This template is an application that utilizes Amazon Kendra, a machine learning powered search service, and Anthropic Claude for text generation. The application retrieves documents using a Retrieval chain to answer questions from your documents.
It uses the
boto3 library to connect with the Bedrock service.
For more context on building RAG applications with Amazon Kendra, check this page.
Please ensure to setup and configure
boto3 to work with your AWS account.
You can follow the guide here.
You should also have a Kendra Index set up before using this template.
You can use this Cloudformation template to create a sample index.
This includes sample data containing AWS online documentation for Amazon Kendra, Amazon Lex, and Amazon SageMaker. Alternatively, you can use your own Amazon Kendra index if you have indexed your own dataset.
The following environment variables need to be set:
AWS_DEFAULT_REGION- This should reflect the correct AWS region. Default is
AWS_PROFILE- This should reflect your AWS profile. Default is
KENDRA_INDEX_ID- This should have the Index ID of the Kendra index. Note that the Index ID is a 36 character alphanumeric value that can be found in the index detail page.
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package rag-aws-kendra
If you want to add this to an existing project, you can just run:
langchain app add rag-aws-kendra
And add the following code to your
from rag_aws_kendra.chain import chain as rag_aws_kendra_chain
add_routes(app, rag_aws_kendra_chain, path="/rag-aws-kendra")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. 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, then you can spin up a LangServe instance directly by:
This will start the FastAPI app with a server running locally at http://localhost:8000
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-aws-kendra")