CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale
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.
Cite
@article{arxiv.2601.06564,
title = {CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale},
author = {Rajpreet Singh and Novak Boškov and Lawrence Drabeck and Aditya Gudal and Manzoor A. Khan},
journal= {arXiv preprint arXiv:2601.06564},
year = {2026}
}