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Routing End User Queries to Enterprise Databases

This research addresses the complex challenge of accurately routing natural language queries to the appropriate database within heterogeneous enterprise environments. The authors identify significant flaws in previous benchmarks, noting that prior studies utilized imbalanced datasets that failed to realistically simulate the difficulty of distinguishing between databases with overlapping domains. To correct this, they introduce two robust benchmarks, Spider-Route and Bird-Route, which unify database repositories to create a more rigorously balanced testing ground for both in-domain and cross-domain scenarios. The study creates a novel, training-free modular reasoning approach that outperforms standard embedding models and direct Large Language Model (LLM) prompting by decomposing the routing task into verifiable steps. This method first uses an LLM to map query phrases to specific schema entities and then applies an algorithmic check to ensure the mapped tables form a connected subgraph, thereby validating that the database can structurally support the necessary joins to answer the query. By explicitly calculating scores based on schema coverage and connectivity, this strategy achieves state-of-the-art performance and significantly reduces errors where models confuse semantically similar databases. https://arxiv.org/pdf/2601.19825