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vectordb-js

v1.0.1

Published

用typescript实现的vector db

Downloads

40

Readme

vertor store

用typescript实现的vector db

为什么会有这个东西呢? 因为在做公司内部的文档机器人的时候,我悲剧的发现内部npm没有langchain 因此只能蛋疼的手搓一个vector store以供迁移

优势

在公司的开发中,如果要引入GPT等工具做增效工具,我们不可避免地需要考虑信安等因素

大部分公司的解决方案都是内部封装一个GPT的api,由信安部门做过滤

但是当我们使用这种api搭建基于vector db的文档机器人这类的产品时,就会遇到一个问题,那就是目前流行的提供此类功能的工具库大多是高度集成的(此处点名langchain),这意味着我们很难把其内部调用的api换成公司内部的api

本项目提出的背景也是基于此,本项目将获取embedding与构建vector store剥离开来,方便受限情况下的文档机器人的搭建

使用

  1. npm i vectordb-js
  2. 接入自己编写的获取openai embedding结果的函数,以azure openai版本为例:
    import { vectorStore } from "vectordb-js";
     import { OpenAIClient, AzureKeyCredential } from "@azure/openai";
     import { ISplitedDocument,IVector } from "./types";
     require("dotenv").config();
    
     const endpoint = process.env.AZURE_OPENAI_ENDPOINT || "";
     const azureApiKey = process.env.AZURE_OPENAI_KEY || "";
    
     const DOC_PATH = "documents";
    
     const client = new OpenAIClient(endpoint, new AzureKeyCredential(azureApiKey));
     const deploymentId = "thy-openai-embedding";
    
     async function openaiEmbedding(
         splitDocs: ISplitedDocument[]
     ): Promise<IVector[]> {
         const res = await client.getEmbeddings(
             deploymentId,
             splitDocs.map((doc) => doc.content)
         );
         const vectors: IVector[] = res.data.map((vector, idx) => {
             return {
                 embedding: vector.embedding,
                 metadata: {
                     path: splitDocs[idx].metaData.path,
                     startIndex: splitDocs[idx].metaData.startIndex,
                     endIndex: splitDocs[idx].metaData.endIndex,
                 },
             };
         });
         return vectors;
     }
    
     async function main(queryQuestion: string) {
     const openaiVectorStore = await vectorStore({
         docPath: DOC_PATH,
         embeddingFunc: openaiEmbedding,
     });
     const inputQuery = await client.getEmbeddings(deploymentId, [
         queryQuestion,
     ]);
    
     const queryVector = inputQuery.data[0].embedding;
     const res2 = openaiVectorStore.query(queryVector, 2);
    
     console.log(res2);
     }
    
     main("线程与进程的区别是什么?");