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.
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
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
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_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, you can spin up a LangServe instance directly by:
This will start the FastAPI app with a server running locally at http://localhost:8000
You can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-aws-bedrock")