Datrics Text2SQL：一种自然语言转SQL查询生成框架
Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
- Datrics
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对Text-to-SQL系统在歧义理解、领域词汇和复杂模式处理上的难题，提出基于RAG的Datrics Text2SQL框架，通过知识库检索生成准确SQL，降低数据分析的使用门槛。
AI中文摘要：
Text-to-SQL系统支持用户使用自然语言查询数据库，让数据分析的访问门槛大幅降低。但这类系统在理解歧义表述、领域特定词汇和复杂模式关系方面面临挑战。本文提出Datrics Text2SQL，这是一种基于检索增强生成（Retrieval-Augmented Generation，RAG）的框架，旨在通过利用结构化文档、基于示例的学习和领域特定规则生成准确的SQL查询。该系统从数据库文档和问题-查询示例中构建丰富的知识库，内容以向量嵌入形式存储，并通过语义相似度进行检索。随后系统利用这些上下文生成语法正确、语义对齐的SQL代码。本文详细介绍了其架构、训练方法和检索逻辑，阐明了该系统如何在无需用户具备SQL专业知识的情况下，弥合用户意图与数据库结构之间的差距。
英文摘要：
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.
