AWS Big Data Blog
Author: Dhaval Shah
Dhaval Shah is a Principal Solutions Architect with Amazon Web Services based out of New York, where he guides global financial services customers to build highly secure, scalable, reliable, and cost-efficient applications on the cloud. He brings over 20 years of technology experience on Software Development and Architecture, Data Engineering, and IT Management.
Build a decentralized semantic search engine on heterogeneous data stores using autonomous agents
In this post, we show how to build a Q&A bot with RAG (Retrieval Augmented Generation). RAG uses data sources like Amazon Redshift and Amazon OpenSearch Service to retrieve documents that augment the LLM prompt. For getting data from Amazon Redshift, we use the Anthropic Claude 2.0 on Amazon Bedrock, summarizing the final response based on pre-defined prompt template libraries from LangChain. To get data from Amazon OpenSearch Service, we chunk, and convert the source data chunks to vectors using Amazon Titan Text Embeddings model.