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The development of Natural Language Interfaces to Databases (NLIDBs) has beengreatly advanced by the advent of large language models (LLMs), which providean intuitive way to translate natural language (NL) questions into StructuredQuery Language (SQL) queries. While significant progress has been made inLLM-based NL2SQL, existing approaches face several challenges in real-worldscenarios of natural language querying over massive databases. In this paper,we present DBCopilot, a framework that addresses these challenges by employinga compact and flexible copilot model for routing over massive databases.Specifically, DBCopilot decouples schema-agnostic NL2SQL into schema routingand SQL generation. This framework utilizes a single lightweight differentiablesearch index to construct semantic mappings for massive database schemata, andnavigates natural language questions to their target databases and tables in arelation-aware joint retrieval manner. The routed schemata and questions arethen fed into LLMs for effective SQL generation. Furthermore, DBCopilotintroduces a reverse schema-to-question generation paradigm that canautomatically learn and adapt the router over massive databases without manualintervention. Experimental results verify that DBCopilot is a scalable andeffective solution for schema-agnostic NL2SQL, providing a significant advancein handling natural language querying over massive databases for NLIDBs.

DBCopilot: Natural Language Querying over Massive Databases via Schema Routing