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We've seen a surge in GenAI-powered apps. While these apps promise a completely new way to interact with computers, they often don't meet user expectations. Here are my thoughts on improving Retrieval-Augmented Generation (RAG) applications, starting with better indexing and exploring the exciting potential of multi-agent systems. I've built this demonstration app called "SmartRAG" that showcases these concepts.
While these indexing improvements significantly enhance the retrieval capabilities of RAG applications, sometimes a single question-answer interaction isn't sufficient for complex queries. This is where multi-agent systems come into play.
SmartRAG has an experimental feature called "Multi-Agent Research". Using Microsoft's AutoGen framework, this feature creates an ensemble of AI agents that collaborate to research more complex topics. Here's how it works:
A reviewer agent oversees the process, guiding the research and synthesizing the findings. This agent also decides when the goal has been reached.
