Adaptive Query Routing Addresses Key Failure Points in Production RAG Systems

Basic Retrieval-Augmented Generation (RAG) pipelines fail in production when they apply vector search indiscriminately to all user queries, including simple greetings, vague questions, and out-of-domain requests. A developer has outlined an Adaptive RAG approach that classifies each query and routes it to one of three handlers: a vector store, a web search fallback, or a direct LLM response. The system uses LangChain, Pydantic, and FastAPI, with a structured router built on Google's Gemini model to make routing decisions. A relevance-grading step filters retrieved documents before they reach the LLM, reducing hallucinations caused by poor context. The approach also cuts latency for simple queries to under 300 milliseconds by bypassing embedding generation and vector lookups entirely.