The integration of Large Language Models (LLMs) with enterprise relational databases has been largely confined to read-only Retrieval-Augmented Generation (RAG) systems. This paper transcends that limitation, presenting a comprehensive architectural framework for building conversational AI agents capable of both reading and writing to a relational database via a Knowledge Graph (KG) intermediary. We will dissect the core architectural challenge, evaluate multiple design patterns — including KG as a cache, KG as a source of truth, and a sophisticated Command Query Responsibility Segregation (CQRS) pattern. This document provides an exhaustive, production-ready guide, complete with data modeling strategies, detailed prompt engineering for both query and command generation, Mermaid architecture diagrams, and best practices for security, validation, and transaction management. This is the blueprint for creating the next generation of truly interactive, data-manipulating chatbots. — Read More

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