Home icon

Elevate RAG for numerical analysis using Amazon Bedrock Knowledge Bases

Machine Learning Blog

The article discusses how Amazon Bedrock Knowledge Bases can be used to enhance the Retrieval Augmented Generation (RAG) technique for numerical analysis, particularly when dealing with complex nested tables across multiple documents.

Specifically, the article covers:

  • The power of RAG and its limitations in handling non-textual elements like tables
  • How Amazon Bedrock Knowledge Bases addresses these limitations through hybrid search, chunking data in fixed sizes, and retrieving a larger number of chunks
  • A solution overview demonstrating the architecture and workflow
  • Steps to create an S3 bucket, knowledge base, and a Streamlit application using CloudFormation
  • Testing the solution with examples and results
  • Conclusion highlighting the benefits of this solution for numerical analysis
Go to article

The AWS News Feed is currently looking for gold sponsors. If you want to support the AWS community and reach a large audience of AWS professionals, consider sponsoring the AWS News Feed.

Related articles

Sep 23
2024
Generate synthetic data for evaluating RAG systems using Amazon Bedrock
Oct 4
2024
Add Retrieval Augmented Generation (RAG) to your .NET applications with Amazon Bedrock Knowledge Bases
Dec 2
2024
Amazon Bedrock Knowledge Bases now supports RAG evaluation (Preview)
Nov 22
2024
Amazon Bedrock Knowledge Bases now supports binary vector embeddings to build RAG applications

The AWS News Feed is currently looking for silver sponsors. If you want to support the AWS community and reach a large audience of AWS professionals, consider sponsoring the AWS News Feed.