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Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation

arXiv 2506.12234 ↗First posted 3 Apr 2025cs.DB cs.AI cs.CLcited by 0

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Abstract (arXiv)

Text-to-SQL systems enable users to query databases using natural language, democratizing access to data analytics. However, they face challenges in understanding ambiguous phrasing, domain-specific vocabulary, and complex schema relationships. This paper introduces Datrics Text2SQL, a Retrieval-Augmented Generation (RAG)-based framework designed to generate accurate SQL queries by leveraging structured documentation, example-based learning, and domain-specific rules. The system builds a rich Knowledge Base from database documentation and question-query examples, which are stored as vector embeddings and retrieved through semantic similarity. It then uses this context to generate syntactically correct and semantically aligned SQL code. The paper details the architecture, training methodology, and retrieval logic, highlighting how the system bridges the gap between user intent and database structure without requiring SQL expertise.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 3 Apr 2025.

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7 of 21 references.

References · 7

  1. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 120
  2. 2021Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalizationcited by 92
  3. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  4. 2024AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queriescited by 35
  5. 2023Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledgecited by 18
  6. 2024ChatBI: Towards Natural Language to Complex Business Intelligence SQLcited by 9
  7. 2022Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQLcited by 6

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  1. 2024From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems
  2. 2026CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale
  3. 2024Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning
  4. 2025DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction
  5. 2025Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL
  6. 2024Multi-Turn Interactions for Text-to-SQL with Large Language Models
  7. 2024Text2SQL is Not Enough: Unifying AI and Databases with TAG
  8. 2025DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL