Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
It addresses the challenge of ambiguous phrasing and domain-specific vocabulary in text-to-SQL systems, making data analytics more accessible, though it appears incremental as it builds on existing RAG methods.
The paper tackles the problem of generating accurate SQL queries from natural language by introducing Datrics Text2SQL, a Retrieval-Augmented Generation-based framework that leverages structured documentation and examples, resulting in improved accuracy and usability for non-experts.
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.