Build cost-effective RAG applications with Binary Embeddings in Amazon Titan Text Embeddings V2, Amazon OpenSearch Serverless, and Amazon Bedrock Knowledge Bases

This article discusses how AWS customers can build cost-effective Retrieval Augmented Generation (RAG) applications using Binary Embeddings in Amazon Titan Text Embeddings V2, Amazon OpenSearch Serverless, and Amazon Bedrock Knowledge Bases.

Specifically, the article covers:

• How Binary Embeddings in Amazon Titan Text Embeddings V2 can represent data as binary vectors for more efficient storage and computation
• How Amazon OpenSearch Serverless now supports storing and searching binary vectors, enabling lower storage costs
• How to configure Amazon Bedrock Knowledge Bases to use Binary Embeddings and binary vector stores for RAG applications
• Benchmarks showing reduced memory usage, storage requirements, and costs while maintaining high retrieval accuracy with Binary Embeddings
• Conclusion summarizing the benefits of using these technologies together for cost-effective RAG applications