Advanced RAG patterns on Amazon SageMaker

This article discusses advanced techniques for Retrieval Augmented Generation (RAG) using large language models (LLMs) like Mixtral-8x7B Instruct on Amazon SageMaker. It covers:

• Challenges with regular RAG like inaccurate retrieval, context overflow, and document size/complexity
• Using parent document retriever to split large documents into smaller child documents for better embedding
• Using contextual compression to filter retrieved documents based on relevance to the query
• Implementing these techniques with LangChain, deploying Mixtral-8x7B and BGE embeddings on SageMaker JumpStart
• Comparing results from regular retriever chain vs advanced techniques like parent document retriever and contextual compression
• Conclusion highlighting benefits of advanced RAG for better quality, relevance and efficiency