Multi-Agent SQL Assistant, Part 2: Building a RAG Manager

A RAG (Retrieval-Augmented Generation) manager is developed to optimize a multi-agent SQL assistant by avoiding the need to send large, full database schemas to an LLM. The system implements and compares four distinct strategies: a baseline with no RAG, a keyword-matching method, and two vector-based searches using FAISS and ChromaDB. This approach dynamically selects only the most relevant parts of the schema for the LLM's context, aiming to reduce token usage and improve response accuracy. The guide provides code examples, starting with a `BaseRAG` abstract class to ensure a modular and extensible design for different retrieval techniques.