Build Performant RAG Applications Using Couchbase Vector Search and Amazon Bedrock

Generative AI has the potential to automate 60-70% of employees' time, but its knowledge is confined to training data, leading to "hallucinations" that undermine trust and credibility. The Retrieval-Augmented Generation (RAG) technique can augment LLMs with proprietary data, grounding responses in current facts. A highly scalable database, vector database, and LLM cache are required for successful RAG implementation. Couchbase and Amazon Bedrock offer an end-to-end platform to build performant RAG applications across industries, leveraging a cloud-native high-performance DBaaS called Capella. This platform provides hybrid search capabilities, allowing seamless integration of Capella as a knowledge base or vector DB with leading GenAI platforms like Amazon Bedrock. A production-grade RAG pipeline can be built using orchestration frameworks such as LangChain or LlamaIndex.

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