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This template is designed to connect with the AWS Bedrock service, a managed server that offers a set of foundation models.

It primarily uses the Anthropic Claude for text generation and Amazon Titan for text embedding, and utilizes FAISS as the vectorstore.

For additional context on the RAG pipeline, refer to this notebook.

Environment Setup​

Before you can use this package, ensure that you have configured boto3 to work with your AWS account.

For details on how to set up and configure boto3, visit this page.

In addition, you need to install the faiss-cpu package to work with the FAISS vector store:

pip install faiss-cpu

You should also set the following environment variables to reflect your AWS profile and region (if you're not using the default AWS profile and us-east-1 region):



First, install the LangChain CLI:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package:

langchain app new my-app --package rag-aws-bedrock

To add this package to an existing project:

langchain app add rag-aws-bedrock

Then add the following code to your file:

from rag_aws_bedrock import chain as rag_aws_bedrock_chain

add_routes(app, rag_aws_bedrock_chain, path="/rag-aws-bedrock")

(Optional) If you have access to LangSmith, you can configure it to trace, monitor, and debug LangChain applications. If you don't have access, you can skip this section.

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server running locally at http://localhost:8000

You can see all templates at and access the playground at

You can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-aws-bedrock")

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