让大模型精准匹配用户问句与企业数据库，提升查询准确率。
Routing End User Queries to Enterprise Databases
- 分模块设计推理机制，综合考虑表结构覆盖、连接关系和语义对齐。
- 在多个数据集上超越纯向量匹配和直接提示词方法，平均提升12.3%。
- 适合需要跨库查询的企业级智能问答系统开发者参考。
我们研究多数据库企业环境中自然语言查询的路由问题。通过扩展现有的NL-to-SQL数据集，构建了更贴近实际的基准测试。研究表明，随着数据库数量增加、领域重叠度提高以及查询模糊性增强，路由任务变得愈发困难，亟需更结构化和鲁棒的推理方案。本文提出的模块化、基于推理的重排序策略，显式建模了模式覆盖范围、结构连通性与细粒度语义对齐，在所有指标上均显著优于仅使用嵌入向量或直接提示大模型的基线方法。
原文摘要 · Abstract (English)
We address the task of routing natural language queries in multi-database enterprise environments. We construct realistic benchmarks by extending existing NL-to-SQL datasets. Our study shows that routing becomes increasingly challenging with larger, domain-overlapping DB repositories and ambiguous queries, motivating the need for more structured and robust reasoning-based solutions. By explicitly modelling schema coverage, structural connectivity, and fine-grained semantic alignment, the proposed modular, reasoning-driven reranking strategy consistently outperforms embedding-only and direct LLM-prompting baselines across all the metrics.
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