Deploy RAG applications on Amazon SageMaker JumpStart using FAISS

This article details how to deploy Retrieval Augmented Generation (RAG) applications on Amazon SageMaker JumpStart using FAISS, demonstrating a method to improve generative AI outputs by incorporating external knowledge sources.

• Uses Meta Llama 3 and BGE Hugging Face embeddings models
• Leverages LangChain to simplify RAG workflow components
• Utilizes FAISS as a vector store for efficient similarity search
• Demonstrates RAG technique using Amazon's Letter to Shareholders as sample document corpus
• Provides step-by-step guide for model deployment, data preparation, and vector store setup

The solution highlights how RAG can enhance generative AI applications by dynamically retrieving relevant context to improve response accuracy and relevance, without requiring costly model retraining.