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*   [Introduction](/v0.2/docs/introduction)
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    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
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    *   [Build a Retrieval Augmented Generation (RAG) App](/v0.2/docs/tutorials/rag)
    *   [Build a Question/Answering system over SQL data](/v0.2/docs/tutorials/sql_qa)
*   [How-to guides](/v0.2/docs/how_to/)
    
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    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
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    *   [How to do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
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    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
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    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
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    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
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    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
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    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
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*   How use a vector store to retrieve data

On this page

How use a vector store to retrieve data
=======================================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Vector stores](/v0.2/docs/concepts/#vectorstores)
*   [Retrievers](/v0.2/docs/concepts/#retrievers)
*   [Text splitters](/v0.2/docs/concepts#text-splitters)
*   [Chaining runnables](/v0.2/docs/how_to/sequence/)

Vector stores can be converted into retrievers using the [`.asRetriever()`](https://v02.api.js.langchain.com/classes/langchain_core_vectorstores.VectorStore.html#asRetriever) method, which allows you to more easily compose them in chains.

Below, we show a retrieval-augmented generation (RAG) chain that performs question answering over documents using the following steps:

1.  Initialize an vector store
2.  Create a retriever from that vector store
3.  Compose a question answering chain
4.  Ask questions!

Each of the steps has multiple sub steps and potential configurations, but we'll go through one common flow. First, install the required dependency:

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

You can download the `state_of_the_union.txt` file [here](https://github.com/langchain-ai/langchain/blob/master/docs/docs/modules/state_of_the_union.txt).

    import * as fs from "node:fs";import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";import { MemoryVectorStore } from "langchain/vectorstores/memory";import {  RunnablePassthrough,  RunnableSequence,} from "@langchain/core/runnables";import { StringOutputParser } from "@langchain/core/output_parsers";import { ChatPromptTemplate } from "@langchain/core/prompts";import type { Document } from "@langchain/core/documents";const formatDocumentsAsString = (documents: Document[]) => {  return documents.map((document) => document.pageContent).join("\n\n");};// Initialize the LLM to use to answer the question.const model = new ChatOpenAI({  model: "gpt-4o",});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 MemoryVectorStore.fromDocuments(  docs,  new OpenAIEmbeddings());// Initialize a retriever wrapper around the vector storeconst vectorStoreRetriever = vectorStore.asRetriever();// Create a system & human prompt for the chat modelconst SYSTEM_TEMPLATE = `Use the following pieces of context to answer the question at the end.If you don't know the answer, just say that you don't know, don't try to make up an answer.----------------{context}`;const prompt = ChatPromptTemplate.fromMessages([  ["system", SYSTEM_TEMPLATE],  ["human", "{question}"],]);const chain = RunnableSequence.from([  {    context: vectorStoreRetriever.pipe(formatDocumentsAsString),    question: new RunnablePassthrough(),  },  prompt,  model,  new StringOutputParser(),]);const answer = await chain.invoke(  "What did the president say about Justice Breyer?");console.log({ answer });/*  {    answer: 'The president honored Justice Stephen Breyer by recognizing his dedication to serving the country as an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. He thanked Justice Breyer for his service.'  }*/

#### API Reference:

*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [RecursiveCharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `@langchain/textsplitters`
*   [MemoryVectorStore](https://v02.api.js.langchain.com/classes/langchain_vectorstores_memory.MemoryVectorStore.html) from `langchain/vectorstores/memory`
*   [RunnablePassthrough](https://v02.api.js.langchain.com/classes/langchain_core_runnables.RunnablePassthrough.html) from `@langchain/core/runnables`
*   [RunnableSequence](https://v02.api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables`
*   [StringOutputParser](https://v02.api.js.langchain.com/classes/langchain_core_output_parsers.StringOutputParser.html) from `@langchain/core/output_parsers`
*   [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts`
*   [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`

Let's walk through what's happening here.

1.  We first load a long text and split it into smaller documents using a text splitter. We then load those documents (which also embeds the documents using the passed `OpenAIEmbeddings` instance) into HNSWLib, our vector store, creating our index.
    
2.  Though we can query the vector store directly, we convert the vector store into a retriever to return retrieved documents in the right format for the question answering chain.
    
3.  We initialize a retrieval chain, which we'll call later in step 4.
    
4.  We ask questions!
    

Next steps[​](#next-steps "Direct link to Next steps")
------------------------------------------------------

You've now learned how to convert a vector store as a retriever.

See the individual sections for deeper dives on specific retrievers, the [broader tutorial on RAG](/v0.2/docs/tutorials/rag), or this section to learn how to [create your own custom retriever over any data source](/v0.2/docs/how_to/custom_retriever/).

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