How to Implement RAG With Amazon Bedrock and LangChain

This article explores the implementation of RAG (retrieval-augmented generation) applications using Amazon Bedrock and LangChain. It covers setting up Amazon Bedrock, integrating with LangChain, and utilizing the potent Amazon Titan model for large language model (LLM) applications. The text also discusses how pgvector on Timescale's PostgreSQL cloud platform makes it easier to set up a vector database optimized for efficient storage and powering LLM applications with RAG.