Build Performant RAG Applications Using Couchbase Vector Search and Amazon Bedrock

This demo showcases how to build a Retrieval-Augmented Generation (RAG) application using a vector database and Amazon Bedrock. The process begins with creating a vector index in the vector database to store, index, and retrieve embeddings efficiently. Amazon Bedrock is then integrated to access foundation models like Anthropic Claude for embeddings and large language model (LLM) responses. The demo also highlights the development of a production-grade RAG pipeline using orchestration frameworks such as LangChain or LlamaIndex. By combining vector search capabilities with foundation models, this approach enables highly granular and contextually accurate applications across industries, supporting use cases like content creation, question answering, summarization, and report generation.