Integrate sparse and dense vectors to enhance knowledge retrieval in RAG using Amazon OpenSearch Service

This article discusses how to integrate sparse and dense vectors to enhance knowledge retrieval in Retrieval-Augmented Generation (RAG) using Amazon OpenSearch Service. It highlights the advantages of combining sparse vector retrieval with dense vector retrieval for knowledge retrieval in RAG scenarios.

Specifically, the article covers:

• What is sparse vector retrieval and its advantages over traditional BM25 algorithm
• Steps to deploy dense vector model using Amazon Bedrock and sparse vector model using Amazon SageMaker
• Creating pipelines for ingestion and search to combine sparse and dense vectors
• Testing methodology and datasets used for evaluation
• Performance results showing improved recall metrics when combining sparse and dense vectors
• Conclusion highlighting the benefits of this approach for knowledge retrieval in RAG