Enhance RAG for numerical computation with Amazon Bedrock Knowledge Bases | AWS Machine Learning Blog

Introduction to Retrieval Augmented Generation (RAG)

In the realm of generative artificial intelligence (AI), Retrieval Augmented Generation (RAG) has emerged as a powerful technique, enabling foundation models (FMs) to use external knowledge sources for enhanced text generation.

Amazon Bedrock and Knowledge Bases

Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies. Amazon Bedrock Knowledge Bases is a fully managed capability that helps implement the entire RAG workflow.

Numerical Analysis Challenges and Solutions

RAG faces challenges, especially when used for numerical analysis. Amazon Bedrock Knowledge Bases provide capabilities to enhance numerical analysis across documents containing tables.

Enhancing Numerical Analysis with Amazon Bedrock Knowledge Bases

Amazon Bedrock Knowledge Bases provide three capabilities to resolve issues related to numerical analysis across multiple documents.

Document Ingestion and Querying Workflows

The UI experience can be divided into two phases: document ingestion and document querying workflows.

Testing and Results

The solution was tested on Amazon earnings documents, showing improved results with the hybrid search option.

Conclusion and Deployment Steps

This post discussed Amazon Bedrock Knowledge Bases as a powerful solution for numerical analysis. Detailed deployment steps were provided for setting up and testing the solution.