arXiv:2606.28387v1 Announce Type: cross Abstract: Enterprise text-to-SQL systems often fail before SQL is generated: the model receives the wrong schema context. Modern warehouses contain thousands of tables, abbreviated columns, informal metrics, hidden join conventions, and permission boundaries that are not captured by raw table names. We introduce Schema-First Retrieval, a retrieval layer that embeds catalog metadata rather than warehouse rows. The system indexes five typed catalog objects, tables, columns, metrics, relationships, and query history, using object-specific text templates. At
The proliferation of massive datasets and the increasing demand for intuitive data interaction drive the immediate need for more robust text-to-SQL systems, addressing current limitations in schema context handling.
This development enhances the accuracy and accessibility of enterprise data analytics, allowing non-technical users to query complex databases more effectively and reducing friction in data-driven decision-making.
Traditional text-to-SQL systems focused primarily on SQL generation; now, the emphasis shifts to intelligent schema retrieval, making the foundational step of understanding data catalogs smarter and more reliable.
- · Enterprise data analytics platforms
- · Data scientists and analysts
- · Companies with complex data warehouses
- · AI-powered SaaS providers
- · Inefficient manual data cataloging processes
Improved efficiency and accuracy in querying large, complex enterprise databases using natural language.
Reduced need for specialized SQL knowledge across various business functions, democratizing data access.
Acceleration of AI adoption in business intelligence and operational decision-making as data becomes more readily actionable at scale.
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Read at arXiv cs.AI