SOPRAG：面向工业标准操作规程的多视图图专家检索
SOPRAG: Multi-view Graph Experts Retrieval for Industrial Standard Operating Procedures
AI总结：
SOPRAG通过多视图图专家检索框架，解决工业SOP检索中的结构复杂性和条件依赖性问题，提升检索准确性和响应实用性。
摘要：
标准操作规程（SOPs）对于确保工业环境中的操作安全性和一致性至关重要。然而，检索和遵循这些规程存在独特挑战，例如固有的专有结构、条件依赖的相关性以及可操作的执行要求，这些是标准语义驱动的检索增强生成（RAG）范式无法解决的。受混合专家（MoE）范式的启发，我们提出了SOPRAG，一种专门设计解决SOP检索上述痛点的新框架。SOPRAG用专门的实体、因果和流程图专家取代了扁平分块，以解决工业结构和逻辑复杂性。为了优化和协调这些专家，我们提出了一种过程卡层，通过修剪搜索空间来消除计算噪声，并提出了一种由大型语言模型引导的门控机制，动态加权这些专家以使检索与操作员意图对齐。为了解决领域特定数据稀缺的问题，我们还引入了自动化、多代理的工作流来构建基准。在四个工业领域的广泛实验中，SOPRAG在检索准确性和响应实用性方面均显著优于强大的词法、密集和图基于的RAG基线，实现了在现实世界关键任务中的完美执行评分。
英文摘要：
Standard Operating Procedures (SOPs) are essential for ensuring operational safety and consistency in industrial environments. However, retrieving and following these procedures presents unique challenges, such as rigid proprietary structures, condition-dependent relevance, and actionable execution requirement, which standard semantic-driven Retrieval-Augmented Generation (RAG) paradigms fail to address. Inspired by the Mixture-of-Experts (MoE) paradigm, we propose SOPRAG, a novel framework specifically designed to address the above pain points in SOP retrieval. SOPRAG replaces flat chunking with specialized Entity, Causal, and Flow graph experts to resolve industrial structural and logical complexities. To optimize and coordinate these experts, we propose a Procedure Card layer that prunes the search space to eliminate computational noise, and an LLM-Guided gating mechanism that dynamically weights these experts to align retrieval with operator intent. To address the scarcity of domain-specific data, we also introduce an automated, multi-agent workflow for benchmark construction. Extensive experiments across four industrial domains demonstrate that SOPRAG significantly outperforms strong lexical, dense, and graph-based RAG baselines in both retrieval accuracy and response utility, achieving perfect execution scores in real-world critical tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
