Dynamic Routing in RAG: Directing User Queries to the Right Vector Store with Open Source Models

Generative AI applications can be optimized by integrating a semantic routing mechanism in the Retrieval-Augmented Generation (RAG) framework. This involves analyzing user queries and directing them to the most relevant vector stores, enhancing both accuracy and efficiency. The post demonstrates implementing a semantic router using a Nomic embedding model and Llama 3.1 for embeddings, covering machine learning, computer science, and economics topics. Advanced techniques like Multi-query translation and HyDE further refine the process, ensuring users receive pertinent information from diverse sources.