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*   [](/v0.1/)
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*   [Retrieval](/v0.1/docs/modules/data_connection/)
*   [Retrievers](/v0.1/docs/modules/data_connection/retrievers/)
*   Vector store-backed retriever

On this page

Vector store-backed retriever
=============================

A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the Vector Store class to make it conform to the Retriever interface. It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store.

Once you construct a Vector store, it's very easy to construct a retriever. Let's walk through an example.

    const vectorStore = ...const retriever = vectorStore.asRetriever();

Here's a more end-to-end example:

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

    yarn add @langchain/openai

    pnpm add @langchain/openai

    import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";import { HNSWLib } from "langchain/vectorstores/hnswlib";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());// Initialize a retriever wrapper around the vector storeconst retriever = vectorStore.asRetriever();const docs = await retriever.getRelevantDocuments(  "what did he say about ketanji brown jackson");

Configuration[​](#configuration "Direct link to Configuration")
---------------------------------------------------------------

You can specify a maximum number of documents to retrieve as well as a vector store-specific filter to use when retrieving.

    // Return up to 2 documents with `metadataField` set to `"value"`const retriever = vectorStore.asRetriever(2, { metadataField: "value" });const docs = retriever.getRelevantDocuments(  "what did he say about ketanji brown jackson");

* * *

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*   [Configuration](#configuration)

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