2025/04/03 by Tetiana Gladkykh, Gladkykh, Tetiana, Kyrylo Kirykov +1
Computer Science · Decision Sciences · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Databases (cs.DB) #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #H.2.3 #I.2.7 #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2506.12234
openalex publication_date 2025/04/03 · openalex created_date 2025/10/13 · openalex updated_date 2026/07/28
Text-to-SQL systems enable users to query databases using natural language, democratizing access to data analytics. However, they face challenges in understanding ambiguous phrasing, domain-specific vocabulary, and complex schema relationships. This paper introduces Datrics Text2SQL, a Retrieval-Augmented Generation (RAG)-based framework designed to generate accurate SQL queries by leveraging structured documentation, example-based learning, and domain-specific rules. The system builds a rich Knowledge Base from database documentation and question-query examples, which are stored as vector embeddings and retrieved through semantic similarity. It then uses this context to generate syntactically correct and semantically aligned SQL code. The paper details the architecture, training methodology, and retrieval logic, highlighting how the system bridges the gap between user intent and database structure without requiring SQL expertise.