Most open-source Text2SQL engines struggle with major issues: - Poor retrieval mechanisms – They fail to fetch the right tables & columns before SQL generation

Most open-source Text2SQL engines struggle with major issues: - Poor retrieval mechanisms – They fail to fetch the right tables & columns before SQL generation. - Ambiguity in documentation – Many models cannot effectively resolve vague schema descriptions, leading to errors. - Poor generalization on real-world queries – Models work on benchmarks but break on actual user inputs. We built Datrics Text2SQL to fix this. Our approach provides: - A well-tuned RAG pipeline that retrieves schema context with high precision. - Better disambiguation algorithms for handling unclear database documentation. - Improved generalization with real-world query adaptation, not just benchmark scores. If you’ve worked with Text2SQL and faced these issues, we’d love your feedback! Whitepaper: https://www.researchgate.net/publication/389944067_Datrics_Text2SQL_A_Framework_for_Natural_Language_to_SQL_Query_Generation https://www.researchgate.net/publication/389944067_Datrics_T... GitHub: https://github.com/datrics-ai/text2sql https://github.com/datrics-ai/text2sql