Extending a conversational assistant with RAG for conversational search across multiple user and user-group embeddings
Retrieval Augmented Generation (RAG), which utilises a LLM, makes it relatively straightfoward to surface information through a conversational assistant. This is potentially transformative for HR & talent management and customer care use cases where information contained in policies, guidelines, handbooks and other unstructured natural language formats can be made more accessible and conveniently queried through an assistant’s natural language interface. Here I share an architecture that extends a conversational assistant with RAG, routing searches to collections mapped to a user and intent.
Nov 4, 2023