mirror of https://github.com/hwchase17/langchain
Add dashvector vectorstore (#9163)
## Description Add `Dashvector` vectorstore for langchain - [dashvector quick start](https://help.aliyun.com/document_detail/2510223.html) - [dashvector package description](https://pypi.org/project/dashvector/) ## How to use ```python from langchain.vectorstores.dashvector import DashVector dashvector = DashVector.from_documents(docs, embeddings) ``` --------- Co-authored-by: smallrain.xuxy <smallrain.xuxy@alibaba-inc.com> Co-authored-by: Bagatur <baskaryan@gmail.com>pull/9278/head
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# DashVector
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> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.
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This document demonstrates to leverage DashVector within the LangChain ecosystem. In particular, it shows how to install DashVector, and how to use it as a VectorStore plugin in LangChain.
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It is broken into two parts: installation and setup, and then references to specific DashVector wrappers.
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## Installation and Setup
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Install the Python SDK:
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```bash
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pip install dashvector
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```
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## VectorStore
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A DashVector Collection is wrapped as a familiar VectorStore for native usage within LangChain,
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which allows it to be readily used for various scenarios, such as semantic search or example selection.
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You may import the vectorstore by:
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```python
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from langchain.vectorstores import DashVector
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```
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For a detailed walkthrough of the DashVector wrapper, please refer to [this notebook](/docs/integrations/vectorstores/dashvector.html)
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# DashVector\n",
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"\n",
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"> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.\n",
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"\n",
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"This notebook shows how to use functionality related to the `DashVector` vector database.\n",
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"\n",
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"To use DashVector, you must have an API key.\n",
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"Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Install"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"!pip install dashvector dashscope"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"We want to use `DashScopeEmbeddings` so we also have to get the Dashscope API Key."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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},
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"ExecuteTime": {
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"end_time": "2023-08-11T10:37:15.091585Z",
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"start_time": "2023-08-11T10:36:51.859753Z"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ[\"DASHVECTOR_API_KEY\"] = getpass.getpass(\"DashVector API Key:\")\n",
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"os.environ[\"DASHSCOPE_API_KEY\"] = getpass.getpass(\"DashScope API Key:\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"pycharm": {
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"name": "#%%\n",
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"is_executing": true
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},
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"ExecuteTime": {
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"end_time": "2023-08-11T10:42:30.243460Z",
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"start_time": "2023-08-11T10:42:27.783785Z"
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}
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings.dashscope import DashScopeEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import DashVector"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"pycharm": {
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"is_executing": true,
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"name": "#%%\n"
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},
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"ExecuteTime": {
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"end_time": "2023-08-11T10:42:30.391580Z",
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"start_time": "2023-08-11T10:42:30.249021Z"
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}
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},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"\n",
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"loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = DashScopeEmbeddings()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"We can create DashVector from documents."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"dashvector = DashVector.from_documents(docs, embeddings)\n",
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"\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = dashvector.similarity_search(query)\n",
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"print(docs)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"We can add texts with meta datas and ids, and search with meta filter."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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},
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"ExecuteTime": {
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"end_time": "2023-08-11T10:42:51.641309Z",
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"start_time": "2023-08-11T10:42:51.132109Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[Document(page_content='baz', metadata={'key': 2})]\n"
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]
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}
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],
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"source": [
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"texts = [\"foo\", \"bar\", \"baz\"]\n",
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"metadatas = [{\"key\": i} for i in range(len(texts))]\n",
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"ids = [\"0\", \"1\", \"2\"]\n",
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"\n",
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"dashvector.add_texts(texts, metadatas=metadatas, ids=ids)\n",
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"\n",
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"docs = dashvector.similarity_search(\"foo\", filter=\"key = 2\")\n",
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"print(docs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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"""Wrapper around DashVector vector database."""
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from __future__ import annotations
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import logging
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import uuid
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from typing import (
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Any,
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Iterable,
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List,
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Optional,
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Tuple,
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)
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import numpy as np
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.utils import get_from_env
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from langchain.vectorstores.base import VectorStore
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from langchain.vectorstores.utils import maximal_marginal_relevance
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logger = logging.getLogger(__name__)
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class DashVector(VectorStore):
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"""Wrapper around DashVector vector database.
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To use, you should have the ``dashvector`` python package installed.
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Example:
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.. code-block:: python
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from langchain.vectorstores import dashvector
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from langchain.embeddings.openai import OpenAIEmbeddings
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import dashvector
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client = dashvector.Client.init(api_key="***")
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client.create("langchain")
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collection = client.get("langchain")
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embeddings = OpenAIEmbeddings()
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vectorstore = Dashvector(collection, embeddings.embed_query, "text")
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"""
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def __init__(
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self,
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collection: Any,
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embedding: Embeddings,
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text_field: str,
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):
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"""Initialize with DashVector collection."""
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try:
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import dashvector
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except ImportError:
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raise ValueError(
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"Could not import dashvector python package. "
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"Please install it with `pip install dashvector`."
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)
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if not isinstance(collection, dashvector.Collection):
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raise ValueError(
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f"collection should be an instance of dashvector.Collection, "
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f"bug got {type(collection)}"
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)
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self._collection = collection
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self._embedding = embedding
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self._text_field = text_field
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def _similarity_search_with_score_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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filter: Optional[str] = None,
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query vector, along with scores"""
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# query by vector
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ret = self._collection.query(embedding, topk=k, filter=filter)
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if not ret:
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raise ValueError(
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f"Fail to query docs by vector, error {self._collection.message}"
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)
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docs = []
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for doc in ret:
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metadata = doc.fields
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text = metadata.pop(self._text_field)
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score = doc.score
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docs.append((Document(page_content=text, metadata=metadata), score))
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return docs
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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ids: Optional[List[str]] = None,
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batch_size: int = 25,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embeddings and add to the vectorstore.
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Args:
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texts: Iterable of strings to add to the vectorstore.
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metadatas: Optional list of metadatas associated with the texts.
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ids: Optional list of ids associated with the texts.
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batch_size: Optional batch size to upsert docs.
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kwargs: vectorstore specific parameters
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Returns:
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List of ids from adding the texts into the vectorstore.
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"""
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ids = ids or [str(uuid.uuid4().hex) for _ in texts]
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text_list = list(texts)
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for i in range(0, len(text_list), batch_size):
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# batch end
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end = min(i + batch_size, len(text_list))
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batch_texts = text_list[i:end]
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batch_ids = ids[i:end]
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batch_embeddings = self._embedding.embed_documents(list(batch_texts))
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# batch metadatas
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if metadatas:
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batch_metadatas = metadatas[i:end]
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else:
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batch_metadatas = [{} for _ in range(i, end)]
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for metadata, text in zip(batch_metadatas, batch_texts):
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metadata[self._text_field] = text
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# batch upsert to collection
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docs = list(zip(batch_ids, batch_embeddings, batch_metadatas))
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ret = self._collection.upsert(docs)
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if not ret:
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raise ValueError(
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f"Fail to upsert docs to dashvector vector database,"
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f"Error: {ret.message}"
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)
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return ids
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def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool:
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"""Delete by vector ID.
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Args:
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ids: List of ids to delete.
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Returns:
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True if deletion is successful,
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False otherwise.
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"""
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return bool(self._collection.delete(ids))
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def similarity_search(
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self,
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query: str,
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k: int = 4,
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filter: Optional[str] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs most similar to query.
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Args:
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query: Text to search documents similar to.
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k: Number of documents to return. Default to 4.
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filter: Doc fields filter conditions that meet the SQL where clause
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specification.
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Returns:
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List of Documents most similar to the query text.
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"""
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docs_and_scores = self.similarity_search_with_relevance_scores(query, k, filter)
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return [doc for doc, _ in docs_and_scores]
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def similarity_search_with_relevance_scores(
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self,
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query: str,
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k: int = 4,
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filter: Optional[str] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query text , alone with relevance scores.
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Less is more similar, more is more dissimilar.
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Args:
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query: input text
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k: Number of Documents to return. Defaults to 4.
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filter: Doc fields filter conditions that meet the SQL where clause
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specification.
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Returns:
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List of Tuples of (doc, similarity_score)
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"""
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embedding = self._embedding.embed_query(query)
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return self._similarity_search_with_score_by_vector(
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embedding, k=k, filter=filter
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)
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def similarity_search_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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filter: Optional[str] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs most similar to embedding vector.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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filter: Doc fields filter conditions that meet the SQL where clause
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specification.
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Returns:
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List of Documents most similar to the query vector.
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"""
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docs_and_scores = self._similarity_search_with_score_by_vector(
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embedding, k, filter
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)
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return [doc for doc, _ in docs_and_scores]
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def max_marginal_relevance_search(
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self,
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query: str,
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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filter: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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filter: Doc fields filter conditions that meet the SQL where clause
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specification.
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Returns:
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List of Documents selected by maximal marginal relevance.
|
||||
"""
|
||||
embedding = self._embedding.embed_query(query)
|
||||
return self.max_marginal_relevance_search_by_vector(
|
||||
embedding, k, fetch_k, lambda_mult, filter
|
||||
)
|
||||
|
||||
def max_marginal_relevance_search_by_vector(
|
||||
self,
|
||||
embedding: List[float],
|
||||
k: int = 4,
|
||||
fetch_k: int = 20,
|
||||
lambda_mult: float = 0.5,
|
||||
filter: Optional[dict] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Return docs selected using the maximal marginal relevance.
|
||||
|
||||
Maximal marginal relevance optimizes for similarity to query AND diversity
|
||||
among selected documents.
|
||||
|
||||
Args:
|
||||
embedding: Embedding to look up documents similar to.
|
||||
k: Number of Documents to return. Defaults to 4.
|
||||
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
|
||||
lambda_mult: Number between 0 and 1 that determines the degree
|
||||
of diversity among the results with 0 corresponding
|
||||
to maximum diversity and 1 to minimum diversity.
|
||||
Defaults to 0.5.
|
||||
filter: Doc fields filter conditions that meet the SQL where clause
|
||||
specification.
|
||||
|
||||
Returns:
|
||||
List of Documents selected by maximal marginal relevance.
|
||||
"""
|
||||
|
||||
# query by vector
|
||||
ret = self._collection.query(
|
||||
embedding, topk=fetch_k, filter=filter, include_vector=True
|
||||
)
|
||||
if not ret:
|
||||
raise ValueError(
|
||||
f"Fail to query docs by vector, error {self._collection.message}"
|
||||
)
|
||||
|
||||
candidate_embeddings = [doc.vector for doc in ret]
|
||||
mmr_selected = maximal_marginal_relevance(
|
||||
np.array(embedding), candidate_embeddings, lambda_mult, k
|
||||
)
|
||||
|
||||
metadatas = [ret.output[i].fields for i in mmr_selected]
|
||||
return [
|
||||
Document(page_content=metadata.pop(self._text_field), metadata=metadata)
|
||||
for metadata in metadatas
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def from_texts(
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
dashvector_api_key: Optional[str] = None,
|
||||
collection_name: str = "langchain",
|
||||
text_field: str = "text",
|
||||
batch_size: int = 25,
|
||||
ids: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> DashVector:
|
||||
"""Return DashVector VectorStore initialized from texts and embeddings.
|
||||
|
||||
This is the quick way to get started with dashvector vector store.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.vectorstores import DashVector
|
||||
from langchain.embeddings import OpenAIEmbeddings
|
||||
import dashvector
|
||||
|
||||
embeddings = OpenAIEmbeddings()
|
||||
dashvector = DashVector.from_documents(
|
||||
docs,
|
||||
embeddings,
|
||||
dashvector_api_key="{DASHVECTOR_API_KEY}"
|
||||
)
|
||||
"""
|
||||
try:
|
||||
import dashvector
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import dashvector python package. "
|
||||
"Please install it with `pip install dashvector`."
|
||||
)
|
||||
|
||||
dashvector_api_key = dashvector_api_key or get_from_env(
|
||||
"dashvector_api_key", "DASHVECTOR_API_KEY"
|
||||
)
|
||||
|
||||
dashvector_client = dashvector.Client(api_key=dashvector_api_key)
|
||||
dashvector_client.delete(collection_name)
|
||||
collection = dashvector_client.get(collection_name)
|
||||
if not collection:
|
||||
dim = len(embedding.embed_query(texts[0]))
|
||||
# create collection if not existed
|
||||
resp = dashvector_client.create(collection_name, dimension=dim)
|
||||
if resp:
|
||||
collection = dashvector_client.get(collection_name)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Fail to create collection. " f"Error: {resp.message}."
|
||||
)
|
||||
|
||||
dashvector_vector_db = cls(collection, embedding, text_field)
|
||||
dashvector_vector_db.add_texts(texts, metadatas, ids, batch_size)
|
||||
return dashvector_vector_db
|
@ -0,0 +1,75 @@
|
||||
from time import sleep
|
||||
|
||||
from langchain.schema import Document
|
||||
from langchain.vectorstores import DashVector
|
||||
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
|
||||
|
||||
texts = ["foo", "bar", "baz"]
|
||||
ids = ["1", "2", "3"]
|
||||
|
||||
|
||||
def test_dashvector_from_texts() -> None:
|
||||
dashvector = DashVector.from_texts(
|
||||
texts=texts,
|
||||
embedding=FakeEmbeddings(),
|
||||
ids=ids,
|
||||
)
|
||||
|
||||
# the vector insert operation is async by design, we wait here a bit for the
|
||||
# insertion to complete.
|
||||
sleep(0.5)
|
||||
output = dashvector.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
def test_dashvector_with_text_with_metadatas() -> None:
|
||||
metadatas = [{"meta": i} for i in range(len(texts))]
|
||||
dashvector = DashVector.from_texts(
|
||||
texts=texts,
|
||||
embedding=FakeEmbeddings(),
|
||||
metadatas=metadatas,
|
||||
ids=ids,
|
||||
)
|
||||
|
||||
# the vector insert operation is async by design, we wait here a bit for the
|
||||
# insertion to complete.
|
||||
sleep(0.5)
|
||||
output = dashvector.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo", metadata={"meta": 0})]
|
||||
|
||||
|
||||
def test_dashvector_search_with_filter() -> None:
|
||||
metadatas = [{"meta": i} for i in range(len(texts))]
|
||||
dashvector = DashVector.from_texts(
|
||||
texts=texts,
|
||||
embedding=FakeEmbeddings(),
|
||||
metadatas=metadatas,
|
||||
ids=ids,
|
||||
)
|
||||
|
||||
# the vector insert operation is async by design, we wait here a bit for the
|
||||
# insertion to complete.
|
||||
sleep(0.5)
|
||||
output = dashvector.similarity_search("foo", filter="meta=2")
|
||||
assert output == [Document(page_content="baz", metadata={"meta": 2})]
|
||||
|
||||
|
||||
def test_dashvector_search_with_scores() -> None:
|
||||
dashvector = DashVector.from_texts(
|
||||
texts=texts,
|
||||
embedding=FakeEmbeddings(),
|
||||
ids=ids,
|
||||
)
|
||||
|
||||
# the vector insert operation is async by design, we wait here a bit for the
|
||||
# insertion to complete.
|
||||
sleep(0.5)
|
||||
output = dashvector.similarity_search_with_relevance_scores("foo")
|
||||
docs, scores = zip(*output)
|
||||
|
||||
assert scores[0] < scores[1] < scores[2]
|
||||
assert list(docs) == [
|
||||
Document(page_content="foo"),
|
||||
Document(page_content="bar"),
|
||||
Document(page_content="baz"),
|
||||
]
|
Loading…
Reference in New Issue