Large Language Models (LLMs) have emerged as a promising solution for converting natural language queries into SQL commands, enabling seamless database interaction. However, these Text-to-SQL (Text2SQL) systems face inherent limitations, hallucinations, outdated knowledge, and untraceable reasoning. To address these challenges, the integration of retrieval-augmented generation (RAG) with Text2SQL models has gained traction. RAG serves as a retrieval mechanism, providing essential contextual information, such as table schemas and metadata, to enhance the query generation process. Despite their potential, RAG + Text2SQL systems are susceptible to the quality and size of retrieved documents. While richer document content can improve schema relevance and retrieval accuracy, it also introduces noise, increasing the risk of hallucinations and reducing query fidelity as the prompt size of the Text2SQL model increases. This research investigates the nuanced trade-off between document size and quality, aiming to strike a balance that optimizes system performance. Key thresholds are identified where performance degradation occurs, along with actionable strategies to mitigate these challenges. Additionally, we explore the phenomenon of hallucinations in Text2SQL models, emphasizing the critical role of curated document presentation in minimizing errors. Our findings provide a roadmap for enhancing the robustness of RAG + Text2SQL systems, offering practical insights for real-world applications.

翻译：大型语言模型（LLMs）已成为将自然语言查询转换为SQL命令的有前景解决方案，实现了无缝的数据库交互。然而，这些文本到SQL（Text2SQL）系统面临固有的局限性，包括幻觉、知识过时和推理过程不可追溯等问题。为应对这些挑战，将检索增强生成（RAG）与Text2SQL模型相结合的方法日益受到关注。RAG作为一种检索机制，通过提供表结构和元数据等关键上下文信息来增强查询生成过程。尽管具有潜力，RAG + Text2SQL系统仍易受检索文档质量和规模的影响。虽然更丰富的文档内容能提升模式相关性和检索准确性，但也会引入噪声——随着Text2SQL模型提示规模的增加，幻觉风险相应提高，查询保真度随之下降。本研究深入探讨文档规模与质量之间的微妙权衡关系，旨在寻求优化系统性能的平衡点。我们识别了导致性能下降的关键阈值，并提出可操作的缓解策略。此外，本研究深入探究Text2SQL模型中的幻觉现象，强调精细化文档呈现方式对于最小化误差的关键作用。我们的研究成果为增强RAG + Text2SQL系统的鲁棒性提供了技术路线图，并为实际应用提供了具有实践价值的见解。