LangChain allows us to easily create LLM applications using a simple chain-like structure. We can integrate the capabilities of LangChain with AWS Bedrock Knowledge Bases and foundational models to create chatbots.
In this Cloud Lab, you’ll learn how to build a Retrieval-Augmented Generation (RAG) chatbot using LangChain and Amazon Bedrock. You’ll start by setting up an Amazon Bedrock Knowledge Base with an Aurora Serverless instance as its vector store. Also, you’ll create an S3 bucket to store the source files for the knowledge base. The knowledge base will access the S3 bucket through an IAM role. Then, you’ll use LangChain to create a retriever and generator chain. You’ll use the knowledge base as the retriever and the Anthropic Claude model as the generator. Finally, you’ll bring your application to life with a Streamlit frontend to test your RAG model.
By the end of this Cloud Lab, you’ll be well-equipped to use Bedrock Knowledge Bases and base models in your AI applications. The architecture diagram shows the infrastructure you’ll build in this Cloud Lab:
