CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale

• URL: http://arxiv.org/abs/2601.06564v1
• Date: Sat, 10 Jan 2026 13:20:07 GMT
• Title: CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale
• Authors: Rajpreet Singh, Novak Boškov, Lawrence Drabeck, Aditya Gudal, Manzoor A. Khan,
• Abstract summary: We propose a novel hybrid Retrieval Augmented Generation (RAG) system consisting of contextual, structural, and relational retrieval.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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