Domain-specific SQL generation with LLMs: A hybrid framework combining knowledge graphs and retrieval-augmentation

Municipal sewer asset management personnel without SQL expertise often rely on additional IT support to retrieve necessary information from their organization’s database, which hinders timely decision-making. This study presents a schema-guided RAG, a hybrid text-to-SQL framework that integrates structured knowledge graphs (KGs) for explicit join-path and relational reasoning with retrieval-augmented generation (RAG) for contextual grounding. Designed for domain-specific and multi-relational databases, the schema-guided RAG enables large language models (LLMs) to reason over complex relational structures while mitigating data scarcity through semantic retrieval. The framework was evaluated using both proprietary and open-source LLMs and applied in the sewer asset management domain. Results show consistent improvements in execution accuracy, logical form accuracy, and exact match, with particularly strong gains for queries requiring multi-table joins and nested logic. The schema-guided RAG offers an interpretable approach to natural language queries, supporting efficient, accurate, and explainable access to infrastructure data.