Documents

These are the core chains for working with Documents. They are useful for summarizing documents, answering questions over documents, extracting information from documents, and more.

These chains are all loaded in a similar way:

tip

See this section for general instructions on installing integration packages.

npm

npm install @langchain/openai

Yarn

yarn add @langchain/openai

pnpm

pnpm add @langchain/openai

import { OpenAI } from "@langchain/openai";
import {
  loadQAStuffChain,
  loadQAMapReduceChain,
  loadQARefineChain,
} from "langchain/chains";
import { Document } from "langchain/document";

// This first example uses the `StuffDocumentsChain`.
const llmA = new OpenAI({});
const chainA = loadQAStuffChain(llmA);
const docs = [
  new Document({ pageContent: "Harrison went to Harvard." }),
  new Document({ pageContent: "Ankush went to Princeton." }),
];
const resA = await chainA.call({
  input_documents: docs,
  question: "Where did Harrison go to college?",
});
console.log({ resA });
// { resA: { text: ' Harrison went to Harvard.' } }

// This second example uses the `MapReduceChain`.
// Optionally limit the number of concurrent requests to the language model.
const llmB = new OpenAI({ maxConcurrency: 10 });
const chainB = loadQAMapReduceChain(llmB);
const resB = await chainB.call({
  input_documents: docs,
  question: "Where did Harrison go to college?",
});
console.log({ resB });
// { resB: { text: ' Harrison went to Harvard.' } }