGroup?: number
   /**
@@ -1318,21 +1259,21 @@ 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',
 }
 
 /**
@@ -1344,14 +1285,14 @@ 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')
+  )
 }
 
 /**
@@ -1361,15 +1302,15 @@ 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({ uri: `s3://lancedb-integtest?mirroredStore=${dir}`, storageOptions: { allowHttp: 'true' } })
+    const conn = await lancedb.connect(`s3://lancedb-integtest?mirroredStore=${dir}`)
     const data = Array(200).fill({ vector: Array(128).fill(1.0), id: 0 })
     data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 1 }))
     data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 2 }))

node/src/query.ts

@@ -38,7 +38,7 @@ export class Query<T = number[]> {
   constructor (query?: T, tbl?: any, embeddings?: EmbeddingFunction<T>) {
     this._tbl = tbl
     this._query = query
-    this._limit = 10
+    this._limit = undefined
     this._nprobes = 20
     this._refineFactor = undefined
     this._select = undefined
@@ -50,7 +50,6 @@ export class Query<T = number[]> {
 
   /***
      * Sets the number of results that will be returned
-     * default value is 10
      * @param value number of results
      */
   limit (value: number): Query<T> {

node/src/remote/client.ts

@@ -103,22 +103,6 @@ function toLanceRes (res: AxiosResponse): RemoteResponse {
   }
 }
 
-async function decodeErrorData(
-  res: RemoteResponse,
-  responseType?: ResponseType
-): Promise<string> {
-  const errorData = await res.body()
-  if (responseType === 'arraybuffer') {
-      return new TextDecoder().decode(errorData)
-  } else {
-    if (typeof errorData === 'object') {
-      return JSON.stringify(errorData)
-    }
-
-    return errorData
-  }
-}
-
 export class HttpLancedbClient {
   private readonly _url: string
   private readonly _apiKey: () => string
@@ -196,7 +180,7 @@ export class HttpLancedbClient {
     }
 
     if (response.status !== 200) {
-      const errorData = await decodeErrorData(response)
+      const errorData = new TextDecoder().decode(await response.body())
       throw new Error(
         `Server Error, status: ${response.status}, ` +
         `message: ${response.statusText}: ${errorData}`
@@ -242,7 +226,7 @@ export class HttpLancedbClient {
     }
 
     if (response.status !== 200) {
-      const errorData = await decodeErrorData(response, responseType)
+      const errorData = new TextDecoder().decode(await response.body())
       throw new Error(
         `Server Error, status: ${response.status}, ` +
         `message: ${response.statusText}: ${errorData}`

node/src/remote/index.ts

@@ -38,7 +38,7 @@ import {
   fromRecordsToStreamBuffer,
   fromTableToStreamBuffer
 } from '../arrow'
-import { toSQL, TTLCache } from '../util'
+import { toSQL } from '../util'
 import { type HttpMiddleware } from '../middleware'
 
 /**
@@ -47,7 +47,6 @@ 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://')) {
@@ -90,9 +89,6 @@ 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
   }
 
@@ -105,12 +101,6 @@ 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 {
@@ -140,9 +130,6 @@ export class RemoteConnection implements Connection {
       schema = nameOrOpts.schema
       embeddings = nameOrOpts.embeddingFunction
       tableName = nameOrOpts.name
-      if (data === undefined) {
-        data = nameOrOpts.data
-      }
     }
 
     let buffer: Buffer
@@ -169,7 +156,7 @@ export class RemoteConnection implements Connection {
     }
 
     const res = await this._client.post(
-      `/v1/table/${encodeURIComponent(tableName)}/create/`,
+      `/v1/table/${tableName}/create/`,
       buffer,
       undefined,
       'application/vnd.apache.arrow.stream'
@@ -182,7 +169,6 @@ export class RemoteConnection implements Connection {
       )
     }
 
-    this._tableCache.set(tableName, true)
     if (embeddings === undefined) {
       return new RemoteTable(this._client, tableName)
     } else {
@@ -191,8 +177,7 @@ export class RemoteConnection implements Connection {
   }
 
   async dropTable (name: string): Promise<void> {
-    await this._client.post(`/v1/table/${encodeURIComponent(name)}/drop/`)
-    this._tableCache.delete(name)
+    await this._client.post(`/v1/table/${name}/drop/`)
   }
 
   withMiddleware (middleware: HttpMiddleware): Connection {
@@ -283,7 +268,7 @@ export class RemoteTable<T = number[]> implements Table<T> {
 
   get schema (): Promise<any> {
     return this._client
-      .post(`/v1/table/${encodeURIComponent(this._name)}/describe/`)
+      .post(`/v1/table/${this._name}/describe/`)
       .then(async (res) => {
         if (res.status !== 200) {
           throw new Error(
@@ -297,7 +282,7 @@ export class RemoteTable<T = number[]> implements Table<T> {
   }
 
   search (query: T): Query<T> {
-    return new RemoteQuery(query, this._client, encodeURIComponent(this._name)) //, this._embeddings_new)
+    return new RemoteQuery(query, this._client, this._name) //, this._embeddings_new)
   }
 
   filter (where: string): Query<T> {
@@ -339,7 +324,7 @@ export class RemoteTable<T = number[]> implements Table<T> {
 
     const buffer = await fromTableToStreamBuffer(tbl, this._embeddings)
     const res = await this._client.post(
-      `/v1/table/${encodeURIComponent(this._name)}/merge_insert/`,
+      `/v1/table/${this._name}/merge_insert/`,
       buffer,
       queryParams,
       'application/vnd.apache.arrow.stream'
@@ -363,7 +348,7 @@ export class RemoteTable<T = number[]> implements Table<T> {
 
     const buffer = await fromTableToStreamBuffer(tbl, this._embeddings)
     const res = await this._client.post(
-      `/v1/table/${encodeURIComponent(this._name)}/insert/`,
+      `/v1/table/${this._name}/insert/`,
       buffer,
       {
         mode: 'append'
@@ -389,7 +374,7 @@ export class RemoteTable<T = number[]> implements Table<T> {
     }
     const buffer = await fromTableToStreamBuffer(tbl, this._embeddings)
     const res = await this._client.post(
-      `/v1/table/${encodeURIComponent(this._name)}/insert/`,
+      `/v1/table/${this._name}/insert/`,
       buffer,
       {
         mode: 'overwrite'
@@ -436,7 +421,7 @@ export class RemoteTable<T = number[]> implements Table<T> {
       index_cache_size: indexCacheSize
     }
     const res = await this._client.post(
-      `/v1/table/${encodeURIComponent(this._name)}/create_index/`,
+      `/v1/table/${this._name}/create_index/`,
       data
     )
     if (res.status !== 200) {
@@ -457,7 +442,7 @@ export class RemoteTable<T = number[]> implements Table<T> {
       replace: true
     }
     const res = await this._client.post(
-      `/v1/table/${encodeURIComponent(this._name)}/create_scalar_index/`,
+      `/v1/table/${this._name}/create_scalar_index/`,
       data
     )
     if (res.status !== 200) {
@@ -469,15 +454,13 @@ export class RemoteTable<T = number[]> implements Table<T> {
     }
   }
 
-  async countRows (filter?: string): Promise<number> {
-    const result = await this._client.post(`/v1/table/${encodeURIComponent(this._name)}/count_rows/`, {
-      predicate: filter
-    })
-    return (await result.body())
+  async countRows (): Promise<number> {
+    const result = await this._client.post(`/v1/table/${this._name}/describe/`)
+    return (await result.body())?.stats?.num_rows
   }
 
   async delete (filter: string): Promise<void> {
-    await this._client.post(`/v1/table/${encodeURIComponent(this._name)}/delete/`, {
+    await this._client.post(`/v1/table/${this._name}/delete/`, {
       predicate: filter
     })
   }
@@ -496,7 +479,7 @@ export class RemoteTable<T = number[]> implements Table<T> {
         updates[key] = toSQL(value)
       }
     }
-    await this._client.post(`/v1/table/${encodeURIComponent(this._name)}/update/`, {
+    await this._client.post(`/v1/table/${this._name}/update/`, {
       predicate: filter,
       updates: Object.entries(updates).map(([key, value]) => [key, value])
     })
@@ -504,27 +487,23 @@ export class RemoteTable<T = number[]> implements Table<T> {
 
   async listIndices (): Promise<VectorIndex[]> {
     const results = await this._client.post(
-      `/v1/table/${encodeURIComponent(this._name)}/index/list/`
+      `/v1/table/${this._name}/index/list/`
     )
     return (await results.body()).indexes?.map((index: any) => ({
       columns: index.columns,
       name: index.index_name,
-      uuid: index.index_uuid,
-      status: index.status
+      uuid: index.index_uuid
     }))
   }
 
   async indexStats (indexUuid: string): Promise<IndexStats> {
     const results = await this._client.post(
-      `/v1/table/${encodeURIComponent(this._name)}/index/${indexUuid}/stats/`
+      `/v1/table/${this._name}/index/${indexUuid}/stats/`
     )
     const body = await results.body()
     return {
       numIndexedRows: body?.num_indexed_rows,
-      numUnindexedRows: body?.num_unindexed_rows,
-      indexType: body?.index_type,
-      distanceType: body?.distance_type,
-      completedAt: body?.completed_at
+      numUnindexedRows: body?.num_unindexed_rows
     }
   }
 

node/src/sanitize.ts

@@ -32,7 +32,7 @@ import {
   Bool,
   Date_,
   Decimal,
-  type DataType,
+  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

@@ -42,36 +42,3 @@ 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

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