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