mirror of
https://github.com/lancedb/lancedb.git
synced 2025-12-23 05:19:58 +00:00
Compare commits
7 Commits
v0.1.4-pyt
...
v0.1.3-nod
| Author | SHA1 | Date | |
|---|---|---|---|
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|
091fb9b665 | ||
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03013a4434 | ||
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3e14b357e7 | ||
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99cbda8b07 | ||
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e50b642d80 | ||
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6d8cf52e01 | ||
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53f3882d6e |
48
.github/workflows/make_release_commit.yml
vendored
Normal file
48
.github/workflows/make_release_commit.yml
vendored
Normal file
@@ -0,0 +1,48 @@
|
||||
name: Create release commit
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
dry_run:
|
||||
description: 'Just create the local commit/tags but do not push it'
|
||||
required: true
|
||||
default: "false"
|
||||
type: choice
|
||||
options:
|
||||
- "true"
|
||||
- "false"
|
||||
part:
|
||||
description: 'What kind of release is this?'
|
||||
required: true
|
||||
default: 'patch'
|
||||
type: choice
|
||||
options:
|
||||
- patch
|
||||
- minor
|
||||
- major
|
||||
|
||||
jobs:
|
||||
bump-version:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Check out main
|
||||
uses: actions/checkout@v3
|
||||
with:
|
||||
ref: main
|
||||
persist-credentials: false
|
||||
fetch-depth: 0
|
||||
lfs: true
|
||||
- name: Install cargo utils
|
||||
run: cargo install cargo-edit
|
||||
- name: Bump versions
|
||||
run: |
|
||||
NEW_VERSION=$(bash ci/bump_versions.sh ${{ inputs.part }})
|
||||
echo "New version: v$NEW_VERSION"
|
||||
git tag v$NEW_VERSION
|
||||
- name: Push new version and tag
|
||||
if: ${{ inputs.dry_run }} == "false"
|
||||
uses: ad-m/github-push-action@master
|
||||
with:
|
||||
github_token: ${{ secrets.RELEASE_TOKEN }}
|
||||
branch: main
|
||||
tags: true
|
||||
12
.github/workflows/node.yml
vendored
12
.github/workflows/node.yml
vendored
@@ -67,8 +67,12 @@ jobs:
|
||||
- name: Build
|
||||
run: |
|
||||
npm ci
|
||||
npm run build
|
||||
npm run tsc
|
||||
npm run build
|
||||
npm run pack-build
|
||||
npm install --no-save ./dist/lancedb-vectordb-*.tgz
|
||||
# Remove index.node to test with dependency installed
|
||||
rm index.node
|
||||
- name: Test
|
||||
run: npm run test
|
||||
macos:
|
||||
@@ -94,8 +98,12 @@ jobs:
|
||||
- name: Build
|
||||
run: |
|
||||
npm ci
|
||||
npm run build
|
||||
npm run tsc
|
||||
npm run build
|
||||
npm run pack-build
|
||||
npm install --no-save ./dist/lancedb-vectordb-*.tgz
|
||||
# Remove index.node to test with dependency installed
|
||||
rm index.node
|
||||
- name: Test
|
||||
run: |
|
||||
npm run test
|
||||
|
||||
167
.github/workflows/release.yml
vendored
Normal file
167
.github/workflows/release.yml
vendored
Normal file
@@ -0,0 +1,167 @@
|
||||
name: Prepare Release
|
||||
|
||||
# NOTE: Python is a separate release for now.
|
||||
|
||||
# Currently disabled until it can be completed.
|
||||
# on:
|
||||
# push:
|
||||
# tags:
|
||||
# - v*
|
||||
|
||||
jobs:
|
||||
draft-release:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: softprops/action-gh-release@v1
|
||||
with:
|
||||
draft: true
|
||||
prerelease: true # hardcoded on for now
|
||||
generate_release_notes: true
|
||||
|
||||
rust:
|
||||
runs-on: ubuntu-latest
|
||||
needs: draft-release
|
||||
defaults:
|
||||
run:
|
||||
shell: bash
|
||||
working-directory: rust/vectordb
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
with:
|
||||
fetch-depth: 0
|
||||
lfs: true
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
sudo apt update
|
||||
sudo apt install -y protobuf-compiler libssl-dev
|
||||
- name: Package Rust
|
||||
run: cargo package --all-features
|
||||
- uses: softprops/action-gh-release@v1
|
||||
with:
|
||||
draft: true
|
||||
files: target/package/vectordb-*.crate
|
||||
fail_on_unmatched_files: true
|
||||
|
||||
node:
|
||||
runs-on: ubuntu-latest
|
||||
needs: draft-release
|
||||
defaults:
|
||||
run:
|
||||
shell: bash
|
||||
working-directory: node
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v3
|
||||
- uses: actions/setup-node@v3
|
||||
with:
|
||||
node-version: 20
|
||||
cache: 'npm'
|
||||
cache-dependency-path: node/package-lock.json
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
sudo apt update
|
||||
sudo apt install -y protobuf-compiler libssl-dev
|
||||
- name: Build
|
||||
run: |
|
||||
npm ci
|
||||
npm run tsc
|
||||
npm pack
|
||||
- uses: softprops/action-gh-release@v1
|
||||
with:
|
||||
draft: true
|
||||
files: node/vectordb-*.tgz
|
||||
fail_on_unmatched_files: true
|
||||
|
||||
node-macos:
|
||||
runs-on: macos-12
|
||||
needs: draft-release
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
target: [x86_64-apple-darwin, aarch64-apple-darwin]
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v33
|
||||
- name: Install system dependencies
|
||||
run: brew install protobuf
|
||||
- name: Install npm dependencies
|
||||
run: |
|
||||
cd node
|
||||
npm ci
|
||||
- name: Install rustup target
|
||||
if: ${{ matrix.target == 'aarch64-apple-darwin' }}
|
||||
run: rustup target add aarch64-apple-darwin
|
||||
- name: Build MacOS native node modules
|
||||
run: bash ci/build_macos_artifacts.sh ${{ matrix.target }}
|
||||
- uses: softprops/action-gh-release@v1
|
||||
with:
|
||||
draft: true
|
||||
files: node/dist/lancedb-vectordb-darwin*.tgz
|
||||
fail_on_unmatched_files: true
|
||||
|
||||
node-linux:
|
||||
name: node-linux (${{ matrix.arch}}-unknown-linux-${{ matrix.libc }})
|
||||
runs-on: ubuntu-latest
|
||||
needs: draft-release
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
libc:
|
||||
- gnu
|
||||
# TODO: re-enable musl once we have refactored to pre-built containers
|
||||
# Right now we have to build node from source which is too expensive.
|
||||
# - musl
|
||||
arch:
|
||||
- x86_64
|
||||
# Building on aarch64 is too slow for now
|
||||
# - aarch64
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v3
|
||||
- name: Change owner to root (for npm)
|
||||
# The docker container is run as root, so we need the files to be owned by root
|
||||
# Otherwise npm is a nightmare: https://github.com/npm/cli/issues/3773
|
||||
run: sudo chown -R root:root .
|
||||
- name: Set up QEMU
|
||||
if: ${{ matrix.arch == 'aarch64' }}
|
||||
uses: docker/setup-qemu-action@v2
|
||||
with:
|
||||
platforms: arm64
|
||||
- name: Build Linux GNU native node modules
|
||||
if: ${{ matrix.libc == 'gnu' }}
|
||||
run: |
|
||||
docker run \
|
||||
-v $(pwd):/io -w /io \
|
||||
quay.io/pypa/manylinux2014_${{ matrix.arch }} \
|
||||
bash ci/build_linux_artifacts.sh ${{ matrix.arch }}-unknown-linux-gnu
|
||||
- name: Build musl Linux native node modules
|
||||
if: ${{ matrix.libc == 'musl' }}
|
||||
run: |
|
||||
docker run --platform linux/arm64/v8 \
|
||||
-v $(pwd):/io -w /io \
|
||||
quay.io/pypa/musllinux_1_1_${{ matrix.arch }} \
|
||||
bash ci/build_linux_artifacts.sh ${{ matrix.arch }}-unknown-linux-musl
|
||||
- uses: softprops/action-gh-release@v1
|
||||
with:
|
||||
draft: true
|
||||
files: node/dist/lancedb-vectordb-linux*.tgz
|
||||
fail_on_unmatched_files: true
|
||||
|
||||
release:
|
||||
needs: [rust, node, node-macos, node-linux]
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/download-artifact@v3
|
||||
- name: Publish to NPM
|
||||
run: |
|
||||
for filename in node/dist/*.tgz; do
|
||||
npm publish --dry-run $filename
|
||||
done
|
||||
- name: Publish to crates.io
|
||||
env:
|
||||
CARGO_REGISTRY_TOKEN: ${{ secrets.CARGO_REGISTRY_TOKEN }}
|
||||
run: |
|
||||
cargo publish --dry-run --no-verify rust/target/vectordb-*.crate
|
||||
# - uses: softprops/action-gh-release@v1
|
||||
# with:
|
||||
# draft: false
|
||||
2
.gitignore
vendored
2
.gitignore
vendored
@@ -4,6 +4,8 @@
|
||||
**/__pycache__
|
||||
.DS_Store
|
||||
|
||||
.vscode
|
||||
|
||||
rust/target
|
||||
rust/Cargo.lock
|
||||
|
||||
|
||||
5
Cargo.lock
generated
5
Cargo.lock
generated
@@ -3358,7 +3358,7 @@ checksum = "accd4ea62f7bb7a82fe23066fb0957d48ef677f6eeb8215f372f52e48bb32426"
|
||||
|
||||
[[package]]
|
||||
name = "vectordb"
|
||||
version = "0.0.1"
|
||||
version = "0.1.2"
|
||||
dependencies = [
|
||||
"arrow-array",
|
||||
"arrow-data",
|
||||
@@ -3366,13 +3366,14 @@ dependencies = [
|
||||
"lance",
|
||||
"object_store",
|
||||
"rand",
|
||||
"snafu",
|
||||
"tempfile",
|
||||
"tokio",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "vectordb-node"
|
||||
version = "0.1.0"
|
||||
version = "0.1.2"
|
||||
dependencies = [
|
||||
"arrow-array",
|
||||
"arrow-ipc",
|
||||
|
||||
91
ci/build_linux_artifacts.sh
Normal file
91
ci/build_linux_artifacts.sh
Normal file
@@ -0,0 +1,91 @@
|
||||
#!/bin/bash
|
||||
# Builds the Linux artifacts (node binaries).
|
||||
# Usage: ./build_linux_artifacts.sh [target]
|
||||
# Targets supported:
|
||||
# - x86_64-unknown-linux-gnu:centos
|
||||
# - aarch64-unknown-linux-gnu:centos
|
||||
# - aarch64-unknown-linux-musl
|
||||
# - x86_64-unknown-linux-musl
|
||||
|
||||
# TODO: refactor this into a Docker container we can pull
|
||||
|
||||
set -e
|
||||
|
||||
setup_dependencies() {
|
||||
echo "Installing system dependencies..."
|
||||
if [[ $1 == *musl ]]; then
|
||||
# musllinux
|
||||
apk add openssl-dev
|
||||
else
|
||||
# manylinux2014
|
||||
yum install -y openssl-devel unzip
|
||||
fi
|
||||
|
||||
if [[ $1 == x86_64* ]]; then
|
||||
ARCH=x86_64
|
||||
else
|
||||
# gnu target
|
||||
ARCH=aarch_64
|
||||
fi
|
||||
|
||||
# Install new enough protobuf (yum-provided is old)
|
||||
PB_REL=https://github.com/protocolbuffers/protobuf/releases
|
||||
PB_VERSION=23.1
|
||||
curl -LO $PB_REL/download/v$PB_VERSION/protoc-$PB_VERSION-linux-$ARCH.zip
|
||||
unzip protoc-$PB_VERSION-linux-$ARCH.zip -d /usr/local
|
||||
}
|
||||
|
||||
install_node() {
|
||||
echo "Installing node..."
|
||||
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.34.0/install.sh | bash
|
||||
source "$HOME"/.bashrc
|
||||
|
||||
if [[ $1 == *musl ]]; then
|
||||
# This node version is 15, we need 16 or higher:
|
||||
# apk add nodejs-current npm
|
||||
# So instead we install from source (nvm doesn't provide binaries for musl):
|
||||
nvm install -s --no-progress 17
|
||||
else
|
||||
nvm install --no-progress 17 # latest that supports glibc 2.17
|
||||
fi
|
||||
}
|
||||
|
||||
install_rust() {
|
||||
echo "Installing rust..."
|
||||
curl https://sh.rustup.rs -sSf | bash -s -- -y
|
||||
export PATH="$PATH:/root/.cargo/bin"
|
||||
}
|
||||
|
||||
build_node_binary() {
|
||||
echo "Building node library for $1..."
|
||||
pushd node
|
||||
|
||||
npm ci
|
||||
|
||||
if [[ $1 == *musl ]]; then
|
||||
# This is needed for cargo to allow build cdylibs with musl
|
||||
export RUSTFLAGS="-C target-feature=-crt-static"
|
||||
fi
|
||||
|
||||
# Cargo can run out of memory while pulling dependencies, espcially when running
|
||||
# in QEMU. This is a workaround for that.
|
||||
export CARGO_NET_GIT_FETCH_WITH_CLI=true
|
||||
|
||||
# We don't pass in target, since the native target here already matches
|
||||
# and openblas-src doesn't do well with cross-compilation.
|
||||
npm run build-release
|
||||
npm run pack-build
|
||||
|
||||
popd
|
||||
}
|
||||
|
||||
TARGET=${1:-x86_64-unknown-linux-gnu}
|
||||
# Others:
|
||||
# aarch64-unknown-linux-gnu
|
||||
# x86_64-unknown-linux-musl
|
||||
# aarch64-unknown-linux-musl
|
||||
|
||||
setup_dependencies $TARGET
|
||||
install_node $TARGET
|
||||
install_rust
|
||||
build_node_binary $TARGET
|
||||
33
ci/build_macos_artifacts.sh
Normal file
33
ci/build_macos_artifacts.sh
Normal file
@@ -0,0 +1,33 @@
|
||||
# Builds the macOS artifacts (node binaries).
|
||||
# Usage: ./ci/build_macos_artifacts.sh [target]
|
||||
# Targets supported: x86_64-apple-darwin aarch64-apple-darwin
|
||||
|
||||
prebuild_rust() {
|
||||
# Building here for the sake of easier debugging.
|
||||
pushd rust/ffi/node
|
||||
echo "Building rust library for $1"
|
||||
export RUST_BACKTRACE=1
|
||||
cargo build --release --target $1
|
||||
popd
|
||||
}
|
||||
|
||||
build_node_binaries() {
|
||||
pushd node
|
||||
echo "Building node library for $1"
|
||||
npm run build-release -- --target $1
|
||||
npm run pack-build -- --target $1
|
||||
popd
|
||||
}
|
||||
|
||||
if [ -n "$1" ]; then
|
||||
targets=$1
|
||||
else
|
||||
targets="x86_64-apple-darwin aarch64-apple-darwin"
|
||||
fi
|
||||
|
||||
echo "Building artifacts for targets: $targets"
|
||||
for target in $targets
|
||||
do
|
||||
prebuild_rust $target
|
||||
build_node_binaries $target
|
||||
done
|
||||
58
ci/bump_versions.sh
Normal file
58
ci/bump_versions.sh
Normal file
@@ -0,0 +1,58 @@
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
# if cargo bump isn't installed return an error
|
||||
if ! cargo set-version &> /dev/null
|
||||
then
|
||||
echo "cargo-edit could not be found. Install with `cargo install cargo-edit`"
|
||||
exit
|
||||
fi
|
||||
|
||||
BUMP_PART=${1:-patch}
|
||||
|
||||
# if BUMP_PART isn't patch, minor, or major return an error
|
||||
if [ "$BUMP_PART" != "patch" ] && [ "$BUMP_PART" != "minor" ] && [ "$BUMP_PART" != "major" ]
|
||||
then
|
||||
echo "BUMP_PART must be one of patch, minor, or major"
|
||||
exit
|
||||
fi
|
||||
|
||||
function get_crate_version() {
|
||||
cargo pkgid -p $1 | cut -d@ -f2 | cut -d# -f2
|
||||
}
|
||||
|
||||
# First, validate versions are starting as same
|
||||
VECTORDB_VERSION=$(get_crate_version vectordb)
|
||||
FFI_NODE_VERSION=$(get_crate_version vectordb-node)
|
||||
|
||||
# FYI, we pipe all output to /dev/null because the only thing we want to ouput
|
||||
# if success is the new tag. This way it can be then used with `git tag`.
|
||||
pushd node > /dev/null
|
||||
NODE_VERSION=$(npm pkg get version | xargs echo)
|
||||
popd > /dev/null
|
||||
|
||||
if [ "$VECTORDB_VERSION" != "$FFI_NODE_VERSION" ] || [ "$VECTORDB_VERSION" != "$NODE_VERSION" ]
|
||||
then
|
||||
echo "Version mismatch between rust/vectordb, rust/ffi/node, and node"
|
||||
echo "rust/vectordb: $VECTORDB_VERSION"
|
||||
echo "rust/ffi/node: $FFI_NODE_VERSION"
|
||||
echo "node: $NODE_VERSION"
|
||||
exit
|
||||
fi
|
||||
|
||||
cargo set-version --bump $BUMP_PART > /dev/null 2>&1
|
||||
NEW_VERSION=$(get_crate_version vectordb)
|
||||
|
||||
pushd node > /dev/null
|
||||
npm version $BUMP_PART > /dev/null
|
||||
|
||||
# Also need to update version of the native modules
|
||||
NATIVE_MODULES=$(npm pkg get optionalDependencies | jq 'keys[]' | grep @vectordb/ | tr -d '"')
|
||||
for module in $NATIVE_MODULES
|
||||
do
|
||||
npm install $module@$NEW_VERSION --save-optional > /dev/null
|
||||
done
|
||||
popd > /dev/null
|
||||
|
||||
|
||||
echo $NEW_VERSION
|
||||
136
ci/release_process.md
Normal file
136
ci/release_process.md
Normal file
@@ -0,0 +1,136 @@
|
||||
# How to release
|
||||
|
||||
This is for the Rust crate and Node module. For now, the Python module is
|
||||
released separately.
|
||||
|
||||
<!--
|
||||
The release is started by bumping the versions and pushing a new tag. To do this
|
||||
automatically, use the `make_release_commit` GitHub action.
|
||||
|
||||
When the tag is pushed, GitHub actions will start building the libraries and
|
||||
will upload them to a draft release.
|
||||
|
||||
While those jobs are running, edit the release notes as needed. For example,
|
||||
bring relevant new features and bugfixes to the top of the notes and the testing
|
||||
and CI changes to the bottom.
|
||||
|
||||
Once the jobs have finished, the release will be marked as not draft and the
|
||||
artifacts will be released to crates.io, NPM, and PyPI.
|
||||
|
||||
-->
|
||||
|
||||
## Manual process
|
||||
|
||||
The manual release process can be completed on a MacOS machine.
|
||||
|
||||
### Bump the versions
|
||||
|
||||
You can use the script `ci/bump_versions.sh` to bump the versions. It defaults
|
||||
to a `patch` bump, but you can also pass `minor` and `major`. Once you have the
|
||||
tag created, push it to GitHub.
|
||||
|
||||
```shell
|
||||
VERSION=$(bash ci/bump_versions.sh)
|
||||
git tag v$VERSION
|
||||
git push origin v$VERSION
|
||||
```
|
||||
|
||||
### Build the MacOS release libraries
|
||||
|
||||
One-time setup:
|
||||
|
||||
```shell
|
||||
rustup target add x86_64-apple-darwin aarch64-apple-darwin
|
||||
```
|
||||
|
||||
To build both x64 and arm64, run `ci/build_macos_artifacts.sh` without any args:
|
||||
|
||||
```shell
|
||||
bash ci/build_macos_artifacts.sh
|
||||
```
|
||||
|
||||
### Build the Linux release libraries
|
||||
|
||||
To build a Linux library, we need to use docker with a different build script:
|
||||
|
||||
```shell
|
||||
ARCH=aarch64
|
||||
docker run \
|
||||
-v $(pwd):/io -w /io \
|
||||
quay.io/pypa/manylinux2014_$ARCH \
|
||||
bash ci/build_linux_artifacts.sh $ARCH-unknown-linux-gnu
|
||||
```
|
||||
|
||||
For x64, change `ARCH` to `x86_64`. NOTE: compiling for a different architecture
|
||||
than your machine in Docker is very slow. It's best to do this on a machine with
|
||||
matching architecture.
|
||||
|
||||
|
||||
<!--
|
||||
Similar script for musl binaries (not yet working):
|
||||
|
||||
```shell
|
||||
ARCH=aarch64
|
||||
docker run \
|
||||
--user $(id -u) \
|
||||
-v $(pwd):/io -w /io \
|
||||
quay.io/pypa/musllinux_1_1_$ARCH \
|
||||
bash ci/build_linux_artifacts.sh $ARCH-unknown-linux-musl
|
||||
```
|
||||
|
||||
-->
|
||||
|
||||
<!--
|
||||
|
||||
For debugging, use these snippets:
|
||||
|
||||
```shell
|
||||
ARCH=aarch64
|
||||
docker run -it \
|
||||
-v $(pwd):/io -w /io \
|
||||
quay.io/pypa/manylinux2014_$ARCH \
|
||||
bash
|
||||
```
|
||||
|
||||
```shell
|
||||
ARCH=aarch64
|
||||
docker run -it \
|
||||
-v $(pwd):/io -w /io \
|
||||
quay.io/pypa/musllinux_1_1_$ARCH \
|
||||
bash
|
||||
```
|
||||
|
||||
Note: musllinux_1_1 is Alpine Linux 3.12
|
||||
-->
|
||||
|
||||
|
||||
### Build the npm module
|
||||
|
||||
To build the typescript and create a release tarball, run:
|
||||
|
||||
```shell
|
||||
npm ci
|
||||
npm tsc
|
||||
npm pack
|
||||
```
|
||||
|
||||
### Release to npm
|
||||
|
||||
Assuming you still have `VERSION` set from earlier:
|
||||
|
||||
```shell
|
||||
pushd node
|
||||
npm publish lancedb-vectordb-$VERSION.tgz
|
||||
for tarball in ./dist/lancedb-vectordb-*-$VERSION.tgz;
|
||||
do
|
||||
npm publish $tarball
|
||||
done
|
||||
popd
|
||||
```
|
||||
|
||||
### Release to crates.io
|
||||
|
||||
```shell
|
||||
cargo publish -p vectordb
|
||||
cargo publish -p vectordb-node
|
||||
```
|
||||
@@ -38,7 +38,7 @@ result = table.search([100, 100]).limit(2).to_df()
|
||||
## Complete Demos
|
||||
|
||||
We will be adding completed demo apps built using LanceDB.
|
||||
- [YouTube Transcript Search](../notebooks/youtube_transcript_search.ipynb)
|
||||
- [YouTube Transcript Search](../../notebooks/youtube_transcript_search.ipynb)
|
||||
|
||||
|
||||
## Documentation Quick Links
|
||||
|
||||
4
node/.npmignore
Normal file
4
node/.npmignore
Normal file
@@ -0,0 +1,4 @@
|
||||
gen_test_data.py
|
||||
index.node
|
||||
dist/lancedb*.tgz
|
||||
vectordb*.tgz
|
||||
@@ -8,6 +8,10 @@ A JavaScript / Node.js library for [LanceDB](https://github.com/lancedb/lancedb)
|
||||
npm install vectordb
|
||||
```
|
||||
|
||||
This will download the appropriate native library for your platform. We currently
|
||||
support x86_64 Linux, aarch64 Linux, Intel MacOS, and ARM (M1/M2) MacOS. We do not
|
||||
yet support Windows or musl-based Linux (such as Alpine Linux).
|
||||
|
||||
## Usage
|
||||
|
||||
### Basic Example
|
||||
@@ -24,17 +28,33 @@ The [examples](./examples) folder contains complete examples.
|
||||
|
||||
## Development
|
||||
|
||||
The LanceDB javascript is built with npm:
|
||||
To build everything fresh:
|
||||
|
||||
```bash
|
||||
npm install
|
||||
npm run tsc
|
||||
npm run build
|
||||
```
|
||||
|
||||
Then you should be able to run the tests with:
|
||||
|
||||
```bash
|
||||
npm test
|
||||
```
|
||||
|
||||
### Rebuilding Rust library
|
||||
|
||||
```bash
|
||||
npm run build
|
||||
```
|
||||
|
||||
### Rebuilding Typescript
|
||||
|
||||
```bash
|
||||
npm run tsc
|
||||
```
|
||||
|
||||
Run the tests with
|
||||
|
||||
```bash
|
||||
npm test
|
||||
```
|
||||
### Fix lints
|
||||
|
||||
To run the linter and have it automatically fix all errors
|
||||
|
||||
|
||||
41
node/examples/js-openai/index.js
Normal file
41
node/examples/js-openai/index.js
Normal file
@@ -0,0 +1,41 @@
|
||||
// Copyright 2023 Lance Developers.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
'use strict'
|
||||
|
||||
async function example () {
|
||||
const lancedb = require('vectordb')
|
||||
// You need to provide an OpenAI API key, here we read it from the OPENAI_API_KEY environment variable
|
||||
const apiKey = process.env.OPENAI_API_KEY
|
||||
// The embedding function will create embeddings for the 'text' column(text in this case)
|
||||
const embedding = new lancedb.OpenAIEmbeddingFunction('text', apiKey)
|
||||
|
||||
const db = await lancedb.connect('data/sample-lancedb')
|
||||
|
||||
const data = [
|
||||
{ id: 1, text: 'Black T-Shirt', price: 10 },
|
||||
{ id: 2, text: 'Leather Jacket', price: 50 }
|
||||
]
|
||||
|
||||
const table = await db.createTable('vectors', data, embedding)
|
||||
console.log(await db.tableNames())
|
||||
|
||||
const results = await table
|
||||
.search('keeps me warm')
|
||||
.limit(1)
|
||||
.execute()
|
||||
console.log(results[0].text)
|
||||
}
|
||||
|
||||
example().then(_ => { console.log('All done!') })
|
||||
15
node/examples/js-openai/package.json
Normal file
15
node/examples/js-openai/package.json
Normal file
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"name": "vectordb-example-js-openai",
|
||||
"version": "1.0.0",
|
||||
"description": "",
|
||||
"main": "index.js",
|
||||
"scripts": {
|
||||
"test": "echo \"Error: no test specified\" && exit 1"
|
||||
},
|
||||
"author": "Lance Devs",
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"vectordb": "file:../..",
|
||||
"openai": "^3.2.1"
|
||||
}
|
||||
}
|
||||
@@ -9,6 +9,6 @@
|
||||
"author": "Lance Devs",
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"vectordb": "^0.1.0"
|
||||
"vectordb": "file:../.."
|
||||
}
|
||||
}
|
||||
|
||||
@@ -17,6 +17,6 @@
|
||||
"typescript": "*"
|
||||
},
|
||||
"dependencies": {
|
||||
"vectordb": "^0.1.0"
|
||||
"vectordb": "file:../.."
|
||||
}
|
||||
}
|
||||
|
||||
@@ -12,29 +12,26 @@
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
const { currentTarget } = require('@neon-rs/load');
|
||||
|
||||
let nativeLib;
|
||||
|
||||
function getPlatformLibrary() {
|
||||
if (process.platform === "darwin" && process.arch == "arm64") {
|
||||
return require('./aarch64-apple-darwin.node');
|
||||
} else if (process.platform === "darwin" && process.arch == "x64") {
|
||||
return require('./x86_64-apple-darwin.node');
|
||||
} else if (process.platform === "linux" && process.arch == "x64") {
|
||||
return require('./x86_64-unknown-linux-gnu.node');
|
||||
} else {
|
||||
throw new Error(`vectordb: unsupported platform ${process.platform}_${process.arch}. Please file a bug report at https://github.com/lancedb/lancedb/issues`)
|
||||
}
|
||||
}
|
||||
|
||||
try {
|
||||
nativeLib = require('./index.node')
|
||||
nativeLib = require(`@lancedb/vectordb-${currentTarget()}`);
|
||||
} catch (e) {
|
||||
if (e.code === "MODULE_NOT_FOUND") {
|
||||
nativeLib = getPlatformLibrary();
|
||||
} else {
|
||||
throw new Error('vectordb: failed to load native library. Please file a bug report at https://github.com/lancedb/lancedb/issues');
|
||||
try {
|
||||
// Might be developing locally, so try that. But don't expose that error
|
||||
// to the user.
|
||||
nativeLib = require("./index.node");
|
||||
} catch {
|
||||
throw new Error(`vectordb: failed to load native library.
|
||||
You may need to run \`npm install @lancedb/vectordb-${currentTarget()}\`.
|
||||
|
||||
If that does not work, please file a bug report at https://github.com/lancedb/lancedb/issues
|
||||
|
||||
Source error: ${e}`);
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = nativeLib
|
||||
|
||||
// Dynamic require for runtime.
|
||||
module.exports = nativeLib;
|
||||
|
||||
548
node/package-lock.json
generated
548
node/package-lock.json
generated
@@ -1,18 +1,28 @@
|
||||
{
|
||||
"name": "vectordb",
|
||||
"version": "0.1.1",
|
||||
"version": "0.1.2",
|
||||
"lockfileVersion": 2,
|
||||
"requires": true,
|
||||
"packages": {
|
||||
"": {
|
||||
"name": "vectordb",
|
||||
"version": "0.1.1",
|
||||
"version": "0.1.2",
|
||||
"cpu": [
|
||||
"x64",
|
||||
"arm64"
|
||||
],
|
||||
"license": "Apache-2.0",
|
||||
"os": [
|
||||
"darwin",
|
||||
"linux"
|
||||
],
|
||||
"dependencies": {
|
||||
"@apache-arrow/ts": "^12.0.0",
|
||||
"@neon-rs/load": "^0.0.74",
|
||||
"apache-arrow": "^12.0.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@neon-rs/cli": "^0.0.74",
|
||||
"@types/chai": "^4.3.4",
|
||||
"@types/mocha": "^10.0.1",
|
||||
"@types/node": "^18.16.2",
|
||||
@@ -26,10 +36,18 @@
|
||||
"eslint-plugin-n": "^15.7.0",
|
||||
"eslint-plugin-promise": "^6.1.1",
|
||||
"mocha": "^10.2.0",
|
||||
"openai": "^3.2.1",
|
||||
"sinon": "^15.1.0",
|
||||
"temp": "^0.9.4",
|
||||
"ts-node": "^10.9.1",
|
||||
"ts-node-dev": "^2.0.0",
|
||||
"typescript": "*"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@lancedb/vectordb-darwin-arm64": "0.1.2",
|
||||
"@lancedb/vectordb-darwin-x64": "0.1.2",
|
||||
"@lancedb/vectordb-linux-arm64-gnu": "0.1.2",
|
||||
"@lancedb/vectordb-linux-x64-gnu": "0.1.2"
|
||||
}
|
||||
},
|
||||
"node_modules/@apache-arrow/ts": {
|
||||
@@ -197,6 +215,46 @@
|
||||
"@jridgewell/sourcemap-codec": "^1.4.10"
|
||||
}
|
||||
},
|
||||
"node_modules/@lancedb/vectordb-darwin-arm64": {
|
||||
"version": "0.1.2",
|
||||
"resolved": "https://npm.pkg.github.com/download/@lancedb/vectordb-darwin-arm64/0.1.2/84d71331e03e8aaeb9fb12cdacc759dc82cfd3b0",
|
||||
"integrity": "sha512-DU6tHmmn/coSj5r5FGwTMXMQfsSSxQN1ozOl9mFUXr0aVtlx5nlA8ZY5BAF/V371yL5QzNPKtaNpogP6iw51NA==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"license": "Apache-2.0",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"darwin"
|
||||
]
|
||||
},
|
||||
"node_modules/@lancedb/vectordb-linux-arm64-gnu": {
|
||||
"version": "0.1.2",
|
||||
"resolved": "https://npm.pkg.github.com/download/@lancedb/vectordb-linux-arm64-gnu/0.1.2/d5a9d66c3969494cf3546195fb5511f9f49aa295",
|
||||
"integrity": "sha512-LZZ4KgoGqD5AzKX/utBrsxrwXq6whpUNa02tWxl/ND/601ruNi9ZUaXCTb1rSVUWJkgMR2wASk15kssyaPRSjw==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"license": "Apache-2.0",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@neon-rs/cli": {
|
||||
"version": "0.0.74",
|
||||
"resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.74.tgz",
|
||||
"integrity": "sha512-9lPmNmjej5iKKOTMPryOMubwkgMRyTWRuaq1yokASvI5mPhr2kzPN7UVjdCOjQvpunNPngR9yAHoirpjiWhUHw==",
|
||||
"dev": true,
|
||||
"bin": {
|
||||
"neon": "index.js"
|
||||
}
|
||||
},
|
||||
"node_modules/@neon-rs/load": {
|
||||
"version": "0.0.74",
|
||||
"resolved": "https://registry.npmjs.org/@neon-rs/load/-/load-0.0.74.tgz",
|
||||
"integrity": "sha512-/cPZD907UNz55yrc/ud4wDgQKtU1TvkD9jeqZWG6J4IMmZkp6zgjkQcKA8UvpkZlcpPHvc8J17sGzLFbP/LUYg=="
|
||||
},
|
||||
"node_modules/@nodelib/fs.scandir": {
|
||||
"version": "2.1.5",
|
||||
"resolved": "https://registry.npmjs.org/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz",
|
||||
@@ -232,6 +290,50 @@
|
||||
"node": ">= 8"
|
||||
}
|
||||
},
|
||||
"node_modules/@sinonjs/commons": {
|
||||
"version": "3.0.0",
|
||||
"resolved": "https://registry.npmjs.org/@sinonjs/commons/-/commons-3.0.0.tgz",
|
||||
"integrity": "sha512-jXBtWAF4vmdNmZgD5FoKsVLv3rPgDnLgPbU84LIJ3otV44vJlDRokVng5v8NFJdCf/da9legHcKaRuZs4L7faA==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"type-detect": "4.0.8"
|
||||
}
|
||||
},
|
||||
"node_modules/@sinonjs/fake-timers": {
|
||||
"version": "10.2.0",
|
||||
"resolved": "https://registry.npmjs.org/@sinonjs/fake-timers/-/fake-timers-10.2.0.tgz",
|
||||
"integrity": "sha512-OPwQlEdg40HAj5KNF8WW6q2KG4Z+cBCZb3m4ninfTZKaBmbIJodviQsDBoYMPHkOyJJMHnOJo5j2+LKDOhOACg==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"@sinonjs/commons": "^3.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@sinonjs/samsam": {
|
||||
"version": "8.0.0",
|
||||
"resolved": "https://registry.npmjs.org/@sinonjs/samsam/-/samsam-8.0.0.tgz",
|
||||
"integrity": "sha512-Bp8KUVlLp8ibJZrnvq2foVhP0IVX2CIprMJPK0vqGqgrDa0OHVKeZyBykqskkrdxV6yKBPmGasO8LVjAKR3Gew==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"@sinonjs/commons": "^2.0.0",
|
||||
"lodash.get": "^4.4.2",
|
||||
"type-detect": "^4.0.8"
|
||||
}
|
||||
},
|
||||
"node_modules/@sinonjs/samsam/node_modules/@sinonjs/commons": {
|
||||
"version": "2.0.0",
|
||||
"resolved": "https://registry.npmjs.org/@sinonjs/commons/-/commons-2.0.0.tgz",
|
||||
"integrity": "sha512-uLa0j859mMrg2slwQYdO/AkrOfmH+X6LTVmNTS9CqexuE2IvVORIkSpJLqePAbEnKJ77aMmCwr1NUZ57120Xcg==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"type-detect": "4.0.8"
|
||||
}
|
||||
},
|
||||
"node_modules/@sinonjs/text-encoding": {
|
||||
"version": "0.7.2",
|
||||
"resolved": "https://registry.npmjs.org/@sinonjs/text-encoding/-/text-encoding-0.7.2.tgz",
|
||||
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@@ -6222,6 +6716,16 @@
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@@ -6484,6 +6997,37 @@
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|
||||
"nise": "^5.1.4",
|
||||
"supports-color": "^7.2.0"
|
||||
},
|
||||
"dependencies": {
|
||||
"diff": {
|
||||
"version": "5.1.0",
|
||||
"resolved": "https://registry.npmjs.org/diff/-/diff-5.1.0.tgz",
|
||||
"integrity": "sha512-D+mk+qE8VC/PAUrlAU34N+VfXev0ghe5ywmpqrawphmVZc1bEfn56uo9qpyGp1p4xpzOHkSW4ztBd6L7Xx4ACw==",
|
||||
"dev": true
|
||||
},
|
||||
"supports-color": {
|
||||
"version": "7.2.0",
|
||||
"resolved": "https://registry.npmjs.org/supports-color/-/supports-color-7.2.0.tgz",
|
||||
"integrity": "sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
"has-flag": "^4.0.0"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"slash": {
|
||||
"version": "3.0.0",
|
||||
"resolved": "https://registry.npmjs.org/slash/-/slash-3.0.0.tgz",
|
||||
|
||||
@@ -1,15 +1,18 @@
|
||||
{
|
||||
"name": "vectordb",
|
||||
"version": "0.1.1",
|
||||
"version": "0.1.2",
|
||||
"description": " Serverless, low-latency vector database for AI applications",
|
||||
"main": "dist/index.js",
|
||||
"types": "dist/index.d.ts",
|
||||
"scripts": {
|
||||
"tsc": "tsc -b",
|
||||
"build": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json-render-diagnostics",
|
||||
"build": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json",
|
||||
"build-release": "npm run build -- --release",
|
||||
"cross-release": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cross build --message-format=json --release -p vectordb-node",
|
||||
"test": "mocha -recursive dist/test",
|
||||
"lint": "eslint src --ext .js,.ts"
|
||||
"lint": "eslint src --ext .js,.ts",
|
||||
"pack-build": "neon pack-build",
|
||||
"check-npm": "printenv && which node && which npm && npm --version"
|
||||
},
|
||||
"repository": {
|
||||
"type": "git",
|
||||
@@ -24,9 +27,11 @@
|
||||
"author": "Lance Devs",
|
||||
"license": "Apache-2.0",
|
||||
"devDependencies": {
|
||||
"@neon-rs/cli": "^0.0.74",
|
||||
"@types/chai": "^4.3.4",
|
||||
"@types/mocha": "^10.0.1",
|
||||
"@types/node": "^18.16.2",
|
||||
"@types/sinon": "^10.0.15",
|
||||
"@types/temp": "^0.9.1",
|
||||
"@typescript-eslint/eslint-plugin": "^5.59.1",
|
||||
"cargo-cp-artifact": "^0.1",
|
||||
@@ -37,6 +42,8 @@
|
||||
"eslint-plugin-n": "^15.7.0",
|
||||
"eslint-plugin-promise": "^6.1.1",
|
||||
"mocha": "^10.2.0",
|
||||
"sinon": "^15.1.0",
|
||||
"openai": "^3.2.1",
|
||||
"temp": "^0.9.4",
|
||||
"ts-node": "^10.9.1",
|
||||
"ts-node-dev": "^2.0.0",
|
||||
@@ -44,6 +51,29 @@
|
||||
},
|
||||
"dependencies": {
|
||||
"@apache-arrow/ts": "^12.0.0",
|
||||
"@neon-rs/load": "^0.0.74",
|
||||
"apache-arrow": "^12.0.0"
|
||||
},
|
||||
"os": [
|
||||
"darwin",
|
||||
"linux"
|
||||
],
|
||||
"cpu": [
|
||||
"x64",
|
||||
"arm64"
|
||||
],
|
||||
"neon": {
|
||||
"targets": {
|
||||
"x86_64-apple-darwin": "@lancedb/vectordb-darwin-x64",
|
||||
"aarch64-apple-darwin": "@lancedb/vectordb-darwin-arm64",
|
||||
"x86_64-unknown-linux-gnu": "@lancedb/vectordb-linux-x64-gnu",
|
||||
"aarch64-unknown-linux-gnu": "@lancedb/vectordb-linux-arm64-gnu"
|
||||
}
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@lancedb/vectordb-darwin-arm64": "0.1.2",
|
||||
"@lancedb/vectordb-darwin-x64": "0.1.2",
|
||||
"@lancedb/vectordb-linux-x64-gnu": "0.1.2",
|
||||
"@lancedb/vectordb-linux-arm64-gnu": "0.1.2"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -24,7 +24,7 @@ import {
|
||||
} from 'apache-arrow'
|
||||
import { type EmbeddingFunction } from './index'
|
||||
|
||||
export function convertToTable<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Table {
|
||||
export async function convertToTable<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Table> {
|
||||
if (data.length === 0) {
|
||||
throw new Error('At least one record needs to be provided')
|
||||
}
|
||||
@@ -51,7 +51,7 @@ export function convertToTable<T> (data: Array<Record<string, unknown>>, embeddi
|
||||
}
|
||||
|
||||
if (columnsKey === embeddings?.sourceColumn) {
|
||||
const vectors = embeddings.embed(values as T[])
|
||||
const vectors = await embeddings.embed(values as T[])
|
||||
const listBuilder = newVectorListBuilder()
|
||||
vectors.map(v => listBuilder.append(v))
|
||||
records.vector = listBuilder.finish().toVector()
|
||||
@@ -79,7 +79,7 @@ function newVectorListBuilder (): ListBuilder<Float32, any> {
|
||||
}
|
||||
|
||||
export async function fromRecordsToBuffer<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Buffer> {
|
||||
const table = convertToTable(data, embeddings)
|
||||
const table = await convertToTable(data, embeddings)
|
||||
const writer = RecordBatchFileWriter.writeAll(table)
|
||||
return Buffer.from(await writer.toUint8Array())
|
||||
}
|
||||
|
||||
28
node/src/embedding/embedding_function.ts
Normal file
28
node/src/embedding/embedding_function.ts
Normal file
@@ -0,0 +1,28 @@
|
||||
// Copyright 2023 Lance Developers.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
/**
|
||||
* An embedding function that automatically creates vector representation for a given column.
|
||||
*/
|
||||
export interface EmbeddingFunction<T> {
|
||||
/**
|
||||
* The name of the column that will be used as input for the Embedding Function.
|
||||
*/
|
||||
sourceColumn: string
|
||||
|
||||
/**
|
||||
* Creates a vector representation for the given values.
|
||||
*/
|
||||
embed: (data: T[]) => Promise<number[][]>
|
||||
}
|
||||
51
node/src/embedding/openai.ts
Normal file
51
node/src/embedding/openai.ts
Normal file
@@ -0,0 +1,51 @@
|
||||
// Copyright 2023 Lance Developers.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
import { type EmbeddingFunction } from '../index'
|
||||
|
||||
export class OpenAIEmbeddingFunction implements EmbeddingFunction<string> {
|
||||
private readonly _openai: any
|
||||
private readonly _modelName: string
|
||||
|
||||
constructor (sourceColumn: string, openAIKey: string, modelName: string = 'text-embedding-ada-002') {
|
||||
let openai
|
||||
try {
|
||||
// eslint-disable-next-line @typescript-eslint/no-var-requires
|
||||
openai = require('openai')
|
||||
} catch {
|
||||
throw new Error('please install openai using npm install openai')
|
||||
}
|
||||
|
||||
this.sourceColumn = sourceColumn
|
||||
const configuration = new openai.Configuration({
|
||||
apiKey: openAIKey
|
||||
})
|
||||
this._openai = new openai.OpenAIApi(configuration)
|
||||
this._modelName = modelName
|
||||
}
|
||||
|
||||
async embed (data: string[]): Promise<number[][]> {
|
||||
const response = await this._openai.createEmbedding({
|
||||
model: this._modelName,
|
||||
input: data
|
||||
})
|
||||
const embeddings: number[][] = []
|
||||
for (let i = 0; i < response.data.data.length; i++) {
|
||||
embeddings.push(response.data.data[i].embedding as number[])
|
||||
}
|
||||
return embeddings
|
||||
}
|
||||
|
||||
sourceColumn: string
|
||||
}
|
||||
@@ -19,10 +19,14 @@ import {
|
||||
Vector
|
||||
} from 'apache-arrow'
|
||||
import { fromRecordsToBuffer } from './arrow'
|
||||
import type { EmbeddingFunction } from './embedding/embedding_function'
|
||||
|
||||
// eslint-disable-next-line @typescript-eslint/no-var-requires
|
||||
const { databaseNew, databaseTableNames, databaseOpenTable, tableCreate, tableSearch, tableAdd, tableCreateVectorIndex } = require('../native.js')
|
||||
|
||||
export type { EmbeddingFunction }
|
||||
export { OpenAIEmbeddingFunction } from './embedding/openai'
|
||||
|
||||
/**
|
||||
* Connect to a LanceDB instance at the given URI
|
||||
* @param uri The uri of the database.
|
||||
@@ -135,14 +139,8 @@ export class Table<T = number[]> {
|
||||
* Creates a search query to find the nearest neighbors of the given search term
|
||||
* @param query The query search term
|
||||
*/
|
||||
search (query: T): Query {
|
||||
let queryVector: number[]
|
||||
if (this._embeddings !== undefined) {
|
||||
queryVector = this._embeddings.embed([query])[0]
|
||||
} else {
|
||||
queryVector = query as number[]
|
||||
}
|
||||
return new Query(this._tbl, queryVector)
|
||||
search (query: T): Query<T> {
|
||||
return new Query(this._tbl, query, this._embeddings)
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -228,32 +226,35 @@ export type VectorIndexParams = IvfPQIndexConfig
|
||||
/**
|
||||
* A builder for nearest neighbor queries for LanceDB.
|
||||
*/
|
||||
export class Query {
|
||||
export class Query<T = number[]> {
|
||||
private readonly _tbl: any
|
||||
private readonly _queryVector: number[]
|
||||
private readonly _query: T
|
||||
private _queryVector?: number[]
|
||||
private _limit: number
|
||||
private _refineFactor?: number
|
||||
private _nprobes: number
|
||||
private readonly _columns?: string[]
|
||||
private _filter?: string
|
||||
private _metricType?: MetricType
|
||||
private readonly _embeddings?: EmbeddingFunction<T>
|
||||
|
||||
constructor (tbl: any, queryVector: number[]) {
|
||||
constructor (tbl: any, query: T, embeddings?: EmbeddingFunction<T>) {
|
||||
this._tbl = tbl
|
||||
this._queryVector = queryVector
|
||||
this._query = query
|
||||
this._limit = 10
|
||||
this._nprobes = 20
|
||||
this._refineFactor = undefined
|
||||
this._columns = undefined
|
||||
this._filter = undefined
|
||||
this._metricType = undefined
|
||||
this._embeddings = embeddings
|
||||
}
|
||||
|
||||
/***
|
||||
* Sets the number of results that will be returned
|
||||
* @param value number of results
|
||||
*/
|
||||
limit (value: number): Query {
|
||||
limit (value: number): Query<T> {
|
||||
this._limit = value
|
||||
return this
|
||||
}
|
||||
@@ -262,7 +263,7 @@ export class Query {
|
||||
* Refine the results by reading extra elements and re-ranking them in memory.
|
||||
* @param value refine factor to use in this query.
|
||||
*/
|
||||
refineFactor (value: number): Query {
|
||||
refineFactor (value: number): Query<T> {
|
||||
this._refineFactor = value
|
||||
return this
|
||||
}
|
||||
@@ -271,7 +272,7 @@ export class Query {
|
||||
* The number of probes used. A higher number makes search more accurate but also slower.
|
||||
* @param value The number of probes used.
|
||||
*/
|
||||
nprobes (value: number): Query {
|
||||
nprobes (value: number): Query<T> {
|
||||
this._nprobes = value
|
||||
return this
|
||||
}
|
||||
@@ -280,7 +281,7 @@ export class Query {
|
||||
* A filter statement to be applied to this query.
|
||||
* @param value A filter in the same format used by a sql WHERE clause.
|
||||
*/
|
||||
filter (value: string): Query {
|
||||
filter (value: string): Query<T> {
|
||||
this._filter = value
|
||||
return this
|
||||
}
|
||||
@@ -289,7 +290,7 @@ export class Query {
|
||||
* The MetricType used for this Query.
|
||||
* @param value The metric to the. @see MetricType for the different options
|
||||
*/
|
||||
metricType (value: MetricType): Query {
|
||||
metricType (value: MetricType): Query<T> {
|
||||
this._metricType = value
|
||||
return this
|
||||
}
|
||||
@@ -298,6 +299,12 @@ export class Query {
|
||||
* Execute the query and return the results as an Array of Objects
|
||||
*/
|
||||
async execute<T = Record<string, unknown>> (): Promise<T[]> {
|
||||
if (this._embeddings !== undefined) {
|
||||
this._queryVector = (await this._embeddings.embed([this._query]))[0]
|
||||
} else {
|
||||
this._queryVector = this._query as number[]
|
||||
}
|
||||
|
||||
const buffer = await tableSearch.call(this._tbl, this)
|
||||
const data = tableFromIPC(buffer)
|
||||
return data.toArray().map((entry: Record<string, unknown>) => {
|
||||
@@ -319,21 +326,6 @@ export enum WriteMode {
|
||||
Append = 'append'
|
||||
}
|
||||
|
||||
/**
|
||||
* An embedding function that automatically creates vector representation for a given column.
|
||||
*/
|
||||
export interface EmbeddingFunction<T> {
|
||||
/**
|
||||
* The name of the column that will be used as input for the Embedding Function.
|
||||
*/
|
||||
sourceColumn: string
|
||||
|
||||
/**
|
||||
* Creates a vector representation for the given values.
|
||||
*/
|
||||
embed: (data: T[]) => number[][]
|
||||
}
|
||||
|
||||
/**
|
||||
* Distance metrics type.
|
||||
*/
|
||||
|
||||
50
node/src/test/embedding/openai.ts
Normal file
50
node/src/test/embedding/openai.ts
Normal file
@@ -0,0 +1,50 @@
|
||||
// Copyright 2023 Lance Developers.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
import { describe } from 'mocha'
|
||||
import { assert } from 'chai'
|
||||
|
||||
import { OpenAIEmbeddingFunction } from '../../embedding/openai'
|
||||
|
||||
// eslint-disable-next-line @typescript-eslint/no-var-requires
|
||||
const { OpenAIApi } = require('openai')
|
||||
// eslint-disable-next-line @typescript-eslint/no-var-requires
|
||||
const { stub } = require('sinon')
|
||||
|
||||
describe('OpenAPIEmbeddings', function () {
|
||||
const stubValue = {
|
||||
data: {
|
||||
data: [
|
||||
{
|
||||
embedding: Array(1536).fill(1.0)
|
||||
},
|
||||
{
|
||||
embedding: Array(1536).fill(2.0)
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
describe('#embed', function () {
|
||||
it('should create vector embeddings', async function () {
|
||||
const openAIStub = stub(OpenAIApi.prototype, 'createEmbedding').returns(stubValue)
|
||||
const f = new OpenAIEmbeddingFunction('text', 'sk-key')
|
||||
const vectors = await f.embed(['abc', 'def'])
|
||||
assert.isTrue(openAIStub.calledOnce)
|
||||
assert.equal(vectors.length, 2)
|
||||
assert.deepEqual(vectors[0], stubValue.data.data[0].embedding)
|
||||
assert.deepEqual(vectors[1], stubValue.data.data[1].embedding)
|
||||
})
|
||||
})
|
||||
})
|
||||
@@ -154,7 +154,7 @@ describe('LanceDB client', function () {
|
||||
['bar', [3.1, 3.2]]
|
||||
])
|
||||
|
||||
embed (data: string[]): number[][] {
|
||||
async embed (data: string[]): Promise<number[][]> {
|
||||
return data.map(datum => this._embedding_map.get(datum) ?? [0.0, 0.0])
|
||||
}
|
||||
}
|
||||
|
||||
108
notebooks/diffusiondb/datagen.py
Executable file
108
notebooks/diffusiondb/datagen.py
Executable file
@@ -0,0 +1,108 @@
|
||||
#!/usr/bin/env python
|
||||
#
|
||||
# Copyright 2023 LanceDB Developers
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""Dataset hf://poloclub/diffusiondb
|
||||
"""
|
||||
|
||||
import io
|
||||
from argparse import ArgumentParser
|
||||
from multiprocessing import Pool
|
||||
|
||||
import lance
|
||||
import lancedb
|
||||
import pyarrow as pa
|
||||
from datasets import load_dataset
|
||||
from PIL import Image
|
||||
from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast
|
||||
|
||||
MODEL_ID = "openai/clip-vit-base-patch32"
|
||||
|
||||
device = "cuda"
|
||||
|
||||
tokenizer = CLIPTokenizerFast.from_pretrained(MODEL_ID)
|
||||
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
|
||||
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
|
||||
|
||||
schema = pa.schema(
|
||||
[
|
||||
pa.field("prompt", pa.string()),
|
||||
pa.field("seed", pa.uint32()),
|
||||
pa.field("step", pa.uint16()),
|
||||
pa.field("cfg", pa.float32()),
|
||||
pa.field("sampler", pa.string()),
|
||||
pa.field("width", pa.uint16()),
|
||||
pa.field("height", pa.uint16()),
|
||||
pa.field("timestamp", pa.timestamp("s")),
|
||||
pa.field("image_nsfw", pa.float32()),
|
||||
pa.field("prompt_nsfw", pa.float32()),
|
||||
pa.field("vector", pa.list_(pa.float32(), 512)),
|
||||
pa.field("image", pa.binary()),
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
def pil_to_bytes(img) -> list[bytes]:
|
||||
buf = io.BytesIO()
|
||||
img.save(buf, format="PNG")
|
||||
return buf.getvalue()
|
||||
|
||||
|
||||
def generate_clip_embeddings(batch) -> pa.RecordBatch:
|
||||
image = processor(text=None, images=batch["image"], return_tensors="pt")[
|
||||
"pixel_values"
|
||||
].to(device)
|
||||
img_emb = model.get_image_features(image)
|
||||
batch["vector"] = img_emb.cpu().tolist()
|
||||
|
||||
with Pool() as p:
|
||||
batch["image_bytes"] = p.map(pil_to_bytes, batch["image"])
|
||||
return batch
|
||||
|
||||
|
||||
def datagen(args):
|
||||
"""Generate DiffusionDB dataset, and use CLIP model to generate image embeddings."""
|
||||
dataset = load_dataset("poloclub/diffusiondb", args.subset)
|
||||
data = []
|
||||
for b in dataset.map(
|
||||
generate_clip_embeddings, batched=True, batch_size=256, remove_columns=["image"]
|
||||
)["train"]:
|
||||
b["image"] = b["image_bytes"]
|
||||
del b["image_bytes"]
|
||||
data.append(b)
|
||||
tbl = pa.Table.from_pylist(data, schema=schema)
|
||||
return tbl
|
||||
|
||||
|
||||
def main():
|
||||
parser = ArgumentParser()
|
||||
parser.add_argument(
|
||||
"-o", "--output", metavar="DIR", help="Output lance directory", required=True
|
||||
)
|
||||
parser.add_argument(
|
||||
"-s",
|
||||
"--subset",
|
||||
choices=["2m_all", "2m_first_10k", "2m_first_100k"],
|
||||
default="2m_first_10k",
|
||||
help="subset of the hg dataset",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
batches = datagen(args)
|
||||
lance.write_dataset(batches, args.output)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
9
notebooks/diffusiondb/requirements.txt
Normal file
9
notebooks/diffusiondb/requirements.txt
Normal file
@@ -0,0 +1,9 @@
|
||||
datasets
|
||||
Pillow
|
||||
lancedb
|
||||
isort
|
||||
black
|
||||
transformers
|
||||
--index-url https://download.pytorch.org/whl/cu118
|
||||
torch
|
||||
torchvision
|
||||
240
notebooks/multimodal_search.ipynb
Normal file
240
notebooks/multimodal_search.ipynb
Normal file
@@ -0,0 +1,240 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
|
||||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
|
||||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!pip install --quiet -U lancedb\n",
|
||||
"!pip install --quiet gradio transformers torch torchvision"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import io\n",
|
||||
"import PIL\n",
|
||||
"import duckdb\n",
|
||||
"import lancedb"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## First run setup: Download data and pre-process"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<lance.dataset.LanceDataset at 0x3045db590>"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# remove null prompts\n",
|
||||
"import lance\n",
|
||||
"import pyarrow.compute as pc\n",
|
||||
"\n",
|
||||
"# download s3://eto-public/datasets/diffusiondb/small_10k.lance to this uri\n",
|
||||
"data = lance.dataset(\"~/datasets/rawdata.lance\").to_table()\n",
|
||||
"\n",
|
||||
"# First data processing and full-text-search index\n",
|
||||
"db = lancedb.connect(\"~/datasets/demo\")\n",
|
||||
"tbl = db.create_table(\"diffusiondb\", data.filter(~pc.field(\"prompt\").is_null()))\n",
|
||||
"tbl = tbl.create_fts_index([\"prompt\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create / Open LanceDB Table"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"db = lancedb.connect(\"~/datasets/demo\")\n",
|
||||
"tbl = db.open_table(\"diffusiondb\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create CLIP embedding function for the text"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast\n",
|
||||
"\n",
|
||||
"MODEL_ID = \"openai/clip-vit-base-patch32\"\n",
|
||||
"\n",
|
||||
"tokenizer = CLIPTokenizerFast.from_pretrained(MODEL_ID)\n",
|
||||
"model = CLIPModel.from_pretrained(MODEL_ID)\n",
|
||||
"processor = CLIPProcessor.from_pretrained(MODEL_ID)\n",
|
||||
"\n",
|
||||
"def embed_func(query):\n",
|
||||
" inputs = tokenizer([query], padding=True, return_tensors=\"pt\")\n",
|
||||
" text_features = model.get_text_features(**inputs)\n",
|
||||
" return text_features.detach().numpy()[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Search functions for Gradio"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def find_image_vectors(query):\n",
|
||||
" emb = embed_func(query)\n",
|
||||
" return _extract(tbl.search(emb).limit(9).to_df())\n",
|
||||
"\n",
|
||||
"def find_image_keywords(query):\n",
|
||||
" return _extract(tbl.search(query).limit(9).to_df())\n",
|
||||
"\n",
|
||||
"def find_image_sql(query):\n",
|
||||
" diffusiondb = tbl.to_lance()\n",
|
||||
" return _extract(duckdb.query(query).to_df())\n",
|
||||
"\n",
|
||||
"def _extract(df):\n",
|
||||
" image_col = \"image\"\n",
|
||||
" return [(PIL.Image.open(io.BytesIO(row[image_col])), row[\"prompt\"]) for _, row in df.iterrows()]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup Gradio interface"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Running on local URL: http://127.0.0.1:7867\n",
|
||||
"\n",
|
||||
"To create a public link, set `share=True` in `launch()`.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div><iframe src=\"http://127.0.0.1:7867/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": []
|
||||
},
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import gradio as gr\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"with gr.Blocks() as demo:\n",
|
||||
"\n",
|
||||
" with gr.Row():\n",
|
||||
" with gr.Tab(\"Embeddings\"):\n",
|
||||
" vector_query = gr.Textbox(value=\"portraits of a person\", show_label=False)\n",
|
||||
" b1 = gr.Button(\"Submit\")\n",
|
||||
" with gr.Tab(\"Keywords\"):\n",
|
||||
" keyword_query = gr.Textbox(value=\"ninja turtle\", show_label=False)\n",
|
||||
" b2 = gr.Button(\"Submit\")\n",
|
||||
" with gr.Tab(\"SQL\"):\n",
|
||||
" sql_query = gr.Textbox(value=\"SELECT * from diffusiondb WHERE image_nsfw >= 2 LIMIT 9\", show_label=False)\n",
|
||||
" b3 = gr.Button(\"Submit\")\n",
|
||||
" with gr.Row():\n",
|
||||
" gallery = gr.Gallery(\n",
|
||||
" label=\"Found images\", show_label=False, elem_id=\"gallery\"\n",
|
||||
" ).style(columns=[3], rows=[3], object_fit=\"contain\", height=\"auto\") \n",
|
||||
" \n",
|
||||
" b1.click(find_image_vectors, inputs=vector_query, outputs=gallery)\n",
|
||||
" b2.click(find_image_keywords, inputs=keyword_query, outputs=gallery)\n",
|
||||
" b3.click(find_image_sql, inputs=sql_query, outputs=gallery)\n",
|
||||
" \n",
|
||||
"demo.launch()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
@@ -253,8 +253,7 @@ def _sanitize_vector_column(data: pa.Table, vector_column_name: str) -> pa.Table
|
||||
vector_column_name: str
|
||||
The name of the vector column.
|
||||
"""
|
||||
i = data.column_names.index(vector_column_name)
|
||||
if i < 0:
|
||||
if vector_column_name not in data.column_names:
|
||||
raise ValueError(f"Missing vector column: {vector_column_name}")
|
||||
vec_arr = data[vector_column_name].combine_chunks()
|
||||
if pa.types.is_fixed_size_list(vec_arr.type):
|
||||
@@ -266,4 +265,4 @@ def _sanitize_vector_column(data: pa.Table, vector_column_name: str) -> pa.Table
|
||||
values = values.cast(pa.float32())
|
||||
list_size = len(values) / len(data)
|
||||
vec_arr = pa.FixedSizeListArray.from_arrays(values, list_size)
|
||||
return data.set_column(i, vector_column_name, vec_arr)
|
||||
return data.set_column(data.column_names.index(vector_column_name), vector_column_name, vec_arr)
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "vectordb-node"
|
||||
version = "0.1.0"
|
||||
version = "0.1.2"
|
||||
description = "Serverless, low-latency vector database for AI applications"
|
||||
license = "Apache-2.0"
|
||||
edition = "2018"
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "vectordb"
|
||||
version = "0.0.1"
|
||||
version = "0.1.2"
|
||||
edition = "2021"
|
||||
description = "Serverless, low-latency vector database for AI applications"
|
||||
license = "Apache-2.0"
|
||||
@@ -13,7 +13,7 @@ arrow-array = "37.0"
|
||||
arrow-data = "37.0"
|
||||
arrow-schema = "37.0"
|
||||
object_store = "0.5.6"
|
||||
|
||||
snafu = "0.7.4"
|
||||
lance = "0.4.17"
|
||||
tokio = { version = "1.23", features = ["rt-multi-thread"] }
|
||||
|
||||
|
||||
@@ -17,8 +17,9 @@ use std::path::Path;
|
||||
|
||||
use arrow_array::RecordBatchReader;
|
||||
use lance::io::object_store::ObjectStore;
|
||||
use snafu::prelude::*;
|
||||
|
||||
use crate::error::Result;
|
||||
use crate::error::{CreateDirSnafu, Result};
|
||||
use crate::table::Table;
|
||||
|
||||
pub struct Database {
|
||||
@@ -43,10 +44,7 @@ impl Database {
|
||||
pub async fn connect(uri: &str) -> Result<Database> {
|
||||
let object_store = ObjectStore::new(uri).await?;
|
||||
if object_store.is_local() {
|
||||
let path = Path::new(uri);
|
||||
if !path.try_exists()? {
|
||||
create_dir_all(&path)?;
|
||||
}
|
||||
Self::try_create_dir(uri).context(CreateDirSnafu { path: uri })?;
|
||||
}
|
||||
Ok(Database {
|
||||
uri: uri.to_string(),
|
||||
@@ -54,6 +52,15 @@ impl Database {
|
||||
})
|
||||
}
|
||||
|
||||
/// Try to create a local directory to store the lancedb dataset
|
||||
fn try_create_dir(path: &str) -> core::result::Result<(), std::io::Error> {
|
||||
let path = Path::new(path);
|
||||
if !path.try_exists()? {
|
||||
create_dir_all(&path)?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Get the names of all tables in the database.
|
||||
///
|
||||
/// # Returns
|
||||
|
||||
@@ -12,44 +12,50 @@
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#[derive(Debug)]
|
||||
pub enum Error {
|
||||
IO(String),
|
||||
Lance(String),
|
||||
}
|
||||
use snafu::Snafu;
|
||||
|
||||
impl std::fmt::Display for Error {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
let (catalog, message) = match self {
|
||||
Self::IO(s) => ("I/O", s.as_str()),
|
||||
Self::Lance(s) => ("Lance", s.as_str()),
|
||||
};
|
||||
write!(f, "LanceDBError({catalog}): {message}")
|
||||
}
|
||||
#[derive(Debug, Snafu)]
|
||||
#[snafu(visibility(pub(crate)))]
|
||||
pub enum Error {
|
||||
#[snafu(display("LanceDBError: Invalid table name: {name}"))]
|
||||
InvalidTableName { name: String },
|
||||
#[snafu(display("LanceDBError: Table '{name}' was not found"))]
|
||||
TableNotFound { name: String },
|
||||
#[snafu(display("LanceDBError: Table '{name}' already exists"))]
|
||||
TableAlreadyExists { name: String },
|
||||
#[snafu(display("LanceDBError: Unable to created lance dataset at {path}: {source}"))]
|
||||
CreateDir {
|
||||
path: String,
|
||||
source: std::io::Error,
|
||||
},
|
||||
#[snafu(display("LanceDBError: {message}"))]
|
||||
Store { message: String },
|
||||
#[snafu(display("LanceDBError: {message}"))]
|
||||
Lance { message: String },
|
||||
}
|
||||
|
||||
pub type Result<T> = std::result::Result<T, Error>;
|
||||
|
||||
impl From<std::io::Error> for Error {
|
||||
fn from(e: std::io::Error) -> Self {
|
||||
Self::IO(e.to_string())
|
||||
}
|
||||
}
|
||||
|
||||
impl From<lance::Error> for Error {
|
||||
fn from(e: lance::Error) -> Self {
|
||||
Self::Lance(e.to_string())
|
||||
Self::Lance {
|
||||
message: e.to_string(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl From<object_store::Error> for Error {
|
||||
fn from(e: object_store::Error) -> Self {
|
||||
Self::IO(e.to_string())
|
||||
Self::Store {
|
||||
message: e.to_string(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl From<object_store::path::Error> for Error {
|
||||
fn from(e: object_store::path::Error) -> Self {
|
||||
Self::IO(e.to_string())
|
||||
Self::Store {
|
||||
message: e.to_string(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -18,8 +18,9 @@ use std::sync::Arc;
|
||||
use arrow_array::{Float32Array, RecordBatchReader};
|
||||
use lance::dataset::{Dataset, WriteMode, WriteParams};
|
||||
use lance::index::IndexType;
|
||||
use snafu::prelude::*;
|
||||
|
||||
use crate::error::{Error, Result};
|
||||
use crate::error::{Error, InvalidTableNameSnafu, Result};
|
||||
use crate::index::vector::VectorIndexBuilder;
|
||||
use crate::query::Query;
|
||||
|
||||
@@ -27,6 +28,7 @@ pub const VECTOR_COLUMN_NAME: &str = "vector";
|
||||
pub const LANCE_FILE_EXTENSION: &str = "lance";
|
||||
|
||||
/// A table in a LanceDB database.
|
||||
#[derive(Debug)]
|
||||
pub struct Table {
|
||||
name: String,
|
||||
uri: String,
|
||||
@@ -57,9 +59,16 @@ impl Table {
|
||||
let uri = table_uri
|
||||
.as_path()
|
||||
.to_str()
|
||||
.ok_or(Error::IO(format!("Invalid table name: {}", name)))?;
|
||||
.context(InvalidTableNameSnafu { name })?;
|
||||
|
||||
let dataset = Dataset::open(&uri).await?;
|
||||
let dataset = Dataset::open(&uri).await.map_err(|e| match e {
|
||||
lance::Error::DatasetNotFound { .. } => Error::TableNotFound {
|
||||
name: name.to_string(),
|
||||
},
|
||||
e => Error::Lance {
|
||||
message: e.to_string(),
|
||||
},
|
||||
})?;
|
||||
Ok(Table {
|
||||
name: name.to_string(),
|
||||
uri: uri.to_string(),
|
||||
@@ -88,14 +97,22 @@ impl Table {
|
||||
let uri = table_uri
|
||||
.as_path()
|
||||
.to_str()
|
||||
.ok_or(Error::IO(format!("Invalid table name: {}", name)))?
|
||||
.context(InvalidTableNameSnafu { name })?
|
||||
.to_string();
|
||||
let dataset =
|
||||
Arc::new(Dataset::write(&mut batches, &uri, Some(WriteParams::default())).await?);
|
||||
let dataset = Dataset::write(&mut batches, &uri, Some(WriteParams::default()))
|
||||
.await
|
||||
.map_err(|e| match e {
|
||||
lance::Error::DatasetAlreadyExists { .. } => Error::TableAlreadyExists {
|
||||
name: name.to_string(),
|
||||
},
|
||||
e => Error::Lance {
|
||||
message: e.to_string(),
|
||||
},
|
||||
})?;
|
||||
Ok(Table {
|
||||
name: name.to_string(),
|
||||
uri,
|
||||
dataset,
|
||||
dataset: Arc::new(dataset),
|
||||
})
|
||||
}
|
||||
|
||||
@@ -178,15 +195,6 @@ mod tests {
|
||||
use super::*;
|
||||
use crate::index::vector::IvfPQIndexBuilder;
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_new_table_not_exists() {
|
||||
let tmp_dir = tempdir().unwrap();
|
||||
let uri = tmp_dir.path().to_str().unwrap();
|
||||
|
||||
let table = Table::open(&uri, "test").await;
|
||||
assert!(table.is_err());
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_open() {
|
||||
let tmp_dir = tempdir().unwrap();
|
||||
@@ -203,6 +211,14 @@ mod tests {
|
||||
assert_eq!(table.name, "test")
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_open_not_found() {
|
||||
let tmp_dir = tempdir().unwrap();
|
||||
let uri = tmp_dir.path().to_str().unwrap();
|
||||
let table = Table::open(uri, "test").await;
|
||||
assert!(matches!(table.unwrap_err(), Error::TableNotFound { .. }));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_object_store_path() {
|
||||
use std::path::Path as StdPath;
|
||||
@@ -211,6 +227,23 @@ mod tests {
|
||||
assert_eq!(c.to_str().unwrap(), "s3://bucket/path/to/file/subfile");
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_create_already_exists() {
|
||||
let tmp_dir = tempdir().unwrap();
|
||||
let uri = tmp_dir.path().to_str().unwrap();
|
||||
|
||||
let batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
|
||||
let schema = batches.schema().clone();
|
||||
Table::create(&uri, "test", batches).await.unwrap();
|
||||
|
||||
let batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
|
||||
let result = Table::create(&uri, "test", batches).await;
|
||||
assert!(matches!(
|
||||
result.unwrap_err(),
|
||||
Error::TableAlreadyExists { .. }
|
||||
));
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_add() {
|
||||
let tmp_dir = tempdir().unwrap();
|
||||
|
||||
Reference in New Issue
Block a user