Datrics Text2SQL: Open-Source, High-Accuracy Natural Language to SQL Conversion (github.com) 1 points by Datkiri 1y ago ↗ HN
[–] Datkiri 1y ago ↗ 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_T...GitHub: https://github.com/datrics-ai/text2sql
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[ 3.3 ms ] story [ 15.3 ms ] thread- 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_T...
GitHub: https://github.com/datrics-ai/text2sql