🤖 AI Summary
To address scalability bottlenecks in Text-to-SQL for enterprise-scale databases, this paper proposes a domain-agnostic, retrieval-augmented schema linking framework. Methodologically, it introduces a modular semantic indexing architecture that decouples database schemas and metadata into fine-grained semantic units for independent vectorization; further, it designs a table-level prioritized identification mechanism coupled with column-level contextual fusion, integrating chunked indexing, semantic retrieval, recall re-ranking, and context budget control. Crucially, the approach fully leverages intrinsic semantic cues from raw metadata without requiring domain-specific fine-tuning, enabling high-precision schema matching. Experimental results demonstrate that our method consistently outperforms mainstream baselines across heterogeneous, multi-source data catalogs—achieving both high recall and high accuracy. Its plug-and-play compatibility facilitates seamless enterprise deployment.
📝 Abstract
Despite advances in large language model (LLM)-based natural language interfaces for databases, scaling to enterprise-level data catalogs remains an under-explored challenge. Prior works addressing this challenge rely on domain-specific fine-tuning - complicating deployment - and fail to leverage important semantic context contained within database metadata. To address these limitations, we introduce a component-based retrieval architecture that decomposes database schemas and metadata into discrete semantic units, each separately indexed for targeted retrieval. Our approach prioritizes effective table identification while leveraging column-level information, ensuring the total number of retrieved tables remains within a manageable context budget. Experiments demonstrate that our method maintains high recall and accuracy, with our system outperforming baselines over massive databases with varying structure and available metadata. Our solution enables practical text-to-SQL systems deployable across diverse enterprise settings without specialized fine-tuning, addressing a critical scalability gap in natural language database interfaces.