CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914245364416512 |
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| author | Singh, Rajpreet Boškov, Novak Drabeck, Lawrence Gudal, Aditya Khan, Manzoor A. |
| author_facet | Singh, Rajpreet Boškov, Novak Drabeck, Lawrence Gudal, Aditya Khan, Manzoor A. |
| contents | Natural language to SQL translation (Text-to-SQL) is one of the long-standing problems that has recently benefited from advances in Large Language Models (LLMs). While most academic Text-to-SQL benchmarks request schema description as a part of natural language input, enterprise-scale applications often require table retrieval before SQL query generation. To address this need, we propose a novel hybrid Retrieval Augmented Generation (RAG) system consisting of contextual, structural, and relational retrieval (CSR-RAG) to achieve computationally efficient yet sufficiently accurate retrieval for enterprise-scale databases. Through extensive enterprise benchmarks, we demonstrate that CSR-RAG achieves up to 40% precision and over 80% recall while incurring a negligible average query generation latency of only 30ms on commodity data center hardware, which makes it appropriate for modern LLM-based enterprise-scale systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06564 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale Singh, Rajpreet Boškov, Novak Drabeck, Lawrence Gudal, Aditya Khan, Manzoor A. Computation and Language Natural language to SQL translation (Text-to-SQL) is one of the long-standing problems that has recently benefited from advances in Large Language Models (LLMs). While most academic Text-to-SQL benchmarks request schema description as a part of natural language input, enterprise-scale applications often require table retrieval before SQL query generation. To address this need, we propose a novel hybrid Retrieval Augmented Generation (RAG) system consisting of contextual, structural, and relational retrieval (CSR-RAG) to achieve computationally efficient yet sufficiently accurate retrieval for enterprise-scale databases. Through extensive enterprise benchmarks, we demonstrate that CSR-RAG achieves up to 40% precision and over 80% recall while incurring a negligible average query generation latency of only 30ms on commodity data center hardware, which makes it appropriate for modern LLM-based enterprise-scale systems. |
| title | CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.06564 |