Advanced RAG – Dynamically Selecting the best Retrievers for Queries with AI

Retrieval Augmented Generation (RAG) uses data from retrieval systems such as Vector DBs to find the relevant information to answer a user query. When multiple retrieval systems are to be used, selecting the optimal retrieval system for a query can be a challenge. Marco Frodl will present the MultiRouteChain paradigm from the LangChain framework, which enables AI-based dynamic retrieval system selection based on semantic matching of the user query and the retrieval system focus. The live coding will illustrate how MultiRouteChain improves the performance of RAG for answering user queries by selecting the most relevant QA chain for retrieval for each query.

The talk for the AI Meetup Frankfurt will be very hands-on with lots of live demos and coding in Python, LangChain framework and MistralAI to show the difference between a simple RAG approach and this more real-world approach.