检索问答

RetrievalQAChain 是将 Retriever 和 QA 链（上文中所述)组合起来的链。它用于从 Retriever 检索文档，然后使用 QA 链根据检索到的文档回答问题。

使用

在下面的示例中，我们使用 VectorStore 作为 Retriever。默认情况下，将使用 StuffDocumentsChain 作为 QA 链。

import { OpenAI } from "langchain/llms/openai";
import { RetrievalQAChain } from "langchain/chains";
import { HNSWLib } from "langchain/vectorstores/hnswlib";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import * as fs from "fs";

export const run = async () => {
  // Initialize the LLM to use to answer the question.
  const model = new OpenAI({});
  const text = fs.readFileSync("state_of_the_union.txt", "utf8");
  const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
  const docs = await textSplitter.createDocuments([text]);

  // Create a vector store from the documents.
  const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());

  // Create a chain that uses the OpenAI LLM and HNSWLib vector store.
  const chain = RetrievalQAChain.fromLLM(model, vectorStore.asRetriever());
  const res = await chain.call({
    query: "What did the president say about Justice Breyer?",
  });
  console.log({ res });
  /*
  {
    res: {
      text: 'The president said that Justice Breyer was an Army veteran, Constitutional scholar,
      and retiring Justice of the United States Supreme Court and thanked him for his service.'
    }
  }
  */
};

使用自定义的 QA 链

在下面的示例中，我们使用 VectorStore 作为 Retriever，并使用 RefineDocumentsChain 作为 QA 链。

import { OpenAI } from "langchain/llms/openai";
import { RetrievalQAChain, loadQARefineChain } from "langchain/chains";
import { HNSWLib } from "langchain/vectorstores/hnswlib";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import * as fs from "fs";

// Initialize the LLM to use to answer the question.
const model = new OpenAI({});
const text = fs.readFileSync("state_of_the_union.txt", "utf8");
const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
const docs = await textSplitter.createDocuments([text]);

// Create a vector store from the documents.
const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());

// Create a chain that uses a Refine chain and HNSWLib vector store.
const chain = new RetrievalQAChain({
  combineDocumentsChain: loadQARefineChain(model),
  retriever: vectorStore.asRetriever(),
});
const res = await chain.call({
  query: "What did the president say about Justice Breyer?",
});
console.log({ res });
/*
{
  res: {
    output_text: '\n' +
      '\n' +
      "The president said that Justice Breyer has dedicated his life to serve his country, and thanked him for his service. He also said that Judge Ketanji Brown Jackson will continue Justice Breyer's legacy of excellence, emphasizing the importance of protecting the rights of citizens, especially women, LGBTQ+ Americans, and access to healthcare. He also expressed his commitment to supporting the younger transgender Americans in America and ensuring they are able to reach their full potential, offering a Unity Agenda for the Nation to beat the opioid epidemic and increase funding for prevention, treatment, harm reduction, and recovery."
  }
}
*/