用图结构规划跨工具查询，让大模型更可靠地处理复杂数据请求。
QueryGraph: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation
- 将自然语言转为带依赖关系的图结构，实现多工具协同
- 基于深度优先搜索规划执行路径，准确率高且支持本地部署小模型
- 适合需要跨应用查询个人数据的智能助手场景
针对个人数据查询需跨多个应用、仅靠单一工具无法满足的问题，现有大模型虽具较强推理与工具调用能力，但跨工具多步查询仍难保证可靠性。本文提出一种系统，将自然语言查询转化为结构化图，并通过确定性规划器执行。该方法采用深度优先搜索处理工具间依赖关系，整合多源结果，显著提升执行可靠性，支持超出传统关键词搜索的复杂查询。实验表明，即使使用较小或本地部署的大模型，也能保持高准确率。
原文摘要 · Abstract (English)
Many real-world queries over personal data span multiple applications and require structured planning, as individual tools expose only partial information. While LLMs show strong reasoning and tool use, reliably executing multi-step, cross-tool queries remains challenging. We introduce a system that converts natural language queries into structured graphs and executes them via a deterministic planner. Our approach uses depth-first search to resolve dependencies and combine results across tools, improving reliability and enabling queries beyond traditional keyword-based search. We demonstrate high accuracy even with smaller or locally hosted LLMs.
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