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VectorStore Agent Toolkit

tip

See this section for general instructions on installing integration packages.

npm install @langchain/openai @langchain/community

This example shows how to load and use an agent with a vectorstore toolkit.

import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import * as fs from "fs";
import {
  VectorStoreToolkit,
  createVectorStoreAgent,
  VectorStoreInfo,
} from "langchain/agents";

const model = new OpenAI({ temperature: 0 });
/* Load in the file we want to do question answering over */
const text = fs.readFileSync("state_of_the_union.txt", "utf8");
/* Split the text into chunks using character, not token, size */
const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
const docs = await textSplitter.createDocuments([text]);
/* Create the vectorstore */
const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());

/* Create the agent */
const vectorStoreInfo: VectorStoreInfo = {
  name: "state_of_union_address",
  description: "the most recent state of the Union address",
  vectorStore,
};

const toolkit = new VectorStoreToolkit(vectorStoreInfo, model);
const agent = createVectorStoreAgent(model, toolkit);

const input =
  "What did biden say about Ketanji Brown Jackson is the state of the union address?";
console.log(`Executing: ${input}`);

const result = await agent.invoke({ input });
console.log(`Got output ${result.output}`);
console.log(
  `Got intermediate steps ${JSON.stringify(result.intermediateSteps, null, 2)}`
);

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