RAG-Enhanced NL-to-SQL System for Power Marketing

Abstract

The rapid evolution of intelligent power systems and wireless communication infrastructures has accelerated the demand for efficient natural language interaction with large-scale power marketing databases. To address the limitations of conventional database querying, this study proposes a Retrieval-Augmented Generation (RAG)-enhanced Natural Language-to-SQL (NL2SQL) architecture that integrates domain knowledge retrieval with automated SQL generation. The proposed framework combines intention understanding, hybrid knowledge retrieval, schema-aware query generation, and compliance verification into a unified workflow for intelligent power marketing services. By incorporating database schemas and business rules through RAG, the system effectively reduces semantic ambiguity and improves SQL generation reliability in complex domain-specific scenarios. Case studies involving electricity bill inquiries and abnormal power consumption analysis demonstrate that the architecture achieves over 92% query accuracy while reducing response time to less than one minute. The proposed framework provides an efficient solution for intelligent data interaction in power systems and offers methodological support for communication-assisted smart energy services, where reliable information transmission and large-scale data access are essential. Its architecture also presents a transferable paradigm for knowledge-driven query systems deployed in digitally connected industrial environments.