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*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [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)
    *   [Build a Query Analysis System](/v0.2/docs/tutorials/query_analysis)
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    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
    *   [Conversational RAG](/v0.2/docs/tutorials/qa_chat_history)
    *   [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/)
    
    *   [How-to guides](/v0.2/docs/how_to/)
    *   [How to use example selectors](/v0.2/docs/how_to/example_selectors)
    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
    *   [How to create a custom LLM class](/v0.2/docs/how_to/custom_llm)
    *   [How to use few shot examples](/v0.2/docs/how_to/few_shot_examples)
    *   [How to use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
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    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
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    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
    *   [How to compose prompts together](/v0.2/docs/how_to/prompts_composition)
    *   [How to use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)
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    *   [How to cache embedding results](/v0.2/docs/how_to/caching_embeddings)
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    *   [How to manage memory](/v0.2/docs/how_to/chatbots_memory)
    *   [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)
    *   [How to do retrieval with contextual compression](/v0.2/docs/how_to/contextual_compression)
    *   [How to write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
    *   [How to create custom Tools](/v0.2/docs/how_to/custom_tools)
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    *   [How to load data from a directory](/v0.2/docs/how_to/document_loader_directory)
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    *   [How to load JSON data](/v0.2/docs/how_to/document_loaders_json)
    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
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    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
    *   [How to handle long text](/v0.2/docs/how_to/extraction_long_text)
    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
    *   [How to run custom functions](/v0.2/docs/how_to/functions)
    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
    *   [How to map values to a database](/v0.2/docs/how_to/graph_mapping)
    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [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)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [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)
    *   [How to return sources](/v0.2/docs/how_to/qa_sources)
    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [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)
    *   [How to split text by tokens](/v0.2/docs/how_to/split_by_token)
    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [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)
*   [Conceptual guide](/v0.2/docs/concepts)
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*   [How-to guides](/v0.2/docs/how_to/)
*   How to generate multiple queries to retrieve data for

On this page

How to generate multiple queries to retrieve data for
=====================================================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Vector stores](/v0.2/docs/concepts/#vectorstores)
*   [Retrievers](/v0.2/docs/concepts/#retrievers)
*   [Retrieval-augmented generation (RAG)](/v0.2/docs/tutorials/rag)

Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on “distance”. But retrieval may produce different results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious.

The [`MultiQueryRetriever`](https://v02.api.js.langchain.com/classes/langchain_retrievers_multi_query.MultiQueryRetriever.html) automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` can help overcome some of the limitations of the distance-based retrieval and get a richer set of results.

Get started[​](#get-started "Direct link to Get started")
---------------------------------------------------------

tip

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

*   npm
*   yarn
*   pnpm

    npm i @langchain/anthropic @langchain/cohere

    yarn add @langchain/anthropic @langchain/cohere

    pnpm add @langchain/anthropic @langchain/cohere

    import { MemoryVectorStore } from "langchain/vectorstores/memory";import { CohereEmbeddings } from "@langchain/cohere";import { MultiQueryRetriever } from "langchain/retrievers/multi_query";import { ChatAnthropic } from "@langchain/anthropic";const embeddings = new CohereEmbeddings();const vectorstore = await MemoryVectorStore.fromTexts(  [    "Buildings are made out of brick",    "Buildings are made out of wood",    "Buildings are made out of stone",    "Cars are made out of metal",    "Cars are made out of plastic",    "mitochondria is the powerhouse of the cell",    "mitochondria is made of lipids",  ],  [{ id: 1 }, { id: 2 }, { id: 3 }, { id: 4 }, { id: 5 }],  embeddings);const model = new ChatAnthropic({  model: "claude-3-sonnet-20240229",});const retriever = MultiQueryRetriever.fromLLM({  llm: model,  retriever: vectorstore.asRetriever(),});const query = "What are mitochondria made of?";const retrievedDocs = await retriever.invoke(query);/*  Generated queries: What are the components of mitochondria?,What substances comprise the mitochondria organelle?  ,What is the molecular composition of mitochondria?*/console.log(retrievedDocs);

    [  Document {    pageContent: "mitochondria is made of lipids",    metadata: {}  },  Document {    pageContent: "mitochondria is the powerhouse of the cell",    metadata: {}  },  Document {    pageContent: "Buildings are made out of brick",    metadata: { id: 1 }  },  Document {    pageContent: "Buildings are made out of wood",    metadata: { id: 2 }  }]

Customization[​](#customization "Direct link to Customization")
---------------------------------------------------------------

You can also supply a custom prompt to tune what types of questions are generated. You can also pass a custom output parser to parse and split the results of the LLM call into a list of queries.

    import { LLMChain } from "langchain/chains";import { pull } from "langchain/hub";import { BaseOutputParser } from "@langchain/core/output_parsers";import { PromptTemplate } from "@langchain/core/prompts";type LineList = {  lines: string[];};class LineListOutputParser extends BaseOutputParser<LineList> {  static lc_name() {    return "LineListOutputParser";  }  lc_namespace = ["langchain", "retrievers", "multiquery"];  async parse(text: string): Promise<LineList> {    const startKeyIndex = text.indexOf("<questions>");    const endKeyIndex = text.indexOf("</questions>");    const questionsStartIndex =      startKeyIndex === -1 ? 0 : startKeyIndex + "<questions>".length;    const questionsEndIndex = endKeyIndex === -1 ? text.length : endKeyIndex;    const lines = text      .slice(questionsStartIndex, questionsEndIndex)      .trim()      .split("\n")      .filter((line) => line.trim() !== "");    return { lines };  }  getFormatInstructions(): string {    throw new Error("Not implemented.");  }}// Default prompt is available at: https://smith.langchain.com/hub/jacob/multi-vector-retriever-germanconst prompt: PromptTemplate = await pull(  "jacob/multi-vector-retriever-german");const vectorstore = await MemoryVectorStore.fromTexts(  [    "Gebäude werden aus Ziegelsteinen hergestellt",    "Gebäude werden aus Holz hergestellt",    "Gebäude werden aus Stein hergestellt",    "Autos werden aus Metall hergestellt",    "Autos werden aus Kunststoff hergestellt",    "Mitochondrien sind die Energiekraftwerke der Zelle",    "Mitochondrien bestehen aus Lipiden",  ],  [{ id: 1 }, { id: 2 }, { id: 3 }, { id: 4 }, { id: 5 }],  embeddings);const model = new ChatAnthropic({});const llmChain = new LLMChain({  llm: model,  prompt,  outputParser: new LineListOutputParser(),});const retriever = new MultiQueryRetriever({  retriever: vectorstore.asRetriever(),  llmChain,});const query = "What are mitochondria made of?";const retrievedDocs = await retriever.invoke(query);/*  Generated queries: Was besteht ein Mitochondrium?,Aus welchen Komponenten setzt sich ein Mitochondrium zusammen?  ,Welche Moleküle finden sich in einem Mitochondrium?*/console.log(retrievedDocs);

    [  Document {    pageContent: "Mitochondrien bestehen aus Lipiden",    metadata: {}  },  Document {    pageContent: "Mitochondrien sind die Energiekraftwerke der Zelle",    metadata: {}  },  Document {    pageContent: "Gebäude werden aus Stein hergestellt",    metadata: { id: 3 }  },  Document {    pageContent: "Autos werden aus Metall hergestellt",    metadata: { id: 4 }  },  Document {    pageContent: "Gebäude werden aus Holz hergestellt",    metadata: { id: 2 }  },  Document {    pageContent: "Gebäude werden aus Ziegelsteinen hergestellt",    metadata: { id: 1 }  }]

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

You’ve now learned how to use the `MultiQueryRetriever` to query a vector store with automatically generated queries.

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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