[python] Bump version: 0.3.6 → 0.4.0

pass vector column name to remote as well.

`vector_column` is already part of `Query` just declearing it as part to
`remote.VectorQuery` as well

.bumpversion.cfg

@@ -0,0 +1,12 @@
+[bumpversion]
+current_version = 0.3.11
+commit = True
+message = Bump version: {current_version} → {new_version}
+tag = True
+tag_name = v{new_version}
+
+[bumpversion:file:node/package.json]
+
+[bumpversion:file:rust/ffi/node/Cargo.toml]
+
+[bumpversion:file:rust/vectordb/Cargo.toml]

.bumpversion.toml

@@ -1,110 +0,0 @@
-[tool.bumpversion]
-current_version = "0.19.0-beta.5"
-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 = ""
-
-# Java maven files
-pre_commit_hooks = [
-  """
-    NEW_VERSION="${BVHOOK_NEW_MAJOR}.${BVHOOK_NEW_MINOR}.${BVHOOK_NEW_PATCH}"
-    if [ ! -z "$BVHOOK_NEW_PRE_L" ] && [ ! -z "$BVHOOK_NEW_PRE_N" ]; then
-        NEW_VERSION="${NEW_VERSION}-${BVHOOK_NEW_PRE_L}.${BVHOOK_NEW_PRE_N}"
-    fi
-    echo "Constructed new version: $NEW_VERSION"
-    cd java && mvn versions:set -DnewVersion=$NEW_VERSION && mvn versions:commit
-
-    # Check for any modified but unstaged pom.xml files
-    MODIFIED_POMS=$(git ls-files -m | grep pom.xml)
-    if [ ! -z "$MODIFIED_POMS" ]; then
-        echo "The following pom.xml files were modified but not staged. Adding them now:"
-        echo "$MODIFIED_POMS" | while read -r file; do
-            git add "$file"
-            echo "Added: $file"
-        done
-    fi
-    """,
-]
-
-[tool.bumpversion.parts.pre_l]
-optional_value = "final"
-values = ["beta", "final"]
-
-[[tool.bumpversion.files]]
-filename = "node/package.json"
-replace = "\"version\": \"{new_version}\","
-search = "\"version\": \"{current_version}\","
-
-[[tool.bumpversion.files]]
-filename = "nodejs/package.json"
-replace = "\"version\": \"{new_version}\","
-search = "\"version\": \"{current_version}\","
-
-# nodejs binary packages
-[[tool.bumpversion.files]]
-glob = "nodejs/npm/*/package.json"
-replace = "\"version\": \"{new_version}\","
-search = "\"version\": \"{current_version}\","
-
-# vectodb node binary packages
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-darwin-arm64\": \"{new_version}\""
-search = "\"@lancedb/vectordb-darwin-arm64\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-darwin-x64\": \"{new_version}\""
-search = "\"@lancedb/vectordb-darwin-x64\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-linux-arm64-gnu\": \"{new_version}\""
-search = "\"@lancedb/vectordb-linux-arm64-gnu\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-linux-x64-gnu\": \"{new_version}\""
-search = "\"@lancedb/vectordb-linux-x64-gnu\": \"{current_version}\""
-
-[[tool.bumpversion.files]]
-glob = "node/package.json"
-replace = "\"@lancedb/vectordb-win32-x64-msvc\": \"{new_version}\""
-search = "\"@lancedb/vectordb-win32-x64-msvc\": \"{current_version}\""
-
-# Cargo files
-# ------------
-[[tool.bumpversion.files]]
-filename = "rust/ffi/node/Cargo.toml"
-replace = "\nversion = \"{new_version}\""
-search = "\nversion = \"{current_version}\""
-
-[[tool.bumpversion.files]]
-filename = "rust/lancedb/Cargo.toml"
-replace = "\nversion = \"{new_version}\""
-search = "\nversion = \"{current_version}\""
-
-[[tool.bumpversion.files]]
-filename = "nodejs/Cargo.toml"
-replace = "\nversion = \"{new_version}\""
-search = "\nversion = \"{current_version}\""

.cargo/config.toml

@@ -1,51 +0,0 @@
-[profile.release]
-lto = "fat"
-codegen-units = 1
-
-[profile.release-with-debug]
-inherits = "release"
-debug = true
-# Prioritize compile time over runtime performance
-codegen-units = 16
-lto = "thin"
-
-[target.'cfg(all())']
-rustflags = [
-    "-Wclippy::all",
-    "-Wclippy::style",
-    "-Wclippy::fallible_impl_from",
-    "-Wclippy::manual_let_else",
-    "-Wclippy::redundant_pub_crate",
-    "-Wclippy::string_add_assign",
-    "-Wclippy::string_add",
-    "-Wclippy::string_lit_as_bytes",
-    "-Wclippy::string_to_string",
-    "-Wclippy::use_self",
-    "-Dclippy::cargo",
-    "-Dclippy::dbg_macro",
-    # not too much we can do to avoid multiple crate versions
-    "-Aclippy::multiple-crate-versions",
-    "-Aclippy::wildcard_dependencies",
-]
-
-[target.x86_64-unknown-linux-gnu]
-rustflags = ["-C", "target-cpu=haswell", "-C", "target-feature=+avx2,+fma,+f16c"]
-
-[target.x86_64-unknown-linux-musl]
-rustflags = ["-C", "target-cpu=haswell", "-C", "target-feature=-crt-static,+avx2,+fma,+f16c"]
-
-[target.aarch64-unknown-linux-musl]
-linker = "aarch64-linux-musl-gcc"
-rustflags = ["-C", "target-feature=-crt-static"]
-
-[target.aarch64-apple-darwin]
-rustflags = ["-C", "target-cpu=apple-m1", "-C", "target-feature=+neon,+fp16,+fhm,+dotprod"]
-
-# Not all Windows systems have the C runtime installed, so this avoids library
-# not found errors on systems that are missing it.
-[target.x86_64-pc-windows-msvc]
-rustflags = ["-Ctarget-feature=+crt-static"]
-
-# Experimental target for Arm64 Windows
-[target.aarch64-pc-windows-msvc]
-rustflags = ["-Ctarget-feature=+crt-static"]

.github/ISSUE_TEMPLATE/bug-node.yml

@@ -1,33 +0,0 @@
-name: Bug Report - Node / Typescript
-description: File a bug report
-title: "bug(node): "
-labels: [bug, typescript]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to fill out this bug report!
-  - type: input
-    id: version
-    attributes:
-      label: LanceDB version
-      description: What version of LanceDB are you using? `npm list | grep vectordb`.
-      placeholder: v0.3.2
-    validations:
-      required: false
-  - type: textarea
-    id: what-happened
-    attributes:
-      label: What happened?
-      description: Also tell us, what did you expect to happen?
-    validations:
-      required: true
-  - type: textarea
-    id: reproduction
-    attributes:
-      label: Are there known steps to reproduce?
-      description: |
-        Let us know how to reproduce the bug and we may be able to fix it more
-        quickly. This is not required, but it is helpful.
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/bug-python.yml

@@ -1,33 +0,0 @@
-name: Bug Report - Python
-description: File a bug report
-title: "bug(python): "
-labels: [bug, python]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to fill out this bug report!
-  - type: input
-    id: version
-    attributes:
-      label: LanceDB version
-      description: What version of LanceDB are you using? `python -c "import lancedb; print(lancedb.__version__)"`.
-      placeholder: v0.3.2
-    validations:
-      required: false
-  - type: textarea
-    id: what-happened
-    attributes:
-      label: What happened?
-      description: Also tell us, what did you expect to happen?
-    validations:
-      required: true
-  - type: textarea
-    id: reproduction
-    attributes:
-      label: Are there known steps to reproduce?
-      description: |
-        Let us know how to reproduce the bug and we may be able to fix it more
-        quickly. This is not required, but it is helpful.
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/config.yml

@@ -1,5 +0,0 @@
-blank_issues_enabled: true
-contact_links:
-  - name: Discord Community Support
-    url: https://discord.com/invite/zMM32dvNtd
-    about: Please ask and answer questions here.

.github/ISSUE_TEMPLATE/documentation.yml

@@ -1,23 +0,0 @@
-name: 'Documentation improvement'
-description: Report an issue with the documentation.
-labels: [documentation]
-
-body:
-  - type: textarea
-    id: description
-    attributes:
-      label: Description
-      description: >
-        Describe the issue with the documentation and how it can be fixed or improved.
-    validations:
-      required: true
-
-  - type: input
-    id: link
-    attributes:
-      label: Link
-      description: >
-        Provide a link to the existing documentation, if applicable.
-      placeholder: ex. https://lancedb.github.io/lancedb/guides/tables/...
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/feature.yml

@@ -1,31 +0,0 @@
-name: Feature suggestion
-description: Suggestion a new feature for LanceDB
-title: "Feature: "
-labels: [enhancement]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Share a new idea for a feature or improvement. Be sure to search existing
-        issues first to avoid duplicates.
-  - type: dropdown
-    id: sdk
-    attributes:
-      label: SDK
-      description: Which SDK are you using? This helps us prioritize.
-      options:
-        - Python
-        - Node
-        - Rust
-      default: 0
-    validations:
-      required: false
-  - type: textarea
-    id: description
-    attributes:
-      label: Description
-      description: |
-        Describe the feature and why it would be useful. If applicable, consider
-        providing a code example of what it might be like to use the feature.
-    validations:
-      required: true

.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

@@ -1,57 +0,0 @@
-# We create a composite action to be re-used both for testing and for releasing
-name: build-linux-wheel
-description: "Build a manylinux wheel for lance"
-inputs:
-  python-minor-version:
-    description: "8, 9, 10, 11, 12"
-    required: true
-  args:
-    description: "--release"
-    required: false
-    default: ""
-  arm-build:
-    description: "Build for arm64 instead of x86_64"
-    # 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:
-    - name: CONFIRM ARM BUILD
-      shell: bash
-      run: |
-        echo "ARM BUILD: ${{ inputs.arm-build }}"
-    - name: Build x86_64 Manylinux wheel
-      if: ${{ inputs.arm-build == 'false' }}
-      uses: PyO3/maturin-action@v1
-      with:
-        command: build
-        working-directory: python
-        target: x86_64-unknown-linux-gnu
-        manylinux: ${{ inputs.manylinux }}
-        args: ${{ inputs.args }}
-        before-script-linux: |
-          set -e
-          curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-$(uname -m).zip > /tmp/protoc.zip \
-            && unzip /tmp/protoc.zip -d /usr/local \
-            && rm /tmp/protoc.zip
-    - name: Build Arm Manylinux Wheel
-      if: ${{ inputs.arm-build == 'true' }}
-      uses: PyO3/maturin-action@v1
-      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 }}
-        args: ${{ inputs.args }}
-        before-script-linux: |
-          set -e
-          yum install -y clang \
-            && curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-aarch_64.zip > /tmp/protoc.zip \
-            && unzip /tmp/protoc.zip -d /usr/local \
-            && rm /tmp/protoc.zip

.github/workflows/build_mac_wheel/action.yml

@@ -1,26 +0,0 @@
-# We create a composite action to be re-used both for testing and for releasing
-name: build_wheel
-description: "Build a lance wheel"
-inputs:
-  python-minor-version:
-    description: "8, 9, 10, 11"
-    required: true
-  args:
-    description: "--release"
-    required: false
-    default: ""
-runs:
-  using: "composite"
-  steps:
-    - name: Install macos dependency
-      shell: bash
-      run: |
-        brew install protobuf
-    - name: Build wheel
-      uses: PyO3/maturin-action@v1
-      with:
-        command: build
-        # TODO: pass through interpreter
-        args: ${{ inputs.args }}
-        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
-        working-directory: python

.github/workflows/build_windows_wheel/action.yml

@@ -1,34 +0,0 @@
-# We create a composite action to be re-used both for testing and for releasing
-name: build_wheel
-description: "Build a lance wheel"
-inputs:
-  python-minor-version:
-    description: "8, 9, 10, 11"
-    required: true
-  args:
-    description: "--release"
-    required: false
-    default: ""
-runs:
-  using: "composite"
-  steps:
-    - name: Install Protoc v21.12
-      working-directory: C:\
-      run: |
-        New-Item -Path 'C:\protoc' -ItemType Directory
-        Set-Location C:\protoc
-        Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
-        7z x protoc.zip
-        Add-Content $env:GITHUB_PATH "C:\protoc\bin"
-      shell: powershell
-    - name: Build wheel
-      uses: PyO3/maturin-action@v1
-      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@v4
-      with:
-        name: windows-wheels
-        path: python\target\wheels

.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:
@@ -23,7 +16,7 @@ jobs:
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
       - uses: Swatinem/rust-cache@v2
         with:
           workspaces: rust
@@ -33,4 +26,4 @@ jobs:
           sudo apt install -y protobuf-compiler libssl-dev
       - name: Publish the package
         run: |
-          cargo publish -p lancedb --all-features --token ${{ secrets.CARGO_REGISTRY_TOKEN }}
+          cargo publish -p vectordb --all-features --token ${{ secrets.CARGO_REGISTRY_TOKEN }}

.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/docs.yml

@@ -24,16 +24,12 @@ jobs:
     environment:
       name: github-pages
       url: ${{ steps.deployment.outputs.page_url }}
-    runs-on: buildjet-8vcpu-ubuntu-2204
+    runs-on: ubuntu-22.04
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install dependecies needed for ubuntu
-        run: |
-          sudo apt install -y protobuf-compiler libssl-dev
-          rustup update && rustup default
+        uses: actions/checkout@v3
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: "3.10"
           cache: "pip"
@@ -41,12 +37,12 @@ jobs:
       - name: Build Python
         working-directory: python
         run: |
-          python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .
-          python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -r ../docs/requirements.txt
+          python -m pip install -e .
+          python -m pip install -r ../docs/requirements.txt
       - name: Set up node
         uses: actions/setup-node@v3
         with:
-          node-version: 20
+          node-version: ${{ matrix.node-version }}
           cache: 'npm'
           cache-dependency-path: node/package-lock.json
       - uses: Swatinem/rust-cache@v2
@@ -66,15 +62,14 @@ jobs:
         run: |
           npx typedoc --plugin typedoc-plugin-markdown --out ../docs/src/javascript src/index.ts
       - name: Build docs
-        working-directory: docs
         run: |
-          PYTHONPATH=. mkdocs build
+          PYTHONPATH=. mkdocs build -f docs/mkdocs.yml
       - name: Setup Pages
         uses: actions/configure-pages@v2
       - name: Upload artifact
-        uses: actions/upload-pages-artifact@v3
+        uses: actions/upload-pages-artifact@v1
         with:
           path: "docs/site"
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v4
+        uses: actions/deploy-pages@v1

.github/workflows/docs_test.yml

@@ -18,38 +18,30 @@ on:
 env:
   # 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 -C target-cpu=haswell -C target-feature=+f16c,+avx2,+fma"
+  RUSTFLAGS: "-C debuginfo=1"
   RUST_BACKTRACE: "1"
 
 jobs:
   test-python:
     name: Test doc python code
-    runs-on: ubuntu-24.04
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        python-minor-version: [ "11" ]
+        os: ["ubuntu-22.04"]
     steps:
     - name: Checkout
-      uses: actions/checkout@v4
-    - name: Print CPU capabilities
-      run: cat /proc/cpuinfo
-    - name: Install protobuf
-      run: |
-        sudo apt update
-        sudo apt install -y protobuf-compiler
-    - name: Install dependecies needed for ubuntu
-      run: |
-        sudo apt install -y libssl-dev
-        rustup update && rustup default
+      uses: actions/checkout@v3
     - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v4
       with:
-        python-version: 3.11
+        python-version: 3.${{ matrix.python-minor-version }}
         cache: "pip"
         cache-dependency-path: "docs/test/requirements.txt"
-    - name: Rust cache
-      uses: swatinem/rust-cache@v2
     - name: Build Python
       working-directory: docs/test
       run:
-        python -m pip install --extra-index-url https://pypi.fury.io/lancedb/ -r requirements.txt
+        python -m pip install -r requirements.txt
     - name: Create test files
       run: |
         cd docs/test
@@ -60,49 +52,42 @@ jobs:
         for d in *; do cd "$d"; echo "$d".py; python "$d".py; cd ..; done
   test-node:
     name: Test doc nodejs code
-    runs-on: ubuntu-24.04
-    timeout-minutes: 60
+    runs-on: ${{ matrix.os }}
     strategy:
-      fail-fast: false
+      matrix:
+        node-version: [ "18" ]
+        os: ["ubuntu-22.04"]
     steps:
     - name: Checkout
-      uses: actions/checkout@v4
+      uses: actions/checkout@v3
       with:
         fetch-depth: 0
         lfs: true
-    - name: Print CPU capabilities
-      run: cat /proc/cpuinfo
     - name: Set up Node
-      uses: actions/setup-node@v4
+      uses: actions/setup-node@v3
       with:
-        node-version: 20
-    - name: Install protobuf
-      run: |
-        sudo apt update
-        sudo apt install -y protobuf-compiler
+        node-version: ${{ matrix.node-version }}
     - name: Install dependecies needed for ubuntu
+      if: ${{ matrix.os == 'ubuntu-22.04' }}
       run: |
-        sudo apt install -y libssl-dev
-        rustup update && rustup default
-    - name: Rust cache
-      uses: swatinem/rust-cache@v2
+        sudo apt install -y protobuf-compiler libssl-dev
     - name: Install node dependencies
       run: |
-        sudo swapoff -a
-        sudo fallocate -l 8G /swapfile
-        sudo chmod 600 /swapfile
-        sudo mkswap /swapfile
-        sudo swapon /swapfile
-        sudo swapon --show
-        cd node
+        cd docs/test
+        npm install 
+    - name: Rust cache
+      uses: swatinem/rust-cache@v2
+    - name: Install LanceDB
+      run: |
+        cd docs/test/node_modules/vectordb 
         npm ci
         npm run build-release
-        cd ../docs
-        npm install
-    - name: Test
-      env:
-        LANCEDB_URI: ${{ secrets.LANCEDB_URI }}
-        LANCEDB_DEV_API_KEY: ${{ secrets.LANCEDB_DEV_API_KEY }}
+        npm run tsc
+    - name: Create test files
       run: |
-        cd docs
-        npm t
+        cd docs/test
+        node md_testing.js
+    - name: Test
+      run: |
+        cd docs/test/node
+        for d in *; do cd "$d"; echo "$d".js; node "$d".js; cd ..; done

.github/workflows/java-publish.yml

@@ -1,114 +0,0 @@
-name: Build and publish Java packages
-on:
-  release:
-    types: [released]
-  pull_request:
-    paths:
-      - .github/workflows/java-publish.yml
-
-jobs:
-  macos-arm64:
-    name: Build on MacOS Arm64
-    runs-on: macos-14
-    timeout-minutes: 45
-    defaults:
-      run:
-        working-directory: ./java/core/lancedb-jni
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      - uses: Swatinem/rust-cache@v2
-      - name: Install dependencies
-        run: |
-          brew install protobuf
-      - name: Build release
-        run: |
-          cargo build --release
-      - uses: actions/upload-artifact@v4
-        with:
-          name: liblancedb_jni_darwin_aarch64.zip
-          path: target/release/liblancedb_jni.dylib
-          retention-days: 1
-          if-no-files-found: error
-  linux-arm64:
-    name: Build on Linux Arm64
-    runs-on: warp-ubuntu-2204-arm64-8x
-    timeout-minutes: 45
-    defaults:
-      run:
-        working-directory: ./java/core/lancedb-jni
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      - uses: Swatinem/rust-cache@v2
-      - uses: actions-rust-lang/setup-rust-toolchain@v1
-        with:
-          toolchain: "1.81.0"
-          cache-workspaces: "./java/core/lancedb-jni"
-          # Disable full debug symbol generation to speed up CI build and keep memory down
-          # "1" means line tables only, which is useful for panic tracebacks.
-          rustflags: "-C debuginfo=1"
-      - name: Install dependencies
-        run: |
-          sudo apt -y -qq update
-          sudo apt install -y protobuf-compiler libssl-dev pkg-config
-      - name: Build release
-        run: |
-          cargo build --release
-      - uses: actions/upload-artifact@v4
-        with:
-          name: liblancedb_jni_linux_aarch64.zip
-          path: target/release/liblancedb_jni.so
-          retention-days: 1
-          if-no-files-found: error
-  linux-x86:
-    runs-on: warp-ubuntu-2204-x64-8x
-    timeout-minutes: 30
-    needs: [macos-arm64, linux-arm64]
-    defaults:
-      run:
-        working-directory: ./java
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      - uses: Swatinem/rust-cache@v2
-      - name: Set up Java 8
-        uses: actions/setup-java@v4
-        with:
-          distribution: temurin
-          java-version: 8
-          cache: "maven"
-          server-id: ossrh
-          server-username: SONATYPE_USER
-          server-password: SONATYPE_TOKEN
-          gpg-private-key: ${{ secrets.GPG_PRIVATE_KEY }}
-          gpg-passphrase: ${{ secrets.GPG_PASSPHRASE }}
-      - name: Install dependencies
-        run: |
-          sudo apt -y -qq update
-          sudo apt install -y protobuf-compiler libssl-dev pkg-config
-      - name: Download artifact
-        uses: actions/download-artifact@v4
-      - name: Copy native libs
-        run: |
-          mkdir -p ./core/target/classes/nativelib/darwin-aarch64 ./core/target/classes/nativelib/linux-aarch64
-          cp ../liblancedb_jni_darwin_aarch64.zip/liblancedb_jni.dylib ./core/target/classes/nativelib/darwin-aarch64/liblancedb_jni.dylib
-          cp ../liblancedb_jni_linux_aarch64.zip/liblancedb_jni.so ./core/target/classes/nativelib/linux-aarch64/liblancedb_jni.so
-      - name: Dry run
-        if: github.event_name == 'pull_request'
-        run: |
-          mvn --batch-mode -DskipTests -Drust.release.build=true package
-      - name: Set github
-        run: |
-          git config --global user.email "LanceDB Github Runner"
-          git config --global user.name "dev+gha@lancedb.com"
-      - name: Publish with Java 8
-        if: github.event_name == 'release'
-        run: |
-          echo "use-agent" >> ~/.gnupg/gpg.conf
-          echo "pinentry-mode loopback" >> ~/.gnupg/gpg.conf
-          export GPG_TTY=$(tty)
-          mvn --batch-mode -DskipTests -Drust.release.build=true -DpushChanges=false -Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }} deploy -P deploy-to-ossrh
-        env:
-          SONATYPE_USER: ${{ secrets.SONATYPE_USER }}
-          SONATYPE_TOKEN: ${{ secrets.SONATYPE_TOKEN }}

.github/workflows/java.yml

@@ -1,113 +0,0 @@
-name: Build and Run Java JNI Tests
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - java/**
-  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-java-11:
-    runs-on: ubuntu-22.04
-    name: ubuntu-22.04 + Java 11
-    defaults:
-      run:
-        working-directory: ./java
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      - uses: Swatinem/rust-cache@v2
-        with:
-          workspaces: java/core/lancedb-jni
-      - name: Run cargo fmt
-        run: cargo fmt --check
-        working-directory: ./java/core/lancedb-jni
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler libssl-dev
-      - name: Install Java 11
-        uses: actions/setup-java@v4
-        with:
-          distribution: temurin
-          java-version: 11
-          cache: "maven"
-      - name: Java Style Check
-        run: mvn checkstyle:check
-      # Disable because of issues in lancedb rust core code
-      # - name: Rust Clippy
-      #   working-directory: java/core/lancedb-jni
-      #   run: cargo clippy --all-targets -- -D warnings
-      - name: Running tests with Java 11
-        run: mvn clean test
-  linux-build-java-17:
-    runs-on: ubuntu-22.04
-    name: ubuntu-22.04 + Java 17
-    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" >> $GITHUB_ENV
-      - name: Java Style Check
-        run: mvn checkstyle:check
-      # Disable because of issues in lancedb rust core code
-      # - name: Rust Clippy
-      #   working-directory: java/core/lancedb-jni
-      #   run: cargo clippy --all-targets -- -D warnings
-      - name: Running tests with Java 17
-        run: |
-          export JAVA_TOOL_OPTIONS="$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/license-header-check.yml

@@ -1,31 +0,0 @@
-name: Check license headers
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    paths:
-      - rust/**
-      - python/**
-      - nodejs/**
-      - java/**
-      - .github/workflows/license-header-check.yml
-jobs:
-  check-licenses:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-      - name: Install license-header-checker
-        working-directory: /tmp
-        run: |
-            curl -s https://raw.githubusercontent.com/lluissm/license-header-checker/master/install.sh | bash
-            mv /tmp/bin/license-header-checker /usr/local/bin/
-      - name: Check license headers (rust)
-        run: license-header-checker -a -v ./rust/license_header.txt ./ rs && [[ -z `git status -s` ]]
-      - name: Check license headers (python)
-        run: license-header-checker -a -v ./python/license_header.txt python py && [[ -z `git status -s` ]]
-      - name: Check license headers (typescript)
-        run: license-header-checker -a -v ./nodejs/license_header.txt nodejs ts && [[ -z `git status -s` ]]
-      - name: Check license headers (java)
-        run: license-header-checker -a -v ./nodejs/license_header.txt java java && [[ -z `git status -s` ]]

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

@@ -1,102 +1,59 @@
 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/Java 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.
-    runs-on: ubuntu-24.04
-    permissions:
-      contents: write
+  bump-version:
+    runs-on: ubuntu-latest
     steps:
-      - name: Output Inputs
-        run: echo "${{ toJSON(github.event.inputs) }}"
-      - uses: actions/checkout@v4
+      - name: Check out main
+        uses: actions/checkout@v3
         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: Validate Lance dependency is at stable version
-        if: ${{ inputs.type == 'stable' }}
-        run: python ci/validate_stable_lance.py
       - name: Set git configs for bumpversion
         shell: bash
         run: |
           git config user.name 'Lance Release'
           git config user.email 'lance-dev@lancedb.com'
-      - name: Bump Python version
-        if: ${{ inputs.python }}
-        working-directory: python
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Bump version, create tag and commit
         run: |
-          # Need to get the commit before bumping the version, so we can
-          # determine if there are breaking changes in the next step as well.
-          echo "COMMIT_BEFORE_BUMP=$(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 }}
-      - uses: ./.github/workflows/update_package_lock_nodejs
-        if: ${{ !inputs.dry_run && inputs.other }}
-        with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+

.github/workflows/node.yml

@@ -18,26 +18,44 @@ concurrency:
 env:
   # 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.
-  #
-  # Use native CPU to accelerate tests if possible, especially for f16
-  # target-cpu=haswell fixes failing ci build
-  RUSTFLAGS: "-C debuginfo=1 -C target-cpu=haswell -C target-feature=+f16c,+avx2,+fma"
+  RUSTFLAGS: "-C debuginfo=1"
   RUST_BACKTRACE: "1"
 
 jobs:
+  lint:
+    name: Lint
+    runs-on: ubuntu-22.04
+    defaults:
+      run:
+        shell: bash
+        working-directory: node
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        lfs: true
+    - uses: actions/setup-node@v3
+      with:
+        node-version: 18
+        cache: 'npm'
+        cache-dependency-path: node/package-lock.json
+    - name: Lint
+      run: |
+        npm ci
+        npm run lint
   linux:
     name: Linux (Node ${{ matrix.node-version }})
     timeout-minutes: 30
     strategy:
       matrix:
-        node-version: [ "18", "20" ]
+        node-version: [ "16", "18" ]
     runs-on: "ubuntu-22.04"
     defaults:
       run:
         shell: bash
         working-directory: node
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v3
       with:
         fetch-depth: 0
         lfs: true
@@ -54,6 +72,7 @@ jobs:
     - name: Build
       run: |
         npm ci
+        npm run tsc
         npm run build
         npm run pack-build
         npm install --no-save ./dist/lancedb-vectordb-*.tgz
@@ -69,13 +88,13 @@ jobs:
         shell: bash
         working-directory: node
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v3
       with:
         fetch-depth: 0
         lfs: true
     - uses: actions/setup-node@v3
       with:
-        node-version: 20
+        node-version: 18
         cache: 'npm'
         cache-dependency-path: node/package-lock.json
     - uses: Swatinem/rust-cache@v2
@@ -84,6 +103,7 @@ jobs:
     - name: Build
       run: |
         npm ci
+        npm run tsc
         npm run build
         npm run pack-build
         npm install --no-save ./dist/lancedb-vectordb-*.tgz
@@ -107,15 +127,14 @@ jobs:
       AWS_ENDPOINT: http://localhost:4566
       # this one is for dynamodb
       DYNAMODB_ENDPOINT: http://localhost:4566
-      ALLOW_HTTP: true
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v3
       with:
         fetch-depth: 0
         lfs: true
     - uses: actions/setup-node@v3
       with:
-        node-version: 20
+        node-version: 18
         cache: 'npm'
         cache-dependency-path: node/package-lock.json
     - name: start local stack
@@ -138,6 +157,7 @@ jobs:
     - name: Build
       run: |
         npm ci
+        npm run tsc
         npm run build
         npm run pack-build
         npm install --no-save ./dist/lancedb-vectordb-*.tgz

.github/workflows/nodejs.yml

@@ -1,149 +0,0 @@
-name: NodeJS (NAPI)
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    paths:
-      - nodejs/**
-      - .github/workflows/nodejs.yml
-      - docker-compose.yml
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-env:
-  # 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"
-
-jobs:
-  lint:
-    name: Lint
-    runs-on: ubuntu-22.04
-    defaults:
-      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:
-        fetch-depth: 0
-        lfs: true
-    - uses: actions/setup-node@v3
-      with:
-        node-version: 20
-        cache: 'npm'
-        cache-dependency-path: nodejs/package-lock.json
-    - uses: Swatinem/rust-cache@v2
-    - name: Install dependencies
-      run: |
-        sudo apt update
-        sudo apt install -y protobuf-compiler libssl-dev
-    - name: Lint
-      run: |
-        cargo fmt --all -- --check
-        cargo clippy --all --all-features -- -D warnings
-        npm ci
-        npm run lint-ci
-    - name: Lint examples
-      working-directory: nodejs/examples
-      run: npm ci && npm run lint-ci
-  linux:
-    name: Linux (NodeJS ${{ matrix.node-version }})
-    timeout-minutes: 30
-    strategy:
-      matrix:
-        node-version: [ "18", "20" ]
-    runs-on: "ubuntu-22.04"
-    defaults:
-      run:
-        shell: bash
-        working-directory: nodejs
-    steps:
-    - uses: actions/checkout@v4
-      with:
-        fetch-depth: 0
-        lfs: true
-    - uses: actions/setup-node@v3
-      with:
-        node-version: ${{ matrix.node-version }}
-        cache: 'npm'
-        cache-dependency-path: node/package-lock.json
-    - uses: Swatinem/rust-cache@v2
-    - name: Install dependencies
-      run: |
-        sudo apt update
-        sudo apt install -y protobuf-compiler libssl-dev
-        npm install -g @napi-rs/cli
-    - name: Build
-      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
-    - name: Setup examples
-      working-directory: nodejs/examples
-      run: npm ci
-    - name: Test examples
-      working-directory: ./
-      env:
-        OPENAI_API_KEY: test
-        OPENAI_BASE_URL: http://0.0.0.0:8000
-      run: |
-        python ci/mock_openai.py &
-        cd nodejs/examples
-        npm test
-    - name: Check docs
-      run: |
-        # We run this as part of the job because the binary needs to be built
-        # first to export the types of the native code.
-        set -e
-        npm ci
-        npm run docs
-        if ! git diff --exit-code; then
-          echo "Docs need to be updated"
-          echo "Run 'npm run docs', fix any warnings, and commit the changes."
-          exit 1
-        fi
-  macos:
-    timeout-minutes: 30
-    runs-on: "macos-14"
-    defaults:
-      run:
-        shell: bash
-        working-directory: nodejs
-    steps:
-    - uses: actions/checkout@v4
-      with:
-        fetch-depth: 0
-        lfs: true
-    - uses: actions/setup-node@v3
-      with:
-        node-version: 20
-        cache: 'npm'
-        cache-dependency-path: node/package-lock.json
-    - uses: Swatinem/rust-cache@v2
-    - name: Install dependencies
-      run: |
-        brew install protobuf
-        npm install -g @napi-rs/cli
-    - name: Build
-      run: |
-        npm ci
-        npm run build
-    - name: Test
-      run: |
-        npm run test

.github/workflows/npm-publish.yml

@@ -1,390 +1,25 @@
 name: NPM Publish
 
-env:
-  MACOSX_DEPLOYMENT_TARGET: '10.13'
-  CARGO_INCREMENTAL: '0'
-
-permissions:
-  contents: write
-  id-token: write
-
 on:
-  push:
-    branches:
-      - main
-    tags:
-      - "v*"
-  pull_request:
-    # This should trigger a dry run (we skip the final publish step)
-    paths:
-      - .github/workflows/npm-publish.yml
-      - Cargo.toml # Change in dependency frequently breaks builds
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
+  release:
+    types: [ published ]
 
 jobs:
-  gh-release:
-    if: startsWith(github.ref, 'refs/tags/v')
-    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: ${{ steps.release_notes.outputs.changelog }}
-
-  build-lancedb:
-    strategy:
-      fail-fast: false
-      matrix:
-        settings:
-          - target: x86_64-apple-darwin
-            host: macos-latest
-            features: ","
-            pre_build: |-
-                brew install protobuf
-                rustup target add x86_64-apple-darwin
-          - target: aarch64-apple-darwin
-            host: macos-latest
-            features: fp16kernels
-            pre_build: brew install protobuf
-          - target: x86_64-pc-windows-msvc
-            host: windows-latest
-            features: ","
-            pre_build: |-
-              choco install --no-progress protoc ninja nasm
-              tail -n 1000 /c/ProgramData/chocolatey/logs/chocolatey.log
-              # There is an issue where choco doesn't add nasm to the path
-              export PATH="$PATH:/c/Program Files/NASM"
-              nasm -v
-          - target: aarch64-pc-windows-msvc
-            host: windows-latest
-            features: ","
-            pre_build: |-
-                choco install --no-progress protoc
-                rustup target add aarch64-pc-windows-msvc
-          - target: x86_64-unknown-linux-gnu
-            host: ubuntu-latest
-            features: fp16kernels
-            # https://github.com/napi-rs/napi-rs/blob/main/debian.Dockerfile
-            docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-debian
-            pre_build: |-
-              set -e &&
-              apt-get update &&
-              apt-get install -y protobuf-compiler pkg-config
-          - target: x86_64-unknown-linux-musl
-            # This one seems to need some extra memory
-            host: ubuntu-2404-8x-x64
-            # https://github.com/napi-rs/napi-rs/blob/main/alpine.Dockerfile
-            docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-alpine
-            features: fp16kernels
-            pre_build: |-
-              set -e &&
-              apk add protobuf-dev curl &&
-              ln -s /usr/lib/gcc/x86_64-alpine-linux-musl/14.2.0/crtbeginS.o /usr/lib/crtbeginS.o &&
-              ln -s /usr/lib/libgcc_s.so /usr/lib/libgcc.so &&
-              CC=gcc &&
-              CXX=g++
-          - target: aarch64-unknown-linux-gnu
-            host: ubuntu-2404-8x-x64
-            # https://github.com/napi-rs/napi-rs/blob/main/debian-aarch64.Dockerfile
-            docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-debian-aarch64
-            features: "fp16kernels"
-            pre_build: |-
-              set -e &&
-              apt-get update &&
-              apt-get install -y protobuf-compiler pkg-config &&
-              # https://github.com/aws/aws-lc-rs/issues/737#issuecomment-2725918627
-              ln -s /usr/aarch64-unknown-linux-gnu/lib/gcc/aarch64-unknown-linux-gnu/4.8.5/crtbeginS.o /usr/aarch64-unknown-linux-gnu/aarch64-unknown-linux-gnu/sysroot/usr/lib/crtbeginS.o &&
-              ln -s /usr/aarch64-unknown-linux-gnu/lib/gcc /usr/aarch64-unknown-linux-gnu/aarch64-unknown-linux-gnu/sysroot/usr/lib/gcc &&
-              rustup target add aarch64-unknown-linux-gnu
-          - target: aarch64-unknown-linux-musl
-            host: ubuntu-2404-8x-x64
-            # https://github.com/napi-rs/napi-rs/blob/main/alpine.Dockerfile
-            docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-alpine
-            features: ","
-            pre_build: |-
-              set -e &&
-              apk add protobuf-dev &&
-              rustup target add aarch64-unknown-linux-musl &&
-              export CC_aarch64_unknown_linux_musl=aarch64-linux-musl-gcc &&
-              export CXX_aarch64_unknown_linux_musl=aarch64-linux-musl-g++
-    name: build - ${{ matrix.settings.target }}
-    runs-on: ${{ matrix.settings.host }}
-    defaults:
-      run:
-        working-directory: nodejs
-    steps:
-      - uses: actions/checkout@v4
-      - name: Setup node
-        uses: actions/setup-node@v4
-        if: ${{ !matrix.settings.docker }}
-        with:
-          node-version: 20
-          cache: npm
-          cache-dependency-path: nodejs/package-lock.json
-      - name: Install
-        uses: dtolnay/rust-toolchain@stable
-        if: ${{ !matrix.settings.docker }}
-        with:
-          toolchain: stable
-          targets: ${{ matrix.settings.target }}
-      - name: Cache cargo
-        uses: actions/cache@v4
-        with:
-          path: |
-            ~/.cargo/registry/index/
-            ~/.cargo/registry/cache/
-            ~/.cargo/git/db/
-            .cargo-cache
-            target/
-          key: nodejs-${{ matrix.settings.target }}-cargo-${{ matrix.settings.host }}
-      - name: Setup toolchain
-        run: ${{ matrix.settings.setup }}
-        if: ${{ matrix.settings.setup }}
-        shell: bash
-      - name: Install dependencies
-        run: npm ci
-      - name: Build in docker
-        uses: addnab/docker-run-action@v3
-        if: ${{ matrix.settings.docker }}
-        with:
-          image: ${{ matrix.settings.docker }}
-          options: "--user 0:0 -v ${{ github.workspace }}/.cargo-cache/git/db:/usr/local/cargo/git/db \
-                    -v ${{ github.workspace }}/.cargo/registry/cache:/usr/local/cargo/registry/cache \
-                    -v ${{ github.workspace }}/.cargo/registry/index:/usr/local/cargo/registry/index \
-                    -v ${{ github.workspace }}:/build -w /build/nodejs"
-          run: |
-            set -e
-            ${{ matrix.settings.pre_build }}
-            npx napi build --platform  --release --no-const-enum \
-              --features ${{ matrix.settings.features }} \
-              --target ${{ matrix.settings.target }} \
-              --dts ../lancedb/native.d.ts \
-              --js ../lancedb/native.js \
-              --strip \
-              dist/
-      - name: Build
-        run: |
-          ${{ matrix.settings.pre_build }}
-          npx napi build --platform  --release --no-const-enum \
-              --features ${{ matrix.settings.features }} \
-              --target ${{ matrix.settings.target }} \
-              --dts ../lancedb/native.d.ts \
-              --js ../lancedb/native.js \
-              --strip \
-              $EXTRA_ARGS \
-              dist/
-        if: ${{ !matrix.settings.docker }}
-        shell: bash
-      - name: Upload artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: lancedb-${{ matrix.settings.target }}
-          path: nodejs/dist/*.node
-          if-no-files-found: error
-      # The generic files are the same in all distros so we just pick
-      # one to do the upload.
-      - name: Make generic artifacts
-        if: ${{ matrix.settings.target == 'aarch64-apple-darwin' }}
-        run: npm run tsc
-      - name: Upload Generic Artifacts
-        if: ${{ matrix.settings.target == 'aarch64-apple-darwin' }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: nodejs-dist
-          path: |
-            nodejs/dist/*
-            !nodejs/dist/*.node
-  test-lancedb:
-    name: "Test: ${{ matrix.settings.target }} - node@${{ matrix.node }}"
-    needs:
-      - build-lancedb
-    strategy:
-      fail-fast: false
-      matrix:
-        settings:
-          # TODO: Get tests passing on Windows (failing from test tmpdir issue)
-          # - host: windows-latest
-          #   target: x86_64-pc-windows-msvc
-          - host: macos-latest
-            target: aarch64-apple-darwin
-          - target: x86_64-unknown-linux-gnu
-            host: ubuntu-latest
-          - target: aarch64-unknown-linux-gnu
-            host: buildjet-16vcpu-ubuntu-2204-arm
-        node:
-          - '20'
-    runs-on: ${{ matrix.settings.host }}
-    defaults:
-      run:
-        shell: bash
-        working-directory: nodejs
-    steps:
-      - uses: actions/checkout@v4
-      - name: Setup node
-        uses: actions/setup-node@v4
-        with:
-          node-version: ${{ matrix.node }}
-          cache: npm
-          cache-dependency-path: nodejs/package-lock.json
-      - name: Install dependencies
-        run: npm ci
-      - name: Download artifacts
-        uses: actions/download-artifact@v4
-        with:
-          name: lancedb-${{ matrix.settings.target }}
-          path: nodejs/dist/
-          # For testing purposes:
-          # run-id: 13982782871
-          # github-token: ${{ secrets.GITHUB_TOKEN }} # token with actions:read permissions on target repo
-      - uses: actions/download-artifact@v4
-        with:
-          name: nodejs-dist
-          path: nodejs/dist
-          # For testing purposes:
-          # github-token: ${{ secrets.GITHUB_TOKEN }} # token with actions:read permissions on target repo
-          # run-id: 13982782871
-      - name: List packages
-        run: ls -R dist
-      - name: Move built files
-        run: cp dist/native.d.ts dist/native.js dist/*.node lancedb/
-      - name: Test bindings
-        run: npm test
-  publish:
-    name: Publish
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        shell: bash
-        working-directory: nodejs
-    needs:
-      - test-lancedb
-    steps:
-      - uses: actions/checkout@v4
-      - name: Setup node
-        uses: actions/setup-node@v4
-        with:
-          node-version: 20
-          cache: npm
-          cache-dependency-path: nodejs/package-lock.json
-          registry-url: "https://registry.npmjs.org"
-      - name: Install dependencies
-        run: npm ci
-      - uses: actions/download-artifact@v4
-        with:
-          name: nodejs-dist
-          path: nodejs/dist
-          # For testing purposes:
-          # run-id: 13982782871
-          # github-token: ${{ secrets.GITHUB_TOKEN }} # token with actions:read permissions on target repo
-      - uses: actions/download-artifact@v4
-        name: Download arch-specific binaries
-        with:
-          pattern: lancedb-*
-          path: nodejs/nodejs-artifacts
-          merge-multiple: true
-          # For testing purposes:
-          # run-id: 13982782871
-          # github-token: ${{ secrets.GITHUB_TOKEN }} # token with actions:read permissions on target repo
-      - name: Display structure of downloaded files
-        run: find dist && find nodejs-artifacts
-      - name: Move artifacts
-        run: npx napi artifacts -d nodejs-artifacts
-      - name: List packages
-        run: find npm
-      - name: Publish
-        env:
-          NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
-          DRY_RUN: ${{ !startsWith(github.ref, 'refs/tags/v') }}
-        run: |
-          ARGS="--access public"
-          if [[ $DRY_RUN == "true" ]]; then
-            ARGS="$ARGS --dry-run"
-          fi
-          if [[ $GITHUB_REF =~ refs/tags/v(.*)-beta.* ]]; then
-            ARGS="$ARGS --tag preview"
-          fi
-          npm publish $ARGS
-
-
-  # ----------------------------------------------------------------------------
-  # vectordb release (legacy)
-  # ----------------------------------------------------------------------------
-  # TODO: delete this when we drop vectordb
   node:
-    name: vectordb Typescript
     runs-on: ubuntu-latest
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
     defaults:
       run:
         shell: bash
         working-directory: node
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
       - uses: actions/setup-node@v3
         with:
           node-version: 20
-          cache: "npm"
+          cache: 'npm'
           cache-dependency-path: node/package-lock.json
       - name: Install dependencies
         run: |
@@ -396,14 +31,13 @@ jobs:
           npm run tsc
           npm pack
       - name: Upload Linux Artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v3
         with:
           name: node-package
           path: |
             node/vectordb-*.tgz
 
   node-macos:
-    name: vectordb ${{ matrix.config.arch }}
     strategy:
       matrix:
         config:
@@ -411,11 +45,13 @@ jobs:
             runner: macos-13
           - arch: aarch64-apple-darwin
             # xlarge is implicitly arm64.
-            runner: macos-14
+            runner: macos-13-xlarge
     runs-on: ${{ matrix.config.runner }}
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
       - name: Install system dependencies
         run: brew install protobuf
       - name: Install npm dependencies
@@ -425,15 +61,18 @@ jobs:
       - name: Build MacOS native node modules
         run: bash ci/build_macos_artifacts.sh ${{ matrix.config.arch }}
       - name: Upload Darwin Artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v3
         with:
-          name: node-native-darwin-${{ matrix.config.arch }}
+          name: native-darwin
           path: |
             node/dist/lancedb-vectordb-darwin*.tgz
          
-  node-linux-gnu:
-    name: vectordb (${{ matrix.config.arch}}-unknown-linux-gnu)
+
+  node-linux:
+    name: node-linux (${{ matrix.config.arch}}-unknown-linux-gnu
     runs-on: ${{ matrix.config.runner }}
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
     strategy:
       fail-fast: false
       matrix:
@@ -441,44 +80,31 @@ jobs:
           - arch: x86_64
             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-4vcpu-ubuntu-2204-arm
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
-      # To avoid OOM errors on ARM, we create a swap file.
-      - name: Configure aarch64 build
-        if: ${{ matrix.config.arch == 'aarch64' }}
-        run: |
-          free -h
-          sudo fallocate -l 16G /swapfile
-          sudo chmod 600 /swapfile
-          sudo mkswap /swapfile
-          sudo swapon /swapfile
-          echo "/swapfile swap swap defaults 0 0" >> sudo /etc/fstab
-          # print info
-          swapon --show
-          free -h
+        uses: actions/checkout@v3
       - name: Build Linux Artifacts
         run: |
-          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }} ${{ matrix.config.arch }}-unknown-linux-gnu
+          bash ci/build_linux_artifacts.sh ${{ matrix.config.arch }}
       - name: Upload Linux Artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v3
         with:
-          name: node-native-linux-${{ matrix.config.arch }}-gnu
+          name: native-linux
           path: |
             node/dist/lancedb-vectordb-linux*.tgz
 
   node-windows:
-    name: vectordb ${{ matrix.target }}
     runs-on: windows-2022
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
     strategy:
       fail-fast: false
       matrix:
         target: [x86_64-pc-windows-msvc]
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
       - name: Install Protoc v21.12
         working-directory: C:\
         run: |
@@ -495,72 +121,45 @@ jobs:
       - name: Build Windows native node modules
         run: .\ci\build_windows_artifacts.ps1 ${{ matrix.target }}
       - name: Upload Windows Artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v3
         with:
-          name: node-native-windows
+          name: native-windows
           path: |
             node/dist/lancedb-vectordb-win32*.tgz
 
   release:
-    name: vectordb NPM Publish
-    needs: [node, node-macos, node-linux-gnu, node-windows]
+    needs: [node, node-macos, node-linux, node-windows]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
     steps:
-      - uses: actions/download-artifact@v4
-        with:
-          pattern: node-*
+      - uses: actions/download-artifact@v3
       - name: Display structure of downloaded files
         run: ls -R
       - uses: actions/setup-node@v3
         with:
           node-version: 20
-          registry-url: "https://registry.npmjs.org"
+          registry-url: 'https://registry.npmjs.org'
       - name: Publish to NPM
         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
-      - name: Deprecate
-        env:
-          NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
-        # We need to deprecate the old package to avoid confusion.
-        # Each time we publish a new version, it gets undeprecated.
-        run: npm deprecate vectordb "Use @lancedb/lancedb instead."
-      - name: Notify Slack Action
-        uses: ravsamhq/notify-slack-action@2.3.0
-        if: ${{ always() }}
-        with:
-          status: ${{ job.status }}
-          notify_when: "failure"
-          notification_title: "{workflow} is failing"
-        env:
-          SLACK_WEBHOOK_URL: ${{ secrets.ACTION_MONITORING_SLACK }}
 
   update-package-lock:
-    if: startsWith(github.ref, 'refs/tags/v')
     needs: [release]
     runs-on: ubuntu-latest
-    permissions:
-      contents: write
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
         with:
           ref: main
-          token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          persist-credentials: false
           fetch-depth: 0
           lfs: true
       - uses: ./.github/workflows/update_package_lock
         with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}

.github/workflows/pypi-publish.yml

@@ -1,175 +1,31 @@
 name: PyPI Publish
 
 on:
-  push:
-    tags:
-      - 'python-v*'
-  pull_request:
-    # This should trigger a dry run (we skip the final publish step)
-    paths:
-      - .github/workflows/pypi-publish.yml
-      - Cargo.toml # Change in dependency frequently breaks builds
+  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: ""
-            runner: ubuntu-22.04
-          - platform: x86_64
-            manylinux: "2_28"
-            extra_args: "--features fp16kernels"
-            runner: ubuntu-22.04
-          - platform: aarch64
-            manylinux: "2_17"
-            extra_args: ""
-            # For successful fat LTO builds, we need a large runner to avoid OOM errors.
-            runner: ubuntu-2404-8x-arm64
-          - platform: aarch64
-            manylinux: "2_28"
-            extra_args: "--features fp16kernels"
-            runner: ubuntu-2404-8x-arm64
-    runs-on: ${{ matrix.config.runner }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: 3.8
-      - 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 }}
-      - uses: ./.github/workflows/upload_wheel
-        if: startsWith(github.ref, 'refs/tags/python-v')
-        with:
-          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          fury_token: ${{ secrets.FURY_TOKEN }}
-  mac:
-    timeout-minutes: 60
-    runs-on: ${{ matrix.config.runner }}
-    strategy:
-      matrix:
-        config:
-          - target: x86_64-apple-darwin
-            runner: macos-13
-          - target: aarch64-apple-darwin
-            runner: macos-14
-    env:
-      MACOSX_DEPLOYMENT_TARGET: 10.15
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: 3.12
-      - uses: ./.github/workflows/build_mac_wheel
-        with:
-          python-minor-version: 8
-          args: "--release --strip --target ${{ matrix.config.target }} --features fp16kernels"
-      - uses: ./.github/workflows/upload_wheel
-        if: startsWith(github.ref, 'refs/tags/python-v')
-        with:
-          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          fury_token: ${{ secrets.FURY_TOKEN }}
-  windows:
-    timeout-minutes: 60
-    runs-on: windows-latest
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: 3.12
-      - uses: ./.github/workflows/build_windows_wheel
-        with:
-          python-minor-version: 8
-          args: "--release --strip"
-          vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
-      - uses: ./.github/workflows/upload_wheel
-        if: startsWith(github.ref, 'refs/tags/python-v')
-        with:
-          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
-          fury_token: ${{ secrets.FURY_TOKEN }}
-  gh-release:
-    if: startsWith(github.ref, 'refs/tags/python-v')
+  publish:
     runs-on: ubuntu-latest
-    permissions:
-      contents: write
+    # Only runs on tags that matches the python-make-release action
+    if: startsWith(github.ref, 'refs/tags/python-v')
+    defaults:
+      run:
+        shell: bash
+        working-directory: python
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
         with:
-          fetch-depth: 0
-          lfs: true
-      - name: Extract version
-        id: extract_version
-        env:
-          GITHUB_REF: ${{ github.ref }}
+          python-version: "3.8"
+      - name: Build distribution
         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
+          ls -la
+          pip install wheel setuptools --upgrade
+          python setup.py sdist bdist_wheel
+      - name: Publish
+        uses: pypa/gh-action-pypi-publish@v1.8.5
         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 }}
+          password: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          packages-dir: python/dist

.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@v3
+      with:
+        ref: main
+        persist-credentials: false
+        fetch-depth: 0
+        lfs: true
+    - name: Set git configs for bumpversion
+      shell: bash
+      run: |
+        git config user.name 'Lance Release'
+        git config user.email 'lance-dev@lancedb.com'
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.10"
+    - name: Bump version, create tag and commit
+      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

@@ -13,222 +13,86 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
   cancel-in-progress: true
 
-env:
-  # Color output for pytest is off by default.
-  PYTEST_ADDOPTS: "--color=yes"
-  FORCE_COLOR: "1"
-
 jobs:
-  lint:
-    name: "Lint"
-    timeout-minutes: 30
-    runs-on: "ubuntu-22.04"
-    defaults:
-      run:
-        shell: bash
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-      - name: Install ruff
-        run: |
-          pip install ruff==0.9.9
-      - name: Format check
-        run: ruff format --check .
-      - name: Lint
-        run: ruff check .
-
-  type-check:
-    name: "Type Check"
-    timeout-minutes: 30
-    runs-on: "ubuntu-22.04"
-    defaults:
-      run:
-        shell: bash
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-      - name: Install protobuf compiler
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler
-          pip install toml
-      - name: Install dependencies
-        run: |
-          python ../ci/parse_requirements.py pyproject.toml --extras dev,tests,embeddings > requirements.txt
-          pip install -r requirements.txt
-      - name: Run pyright
-        run: pyright
-
-  doctest:
-    name: "Doctest"
-    timeout-minutes: 30
-    runs-on: "ubuntu-24.04"
-    defaults:
-      run:
-        shell: bash
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-          cache: "pip"
-      - name: Install protobuf
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler
-      - uses: Swatinem/rust-cache@v2
-        with:
-          workspaces: python
-      - name: Install
-        run: |
-          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests,dev,embeddings]
-          pip install tantivy
-          pip install mlx
-      - name: Doctest
-        run: pytest --doctest-modules python/lancedb
   linux:
-    name: "Linux: python-3.${{ matrix.python-minor-version }}"
     timeout-minutes: 30
     strategy:
       matrix:
-        python-minor-version: ["9", "12"]
-    runs-on: "ubuntu-24.04"
+        python-minor-version: [ "8", "9", "10", "11" ]
+    runs-on: "ubuntu-22.04"
     defaults:
       run:
         shell: bash
         working-directory: python
     steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Install protobuf
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: 3.${{ matrix.python-minor-version }}
-      - uses: Swatinem/rust-cache@v2
-        with:
-          workspaces: python
-      - uses: ./.github/workflows/build_linux_wheel
-      - uses: ./.github/workflows/run_tests
-        with:
-          integration: true
-      - name: Test without pylance
-        run: |
-          pip uninstall -y pylance
-          pytest -vv python/tests/test_table.py
-      # Make sure wheels are not included in the Rust cache
-      - name: Delete wheels
-        run: rm -rf target/wheels
-  platform:
-    name: "Mac: ${{ matrix.config.name }}"
+    - uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        lfs: true
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.${{ matrix.python-minor-version }}
+    - name: Install lancedb
+      run: |
+        pip install -e .[tests]
+        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+        pip install pytest pytest-mock ruff
+    - name: Lint
+      run: ruff format --check .
+    - name: Run tests
+      run: pytest -m "not slow" -x -v --durations=30 tests
+    - name: doctest
+      run: pytest --doctest-modules lancedb
+  mac:
     timeout-minutes: 30
     strategy:
       matrix:
-        config:
-          - name: x86
-            runner: macos-13
-          - name: Arm
-            runner: macos-14
-    runs-on: "${{ matrix.config.runner }}"
+        mac-runner: [ "macos-13", "macos-13-xlarge" ]
+    runs-on: "${{ matrix.mac-runner }}"
     defaults:
       run:
         shell: bash
         working-directory: python
     steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-      - uses: Swatinem/rust-cache@v2
-        with:
-          workspaces: python
-      - uses: ./.github/workflows/build_mac_wheel
-      - uses: ./.github/workflows/run_tests
-      # Make sure wheels are not included in the Rust cache
-      - name: Delete wheels
-        run: rm -rf target/wheels
-  windows:
-    name: "Windows: ${{ matrix.config.name }}"
-    timeout-minutes: 60
-    strategy:
-      matrix:
-        config:
-          - name: x86
-            runner: windows-latest
-    runs-on: "${{ matrix.config.runner }}"
-    defaults:
-      run:
-        shell: bash
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-      - uses: Swatinem/rust-cache@v2
-        with:
-          workspaces: python
-      - uses: ./.github/workflows/build_windows_wheel
-      - uses: ./.github/workflows/run_tests
-      # Make sure wheels are not included in the Rust cache
-      - name: Delete wheels
-        run: rm -rf target/wheels
+    - uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        lfs: true
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.11"
+    - name: Install lancedb
+      run: |
+        pip install -e .[tests]
+        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+        pip install pytest pytest-mock black
+    - name: Run tests
+      run: pytest -m "not slow" -x -v --durations=30 tests
   pydantic1x:
     timeout-minutes: 30
-    runs-on: "ubuntu-24.04"
+    runs-on: "ubuntu-22.04"
     defaults:
       run:
         shell: bash
         working-directory: python
     steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: 3.9
-      - name: Install lancedb
-        run: |
-          pip install "pydantic<2"
-          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests]
-          pip install tantivy
-      - name: Run tests
-        run: pytest -m "not slow and not s3_test" -x -v --durations=30 python/tests
+    - uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        lfs: true
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.9
+    - name: Install lancedb
+      run: |
+        pip install "pydantic<2"
+        pip install -e .[tests]
+        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+        pip install pytest pytest-mock
+    - name: Run tests
+      run: pytest -m "not slow" -x -v --durations=30 tests
+    - name: doctest
+      run: pytest --doctest-modules lancedb

.github/workflows/run_tests/action.yml

@@ -1,31 +0,0 @@
-name: run-tests
-
-description: "Install lance wheel and run unit tests"
-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' }}
-      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

@@ -22,123 +22,46 @@ env:
   # "1" means line tables only, which is useful for panic tracebacks.
   RUSTFLAGS: "-C debuginfo=1"
   RUST_BACKTRACE: "1"
-  CARGO_INCREMENTAL: 0
 
 jobs:
-  lint:
-    timeout-minutes: 30
-    runs-on: ubuntu-24.04
-    defaults:
-      run:
-        shell: bash
-    env:
-      # Need up-to-date compilers for kernels
-      CC: clang-18
-      CXX: clang++-18
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - uses: Swatinem/rust-cache@v2
-        with:
-          workspaces: rust
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler libssl-dev
-      - name: Run format
-        run: cargo fmt --all -- --check
-      - name: Run clippy
-        run: cargo clippy --workspace --tests --all-features -- -D warnings
-
-  build-no-lock:
-    runs-on: ubuntu-24.04
-    timeout-minutes: 30
-    env:
-      # Need up-to-date compilers for kernels
-      CC: clang
-      CXX: clang++
-    steps:
-      - uses: actions/checkout@v4
-      # Building without a lock file often requires the latest Rust version since downstream
-      # dependencies may have updated their minimum Rust version.
-      - uses: actions-rust-lang/setup-rust-toolchain@v1
-        with:
-          toolchain: "stable"
-      # Remove cargo.lock to force a fresh build
-      - name: Remove Cargo.lock
-        run: rm -f Cargo.lock
-      - uses: rui314/setup-mold@v1
-      - uses: Swatinem/rust-cache@v2
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler libssl-dev
-      - name: Build all
-        run: |
-          cargo build --benches --all-features --tests
-
   linux:
     timeout-minutes: 30
-    # To build all features, we need more disk space than is available
-    # on the free OSS github runner. This is mostly due to the the
-    # sentence-transformers feature.
-    runs-on: ubuntu-2404-4x-x64
+    runs-on: ubuntu-22.04
     defaults:
       run:
         shell: bash
         working-directory: rust
-    env:
-      # Need up-to-date compilers for kernels
-      CC: clang-18
-      CXX: clang++-18
     steps:
-      - uses: actions/checkout@v4
-        with:
+    - uses: actions/checkout@v3
+      with:
           fetch-depth: 0
           lfs: true
-      - uses: Swatinem/rust-cache@v2
-        with:
+    - uses: Swatinem/rust-cache@v2
+      with:
           workspaces: rust
-      - name: Install dependencies
-        run: |
-          # This shaves 2 minutes off this step in CI. This doesn't seem to be
-          # necessary in standard runners, but it is in the 4x runners.
-          sudo rm /var/lib/man-db/auto-update
+    - name: Install dependencies
+      run: |
+          sudo apt update
           sudo apt install -y protobuf-compiler libssl-dev
-      - uses: rui314/setup-mold@v1
-      - name: Make Swap
-        run: |
-          sudo fallocate -l 16G /swapfile
-          sudo chmod 600 /swapfile
-          sudo mkswap /swapfile
-          sudo swapon /swapfile
-      - name: Start S3 integration test environment
-        working-directory: .
-        run: docker compose up --detach --wait
-      - name: Build
-        run: cargo build --all-features --tests --locked --examples
-      - name: Run tests
-        run: cargo test --all-features --locked
-      - name: Run examples
-        run: cargo run --example simple --locked
-
+    - name: Build
+      run: cargo build --all-features
+    - name: Run tests
+      run: cargo test --all-features
   macos:
     timeout-minutes: 30
     strategy:
       matrix:
-        mac-runner: ["macos-13", "macos-14"]
+        mac-runner: [ "macos-13", "macos-13-xlarge" ]
     runs-on: "${{ matrix.mac-runner }}"
     defaults:
       run:
         shell: bash
         working-directory: rust
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
         with:
-          fetch-depth: 0
-          lfs: true
+            fetch-depth: 0
+            lfs: true
       - name: CPU features
         run: sysctl -a | grep cpu
       - uses: Swatinem/rust-cache@v2
@@ -146,78 +69,28 @@ jobs:
           workspaces: rust
       - name: Install dependencies
         run: brew install protobuf
+      - name: Build
+        run: cargo build --all-features
       - name: Run tests
-        run: |
-          # Don't run the s3 integration tests since docker isn't available
-          # on this image.
-          ALL_FEATURES=`cargo metadata --format-version=1 --no-deps \
-            | jq -r '.packages[] | .features | keys | .[]' \
-            | grep -v s3-test | sort | uniq | paste -s -d "," -`
-          cargo test --features $ALL_FEATURES --locked
-
+        run: cargo test --all-features
   windows:
     runs-on: windows-2022
-    strategy:
-      matrix:
-        target:
-        - x86_64-pc-windows-msvc
-        - aarch64-pc-windows-msvc
-    defaults:
-      run:
-        working-directory: rust/lancedb
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
       - uses: Swatinem/rust-cache@v2
         with:
           workspaces: rust
       - name: Install Protoc v21.12
-        run: choco install --no-progress protoc
-      - name: Build
+        working-directory: C:\
         run: |
-          rustup target add ${{ matrix.target }}
-          $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
-          cargo build --features remote --tests --locked --target ${{ matrix.target }}
+          New-Item -Path 'C:\protoc' -ItemType Directory
+          Set-Location C:\protoc
+          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+          7z x protoc.zip
+          Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+        shell: powershell
       - name: Run tests
-        # Can only run tests when target matches host
-        if: ${{ matrix.target == 'x86_64-pc-windows-msvc' }}
         run: |
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
-          cargo test --features remote --locked
-
-  msrv:
-    # Check the minimum supported Rust version
-    name: MSRV Check - Rust v${{ matrix.msrv }}
-    runs-on: ubuntu-24.04
-    strategy:
-      matrix:
-        msrv: ["1.78.0"] # This should match up with rust-version in Cargo.toml
-    env:
-      # Need up-to-date compilers for kernels
-      CC: clang-18
-      CXX: clang++-18
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: true
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler libssl-dev
-      - name: Install ${{ matrix.msrv }}
-        uses: dtolnay/rust-toolchain@master
-        with:
-          toolchain: ${{ matrix.msrv }}
-      - name: Downgrade  dependencies
-        # These packages have newer requirements for MSRV
-        run: |
-          cargo update -p aws-sdk-bedrockruntime --precise 1.64.0
-          cargo update -p aws-sdk-dynamodb --precise 1.55.0
-          cargo update -p aws-config --precise 1.5.10
-          cargo update -p aws-sdk-kms --precise 1.51.0
-          cargo update -p aws-sdk-s3 --precise 1.65.0
-          cargo update -p aws-sdk-sso --precise 1.50.0
-          cargo update -p aws-sdk-ssooidc --precise 1.51.0
-          cargo update -p aws-sdk-sts --precise 1.51.0
-          cargo update -p home --precise 0.5.9
-      - name: cargo +${{ matrix.msrv }} check
-        run: cargo check --workspace --tests --benches --all-features
+          cargo build
+          cargo test

.github/workflows/update_package_lock_nodejs/action.yml

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

.github/workflows/update_package_lock_run.yml

@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
         with:
           ref: main
           persist-credentials: false

.github/workflows/update_package_lock_run_nodejs.yml

@@ -1,19 +0,0 @@
-name: Update NodeJs package-lock.json
-
-on:
-  workflow_dispatch:
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          ref: main
-          persist-credentials: false
-          fetch-depth: 0
-          lfs: true
-      - uses: ./.github/workflows/update_package_lock_nodejs
-        with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}

.github/workflows/upload_wheel/action.yml

@@ -1,45 +0,0 @@
-name: upload-wheel
-
-description: "Upload wheels to Pypi"
-inputs:
-  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
-      python3 -m pip install --upgrade pkginfo
-  - name: Choose repo
-    shell: bash
-    id: choose_repo
-    run: |
-      if [[ ${{ github.ref }} == *beta* ]]; then
-        echo "repo=fury" >> $GITHUB_OUTPUT
-      else
-        echo "repo=pypi" >> $GITHUB_OUTPUT
-      fi
-  - name: Publish to PyPI
-    shell: bash
-    env:
-      FURY_TOKEN: ${{ inputs.fury_token }}
-      PYPI_TOKEN: ${{ inputs.pypi_token }}
-    run: |
-      if [[ ${{ steps.choose_repo.outputs.repo }} == fury ]]; then
-        WHEEL=$(ls target/wheels/lancedb-*.whl 2> /dev/null | head -n 1)
-        echo "Uploading $WHEEL to Fury"
-        curl -f -F package=@$WHEEL https://$FURY_TOKEN@push.fury.io/lancedb/
-      else
-        twine upload --repository ${{ steps.choose_repo.outputs.repo }} \
-          --username __token__ \
-          --password $PYPI_TOKEN \
-          target/wheels/lancedb-*.whl
-      fi

.gitignore

@@ -4,11 +4,11 @@
 **/__pycache__
 .DS_Store
 venv
-.venv
 
 .vscode
-.zed
+
 rust/target
+rust/Cargo.lock
 
 site
 
@@ -22,11 +22,6 @@ python/dist
 
 **/.hypothesis
 
-# Compiled Dynamic libraries
-*.so
-*.dylib
-*.dll
-
 ## Javascript
 *.node
 **/node_modules
@@ -34,10 +29,8 @@ python/dist
 node/dist
 node/examples/**/package-lock.json
 node/examples/**/dist
-nodejs/lancedb/native*
-dist
 
 ## Rust
 target
 
-**/sccache.log
+Cargo.lock

.pre-commit-config.yaml

@@ -1,27 +1,21 @@
 repos:
-  - repo: https://github.com/pre-commit/pre-commit-hooks
+-   repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v3.2.0
     hooks:
-      - id: check-yaml
-      - id: end-of-file-fixer
-      - id: trailing-whitespace
-  - repo: https://github.com/astral-sh/ruff-pre-commit
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+-   repo: https://github.com/psf/black
+    rev: 22.12.0
+    hooks:
+    -   id: black
+-   repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.9.9
+    rev: v0.0.277
     hooks:
-      - id: ruff
-  # - repo: https://github.com/RobertCraigie/pyright-python
-  #   rev: v1.1.395
-  #   hooks:
-  #     - id: pyright
-  #       args: ["--project", "python"]
-  #       additional_dependencies: [pyarrow-stubs]
-  - repo: local
+    - id: ruff
+-   repo: https://github.com/pycqa/isort
+    rev: 5.12.0
     hooks:
-      - id: local-biome-check
-        name: biome check
-        entry: npx @biomejs/biome@1.8.3 check --config-path nodejs/biome.json nodejs/
-        language: system
-        types: [text]
-        files: "nodejs/.*"
-        exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*|nodejs/examples/.*
+    - id: isort
+      name: isort (python)

CONTRIBUTING.md

@@ -1,78 +0,0 @@
-# Contributing to LanceDB
-
-LanceDB is an open-source project and we welcome contributions from the community.
-This document outlines the process for contributing to LanceDB.
-
-## Reporting Issues
-
-If you encounter a bug or have a feature request, please open an issue on the
-[GitHub issue tracker](https://github.com/lancedb/lancedb).
-
-## Picking an issue
-
-We track issues on the GitHub issue tracker. If you are looking for something to
-work on, check the [good first issue](https://github.com/lancedb/lancedb/contribute) label. These issues are typically the best described and have the smallest scope.
-
-If there's an issue you are interested in working on, please leave a comment on the issue. This will help us avoid duplicate work. Additionally, if you have questions about the issue, please ask them in the issue comments. We are happy to provide guidance on how to approach the issue.
-
-## Configuring Git
-
-First, fork the repository on GitHub, then clone your fork:
-
-```bash
-git clone https://github.com/<username>/lancedb.git
-cd lancedb
-```
-
-Then add the main repository as a remote:
-
-```bash
-git remote add upstream https://github.com/lancedb/lancedb.git
-git fetch upstream
-```
-
-## Setting up your development environment
-
-We have development environments for Python, Typescript, and Java. Each environment has its own setup instructions.
-
-* [Python](python/CONTRIBUTING.md)
-* [Typescript](nodejs/CONTRIBUTING.md)
-<!-- TODO: add Java contributing guide -->
-* [Documentation](docs/README.md)
-
-
-## Best practices for pull requests
-
-For the best chance of having your pull request accepted, please follow these guidelines:
-
-1. Unit test all bug fixes and new features. Your code will not be merged if it
-   doesn't have tests.
-1. If you change the public API, update the documentation in the `docs` directory.
-1. Aim to minimize the number of changes in each pull request. Keep to solving
-   one problem at a time, when possible.
-1. Before marking a pull request ready-for-review, do a self review of your code.
-   Is it clear why you are making the changes? Are the changes easy to understand?
-1. Use [conventional commit messages](https://www.conventionalcommits.org/en/) as pull request titles. Examples:
-    * New feature: `feat: adding foo API`
-    * Bug fix: `fix: issue with foo API`
-    * Documentation change: `docs: adding foo API documentation`
-1. If your pull request is a work in progress, leave the pull request as a draft.
-   We will assume the pull request is ready for review when it is opened.
-1. When writing tests, test the error cases. Make sure they have understandable
-   error messages.
-
-## Project structure
-
-The core library is written in Rust. The Python, Typescript, and Java libraries
-are wrappers around the Rust library.
-
-* `src/lancedb`: Rust library source code
-* `python`: Python package source code
-* `nodejs`: Typescript package source code
-* `node`: **Deprecated** Typescript package source code
-* `java`: Java package source code
-* `docs`: Documentation source code
-
-## Release process
-
-For information on the release process, see: [release_process.md](release_process.md)

Cargo.toml

@@ -1,77 +1,28 @@
 [workspace]
-members = [
-    "rust/ffi/node",
-    "rust/lancedb",
-    "nodejs",
-    "python",
-    "java/core/lancedb-jni",
-]
+members = ["rust/ffi/node", "rust/vectordb"]
 # Python package needs to be built by maturin.
 exclude = ["python"]
 resolver = "2"
 
-[workspace.package]
-edition = "2021"
-authors = ["LanceDB Devs <dev@lancedb.com>"]
-license = "Apache-2.0"
-repository = "https://github.com/lancedb/lancedb"
-description = "Serverless, low-latency vector database for AI applications"
-keywords = ["lancedb", "lance", "database", "vector", "search"]
-categories = ["database-implementations"]
-rust-version = "1.78.0"
-
 [workspace.dependencies]
-lance = { "version" = "=0.25.3", "features" = [
-    "dynamodb",
-], tag = "v0.25.3-beta.2", git = "https://github.com/lancedb/lance" }
-lance-io = { version = "=0.25.3", tag = "v0.25.3-beta.2", git = "https://github.com/lancedb/lance" }
-lance-index = { version = "=0.25.3", tag = "v0.25.3-beta.2", git = "https://github.com/lancedb/lance" }
-lance-linalg = { version = "=0.25.3", tag = "v0.25.3-beta.2", git = "https://github.com/lancedb/lance" }
-lance-table = { version = "=0.25.3", tag = "v0.25.3-beta.2", git = "https://github.com/lancedb/lance" }
-lance-testing = { version = "=0.25.3", tag = "v0.25.3-beta.2", git = "https://github.com/lancedb/lance" }
-lance-datafusion = { version = "=0.25.3", tag = "v0.25.3-beta.2", git = "https://github.com/lancedb/lance" }
-lance-encoding = { version = "=0.25.3", tag = "v0.25.3-beta.2", git = "https://github.com/lancedb/lance" }
+lance = { "version" = "=0.9.0", "features" = ["dynamodb"] }
+lance-index = { "version" = "=0.9.0" }
+lance-linalg = { "version" = "=0.9.0" }
+lance-testing = { "version" = "=0.9.0" }
 # Note that this one does not include pyarrow
-arrow = { version = "54.1", optional = false }
-arrow-array = "54.1"
-arrow-data = "54.1"
-arrow-ipc = "54.1"
-arrow-ord = "54.1"
-arrow-schema = "54.1"
-arrow-arith = "54.1"
-arrow-cast = "54.1"
-async-trait = "0"
-datafusion = { version = "46.0", default-features = false }
-datafusion-catalog = "46.0"
-datafusion-common = { version = "46.0", default-features = false }
-datafusion-execution = "46.0"
-datafusion-expr = "46.0"
-datafusion-physical-plan = "46.0"
-env_logger = "0.11"
-half = { "version" = "=2.4.1", default-features = false, features = [
+arrow = { version = "47.0.0", optional = false }
+arrow-array = "47.0"
+arrow-data = "47.0"
+arrow-ipc = "47.0"
+arrow-ord = "47.0"
+arrow-schema = "47.0"
+arrow-arith = "47.0"
+arrow-cast = "47.0"
+chrono = "0.4.23"
+half = { "version" = "=2.3.1", default-features = false, features = [
     "num-traits",
 ] }
-futures = "0"
 log = "0.4"
-moka = { version = "0.12", features = ["future"] }
-object_store = "0.11.0"
-pin-project = "1.0.7"
-snafu = "0.8"
+object_store = "0.7.1"
+snafu = "0.7.4"
 url = "2"
-num-traits = "0.2"
-rand = "0.8"
-regex = "1.10"
-lazy_static = "1"
-semver = "1.0.25"
-
-# Temporary pins to work around downstream issues
-# https://github.com/apache/arrow-rs/commit/2fddf85afcd20110ce783ed5b4cdeb82293da30b
-chrono = "=0.4.39"
-# https://github.com/RustCrypto/formats/issues/1684
-base64ct = "=1.6.0"
-
-# Workaround for: https://github.com/eira-fransham/crunchy/issues/13
-crunchy = "=0.2.2"
-
-# Workaround for: https://github.com/Lokathor/bytemuck/issues/306
-bytemuck_derive = ">=1.8.1, <1.9.0"

README.md

@@ -1,24 +1,15 @@
-<a href="https://cloud.lancedb.com" target="_blank">
-  <img src="https://github.com/user-attachments/assets/92dad0a2-2a37-4ce1-b783-0d1b4f30a00c" alt="LanceDB Cloud Public Beta" width="100%" style="max-width: 100%;">
-</a>
-
 <div align="center">
 <p align="center">
 
-<picture>
-  <source media="(prefers-color-scheme: dark)" srcset="https://github.com/user-attachments/assets/ac270358-333e-4bea-a132-acefaa94040e">
-  <source media="(prefers-color-scheme: light)" srcset="https://github.com/user-attachments/assets/b864d814-0d29-4784-8fd9-807297c758c0">
-  <img alt="LanceDB Logo" src="https://github.com/user-attachments/assets/b864d814-0d29-4784-8fd9-807297c758c0" width=300>
-</picture>
+<img width="275" alt="LanceDB Logo" src="https://user-images.githubusercontent.com/917119/226205734-6063d87a-1ecc-45fe-85be-1dea6383a3d8.png">
 
-**Search More, Manage Less**
+**Developer-friendly, serverless vector database for AI applications**
 
 <a href='https://github.com/lancedb/vectordb-recipes/tree/main' target="_blank"><img alt='LanceDB' src='https://img.shields.io/badge/VectorDB_Recipes-100000?style=for-the-badge&logo=LanceDB&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
 <a href='https://lancedb.github.io/lancedb/' target="_blank"><img alt='lancdb' src='https://img.shields.io/badge/DOCS-100000?style=for-the-badge&logo=lancdb&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
-[![Blog](https://img.shields.io/badge/Blog-12100E?style=for-the-badge&logoColor=white)](https://blog.lancedb.com/)
+[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/) 
 [![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/zMM32dvNtd) 
 [![Twitter](https://img.shields.io/badge/Twitter-%231DA1F2.svg?style=for-the-badge&logo=Twitter&logoColor=white)](https://twitter.com/lancedb)
-[![Gurubase](https://img.shields.io/badge/Gurubase-Ask%20LanceDB%20Guru-006BFF?style=for-the-badge)](https://gurubase.io/g/lancedb)
 
 </p>
 
@@ -29,7 +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:
 
@@ -45,7 +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.
 
@@ -53,24 +44,19 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
 
 **Javascript**
 ```shell
-npm install @lancedb/lancedb
+npm install vectordb
 ```
 
 ```javascript
-import * as lancedb from "@lancedb/lancedb";
+const lancedb = require('vectordb');
+const db = await lancedb.connect('data/sample-lancedb');
 
-const db = await lancedb.connect("data/sample-lancedb");
-const table = await db.createTable("vectors", [
-	{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
-	{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },
-], {mode: 'overwrite'});
+const table = await db.createTable('vectors',
+      [{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
+       { id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }])
 
-
-const query = table.vectorSearch([0.1, 0.3]).limit(2);
-const results = await query.toArray();
-
-// You can also search for rows by specific criteria without involving a vector search.
-const rowsByCriteria = await table.query().where("price >= 10").toArray();
+const query = table.search([0.1, 0.3]).limit(2);
+const results = await query.execute();
 ```
 
 **Python**
@@ -90,5 +76,5 @@ 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://github.com/lancedb/vectordb-recipes/tree/main/examples/Youtube-Search-QA-Bot">Build a question and answer bot with LanceDB</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/build_linux_artifacts.sh

@@ -1,7 +1,6 @@
 #!/bin/bash
 set -e
 ARCH=${1:-x86_64}
-TARGET_TRIPLE=${2:-x86_64-unknown-linux-gnu}
 
 # We pass down the current user so that when we later mount the local files 
 # into the container, the files are accessible by the current user.
@@ -14,9 +13,7 @@ docker build \
     .
 popd
 
-# We turn on memory swap to avoid OOM killer
 docker run \
     -v $(pwd):/io -w /io \
-    --memory-swap=-1 \
     lancedb-node-manylinux \
-    bash ci/manylinux_node/build_vectordb.sh $ARCH $TARGET_TRIPLE
+    bash ci/manylinux_node/build.sh $ARCH

ci/build_windows_artifacts.ps1

@@ -3,7 +3,6 @@
 # Targets supported:
 # - x86_64-pc-windows-msvc
 # - i686-pc-windows-msvc
-# - aarch64-pc-windows-msvc
 
 function Prebuild-Rust {
     param (
@@ -32,7 +31,7 @@ function Build-NodeBinaries {
 
 $targets = $args[0]
 if (-not $targets) {
-    $targets = "x86_64-pc-windows-msvc", "aarch64-pc-windows-msvc"
+    $targets = "x86_64-pc-windows-msvc"
 }
 
 Write-Host "Building artifacts for targets: $targets"

ci/build_windows_artifacts_nodejs.ps1

@@ -1,42 +0,0 @@
-# Builds the Windows artifacts (nodejs binaries).
-# Usage:  .\ci\build_windows_artifacts_nodejs.ps1 [target]
-# Targets supported:
-# - x86_64-pc-windows-msvc
-# - i686-pc-windows-msvc
-# - aarch64-pc-windows-msvc
-
-function Prebuild-Rust {
-    param (
-        [string]$target
-    )
-
-    # Building here for the sake of easier debugging.
-    Push-Location -Path "rust/lancedb"
-    Write-Host "Building rust library for $target"
-    $env:RUST_BACKTRACE=1
-    cargo build --release --target $target
-    Pop-Location
-}
-
-function Build-NodeBinaries {
-    param (
-        [string]$target
-    )
-
-    Push-Location -Path "nodejs"
-    Write-Host "Building nodejs library for $target"
-    $env:RUST_TARGET=$target
-    npm run build-release
-    Pop-Location
-}
-
-$targets = $args[0]
-if (-not $targets) {
-    $targets = "x86_64-pc-windows-msvc", "aarch64-pc-windows-msvc"
-}
-
-Write-Host "Building artifacts for targets: $targets"
-foreach ($target in $targets) {
-    Prebuild-Rust $target
-    Build-NodeBinaries $target
-}

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/manylinux_node/Dockerfile

@@ -4,18 +4,22 @@
 # range of linux distributions.
 ARG ARCH=x86_64
 
-FROM quay.io/pypa/manylinux_2_28_${ARCH}
+FROM quay.io/pypa/manylinux2014_${ARCH}
 
 ARG ARCH=x86_64
 ARG DOCKER_USER=default_user
 
+# Install static openssl
+COPY install_openssl.sh install_openssl.sh
+RUN ./install_openssl.sh ${ARCH} > /dev/null
+
 # Protobuf is also installed as root.
 COPY install_protobuf.sh install_protobuf.sh
 RUN ./install_protobuf.sh ${ARCH}
 
 ENV DOCKER_USER=${DOCKER_USER}
-# Create a group and user, but only if it doesn't exist
-RUN echo ${ARCH} && id -u ${DOCKER_USER} >/dev/null 2>&1 || adduser --user-group --create-home --uid ${DOCKER_USER} build_user
+# Create a group and user
+RUN echo ${ARCH} && adduser --user-group --create-home --uid ${DOCKER_USER} build_user
 
 # We switch to the user to install Rust and Node, since those like to be 
 # installed at the user level.

ci/manylinux_node/build.sh

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

ci/manylinux_node/build_vectordb.sh

@@ -1,13 +0,0 @@
-#!/bin/bash
-# Builds the node module for manylinux. Invoked by ci/build_linux_artifacts.sh.
-set -e
-ARCH=${1:-x86_64}
-TARGET_TRIPLE=${2:-x86_64-unknown-linux-gnu}
-
-#Alpine doesn't have .bashrc
-FILE=$HOME/.bashrc && test -f $FILE && source $FILE
-
-cd node
-npm ci
-npm run build-release
-npm run pack-build -- -t $TARGET_TRIPLE

ci/manylinux_node/install_openssl.sh

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

ci/manylinux_node/prepare_manylinux_node.sh

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

ci/mock_openai.py

@@ -1,57 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-"""A zero-dependency mock OpenAI embeddings API endpoint for testing purposes."""
-import argparse
-import json
-import http.server
-
-
-class MockOpenAIRequestHandler(http.server.BaseHTTPRequestHandler):
-    def do_POST(self):
-        content_length = int(self.headers["Content-Length"])
-        post_data = self.rfile.read(content_length)
-        post_data = json.loads(post_data.decode("utf-8"))
-        # See: https://platform.openai.com/docs/api-reference/embeddings/create
-
-        if isinstance(post_data["input"], str):
-            num_inputs = 1
-        else:
-            num_inputs = len(post_data["input"])
-
-        model = post_data.get("model", "text-embedding-ada-002")
-
-        data = []
-        for i in range(num_inputs):
-            data.append({
-                "object": "embedding",
-                "embedding": [0.1] * 1536,
-                "index": i,
-            })
-
-        response = {
-            "object": "list",
-            "data": data,
-            "model": model,
-            "usage": {
-                "prompt_tokens": 0,
-                "total_tokens": 0,
-            }
-        }
-
-        self.send_response(200)
-        self.send_header("Content-type", "application/json")
-        self.end_headers()
-        self.wfile.write(json.dumps(response).encode("utf-8"))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Mock OpenAI embeddings API endpoint")
-    parser.add_argument("--port", type=int, default=8000, help="Port to listen on")
-    args = parser.parse_args()
-    port = args.port
-
-    print(f"server started on port {port}. Press Ctrl-C to stop.")
-    print(f"To use, set OPENAI_BASE_URL=http://localhost:{port} in your environment.")
-
-    with http.server.HTTPServer(("0.0.0.0", port), MockOpenAIRequestHandler) as server:
-        server.serve_forever()

ci/parse_requirements.py

@@ -1,41 +0,0 @@
-import argparse
-import toml
-
-
-def parse_dependencies(pyproject_path, extras=None):
-    with open(pyproject_path, "r") as file:
-        pyproject = toml.load(file)
-
-    dependencies = pyproject.get("project", {}).get("dependencies", [])
-    for dependency in dependencies:
-        print(dependency)
-
-    optional_dependencies = pyproject.get("project", {}).get(
-        "optional-dependencies", {}
-    )
-
-    if extras:
-        for extra in extras.split(","):
-            for dep in optional_dependencies.get(extra, []):
-                print(dep)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Generate requirements.txt from pyproject.toml"
-    )
-    parser.add_argument("path", type=str, help="Path to pyproject.toml")
-    parser.add_argument(
-        "--extras",
-        type=str,
-        help="Comma-separated list of extras to include",
-        default="",
-    )
-
-    args = parser.parse_args()
-
-    parse_dependencies(args.path, args.extras)
-
-
-if __name__ == "__main__":
-    main()

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)

ci/sysroot-aarch64-pc-windows-msvc.sh

@@ -1,105 +0,0 @@
-#!/bin/sh
-
-# https://github.com/mstorsjo/msvc-wine/blob/master/vsdownload.py
-# https://github.com/mozilla/gecko-dev/blob/6027d1d91f2d3204a3992633b3ef730ff005fc64/build/vs/vs2022-car.yaml
-
-# function dl() {
-# 	curl -O https://download.visualstudio.microsoft.com/download/pr/$1
-# }
-
-# [[.h]]
-
-# "id": "Win11SDK_10.0.26100"
-# "version": "10.0.26100.7"
-
-# libucrt.lib
-
-# example: <assert.h>
-# dir: ucrt/
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/2ee3a5fc6e9fc832af7295b138e93839/universal%20crt%20headers%20libraries%20and%20sources-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/b1aa09b90fe314aceb090f6ec7626624/16ab2ea2187acffa6435e334796c8c89.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/400609bb0ff5804e36dbe6dcd42a7f01/6ee7bbee8435130a869cf971694fd9e2.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/2ac327317abb865a0e3f56b2faefa918/78fa3c824c2c48bd4a49ab5969adaaf7.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/f034bc0b2680f67dccd4bfeea3d0f932/7afc7b670accd8e3cc94cfffd516f5cb.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/7ed5e12f9d50f80825a8b27838cf4c7f/96076045170fe5db6d5dcf14b6f6688e.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/764edc185a696bda9e07df8891dddbbb/a1e2a83aa8a71c48c742eeaff6e71928.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/66854bedc6dbd5ccb5dd82c8e2412231/b2f03f34ff83ec013b9e45c7cd8e8a73.cab
-
-# example: <windows.h>
-# dir: um/
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/b286efac4d83a54fc49190bddef1edc9/windows%20sdk%20for%20windows%20store%20apps%20headers-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/e0dc3811d92ab96fcb72bf63d6c08d71/766c0ffd568bbb31bf7fb6793383e24a.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/613503da4b5628768497822826aed39f/8125ee239710f33ea485965f76fae646.cab
-
-# example: <winapifamily.h>
-# dir: /shared
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/122979f0348d3a2a36b6aa1a111d5d0c/windows%20sdk%20for%20windows%20store%20apps%20headers%20onecoreuap-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/766e04beecdfccff39e91dd9eb32834a/e89e3dcbb016928c7e426238337d69eb.cab
-
-
-# "id": "Microsoft.VisualC.14.16.CRT.Headers"
-# "version": "14.16.27045"
-
-# example: <vcruntime.h>
-# dir: MSVC/
-curl -O https://download.visualstudio.microsoft.com/download/pr/bac0afd7-cc9e-4182-8a83-9898fa20e092/87bbe41e09a2f83711e72696f49681429327eb7a4b90618c35667a6ba2e2880e/Microsoft.VisualC.14.16.CRT.Headers.vsix
-
-# [[.lib]]
-
-# advapi32.lib bcrypt.lib kernel32.lib ntdll.lib user32.lib uuid.lib ws2_32.lib userenv.lib cfgmgr32.lib runtimeobject.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/944c4153b849a1f7d0c0404a4f1c05ea/windows%20sdk%20for%20windows%20store%20apps%20libs-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/5306aed3e1a38d1e8bef5934edeb2a9b/05047a45609f311645eebcac2739fc4c.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/13c8a73a0f5a6474040b26d016a26fab/13d68b8a7b6678a368e2d13ff4027521.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/149578fb3b621cdb61ee1813b9b3e791/463ad1b0783ebda908fd6c16a4abfe93.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/5c986c4f393c6b09d5aec3b539e9fb4a/5a22e5cde814b041749fb271547f4dd5.cab
-
-# dbghelp.lib fwpuclnt.lib arm64rt.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/7a332420d812f7c1d41da865ae5a7c52/windows%20sdk%20desktop%20libs%20arm64-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/19de98ed4a79938d0045d19c047936b3/3e2f7be479e3679d700ce0782e4cc318.cab
-
-# libcmt.lib libvcruntime.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/bac0afd7-cc9e-4182-8a83-9898fa20e092/227f40682a88dc5fa0ccb9cadc9ad30af99ad1f1a75db63407587d079f60d035/Microsoft.VisualC.14.16.CRT.ARM64.Desktop.vsix
-
-
-msiextract universal%20crt%20headers%20libraries%20and%20sources-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20headers-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20headers%20onecoreuap-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20libs-x86_en-us.msi
-msiextract windows%20sdk%20desktop%20libs%20arm64-x86_en-us.msi
-unzip -o Microsoft.VisualC.14.16.CRT.Headers.vsix
-unzip -o Microsoft.VisualC.14.16.CRT.ARM64.Desktop.vsix
-
-mkdir -p /usr/aarch64-pc-windows-msvc/usr/include
-mkdir -p /usr/aarch64-pc-windows-msvc/usr/lib
-
-# lowercase folder/file names
-echo "$(find . -regex ".*/[^/]*[A-Z][^/]*")" | xargs -I{} sh -c 'mv "$(echo "{}" | sed -E '"'"'s/(.*\/)/\L\1/'"'"')" "$(echo "{}" | tr [A-Z] [a-z])"'
-
-# .h
-(cd 'program files/windows kits/10/include/10.0.26100.0' && cp -r ucrt/* um/* shared/* -t /usr/aarch64-pc-windows-msvc/usr/include)
-
-cp -r contents/vc/tools/msvc/14.16.27023/include/* /usr/aarch64-pc-windows-msvc/usr/include
-
-# lowercase #include "" and #include <>
-find /usr/aarch64-pc-windows-msvc/usr/include -type f -exec sed -i -E 's/(#include <[^<>]*?[A-Z][^<>]*?>)|(#include "[^"]*?[A-Z][^"]*?")/\L\1\2/' "{}" ';'
-
-# ARM intrinsics
-# original dir: MSVC/
-
-# '__n128x4' redefined in arm_neon.h
-# "arm64_neon.h" included from intrin.h
-
-(cd /usr/lib/llvm19/lib/clang/19/include && cp arm_neon.h intrin.h -t /usr/aarch64-pc-windows-msvc/usr/include)
-
-# .lib
-
-# _Interlocked intrinsics
-# must always link with arm64rt.lib
-# reason: https://developercommunity.visualstudio.com/t/libucrtlibstreamobj-error-lnk2001-unresolved-exter/1544787#T-ND1599818
-# I don't understand the 'correct' fix for this, arm64rt.lib is supposed to be the workaround
-
-(cd 'program files/windows kits/10/lib/10.0.26100.0/um/arm64' && cp advapi32.lib bcrypt.lib kernel32.lib ntdll.lib user32.lib uuid.lib ws2_32.lib userenv.lib cfgmgr32.lib runtimeobject.lib dbghelp.lib fwpuclnt.lib arm64rt.lib -t /usr/aarch64-pc-windows-msvc/usr/lib)
-
-(cd 'contents/vc/tools/msvc/14.16.27023/lib/arm64' && cp libcmt.lib libvcruntime.lib -t /usr/aarch64-pc-windows-msvc/usr/lib)
-
-cp 'program files/windows kits/10/lib/10.0.26100.0/ucrt/arm64/libucrt.lib' /usr/aarch64-pc-windows-msvc/usr/lib

ci/sysroot-x86_64-pc-windows-msvc.sh

@@ -1,105 +0,0 @@
-#!/bin/sh
-
-# https://github.com/mstorsjo/msvc-wine/blob/master/vsdownload.py
-# https://github.com/mozilla/gecko-dev/blob/6027d1d91f2d3204a3992633b3ef730ff005fc64/build/vs/vs2022-car.yaml
-
-# function dl() {
-# 	curl -O https://download.visualstudio.microsoft.com/download/pr/$1
-# }
-
-# [[.h]]
-
-# "id": "Win11SDK_10.0.26100"
-# "version": "10.0.26100.7"
-
-# libucrt.lib
-
-# example: <assert.h>
-# dir: ucrt/
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/2ee3a5fc6e9fc832af7295b138e93839/universal%20crt%20headers%20libraries%20and%20sources-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/b1aa09b90fe314aceb090f6ec7626624/16ab2ea2187acffa6435e334796c8c89.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/400609bb0ff5804e36dbe6dcd42a7f01/6ee7bbee8435130a869cf971694fd9e2.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/2ac327317abb865a0e3f56b2faefa918/78fa3c824c2c48bd4a49ab5969adaaf7.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/f034bc0b2680f67dccd4bfeea3d0f932/7afc7b670accd8e3cc94cfffd516f5cb.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/7ed5e12f9d50f80825a8b27838cf4c7f/96076045170fe5db6d5dcf14b6f6688e.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/764edc185a696bda9e07df8891dddbbb/a1e2a83aa8a71c48c742eeaff6e71928.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/66854bedc6dbd5ccb5dd82c8e2412231/b2f03f34ff83ec013b9e45c7cd8e8a73.cab
-
-# example: <windows.h>
-# dir: um/
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/b286efac4d83a54fc49190bddef1edc9/windows%20sdk%20for%20windows%20store%20apps%20headers-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/e0dc3811d92ab96fcb72bf63d6c08d71/766c0ffd568bbb31bf7fb6793383e24a.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/613503da4b5628768497822826aed39f/8125ee239710f33ea485965f76fae646.cab
-
-# example: <winapifamily.h>
-# dir: /shared
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/122979f0348d3a2a36b6aa1a111d5d0c/windows%20sdk%20for%20windows%20store%20apps%20headers%20onecoreuap-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/766e04beecdfccff39e91dd9eb32834a/e89e3dcbb016928c7e426238337d69eb.cab
-
-
-# "id": "Microsoft.VisualC.14.16.CRT.Headers"
-# "version": "14.16.27045"
-
-# example: <vcruntime.h>
-# dir: MSVC/
-curl -O https://download.visualstudio.microsoft.com/download/pr/bac0afd7-cc9e-4182-8a83-9898fa20e092/87bbe41e09a2f83711e72696f49681429327eb7a4b90618c35667a6ba2e2880e/Microsoft.VisualC.14.16.CRT.Headers.vsix
-
-# [[.lib]]
-
-# advapi32.lib bcrypt.lib kernel32.lib ntdll.lib user32.lib uuid.lib ws2_32.lib userenv.lib cfgmgr32.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/944c4153b849a1f7d0c0404a4f1c05ea/windows%20sdk%20for%20windows%20store%20apps%20libs-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/5306aed3e1a38d1e8bef5934edeb2a9b/05047a45609f311645eebcac2739fc4c.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/13c8a73a0f5a6474040b26d016a26fab/13d68b8a7b6678a368e2d13ff4027521.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/149578fb3b621cdb61ee1813b9b3e791/463ad1b0783ebda908fd6c16a4abfe93.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/5c986c4f393c6b09d5aec3b539e9fb4a/5a22e5cde814b041749fb271547f4dd5.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/bfc3904a0195453419ae4dfea7abd6fb/e10768bb6e9d0ea730280336b697da66.cab
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/637f9f3be880c71f9e3ca07b4d67345c/f9b24c8280986c0683fbceca5326d806.cab
-
-# dbghelp.lib fwpuclnt.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/9f51690d5aa804b1340ce12d1ec80f89/windows%20sdk%20desktop%20libs%20x64-x86_en-us.msi
-curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/d3a7df4ca3303a698640a29e558a5e5b/58314d0646d7e1a25e97c902166c3155.cab
-
-# libcmt.lib libvcruntime.lib
-curl -O https://download.visualstudio.microsoft.com/download/pr/bac0afd7-cc9e-4182-8a83-9898fa20e092/8728f21ae09940f1f4b4ee47b4a596be2509e2a47d2f0c83bbec0ea37d69644b/Microsoft.VisualC.14.16.CRT.x64.Desktop.vsix
-
-
-msiextract universal%20crt%20headers%20libraries%20and%20sources-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20headers-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20headers%20onecoreuap-x86_en-us.msi
-msiextract windows%20sdk%20for%20windows%20store%20apps%20libs-x86_en-us.msi
-msiextract windows%20sdk%20desktop%20libs%20x64-x86_en-us.msi
-unzip -o Microsoft.VisualC.14.16.CRT.Headers.vsix
-unzip -o Microsoft.VisualC.14.16.CRT.x64.Desktop.vsix
-
-mkdir -p /usr/x86_64-pc-windows-msvc/usr/include
-mkdir -p /usr/x86_64-pc-windows-msvc/usr/lib
-
-# lowercase folder/file names
-echo "$(find . -regex ".*/[^/]*[A-Z][^/]*")" | xargs -I{} sh -c 'mv "$(echo "{}" | sed -E '"'"'s/(.*\/)/\L\1/'"'"')" "$(echo "{}" | tr [A-Z] [a-z])"'
-
-# .h
-(cd 'program files/windows kits/10/include/10.0.26100.0' && cp -r ucrt/* um/* shared/* -t /usr/x86_64-pc-windows-msvc/usr/include)
-
-cp -r contents/vc/tools/msvc/14.16.27023/include/* /usr/x86_64-pc-windows-msvc/usr/include
-
-# lowercase #include "" and #include <>
-find /usr/x86_64-pc-windows-msvc/usr/include -type f -exec sed -i -E 's/(#include <[^<>]*?[A-Z][^<>]*?>)|(#include "[^"]*?[A-Z][^"]*?")/\L\1\2/' "{}" ';'
-
-# x86 intrinsics
-# original dir: MSVC/
-
-# '_mm_movemask_epi8' defined in emmintrin.h
-# '__v4sf' defined in xmmintrin.h
-# '__v2si' defined in mmintrin.h
-# '__m128d' redefined in immintrin.h
-# '__m128i' redefined in intrin.h
-# '_mm_comlt_epu8' defined in ammintrin.h
-
-(cd /usr/lib/llvm19/lib/clang/19/include && cp emmintrin.h xmmintrin.h mmintrin.h immintrin.h intrin.h ammintrin.h -t /usr/x86_64-pc-windows-msvc/usr/include)
-
-# .lib
-(cd 'program files/windows kits/10/lib/10.0.26100.0/um/x64' && cp advapi32.lib bcrypt.lib kernel32.lib ntdll.lib user32.lib uuid.lib ws2_32.lib userenv.lib cfgmgr32.lib dbghelp.lib fwpuclnt.lib -t /usr/x86_64-pc-windows-msvc/usr/lib)
-
-(cd 'contents/vc/tools/msvc/14.16.27023/lib/x64' && cp libcmt.lib libvcruntime.lib -t /usr/x86_64-pc-windows-msvc/usr/lib)
-
-cp 'program files/windows kits/10/lib/10.0.26100.0/ucrt/x64/libucrt.lib' /usr/x86_64-pc-windows-msvc/usr/lib

ci/validate_stable_lance.py

@@ -1,34 +0,0 @@
-import tomllib
-
-found_preview_lance = False
-
-with open("Cargo.toml", "rb") as f:
-    cargo_data = tomllib.load(f)
-
-    for name, dep in cargo_data["workspace"]["dependencies"].items():
-        if name == "lance" or name.startswith("lance-"):
-            if isinstance(dep, str):
-                version = dep
-            elif isinstance(dep, dict):
-                # Version doesn't have the beta tag in it, so we instead look
-                # at the git tag.
-                version = dep.get('tag', dep.get('version'))
-            else:
-                raise ValueError("Unexpected type for dependency: " + str(dep))
-
-            if "beta" in version:
-                found_preview_lance = True
-                print(f"Dependency '{name}' is a preview version: {version}")
-
-with open("python/pyproject.toml", "rb") as f:
-    py_proj_data = tomllib.load(f)
-
-    for dep in py_proj_data["project"]["dependencies"]:
-        if dep.startswith("pylance"):
-            if "b" in dep:
-                found_preview_lance = True
-                print(f"Dependency '{dep}' is a preview version")
-            break  # Only one pylance dependency
-
-if found_preview_lance:
-    raise ValueError("Found preview version of Lance in dependencies")

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

dockerfiles/Dockerfile

@@ -1,27 +0,0 @@
-#Simple base dockerfile that supports basic dependencies required to run lance with FTS and Hybrid Search
-#Usage docker build -t lancedb:latest -f Dockerfile .
-FROM python:3.10-slim-buster
-
-# Install Rust
-RUN apt-get update && apt-get install -y curl build-essential && \
-  curl https://sh.rustup.rs -sSf | sh -s -- -y
-
-# Set the environment variable for Rust
-ENV PATH="/root/.cargo/bin:${PATH}"
-
-# Install protobuf compiler
-RUN apt-get install -y protobuf-compiler && \
-  apt-get clean && \
-  rm -rf /var/lib/apt/lists/*
-
-RUN apt-get -y update &&\
-  apt-get -y upgrade && \
-  apt-get -y install git
-
-
-# Verify installations
-RUN python --version && \
-  rustc --version && \
-  protoc --version
-
-RUN pip install tantivy lancedb

docs/README.md

@@ -9,81 +9,18 @@ unreleased features.
 ## Building the docs
 
 ### Setup
-1. Install LanceDB Python. See setup in [Python contributing guide](../python/CONTRIBUTING.md).
-   Run `make develop` to install the Python package.
-2. Install documentation dependencies. From LanceDB repo root: `pip install -r docs/requirements.txt`
+1. Install LanceDB. From LanceDB repo root: `pip install -e python`
+2. Install dependencies. From LanceDB repo root: `pip install -r docs/requirements.txt`
+3. Make sure you have node and npm setup
+4. Make sure protobuf and libssl are installed
 
-### Preview the docs
+### Building node module and create markdown files
 
-```shell
-cd docs
-mkdocs serve
-```
+See [Javascript docs README](docs/src/javascript/README.md)
 
-If you want to just generate the HTML files:
+### Build docs
+From LanceDB repo root:
 
-```shell
-PYTHONPATH=. mkdocs build -f docs/mkdocs.yml
-```
+Run: `PYTHONPATH=. mkdocs build -f docs/mkdocs.yml`
 
 If successful, you should see a `docs/site` directory that you can verify locally.
-
-## Adding examples
-
-To make sure examples are correct, we put examples in test files so they can be
-run as part of our test suites.
-
-You can see the tests are at:
-
-* Python: `python/python/tests/docs`
-* Typescript: `nodejs/examples/`
-
-### Checking python examples
-
-```shell
-cd python
-pytest -vv python/tests/docs
-```
-
-### Checking typescript examples
-
-The `@lancedb/lancedb` package must be built before running the tests:
-
-```shell
-pushd nodejs
-npm ci
-npm run build
-popd
-```
-
-Then you can run the examples by going to the `nodejs/examples` directory and
-running the tests like a normal npm package:
-
-```shell
-pushd nodejs/examples
-npm ci
-npm test
-popd
-```
-
-## API documentation
-
-### Python
-
-The Python API documentation is organized based on the file `docs/src/python/python.md`.
-We manually add entries there so we can control the organization of the reference page.
-**However, this means any new types must be manually added to the file.** No additional
-steps are needed to generate the API documentation.
-
-### Typescript
-
-The typescript API documentation is generated from the typescript source code using [typedoc](https://typedoc.org/).
-
-When new APIs are added, you must manually re-run the typedoc command to update the API documentation.
-The new files should be checked into the repository.
-
-```shell
-pushd nodejs
-npm run docs
-popd
-```

docs/mkdocs.yml

@@ -1,386 +1,160 @@
-site_name: LanceDB
+site_name: LanceDB Docs
 site_url: https://lancedb.github.io/lancedb/
 repo_url: https://github.com/lancedb/lancedb
 edit_uri: https://github.com/lancedb/lancedb/tree/main/docs/src
 repo_name: lancedb/lancedb
 docs_dir: src
-watch:
-  - src
-  - ../python/python
 
 theme:
   name: "material"
   logo: assets/logo.png
   favicon: assets/logo.png
-  palette:
-    # Palette toggle for light mode
-    - scheme: lancedb
-      primary: custom
-      toggle:
-        icon: material/weather-night
-        name: Switch to dark mode
-    # Palette toggle for dark mode
-    - scheme: slate
-      primary: custom
-      toggle:
-        icon: material/weather-sunny
-        name: Switch to light mode
   features:
     - content.code.copy
     - content.tabs.link
     - content.action.edit
-    - content.tooltips
     - toc.follow
+    - toc.integrate
     - navigation.top
     - navigation.tabs
     - navigation.tabs.sticky
     - navigation.footer
     - navigation.tracking
     - navigation.instant
-    - content.footnote.tooltips
+    - navigation.indexes
+    - navigation.expand
   icon:
     repo: fontawesome/brands/github
-    annotation: material/arrow-right-circle
   custom_dir: overrides
 
 plugins:
-  - search
-  - autorefs
-  - mkdocstrings:
-      handlers:
-        python:
-          paths: [../python]
-          options:
-            docstring_style: numpy
-            heading_level: 3
-            show_source: true
-            show_symbol_type_in_heading: true
-            show_signature_annotations: true
-            show_root_heading: true
-            members_order: source
-            docstring_section_style: list
-            signature_crossrefs: true
-            separate_signature: true
-          import:
-            # for cross references
-            - https://arrow.apache.org/docs/objects.inv
-            - https://pandas.pydata.org/docs/objects.inv
-            - https://lancedb.github.io/lance/objects.inv
-            - https://docs.pydantic.dev/latest/objects.inv
-  - mkdocs-jupyter
-  - render_swagger:
-      allow_arbitrary_locations: true
+- search
+- autorefs
+- mkdocstrings:
+    handlers:
+      python:
+        paths: [../python]
+        selection:
+          docstring_style: numpy
+        rendering:
+          heading_level: 4
+          show_source: true
+          show_symbol_type_in_heading: true
+          show_signature_annotations: true
+          show_root_heading: true
+          members_order: source
+        import:
+          # for cross references
+          - https://arrow.apache.org/docs/objects.inv
+          - https://pandas.pydata.org/docs/objects.inv
+- mkdocs-jupyter
 
 markdown_extensions:
-  - admonition
-  - footnotes
-  - pymdownx.critic
-  - pymdownx.caret
-  - pymdownx.keys
-  - pymdownx.mark
-  - pymdownx.tilde
-  - pymdownx.details
-  - pymdownx.highlight:
-      anchor_linenums: true
-      line_spans: __span
-      pygments_lang_class: true
-  - pymdownx.inlinehilite
-  - pymdownx.snippets:
-      base_path: ..
-      dedent_subsections: true
-  - pymdownx.superfences
-  - pymdownx.tabbed:
-      alternate_style: true
-  - md_in_html
-  - abbr
-  - attr_list
-  - pymdownx.snippets
-  - pymdownx.emoji:
-      emoji_index: !!python/name:material.extensions.emoji.twemoji
-      emoji_generator: !!python/name:material.extensions.emoji.to_svg
-  - markdown.extensions.toc:
-      baselevel: 1
-      permalink: ""
+- admonition
+- footnotes
+- pymdownx.superfences
+- pymdownx.details
+- pymdownx.highlight:
+    anchor_linenums: true
+    line_spans: __span
+    pygments_lang_class: true
+- pymdownx.inlinehilite
+- pymdownx.snippets
+- pymdownx.superfences
+- pymdownx.tabbed:
+    alternate_style: true
+- md_in_html
 
 nav:
-  - Home:
-      - LanceDB: index.md
-      - 🏃🏼‍♂️ Quick start: basic.md
-      - 📚 Concepts:
-          - Vector search: concepts/vector_search.md
-          - Indexing:
-              - IVFPQ: concepts/index_ivfpq.md
-              - HNSW: concepts/index_hnsw.md
-          - Storage: concepts/storage.md
-          - Data management: concepts/data_management.md
-      - 🔨 Guides:
-          - Working with tables: guides/tables.md
-          - Building a vector index: ann_indexes.md
-          - Vector Search: search.md
-          - Full-text search (native): fts.md
-          - Full-text search (tantivy-based): fts_tantivy.md
-          - Building a scalar index: guides/scalar_index.md
-          - Hybrid search:
-              - Overview: hybrid_search/hybrid_search.md
-              - Comparing Rerankers: hybrid_search/eval.md
-              - Airbnb financial data example: notebooks/hybrid_search.ipynb
-          - Late interaction with MultiVector search:
-              - Overview: guides/multi-vector.md
-              - Example: notebooks/Multivector_on_LanceDB.ipynb
-          - RAG:
-              - Vanilla RAG: rag/vanilla_rag.md
-              - Multi-head RAG: rag/multi_head_rag.md
-              - Corrective RAG: rag/corrective_rag.md
-              - Agentic RAG: rag/agentic_rag.md
-              - Graph RAG: rag/graph_rag.md
-              - Self RAG: rag/self_rag.md
-              - Adaptive RAG: rag/adaptive_rag.md
-              - SFR RAG: rag/sfr_rag.md
-              - Advanced Techniques:
-                  - HyDE: rag/advanced_techniques/hyde.md
-                  - FLARE: rag/advanced_techniques/flare.md
-          - Reranking:
-              - Quickstart: reranking/index.md
-              - Cohere Reranker: reranking/cohere.md
-              - Linear Combination Reranker: reranking/linear_combination.md
-              - Reciprocal Rank Fusion Reranker: reranking/rrf.md
-              - Cross Encoder Reranker: reranking/cross_encoder.md
-              - ColBERT Reranker: reranking/colbert.md
-              - Jina Reranker: reranking/jina.md
-              - OpenAI Reranker: reranking/openai.md
-              - AnswerDotAi Rerankers: reranking/answerdotai.md
-              - Voyage AI Rerankers: reranking/voyageai.md
-              - Building Custom Rerankers: reranking/custom_reranker.md
-              - Example: notebooks/lancedb_reranking.ipynb
-          - Filtering: sql.md
-          - Versioning & Reproducibility:
-              - sync API: notebooks/reproducibility.ipynb
-              - async API: notebooks/reproducibility_async.ipynb
-          - Configuring Storage: guides/storage.md
-          - Migration Guide: migration.md
-          - Tuning retrieval performance:
-              - Choosing right query type: guides/tuning_retrievers/1_query_types.md
-              - Reranking: guides/tuning_retrievers/2_reranking.md
-              - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
-          - Build MCP with LanceDB: guides/mcp.md
-      - 🧬 Managing embeddings:
-          - Understand Embeddings: embeddings/understanding_embeddings.md
-          - Get Started: embeddings/index.md
-          - Embedding functions: embeddings/embedding_functions.md
-          - Available models:
-              - Overview: embeddings/default_embedding_functions.md
-              - Text Embedding Functions:
-                  - Sentence Transformers: embeddings/available_embedding_models/text_embedding_functions/sentence_transformers.md
-                  - Huggingface Embedding Models: embeddings/available_embedding_models/text_embedding_functions/huggingface_embedding.md
-                  - Ollama Embeddings: embeddings/available_embedding_models/text_embedding_functions/ollama_embedding.md
-                  - OpenAI Embeddings: embeddings/available_embedding_models/text_embedding_functions/openai_embedding.md
-                  - Instructor Embeddings: embeddings/available_embedding_models/text_embedding_functions/instructor_embedding.md
-                  - Gemini Embeddings: embeddings/available_embedding_models/text_embedding_functions/gemini_embedding.md
-                  - Cohere Embeddings: embeddings/available_embedding_models/text_embedding_functions/cohere_embedding.md
-                  - Jina Embeddings: embeddings/available_embedding_models/text_embedding_functions/jina_embedding.md
-                  - AWS Bedrock Text Embedding Functions: embeddings/available_embedding_models/text_embedding_functions/aws_bedrock_embedding.md
-                  - IBM watsonx.ai Embeddings: embeddings/available_embedding_models/text_embedding_functions/ibm_watsonx_ai_embedding.md
-                  - Voyage AI Embeddings: embeddings/available_embedding_models/text_embedding_functions/voyageai_embedding.md
-              - Multimodal Embedding Functions:
-                  - OpenClip embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/openclip_embedding.md
-                  - Imagebind embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md
-                  - Jina Embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md
-          - User-defined embedding functions: embeddings/custom_embedding_function.md
-          - Variables and secrets: embeddings/variables_and_secrets.md
-          - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
-          - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
-      - 🔌 Integrations:
-          - Tools and data formats: integrations/index.md
-          - Pandas and PyArrow: python/pandas_and_pyarrow.md
-          - Polars: python/polars_arrow.md
-          - DuckDB: python/duckdb.md
-          - LangChain:
-              - LangChain 🔗: integrations/langchain.md
-              - LangChain demo: notebooks/langchain_demo.ipynb
-              - LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
-          - LlamaIndex 🦙:
-              - LlamaIndex docs: integrations/llamaIndex.md
-              - LlamaIndex demo: notebooks/llamaIndex_demo.ipynb
-          - Pydantic: python/pydantic.md
-          - Voxel51: integrations/voxel51.md
-          - PromptTools: integrations/prompttools.md
-          - dlt: integrations/dlt.md
-          - phidata: integrations/phidata.md
-      - 🎯 Examples:
-          - Overview: examples/index.md
-          - 🐍 Python:
-              - Overview: examples/examples_python.md
-              - Build From Scratch: examples/python_examples/build_from_scratch.md
-              - Multimodal: examples/python_examples/multimodal.md
-              - Rag: examples/python_examples/rag.md
-              - Vector Search: examples/python_examples/vector_search.md
-              - Chatbot: examples/python_examples/chatbot.md
-              - Evaluation: examples/python_examples/evaluations.md
-              - AI Agent: examples/python_examples/aiagent.md
-              - Recommender System: examples/python_examples/recommendersystem.md
-              - Miscellaneous:
-                  - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
-                  - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
-          - 👾 JavaScript:
-              - Overview: examples/examples_js.md
-              - Serverless Website Chatbot: examples/serverless_website_chatbot.md
-              - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
-              - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
-          - 🦀 Rust:
-              - Overview: examples/examples_rust.md
-      - 📓 Studies:
-          - ↗Improve retrievers with hybrid search and reranking: https://blog.lancedb.com/hybrid-search-and-reranking-report/
-      - 💭 FAQs: faq.md
-      - 🔍 Troubleshooting: troubleshooting.md
-      - ⚙️ API reference:
-          - 🐍 Python: python/python.md
-          - 👾 JavaScript (vectordb): javascript/modules.md
-          - 👾 JavaScript (lancedb): js/globals.md
-          - 🦀 Rust: https://docs.rs/lancedb/latest/lancedb/
+- Home:
+  - 🏢 Home: index.md
+  - 💡 Basics: basic.md
+  - 📚 Guides:
+    - Create Ingest Update Delete: guides/tables.md
+    - Vector Search: search.md
+    - SQL filters: sql.md
+    - Indexing: ann_indexes.md
+    - Versioning & Reproducibility: notebooks/reproducibility.ipynb
+  - 🧬 Embeddings:
+    - embeddings/index.md
+    - Ingest Embedding Functions: embeddings/embedding_functions.md
+    - Available Functions: embeddings/default_embedding_functions.md
+    - Create Custom Embedding Functions: embeddings/api.md
+    - Example - Multi-lingual semantic search: notebooks/multi_lingual_example.ipynb
+    - Example - MultiModal CLIP Embeddings: notebooks/DisappearingEmbeddingFunction.ipynb
+  - 🔍 Python full-text search: fts.md
+  - 🔌 Integrations:
+    - integrations/index.md
+    - Pandas and PyArrow: python/arrow.md
+    - DuckDB: python/duckdb.md
+    - LangChain 🔗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
+    - LangChain JS/TS 🔗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
+    - LlamaIndex 🦙: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
+    - Pydantic: python/pydantic.md
+    - Voxel51: integrations/voxel51.md
+    - PromptTools: integrations/prompttools.md
+  - 🐍 Python examples:
+    - examples/index.md
+    - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+    - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+    - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+    - Example - Calculate CLIP Embeddings with Roboflow Inference: examples/image_embeddings_roboflow.md
+    - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+    - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
+  - 🌐 Javascript examples:
+    - Examples: examples/index_js.md
+    - Serverless Website Chatbot: examples/serverless_website_chatbot.md
+    - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
+    - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
+  - ⚙️ CLI & Config: cli_config.md
 
-  - Quick start: basic.md
-  - Concepts:
-      - Vector search: concepts/vector_search.md
-      - Indexing:
-          - IVFPQ: concepts/index_ivfpq.md
-          - HNSW: concepts/index_hnsw.md
-      - Storage: concepts/storage.md
-      - Data management: concepts/data_management.md
-  - Guides:
-      - Working with tables: guides/tables.md
-      - Building an ANN index: ann_indexes.md
-      - Vector Search: search.md
-      - Full-text search (native): fts.md
-      - Full-text search (tantivy-based): fts_tantivy.md
-      - Building a scalar index: guides/scalar_index.md
-      - Hybrid search:
-          - Overview: hybrid_search/hybrid_search.md
-          - Comparing Rerankers: hybrid_search/eval.md
-          - Airbnb financial data example: notebooks/hybrid_search.ipynb
-      - Late interaction with MultiVector search:
-          - Overview: guides/multi-vector.md
-          - Document search Example: notebooks/Multivector_on_LanceDB.ipynb
-      - RAG:
-          - Vanilla RAG: rag/vanilla_rag.md
-          - Multi-head RAG: rag/multi_head_rag.md
-          - Corrective RAG: rag/corrective_rag.md
-          - Agentic RAG: rag/agentic_rag.md
-          - Graph RAG: rag/graph_rag.md
-          - Self RAG: rag/self_rag.md
-          - Adaptive RAG: rag/adaptive_rag.md
-          - SFR RAG: rag/sfr_rag.md
-          - Advanced Techniques:
-              - HyDE: rag/advanced_techniques/hyde.md
-              - FLARE: rag/advanced_techniques/flare.md
-      - Reranking:
-          - Quickstart: reranking/index.md
-          - Cohere Reranker: reranking/cohere.md
-          - Linear Combination Reranker: reranking/linear_combination.md
-          - Reciprocal Rank Fusion Reranker: reranking/rrf.md
-          - Cross Encoder Reranker: reranking/cross_encoder.md
-          - ColBERT Reranker: reranking/colbert.md
-          - Jina Reranker: reranking/jina.md
-          - OpenAI Reranker: reranking/openai.md
-          - AnswerDotAi Rerankers: reranking/answerdotai.md
-          - Building Custom Rerankers: reranking/custom_reranker.md
-          - Example: notebooks/lancedb_reranking.ipynb
-      - Filtering: sql.md
-      - Versioning & Reproducibility:
-          - sync API: notebooks/reproducibility.ipynb
-          - async API: notebooks/reproducibility_async.ipynb
-      - Configuring Storage: guides/storage.md
-      - Migration Guide: migration.md
-      - Tuning retrieval performance:
-          - Choosing right query type: guides/tuning_retrievers/1_query_types.md
-          - Reranking: guides/tuning_retrievers/2_reranking.md
-          - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
-      - Build MCP with LanceDB: guides/mcp.md
-  - Managing Embeddings:
-      - Understand Embeddings: embeddings/understanding_embeddings.md
-      - Get Started: embeddings/index.md
-      - Embedding functions: embeddings/embedding_functions.md
-      - Available models:
-          - Overview: embeddings/default_embedding_functions.md
-          - Text Embedding Functions:
-              - Sentence Transformers: embeddings/available_embedding_models/text_embedding_functions/sentence_transformers.md
-              - Huggingface Embedding Models: embeddings/available_embedding_models/text_embedding_functions/huggingface_embedding.md
-              - Ollama Embeddings: embeddings/available_embedding_models/text_embedding_functions/ollama_embedding.md
-              - OpenAI Embeddings: embeddings/available_embedding_models/text_embedding_functions/openai_embedding.md
-              - Instructor Embeddings: embeddings/available_embedding_models/text_embedding_functions/instructor_embedding.md
-              - Gemini Embeddings: embeddings/available_embedding_models/text_embedding_functions/gemini_embedding.md
-              - Cohere Embeddings: embeddings/available_embedding_models/text_embedding_functions/cohere_embedding.md
-              - Jina Embeddings: embeddings/available_embedding_models/text_embedding_functions/jina_embedding.md
-              - AWS Bedrock Text Embedding Functions: embeddings/available_embedding_models/text_embedding_functions/aws_bedrock_embedding.md
-              - IBM watsonx.ai Embeddings: embeddings/available_embedding_models/text_embedding_functions/ibm_watsonx_ai_embedding.md
-          - Multimodal Embedding Functions:
-              - OpenClip embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/openclip_embedding.md
-              - Imagebind embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md
-              - Jina Embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md
-      - User-defined embedding functions: embeddings/custom_embedding_function.md
-      - Variables and secrets: embeddings/variables_and_secrets.md
-      - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
-      - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
-  - Integrations:
-      - Overview: integrations/index.md
-      - Pandas and PyArrow: python/pandas_and_pyarrow.md
-      - Polars: python/polars_arrow.md
-      - DuckDB: python/duckdb.md
-      - LangChain 🦜️🔗↗: integrations/langchain.md
-      - LangChain.js 🦜️🔗↗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
-      - LlamaIndex 🦙↗: integrations/llamaIndex.md
-      - Pydantic: python/pydantic.md
-      - Voxel51: integrations/voxel51.md
-      - PromptTools: integrations/prompttools.md
-      - dlt: integrations/dlt.md
-      - phidata: integrations/phidata.md
-  - Examples:
-      - examples/index.md
-      - 🐍 Python:
-          - Overview: examples/examples_python.md
-          - Build From Scratch: examples/python_examples/build_from_scratch.md
-          - Multimodal: examples/python_examples/multimodal.md
-          - Rag: examples/python_examples/rag.md
-          - Vector Search: examples/python_examples/vector_search.md
-          - Chatbot: examples/python_examples/chatbot.md
-          - Evaluation: examples/python_examples/evaluations.md
-          - AI Agent: examples/python_examples/aiagent.md
-          - Recommender System: examples/python_examples/recommendersystem.md
-          - Miscellaneous:
-              - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
-              - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
-      - 👾 JavaScript:
-          - Overview: examples/examples_js.md
-          - Serverless Website Chatbot: examples/serverless_website_chatbot.md
-          - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
-          - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
-      - 🦀 Rust:
-          - Overview: examples/examples_rust.md
-  - Studies:
-      - studies/overview.md
-      - ↗Improve retrievers with hybrid search and reranking: https://blog.lancedb.com/hybrid-search-and-reranking-report/
-  - API reference:
-      - Overview: api_reference.md
-      - Python: python/python.md
-      - Javascript (vectordb): javascript/modules.md
-      - Javascript (lancedb): js/globals.md
-      - Rust: https://docs.rs/lancedb/latest/lancedb/index.html
+- Basics: basic.md
+- Guides:
+  - Create Ingest Update Delete: guides/tables.md
+  - Vector Search: search.md
+  - SQL filters: sql.md
+  - Indexing: ann_indexes.md
+  - Versioning & Reproducibility: notebooks/reproducibility.ipynb
+- Embeddings:
+  - embeddings/index.md
+  - Ingest Embedding Functions: embeddings/embedding_functions.md
+  - Available Functions: embeddings/default_embedding_functions.md
+  - Create Custom Embedding Functions: embeddings/api.md
+  - Example - Multi-lingual semantic search: notebooks/multi_lingual_example.ipynb
+  - Example - MultiModal CLIP Embeddings: notebooks/DisappearingEmbeddingFunction.ipynb
+- Python full-text search: fts.md
+- Integrations:
+  - integrations/index.md
+  - Pandas and PyArrow: python/arrow.md
+  - DuckDB: python/duckdb.md
+  - LangChain 🦜️🔗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
+  - LangChain JS/TS 🦜️🔗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
+  - LlamaIndex 🦙: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
+  - Pydantic: python/pydantic.md
+  - Voxel51: integrations/voxel51.md
+  - PromptTools: integrations/prompttools.md
+- Python examples:
+  - examples/index.md
+  - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+  - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+  - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+  - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+  - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
+- Javascript examples:
+  - examples/index_js.md
+  - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
+  - Serverless Chatbot from any website: examples/serverless_website_chatbot.md
+  - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
+- API references:
+  - OSS Python API: python/python.md
+  - SaaS Python API: python/saas-python.md
+  - Javascript API: javascript/modules.md
+- LanceDB Cloud↗: https://noteforms.com/forms/lancedb-mailing-list-cloud-kty1o5?notionforms=1&utm_source=notionforms
 
 extra_css:
   - styles/global.css
-  - styles/extra.css
-
-extra_javascript:
-  - "extra_js/init_ask_ai_widget.js"
-  - "extra_js/reo.js"
 
 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/openapi.yml

@@ -1,513 +0,0 @@
-openapi: 3.1.0
-info:
-  version: 1.0.0
-  title: LanceDB Cloud API
-  description: |
-    LanceDB Cloud API is a RESTful API that allows users to access and modify data stored in LanceDB Cloud.
-    Table actions are considered temporary resource creations and all use POST method.
-  contact:
-    name: LanceDB support
-    url: https://lancedb.com
-    email: contact@lancedb.com
-
-servers:
-  - url: https://{db}.{region}.api.lancedb.com
-    description: LanceDB Cloud REST endpoint.
-    variables:
-      db:
-        default: ""
-        description: the name of DB
-      region:
-        default: "us-east-1"
-        description: the service region of the DB
-
-security:
-  - key_auth: []
-
-components:
-  securitySchemes:
-    key_auth:
-      name: x-api-key
-      type: apiKey
-      in: header
-  parameters:
-    table_name:
-      name: name
-      in: path
-      description: name of the table
-      required: true
-      schema:
-        type: string
-    index_name:
-      name: index_name
-      in: path
-      description: name of the index
-      required: true
-      schema:
-        type: string
-  responses:
-    invalid_request:
-      description: Invalid request
-      content:
-        text/plain:
-          schema:
-            type: string
-    not_found:
-      description: Not found
-      content:
-        text/plain:
-          schema:
-            type: string
-    unauthorized:
-      description: Unauthorized
-      content:
-        text/plain:
-          schema:
-            type: string
-  requestBodies:
-    arrow_stream_buffer:
-      description: Arrow IPC stream buffer
-      required: true
-      content:
-        application/vnd.apache.arrow.stream:
-          schema:
-            type: string
-            format: binary
-
-paths:
-  /v1/table/:
-    get:
-      description: List tables, optionally, with pagination.
-      tags:
-        - Tables
-      summary: List Tables
-      operationId: listTables
-      parameters:
-        - name: limit
-          in: query
-          description: Limits the number of items to return.
-          schema:
-            type: integer
-        - name: page_token
-          in: query
-          description: Specifies the starting position of the next query
-          schema:
-            type: string
-      responses:
-        "200":
-          description: Successfully returned a list of tables in the DB
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  tables:
-                    type: array
-                    items:
-                      type: string
-                  page_token:
-                    type: string
-
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/create/:
-    post:
-      description: Create a new table
-      summary: Create a new table
-      operationId: createTable
-      tags:
-        - Tables
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Table successfully created
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/query/:
-    post:
-      description: Vector Query
-      url: https://{db-uri}.{aws-region}.api.lancedb.com/v1/table/{name}/query/
-      tags:
-        - Data
-      summary: Vector Query
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                vector:
-                  type: FixedSizeList
-                  description: |
-                    The targetted vector to search for. Required.
-                vector_column:
-                  type: string
-                  description: |
-                    The column to query, it can be inferred from the schema if there is only one vector column.
-                prefilter:
-                  type: boolean
-                  description: |
-                    Whether to prefilter the data. Optional.
-                k:
-                  type: integer
-                  description: |
-                    The number of search results to return. Default is 10.
-                distance_type:
-                  type: string
-                  description: |
-                    The distance metric to use for search. l2, Cosine, Dot and Hamming are supported. Default is l2.
-                bypass_vector_index:
-                  type: boolean
-                  description: |
-                    Whether to bypass vector index. Optional.
-                filter:
-                  type: string
-                  description: |
-                    A filter expression that specifies the rows to query. Optional.
-                columns:
-                  type: array
-                  items:
-                    type: string
-                  description: |
-                    The columns to return. Optional.
-                nprobe:
-                  type: integer
-                  description: |
-                    The number of probes to use for search. Optional.
-                refine_factor:
-                  type: integer
-                  description: |
-                    The refine factor to use for search. Optional.
-                  default: null
-                fast_search:
-                  type: boolean
-                  description: |
-                    Whether to use fast search. Optional.
-                  default: false
-              required:
-                - vector
-
-      responses:
-        "200":
-          description: top k results if query is successfully executed
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  results:
-                    type: array
-                    items:
-                      type: object
-                      properties:
-                        id:
-                          type: integer
-                        selected_col_1_to_return:
-                          type: col_1_type
-                        selected_col_n_to_return:
-                          type: col_n_type
-                        _distance:
-                          type: float
-
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/insert/:
-    post:
-      description: Insert new data to the Table.
-      tags:
-        - Data
-      operationId: insertData
-      summary: Insert new data.
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Insert successful
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/merge_insert/:
-    post:
-      description: Create a "merge insert" operation
-        This operation can add rows, update rows, and remove rows all in a single
-        transaction. See python method `lancedb.table.Table.merge_insert` for examples.
-      tags:
-        - Data
-      summary: Merge Insert
-      operationId: mergeInsert
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-        - name: on
-          in: query
-          description: |
-            The column to use as the primary key for the merge operation.
-          required: true
-          schema:
-            type: string
-        - name: when_matched_update_all
-          in: query
-          description: |
-            Rows that exist in both the source table (new data) and
-            the target table (old data) will be updated, replacing
-            the old row with the corresponding matching row.
-          required: false
-          schema:
-            type: boolean
-        - name: when_matched_update_all_filt
-          in: query
-          description: |
-            If present then only rows that satisfy the filter expression will
-            be updated
-          required: false
-          schema:
-            type: string
-        - name: when_not_matched_insert_all
-          in: query
-          description: |
-            Rows that exist only in the source table (new data) will be
-            inserted into the target table (old data).
-          required: false
-          schema:
-            type: boolean
-        - name: when_not_matched_by_source_delete
-          in: query
-          description: |
-            Rows that exist only in the target table (old data) will be
-            deleted. An optional condition (`when_not_matched_by_source_delete_filt`)
-            can be provided to limit what data is deleted.
-          required: false
-          schema:
-            type: boolean
-        - name: when_not_matched_by_source_delete_filt
-          in: query
-          description: |
-            The filter expression that specifies the rows to delete.
-          required: false
-          schema:
-            type: string
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Merge Insert successful
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/delete/:
-    post:
-      description: Delete rows from a table.
-      tags:
-        - Data
-      summary: Delete rows from a table
-      operationId: deleteData
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                predicate:
-                  type: string
-                  description: |
-                    A filter expression that specifies the rows to delete.
-      responses:
-        "200":
-          description: Delete successful
-        "401":
-          $ref: "#/components/responses/unauthorized"
-  /v1/table/{name}/drop/:
-    post:
-      description: Drop a table
-      tags:
-        - Tables
-      summary: Drop a table
-      operationId: dropTable
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Drop successful
-        "401":
-          $ref: "#/components/responses/unauthorized"
-
-  /v1/table/{name}/describe/:
-    post:
-      description: Describe a table and return Table Information.
-      tags:
-        - Tables
-      summary: Describe a table
-      operationId: describeTable
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      responses:
-        "200":
-          description: Table information
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  table:
-                    type: string
-                  version:
-                    type: integer
-                  schema:
-                    type: string
-                  stats:
-                    type: object
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/index/list/:
-    post:
-      description: List indexes of a table
-      tags:
-        - Tables
-      summary: List indexes of a table
-      operationId: listIndexes
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      responses:
-        "200":
-          description: Available list of indexes on the table.
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  indexes:
-                    type: array
-                    items:
-                      type: object
-                      properties:
-                        columns:
-                          type: array
-                          items:
-                            type: string
-                        index_name:
-                          type: string
-                        index_uuid:
-                          type: string
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/create_index/:
-    post:
-      description: Create vector index on a Table
-      tags:
-        - Tables
-      summary: Create vector index on a Table
-      operationId: createIndex
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                column:
-                  type: string
-                metric_type:
-                  type: string
-                  nullable: false
-                  description: |
-                    The metric type to use for the index. l2, Cosine, Dot are supported.
-                index_type:
-                  type: string
-      responses:
-        "200":
-          description: Index successfully created
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/create_scalar_index/:
-    post:
-      description: Create a scalar index on a table
-      tags:
-        - Tables
-      summary: Create a scalar index on a table
-      operationId: createScalarIndex
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                column:
-                  type: string
-                index_type:
-                  type: string
-                  required: false
-      responses:
-        "200":
-          description: Scalar Index successfully created
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/index/{index_name}/drop/:
-    post:
-      description: Drop an index from the table
-      tags:
-        - Tables
-      summary: Drop an index from the table
-      operationId: dropIndex
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-        - $ref: "#/components/parameters/index_name"
-      responses:
-        "200":
-          description: Index successfully dropped
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"

docs/package-lock.json

@@ -1,135 +0,0 @@
-{
-  "name": "lancedb-docs-test",
-  "version": "1.0.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "lancedb-docs-test",
-      "version": "1.0.0",
-      "license": "Apache 2",
-      "dependencies": {
-        "apache-arrow": "file:../node/node_modules/apache-arrow",
-        "vectordb": "file:../node"
-      },
-      "devDependencies": {
-        "@types/node": "^20.11.8",
-        "typescript": "^5.3.3"
-      }
-    },
-    "../node": {
-      "name": "vectordb",
-      "version": "0.12.0",
-      "cpu": [
-        "x64",
-        "arm64"
-      ],
-      "license": "Apache-2.0",
-      "os": [
-        "darwin",
-        "linux",
-        "win32"
-      ],
-      "dependencies": {
-        "@neon-rs/load": "^0.0.74",
-        "axios": "^1.4.0"
-      },
-      "devDependencies": {
-        "@neon-rs/cli": "^0.0.160",
-        "@types/chai": "^4.3.4",
-        "@types/chai-as-promised": "^7.1.5",
-        "@types/mocha": "^10.0.1",
-        "@types/node": "^18.16.2",
-        "@types/sinon": "^10.0.15",
-        "@types/temp": "^0.9.1",
-        "@types/uuid": "^9.0.3",
-        "@typescript-eslint/eslint-plugin": "^5.59.1",
-        "apache-arrow-old": "npm:apache-arrow@13.0.0",
-        "cargo-cp-artifact": "^0.1",
-        "chai": "^4.3.7",
-        "chai-as-promised": "^7.1.1",
-        "eslint": "^8.39.0",
-        "eslint-config-standard-with-typescript": "^34.0.1",
-        "eslint-plugin-import": "^2.26.0",
-        "eslint-plugin-n": "^15.7.0",
-        "eslint-plugin-promise": "^6.1.1",
-        "mocha": "^10.2.0",
-        "openai": "^4.24.1",
-        "sinon": "^15.1.0",
-        "temp": "^0.9.4",
-        "ts-node": "^10.9.1",
-        "ts-node-dev": "^2.0.0",
-        "typedoc": "^0.24.7",
-        "typedoc-plugin-markdown": "^3.15.3",
-        "typescript": "^5.1.0",
-        "uuid": "^9.0.0"
-      },
-      "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.12.0",
-        "@lancedb/vectordb-darwin-x64": "0.12.0",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.12.0",
-        "@lancedb/vectordb-linux-x64-gnu": "0.12.0",
-        "@lancedb/vectordb-win32-x64-msvc": "0.12.0"
-      },
-      "peerDependencies": {
-        "@apache-arrow/ts": "^14.0.2",
-        "apache-arrow": "^14.0.2"
-      }
-    },
-    "../node/node_modules/apache-arrow": {
-      "version": "14.0.2",
-      "license": "Apache-2.0",
-      "dependencies": {
-        "@types/command-line-args": "5.2.0",
-        "@types/command-line-usage": "5.0.2",
-        "@types/node": "20.3.0",
-        "@types/pad-left": "2.1.1",
-        "command-line-args": "5.2.1",
-        "command-line-usage": "7.0.1",
-        "flatbuffers": "23.5.26",
-        "json-bignum": "^0.0.3",
-        "pad-left": "^2.1.0",
-        "tslib": "^2.5.3"
-      },
-      "bin": {
-        "arrow2csv": "bin/arrow2csv.js"
-      }
-    },
-    "node_modules/@types/node": {
-      "version": "20.11.8",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.11.8.tgz",
-      "integrity": "sha512-i7omyekpPTNdv4Jb/Rgqg0RU8YqLcNsI12quKSDkRXNfx7Wxdm6HhK1awT3xTgEkgxPn3bvnSpiEAc7a7Lpyow==",
-      "dev": true,
-      "dependencies": {
-        "undici-types": "~5.26.4"
-      }
-    },
-    "node_modules/apache-arrow": {
-      "resolved": "../node/node_modules/apache-arrow",
-      "link": true
-    },
-    "node_modules/typescript": {
-      "version": "5.3.3",
-      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.3.3.tgz",
-      "integrity": "sha512-pXWcraxM0uxAS+tN0AG/BF2TyqmHO014Z070UsJ+pFvYuRSq8KH8DmWpnbXe0pEPDHXZV3FcAbJkijJ5oNEnWw==",
-      "dev": true,
-      "bin": {
-        "tsc": "bin/tsc",
-        "tsserver": "bin/tsserver"
-      },
-      "engines": {
-        "node": ">=14.17"
-      }
-    },
-    "node_modules/undici-types": {
-      "version": "5.26.5",
-      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
-      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
-      "dev": true
-    },
-    "node_modules/vectordb": {
-      "resolved": "../node",
-      "link": true
-    }
-  }
-}

docs/package.json

@@ -1,20 +0,0 @@
-{
-  "name": "lancedb-docs-test",
-  "version": "1.0.0",
-  "description": "auto-generated tests from doc",
-  "author": "dev@lancedb.com",
-  "license": "Apache 2",
-  "dependencies": {
-    "apache-arrow": "file:../node/node_modules/apache-arrow",
-    "vectordb": "file:../node"
-  },
-  "scripts": {
-    "build": "tsc -b && cd ../node && npm run build-release",
-    "example": "npm run build && node",
-    "test": "npm run build && ls dist/*.js | xargs -n 1 node"
-  },
-  "devDependencies": {
-    "@types/node": "^20.11.8",
-    "typescript": "^5.3.3"
-  }
-}

docs/requirements.txt

@@ -1,7 +1,4 @@
-mkdocs==1.5.3
+mkdocs==1.4.2
 mkdocs-jupyter==0.24.1
-mkdocs-material==9.5.3
-mkdocstrings[python]==0.25.2
-griffe
-mkdocs-render-swagger-plugin
-pydantic
+mkdocs-material==9.1.3
+mkdocstrings[python]==0.20.0

docs/src/ann_indexes.md

@@ -1,93 +1,67 @@
-# Approximate Nearest Neighbor (ANN) Indexes
+# ANN (Approximate Nearest Neighbor) Indexes
 
-An ANN or a vector index is a data structure specifically designed to efficiently organize and
-search vector data based on their similarity via the chosen distance metric.
-By constructing a vector index, the search space is effectively narrowed down, avoiding the need
-for brute-force scanning of the entire vector space.
-A vector index is faster but less accurate than exhaustive search (kNN or flat search).
+You can create an index over your vector data to make search faster.
+Vector indexes are faster but less accurate than exhaustive search (KNN or Flat Search).
 LanceDB provides many parameters to fine-tune the index's size, the speed of queries, and the accuracy of results.
 
-## Disk-based Index
+Currently, LanceDB does *not* automatically create the ANN index.
+LanceDB has optimized code for KNN as well. For many use-cases, datasets under 100K vectors won't require index creation at all.
+If you can live with < 100ms latency, skipping index creation is a simpler workflow while guaranteeing 100% recall.
 
-Lance provides an `IVF_PQ` disk-based index. It uses **Inverted File Index (IVF)** to first divide
-the dataset into `N` partitions, and then applies **Product Quantization** to compress vectors in each partition.
-See the [indexing](concepts/index_ivfpq.md) concepts guide for more information on how this works.
+In the future we will look to automatically create and configure the ANN index.
+
+## Types of Index
+
+Lance can support multiple index types, the most widely used one is `IVF_PQ`.
+
+* `IVF_PQ`: use **Inverted File Index (IVF)** to first divide the dataset into `N` partitions,
+   and then use **Product Quantization** to compress vectors in each partition.
+* `DISKANN` (**Experimental**): organize the vector as a on-disk graph, where the vertices approximately
+   represent the nearest neighbors of each vector.
 
 ## Creating an IVF_PQ Index
 
 Lance supports `IVF_PQ` index type by default.
 
 === "Python"
-    === "Sync API"
+     Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
 
-        Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
+     ```python
+     import lancedb
+     import numpy as np
+     uri = "data/sample-lancedb"
+     db = lancedb.connect(uri)
 
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-numpy"
-        --8<-- "python/python/tests/docs/test_guide_index.py:create_ann_index"
-        ```
-    === "Async API"
-        Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
+     # Create 10,000 sample vectors
+     data = [{"vector": row, "item": f"item {i}"}
+        for i, row in enumerate(np.random.random((10_000, 1536)).astype('float32'))]
 
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-numpy"
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb-ivfpq"
-        --8<-- "python/python/tests/docs/test_guide_index.py:create_ann_index_async"
-        ```
+     # Add the vectors to a table
+     tbl = db.create_table("my_vectors", data=data)
 
-=== "TypeScript"
+     # Create and train the index - you need to have enough data in the table for an effective training step
+     tbl.create_index(num_partitions=256, num_sub_vectors=96)
+     ```
 
-    === "@lancedb/lancedb"
+=== "Javascript"
+     ```javascript
+     const vectordb = require('vectordb')
+     const db = await vectordb.connect('data/sample-lancedb')
 
-        Creating indexes is done via the [lancedb.Table.createIndex](../js/classes/Table.md/#createIndex) method.
+     let data = []
+     for (let i = 0; i < 10_000; i++) {
+         data.push({vector: Array(1536).fill(i), id: `${i}`, content: "", longId: `${i}`},)
+     }
+     const table = await db.createTable('my_vectors', data)
+     await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 256, num_sub_vectors: 96 })
+     ```
 
-        ```typescript
-        --8<--- "nodejs/examples/ann_indexes.test.ts:import"
-
-        --8<-- "nodejs/examples/ann_indexes.test.ts:ingest"
-        ```
-
-    === "vectordb (deprecated)"
-
-        Creating indexes is done via the [lancedb.Table.createIndex](../javascript/interfaces/Table.md/#createIndex) method.
-
-        ```typescript
-        --8<--- "docs/src/ann_indexes.ts:import"
-
-        --8<-- "docs/src/ann_indexes.ts:ingest"
-        ```
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/ivf_pq.rs:create_index"
-    ```
-
-    IVF_PQ index parameters are more fully defined in the [crate docs](https://docs.rs/lancedb/latest/lancedb/index/vector/struct.IvfPqIndexBuilder.html).
-
-The following IVF_PQ paramters can be specified:
-
-- **distance_type**: The distance metric to use. By default it uses euclidean distance "`l2`".
-  We also support "cosine" and "dot" distance as well.
-- **num_partitions**: The number of partitions in the index. The default is the square root
-  of the number of rows.
-
-!!! note
-
-    In the synchronous python SDK and node's `vectordb` the default is 256. This default has
-    changed in the asynchronous python SDK and node's `lancedb`.
-
-- **num_sub_vectors**: The number of sub-vectors (M) that will be created during Product Quantization (PQ).
-  For D dimensional vector, it will be divided into `M` subvectors with dimension `D/M`, each of which is replaced by
-  a single PQ code. The default is the dimension of the vector divided by 16.
-- **num_bits**: The number of bits used to encode each sub-vector. Only 4 and 8 are supported. The higher the number of bits, the higher the accuracy of the index, also the slower search. The default is 8.
-
-!!! note
-
-    In the synchronous python SDK and node's `vectordb` the default is currently 96. This default has
-    changed in the asynchronous python SDK and node's `lancedb`.
+- **metric** (default: "L2"): The distance metric to use. By default it uses euclidean distance "`L2`".
+We also support "cosine" and "dot" distance as well.
+- **num_partitions** (default: 256): The number of partitions of the index.
+- **num_sub_vectors** (default: 96): The number of sub-vectors (M) that will be created during Product Quantization (PQ).
+For D dimensional vector, it will be divided into `M` of `D/M` sub-vectors, each of which is presented by
+a single PQ code.
 
 <figure markdown>
   ![IVF PQ](./assets/ivf_pq.png)
@@ -101,39 +75,38 @@ Using GPU for index creation requires [PyTorch>2.0](https://pytorch.org/) being
 
 You can specify the GPU device to train IVF partitions via
 
-- **accelerator**: Specify to `cuda` or `mps` (on Apple Silicon) to enable GPU training.
+- **accelerator**: Specify to ``cuda`` or ``mps`` (on Apple Silicon) to enable GPU training.
 
 === "Linux"
 
-    <!-- skip-test -->
-    ``` { .python .copy }
-    # Create index using CUDA on Nvidia GPUs.
-    tbl.create_index(
-        num_partitions=256,
-        num_sub_vectors=96,
-        accelerator="cuda"
-    )
-    ```
+     <!-- skip-test -->
+     ``` { .python .copy }
+     # Create index using CUDA on Nvidia GPUs.
+     tbl.create_index(
+          num_partitions=256,
+          num_sub_vectors=96,
+          accelerator="cuda"
+     )
+     ```
 
-=== "MacOS"
+=== "Macos"
 
-    <!-- skip-test -->
-    ```python
-    # Create index using MPS on Apple Silicon.
-    tbl.create_index(
-        num_partitions=256,
-        num_sub_vectors=96,
-        accelerator="mps"
-    )
-    ```
-!!! note
-    GPU based indexing is not yet supported with our asynchronous client.
+     <!-- skip-test -->
+     ```python
+     # Create index using MPS on Apple Silicon.
+     tbl.create_index(
+          num_partitions=256,
+          num_sub_vectors=96,
+          accelerator="mps"
+     )
+     ```
 
-Troubleshooting:
+Trouble shootings:
 
-If you see `AssertionError: Torch not compiled with CUDA enabled`, you need to [install
+If you see ``AssertionError: Torch not compiled with CUDA enabled``, you need to [install
 PyTorch with CUDA support](https://pytorch.org/get-started/locally/).
 
+
 ## Querying an ANN Index
 
 Querying vector indexes is done via the [search](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.search) function.
@@ -142,143 +115,90 @@ There are a couple of parameters that can be used to fine-tune the search:
 
 - **limit** (default: 10): The amount of results that will be returned
 - **nprobes** (default: 20): The number of probes used. A higher number makes search more accurate but also slower.<br/>
-  Most of the time, setting nprobes to cover 5-15% of the dataset should achieve high recall with low latency.<br/>
-    - _For example_, For a dataset of 1 million vectors divided into 256 partitions, `nprobes` should be set to ~20-40. This value can be adjusted to achieve the optimal balance between search latency and search quality. <br/>
-
+  Most of the time, setting nprobes to cover 5-10% of the dataset should achieve high recall with low latency.<br/>
+  e.g., for 1M vectors divided up into 256 partitions, nprobes should be set to ~20-40.<br/>
+  Note: nprobes is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
 - **refine_factor** (default: None): Refine the results by reading extra elements and re-ranking them in memory.<br/>
   A higher number makes search more accurate but also slower. If you find the recall is less than ideal, try refine_factor=10 to start.<br/>
-    - _For example_, For a dataset of 1 million vectors divided into 256 partitions, setting the `refine_factor` to 200 will initially retrieve the top 4,000 candidates (top k * refine_factor) from all searched partitions. These candidates are then reranked to determine the final top 20 results.<br/>
-!!! note
-    Both `nprobes` and `refine_factor` are only applicable if an ANN index is present. If specified on a table without an ANN index, those parameters are ignored.
-
+  e.g., for 1M vectors divided into 256 partitions, if you're looking for top 20, then refine_factor=200 reranks the whole partition.<br/>
+  Note: refine_factor is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
 
 === "Python"
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_async"
-        ```
-
-    ```text
+     ```python
+     tbl.search(np.random.random((1536))) \
+         .limit(2) \
+         .nprobes(20) \
+         .refine_factor(10) \
+         .to_pandas()
+     ```
+     ```
                                               vector       item       _distance
-    0  [0.44949695, 0.8444449, 0.06281311, 0.23338133...  item 1141  103.575333
-    1  [0.48587373, 0.269207, 0.15095535, 0.65531915,...  item 3953  108.393867
-    ```
+     0  [0.44949695, 0.8444449, 0.06281311, 0.23338133...  item 1141  103.575333
+     1  [0.48587373, 0.269207, 0.15095535, 0.65531915,...  item 3953  108.393867
+     ```
 
-=== "TypeScript"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/ann_indexes.test.ts:search1"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/ann_indexes.ts:search1"
-        ```
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/ivf_pq.rs:search1"
-    ```
-
-    Vector search options are more fully defined in the [crate docs](https://docs.rs/lancedb/latest/lancedb/query/struct.Query.html#method.nearest_to).
+=== "Javascript"
+     ```javascript
+     const results_1 = await table
+         .search(Array(1536).fill(1.2))
+         .limit(2)
+         .nprobes(20)
+         .refineFactor(10)
+         .execute()
+     ```
 
 The search will return the data requested in addition to the distance of each item.
 
+
 ### Filtering (where clause)
 
 You can further filter the elements returned by a search using a where clause.
 
 === "Python"
-    === "Sync API"
+     ```python
+     tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
+     ```
 
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_with_filter"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_async_with_filter"
-        ```
-
-=== "TypeScript"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/ann_indexes.test.ts:search2"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```javascript
-        --8<-- "docs/src/ann_indexes.ts:search2"
-        ```
+=== "Javascript"
+     ```javascript
+     const results_2 = await table
+         .search(Array(1536).fill(1.2))
+         .where("id != '1141'")
+         .execute()
+     ```
 
 ### Projections (select clause)
 
 You can select the columns returned by the query using a select clause.
 
 === "Python"
+     ```python
+     tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
+     ```
+     ```
+        vector                                             _distance
+     0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
+     1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
+     ...
+     ```
 
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_with_select"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_async_with_select"
-        ```
-
-    ```text
-                                                vector _distance
-    0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
-    1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
-    ...
-    ```
-
-=== "TypeScript"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/ann_indexes.test.ts:search3"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/ann_indexes.ts:search3"
-        ```
+=== "Javascript"
+     ```javascript
+     const results_3 = await table
+         .search(Array(1536).fill(1.2))
+         .select(["id"])
+         .execute()
+     ```
 
 ## FAQ
 
-### Why do I need to manually create an index?
-
-Currently, LanceDB does _not_ automatically create the ANN index.
-LanceDB is well-optimized for kNN (exhaustive search) via a disk-based index. For many use-cases,
-datasets of the order of ~100K vectors don't require index creation. If you can live with up to
-100ms latency, skipping index creation is a simpler workflow while guaranteeing 100% recall.
-
 ### When is it necessary to create an ANN vector index?
 
-`LanceDB` comes out-of-the-box with highly optimized SIMD code for computing vector similarity.
-In our benchmarks, computing distances for 100K pairs of 1K dimension vectors takes **less than 20ms**.
-We observe that for small datasets (~100K rows) or for applications that can accept 100ms latency,
-vector indices are usually not necessary.
+`LanceDB` has manually-tuned SIMD code for computing vector distances.
+In our benchmarks, computing 100K pairs of 1K dimension vectors takes **less than 20ms**.
+For small datasets (< 100K rows) or applications that can accept 100ms latency, vector indices are usually not necessary.
 
-For large-scale or higher dimension vectors, it can beneficial to create vector index for performance.
+For large-scale or higher dimension vectors, it is beneficial to create vector index.
 
 ### How big is my index, and how many memory will it take?
 
@@ -293,15 +213,7 @@ Product quantization can lead to approximately `16 * sizeof(float32) / 1 = 64` t
 Higher number of partitions could lead to more efficient I/O during queries and better accuracy, but it takes much more time to train.
 On `SIFT-1M` dataset, our benchmark shows that keeping each partition 1K-4K rows lead to a good latency / recall.
 
-`num_sub_vectors` specifies how many Product Quantization (PQ) short codes to generate on each vector. The number should be a factor of the vector dimension. Because
+`num_sub_vectors` specifies how many Product Quantization (PQ) short codes to generate on each vector. Because
 PQ is a lossy compression of the original vector, a higher `num_sub_vectors` usually results in
-less space distortion, and thus yields better accuracy. However, a higher `num_sub_vectors` also causes heavier I/O and more PQ computation, and thus, higher latency. `dimension / num_sub_vectors` should be a multiple of 8 for optimum SIMD efficiency.
-
-!!! note
-    if `num_sub_vectors` is set to be greater than the vector dimension, you will see errors like `attempt to divide by zero`
-
-### How to choose `m` and `ef_construction` for `IVF_HNSW_*` index?
-
-`m` determines the number of connections a new node establishes with its closest neighbors upon entering the graph. Typically, `m` falls within the range of 5 to 48. Lower `m` values are suitable for low-dimensional data or scenarios where recall is less critical. Conversely, higher `m` values are beneficial for high-dimensional data or when high recall is required. In essence, a larger `m` results in a denser graph with increased connectivity, but at the expense of higher memory consumption.
-
-`ef_construction` balances build speed and accuracy. Higher values increase accuracy but slow down the build process. A typical range is 150 to 300. For good search results, a minimum value of 100 is recommended. In most cases, setting this value above 500 offers no additional benefit. Ensure that `ef_construction` is always set to a value equal to or greater than `ef` in the search phase
+less space distortion, and thus yields better accuracy. However, a higher `num_sub_vectors` also causes heavier I/O and
+more PQ computation, and thus, higher latency. `dimension / num_sub_vectors` should be a multiple of 8 for optimum SIMD efficiency.

docs/src/ann_indexes.ts

@@ -1,54 +0,0 @@
-// --8<-- [start:import]
-import * as vectordb from "vectordb";
-// --8<-- [end:import]
-
-(async () => {
-  console.log("ann_indexes.ts: start");
-  // --8<-- [start:ingest]
-  const db = await vectordb.connect("data/sample-lancedb");
-
-  let data = [];
-  for (let i = 0; i < 10_000; i++) {
-    data.push({
-      vector: Array(1536).fill(i),
-      id: `${i}`,
-      content: "",
-      longId: `${i}`,
-    });
-  }
-  const table = await db.createTable("my_vectors", data);
-  await table.createIndex({
-    type: "ivf_pq",
-    column: "vector",
-    num_partitions: 16,
-    num_sub_vectors: 48,
-  });
-  // --8<-- [end:ingest]
-
-  // --8<-- [start:search1]
-  const results_1 = await table
-    .search(Array(1536).fill(1.2))
-    .limit(2)
-    .nprobes(20)
-    .refineFactor(10)
-    .execute();
-  // --8<-- [end:search1]
-
-  // --8<-- [start:search2]
-  const results_2 = await table
-    .search(Array(1536).fill(1.2))
-    .where("id != '1141'")
-    .limit(2)
-    .execute();
-  // --8<-- [end:search2]
-
-  // --8<-- [start:search3]
-  const results_3 = await table
-    .search(Array(1536).fill(1.2))
-    .select(["id"])
-    .limit(2)
-    .execute();
-  // --8<-- [end:search3]
-
-  console.log("ann_indexes.ts: done");
-})();

docs/src/api_reference.md

@@ -1,8 +0,0 @@
-# API Reference
-
-The API reference for the LanceDB client SDKs are available at the following locations:
-
-- [Python](python/python.md)
-- [JavaScript (legacy vectordb package)](javascript/modules.md)
-- [JavaScript (newer @lancedb/lancedb package)](js/globals.md)
-- [Rust](https://docs.rs/lancedb/latest/lancedb/index.html)

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docs/src/assets/python.svg

@@ -1 +0,0 @@
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docs/src/basic.md

@@ -1,655 +1,218 @@
-# Quick start
+# Basic LanceDB Functionality
 
-!!! info "LanceDB can be run in a number of ways:"
+We'll cover the basics of using LanceDB on your local machine in this section.
 
-    * Embedded within an existing backend (like your Django, Flask, Node.js or FastAPI application)
-    * Directly from a client application like a Jupyter notebook for analytical workloads
-    * Deployed as a remote serverless database
+??? info "LanceDB runs embedded on your backend application, so there is no need to run a separate server."
 
-![](assets/lancedb_embedded_explanation.png)
+      <img src="../assets/lancedb_embedded_explanation.png" width="650px" />
 
 ## Installation
 
 === "Python"
-
       ```shell
       pip install lancedb
       ```
 
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
-
-        ```shell
-        npm install @lancedb/lancedb
-        ```
-        !!! note "Bundling `@lancedb/lancedb` apps with Webpack"
-
-            Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.
-
-            ```javascript
-            /** @type {import('next').NextConfig} */
-            module.exports = ({
-            webpack(config) {
-                config.externals.push({ '@lancedb/lancedb': '@lancedb/lancedb' })
-                return config;
-            }
-            })
-            ```
-
-        !!! note "Yarn users"
-
-            Unlike other package managers, Yarn does not automatically resolve peer dependencies. If you are using Yarn, you will need to manually install 'apache-arrow':
-
-            ```shell
-            yarn add apache-arrow
-            ```
-
-    === "vectordb (deprecated)"
-
-        ```shell
-        npm install vectordb
-        ```
-        !!! note "Bundling `vectordb` apps with Webpack"
-
-            Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.
-
-            ```javascript
-            /** @type {import('next').NextConfig} */
-            module.exports = ({
-            webpack(config) {
-                config.externals.push({ vectordb: 'vectordb' })
-                return config;
-            }
-            })
-            ```
-
-        !!! note "Yarn users"
-
-            Unlike other package managers, Yarn does not automatically resolve peer dependencies. If you are using Yarn, you will need to manually install 'apache-arrow':
-
-            ```shell
-            yarn add apache-arrow
-            ```
-
-=== "Rust"
-
-    ```shell
-    cargo add lancedb
-    ```
-
-    !!! info "To use the lancedb create, you first need to install protobuf."
-
-    === "macOS"
-
-        ```shell
-        brew install protobuf
-        ```
-
-    === "Ubuntu/Debian"
-
-        ```shell
-        sudo apt install -y protobuf-compiler libssl-dev
-        ```
-
-    !!! 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"
-
+=== "Javascript"
       ```shell
-      pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
+      npm install vectordb
       ```
 
-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```shell
-        npm install @lancedb/lancedb@preview
-        ```
-    === "vectordb (deprecated)"
-
-        ```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
+## How to connect to a database
 
 === "Python"
-    === "Sync API"
+      ```python
+      import lancedb
+      uri = "data/sample-lancedb"
+      db = lancedb.connect(uri)
+      ```
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:imports"
+      LanceDB will create the directory if it doesn't exist (including parent directories).
 
-        --8<-- "python/python/tests/docs/test_basic.py:set_uri"
-        --8<-- "python/python/tests/docs/test_basic.py:connect"
-        ```
-    === "Async API"
+      If you need a reminder of the uri, use the `db.uri` property.
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:imports"
+=== "Javascript"
+      ```javascript
+      const lancedb = require("vectordb");
 
-        --8<-- "python/python/tests/docs/test_basic.py:set_uri"
-        --8<-- "python/python/tests/docs/test_basic.py:connect_async"
-        ```
+      const uri = "data/sample-lancedb";
+      const db = await lancedb.connect(uri);
+      ```
       
-=== "Typescript[^1]"
+      LanceDB will create the directory if it doesn't exist (including parent directories).
 
-    === "@lancedb/lancedb"
+      If you need a reminder of the uri, you can call `db.uri()`.
 
-        ```typescript
-        import * as lancedb from "@lancedb/lancedb";
-        import * as arrow from "apache-arrow";
-
-        --8<-- "nodejs/examples/basic.test.ts:connect"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:open_db"
-        ```
-
-=== "Rust"
-
-    ```rust
-    #[tokio::main]
-    async fn main() -> Result<()> {
-        --8<-- "rust/lancedb/examples/simple.rs:connect"
-    }
-    ```
-
-    !!! info "See [examples/simple.rs](https://github.com/lancedb/lancedb/tree/main/rust/lancedb/examples/simple.rs) for a full working example."
-
-LanceDB will create the directory if it doesn't exist (including parent directories).
-
-If you need a reminder of the uri, you can call `db.uri()`.
-
-## Create a table
-
-### Create a table from initial data
-
-If you have data to insert into the table at creation time, you can simultaneously create a
-table and insert the data into it. The schema of the data will be used as the schema of the
-table.
+## How to create a table
 
 === "Python"
+      ```python
+      tbl = db.create_table("my_table",
+                        data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                              {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+      ```
 
-    If the table already exists, LanceDB will raise an error by default.
-    If you want to overwrite the table, you can pass in `mode="overwrite"`
-    to the `create_table` method.
+      If the table already exists, LanceDB will raise an error by default.
+      If you want to overwrite the table, you can pass in `mode="overwrite"`
+      to the `create_table` method.
 
-    === "Sync API"
+      You can also pass in a pandas DataFrame directly:
+      ```python
+      import pandas as pd
+      df = pd.DataFrame([{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                        {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+      tbl = db.create_table("table_from_df", data=df)
+      ```
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table"
-        ```
+=== "Javascript"
+      ```javascript
+      const tb = await db.createTable("my_table",
+                        data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                              {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+      ```
       
-        You can also pass in a pandas DataFrame directly:
+!!! warning
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table_pandas"
-        ```
+      If the table already exists, LanceDB will raise an error by default.
+      If you want to overwrite the table, you can pass in `mode="overwrite"`
+      to the `createTable` function.
 
-    === "Async API"
+??? info "Under the hood, LanceDB is converting the input data into an Apache Arrow table and persisting it to disk in [Lance format](https://www.github.com/lancedb/lance)."
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table_async"
-        ```
-
-        You can also pass in a pandas DataFrame directly:
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table_async_pandas"
-        ```
-
-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:create_table"
-        ```
-
-        If the table already exists, LanceDB will raise an error by default.
-        If you want to overwrite the table, you can pass in `mode:"overwrite"`
-        to the `createTable` function.
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/simple.rs:create_table"
-    ```
-
-    If the table already exists, LanceDB will raise an error by default.  See
-    [the mode option](https://docs.rs/lancedb/latest/lancedb/connection/struct.CreateTableBuilder.html#method.mode)
-    for details on how to overwrite (or open) existing tables instead.
-
-    !!! Providing table records in Rust
-
-        The Rust SDK currently expects data to be provided as an Arrow
-        [RecordBatchReader](https://docs.rs/arrow-array/latest/arrow_array/trait.RecordBatchReader.html)
-        Support for additional formats (such as serde or polars) is on the roadmap.
-
-!!! 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
+### Creating an empty table
 
 Sometimes you may not have the data to insert into the table at creation time.
-In this case, you can create an empty table and specify the schema, so that you can add
-data to the table at a later time (as long as it conforms to the schema). This is
-similar to a `CREATE TABLE` statement in SQL.
+In this case, you can create an empty table and specify the schema.
 
 === "Python"
+      ```python
+      import pyarrow as pa
+      schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
+      tbl = db.create_table("empty_table", schema=schema)
+      ```
 
-    === "Sync API"
+## How to open an existing table
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_empty_table"
-        ```
-    === "Async API"
-
-        ```python
-        --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[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_empty_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:create_empty_table"
-        ```
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/simple.rs:create_empty_table"
-    ```
-
-## Open an existing table
-
-Once created, you can open a table as follows:
+Once created, you can open a table using the following code:
 
 === "Python"
+      ```python
+      tbl = db.open_table("my_table")
+      ```
 
-    === "Sync API"
+      If you forget the name of your table, you can always get a listing of all table names:
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:open_table"
-        ```
-    === "Async API"
+      ```python
+      print(db.table_names())
+      ```
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:open_table_async"
-        ```
+=== "Javascript"
+      ```javascript
+      const tbl = await db.openTable("my_table");
+      ```
 
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+      If you forget the name of your table, you can always get a listing of all table names:
 
-        ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:open_table"
-        ```
+      ```javascript
+      console.log(await db.tableNames());
+      ```
 
-    === "vectordb (deprecated)"
+## How to add data to a table
 
-        ```typescript
-        const tbl = await db.openTable("myTable");
-        ```
-
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/simple.rs:open_existing_tbl"
-    ```
-
-If you forget the name of your table, you can always get a listing of all table names:
+After a table has been created, you can always add more data to it using
 
 === "Python"
+      ```python
 
-    === "Sync API"
+      # Option 1: Add a list of dicts to a table
+      data = [{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
+            {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}]
+      tbl.add(data)
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:table_names"
-        ```
-    === "Async API"
+      # Option 2: Add a pandas DataFrame to a table
+      df = pd.DataFrame(data)
+      tbl.add(data)
+      ```
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:table_names_async"
-        ```
+=== "Javascript"
+      ```javascript
+      await tbl.add([{vector: [1.3, 1.4], item: "fizz", price: 100.0},
+              {vector: [9.5, 56.2], item: "buzz", price: 200.0}])
+      ```
 
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
+## How to search for (approximate) nearest neighbors
 
-        ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:table_names"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        console.log(await db.tableNames());
-        ```
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/simple.rs:list_names"
-    ```
-
-## Add data to a table
-
-After a table has been created, you can always add more data to it as follows:
+Once you've embedded the query, you can find its nearest neighbors using the following code:
 
 === "Python"
+      ```python
+      tbl.search([100, 100]).limit(2).to_pandas()
+      ```
 
-    === "Sync API"
+      This returns a pandas DataFrame with the results.
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:add_data"
-        ```
-    === "Async API"
+=== "Javascript"
+      ```javascript
+      const query = await tbl.search([100, 100]).limit(2).execute();
+      ```
 
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:add_data_async"
-        ```
-
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:add_data"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:add"
-        ```
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/simple.rs:add"
-    ```
-
-## Search for nearest neighbors
-
-Once you've embedded the query, you can find its nearest neighbors as follows:
-
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:vector_search"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:vector_search_async"
-        ```
-
-    This returns a pandas DataFrame with the results.
-
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:vector_search"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:search"
-        ```
-
-=== "Rust"
-
-    ```rust
-    use futures::TryStreamExt;
-
-    --8<-- "rust/lancedb/examples/simple.rs:search"
-    ```
-
-    !!! Query vectors in Rust
-        Rust does not yet support automatic execution of embedding functions.  You will need to
-        calculate embeddings yourself.  Support for this is on the roadmap and can be tracked at
-        https://github.com/lancedb/lancedb/issues/994
-
-        Query vectors can be provided as Arrow arrays or a Vec/slice of Rust floats.
-        Support for additional formats (e.g. `polars::series::Series`) is on the roadmap.
-
-By default, LanceDB runs a brute-force scan over dataset to find the K nearest neighbours (KNN).
-For tables with more than 50K vectors, creating an ANN index is recommended to speed up search performance.
-LanceDB allows you to create an ANN index on a table as follows:
-
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_index"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_index_async"
-        ```
-
-=== "Typescript[^1]"
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:create_index"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```{.typescript .ignore}
-        --8<-- "docs/src/basic_legacy.ts:create_index"
-        ```
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/simple.rs:create_index"
-    ```
-
-!!! note "Why do I need to create an index manually?"
-LanceDB does not automatically create the ANN index for two reasons. The first is that it's optimized
-for really fast retrievals via a disk-based index, and the second is that data and query workloads can
-be very diverse, so there's no one-size-fits-all index configuration. LanceDB provides many parameters
-to fine-tune index size, query latency and accuracy. See the section on
-[ANN indexes](ann_indexes.md) for more details.
-
-## Delete rows from a table
+## How to delete rows from a table
 
 Use the `delete()` method on tables to delete rows from a table. To choose
 which rows to delete, provide a filter that matches on the metadata columns.
 This can delete any number of rows that match the filter.
 
 === "Python"
+      ```python
+      tbl.delete('item = "fizz"')
+      ```
 
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:delete_rows"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:delete_rows_async"
-        ```
-
-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:delete_rows"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:delete"
-        ```
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/simple.rs:delete"
-    ```
+=== "Javascript"
+      ```javascript
+      await tbl.delete('item = "fizz"')
+      ```
 
 The deletion predicate is a SQL expression that supports the same expressions
-as the `where()` clause (`only_if()` in Rust) on a search. They can be as
-simple or complex as needed. To see what expressions are supported, see the
-[SQL filters](sql.md) section.
+as the `where()` clause on a search. They can be as simple or complex as needed.
+To see what expressions are supported, see the [SQL filters](sql.md) section.
+
 
 === "Python"
 
-    === "Sync API"
-        Read more: [lancedb.table.Table.delete][]
-    === "Async API"
-        Read more: [lancedb.table.AsyncTable.delete][]
+      Read more: [lancedb.table.Table.delete][]
 
-=== "Typescript[^1]"
+=== "Javascript"
 
-    === "@lancedb/lancedb"
+      Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)
 
-        Read more: [lancedb.Table.delete](javascript/interfaces/Table.md#delete)
-
-    === "vectordb (deprecated)"
-
-        Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)
-
-=== "Rust"
-
-      Read more: [lancedb::Table::delete](https://docs.rs/lancedb/latest/lancedb/table/struct.Table.html#method.delete)
-
-## Drop a table
+## How to remove a table
 
 Use the `drop_table()` method on the database to remove a table.
 
 === "Python"
+      ```python
+      db.drop_table("my_table")
+      ```
 
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:drop_table"
-        ```
-    === "Async API"
-
-        ```python
-        --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`.
-
-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/basic.test.ts:drop_table"
-        ```
-
-    === "vectordb (deprecated)"
-
-        ```typescript
-        --8<-- "docs/src/basic_legacy.ts:drop_table"
-        ```
-
-        This permanently removes the table and is not recoverable, unlike deleting rows.
-        If the table does not exist an exception is raised.
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/simple.rs:drop_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"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_embeddings_optional.py:imports"
-
-        --8<-- "python/python/tests/docs/test_embeddings_optional.py:openai_embeddings"
-        ```
-    === "Async API"
-
-        Coming soon to the async API.
-        https://github.com/lancedb/lancedb/issues/1938
-
-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/embedding.test.ts:imports"
-        --8<-- "nodejs/examples/embedding.test.ts:openai_embeddings"
-        ```
-
-=== "Rust"
-
-    ```rust
-    --8<-- "rust/lancedb/examples/openai.rs:imports"
-    --8<-- "rust/lancedb/examples/openai.rs:openai_embeddings"
-    ```
-
-Learn about using the existing integrations and creating custom embedding functions in the [embedding API guide](./embeddings/index.md).
+This permanently removes the table and is not recoverable, unlike deleting rows.
+By default, if the table does not exist an exception is raised. To suppress this,
+you can pass in `ignore_missing=True`.
 
 
 ## What's next
 
-This section covered the very basics of 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.
+This section covered the very basics of the LanceDB API.
+LanceDB supports many additional features when creating indices to speed up search and options for search.
+These are contained in the next section of the documentation.
 
-If you've already worked with other vector databases, you may want to read the [guides](guides/tables.md) to learn how to work with LanceDB in more detail.
-
-[^1]: The `vectordb` package is a legacy package that is  deprecated in favor of `@lancedb/lancedb`.  The `vectordb` package will continue to receive bug fixes and security updates until September 2024.  We recommend all new projects use `@lancedb/lancedb`.  See the [migration guide](migration.md) for more information.
+## Note: Bundling vectorDB apps with webpack
+Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying on Vercel.
+```javascript
+/** @type {import('next').NextConfig} */
+module.exports = ({
+  webpack(config) {
+    config.externals.push({ vectordb: 'vectordb' })
+    return config;
+  }
+})
+```

docs/src/basic_legacy.ts

@@ -1,126 +0,0 @@
-// --8<-- [start:import]
-import * as lancedb from "vectordb";
-import {
-  Schema,
-  Field,
-  Float32,
-  FixedSizeList,
-  Int32,
-  Float16,
-} from "apache-arrow";
-import * as arrow from "apache-arrow";
-// --8<-- [end:import]
-import * as fs from "fs";
-import { Table as ArrowTable, Utf8 } from "apache-arrow";
-
-const example = async () => {
-  fs.rmSync("data/sample-lancedb", { recursive: true, force: true });
-  // --8<-- [start:open_db]
-  const lancedb = require("vectordb");
-  const uri = "data/sample-lancedb";
-  const db = await lancedb.connect(uri);
-  // --8<-- [end:open_db]
-
-  // --8<-- [start:create_table]
-  const tbl = await db.createTable(
-    "myTable",
-    [
-      { vector: [3.1, 4.1], item: "foo", price: 10.0 },
-      { vector: [5.9, 26.5], item: "bar", price: 20.0 },
-    ],
-    { writeMode: lancedb.WriteMode.Overwrite },
-  );
-  // --8<-- [end:create_table]
-  {
-    // --8<-- [start:create_table_with_schema]
-    const schema = new arrow.Schema([
-      new arrow.Field(
-        "vector",
-        new arrow.FixedSizeList(
-          2,
-          new arrow.Field("item", new arrow.Float32(), true),
-        ),
-      ),
-      new arrow.Field("item", new arrow.Utf8(), true),
-      new arrow.Field("price", new arrow.Float32(), true),
-    ]);
-    const data = [
-      { vector: [3.1, 4.1], item: "foo", price: 10.0 },
-      { vector: [5.9, 26.5], item: "bar", price: 20.0 },
-    ];
-    const tbl = await db.createTable({
-      name: "myTableWithSchema",
-      data,
-      schema,
-    });
-    // --8<-- [end:create_table_with_schema]
-  }
-
-  // --8<-- [start:add]
-  const newData = Array.from({ length: 500 }, (_, i) => ({
-    vector: [i, i + 1],
-    item: "fizz",
-    price: i * 0.1,
-  }));
-  await tbl.add(newData);
-  // --8<-- [end:add]
-
-  // --8<-- [start:create_index]
-  await tbl.createIndex({
-    type: "ivf_pq",
-    num_partitions: 2,
-    num_sub_vectors: 2,
-  });
-  // --8<-- [end:create_index]
-
-  // --8<-- [start:create_empty_table]
-  const schema = new arrow.Schema([
-    new arrow.Field("id", new arrow.Int32()),
-    new arrow.Field("name", new arrow.Utf8()),
-  ]);
-
-  const empty_tbl = await db.createTable({ name: "empty_table", schema });
-  // --8<-- [end:create_empty_table]
-  {
-    // --8<-- [start:create_f16_table]
-    const dim = 16;
-    const total = 10;
-    const schema = new Schema([
-      new Field("id", new Int32()),
-      new Field(
-        "vector",
-        new FixedSizeList(dim, new Field("item", new Float16(), true)),
-        false,
-      ),
-    ]);
-    const data = lancedb.makeArrowTable(
-      Array.from(Array(total), (_, i) => ({
-        id: i,
-        vector: Array.from(Array(dim), Math.random),
-      })),
-      { schema },
-    );
-    const table = await db.createTable("f16_tbl", data);
-    // --8<-- [end:create_f16_table]
-  }
-
-  // --8<-- [start:search]
-  const query = await tbl.search([100, 100]).limit(2).execute();
-  // --8<-- [end:search]
-
-  // --8<-- [start:delete]
-  await tbl.delete('item = "fizz"');
-  // --8<-- [end:delete]
-
-  // --8<-- [start:drop_table]
-  await db.dropTable("myTable");
-  // --8<-- [end:drop_table]
-};
-
-async function main() {
-  console.log("basic_legacy.ts: start");
-  await example();
-  console.log("basic_legacy.ts: done");
-}
-
-main();

docs/src/cli_config.md

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

docs/src/cloud/cloud_faq.md

@@ -1,34 +0,0 @@
-This section provides answers to the most common questions asked about LanceDB Cloud. By following these guidelines, you can ensure a smooth, performant experience with LanceDB Cloud.
-
-### Should I reuse the database connection?
-Yes! It is recommended to establish a single database connection and maintain it throughout your interaction with the tables within. 
-
-LanceDB uses HTTP connections to communicate with the servers. By re-using the Connection object, you avoid the overhead of repeatedly establishing HTTP connections, significantly improving efficiency.
-
-### Should I re-use the `Table` object?
-`table = db.open_table()` should be called once and used for all subsequent table operations. If there are changes to the opened table, `table` always reflect the **latest version** of the data. 
-
-### What should I do if I need to search for rows by `id`?
-LanceDB Cloud currently does not support an ID or primary key column. You are recommended to add a 
-user-defined ID column. To significantly improve the query performance with SQL causes, a scalar BITMAP/BTREE index should be created on this column. 
-
-### What are the vector indexing types supported by LanceDB Cloud?
-We support `IVF_PQ` and `IVF_HNSW_SQ` as the `index_type` which is passed to `create_index`. LanceDB Cloud tunes the indexing parameters automatically to achieve the best tradeoff between query latency and query quality.
-
-### When I add new rows to a table, do I need to manually update the index?
-No! LanceDB Cloud triggers an asynchronous background job to index the new vectors.
-
-Even though indexing is asynchronous, your vectors will still be immediately searchable. LanceDB uses brute-force search to search over unindexed rows. This makes you new data is immediately available, but does increase latency temporarily. To disable the brute-force part of search, set the `fast_search` flag in your query to `true`.
-
-### Do I need to reindex the whole dataset if only a small portion of the data is deleted or updated?
-No! Similar to adding data to the table, LanceDB Cloud triggers an asynchronous background job to update the existing indices. Therefore, no action is needed from users and there is absolutely no 
-downtime expected.
-
-### How do I know whether an index has been created?
-While index creation in LanceDB Cloud is generally fast, querying immediately after a `create_index` call may result in errors. It's recommended to use `list_indices` to verify index creation before querying.
-
-### Why is my query latency higher than expected?
-Multiple factors can impact query latency. To reduce query latency, consider the following:
-- Send pre-warm queries: send a few queries to warm up the cache before an actual user query.
-- Check network latency: LanceDB Cloud is hosted in AWS `us-east-1` region. It is recommended to run queries from an EC2 instance that is in the same region.
-- Create scalar indices: If you are filtering on metadata, it is recommended to create scalar indices on those columns. This will speedup searches with metadata filtering. See [here](../guides/scalar_index.md) for more details on creating a scalar index.

docs/src/cloud/index.md

@@ -1,17 +0,0 @@
-# About LanceDB Cloud
-
-LanceDB Cloud is a SaaS (software-as-a-service) solution that runs serverless in the cloud, clearly separating storage from compute. It's designed to be highly scalable without breaking the bank. LanceDB Cloud is currently in private beta with general availability coming soon, but you can apply for early access with the private beta release by signing up below.
-
-[Try out LanceDB Cloud (Public Beta)](https://cloud.lancedb.com){ .md-button .md-button--primary }
-
-## Architecture
-
-LanceDB Cloud provides the same underlying fast vector store that powers the OSS version, but without the need to maintain your own infrastructure. Because it's serverless, you only pay for the storage you use, and you can scale compute up and down as needed depending on the size of your data and its associated index.
-
-![](../assets/lancedb_cloud.png)
-
-## Transitioning from the OSS to the Cloud version
-
-The OSS version of LanceDB is designed to be embedded in your application, and it runs in-process. This makes it incredibly simple to self-host your own AI retrieval workflows for RAG and more and build and test out your concepts on your own infrastructure. The OSS version is forever free, and you can continue to build and integrate LanceDB into your existing backend applications without any added costs.
-
-Should you decide that you need a managed deployment in production, it's possible to seamlessly transition from the OSS to the cloud version by changing the connection string to point to a remote database instead of a local one. With LanceDB Cloud, you can take your AI application from development to production without major code changes or infrastructure burden.

docs/src/cloud/rest.md

@@ -1 +0,0 @@
-!!swagger ../../openapi.yml!!

docs/src/concepts/data_management.md

@@ -1,62 +0,0 @@
-# Data management
-
-This section covers concepts related to managing your data over time in LanceDB.
-
-## A primer on Lance
-
-Because LanceDB is built on top of the [Lance](https://lancedb.github.io/lance/) data format, it helps to understand some of its core ideas. Just like Apache Arrow, Lance is a fast columnar data format, but it has the added benefit of being versionable, query and train ML models on. Lance is designed to be used with simple and complex data types, like tabular data, images, videos audio, 3D point clouds (which are deeply nested) and more.
-
-The following concepts are important to keep in mind:
-
-- Data storage is columnar and is interoperable with other columnar formats (such as Parquet) via Arrow
-- Data is divided into fragments that represent a subset of the data
-- Data is versioned, with each insert operation creating a new version of the dataset and an update to the manifest that tracks versions via metadata
-
-!!! note
-    1. First, each version contains metadata and just the new/updated data in your transaction. So if you have 100 versions, they aren't 100 duplicates of the same data. However, they do have 100x the metadata overhead of a single version, which can result in slower queries.  
-    2. Second, these versions exist to keep LanceDB scalable and consistent. We do not immediately blow away old versions when creating new ones because other clients might be in the middle of querying the old version. It's important to retain older versions for as long as they might be queried.
-
-## What are fragments?
-
-Fragments are chunks of data in a Lance dataset. Each fragment includes multiple files that contain several columns in the chunk of data that it represents.
-
-## Compaction
-
-As you insert more data, your dataset will grow and you'll need to perform *compaction* to maintain query throughput (i.e., keep latencies down to a minimum). Compaction is the process of merging fragments together to reduce the amount of metadata that needs to be managed, and to reduce the number of files that need to be opened while scanning the dataset.
-
-### How does compaction improve performance?
-
-Compaction performs the following tasks in the background:
-
-- Removes deleted rows from fragments
-- Removes dropped columns from fragments
-- Merges small fragments into larger ones
-
-Depending on the use case and dataset, optimal compaction will have different requirements. As a rule of thumb:
-
-- It’s always better to use *batch* inserts rather than adding 1 row at a time (to avoid too small fragments). If single-row inserts are unavoidable, run compaction on a regular basis to merge them into larger fragments.
-- Keep the number of fragments under 100, which is suitable for most use cases (for *really* large datasets of >500M rows, more fragments might be needed)
-
-## Deletion
-
-Although Lance allows you to delete rows from a dataset, it does not actually delete the data immediately. It simply marks the row as deleted in the `DataFile` that represents a fragment. For a given version of the dataset, each fragment can have up to one deletion file (if no rows were ever deleted from that fragment, it will not have a deletion file). This is important to keep in mind because it means that the data is still there, and can be recovered if needed, as long as that version still exists based on your backup policy.
-
-## Reindexing
-
-Reindexing is the process of updating the index to account for new data, keeping good performance for queries. This applies to either a full-text search (FTS) index or a vector index. For ANN search, new data will always be included in query results, but queries on tables with unindexed data will fallback to slower search methods for the new parts of the table. This is another important operation to run periodically as your data grows, as it also improves performance. This is especially important if you're appending large amounts of data to an existing dataset.
-
-!!! tip
-    When adding new data to a dataset that has an existing index (either FTS or vector), LanceDB doesn't immediately update the index until a reindex operation is complete.
-
-Both LanceDB OSS and Cloud support reindexing, but the process (at least for now) is different for each, depending on the type of index.
-
-When a reindex job is triggered in the background, the entire data is reindexed, but in the interim as new queries come in, LanceDB will combine results from the existing index with exhaustive kNN search on the new data. This is done to ensure that you're still searching on all your data, but it does come at a performance cost. The more data that you add without reindexing, the impact on latency (due to exhaustive search) can be noticeable.
-
-### Vector reindex
-
-* LanceDB Cloud supports incremental reindexing, where a background process will trigger a new index build for you automatically when new data is added to a dataset
-* LanceDB OSS requires you to manually trigger a reindex operation -- we are working on adding incremental reindexing to LanceDB OSS as well
-
-### FTS reindex
-
-FTS reindexing is supported in both LanceDB OSS and Cloud, but requires that it's manually rebuilt once you have a significant enough amount of new data added that needs to be reindexed. We [updated](https://github.com/lancedb/lancedb/pull/762) Tantivy's default heap size from 128MB to 1GB in LanceDB to make it much faster to reindex, by up to 10x from the default settings.

docs/src/concepts/index_hnsw.md

@@ -1,99 +0,0 @@
-
-# Understanding HNSW index
-
-Approximate Nearest Neighbor (ANN) search is a method for finding data points near a given point in a dataset, though not always the exact nearest one. HNSW is one of the most accurate and fastest Approximate Nearest Neighbour search algorithms, It’s beneficial in high-dimensional spaces where finding the same nearest neighbor would be too slow and costly
-
-[Jump to usage](#usage)
-There are three main types of ANN search algorithms:
-
-* **Tree-based search algorithms**: Use a tree structure to organize and store data points.
-* **Hash-based search algorithms**: Use a specialized geometric hash table to store and manage data points. These algorithms typically focus on theoretical guarantees, and don't usually perform as well as the other approaches in practice.
-* **Graph-based search algorithms**: Use a graph structure to store data points, which can be a bit complex. 
-
-HNSW is a graph-based algorithm. All graph-based search algorithms rely on the idea of a k-nearest neighbor (or k-approximate nearest neighbor) graph, which we outline below.  
-HNSW also combines this with the ideas behind a classic 1-dimensional search data structure: the skip list.
-
-## k-Nearest Neighbor Graphs and k-approximate Nearest neighbor Graphs
-The k-nearest neighbor graph actually predates its use for ANN search. Its construction is quite simple:
-
-* Each vector in the dataset is given an associated vertex.
-* Each vertex has outgoing edges to its k nearest neighbors. That is, the k closest other vertices by Euclidean distance between the two corresponding vectors. This can be thought of as a "friend list" for the vertex.
-* For some applications (including nearest-neighbor search), the incoming edges are also added.
-
-Eventually, it was realized that the following greedy search method over such a graph typically results in good approximate nearest neighbors:
-
-* Given a query vector, start at some fixed "entry point" vertex (e.g. the approximate center node).
-* Look at that vertex's neighbors. If any of them are closer to the query vector than the current vertex, then move to that vertex.
-* Repeat until a local optimum is found.
-
-The above algorithm also generalizes to e.g. top 10 approximate nearest neighbors.
-
-Computing a k-nearest neighbor graph is actually quite slow, taking quadratic time in the dataset size. It was quickly realized that near-identical performance can be achieved using a k-approximate nearest neighbor graph. That is, instead of obtaining the k-nearest neighbors for each vertex, an approximate nearest neighbor search data structure is used to build much faster.  
-In fact, another data structure is not needed: This can be done "incrementally".
-That is, if you start with a k-ANN graph for n-1 vertices, you can extend it to a k-ANN graph for n vertices as well by using the graph to obtain the k-ANN for the new vertex.
-
-One downside of k-NN and k-ANN graphs alone is that one must typically build them with a large value of k to get decent results, resulting in a large index.
-
-
-## HNSW: Hierarchical Navigable Small Worlds
-
-HNSW builds on k-ANN in two main ways:
-
-* Instead of getting the k-approximate nearest neighbors for a large value of k, it sparsifies the k-ANN graph using a carefully chosen "edge pruning" heuristic, allowing for the number of edges per vertex to be limited to a relatively small constant.
-* The "entry point" vertex is chosen dynamically using a recursively constructed data structure on a subset of the data, similarly to a skip list.
-
-This recursive structure can be thought of as separating into layers:
-
-* At the bottom-most layer, an k-ANN graph on the whole dataset is present.
-* At the second layer, a k-ANN graph on a fraction of the dataset (e.g. 10%) is present.
-* At the Lth layer, a k-ANN graph is present. It is over a (constant) fraction (e.g. 10%) of the vectors/vertices present in the L-1th layer.
-
-Then the greedy search routine operates as follows:
-
-* At the top layer (using an arbitrary vertex as an entry point), use the greedy local search routine on the k-ANN graph to get an approximate nearest neighbor at that layer.
-* Using the approximate nearest neighbor found in the previous layer as an entry point, find an approximate nearest neighbor in the next layer with the same method.
-* Repeat until the bottom-most layer is reached. Then use the entry point to find multiple nearest neighbors (e.g. top 10).
-
-
-## Usage
-
-There are three key parameters to set when constructing an HNSW index:
-
-* `metric`: Use an `l2` euclidean distance metric. We also support `dot` and `cosine` distance.
-* `m`: The number of neighbors to select for each vector in the HNSW graph.
-* `ef_construction`: The number of candidates to evaluate during the construction of the HNSW graph.
-
-
-We can combine the above concepts to understand how to build and query an HNSW index in LanceDB.
-
-### Construct index
-
-```python
-import lancedb
-import numpy as np
-uri = "/tmp/lancedb"
-db = lancedb.connect(uri)
-
-# Create 10,000 sample vectors
-data = [
-    {"vector": row, "item": f"item {i}"}
-    for i, row in enumerate(np.random.random((10_000, 1536)).astype('float32'))
-]
-
-# Add the vectors to a table
-tbl = db.create_table("my_vectors", data=data)
-
-# Create and train the HNSW index for a 1536-dimensional vector
-# Make sure you have enough data in the table for an effective training step
-tbl.create_index(index_type=IVF_HNSW_SQ)
-
-```
-
-### Query the index
-
-```python
-# Search using a random 1536-dimensional embedding
-tbl.search(np.random.random((1536))) \
-    .limit(2) \
-    .to_pandas()
-```

docs/src/concepts/index_ivfpq.md

@@ -1,86 +0,0 @@
-# Understanding LanceDB's IVF-PQ index
-
-An ANN (Approximate Nearest Neighbors) index is a data structure that represents data in a way that makes it more efficient to search and retrieve. Using an ANN index is faster, but less accurate than kNN or brute force search because, in essence, the index is a lossy representation of the data.
-
-LanceDB is fundamentally different from other vector databases in that it is built on top of [Lance](https://github.com/lancedb/lance), an open-source columnar data format designed for performant ML workloads and fast random access. Due to the design of Lance, LanceDB's indexing philosophy adopts a primarily *disk-based* indexing philosophy.
-
-## IVF-PQ
-
-IVF-PQ is a composite index that combines inverted file index (IVF) and product quantization (PQ). The implementation in LanceDB provides several parameters to fine-tune the index's size, query throughput, latency and recall, which are described later in this section.
-
-### Product quantization
-
-Quantization is a compression technique used to reduce the dimensionality of an embedding to speed up search.
-
-Product quantization (PQ) works by dividing a large, high-dimensional vector of size into equally sized subvectors. Each subvector is assigned a "reproduction value" that maps to the nearest centroid of points for that subvector. The reproduction values are then assigned to a codebook using unique IDs, which can be used to reconstruct the original vector.
-
-![](../assets/ivfpq_pq_desc.png)
-
-It's important to remember that quantization is a *lossy process*, i.e., the reconstructed vector is not identical to the original vector. This results in a trade-off between the size of the index and the accuracy of the search results.
-
-As an example, consider starting with 128-dimensional vector consisting of 32-bit floats. Quantizing it to an 8-bit integer vector with 4 dimensions as in the image above, we can significantly reduce memory requirements.
-
-!!! example "Effect of quantization"
-
-    Original: `128 × 32 = 4096` bits
-    Quantized: `4 × 8 = 32` bits
-
-    Quantization results in a **128x** reduction in memory requirements for each vector in the index, which is substantial.
-
-### Inverted file index
-
-While PQ helps with reducing the size of the index, IVF primarily addresses search performance. The primary purpose of an inverted file index is to facilitate rapid and effective nearest neighbor search by narrowing down the search space.
-
-In IVF, the PQ vector space is divided into *Voronoi cells*, which are essentially partitions that consist of all the points in the space that are within a threshold distance of the given region's seed point. These seed points are initialized by running K-means over the stored vectors. The centroids of K-means turn into the seed points which then each define a region. These regions are then are used to create an inverted index that correlates each centroid with a list of vectors in the space, allowing a search to be restricted to just a subset of vectors in the index.
-
-![](../assets/ivfpq_ivf_desc.webp)
-
-During query time, depending on where the query lands in vector space, it may be close to the border of multiple Voronoi cells, which could make the top-k results ambiguous and span across multiple cells. To address this, the IVF-PQ introduces the `nprobe` parameter, which controls the number of Voronoi cells to search during a query. The higher the `nprobe`, the more accurate the results, but the slower the query.
-
-![](../assets/ivfpq_query_vector.webp)
-
-## Putting it all together
-
-We can combine the above concepts to understand how to build and query an IVF-PQ index in LanceDB.
-
-### Construct index
-
-There are three key parameters to set when constructing an IVF-PQ index:
-
-* `metric`: Use an `l2` euclidean distance metric. We also support `dot` and `cosine` distance.
-* `num_partitions`: The number of partitions in the IVF portion of the index.
-* `num_sub_vectors`: The number of sub-vectors that will be created during Product Quantization (PQ).
-
-In Python, the index can be created as follows:
-
-```python
-# Create and train the index for a 1536-dimensional vector
-# Make sure you have enough data in the table for an effective training step
-tbl.create_index(metric="l2", num_partitions=256, num_sub_vectors=96)
-```
-!!! note
-    `num_partitions`=256 and `num_sub_vectors`=96 does not work for every dataset. Those values needs to be adjusted for your particular dataset.
-
-The `num_partitions` is usually chosen to target a particular number of vectors per partition. `num_sub_vectors` is typically chosen based on the desired recall and the dimensionality of the vector. See [here](../ann_indexes.md/#how-to-choose-num_partitions-and-num_sub_vectors-for-ivf_pq-index) for best practices on choosing these parameters.
-
-
-### Query the index
-
-```python
-# Search using a random 1536-dimensional embedding
-tbl.search(np.random.random((1536))) \
-    .limit(2) \
-    .nprobes(20) \
-    .refine_factor(10) \
-    .to_pandas()
-```
-
-The above query will perform a search on the table `tbl` using the given query vector, with the following parameters:
-
-* `limit`: The number of results to return
-* `nprobes`: The number of probes determines the distribution of vector space. While a higher number enhances search accuracy, it also results in slower performance. Typically, setting `nprobes` to cover 5–10% of the dataset proves effective in achieving high recall with minimal latency.
-* `refine_factor`: Refine the results by reading extra elements and re-ranking them in memory. A higher number makes the search more accurate but also slower (see the [FAQ](../faq.md#do-i-need-to-set-a-refine-factor-when-using-an-index) page for more details on this).
-* `to_pandas()`: Convert the results to a pandas DataFrame
-
-And there you have it! You now understand what an IVF-PQ index is, and how to create and query it in LanceDB.
-To see how to create an IVF-PQ index in LanceDB, take a look at the [ANN indexes](../ann_indexes.md) section.

docs/src/concepts/storage.md

@@ -1,80 +0,0 @@
-# Storage
-
-LanceDB is among the only vector databases built on top of multiple modular components designed from the ground-up to be efficient on disk. This gives it the unique benefit of being flexible enough to support multiple storage backends, including local NVMe, EBS, EFS and many other third-party APIs that connect to the cloud.
-
-It is important to understand the tradeoffs between cost and latency for your specific application and use case. This section will help you understand the tradeoffs between the different storage backends.
-
-## Storage options
-
-We've prepared a simple diagram to showcase the thought process that goes into choosing a storage backend when using LanceDB OSS, Cloud or Enterprise.
-
-![](../assets/lancedb_storage_tradeoffs.png)
-
-When architecting your system, you'd typically ask yourself the following questions to decide on a storage option:
-
-1. **Latency**: How fast do I need results? What do the p50 and also p95 look like?
-2. **Scalability**: Can I scale up the amount of data and QPS easily?
-3. **Cost**: To serve my application, what’s the all-in cost of *both* storage and serving infra?
-4. **Reliability/Availability**: How does replication work? Is disaster recovery addressed?
-
-## Tradeoffs
-
-This section reviews the characteristics of each storage option in four dimensions: latency, scalability, cost and reliability.
-
-**We begin with the lowest cost option, and end with the lowest latency option.**
-
-### 1. S3 / GCS / Azure Blob Storage
-
-!!! tip "Lowest cost, highest latency"
-
-    - **Latency** ⇒ Has the highest latency. p95 latency is also substantially worse than p50. In general you get results in the order of several hundred milliseconds
-    - **Scalability** ⇒ Infinite on storage, however, QPS will be limited by S3 concurrency limits
-    - **Cost** ⇒ Lowest (order of magnitude cheaper than other options)
-    - **Reliability/Availability** ⇒ Highly available, as blob storage like S3 are critical infrastructure that form the backbone of the internet.
-
-Another important point to note is that LanceDB is designed to separate storage from compute, and the underlying Lance format stores the data in numerous immutable fragments. Due to these factors, LanceDB is a great storage option that addresses the _N + 1_ query problem. i.e., when a high query throughput is required, query processes can run in a stateless manner and be scaled up and down as needed.
-
-### 2. EFS / GCS Filestore / Azure File Storage
-
-!!! info "Moderately low cost, moderately low latency (<100ms)"
-
-    - **Latency** ⇒ Much better than object/blob storage but not as good as EBS/Local disk; < 100ms p95 achievable
-    - **Scalability** ⇒ High, but the bottleneck will be the IOPs limit, but when scaling you can provision multiple EFS volumes
-    - **Cost** ⇒ Significantly more expensive than S3 but still very cost effective compared to in-memory dbs. Inactive data in EFS is also automatically tiered to S3-level costs.
-    - **Reliability/Availability** ⇒ Highly available, as query nodes can go down without affecting EFS.  However, EFS does not provide replication / backup - this must be managed manually.
-
-A recommended best practice is to keep a copy of the data on S3 for disaster recovery scenarios. If any downtime is unacceptable, then you would need another EFS with a copy of the data. This is still much cheaper than EC2 instances holding multiple copies of the data.
-
-### 3. Third-party storage solutions
-
-Solutions like [MinIO](https://blog.min.io/lancedb-trusted-steed-against-data-complexity/), WekaFS, etc. that deliver S3 compatible API with much better performance than S3.
-
-!!! info "Moderately low cost, moderately low latency (<100ms)"
-
-    - **Latency** ⇒ Should be similar latency to EFS, better than S3 (<100ms)
-    - **Scalability** ⇒ Up to the solutions architect, who can add as many nodes to their MinIO or other third-party provider's cluster as needed
-    - **Cost** ⇒ Definitely higher than S3. The cost can be marginally higher than EFS until you get to maybe >10TB scale with high utilization
-    - **Reliability/Availability** ⇒ These are all shareable by lots of nodes, quality/cost of replication/backup depends on the vendor
-
-
-### 4. EBS / GCP Persistent Disk / Azure Managed Disk
-
-!!! info "Very low latency (<30ms), higher cost"
-
-    - **Latency** ⇒ Very good, pretty close to local disk. You’re looking at <30ms latency in most cases
-    - **Scalability** ⇒ EBS is not shareable between instances. If deployed via k8s, it can be shared between pods that live on the same instance, but beyond that you would need to shard data or make an additional copy
-    - **Cost** ⇒ Higher than EFS. There are some hidden costs to EBS as well if you’re paying for IO.
-    - **Reliability/Availability** ⇒ Not shareable between instances but can be shared between pods on the same instance. Survives instance termination. No automatic backups.
-
-Just like EFS, an EBS or persistent disk setup requires more manual work to manage data sharding, backups and capacity.
-
-### 5. Local disk (SSD/NVMe)
-
-!!! danger "Lowest latency (<10ms), highest cost"
-
-    - **Latency** ⇒ Lowest latency with modern NVMe drives, <10ms p95
-    - **Scalability** ⇒ Difficult to scale on cloud. Also need additional copies / sharding if QPS needs to be higher
-    - **Cost** ⇒ Highest cost; the main issue with keeping your application and storage tightly integrated is that it’s just not really possible to scale this up in cloud environments
-    - **Reliability/Availability** ⇒ If the instance goes down, so does your data. You have to be _very_ diligent about backing up your data
-
-As a rule of thumb, local disk should be your storage option if you require absolutely *crazy low* latency and you’re willing to do a bunch of data management work to make it happen.

docs/src/concepts/vector_search.md

@@ -1,36 +0,0 @@
-# Vector search
-
-Vector search is a technique used to search for similar items based on their vector representations, called embeddings. It is also known as similarity search, nearest neighbor search, or approximate nearest neighbor search.
-
-Raw data (e.g. text, images, audio, etc.) is converted into embeddings via an embedding model, which are then stored in a vector database like LanceDB. To perform similarity search at scale, an index is created on the stored embeddings, which can then used to perform fast lookups.
-
-![](../assets/vector-db-basics.png)
-
-## Embeddings
-
-Modern machine learning models can be trained to convert raw data into embeddings, represented as arrays (or vectors) of floating point numbers of fixed dimensionality. What makes embeddings useful in practice is that the position of an embedding in vector space captures some of the semantics of the data, depending on the type of model and how it was trained. Points that are close to each other in vector space are considered similar (or appear in similar contexts), and points that are far away are considered dissimilar.
-
-Large datasets of multi-modal data (text, audio, images, etc.) can be converted into embeddings with the appropriate model. Projecting the vectors' principal components in 2D space results in groups of vectors that represent similar concepts clustering together, as shown below.
-
-![](../assets/embedding_intro.png)
-
-## Indexes
-
-Embeddings for a given dataset are made searchable via an **index**. The index is constructed by using data structures that store the embeddings such that it's very efficient to perform scans and lookups on them. A key distinguishing feature of LanceDB is it uses a disk-based index: IVF-PQ, which is a variant of the Inverted File Index (IVF) that uses Product Quantization (PQ) to compress the embeddings.
-
-See the [IVF-PQ](./index_ivfpq.md) page for more details on how it works.
-
-## Brute force search
-
-The simplest way to perform vector search is to perform a brute force search, without an index, where the distance between the query vector and all the vectors in the database are computed, with the top-k closest vectors returned. This is equivalent to a k-nearest neighbours (kNN) search in vector space.
-
-![](../assets/knn_search.png)
-
-As you can imagine, the brute force approach is not scalable for datasets larger than a few hundred thousand vectors, as the latency of the search grows linearly with the size of the dataset. This is where approximate nearest neighbour (ANN) algorithms come in.
-
-## Approximate nearest neighbour (ANN) search
-
-Instead of performing an exhaustive search on the entire database for each and every query, approximate nearest neighbour (ANN) algorithms use an index to narrow down the search space, which significantly reduces query latency. The trade-off is that the results are not guaranteed to be the true nearest neighbors of the query, but are usually "good enough" for most use cases.
-
-
-

docs/src/embeddings/api.md

@@ -0,0 +1,213 @@
+To use your own custom embedding function, you need to follow these 2 simple steps.
+1. Create your embedding function by implementing the `EmbeddingFunction` interface
+2. Register your embedding function in the global `EmbeddingFunctionRegistry`.
+
+Let us see how this looks like in action.
+
+![](../assets/embeddings_api.png)
+
+
+`EmbeddingFunction` & `EmbeddingFunctionRegistry` handle low-level details for serializing schema and model information as metadata. To build a custom embdding function, you don't need to worry about those details and simply focus on setting up the model.
+
+## `TextEmbeddingFunction` Interface
+
+There is another optional layer of abstraction provided in form of `TextEmbeddingFunction`. You can use this if your model isn't multi-modal in nature and only operates on text. In such case both source and vector fields will have the same pathway for vectorization, so you simply just need to setup the model and rest is handled by `TextEmbeddingFunction`. You can read more about the class and its attributes in the class reference.
+
+
+Let's implement `SentenceTransformerEmbeddings` class. All you need to do is implement the `generate_embeddings()` and `ndims` function to handle the input types you expect and register the class in the global `EmbeddingFunctionRegistry`
+
+```python
+from lancedb.embeddings import register
+
+@register("sentence-transformers")
+class SentenceTransformerEmbeddings(TextEmbeddingFunction):
+    name: str = "all-MiniLM-L6-v2"
+    # set more default instance vars like device, etc.
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._ndims = None
+    
+    def generate_embeddings(self, texts):
+        return self._embedding_model().encode(list(texts), ...).tolist()
+
+    def ndims(self):
+        if self._ndims is None:
+            self._ndims = len(self.generate_embeddings("foo")[0])
+        return self._ndims
+
+    @cached(cache={}) 
+    def _embedding_model(self):
+        return sentence_transformers.SentenceTransformer(name)
+
+```
+
+This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and defaul settings.
+
+Now you can use this embedding function to create your table schema and that's it! you can then ingest data and run queries without manually vectorizing the inputs.
+
+```python
+from lancedb.pydantic import LanceModel, Vector
+
+registry = EmbeddingFunctionRegistry.get_instance()
+stransformer = registry.get("sentence-transformers").create()
+
+class TextModelSchema(LanceModel):
+    vector: Vector(stransformer.ndims) = stransformer.VectorField()
+    text: str = stransformer.SourceField()
+
+tbl = db.create_table("table", schema=TextModelSchema)
+
+tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
+result = tbl.search("world").limit(5)
+```
+
+NOTE:
+
+You can always implement the `EmbeddingFunction` interface directly if you want or need to, `TextEmbeddingFunction` just makes it much simpler and faster for you to do so, by setting up the boiler plat for text-specific use case
+
+## Multi-modal embedding function example
+You can also use the `EmbeddingFunction` interface to implement more complex workflows such as multi-modal embedding function support. LanceDB implements `OpenClipEmeddingFunction` class that suppports multi-modal seach. Here's the implementation that you can use as a reference to build your own multi-modal embedding functions.
+
+```python
+@register("open-clip")
+class OpenClipEmbeddings(EmbeddingFunction):
+    name: str = "ViT-B-32"
+    pretrained: str = "laion2b_s34b_b79k"
+    device: str = "cpu"
+    batch_size: int = 64
+    normalize: bool = True
+    _model = PrivateAttr()
+    _preprocess = PrivateAttr()
+    _tokenizer = PrivateAttr()
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        open_clip = self.safe_import("open_clip", "open-clip") # EmbeddingFunction util to import external libs and raise if not found
+        model, _, preprocess = open_clip.create_model_and_transforms(
+            self.name, pretrained=self.pretrained
+        )
+        model.to(self.device)
+        self._model, self._preprocess = model, preprocess
+        self._tokenizer = open_clip.get_tokenizer(self.name)
+        self._ndims = None
+
+    def ndims(self):
+        if self._ndims is None:
+            self._ndims = self.generate_text_embeddings("foo").shape[0]
+        return self._ndims
+
+    def compute_query_embeddings(
+        self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
+    ) -> List[np.ndarray]:
+        """
+        Compute the embeddings for a given user query
+
+        Parameters
+        ----------
+        query : Union[str, PIL.Image.Image]
+            The query to embed. A query can be either text or an image.
+        """
+        if isinstance(query, str):
+            return [self.generate_text_embeddings(query)]
+        else:
+            PIL = self.safe_import("PIL", "pillow")
+            if isinstance(query, PIL.Image.Image):
+                return [self.generate_image_embedding(query)]
+            else:
+                raise TypeError("OpenClip supports str or PIL Image as query")
+
+    def generate_text_embeddings(self, text: str) -> np.ndarray:
+        torch = self.safe_import("torch")
+        text = self.sanitize_input(text)
+        text = self._tokenizer(text)
+        text.to(self.device)
+        with torch.no_grad():
+            text_features = self._model.encode_text(text.to(self.device))
+            if self.normalize:
+                text_features /= text_features.norm(dim=-1, keepdim=True)
+            return text_features.cpu().numpy().squeeze()
+
+    def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
+        """
+        Sanitize the input to the embedding function.
+        """
+        if isinstance(images, (str, bytes)):
+            images = [images]
+        elif isinstance(images, pa.Array):
+            images = images.to_pylist()
+        elif isinstance(images, pa.ChunkedArray):
+            images = images.combine_chunks().to_pylist()
+        return images
+
+    def compute_source_embeddings(
+        self, images: IMAGES, *args, **kwargs
+    ) -> List[np.array]:
+        """
+        Get the embeddings for the given images
+        """
+        images = self.sanitize_input(images)
+        embeddings = []
+        for i in range(0, len(images), self.batch_size):
+            j = min(i + self.batch_size, len(images))
+            batch = images[i:j]
+            embeddings.extend(self._parallel_get(batch))
+        return embeddings
+
+    def _parallel_get(self, images: Union[List[str], List[bytes]]) -> List[np.ndarray]:
+        """
+        Issue concurrent requests to retrieve the image data
+        """
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            futures = [
+                executor.submit(self.generate_image_embedding, image)
+                for image in images
+            ]
+            return [future.result() for future in futures]
+
+    def generate_image_embedding(
+        self, image: Union[str, bytes, "PIL.Image.Image"]
+    ) -> np.ndarray:
+        """
+        Generate the embedding for a single image
+
+        Parameters
+        ----------
+        image : Union[str, bytes, PIL.Image.Image]
+            The image to embed. If the image is a str, it is treated as a uri.
+            If the image is bytes, it is treated as the raw image bytes.
+        """
+        torch = self.safe_import("torch")
+        # TODO handle retry and errors for https
+        image = self._to_pil(image)
+        image = self._preprocess(image).unsqueeze(0)
+        with torch.no_grad():
+            return self._encode_and_normalize_image(image)
+
+    def _to_pil(self, image: Union[str, bytes]):
+        PIL = self.safe_import("PIL", "pillow")
+        if isinstance(image, bytes):
+            return PIL.Image.open(io.BytesIO(image))
+        if isinstance(image, PIL.Image.Image):
+            return image
+        elif isinstance(image, str):
+            parsed = urlparse.urlparse(image)
+            # TODO handle drive letter on windows.
+            if parsed.scheme == "file":
+                return PIL.Image.open(parsed.path)
+            elif parsed.scheme == "":
+                return PIL.Image.open(image if os.name == "nt" else parsed.path)
+            elif parsed.scheme.startswith("http"):
+                return PIL.Image.open(io.BytesIO(url_retrieve(image)))
+            else:
+                raise NotImplementedError("Only local and http(s) urls are supported")
+
+    def _encode_and_normalize_image(self, image_tensor: "torch.Tensor"):
+        """
+        encode a single image tensor and optionally normalize the output
+        """
+        image_features = self._model.encode_image(image_tensor)
+        if self.normalize:
+            image_features /= image_features.norm(dim=-1, keepdim=True)
+        return image_features.cpu().numpy().squeeze()
+```