{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/datrics-text2sql-a-framework-for-natural","title":"Datrics Text2SQL. A Framework for Natural Language to SQL Query Generation","arxiv_id":null,"date":"2025-03-15","proceeding":"- 2025 3","authors":["Tetiana Gladkykh","Kirykov Kyrylo"],"abstract":"Datrics Text2SQL is a Text-to-SQL framework using Retrieval-Augmented Generation (RAG) to enhance accuracy and reliability. By integrating domain knowledge, database structure, and example-based learning, it addresses common SQL generation challenges such as incorrect table selection, faulty joins, and misinterpreted business rules. A knowledge base built from database documentation and successful queries is stored in a vector database, enabling retrieval of relevant context for precise SQL generation. The structured process of knowledge collection, context retrieval, and query generation ensures adaptability to various database schemas and query types.","url_abs":"https://www.researchgate.net/publication/389944067_Datrics_Text2SQL_A_Framework_for_Natural_Language_to_SQL_Query_Generation","url_pdf":"https://www.researchgate.net/publication/389944067_Datrics_Text2SQL_A_Framework_for_Natural_Language_to_SQL_Query_Generation","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"datrics-text2sql-a-framework-for-natural","repo_url":"https://github.com/datrics-ai/text2sql","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"natural-language-queries","task_name":"Natural Language Queries"},{"task_slug":"rag","task_name":"RAG"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"retrieval-augmented-generation","task_name":"Retrieval-augmented Generation"},{"task_slug":"text-to-sql","task_name":"Text to SQL"},{"task_slug":"text-to-sql","task_name":"Text-To-SQL"}],"methods":[{"method_slug":"base","method_name":"BASE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}