!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()||function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}())

[Skip to main content](#__docusaurus_skipToContent_fallback)

You are viewing the **preview** v0.2 docs. View the **stable** v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386).

[

![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png)

](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com)

[More](#)

*   [People](/v0.2/docs/people/)
*   [Community](/v0.2/docs/community)
*   [Tutorials](/v0.2/docs/additional_resources/tutorials)
*   [Contributing](/v0.2/docs/contributing)

[v0.2](#)

*   [v0.2](/v0.2/docs/introduction)
*   [v0.1](https://js.langchain.com/v0.1/docs/get_started/introduction)

[🦜🔗](#)

*   [LangSmith](https://smith.langchain.com)
*   [LangSmith Docs](https://docs.smith.langchain.com)
*   [LangChain Hub](https://smith.langchain.com/hub)
*   [LangServe](https://github.com/langchain-ai/langserve)
*   [Python Docs](https://python.langchain.com/)

[Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs)

Search

*   [Providers](/v0.2/docs/integrations/platforms/)
    
    *   [Providers](/v0.2/docs/integrations/platforms/)
    *   [Anthropic](/v0.2/docs/integrations/platforms/anthropic)
    *   [AWS](/v0.2/docs/integrations/platforms/aws)
    *   [Google](/v0.2/docs/integrations/platforms/google)
    *   [Microsoft](/v0.2/docs/integrations/platforms/microsoft)
    *   [OpenAI](/v0.2/docs/integrations/platforms/openai)
*   [Components](/v0.2/docs/integrations/components)
    
    *   [Chat models](/v0.2/docs/integrations/chat/)
        
    *   [LLMs](/v0.2/docs/integrations/llms/)
        
    *   [Embedding models](/v0.2/docs/integrations/text_embedding)
        
    *   [Document loaders](/v0.2/docs/integrations/document_loaders)
        
    *   [Document transformers](/v0.2/docs/integrations/document_transformers)
        
    *   [Vector stores](/v0.2/docs/integrations/vectorstores)
        
    *   [Retrievers](/v0.2/docs/integrations/retrievers)
        
        *   [Knowledge Bases for Amazon Bedrock](/v0.2/docs/integrations/retrievers/bedrock-knowledge-bases)
        *   [Chaindesk Retriever](/v0.2/docs/integrations/retrievers/chaindesk-retriever)
        *   [ChatGPT Plugin Retriever](/v0.2/docs/integrations/retrievers/chatgpt-retriever-plugin)
        *   [Dria Retriever](/v0.2/docs/integrations/retrievers/dria)
        *   [Exa Search](/v0.2/docs/integrations/retrievers/exa)
        *   [HyDE Retriever](/v0.2/docs/integrations/retrievers/hyde)
        *   [Amazon Kendra Retriever](/v0.2/docs/integrations/retrievers/kendra-retriever)
        *   [Metal Retriever](/v0.2/docs/integrations/retrievers/metal-retriever)
        *   [Supabase Hybrid Search](/v0.2/docs/integrations/retrievers/supabase-hybrid)
        *   [Tavily Search API](/v0.2/docs/integrations/retrievers/tavily)
        *   [Time-Weighted Retriever](/v0.2/docs/integrations/retrievers/time-weighted-retriever)
        *   [Vector Store](/v0.2/docs/integrations/retrievers/vectorstore)
        *   [Vespa Retriever](/v0.2/docs/integrations/retrievers/vespa-retriever)
        *   [Zep Retriever](/v0.2/docs/integrations/retrievers/zep-retriever)
    *   [Tools](/v0.2/docs/integrations/tools)
        
    *   [Toolkits](/v0.2/docs/integrations/toolkits)
        
    *   [Stores](/v0.2/docs/integrations/stores/)
        

*   [](/v0.2/)
*   [Components](/v0.2/docs/integrations/components)
*   [Retrievers](/v0.2/docs/integrations/retrievers)
*   Vespa Retriever

Vespa Retriever
===============

This shows how to use Vespa.ai as a LangChain retriever. Vespa.ai is a platform for highly efficient structured text and vector search. Please refer to [Vespa.ai](https://vespa.ai) for more information.

The following sets up a retriever that fetches results from Vespa's documentation search:

    import { VespaRetriever } from "@langchain/community/retrievers/vespa";export const run = async () => {  const url = "https://doc-search.vespa.oath.cloud";  const query_body = {    yql: "select content from paragraph where userQuery()",    hits: 5,    ranking: "documentation",    locale: "en-us",  };  const content_field = "content";  const retriever = new VespaRetriever({    url,    auth: false,    query_body,    content_field,  });  const result = await retriever.invoke("what is vespa?");  console.log(result);};

#### API Reference:

*   [VespaRetriever](https://v02.api.js.langchain.com/classes/langchain_community_retrievers_vespa.VespaRetriever.html) from `@langchain/community/retrievers/vespa`

Here, up to 5 results are retrieved from the `content` field in the `paragraph` document type, using `documentation` as the ranking method. The `userQuery()` is replaced with the actual query passed from LangChain.

Please refer to the [pyvespa documentation](https://pyvespa.readthedocs.io/en/latest/getting-started-pyvespa.html#Query) for more information.

The URL is the endpoint of the Vespa application. You can connect to any Vespa endpoint, either a remote service or a local instance using Docker. However, most Vespa Cloud instances are protected with mTLS. If this is your case, you can, for instance set up a [CloudFlare Worker](https://cloud.vespa.ai/en/security/cloudflare-workers) that contains the necessary credentials to connect to the instance.

Now you can return the results and continue using them in LangChain.

* * *

#### Was this page helpful?

  

#### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E).

[

Previous

Vector Store

](/v0.2/docs/integrations/retrievers/vectorstore)[

Next

Zep Retriever

](/v0.2/docs/integrations/retrievers/zep-retriever)

Community

*   [Discord](https://discord.gg/cU2adEyC7w)
*   [Twitter](https://twitter.com/LangChainAI)

GitHub

*   [Python](https://github.com/langchain-ai/langchain)
*   [JS/TS](https://github.com/langchain-ai/langchainjs)

More

*   [Homepage](https://langchain.com)
*   [Blog](https://blog.langchain.dev)

Copyright © 2024 LangChain, Inc.