Overview
This Guidance shows how to build an advanced question-answering application using the latest AI tools from AWS and its partners. The architecture includes a database service that stores both operational data and vector data embeddings. A fully managed generative AI service creates these embeddings, which are then stored and managed alongside your most relevant documents based on their proximity to the query vector. This technique, known as Retrieval-Augmented Generation (RAG), enhances AI response accuracy and relevance. As a result, you can provide better, faster answers to your customers' questions using your own data.
Note: See disclaimer below
How it works
This architecture diagram illustrates how to process user queries and generate accurate, contextually relevant responses. It enhances a foundation model (FM) on Amazon Bedrock using Retrieval Augmented Generation (RAG); the vector search capabilities of Amazon DocumentDB and LlamaIndex enable more accurate and informed answers from a customized knowledge base.
Deploy with confidence
Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.
Well-Architected Pillars
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as amany Well-Architected best practices as possible.
Disclaimer
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