SafeMate用AI帮普通人应对突发情况，自动匹配最相关安全指南。
SafeMate: A Modular RAG-Based Agent for Context-Aware Emergency Guidance
- 基于MCP框架动态调用检索与摘要工具，实现上下文感知响应。
- 利用FAISS和余弦相似度从可信源中精准定位应急文档内容。
- 适合公众在灾前准备或危机中快速获取可操作指引。
尽管公共安全文档和应急规程丰富，多数人仍难以在危机中解读并采取行动。传统应急决策支持系统（EDSS）面向专业人员，依赖静态文件如PDF或标准操作流程（SOP），非专业人士在压力下难于使用。这一制度知识与公众可及性之间的鸿沟，严重阻碍了有效应急准备与响应。我们提出SafeMate，一个基于检索增强生成（RAG）的AI助手，能在灾前准备和应急场景中为普通用户提供准确、上下文感知的指导。该系统基于模型上下文协议（MCP），动态调度用户查询至文档检索、清单生成与结构化摘要等工具模块。通过FAISS结合余弦相似度，从可信来源中高效识别相关文本内容。
原文摘要 · Abstract (English)
Despite the abundance of public safety documents and emergency protocols, most individuals remain ill-equipped to interpret and act on such information during crises. Traditional emergency decision support systems (EDSS) are designed for professionals and rely heavily on static documents like PDFs or SOPs, which are difficult for non-experts to navigate under stress. This gap between institutional knowledge and public accessibility poses a critical barrier to effective emergency preparedness and response. We introduce SafeMate, a retrieval-augmented AI assistant that delivers accurate, context-aware guidance to general users in both preparedness and active emergency scenarios. Built on the Model Context Protocol (MCP), SafeMate dynamically routes user queries to tools for document retrieval, checklist generation, and structured summarization. It uses FAISS with cosine similarity to identify relevant content from trusted sources.
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