用大模型+函数调用实现金融数据库自然语言查询，更准更快更省。
FinAI Data Assistant: LLM-based Financial Database Query Processing with the OpenAI Function Calling API
- 不生成SQL，直接调用预设查询函数，提升可靠性。
- 股票价格预测有延迟偏差，但股票代码映射准确率超99%。
- 适合需要稳定、低成本金融数据查询的分析师和开发者。
我们提出FinAI Data Assistant，一种基于大语言模型（LLM）与OpenAI函数调用API的金融数据库自然语言查询方案。系统不通过文本转SQL生成完整查询，而是将用户请求路由至一个经过验证的参数化查询库，以牺牲生成灵活性换取更高的可靠性、低延迟和低成本。我们在股价和基本面数据上进行了三项实证研究：（RQ1）LLM能否在无外部检索情况下可靠回忆或推断时间依赖性金融数据；（RQ2）LLM将公司名称映射为股票代码的准确性；（RQ3）函数调用是否优于文本转SQL完成端到端查询。实验结果表明，纯LLM预测存在显著误差，且对股价存在前瞻偏差（主要源于模型知识截止时间）。对于纳斯达克-100成分股，代码映射准确率接近完美；标普500企业也保持较高准确率。最终，与文本转SQL基线相比，FinAI Data Assistant在查询延迟、成本和可靠性方面均表现更优。论文讨论了设计权衡、局限性及部署前景。
原文摘要 · Abstract (English)
We present FinAI Data Assistant, a practical approach for natural-language querying over financial databases that combines large language models (LLMs) with the OpenAI Function Calling API. Rather than synthesizing complete SQL via text-to-SQL, our system routes user requests to a small library of vetted, parameterized queries, trading generative flexibility for reliability, low latency, and cost efficiency. We empirically study three questions: (RQ1) whether LLMs alone can reliably recall or extrapolate time-dependent financial data without external retrieval; (RQ2) how well LLMs map company names to stock ticker symbols; and (RQ3) whether function calling outperforms text-to-SQL for end-to-end database query processing. Across controlled experiments on prices and fundamentals, LLM-only predictions exhibit non-negligible error and show look-ahead bias primarily for stock prices relative to model knowledge cutoffs. Ticker-mapping accuracy is near-perfect for NASDAQ-100 constituents and high for S\&P~500 firms. Finally, FinAI Data Assistant achieves lower latency and cost and higher reliability than a text-to-SQL baseline on our task suite. We discuss design trade-offs, limitations, and avenues for deployment.
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