- 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
- 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