Compare commits

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18 Commits

Author SHA1 Message Date
ayush chaurasia
db67b27a42 ruff 2024-04-16 12:11:57 +05:30
ayush chaurasia
4b0820ef15 ruff 2024-04-16 12:09:18 +05:30
ayush chaurasia
c7bb919561 update benchmark script 2024-04-16 11:13:52 +05:30
ayush chaurasia
df404b726e ruff 2024-04-16 10:00:47 +05:30
ayush chaurasia
ffbb104648 remove protected namespaces 2024-04-16 09:51:08 +05:30
ayush chaurasia
3ebd561fd9 ruff 2024-04-16 09:26:47 +05:30
ayush chaurasia
6bc488f674 update 2024-04-16 09:24:29 +05:30
ayush chaurasia
ea34c0b4c4 update 2024-04-16 09:07:40 +05:30
ayush chaurasia
1a827925eb update docs 2024-04-16 08:59:36 +05:30
ayush chaurasia
fe5888d661 update usage 2024-04-16 08:24:40 +05:30
ayush chaurasia
6074e6b7ee update 2024-04-15 17:19:04 +05:30
ayush chaurasia
fd8de238bb ruff 2024-04-15 17:11:59 +05:30
ayush chaurasia
d0c1113417 add test 2024-04-15 17:07:29 +05:30
ayush chaurasia
3ca96a852f remove test file 2024-04-15 17:02:58 +05:30
ayush chaurasia
9428c6b565 update 2024-04-15 16:59:16 +05:30
ayush chaurasia
ff00a3242c update 2024-04-15 07:52:04 +05:30
ayush chaurasia
878deb73a0 update 2024-04-15 07:51:05 +05:30
ayush chaurasia
c75bb65609 update 2024-04-15 05:59:26 +05:30
153 changed files with 10611 additions and 14423 deletions

22
.bumpversion.cfg Normal file
View File

@@ -0,0 +1,22 @@
[bumpversion]
current_version = 0.4.17
commit = True
message = Bump version: {current_version} → {new_version}
tag = True
tag_name = v{new_version}
[bumpversion:file:node/package.json]
[bumpversion:file:nodejs/package.json]
[bumpversion:file:nodejs/npm/darwin-x64/package.json]
[bumpversion:file:nodejs/npm/darwin-arm64/package.json]
[bumpversion:file:nodejs/npm/linux-x64-gnu/package.json]
[bumpversion:file:nodejs/npm/linux-arm64-gnu/package.json]
[bumpversion:file:rust/ffi/node/Cargo.toml]
[bumpversion:file:rust/lancedb/Cargo.toml]

View File

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

33
.github/labeler.yml vendored
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@@ -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\\/.*"

View File

@@ -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"]
}
]
}

View File

@@ -1,12 +1,8 @@
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

View File

@@ -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)

View File

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

View File

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

View File

@@ -52,7 +52,8 @@ jobs:
cargo fmt --all -- --check
cargo clippy --all --all-features -- -D warnings
npm ci
npm run lint-ci
npm run lint
npm run chkformat
linux:
name: Linux (NodeJS ${{ matrix.node-version }})
timeout-minutes: 30

View File

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

View File

@@ -1,16 +1,18 @@
name: PyPI Publish
on:
push:
tags:
- 'python-v*'
release:
types: [published]
jobs:
linux:
# Only runs on tags that matches the python-make-release action
if: startsWith(github.ref, 'refs/tags/python-v')
name: Python ${{ matrix.config.platform }} manylinux${{ matrix.config.manylinux }}
timeout-minutes: 60
strategy:
matrix:
python-minor-version: ["8"]
config:
- platform: x86_64
manylinux: "2_17"
@@ -32,22 +34,25 @@ jobs:
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: 3.8
python-version: 3.${{ matrix.python-minor-version }}
- uses: ./.github/workflows/build_linux_wheel
with:
python-minor-version: 8
python-minor-version: ${{ matrix.python-minor-version }}
args: "--release --strip ${{ matrix.config.extra_args }}"
arm-build: ${{ matrix.config.platform == 'aarch64' }}
manylinux: ${{ matrix.config.manylinux }}
- uses: ./.github/workflows/upload_wheel
with:
pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
fury_token: ${{ secrets.FURY_TOKEN }}
token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
repo: "pypi"
mac:
# Only runs on tags that matches the python-make-release action
if: startsWith(github.ref, 'refs/tags/python-v')
timeout-minutes: 60
runs-on: ${{ matrix.config.runner }}
strategy:
matrix:
python-minor-version: ["8"]
config:
- target: x86_64-apple-darwin
runner: macos-13
@@ -58,6 +63,7 @@ jobs:
steps:
- uses: actions/checkout@v4
with:
ref: ${{ inputs.ref }}
fetch-depth: 0
lfs: true
- name: Set up Python
@@ -66,95 +72,38 @@ jobs:
python-version: 3.12
- uses: ./.github/workflows/build_mac_wheel
with:
python-minor-version: 8
python-minor-version: ${{ matrix.python-minor-version }}
args: "--release --strip --target ${{ matrix.config.target }} --features fp16kernels"
- uses: ./.github/workflows/upload_wheel
with:
pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
fury_token: ${{ secrets.FURY_TOKEN }}
python-minor-version: ${{ matrix.python-minor-version }}
token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
repo: "pypi"
windows:
# Only runs on tags that matches the python-make-release action
if: startsWith(github.ref, 'refs/tags/python-v')
timeout-minutes: 60
runs-on: windows-latest
strategy:
matrix:
python-minor-version: ["8"]
steps:
- uses: actions/checkout@v4
with:
ref: ${{ inputs.ref }}
fetch-depth: 0
lfs: true
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: 3.8
python-version: 3.${{ matrix.python-minor-version }}
- uses: ./.github/workflows/build_windows_wheel
with:
python-minor-version: 8
python-minor-version: ${{ matrix.python-minor-version }}
args: "--release --strip"
vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
- uses: ./.github/workflows/upload_wheel
with:
pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
fury_token: ${{ secrets.FURY_TOKEN }}
gh-release:
runs-on: ubuntu-latest
permissions:
contents: write
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
- name: Extract version
id: extract_version
env:
GITHUB_REF: ${{ github.ref }}
run: |
set -e
echo "Extracting tag and version from $GITHUB_REF"
if [[ $GITHUB_REF =~ refs/tags/python-v(.*) ]]; then
VERSION=${BASH_REMATCH[1]}
TAG=python-v$VERSION
echo "tag=$TAG" >> $GITHUB_OUTPUT
echo "version=$VERSION" >> $GITHUB_OUTPUT
else
echo "Failed to extract version from $GITHUB_REF"
exit 1
fi
echo "Extracted version $VERSION from $GITHUB_REF"
if [[ $VERSION =~ beta ]]; then
echo "This is a beta release"
# Get last release (that is not this one)
FROM_TAG=$(git tag --sort='version:refname' \
| grep ^python-v \
| grep -vF "$TAG" \
| python ci/semver_sort.py python-v \
| tail -n 1)
else
echo "This is a stable release"
# Get last stable tag (ignore betas)
FROM_TAG=$(git tag --sort='version:refname' \
| grep ^python-v \
| grep -vF "$TAG" \
| grep -v beta \
| python ci/semver_sort.py python-v \
| tail -n 1)
fi
echo "Found from tag $FROM_TAG"
echo "from_tag=$FROM_TAG" >> $GITHUB_OUTPUT
- name: Create Python Release Notes
id: python_release_notes
uses: mikepenz/release-changelog-builder-action@v4
with:
configuration: .github/release_notes.json
toTag: ${{ steps.extract_version.outputs.tag }}
fromTag: ${{ steps.extract_version.outputs.from_tag }}
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Create Python GH release
uses: softprops/action-gh-release@v2
with:
prerelease: ${{ contains('beta', github.ref) }}
tag_name: ${{ steps.extract_version.outputs.tag }}
token: ${{ secrets.GITHUB_TOKEN }}
generate_release_notes: false
name: Python LanceDB v${{ steps.extract_version.outputs.version }}
body: ${{ steps.python_release_notes.outputs.changelog }}
python-minor-version: ${{ matrix.python-minor-version }}
token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
repo: "pypi"

View File

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

View File

@@ -75,7 +75,7 @@ jobs:
timeout-minutes: 30
strategy:
matrix:
python-minor-version: ["9", "11"]
python-minor-version: ["8", "11"]
runs-on: "ubuntu-22.04"
defaults:
run:

View File

@@ -74,11 +74,11 @@ jobs:
run: |
sudo apt update
sudo apt install -y protobuf-compiler libssl-dev
- name: Build
run: cargo build --all-features
- name: Start S3 integration test environment
working-directory: .
run: docker compose up --detach --wait
- name: Build
run: cargo build --all-features
- name: Run tests
run: cargo test --all-features
- name: Run examples

View File

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

2
.gitignore vendored
View File

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

View File

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

View File

@@ -1,5 +1,5 @@
[workspace]
members = ["rust/ffi/node", "rust/lancedb", "nodejs", "python", "java/core/lancedb-jni"]
members = ["rust/ffi/node", "rust/lancedb", "nodejs", "python"]
# Python package needs to be built by maturin.
exclude = ["python"]
resolver = "2"
@@ -14,22 +14,22 @@ keywords = ["lancedb", "lance", "database", "vector", "search"]
categories = ["database-implementations"]
[workspace.dependencies]
lance = { "version" = "=0.11.0", "features" = ["dynamodb"] }
lance-index = { "version" = "=0.11.0" }
lance-linalg = { "version" = "=0.11.0" }
lance-testing = { "version" = "=0.11.0" }
lance = { "version" = "=0.10.12", "features" = ["dynamodb"] }
lance-index = { "version" = "=0.10.12" }
lance-linalg = { "version" = "=0.10.12" }
lance-testing = { "version" = "=0.10.12" }
# Note that this one does not include pyarrow
arrow = { version = "51.0", optional = false }
arrow-array = "51.0"
arrow-data = "51.0"
arrow-ipc = "51.0"
arrow-ord = "51.0"
arrow-schema = "51.0"
arrow-arith = "51.0"
arrow-cast = "51.0"
arrow = { version = "50.0", optional = false }
arrow-array = "50.0"
arrow-data = "50.0"
arrow-ipc = "50.0"
arrow-ord = "50.0"
arrow-schema = "50.0"
arrow-arith = "50.0"
arrow-cast = "50.0"
async-trait = "0"
chrono = "0.4.35"
half = { "version" = "=2.4.1", default-features = false, features = [
half = { "version" = "=2.3.1", default-features = false, features = [
"num-traits",
] }
futures = "0"

View File

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

View File

@@ -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

View File

@@ -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)

View File

@@ -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)

View File

@@ -0,0 +1,280 @@
import argparse
import os
from llama_index.core import SimpleDirectoryReader
from llama_index.core.llama_dataset import LabelledRagDataset
from llama_index.core.node_parser import SentenceSplitter
from lancedb.embeddings.fine_tuner.dataset import QADataset, TextChunk
from lancedb.embeddings.fine_tuner.llm import Openai
from lancedb.embeddings import get_registry
from llama_index.vector_stores.lancedb import LanceDBVectorStore
from llama_index.core import ServiceContext, VectorStoreIndex, StorageContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.llama_pack import download_llama_pack
import time
import wandb
import pandas as pd
def get_paths_from_dataset(dataset: str, split=True):
"""
Returns paths of:
- downloaded dataset, lance train dataset, lance test dataset, finetuned model
"""
if split:
return (
f"./data/{dataset}",
f"./data/{dataset}_lance_train",
f"./data/{dataset}_lance_test",
f"./data/tuned_{dataset}",
)
return f"./data/{dataset}", f"./data/{dataset}_lance", f"./data/tuned_{dataset}"
def get_llama_dataset(dataset: str):
"""
returns:
- nodes, documents, rag_dataset
"""
if not os.path.exists(f"./data/{dataset}"):
os.system(
f"llamaindex-cli download-llamadataset {dataset} --download-dir ./data/{dataset}" # noqa
)
rag_dataset = LabelledRagDataset.from_json(f"./data/{dataset}/rag_dataset.json")
docs = SimpleDirectoryReader(input_dir=f"./data/{dataset}/source_files").load_data()
parser = SentenceSplitter()
nodes = parser.get_nodes_from_documents(docs)
return nodes, docs, rag_dataset
def lance_dataset_from_llama_nodes(nodes: list, name: str, split=True):
llm = Openai()
chunks = [TextChunk.from_llama_index_node(node) for node in nodes]
# train test split 75-35
if not split:
if os.path.exists(f"./data/{name}_lance"):
ds = QADataset.load(f"./data/{name}_lance")
return ds
ds = QADataset.from_llm(chunks, llm)
ds.save(f"./data/{name}_lance")
return ds
if os.path.exists(f"./data/{name}_lance_train") and os.path.exists(
f"./data/{name}_lance_test"
):
train_ds = QADataset.load(f"./data/{name}_lance_train")
test_ds = QADataset.load(f"./data/{name}_lance_test")
return train_ds, test_ds
# split chunks random
train_size = int(len(chunks) * 0.65)
train_chunks = chunks[:train_size]
test_chunks = chunks[train_size:]
train_ds = QADataset.from_llm(train_chunks, llm)
test_ds = QADataset.from_llm(test_chunks, llm)
train_ds.save(f"./data/{name}_lance_train")
test_ds.save(f"./data/{name}_lance_test")
return train_ds, test_ds
def finetune(
trainset: str, model: str, epochs: int, path: str, valset: str = None, top_k=5
):
print(f"Finetuning {model} for {epochs} epochs")
print(f"trainset query instances: {len(trainset.queries)}")
print(f"valset query instances: {len(valset.queries)}")
valset = valset if valset is not None else trainset
model = get_registry().get("sentence-transformers").create(name=model)
base_result = model.evaluate(valset, path="./data/eval/", top_k=top_k)
base_hit_rate = pd.DataFrame(base_result)["is_hit"].mean()
model.finetune(trainset=trainset, valset=valset, path=path, epochs=epochs)
tuned = get_registry().get("sentence-transformers").create(name=path)
tuned_result = tuned.evaluate(
valset, path=f"./data/eval/{str(time.time())}", top_k=top_k
)
tuned_hit_rate = pd.DataFrame(tuned_result)["is_hit"].mean()
return base_hit_rate, tuned_hit_rate
def do_eval_rag(dataset: str, model: str):
# Requires - pip install llama-index-vector-stores-lancedb
# Requires - pip install llama-index-embeddings-huggingface
nodes, docs, rag_dataset = get_llama_dataset(dataset)
embed_model = HuggingFaceEmbedding(model)
vector_store = LanceDBVectorStore(uri="/tmp/lancedb")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
service_context = ServiceContext.from_defaults(embed_model=embed_model)
index = VectorStoreIndex(
nodes,
service_context=service_context,
show_progress=True,
storage_context=storage_context,
)
# build basic RAG system
index = VectorStoreIndex.from_documents(documents=docs)
query_engine = index.as_query_engine()
# evaluate using the RagEvaluatorPack
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./rag_evaluator_pack")
rag_evaluator_pack = RagEvaluatorPack(
rag_dataset=rag_dataset, query_engine=query_engine
)
metrics = rag_evaluator_pack.run(
batch_size=20, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # seconds to sleep before making an api call
)
return metrics
def main(
dataset,
model,
epochs,
top_k=5,
eval_rag=False,
split=True,
project: str = "lancedb_finetune",
):
nodes, _, _ = get_llama_dataset(dataset)
trainset = None
valset = None
if split:
trainset, valset = lance_dataset_from_llama_nodes(nodes, dataset, split)
data_path, lance_train_path, lance_test_path, tuned_path = (
get_paths_from_dataset(dataset, split=split)
)
else:
trainset = lance_dataset_from_llama_nodes(nodes, dataset, split)
valset = trainset
data_path, lance_path, tuned_path = get_paths_from_dataset(dataset, split=split)
base_hit_rate, tuned_hit_rate = finetune(
trainset, model, epochs, tuned_path, valset, top_k=top_k
)
# Base model model metrics
metrics = do_eval_rag(dataset, model) if eval_rag else {}
# Tuned model metrics
metrics_tuned = do_eval_rag(dataset, tuned_path) if eval_rag else {}
wandb.init(project="lancedb_finetune", name=f"{dataset}_{model}_{epochs}")
wandb.log(
{
"hit_rate": tuned_hit_rate,
}
)
wandb.log(metrics_tuned)
wandb.finish()
wandb.init(project="lancedb_finetune", name=f"{dataset}_{model}_base")
wandb.log(
{
"hit_rate": base_hit_rate,
}
)
wandb.log(metrics)
wandb.finish()
def banchmark_all():
datasets = [
"Uber10KDataset2021",
"MiniTruthfulQADataset",
"MiniSquadV2Dataset",
"MiniEsgBenchDataset",
"MiniCovidQaDataset",
"Llama2PaperDataset",
"HistoryOfAlexnetDataset",
"PatronusAIFinanceBenchDataset",
]
models = ["BAAI/bge-small-en-v1.5"]
top_ks = [5]
for top_k in top_ks:
for model in models:
for dataset in datasets:
main(dataset, model, 5, top_k=top_k)
if __name__ == "__main__":
"""
Benchmark the fine-tuning process for a given dataset and model.
Usage:
- For a single dataset
python lancedb/docs/benchmarks/llama-index-datasets.py --dataset Uber10KDataset2021 --model BAAI/bge-small-en-v1.5 --epochs 4 --top_k 5 --split 1 --project lancedb_finetune
- For all datasets and models across all top_ks
python lancedb/docs/benchmarks/llama-index-datasets.py --benchmark-all
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"--dataset",
type=str,
default="BraintrustCodaHelpDeskDataset",
help="The dataset to use for fine-tuning",
)
parser.add_argument(
"--model",
type=str,
default="BAAI/bge-small-en-v1.5",
help="The model to use for fine-tuning",
)
parser.add_argument(
"--epochs",
type=int,
default=4,
help="The number of epochs to fine-tune the model",
)
parser.add_argument(
"--project",
type=str,
default="lancedb_finetune",
help="The wandb project to log the results",
)
parser.add_argument(
"--top_k", type=int, default=5, help="The number of top results to evaluate"
)
parser.add_argument(
"--split",
type=int,
default=1,
help="Whether to split the dataset into train and test(65-35 split), default is 1",
)
parser.add_argument(
"--eval-rag",
action="store_true",
default=False,
help="Whether to evaluate the model using RAG",
)
parser.add_argument(
"--benchmark-all",
action="store_true",
default=False,
help="Benchmark all datasets across all models and top_ks",
)
args = parser.parse_args()
if args.benchmark_all:
banchmark_all()
else:
main(
args.dataset,
args.model,
args.epochs,
args.top_k,
args.eval_rag,
args.split,
args.project,
)

View File

@@ -119,7 +119,7 @@ nav:
- Polars: python/polars_arrow.md
- DuckDB: python/duckdb.md
- LangChain:
- LangChain 🔗: integrations/langchain.md
- LangChain 🔗: https://python.langchain.com/docs/integrations/vectorstores/lancedb/
- LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
- LlamaIndex 🦙: https://docs.llamaindex.ai/en/stable/examples/vector_stores/LanceDBIndexDemo/
- Pydantic: python/pydantic.md

View File

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

View File

@@ -159,7 +159,7 @@ Allows you to set parameters when registering a `sentence-transformers` object.
from lancedb.embeddings import get_registry
db = lancedb.connect("/tmp/db")
model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
model = get_registry.get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
class Words(LanceModel):
text: str = model.SourceField()
@@ -206,44 +206,6 @@ print(actual.text)
```
### Ollama embeddings
Generate embeddings via the [ollama](https://github.com/ollama/ollama-python) python library. More details:
- [Ollama docs on embeddings](https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings)
- [Ollama blog on embeddings](https://ollama.com/blog/embedding-models)
| Parameter | Type | Default Value | Description |
|------------------------|----------------------------|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|
| `name` | `str` | `nomic-embed-text` | The name of the model. |
| `host` | `str` | `http://localhost:11434` | The Ollama host to connect to. |
| `options` | `ollama.Options` or `dict` | `None` | Additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`. |
| `keep_alive` | `float` or `str` | `"5m"` | Controls how long the model will stay loaded into memory following the request. |
| `ollama_client_kwargs` | `dict` | `{}` | kwargs that can be past to the `ollama.Client`. |
```python
import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry
db = lancedb.connect("/tmp/db")
func = get_registry().get("ollama").create(name="nomic-embed-text")
class Words(LanceModel):
text: str = func.SourceField()
vector: Vector(func.ndims()) = func.VectorField()
table = db.create_table("words", schema=Words, mode="overwrite")
table.add([
{"text": "hello world"},
{"text": "goodbye world"}
])
query = "greetings"
actual = table.search(query).limit(1).to_pydantic(Words)[0]
print(actual.text)
```
### OpenAI embeddings
LanceDB registers the OpenAI embeddings function in the registry by default, as `openai`. Below are the parameters that you can customize when creating the instances:

View File

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

View File

@@ -299,14 +299,6 @@ LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you m
This can also be done with the ``AWS_ENDPOINT`` and ``AWS_DEFAULT_REGION`` environment variables.
!!! tip "Local servers"
For local development, the server often has a `http` endpoint rather than a
secure `https` endpoint. In this case, you must also set the `ALLOW_HTTP`
environment variable to `true` to allow non-TLS connections, or pass the
storage option `allow_http` as `true`. If you do not do this, you will get
an error like `URL scheme is not allowed`.
#### S3 Express
LanceDB supports [S3 Express One Zone](https://aws.amazon.com/s3/storage-classes/express-one-zone/) endpoints, but requires additional configuration. Also, S3 Express endpoints only support connecting from an EC2 instance within the same region.

View File

@@ -13,7 +13,7 @@ Get started using these examples and quick links.
| Integrations | |
|---|---:|
| <h3> LlamaIndex </h3>LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. Llama index integrates with LanceDB as the serverless VectorDB. <h3>[Lean More](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html) </h3> |<img src="../assets/llama-index.jpg" alt="image" width="150" height="auto">|
| <h3>Langchain</h3>Langchain allows building applications with LLMs through composability <h3>[Lean More](https://lancedb.github.io/lancedb/integrations/langchain/) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
| <h3>Langchain</h3>Langchain allows building applications with LLMs through composability <h3>[Lean More](https://python.langchain.com/docs/integrations/vectorstores/lancedb) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
| <h3>Langchain TS</h3> Javascript bindings for Langchain. It integrates with LanceDB's serverless vectordb allowing you to build powerful AI applications through composibility using only serverless functions. <h3>[Learn More]( https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
| <h3>Voxel51</h3> It is an open source toolkit that enables you to build better computer vision workflows by improving the quality of your datasets and delivering insights about your models.<h3>[Learn More](./voxel51.md) | <img src="../assets/voxel.gif" alt="image" width="150" height="auto">|
| <h3>PromptTools</h3> Offers a set of free, open-source tools for testing and experimenting with models, prompts, and configurations. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks. You can use it to experiment with different configurations of LanceDB, and test how LanceDB integrates with the LLM of your choice.<h3>[Learn More](./prompttools.md) | <img src="../assets/prompttools.jpeg" alt="image" width="150" height="auto">|

View File

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

View File

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

View File

@@ -8,7 +8,6 @@ excluded_globs = [
"../src/embedding.md",
"../src/examples/*.md",
"../src/integrations/voxel51.md",
"../src/integrations/langchain.md",
"../src/guides/tables.md",
"../src/python/duckdb.md",
"../src/embeddings/*.md",

View File

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

View File

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

View File

@@ -1,225 +0,0 @@
// Copyright 2024 Lance Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
use std::str::Utf8Error;
use arrow_schema::ArrowError;
use jni::errors::Error as JniError;
use serde_json::Error as JsonError;
use snafu::{Location, Snafu};
type BoxedError = Box<dyn std::error::Error + Send + Sync + 'static>;
/// Java Exception types
pub enum JavaException {
IllegalArgumentException,
IOException,
RuntimeException,
}
impl JavaException {
pub fn as_str(&self) -> &str {
match self {
Self::IllegalArgumentException => "java/lang/IllegalArgumentException",
Self::IOException => "java/io/IOException",
Self::RuntimeException => "java/lang/RuntimeException",
}
}
}
/// TODO(lu) change to lancedb-jni
#[derive(Debug, Snafu)]
#[snafu(visibility(pub))]
pub enum Error {
#[snafu(display("JNI error: {message}, {location}"))]
Jni { message: String, location: Location },
#[snafu(display("Invalid argument: {message}, {location}"))]
InvalidArgument { message: String, location: Location },
#[snafu(display("IO error: {source}, {location}"))]
IO {
source: BoxedError,
location: Location,
},
#[snafu(display("Arrow error: {message}, {location}"))]
Arrow { message: String, location: Location },
#[snafu(display("Index error: {message}, {location}"))]
Index { message: String, location: Location },
#[snafu(display("JSON error: {message}, {location}"))]
JSON { message: String, location: Location },
#[snafu(display("Dataset at path {path} was not found, {location}"))]
DatasetNotFound { path: String, location: Location },
#[snafu(display("Dataset already exists: {uri}, {location}"))]
DatasetAlreadyExists { uri: String, location: Location },
#[snafu(display("Table '{name}' already exists"))]
TableAlreadyExists { name: String },
#[snafu(display("Table '{name}' was not found"))]
TableNotFound { name: String },
#[snafu(display("Invalid table name '{name}': {reason}"))]
InvalidTableName { name: String, reason: String },
#[snafu(display("Embedding function '{name}' was not found: {reason}, {location}"))]
EmbeddingFunctionNotFound {
name: String,
reason: String,
location: Location,
},
#[snafu(display("Other Lance error: {message}, {location}"))]
OtherLance { message: String, location: Location },
#[snafu(display("Other LanceDB error: {message}, {location}"))]
OtherLanceDB { message: String, location: Location },
}
impl Error {
/// Throw as Java Exception
pub fn throw(&self, env: &mut jni::JNIEnv) {
match self {
Self::InvalidArgument { .. }
| Self::DatasetNotFound { .. }
| Self::DatasetAlreadyExists { .. }
| Self::TableAlreadyExists { .. }
| Self::TableNotFound { .. }
| Self::InvalidTableName { .. }
| Self::EmbeddingFunctionNotFound { .. } => {
self.throw_as(env, JavaException::IllegalArgumentException)
}
Self::IO { .. } | Self::Index { .. } => self.throw_as(env, JavaException::IOException),
Self::Arrow { .. }
| Self::JSON { .. }
| Self::OtherLance { .. }
| Self::OtherLanceDB { .. }
| Self::Jni { .. } => self.throw_as(env, JavaException::RuntimeException),
}
}
/// Throw as an concrete Java Exception
pub fn throw_as(&self, env: &mut jni::JNIEnv, exception: JavaException) {
let message = &format!(
"Error when throwing Java exception: {}:{}",
exception.as_str(),
self
);
env.throw_new(exception.as_str(), self.to_string())
.expect(message);
}
}
pub type Result<T> = std::result::Result<T, Error>;
trait ToSnafuLocation {
fn to_snafu_location(&'static self) -> snafu::Location;
}
impl ToSnafuLocation for std::panic::Location<'static> {
fn to_snafu_location(&'static self) -> snafu::Location {
snafu::Location::new(self.file(), self.line(), self.column())
}
}
impl From<JniError> for Error {
#[track_caller]
fn from(source: JniError) -> Self {
Self::Jni {
message: source.to_string(),
location: std::panic::Location::caller().to_snafu_location(),
}
}
}
impl From<Utf8Error> for Error {
#[track_caller]
fn from(source: Utf8Error) -> Self {
Self::InvalidArgument {
message: source.to_string(),
location: std::panic::Location::caller().to_snafu_location(),
}
}
}
impl From<ArrowError> for Error {
#[track_caller]
fn from(source: ArrowError) -> Self {
Self::Arrow {
message: source.to_string(),
location: std::panic::Location::caller().to_snafu_location(),
}
}
}
impl From<JsonError> for Error {
#[track_caller]
fn from(source: JsonError) -> Self {
Self::JSON {
message: source.to_string(),
location: std::panic::Location::caller().to_snafu_location(),
}
}
}
impl From<lance::Error> for Error {
#[track_caller]
fn from(source: lance::Error) -> Self {
match source {
lance::Error::DatasetNotFound {
path,
source: _,
location,
} => Self::DatasetNotFound { path, location },
lance::Error::DatasetAlreadyExists { uri, location } => {
Self::DatasetAlreadyExists { uri, location }
}
lance::Error::IO { source, location } => Self::IO { source, location },
lance::Error::Arrow { message, location } => Self::Arrow { message, location },
lance::Error::Index { message, location } => Self::Index { message, location },
lance::Error::InvalidInput { source, location } => Self::InvalidArgument {
message: source.to_string(),
location,
},
_ => Self::OtherLance {
message: source.to_string(),
location: std::panic::Location::caller().to_snafu_location(),
},
}
}
}
impl From<lancedb::Error> for Error {
#[track_caller]
fn from(source: lancedb::Error) -> Self {
match source {
lancedb::Error::InvalidTableName { name, reason } => {
Self::InvalidTableName { name, reason }
}
lancedb::Error::InvalidInput { message } => Self::InvalidArgument {
message,
location: std::panic::Location::caller().to_snafu_location(),
},
lancedb::Error::TableNotFound { name } => Self::TableNotFound { name },
lancedb::Error::TableAlreadyExists { name } => Self::TableAlreadyExists { name },
lancedb::Error::EmbeddingFunctionNotFound { name, reason } => {
Self::EmbeddingFunctionNotFound {
name,
reason,
location: std::panic::Location::caller().to_snafu_location(),
}
}
lancedb::Error::Arrow { source } => Self::Arrow {
message: source.to_string(),
location: std::panic::Location::caller().to_snafu_location(),
},
lancedb::Error::Lance { source } => Self::from(source),
_ => Self::OtherLanceDB {
message: source.to_string(),
location: std::panic::Location::caller().to_snafu_location(),
},
}
}
}

View File

@@ -1,204 +0,0 @@
// Copyright 2024 Lance Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
use core::slice;
use jni::objects::{JByteBuffer, JObjectArray, JString};
use jni::sys::jobjectArray;
use jni::{objects::JObject, JNIEnv};
use crate::error::{Error, Result};
/// TODO(lu) import from lance-jni without duplicate
/// Extend JNIEnv with helper functions.
pub trait JNIEnvExt {
/// Get integers from Java List<Integer> object.
fn get_integers(&mut self, obj: &JObject) -> Result<Vec<i32>>;
/// Get strings from Java List<String> object.
fn get_strings(&mut self, obj: &JObject) -> Result<Vec<String>>;
/// Get strings from Java String[] object.
/// Note that get Option<Vec<String>> from Java Optional<String[]> just doesn't work.
#[allow(unused)]
fn get_strings_array(&mut self, obj: jobjectArray) -> Result<Vec<String>>;
/// Get Option<String> from Java Optional<String>.
fn get_string_opt(&mut self, obj: &JObject) -> Result<Option<String>>;
/// Get Option<Vec<String>> from Java Optional<List<String>>.
#[allow(unused)]
fn get_strings_opt(&mut self, obj: &JObject) -> Result<Option<Vec<String>>>;
/// Get Option<i32> from Java Optional<Integer>.
fn get_int_opt(&mut self, obj: &JObject) -> Result<Option<i32>>;
/// Get Option<Vec<i32>> from Java Optional<List<Integer>>.
fn get_ints_opt(&mut self, obj: &JObject) -> Result<Option<Vec<i32>>>;
/// Get Option<i64> from Java Optional<Long>.
#[allow(unused)]
fn get_long_opt(&mut self, obj: &JObject) -> Result<Option<i64>>;
/// Get Option<u64> from Java Optional<Long>.
#[allow(unused)]
fn get_u64_opt(&mut self, obj: &JObject) -> Result<Option<u64>>;
/// Get Option<&[u8]> from Java Optional<ByteBuffer>.
#[allow(unused)]
fn get_bytes_opt(&mut self, obj: &JObject) -> Result<Option<&[u8]>>;
fn get_optional<T, F>(&mut self, obj: &JObject, f: F) -> Result<Option<T>>
where
F: FnOnce(&mut JNIEnv, &JObject) -> Result<T>;
}
impl JNIEnvExt for JNIEnv<'_> {
fn get_integers(&mut self, obj: &JObject) -> Result<Vec<i32>> {
let list = self.get_list(obj)?;
let mut iter = list.iter(self)?;
let mut results = Vec::with_capacity(list.size(self)? as usize);
while let Some(elem) = iter.next(self)? {
let int_obj = self.call_method(elem, "intValue", "()I", &[])?;
let int_value = int_obj.i()?;
results.push(int_value);
}
Ok(results)
}
fn get_strings(&mut self, obj: &JObject) -> Result<Vec<String>> {
let list = self.get_list(obj)?;
let mut iter = list.iter(self)?;
let mut results = Vec::with_capacity(list.size(self)? as usize);
while let Some(elem) = iter.next(self)? {
let jstr = JString::from(elem);
let val = self.get_string(&jstr)?;
results.push(val.to_str()?.to_string())
}
Ok(results)
}
fn get_strings_array(&mut self, obj: jobjectArray) -> Result<Vec<String>> {
let jobject_array = unsafe { JObjectArray::from_raw(obj) };
let array_len = self.get_array_length(&jobject_array)?;
let mut res: Vec<String> = Vec::new();
for i in 0..array_len {
let item: JString = self.get_object_array_element(&jobject_array, i)?.into();
res.push(self.get_string(&item)?.into());
}
Ok(res)
}
fn get_string_opt(&mut self, obj: &JObject) -> Result<Option<String>> {
self.get_optional(obj, |env, inner_obj| {
let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
let java_string_obj = java_obj_gen.l()?;
let jstr = JString::from(java_string_obj);
let val = env.get_string(&jstr)?;
Ok(val.to_str()?.to_string())
})
}
fn get_strings_opt(&mut self, obj: &JObject) -> Result<Option<Vec<String>>> {
self.get_optional(obj, |env, inner_obj| {
let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
let java_list_obj = java_obj_gen.l()?;
env.get_strings(&java_list_obj)
})
}
fn get_int_opt(&mut self, obj: &JObject) -> Result<Option<i32>> {
self.get_optional(obj, |env, inner_obj| {
let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
let java_int_obj = java_obj_gen.l()?;
let int_obj = env.call_method(java_int_obj, "intValue", "()I", &[])?;
let int_value = int_obj.i()?;
Ok(int_value)
})
}
fn get_ints_opt(&mut self, obj: &JObject) -> Result<Option<Vec<i32>>> {
self.get_optional(obj, |env, inner_obj| {
let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
let java_list_obj = java_obj_gen.l()?;
env.get_integers(&java_list_obj)
})
}
fn get_long_opt(&mut self, obj: &JObject) -> Result<Option<i64>> {
self.get_optional(obj, |env, inner_obj| {
let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
let java_long_obj = java_obj_gen.l()?;
let long_obj = env.call_method(java_long_obj, "longValue", "()J", &[])?;
let long_value = long_obj.j()?;
Ok(long_value)
})
}
fn get_u64_opt(&mut self, obj: &JObject) -> Result<Option<u64>> {
self.get_optional(obj, |env, inner_obj| {
let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
let java_long_obj = java_obj_gen.l()?;
let long_obj = env.call_method(java_long_obj, "longValue", "()J", &[])?;
let long_value = long_obj.j()?;
Ok(long_value as u64)
})
}
fn get_bytes_opt(&mut self, obj: &JObject) -> Result<Option<&[u8]>> {
self.get_optional(obj, |env, inner_obj| {
let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
let java_byte_buffer_obj = java_obj_gen.l()?;
let j_byte_buffer = JByteBuffer::from(java_byte_buffer_obj);
let raw_data = env.get_direct_buffer_address(&j_byte_buffer)?;
let capacity = env.get_direct_buffer_capacity(&j_byte_buffer)?;
let data = unsafe { slice::from_raw_parts(raw_data, capacity) };
Ok(data)
})
}
fn get_optional<T, F>(&mut self, obj: &JObject, f: F) -> Result<Option<T>>
where
F: FnOnce(&mut JNIEnv, &JObject) -> Result<T>,
{
if obj.is_null() {
return Ok(None);
}
let is_empty = self.call_method(obj, "isEmpty", "()Z", &[])?;
if is_empty.z()? {
// TODO(lu): put get java object into here cuz can only get java Object
Ok(None)
} else {
f(self, obj).map(Some)
}
}
}
#[no_mangle]
pub extern "system" fn Java_com_lancedb_lance_test_JniTestHelper_parseInts(
mut env: JNIEnv,
_obj: JObject,
list_obj: JObject, // List<Integer>
) {
ok_or_throw_without_return!(env, env.get_integers(&list_obj));
}
#[no_mangle]
pub extern "system" fn Java_com_lancedb_lance_test_JniTestHelper_parseIntsOpt(
mut env: JNIEnv,
_obj: JObject,
list_obj: JObject, // Optional<List<Integer>>
) {
ok_or_throw_without_return!(env, env.get_ints_opt(&list_obj));
}

View File

@@ -1,68 +0,0 @@
// Copyright 2024 Lance Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
use lazy_static::lazy_static;
// TODO import from lance-jni without duplicate
#[macro_export]
macro_rules! ok_or_throw {
($env:expr, $result:expr) => {
match $result {
Ok(value) => value,
Err(err) => {
Error::from(err).throw(&mut $env);
return JObject::null();
}
}
};
}
macro_rules! ok_or_throw_without_return {
($env:expr, $result:expr) => {
match $result {
Ok(value) => value,
Err(err) => {
Error::from(err).throw(&mut $env);
return;
}
}
};
}
#[macro_export]
macro_rules! ok_or_throw_with_return {
($env:expr, $result:expr, $ret:expr) => {
match $result {
Ok(value) => value,
Err(err) => {
Error::from(err).throw(&mut $env);
return $ret;
}
}
};
}
mod connection;
pub mod error;
mod ffi;
mod traits;
pub use error::{Error, Result};
lazy_static! {
static ref RT: tokio::runtime::Runtime = tokio::runtime::Builder::new_multi_thread()
.enable_all()
.build()
.expect("Failed to create tokio runtime");
}

View File

@@ -1,122 +0,0 @@
// Copyright 2024 Lance Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
use jni::objects::{JMap, JObject, JString, JValue};
use jni::JNIEnv;
use crate::Result;
pub trait FromJObject<T> {
fn extract(&self) -> Result<T>;
}
/// Convert a Rust type into a Java Object.
pub trait IntoJava {
fn into_java<'a>(self, env: &mut JNIEnv<'a>) -> JObject<'a>;
}
impl FromJObject<i32> for JObject<'_> {
fn extract(&self) -> Result<i32> {
Ok(JValue::from(self).i()?)
}
}
impl FromJObject<i64> for JObject<'_> {
fn extract(&self) -> Result<i64> {
Ok(JValue::from(self).j()?)
}
}
impl FromJObject<f32> for JObject<'_> {
fn extract(&self) -> Result<f32> {
Ok(JValue::from(self).f()?)
}
}
impl FromJObject<f64> for JObject<'_> {
fn extract(&self) -> Result<f64> {
Ok(JValue::from(self).d()?)
}
}
pub trait FromJString {
fn extract(&self, env: &mut JNIEnv) -> Result<String>;
}
impl FromJString for JString<'_> {
fn extract(&self, env: &mut JNIEnv) -> Result<String> {
Ok(env.get_string(self)?.into())
}
}
pub trait JMapExt {
#[allow(dead_code)]
fn get_string(&self, env: &mut JNIEnv, key: &str) -> Result<Option<String>>;
#[allow(dead_code)]
fn get_i32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i32>>;
#[allow(dead_code)]
fn get_i64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i64>>;
#[allow(dead_code)]
fn get_f32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f32>>;
#[allow(dead_code)]
fn get_f64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f64>>;
}
fn get_map_value<T>(env: &mut JNIEnv, map: &JMap, key: &str) -> Result<Option<T>>
where
for<'a> JObject<'a>: FromJObject<T>,
{
let key_obj: JObject = env.new_string(key)?.into();
if let Some(value) = map.get(env, &key_obj)? {
if value.is_null() {
Ok(None)
} else {
Ok(Some(value.extract()?))
}
} else {
Ok(None)
}
}
impl JMapExt for JMap<'_, '_, '_> {
fn get_string(&self, env: &mut JNIEnv, key: &str) -> Result<Option<String>> {
let key_obj: JObject = env.new_string(key)?.into();
if let Some(value) = self.get(env, &key_obj)? {
let value_str: JString = value.into();
Ok(Some(value_str.extract(env)?))
} else {
Ok(None)
}
}
fn get_i32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i32>> {
get_map_value(env, self, key)
}
fn get_i64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i64>> {
get_map_value(env, self, key)
}
fn get_f32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f32>> {
get_map_value(env, self, key)
}
fn get_f64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f64>> {
get_map_value(env, self, key)
}
}

View File

@@ -1,92 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.lancedb</groupId>
<artifactId>lancedb-parent</artifactId>
<version>0.0.3-SNAPSHOT</version>
<relativePath>../pom.xml</relativePath>
</parent>
<artifactId>lancedb-core</artifactId>
<name>LanceDB Core</name>
<packaging>jar</packaging>
<dependencies>
<dependency>
<groupId>org.apache.arrow</groupId>
<artifactId>arrow-vector</artifactId>
</dependency>
<dependency>
<groupId>org.apache.arrow</groupId>
<artifactId>arrow-memory-netty</artifactId>
</dependency>
<dependency>
<groupId>org.apache.arrow</groupId>
<artifactId>arrow-c-data</artifactId>
</dependency>
<dependency>
<groupId>org.apache.arrow</groupId>
<artifactId>arrow-dataset</artifactId>
</dependency>
<dependency>
<groupId>org.json</groupId>
<artifactId>json</artifactId>
</dependency>
<dependency>
<groupId>org.questdb</groupId>
<artifactId>jar-jni</artifactId>
</dependency>
<dependency>
<groupId>org.junit.jupiter</groupId>
<artifactId>junit-jupiter</artifactId>
<scope>test</scope>
</dependency>
</dependencies>
<profiles>
<profile>
<id>build-jni</id>
<activation>
<activeByDefault>true</activeByDefault>
</activation>
<build>
<plugins>
<plugin>
<groupId>org.questdb</groupId>
<artifactId>rust-maven-plugin</artifactId>
<version>1.1.1</version>
<executions>
<execution>
<id>lancedb-jni</id>
<goals>
<goal>build</goal>
</goals>
<configuration>
<path>lancedb-jni</path>
<!--<release>true</release>-->
<!-- Copy native libraries to target/classes for runtime access -->
<copyTo>${project.build.directory}/classes/nativelib</copyTo>
<copyWithPlatformDir>true</copyWithPlatformDir>
</configuration>
</execution>
<execution>
<id>lancedb-jni-test</id>
<goals>
<goal>test</goal>
</goals>
<configuration>
<path>lancedb-jni</path>
<release>false</release>
<verbosity>-v</verbosity>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</profile>
</profiles>
</project>

View File

@@ -1,120 +0,0 @@
/*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.lancedb.lancedb;
import io.questdb.jar.jni.JarJniLoader;
import java.io.Closeable;
import java.util.List;
import java.util.Optional;
/**
* Represents LanceDB database.
*/
public class Connection implements Closeable {
static {
JarJniLoader.loadLib(Connection.class, "/nativelib", "lancedb_jni");
}
private long nativeConnectionHandle;
/**
* Connect to a LanceDB instance.
*/
public static native Connection connect(String uri);
/**
* Get the names of all tables in the database. The names are sorted in
* ascending order.
*
* @return the table names
*/
public List<String> tableNames() {
return tableNames(Optional.empty(), Optional.empty());
}
/**
* Get the names of filtered tables in the database. The names are sorted in
* ascending order.
*
* @param limit The number of results to return.
* @return the table names
*/
public List<String> tableNames(int limit) {
return tableNames(Optional.empty(), Optional.of(limit));
}
/**
* Get the names of filtered tables in the database. The names are sorted in
* ascending order.
*
* @param startAfter If present, only return names that come lexicographically after the supplied
* value. This can be combined with limit to implement pagination
* by setting this to the last table name from the previous page.
* @return the table names
*/
public List<String> tableNames(String startAfter) {
return tableNames(Optional.of(startAfter), Optional.empty());
}
/**
* Get the names of filtered tables in the database. The names are sorted in
* ascending order.
*
* @param startAfter If present, only return names that come lexicographically after the supplied
* value. This can be combined with limit to implement pagination
* by setting this to the last table name from the previous page.
* @param limit The number of results to return.
* @return the table names
*/
public List<String> tableNames(String startAfter, int limit) {
return tableNames(Optional.of(startAfter), Optional.of(limit));
}
/**
* Get the names of filtered tables in the database. The names are sorted in
* ascending order.
*
* @param startAfter If present, only return names that come lexicographically after the supplied
* value. This can be combined with limit to implement pagination
* by setting this to the last table name from the previous page.
* @param limit The number of results to return.
* @return the table names
*/
public native List<String> tableNames(
Optional<String> startAfter, Optional<Integer> limit);
/**
* Closes this connection and releases any system resources associated with it. If
* the connection is
* already closed, then invoking this method has no effect.
*/
@Override
public void close() {
if (nativeConnectionHandle != 0) {
releaseNativeConnection(nativeConnectionHandle);
nativeConnectionHandle = 0;
}
}
/**
* Native method to release the Lance connection resources associated with the
* given handle.
*
* @param handle The native handle to the connection resource.
*/
private native void releaseNativeConnection(long handle);
private Connection() {}
}

View File

@@ -1,135 +0,0 @@
/*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.lancedb.lancedb;
import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertTrue;
import java.nio.file.Path;
import java.util.List;
import java.net.URL;
import org.junit.jupiter.api.BeforeAll;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.io.TempDir;
public class ConnectionTest {
private static final String[] TABLE_NAMES = {
"dataset_version",
"new_empty_dataset",
"test",
"write_stream"
};
@TempDir
static Path tempDir; // Temporary directory for the tests
private static URL lanceDbURL;
@BeforeAll
static void setUp() {
ClassLoader classLoader = ConnectionTest.class.getClassLoader();
lanceDbURL = classLoader.getResource("example_db");
}
@Test
void emptyDB() {
String databaseUri = tempDir.resolve("emptyDB").toString();
try (Connection conn = Connection.connect(databaseUri)) {
List<String> tableNames = conn.tableNames();
assertTrue(tableNames.isEmpty());
}
}
@Test
void tableNames() {
try (Connection conn = Connection.connect(lanceDbURL.toString())) {
List<String> tableNames = conn.tableNames();
assertEquals(4, tableNames.size());
for (int i = 0; i < TABLE_NAMES.length; i++) {
assertEquals(TABLE_NAMES[i], tableNames.get(i));
}
}
}
@Test
void tableNamesStartAfter() {
try (Connection conn = Connection.connect(lanceDbURL.toString())) {
assertTableNamesStartAfter(conn, TABLE_NAMES[0], 3, TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
assertTableNamesStartAfter(conn, TABLE_NAMES[1], 2, TABLE_NAMES[2], TABLE_NAMES[3]);
assertTableNamesStartAfter(conn, TABLE_NAMES[2], 1, TABLE_NAMES[3]);
assertTableNamesStartAfter(conn, TABLE_NAMES[3], 0);
assertTableNamesStartAfter(conn, "a_dataset", 4, TABLE_NAMES[0], TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
assertTableNamesStartAfter(conn, "o_dataset", 2, TABLE_NAMES[2], TABLE_NAMES[3]);
assertTableNamesStartAfter(conn, "v_dataset", 1, TABLE_NAMES[3]);
assertTableNamesStartAfter(conn, "z_dataset", 0);
}
}
private void assertTableNamesStartAfter(Connection conn, String startAfter, int expectedSize, String... expectedNames) {
List<String> tableNames = conn.tableNames(startAfter);
assertEquals(expectedSize, tableNames.size());
for (int i = 0; i < expectedNames.length; i++) {
assertEquals(expectedNames[i], tableNames.get(i));
}
}
@Test
void tableNamesLimit() {
try (Connection conn = Connection.connect(lanceDbURL.toString())) {
for (int i = 0; i <= TABLE_NAMES.length; i++) {
List<String> tableNames = conn.tableNames(i);
assertEquals(i, tableNames.size());
for (int j = 0; j < i; j++) {
assertEquals(TABLE_NAMES[j], tableNames.get(j));
}
}
}
}
@Test
void tableNamesStartAfterLimit() {
try (Connection conn = Connection.connect(lanceDbURL.toString())) {
List<String> tableNames = conn.tableNames(TABLE_NAMES[0], 2);
assertEquals(2, tableNames.size());
assertEquals(TABLE_NAMES[1], tableNames.get(0));
assertEquals(TABLE_NAMES[2], tableNames.get(1));
tableNames = conn.tableNames(TABLE_NAMES[1], 1);
assertEquals(1, tableNames.size());
assertEquals(TABLE_NAMES[2], tableNames.get(0));
tableNames = conn.tableNames(TABLE_NAMES[2], 2);
assertEquals(1, tableNames.size());
assertEquals(TABLE_NAMES[3], tableNames.get(0));
tableNames = conn.tableNames(TABLE_NAMES[3], 2);
assertEquals(0, tableNames.size());
tableNames = conn.tableNames(TABLE_NAMES[0], 0);
assertEquals(0, tableNames.size());
// Limit larger than the number of remaining tables
tableNames = conn.tableNames(TABLE_NAMES[0], 10);
assertEquals(3, tableNames.size());
assertEquals(TABLE_NAMES[1], tableNames.get(0));
assertEquals(TABLE_NAMES[2], tableNames.get(1));
assertEquals(TABLE_NAMES[3], tableNames.get(2));
// Start after a value not in the list
tableNames = conn.tableNames("non_existent_table", 2);
assertEquals(2, tableNames.size());
assertEquals(TABLE_NAMES[2], tableNames.get(0));
assertEquals(TABLE_NAMES[3], tableNames.get(1));
// Start after the last table with a limit
tableNames = conn.tableNames(TABLE_NAMES[3], 1);
assertEquals(0, tableNames.size());
}
}
}

View File

@@ -1 +0,0 @@
$d51afd07-e3cd-4c76-9b9b-787e13fd55b0<62>=id <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>*int3208name <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>*string08

View File

@@ -1 +0,0 @@
$15648e72-076f-4ef1-8b90-10d305b95b3b<33>=id <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>*int3208name <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>*string08

View File

@@ -1 +0,0 @@
$a3689caf-4f6b-4afc-a3c7-97af75661843<34>oitem <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>*string8price <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>*double80vector <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>*fixed_size_list:float:28

View File

@@ -1,219 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.lancedb</groupId>
<artifactId>lancedb-parent</artifactId>
<version>0.0.3-SNAPSHOT</version>
<packaging>pom</packaging>
<name>Lance Parent</name>
<description>LanceDB Java API</description>
<url>http://lancedb.com/</url>
<developers>
<developer>
<name>Lance DB Dev Group</name>
<email>dev@lancedb.com</email>
</developer>
</developers>
<licenses>
<license>
<name>The Apache Software License, Version 2.0</name>
<url>http://www.apache.org/licenses/LICENSE-2.0.txt</url>
</license>
</licenses>
<scm>
<developerConnection>scm:git:git@github.com:lancedb/lancedb.git</developerConnection>
<tag>HEAD</tag>
<url>scm:git:git@github.com:lancedb/lancedb.git</url>
</scm>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<arrow.version>15.0.0</arrow.version>
</properties>
<modules>
<module>core</module>
</modules>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.apache.arrow</groupId>
<artifactId>arrow-vector</artifactId>
<version>${arrow.version}</version>
</dependency>
<dependency>
<groupId>org.apache.arrow</groupId>
<artifactId>arrow-memory-netty</artifactId>
<version>${arrow.version}</version>
</dependency>
<dependency>
<groupId>org.apache.arrow</groupId>
<artifactId>arrow-c-data</artifactId>
<version>${arrow.version}</version>
</dependency>
<dependency>
<groupId>org.apache.arrow</groupId>
<artifactId>arrow-dataset</artifactId>
<version>${arrow.version}</version>
</dependency>
<dependency>
<groupId>org.questdb</groupId>
<artifactId>jar-jni</artifactId>
<version>1.1.1</version>
</dependency>
<dependency>
<groupId>org.junit.jupiter</groupId>
<artifactId>junit-jupiter</artifactId>
<version>5.10.1</version>
</dependency>
<dependency>
<groupId>org.json</groupId>
<artifactId>json</artifactId>
<version>20210307</version>
</dependency>
</dependencies>
</dependencyManagement>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-source-plugin</artifactId>
<version>2.2.1</version>
<executions>
<execution>
<id>attach-sources</id>
<goals>
<goal>jar-no-fork</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-javadoc-plugin</artifactId>
<version>2.9.1</version>
<executions>
<execution>
<id>attach-javadocs</id>
<goals>
<goal>jar</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-checkstyle-plugin</artifactId>
<version>3.3.1</version>
<configuration>
<configLocation>google_checks.xml</configLocation>
<consoleOutput>true</consoleOutput>
<failsOnError>true</failsOnError>
<violationSeverity>warning</violationSeverity>
<linkXRef>false</linkXRef>
</configuration>
<executions>
<execution>
<id>validate</id>
<phase>validate</phase>
<goals>
<goal>check</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
<pluginManagement>
<plugins>
<plugin>
<artifactId>maven-clean-plugin</artifactId>
<version>3.1.0</version>
</plugin>
<plugin>
<artifactId>maven-resources-plugin</artifactId>
<version>3.0.2</version>
</plugin>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.8.1</version>
<configuration>
<compilerArgs>
<arg>-h</arg>
<arg>target/headers</arg>
</compilerArgs>
</configuration>
</plugin>
<plugin>
<artifactId>maven-surefire-plugin</artifactId>
<version>3.2.5</version>
<configuration>
<argLine>--add-opens=java.base/java.nio=ALL-UNNAMED</argLine>
<forkNode implementation="org.apache.maven.plugin.surefire.extensions.SurefireForkNodeFactory" />
<useSystemClassLoader>false</useSystemClassLoader>
</configuration>
</plugin>
<plugin>
<artifactId>maven-jar-plugin</artifactId>
<version>3.0.2</version>
</plugin>
<plugin>
<artifactId>maven-install-plugin</artifactId>
<version>2.5.2</version>
</plugin>
</plugins>
</pluginManagement>
</build>
<profiles>
<profile>
<id>deploy-to-ossrh</id>
<build>
<plugins>
<plugin>
<groupId>org.sonatype.central</groupId>
<artifactId>central-publishing-maven-plugin</artifactId>
<version>0.4.0</version>
<extensions>true</extensions>
<configuration>
<publishingServerId>ossrh</publishingServerId>
<tokenAuth>true</tokenAuth>
</configuration>
</plugin>
<plugin>
<groupId>org.sonatype.plugins</groupId>
<artifactId>nexus-staging-maven-plugin</artifactId>
<version>1.6.13</version>
<extensions>true</extensions>
<configuration>
<serverId>ossrh</serverId>
<nexusUrl>https://s01.oss.sonatype.org/</nexusUrl>
<autoReleaseAfterClose>true</autoReleaseAfterClose>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-gpg-plugin</artifactId>
<version>1.5</version>
<executions>
<execution>
<id>sign-artifacts</id>
<phase>verify</phase>
<goals>
<goal>sign</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</profile>
</profiles>
</project>

74
node/package-lock.json generated
View File

@@ -1,12 +1,12 @@
{
"name": "vectordb",
"version": "0.5.0",
"version": "0.4.17",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "vectordb",
"version": "0.5.0",
"version": "0.4.17",
"cpu": [
"x64",
"arm64"
@@ -52,11 +52,11 @@
"uuid": "^9.0.0"
},
"optionalDependencies": {
"@lancedb/vectordb-darwin-arm64": "0.4.20",
"@lancedb/vectordb-darwin-x64": "0.4.20",
"@lancedb/vectordb-linux-arm64-gnu": "0.4.20",
"@lancedb/vectordb-linux-x64-gnu": "0.4.20",
"@lancedb/vectordb-win32-x64-msvc": "0.4.20"
"@lancedb/vectordb-darwin-arm64": "0.4.17",
"@lancedb/vectordb-darwin-x64": "0.4.17",
"@lancedb/vectordb-linux-arm64-gnu": "0.4.17",
"@lancedb/vectordb-linux-x64-gnu": "0.4.17",
"@lancedb/vectordb-win32-x64-msvc": "0.4.17"
},
"peerDependencies": {
"@apache-arrow/ts": "^14.0.2",
@@ -333,66 +333,6 @@
"@jridgewell/sourcemap-codec": "^1.4.10"
}
},
"node_modules/@lancedb/vectordb-darwin-arm64": {
"version": "0.4.20",
"resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.4.20.tgz",
"integrity": "sha512-ffP2K4sA5mQTgePyARw1y8dPN996FmpvyAYoWO+TSItaXlhcXvc+KVa5udNMCZMDYeEnEv2Xpj6k4PwW3oBz+A==",
"cpu": [
"arm64"
],
"optional": true,
"os": [
"darwin"
]
},
"node_modules/@lancedb/vectordb-darwin-x64": {
"version": "0.4.20",
"resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.4.20.tgz",
"integrity": "sha512-GSYsXE20RIehDu30FjREhJdEzhnwOTV7ZsrSXagStzLY1gr7pyd7sfqxmmUtdD09di7LnQoiM71AOpPTa01YwQ==",
"cpu": [
"x64"
],
"optional": true,
"os": [
"darwin"
]
},
"node_modules/@lancedb/vectordb-linux-arm64-gnu": {
"version": "0.4.20",
"resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.4.20.tgz",
"integrity": "sha512-FpNOjOsz3nJVm6EBGyNgbOW2aFhsWZ/igeY45Z8hbZaaK2YBwrg/DASoNlUzgv6IR8cUaGJ2irNVJfsKR2cG6g==",
"cpu": [
"arm64"
],
"optional": true,
"os": [
"linux"
]
},
"node_modules/@lancedb/vectordb-linux-x64-gnu": {
"version": "0.4.20",
"resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.4.20.tgz",
"integrity": "sha512-pOqWjrRZQSrLTlQPkjidRii7NZDw8Xu9pN6ouVu2JAK8n81FXaPtFCyAI+Y3v9GpnYDN0rvD4eQ36aHAVPsa2g==",
"cpu": [
"x64"
],
"optional": true,
"os": [
"linux"
]
},
"node_modules/@lancedb/vectordb-win32-x64-msvc": {
"version": "0.4.20",
"resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.4.20.tgz",
"integrity": "sha512-5J5SsYSJ7jRCmU/sgwVHdrGz43B/7R2T9OEoFTKyVAtqTZdu75rkytXyn9SyEayXVhlUOaw76N0ASm0hAoDS/A==",
"cpu": [
"x64"
],
"optional": true,
"os": [
"win32"
]
},
"node_modules/@neon-rs/cli": {
"version": "0.0.160",
"resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.160.tgz",

View File

@@ -1,6 +1,6 @@
{
"name": "vectordb",
"version": "0.5.0",
"version": "0.4.17",
"description": " Serverless, low-latency vector database for AI applications",
"main": "dist/index.js",
"types": "dist/index.d.ts",
@@ -88,10 +88,10 @@
}
},
"optionalDependencies": {
"@lancedb/vectordb-darwin-arm64": "0.4.20",
"@lancedb/vectordb-darwin-x64": "0.4.20",
"@lancedb/vectordb-linux-arm64-gnu": "0.4.20",
"@lancedb/vectordb-linux-x64-gnu": "0.4.20",
"@lancedb/vectordb-win32-x64-msvc": "0.4.20"
"@lancedb/vectordb-darwin-arm64": "0.4.17",
"@lancedb/vectordb-darwin-x64": "0.4.17",
"@lancedb/vectordb-linux-arm64-gnu": "0.4.17",
"@lancedb/vectordb-linux-x64-gnu": "0.4.17",
"@lancedb/vectordb-win32-x64-msvc": "0.4.17"
}
}

View File

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

View File

@@ -12,20 +12,19 @@
// See the License for the specific language governing permissions and
// limitations under the License.
import { type Schema, Table as ArrowTable, tableFromIPC } from "apache-arrow";
import { type Schema, Table as ArrowTable, tableFromIPC } from 'apache-arrow'
import {
createEmptyTable,
fromRecordsToBuffer,
fromTableToBuffer,
makeArrowTable
} from "./arrow";
import type { EmbeddingFunction } from "./embedding/embedding_function";
import { RemoteConnection } from "./remote";
import { Query } from "./query";
import { isEmbeddingFunction } from "./embedding/embedding_function";
import { type Literal, toSQL } from "./util";
import { type HttpMiddleware } from "./middleware";
} from './arrow'
import type { EmbeddingFunction } from './embedding/embedding_function'
import { RemoteConnection } from './remote'
import { Query } from './query'
import { isEmbeddingFunction } from './embedding/embedding_function'
import { type Literal, toSQL } from './util'
import { type HttpMiddleware } from './middleware'
const {
databaseNew,
@@ -49,18 +48,14 @@ const {
tableAlterColumns,
tableDropColumns
// eslint-disable-next-line @typescript-eslint/no-var-requires
} = require("../native.js");
} = require('../native.js')
export { Query };
export type { EmbeddingFunction };
export { OpenAIEmbeddingFunction } from "./embedding/openai";
export {
convertToTable,
makeArrowTable,
type MakeArrowTableOptions
} from "./arrow";
export { Query }
export type { EmbeddingFunction }
export { OpenAIEmbeddingFunction } from './embedding/openai'
export { convertToTable, makeArrowTable, type MakeArrowTableOptions } from './arrow'
const defaultAwsRegion = "us-west-2";
const defaultAwsRegion = 'us-west-2'
export interface AwsCredentials {
accessKeyId: string
@@ -133,19 +128,19 @@ export interface ConnectionOptions {
readConsistencyInterval?: number
}
function getAwsArgs(opts: ConnectionOptions): any[] {
const callArgs: any[] = [];
const awsCredentials = opts.awsCredentials;
function getAwsArgs (opts: ConnectionOptions): any[] {
const callArgs: any[] = []
const awsCredentials = opts.awsCredentials
if (awsCredentials !== undefined) {
callArgs.push(awsCredentials.accessKeyId);
callArgs.push(awsCredentials.secretKey);
callArgs.push(awsCredentials.sessionToken);
callArgs.push(awsCredentials.accessKeyId)
callArgs.push(awsCredentials.secretKey)
callArgs.push(awsCredentials.sessionToken)
} else {
callArgs.fill(undefined, 0, 3);
callArgs.fill(undefined, 0, 3)
}
callArgs.push(opts.awsRegion);
return callArgs;
callArgs.push(opts.awsRegion)
return callArgs
}
export interface CreateTableOptions<T> {
@@ -178,56 +173,56 @@ export interface CreateTableOptions<T> {
*
* @see {@link ConnectionOptions} for more details on the URI format.
*/
export async function connect(uri: string): Promise<Connection>;
export async function connect (uri: string): Promise<Connection>
/**
* Connect to a LanceDB instance with connection options.
*
* @param opts The {@link ConnectionOptions} to use when connecting to the database.
*/
export async function connect(
export async function connect (
opts: Partial<ConnectionOptions>
): Promise<Connection>;
export async function connect(
): Promise<Connection>
export async function connect (
arg: string | Partial<ConnectionOptions>
): Promise<Connection> {
let opts: ConnectionOptions;
if (typeof arg === "string") {
opts = { uri: arg };
let opts: ConnectionOptions
if (typeof arg === 'string') {
opts = { uri: arg }
} else {
const keys = Object.keys(arg);
if (keys.length === 1 && keys[0] === "uri" && typeof arg.uri === "string") {
opts = { uri: arg.uri };
const keys = Object.keys(arg)
if (keys.length === 1 && keys[0] === 'uri' && typeof arg.uri === 'string') {
opts = { uri: arg.uri }
} else {
opts = Object.assign(
{
uri: "",
uri: '',
awsCredentials: undefined,
awsRegion: defaultAwsRegion,
apiKey: undefined,
region: defaultAwsRegion
},
arg
);
)
}
}
if (opts.uri.startsWith("db://")) {
if (opts.uri.startsWith('db://')) {
// Remote connection
return new RemoteConnection(opts);
return new RemoteConnection(opts)
}
const storageOptions = opts.storageOptions ?? {};
if (opts.awsCredentials?.accessKeyId !== undefined) {
storageOptions.aws_access_key_id = opts.awsCredentials.accessKeyId;
storageOptions.aws_access_key_id = opts.awsCredentials.accessKeyId
}
if (opts.awsCredentials?.secretKey !== undefined) {
storageOptions.aws_secret_access_key = opts.awsCredentials.secretKey;
storageOptions.aws_secret_access_key = opts.awsCredentials.secretKey
}
if (opts.awsCredentials?.sessionToken !== undefined) {
storageOptions.aws_session_token = opts.awsCredentials.sessionToken;
storageOptions.aws_session_token = opts.awsCredentials.sessionToken
}
if (opts.awsRegion !== undefined) {
storageOptions.region = opts.awsRegion;
storageOptions.region = opts.awsRegion
}
// It's a pain to pass a record to Rust, so we convert it to an array of key-value pairs
const storageOptionsArr = Object.entries(storageOptions);
@@ -236,8 +231,8 @@ export async function connect(
opts.uri,
storageOptionsArr,
opts.readConsistencyInterval
);
return new LocalConnection(db, opts);
)
return new LocalConnection(db, opts)
}
/**
@@ -538,11 +533,7 @@ export interface Table<T = number[]> {
* @param data the new data to insert
* @param args parameters controlling how the operation should behave
*/
mergeInsert: (
on: string,
data: Array<Record<string, unknown>> | ArrowTable,
args: MergeInsertArgs
) => Promise<void>
mergeInsert: (on: string, data: Array<Record<string, unknown>> | ArrowTable, args: MergeInsertArgs) => Promise<void>
/**
* List the indicies on this table.
@@ -567,9 +558,7 @@ export interface Table<T = number[]> {
* expressions will be evaluated for each row in the
* table, and can reference existing columns in the table.
*/
addColumns(
newColumnTransforms: Array<{ name: string, valueSql: string }>
): Promise<void>
addColumns(newColumnTransforms: Array<{ name: string, valueSql: string }>): Promise<void>
/**
* Alter the name or nullability of columns.
@@ -710,23 +699,23 @@ export interface IndexStats {
* A connection to a LanceDB database.
*/
export class LocalConnection implements Connection {
private readonly _options: () => ConnectionOptions;
private readonly _db: any;
private readonly _options: () => ConnectionOptions
private readonly _db: any
constructor(db: any, options: ConnectionOptions) {
this._options = () => options;
this._db = db;
constructor (db: any, options: ConnectionOptions) {
this._options = () => options
this._db = db
}
get uri(): string {
return this._options().uri;
get uri (): string {
return this._options().uri
}
/**
* Get the names of all tables in the database.
*/
async tableNames(): Promise<string[]> {
return databaseTableNames.call(this._db);
async tableNames (): Promise<string[]> {
return databaseTableNames.call(this._db)
}
/**
@@ -734,7 +723,7 @@ export class LocalConnection implements Connection {
*
* @param name The name of the table.
*/
async openTable(name: string): Promise<Table>;
async openTable (name: string): Promise<Table>
/**
* Open a table in the database.
@@ -745,20 +734,23 @@ export class LocalConnection implements Connection {
async openTable<T>(
name: string,
embeddings: EmbeddingFunction<T>
): Promise<Table<T>>;
): Promise<Table<T>>
async openTable<T>(
name: string,
embeddings?: EmbeddingFunction<T>
): Promise<Table<T>>;
): Promise<Table<T>>
async openTable<T>(
name: string,
embeddings?: EmbeddingFunction<T>
): Promise<Table<T>> {
const tbl = await databaseOpenTable.call(this._db, name);
const tbl = await databaseOpenTable.call(
this._db,
name,
)
if (embeddings !== undefined) {
return new LocalTable(tbl, name, this._options(), embeddings);
return new LocalTable(tbl, name, this._options(), embeddings)
} else {
return new LocalTable(tbl, name, this._options());
return new LocalTable(tbl, name, this._options())
}
}
@@ -768,32 +760,32 @@ export class LocalConnection implements Connection {
optsOrEmbedding?: WriteOptions | EmbeddingFunction<T>,
opt?: WriteOptions
): Promise<Table<T>> {
if (typeof name === "string") {
let writeOptions: WriteOptions = new DefaultWriteOptions();
if (typeof name === 'string') {
let writeOptions: WriteOptions = new DefaultWriteOptions()
if (opt !== undefined && isWriteOptions(opt)) {
writeOptions = opt;
writeOptions = opt
} else if (
optsOrEmbedding !== undefined &&
isWriteOptions(optsOrEmbedding)
) {
writeOptions = optsOrEmbedding;
writeOptions = optsOrEmbedding
}
let embeddings: undefined | EmbeddingFunction<T>;
let embeddings: undefined | EmbeddingFunction<T>
if (
optsOrEmbedding !== undefined &&
isEmbeddingFunction(optsOrEmbedding)
) {
embeddings = optsOrEmbedding;
embeddings = optsOrEmbedding
}
return await this.createTableImpl({
name,
data,
embeddingFunction: embeddings,
writeOptions
});
})
}
return await this.createTableImpl(name);
return await this.createTableImpl(name)
}
private async createTableImpl<T>({
@@ -809,27 +801,27 @@ export class LocalConnection implements Connection {
embeddingFunction?: EmbeddingFunction<T> | undefined
writeOptions?: WriteOptions | undefined
}): Promise<Table<T>> {
let buffer: Buffer;
let buffer: Buffer
function isEmpty(
function isEmpty (
data: Array<Record<string, unknown>> | ArrowTable<any>
): boolean {
if (data instanceof ArrowTable) {
return data.data.length === 0;
return data.data.length === 0
}
return data.length === 0;
return data.length === 0
}
if (data === undefined || isEmpty(data)) {
if (schema === undefined) {
throw new Error("Either data or schema needs to defined");
throw new Error('Either data or schema needs to defined')
}
buffer = await fromTableToBuffer(createEmptyTable(schema));
buffer = await fromTableToBuffer(createEmptyTable(schema))
} else if (data instanceof ArrowTable) {
buffer = await fromTableToBuffer(data, embeddingFunction, schema);
buffer = await fromTableToBuffer(data, embeddingFunction, schema)
} else {
// data is Array<Record<...>>
buffer = await fromRecordsToBuffer(data, embeddingFunction, schema);
buffer = await fromRecordsToBuffer(data, embeddingFunction, schema)
}
const tbl = await tableCreate.call(
@@ -838,11 +830,11 @@ export class LocalConnection implements Connection {
buffer,
writeOptions?.writeMode?.toString(),
...getAwsArgs(this._options())
);
)
if (embeddingFunction !== undefined) {
return new LocalTable(tbl, name, this._options(), embeddingFunction);
return new LocalTable(tbl, name, this._options(), embeddingFunction)
} else {
return new LocalTable(tbl, name, this._options());
return new LocalTable(tbl, name, this._options())
}
}
@@ -850,69 +842,69 @@ export class LocalConnection implements Connection {
* Drop an existing table.
* @param name The name of the table to drop.
*/
async dropTable(name: string): Promise<void> {
await databaseDropTable.call(this._db, name);
async dropTable (name: string): Promise<void> {
await databaseDropTable.call(this._db, name)
}
withMiddleware(middleware: HttpMiddleware): Connection {
return this;
withMiddleware (middleware: HttpMiddleware): Connection {
return this
}
}
export class LocalTable<T = number[]> implements Table<T> {
private _tbl: any;
private readonly _name: string;
private readonly _isElectron: boolean;
private readonly _embeddings?: EmbeddingFunction<T>;
private readonly _options: () => ConnectionOptions;
private _tbl: any
private readonly _name: string
private readonly _isElectron: boolean
private readonly _embeddings?: EmbeddingFunction<T>
private readonly _options: () => ConnectionOptions
constructor(tbl: any, name: string, options: ConnectionOptions);
constructor (tbl: any, name: string, options: ConnectionOptions)
/**
* @param tbl
* @param name
* @param options
* @param embeddings An embedding function to use when interacting with this table
*/
constructor(
constructor (
tbl: any,
name: string,
options: ConnectionOptions,
embeddings: EmbeddingFunction<T>
);
constructor(
)
constructor (
tbl: any,
name: string,
options: ConnectionOptions,
embeddings?: EmbeddingFunction<T>
) {
this._tbl = tbl;
this._name = name;
this._embeddings = embeddings;
this._options = () => options;
this._isElectron = this.checkElectron();
this._tbl = tbl
this._name = name
this._embeddings = embeddings
this._options = () => options
this._isElectron = this.checkElectron()
}
get name(): string {
return this._name;
get name (): string {
return this._name
}
/**
* Creates a search query to find the nearest neighbors of the given search term
* @param query The query search term
*/
search(query: T): Query<T> {
return new Query(query, this._tbl, this._embeddings);
search (query: T): Query<T> {
return new Query(query, this._tbl, this._embeddings)
}
/**
* Creates a filter query to find all rows matching the specified criteria
* @param value The filter criteria (like SQL where clause syntax)
*/
filter(value: string): Query<T> {
return new Query(undefined, this._tbl, this._embeddings).filter(value);
filter (value: string): Query<T> {
return new Query(undefined, this._tbl, this._embeddings).filter(value)
}
where = this.filter;
where = this.filter
/**
* Insert records into this Table.
@@ -920,19 +912,16 @@ export class LocalTable<T = number[]> implements Table<T> {
* @param data Records to be inserted into the Table
* @return The number of rows added to the table
*/
async add(
async add (
data: Array<Record<string, unknown>> | ArrowTable
): Promise<number> {
const schema = await this.schema;
let tbl: ArrowTable;
const schema = await this.schema
let tbl: ArrowTable
if (data instanceof ArrowTable) {
tbl = data;
tbl = data
} else {
tbl = makeArrowTable(data, { schema, embeddings: this._embeddings });
tbl = makeArrowTable(data, { schema })
}
return tableAdd
.call(
this._tbl,
@@ -941,8 +930,8 @@ export class LocalTable<T = number[]> implements Table<T> {
...getAwsArgs(this._options())
)
.then((newTable: any) => {
this._tbl = newTable;
});
this._tbl = newTable
})
}
/**
@@ -951,14 +940,14 @@ export class LocalTable<T = number[]> implements Table<T> {
* @param data Records to be inserted into the Table
* @return The number of rows added to the table
*/
async overwrite(
async overwrite (
data: Array<Record<string, unknown>> | ArrowTable
): Promise<number> {
let buffer: Buffer;
let buffer: Buffer
if (data instanceof ArrowTable) {
buffer = await fromTableToBuffer(data, this._embeddings);
buffer = await fromTableToBuffer(data, this._embeddings)
} else {
buffer = await fromRecordsToBuffer(data, this._embeddings);
buffer = await fromRecordsToBuffer(data, this._embeddings)
}
return tableAdd
.call(
@@ -968,8 +957,8 @@ export class LocalTable<T = number[]> implements Table<T> {
...getAwsArgs(this._options())
)
.then((newTable: any) => {
this._tbl = newTable;
});
this._tbl = newTable
})
}
/**
@@ -977,26 +966,26 @@ export class LocalTable<T = number[]> implements Table<T> {
*
* @param indexParams The parameters of this Index, @see VectorIndexParams.
*/
async createIndex(indexParams: VectorIndexParams): Promise<any> {
async createIndex (indexParams: VectorIndexParams): Promise<any> {
return tableCreateVectorIndex
.call(this._tbl, indexParams)
.then((newTable: any) => {
this._tbl = newTable;
});
this._tbl = newTable
})
}
async createScalarIndex(column: string, replace?: boolean): Promise<void> {
async createScalarIndex (column: string, replace?: boolean): Promise<void> {
if (replace === undefined) {
replace = true;
replace = true
}
return tableCreateScalarIndex.call(this._tbl, column, replace);
return tableCreateScalarIndex.call(this._tbl, column, replace)
}
/**
* Returns the number of rows in this table.
*/
async countRows(filter?: string): Promise<number> {
return tableCountRows.call(this._tbl, filter);
async countRows (filter?: string): Promise<number> {
return tableCountRows.call(this._tbl, filter)
}
/**
@@ -1004,10 +993,10 @@ export class LocalTable<T = number[]> implements Table<T> {
*
* @param filter A filter in the same format used by a sql WHERE clause.
*/
async delete(filter: string): Promise<void> {
async delete (filter: string): Promise<void> {
return tableDelete.call(this._tbl, filter).then((newTable: any) => {
this._tbl = newTable;
});
this._tbl = newTable
})
}
/**
@@ -1017,65 +1006,55 @@ export class LocalTable<T = number[]> implements Table<T> {
*
* @returns
*/
async update(args: UpdateArgs | UpdateSqlArgs): Promise<void> {
let filter: string | null;
let updates: Record<string, string>;
async update (args: UpdateArgs | UpdateSqlArgs): Promise<void> {
let filter: string | null
let updates: Record<string, string>
if ("valuesSql" in args) {
filter = args.where ?? null;
updates = args.valuesSql;
if ('valuesSql' in args) {
filter = args.where ?? null
updates = args.valuesSql
} else {
filter = args.where ?? null;
updates = {};
filter = args.where ?? null
updates = {}
for (const [key, value] of Object.entries(args.values)) {
updates[key] = toSQL(value);
updates[key] = toSQL(value)
}
}
return tableUpdate
.call(this._tbl, filter, updates)
.then((newTable: any) => {
this._tbl = newTable;
});
this._tbl = newTable
})
}
async mergeInsert(
on: string,
data: Array<Record<string, unknown>> | ArrowTable,
args: MergeInsertArgs
): Promise<void> {
let whenMatchedUpdateAll = false;
let whenMatchedUpdateAllFilt = null;
if (
args.whenMatchedUpdateAll !== undefined &&
args.whenMatchedUpdateAll !== null
) {
whenMatchedUpdateAll = true;
async mergeInsert (on: string, data: Array<Record<string, unknown>> | ArrowTable, args: MergeInsertArgs): Promise<void> {
let whenMatchedUpdateAll = false
let whenMatchedUpdateAllFilt = null
if (args.whenMatchedUpdateAll !== undefined && args.whenMatchedUpdateAll !== null) {
whenMatchedUpdateAll = true
if (args.whenMatchedUpdateAll !== true) {
whenMatchedUpdateAllFilt = args.whenMatchedUpdateAll;
whenMatchedUpdateAllFilt = args.whenMatchedUpdateAll
}
}
const whenNotMatchedInsertAll = args.whenNotMatchedInsertAll ?? false;
let whenNotMatchedBySourceDelete = false;
let whenNotMatchedBySourceDeleteFilt = null;
if (
args.whenNotMatchedBySourceDelete !== undefined &&
args.whenNotMatchedBySourceDelete !== null
) {
whenNotMatchedBySourceDelete = true;
const whenNotMatchedInsertAll = args.whenNotMatchedInsertAll ?? false
let whenNotMatchedBySourceDelete = false
let whenNotMatchedBySourceDeleteFilt = null
if (args.whenNotMatchedBySourceDelete !== undefined && args.whenNotMatchedBySourceDelete !== null) {
whenNotMatchedBySourceDelete = true
if (args.whenNotMatchedBySourceDelete !== true) {
whenNotMatchedBySourceDeleteFilt = args.whenNotMatchedBySourceDelete;
whenNotMatchedBySourceDeleteFilt = args.whenNotMatchedBySourceDelete
}
}
const schema = await this.schema;
let tbl: ArrowTable;
const schema = await this.schema
let tbl: ArrowTable
if (data instanceof ArrowTable) {
tbl = data;
tbl = data
} else {
tbl = makeArrowTable(data, { schema });
tbl = makeArrowTable(data, { schema })
}
const buffer = await fromTableToBuffer(tbl, this._embeddings, schema);
const buffer = await fromTableToBuffer(tbl, this._embeddings, schema)
this._tbl = await tableMergeInsert.call(
this._tbl,
@@ -1086,7 +1065,7 @@ export class LocalTable<T = number[]> implements Table<T> {
whenNotMatchedBySourceDelete,
whenNotMatchedBySourceDeleteFilt,
buffer
);
)
}
/**
@@ -1104,16 +1083,16 @@ export class LocalTable<T = number[]> implements Table<T> {
* uphold this promise can lead to corrupted tables.
* @returns
*/
async cleanupOldVersions(
async cleanupOldVersions (
olderThan?: number,
deleteUnverified?: boolean
): Promise<CleanupStats> {
return tableCleanupOldVersions
.call(this._tbl, olderThan, deleteUnverified)
.then((res: { newTable: any, metrics: CleanupStats }) => {
this._tbl = res.newTable;
return res.metrics;
});
this._tbl = res.newTable
return res.metrics
})
}
/**
@@ -1127,64 +1106,62 @@ export class LocalTable<T = number[]> implements Table<T> {
* for most tables.
* @returns Metrics about the compaction operation.
*/
async compactFiles(options?: CompactionOptions): Promise<CompactionMetrics> {
const optionsArg = options ?? {};
async compactFiles (options?: CompactionOptions): Promise<CompactionMetrics> {
const optionsArg = options ?? {}
return tableCompactFiles
.call(this._tbl, optionsArg)
.then((res: { newTable: any, metrics: CompactionMetrics }) => {
this._tbl = res.newTable;
return res.metrics;
});
this._tbl = res.newTable
return res.metrics
})
}
async listIndices(): Promise<VectorIndex[]> {
return tableListIndices.call(this._tbl);
async listIndices (): Promise<VectorIndex[]> {
return tableListIndices.call(this._tbl)
}
async indexStats(indexUuid: string): Promise<IndexStats> {
return tableIndexStats.call(this._tbl, indexUuid);
async indexStats (indexUuid: string): Promise<IndexStats> {
return tableIndexStats.call(this._tbl, indexUuid)
}
get schema(): Promise<Schema> {
get schema (): Promise<Schema> {
// empty table
return this.getSchema();
return this.getSchema()
}
private async getSchema(): Promise<Schema> {
const buffer = await tableSchema.call(this._tbl, this._isElectron);
const table = tableFromIPC(buffer);
return table.schema;
private async getSchema (): Promise<Schema> {
const buffer = await tableSchema.call(this._tbl, this._isElectron)
const table = tableFromIPC(buffer)
return table.schema
}
// See https://github.com/electron/electron/issues/2288
private checkElectron(): boolean {
private checkElectron (): boolean {
try {
// eslint-disable-next-line no-prototype-builtins
return (
Object.prototype.hasOwnProperty.call(process?.versions, "electron") ||
navigator?.userAgent?.toLowerCase()?.includes(" electron")
);
Object.prototype.hasOwnProperty.call(process?.versions, 'electron') ||
navigator?.userAgent?.toLowerCase()?.includes(' electron')
)
} catch (e) {
return false;
return false
}
}
async addColumns(
newColumnTransforms: Array<{ name: string, valueSql: string }>
): Promise<void> {
return tableAddColumns.call(this._tbl, newColumnTransforms);
async addColumns (newColumnTransforms: Array<{ name: string, valueSql: string }>): Promise<void> {
return tableAddColumns.call(this._tbl, newColumnTransforms)
}
async alterColumns(columnAlterations: ColumnAlteration[]): Promise<void> {
return tableAlterColumns.call(this._tbl, columnAlterations);
async alterColumns (columnAlterations: ColumnAlteration[]): Promise<void> {
return tableAlterColumns.call(this._tbl, columnAlterations)
}
async dropColumns(columnNames: string[]): Promise<void> {
return tableDropColumns.call(this._tbl, columnNames);
async dropColumns (columnNames: string[]): Promise<void> {
return tableDropColumns.call(this._tbl, columnNames)
}
withMiddleware(middleware: HttpMiddleware): Table<T> {
return this;
withMiddleware (middleware: HttpMiddleware): Table<T> {
return this
}
}
@@ -1207,7 +1184,7 @@ export interface CompactionOptions {
*/
targetRowsPerFragment?: number
/**
* The maximum number of T per group. Defaults to 1024.
* The maximum number of rows per group. Defaults to 1024.
*/
maxRowsPerGroup?: number
/**
@@ -1307,21 +1284,21 @@ export interface IvfPQIndexConfig {
*/
index_cache_size?: number
type: "ivf_pq"
type: 'ivf_pq'
}
export type VectorIndexParams = IvfPQIndexConfig;
export type VectorIndexParams = IvfPQIndexConfig
/**
* Write mode for writing a table.
*/
export enum WriteMode {
/** Create a new {@link Table}. */
Create = "create",
Create = 'create',
/** Overwrite the existing {@link Table} if presented. */
Overwrite = "overwrite",
Overwrite = 'overwrite',
/** Append new data to the table. */
Append = "append",
Append = 'append',
}
/**
@@ -1333,14 +1310,14 @@ export interface WriteOptions {
}
export class DefaultWriteOptions implements WriteOptions {
writeMode = WriteMode.Create;
writeMode = WriteMode.Create
}
export function isWriteOptions(value: any): value is WriteOptions {
export function isWriteOptions (value: any): value is WriteOptions {
return (
Object.keys(value).length === 1 &&
(value.writeMode === undefined || typeof value.writeMode === "string")
);
(value.writeMode === undefined || typeof value.writeMode === 'string')
)
}
/**
@@ -1350,15 +1327,15 @@ export enum MetricType {
/**
* Euclidean distance
*/
L2 = "l2",
L2 = 'l2',
/**
* Cosine distance
*/
Cosine = "cosine",
Cosine = 'cosine',
/**
* Dot product
*/
Dot = "dot",
Dot = 'dot',
}

View File

@@ -51,7 +51,7 @@ describe('LanceDB Mirrored Store Integration test', function () {
const dir = tmpdir()
console.log(dir)
const conn = await lancedb.connect({ uri: `s3://lancedb-integtest?mirroredStore=${dir}`, storageOptions: { allowHttp: 'true' } })
const conn = await lancedb.connect(`s3://lancedb-integtest?mirroredStore=${dir}`)
const data = Array(200).fill({ vector: Array(128).fill(1.0), id: 0 })
data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 1 }))
data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 2 }))

View File

@@ -140,9 +140,6 @@ export class RemoteConnection implements Connection {
schema = nameOrOpts.schema
embeddings = nameOrOpts.embeddingFunction
tableName = nameOrOpts.name
if (data === undefined) {
data = nameOrOpts.data
}
}
let buffer: Buffer

View File

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

File diff suppressed because it is too large Load Diff

3
nodejs/.eslintignore Normal file
View File

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

1
nodejs/.gitignore vendored
View File

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

1
nodejs/.prettierignore Symbolic link
View File

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

View File

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

View File

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

View File

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

View File

@@ -14,11 +14,7 @@
/* eslint-disable @typescript-eslint/naming-convention */
import {
CreateKeyCommand,
KMSClient,
ScheduleKeyDeletionCommand,
} from "@aws-sdk/client-kms";
import { connect } from "../dist";
import {
CreateBucketCommand,
DeleteBucketCommand,
@@ -27,7 +23,11 @@ import {
ListObjectsV2Command,
S3Client,
} from "@aws-sdk/client-s3";
import { connect } from "../lancedb";
import {
CreateKeyCommand,
ScheduleKeyDeletionCommand,
KMSClient,
} from "@aws-sdk/client-kms";
// Skip these tests unless the S3_TEST environment variable is set
const maybeDescribe = process.env.S3_TEST ? describe : describe.skip;
@@ -63,10 +63,9 @@ class S3Bucket {
// Delete the bucket if it already exists
try {
await this.deleteBucket(client, name);
} catch {
} catch (e) {
// It's fine if the bucket doesn't exist
}
// biome-ignore lint/style/useNamingConvention: we dont control s3's api
await client.send(new CreateBucketCommand({ Bucket: name }));
return new S3Bucket(name);
}
@@ -79,32 +78,27 @@ class S3Bucket {
static async deleteBucket(client: S3Client, name: string) {
// Must delete all objects before we can delete the bucket
const objects = await client.send(
// biome-ignore lint/style/useNamingConvention: we dont control s3's api
new ListObjectsV2Command({ Bucket: name }),
);
if (objects.Contents) {
for (const object of objects.Contents) {
await client.send(
// biome-ignore lint/style/useNamingConvention: we dont control s3's api
new DeleteObjectCommand({ Bucket: name, Key: object.Key }),
);
}
}
// biome-ignore lint/style/useNamingConvention: we dont control s3's api
await client.send(new DeleteBucketCommand({ Bucket: name }));
}
public async assertAllEncrypted(path: string, keyId: string) {
const client = S3Bucket.s3Client();
const objects = await client.send(
// biome-ignore lint/style/useNamingConvention: we dont control s3's api
new ListObjectsV2Command({ Bucket: this.name, Prefix: path }),
);
if (objects.Contents) {
for (const object of objects.Contents) {
const metadata = await client.send(
// biome-ignore lint/style/useNamingConvention: we dont control s3's api
new HeadObjectCommand({ Bucket: this.name, Key: object.Key }),
);
expect(metadata.ServerSideEncryption).toBe("aws:kms");
@@ -143,7 +137,6 @@ class KmsKey {
public async delete() {
const client = KmsKey.kmsClient();
// biome-ignore lint/style/useNamingConvention: we dont control s3's api
await client.send(new ScheduleKeyDeletionCommand({ KeyId: this.keyId }));
}
}

View File

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

View File

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

28
nodejs/eslint.config.js Normal file
View File

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

View File

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

View File

@@ -12,10 +12,10 @@
// See the License for the specific language governing permissions and
// limitations under the License.
import { Table as ArrowTable, Schema } from "apache-arrow";
import { fromTableToBuffer, makeArrowTable, makeEmptyTable } from "./arrow";
import { ConnectionOptions, Connection as LanceDbConnection } from "./native";
import { Table } from "./table";
import { Table as ArrowTable, Schema } from "apache-arrow";
/**
* Connect to a LanceDB instance at the given URI.
@@ -77,18 +77,6 @@ export interface OpenTableOptions {
* The available options are described at https://lancedb.github.io/lancedb/guides/storage/
*/
storageOptions?: Record<string, string>;
/**
* Set the size of the index cache, specified as a number of entries
*
* The exact meaning of an "entry" will depend on the type of index:
* - IVF: there is one entry for each IVF partition
* - BTREE: there is one entry for the entire index
*
* This cache applies to the entire opened table, across all indices.
* Setting this value higher will increase performance on larger datasets
* at the expense of more RAM
*/
indexCacheSize?: number;
}
export interface TableNamesOptions {
@@ -172,7 +160,6 @@ export class Connection {
const innerTable = await this.inner.openTable(
name,
cleanseStorageOptions(options?.storageOptions),
options?.indexCacheSize,
);
return new Table(innerTable);
}

View File

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

View File

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

View File

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

View File

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

View File

@@ -1,12 +1,18 @@
{
"name": "@lancedb/lancedb-darwin-arm64",
"version": "0.5.0",
"os": ["darwin"],
"cpu": ["arm64"],
"main": "lancedb.darwin-arm64.node",
"files": ["lancedb.darwin-arm64.node"],
"license": "Apache 2.0",
"engines": {
"node": ">= 18"
}
"name": "@lancedb/lancedb-darwin-arm64",
"version": "0.4.17",
"os": [
"darwin"
],
"cpu": [
"arm64"
],
"main": "lancedb.darwin-arm64.node",
"files": [
"lancedb.darwin-arm64.node"
],
"license": "Apache 2.0",
"engines": {
"node": ">= 18"
}
}

View File

@@ -1,12 +1,18 @@
{
"name": "@lancedb/lancedb-darwin-x64",
"version": "0.5.0",
"os": ["darwin"],
"cpu": ["x64"],
"main": "lancedb.darwin-x64.node",
"files": ["lancedb.darwin-x64.node"],
"license": "Apache 2.0",
"engines": {
"node": ">= 18"
}
"name": "@lancedb/lancedb-darwin-x64",
"version": "0.4.17",
"os": [
"darwin"
],
"cpu": [
"x64"
],
"main": "lancedb.darwin-x64.node",
"files": [
"lancedb.darwin-x64.node"
],
"license": "Apache 2.0",
"engines": {
"node": ">= 18"
}
}

View File

@@ -1,13 +1,21 @@
{
"name": "@lancedb/lancedb-linux-arm64-gnu",
"version": "0.5.0",
"os": ["linux"],
"cpu": ["arm64"],
"main": "lancedb.linux-arm64-gnu.node",
"files": ["lancedb.linux-arm64-gnu.node"],
"license": "Apache 2.0",
"engines": {
"node": ">= 18"
},
"libc": ["glibc"]
"name": "@lancedb/lancedb-linux-arm64-gnu",
"version": "0.4.17",
"os": [
"linux"
],
"cpu": [
"arm64"
],
"main": "lancedb.linux-arm64-gnu.node",
"files": [
"lancedb.linux-arm64-gnu.node"
],
"license": "Apache 2.0",
"engines": {
"node": ">= 18"
},
"libc": [
"glibc"
]
}

View File

@@ -1,13 +1,21 @@
{
"name": "@lancedb/lancedb-linux-x64-gnu",
"version": "0.5.0",
"os": ["linux"],
"cpu": ["x64"],
"main": "lancedb.linux-x64-gnu.node",
"files": ["lancedb.linux-x64-gnu.node"],
"license": "Apache 2.0",
"engines": {
"node": ">= 18"
},
"libc": ["glibc"]
"name": "@lancedb/lancedb-linux-x64-gnu",
"version": "0.4.17",
"os": [
"linux"
],
"cpu": [
"x64"
],
"main": "lancedb.linux-x64-gnu.node",
"files": [
"lancedb.linux-x64-gnu.node"
],
"license": "Apache 2.0",
"engines": {
"node": ">= 18"
},
"libc": [
"glibc"
]
}

View File

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

15661
nodejs/package-lock.json generated

File diff suppressed because it is too large Load Diff

View File

@@ -1,6 +1,6 @@
{
"name": "@lancedb/lancedb",
"version": "0.5.0",
"version": "0.4.17",
"main": "./dist/index.js",
"types": "./dist/index.d.ts",
"napi": {
@@ -18,16 +18,19 @@
},
"license": "Apache 2.0",
"devDependencies": {
"@aws-sdk/client-kms": "^3.33.0",
"@aws-sdk/client-s3": "^3.33.0",
"@biomejs/biome": "^1.7.3",
"@jest/globals": "^29.7.0",
"@aws-sdk/client-kms": "^3.33.0",
"@napi-rs/cli": "^2.18.0",
"@types/jest": "^29.1.2",
"@types/tmp": "^0.2.6",
"@typescript-eslint/eslint-plugin": "^6.19.0",
"@typescript-eslint/parser": "^6.19.0",
"apache-arrow-old": "npm:apache-arrow@13.0.0",
"eslint": "^8.57.0",
"eslint-config-prettier": "^9.1.0",
"eslint-plugin-jsdoc": "^48.2.1",
"jest": "^29.7.0",
"prettier": "^3.1.0",
"shx": "^0.3.4",
"tmp": "^0.2.3",
"ts-jest": "^29.1.2",
@@ -42,26 +45,39 @@
"engines": {
"node": ">= 18"
},
"cpu": ["x64", "arm64"],
"os": ["darwin", "linux", "win32"],
"cpu": [
"x64",
"arm64"
],
"os": [
"darwin",
"linux",
"win32"
],
"scripts": {
"artifacts": "napi artifacts",
"build:debug": "napi build --platform --dts ../lancedb/native.d.ts --js ../lancedb/native.js lancedb",
"build:debug": "napi build --platform --dts ../lancedb/native.d.ts --js ../lancedb/native.js dist/",
"build:release": "napi build --platform --release --dts ../lancedb/native.d.ts --js ../lancedb/native.js dist/",
"build": "npm run build:debug && tsc -b && shx cp lancedb/native.d.ts dist/native.d.ts && shx cp lancedb/*.node dist/",
"build": "npm run build:debug && tsc -b && shx cp lancedb/native.d.ts dist/native.d.ts",
"build-release": "npm run build:release && tsc -b && shx cp lancedb/native.d.ts dist/native.d.ts",
"lint-ci": "biome ci .",
"chkformat": "prettier . --check",
"docs": "typedoc --plugin typedoc-plugin-markdown --out ../docs/src/js lancedb/index.ts",
"lint": "biome check . && biome format .",
"lint-fix": "biome check --apply-unsafe . && biome format --write .",
"lint": "eslint lancedb && eslint __test__",
"prepublishOnly": "napi prepublish -t npm",
"test": "jest --verbose",
"test": "npm run build && jest --verbose",
"integration": "S3_TEST=1 npm run test",
"universal": "napi universal",
"version": "napi version"
},
"optionalDependencies": {
"@lancedb/lancedb-darwin-arm64": "0.4.17",
"@lancedb/lancedb-darwin-x64": "0.4.17",
"@lancedb/lancedb-linux-arm64-gnu": "0.4.17",
"@lancedb/lancedb-linux-x64-gnu": "0.4.17",
"@lancedb/lancedb-win32-x64-msvc": "0.4.17"
},
"dependencies": {
"apache-arrow": "^15.0.0",
"openai": "^4.29.2"
"openai": "^4.29.2",
"apache-arrow": "^15.0.0"
}
}

View File

@@ -176,7 +176,6 @@ impl Connection {
&self,
name: String,
storage_options: Option<HashMap<String, String>>,
index_cache_size: Option<u32>,
) -> napi::Result<Table> {
let mut builder = self.get_inner()?.open_table(&name);
if let Some(storage_options) = storage_options {
@@ -184,9 +183,6 @@ impl Connection {
builder = builder.storage_option(key, value);
}
}
if let Some(index_cache_size) = index_cache_size {
builder = builder.index_cache_size(index_cache_size);
}
let tbl = builder
.execute()
.await

View File

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

8
python/.bumpversion.cfg Normal file
View File

@@ -0,0 +1,8 @@
[bumpversion]
current_version = 0.6.9
commit = True
message = [python] Bump version: {current_version} → {new_version}
tag = True
tag_name = python-v{new_version}
[bumpversion:file:pyproject.toml]

View File

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

View File

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

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