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*   [Components](/v0.1/docs/integrations/components/)
*   [Retrievers](/v0.1/docs/integrations/retrievers/)
*   Knowledge Bases for Amazon Bedrock

Knowledge Bases for Amazon Bedrock
==================================

Knowledge Bases for Amazon Bedrock is a fully managed support for end-to-end RAG workflow provided by Amazon Web Services (AWS). It provides an entire ingestion workflow of converting your documents into embeddings (vector) and storing the embeddings in a specialized vector database. Knowledge Bases for Amazon Bedrock supports popular databases for vector storage, including vector engine for Amazon OpenSearch Serverless, Pinecone, Redis Enterprise Cloud, Amazon Aurora (coming soon), and MongoDB (coming soon).

Setup[​](#setup "Direct link to Setup")
---------------------------------------

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm i @aws-sdk/client-bedrock-agent-runtime @langchain/community

    yarn add @aws-sdk/client-bedrock-agent-runtime @langchain/community

    pnpm add @aws-sdk/client-bedrock-agent-runtime @langchain/community

Usage[​](#usage "Direct link to Usage")
---------------------------------------

    import { AmazonKnowledgeBaseRetriever } from "@langchain/community/retrievers/amazon_knowledge_base";const retriever = new AmazonKnowledgeBaseRetriever({  topK: 10,  knowledgeBaseId: "YOUR_KNOWLEDGE_BASE_ID",  region: "us-east-2",  clientOptions: {    credentials: {      accessKeyId: "YOUR_ACCESS_KEY_ID",      secretAccessKey: "YOUR_SECRET_ACCESS_KEY",    },  },});const docs = await retriever.invoke("How are clouds formed?");console.log(docs);

#### API Reference:

*   [AmazonKnowledgeBaseRetriever](https://api.js.langchain.com/classes/langchain_community_retrievers_amazon_knowledge_base.AmazonKnowledgeBaseRetriever.html) from `@langchain/community/retrievers/amazon_knowledge_base`

* * *

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