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.

翻译：标准操作规程（SOP）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面临着独特的挑战，例如僵化的专有结构、条件依赖的相关性以及可操作执行要求，这些是标准语义驱动的检索增强生成范式所无法解决的。受专家混合范式的启发，我们提出了SOPRAG，这是一个专门为解决SOP检索中上述痛点而设计的新型框架。SOPRAG使用专门的实体图、因果图和流程图专家替代了扁平分块，以解决工业结构及逻辑的复杂性。为了优化和协调这些专家，我们提出了一个规程卡片层来剪枝搜索空间以消除计算噪声，以及一个LLM引导的门控机制来动态加权这些专家，使检索与操作员意图对齐。针对领域特定数据稀缺的问题，我们还引入了一种用于基准构建的自动化多智能体工作流。在四个工业领域的大量实验表明，SOPRAG在检索准确性和响应实用性方面均显著优于强大的基于词法、稠密向量和图结构的RAG基线，并在现实世界关键任务中实现了完美的执行分数。