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*   [Providers](/v0.1/docs/integrations/platforms/)
    
    *   [Providers](/v0.1/docs/integrations/platforms/)
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    *   [Vector stores](/v0.1/docs/integrations/vectorstores/)
        
    *   [Retrievers](/v0.1/docs/integrations/retrievers/)
        
        *   [Knowledge Bases for Amazon Bedrock](/v0.1/docs/integrations/retrievers/bedrock-knowledge-bases/)
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*   [Components](/v0.1/docs/integrations/components/)
*   [Retrievers](/v0.1/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://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.

* * *

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

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