AWS Enhances RAG Applications with Agentic Retrieval on Amazon Bedrock

Amazon Web Services (AWS) has introduced a new approach to Retrieval Augmented Generation (RAG) applications using LangChain and Amazon Bedrock Managed Knowledge Base. The traditional method of handling multi-part questions often results in answers that only cover a fraction of the query, despite appearing relevant. AWS's new agentic retrieval method addresses this by breaking down complex questions into sub-queries, running them individually, and searching again if necessary to ensure comprehensive answers.

The solution leverages Amazon Bedrock Managed Knowledge Base, which simplifies the RAG architecture by managing vector stores, embeddings, and re-ranking models. The walkthrough demonstrates how to create a knowledge base using Amazon Simple Storage Service (Amazon S3) and query it using two APIs: the Retrieve API for standard retrieval and the AgenticRetrieveStream API for multi-step planning. The latter provides trace events that show the planning process, offering insights into how the model generates answers.

To implement this solution, users need an AWS account with access to Amazon Bedrock, specific IAM permissions, Python 3.12 or later, and an S3 bucket with sample documents. The walkthrough covers the necessary permissions, package installations, and steps to create and query the knowledge base. The costs associated with document storage, ingestion, retrieval calls, and foundation model inference should be considered, and resources should be deleted after the experiment.

The implementation involves creating a knowledge base with a managedKnowledgeBaseConfiguration, setting the embeddingModelType to MANAGED, and using the Boto3 client to interact with the knowledge base. This approach enhances the accuracy and comprehensiveness of answers generated by RAG applications, making it a valuable tool for users needing detailed and precise information retrieval.