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        *   [Retrieval augmented generation (RAG)](/v0.1/docs/expression_language/cookbook/retrieval/)
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*   [LangChain Expression Language](/v0.1/docs/expression_language/)
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*   Retrieval augmented generation (RAG)

RAG
===

Let's now look at adding in a retrieval step to a prompt and an LLM, which adds up to a "retrieval-augmented generation" chain:

Interactive tutorial

The screencast below interactively walks through an example. You can update and run the code as it's being written in the video!

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai @langchain/community hnswlib-node

    yarn add @langchain/openai @langchain/community hnswlib-node

    pnpm add @langchain/openai @langchain/community hnswlib-node

    import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";import { formatDocumentsAsString } from "langchain/util/document";import { PromptTemplate } from "@langchain/core/prompts";import {  RunnableSequence,  RunnablePassthrough,} from "@langchain/core/runnables";import { StringOutputParser } from "@langchain/core/output_parsers";const model = new ChatOpenAI({});const vectorStore = await HNSWLib.fromTexts(  ["mitochondria is the powerhouse of the cell"],  [{ id: 1 }],  new OpenAIEmbeddings());const retriever = vectorStore.asRetriever();const prompt =  PromptTemplate.fromTemplate(`Answer the question based only on the following context:{context}Question: {question}`);const chain = RunnableSequence.from([  {    context: retriever.pipe(formatDocumentsAsString),    question: new RunnablePassthrough(),  },  prompt,  model,  new StringOutputParser(),]);const result = await chain.invoke("What is the powerhouse of the cell?");console.log(result);/*  "The powerhouse of the cell is the mitochondria."*/

#### API Reference:

*   [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [HNSWLib](https://api.js.langchain.com/classes/langchain_community_vectorstores_hnswlib.HNSWLib.html) from `@langchain/community/vectorstores/hnswlib`
*   [formatDocumentsAsString](https://api.js.langchain.com/functions/langchain_util_document.formatDocumentsAsString.html) from `langchain/util/document`
*   [PromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts`
*   [RunnableSequence](https://api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables`
*   [RunnablePassthrough](https://api.js.langchain.com/classes/langchain_core_runnables.RunnablePassthrough.html) from `@langchain/core/runnables`
*   [StringOutputParser](https://api.js.langchain.com/classes/langchain_core_output_parsers.StringOutputParser.html) from `@langchain/core/output_parsers`

Conversational Retrieval Chain[​](#conversational-retrieval-chain "Direct link to Conversational Retrieval Chain")
------------------------------------------------------------------------------------------------------------------

Because `RunnableSequence.from` and `runnable.pipe` both accept runnable-like objects, including single-argument functions, we can add in conversation history via a formatting function. This allows us to recreate the popular `ConversationalRetrievalQAChain` to "chat with data":

Interactive tutorial

The screencast below interactively walks through an example. You can update and run the code as it's being written in the video!

    import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";import { formatDocumentsAsString } from "langchain/util/document";import { PromptTemplate } from "@langchain/core/prompts";import {  RunnableSequence,  RunnablePassthrough,} from "@langchain/core/runnables";import { StringOutputParser } from "@langchain/core/output_parsers";const model = new ChatOpenAI({});const condenseQuestionTemplate = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.Chat History:{chat_history}Follow Up Input: {question}Standalone question:`;const CONDENSE_QUESTION_PROMPT = PromptTemplate.fromTemplate(  condenseQuestionTemplate);const answerTemplate = `Answer the question based only on the following context:{context}Question: {question}`;const ANSWER_PROMPT = PromptTemplate.fromTemplate(answerTemplate);const formatChatHistory = (chatHistory: [string, string][]) => {  const formattedDialogueTurns = chatHistory.map(    (dialogueTurn) => `Human: ${dialogueTurn[0]}\nAssistant: ${dialogueTurn[1]}`  );  return formattedDialogueTurns.join("\n");};const vectorStore = await HNSWLib.fromTexts(  [    "mitochondria is the powerhouse of the cell",    "mitochondria is made of lipids",  ],  [{ id: 1 }, { id: 2 }],  new OpenAIEmbeddings());const retriever = vectorStore.asRetriever();type ConversationalRetrievalQAChainInput = {  question: string;  chat_history: [string, string][];};const standaloneQuestionChain = RunnableSequence.from([  {    question: (input: ConversationalRetrievalQAChainInput) => input.question,    chat_history: (input: ConversationalRetrievalQAChainInput) =>      formatChatHistory(input.chat_history),  },  CONDENSE_QUESTION_PROMPT,  model,  new StringOutputParser(),]);const answerChain = RunnableSequence.from([  {    context: retriever.pipe(formatDocumentsAsString),    question: new RunnablePassthrough(),  },  ANSWER_PROMPT,  model,]);const conversationalRetrievalQAChain =  standaloneQuestionChain.pipe(answerChain);const result1 = await conversationalRetrievalQAChain.invoke({  question: "What is the powerhouse of the cell?",  chat_history: [],});console.log(result1);/*  AIMessage { content: "The powerhouse of the cell is the mitochondria." }*/const result2 = await conversationalRetrievalQAChain.invoke({  question: "What are they made out of?",  chat_history: [    [      "What is the powerhouse of the cell?",      "The powerhouse of the cell is the mitochondria.",    ],  ],});console.log(result2);/*  AIMessage { content: "Mitochondria are made out of lipids." }*/

#### API Reference:

*   [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [HNSWLib](https://api.js.langchain.com/classes/langchain_community_vectorstores_hnswlib.HNSWLib.html) from `@langchain/community/vectorstores/hnswlib`
*   [formatDocumentsAsString](https://api.js.langchain.com/functions/langchain_util_document.formatDocumentsAsString.html) from `langchain/util/document`
*   [PromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts`
*   [RunnableSequence](https://api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables`
*   [RunnablePassthrough](https://api.js.langchain.com/classes/langchain_core_runnables.RunnablePassthrough.html) from `@langchain/core/runnables`
*   [StringOutputParser](https://api.js.langchain.com/classes/langchain_core_output_parsers.StringOutputParser.html) from `@langchain/core/output_parsers`

Note that the individual chains we created are themselves `Runnables` and can therefore be piped into each other.

* * *

#### Help us out by providing feedback on this documentation page:

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](/v0.1/docs/expression_language/cookbook/multiple_chains/)[

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](/v0.1/docs/expression_language/cookbook/sql_db/)

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