Intelligent Retrieval and Knowledge Question-Answering System for Semi-Structured Operational Manuals

In the field of industrial production and manufacturing, various management systems such as Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP), and Advanced Planning Systems (APS) play a crucial role in production monitoring, performance evaluation, and decision support. These systems enable managers to obtain real-time production data, analyze process anomalies, and formulate appropriate adjustment strategies to ensure production efficiency and quality consistency. However, when users are unfamiliar with system operations, they often need to search through extensive Standard Operating Procedure (SOP) manuals to find relevant operational guidance, which is both time-consuming and detrimental to operational efficiency. In recent years, the integration of Generative Artificial Intelligence (Generative AI) with Retrieval-Augmented Generation (RAG) technology has enabled systems to automatically retrieve and synthesize relevant knowledge content in response to user queries, providing real-time assistance. However, when handling unstructured or semi-structured data, traditional text segmentation methods may disrupt the original hierarchical structure and contextual relationships of documents, leading to retrieval results that fail to accurately match user queries. Moreover, if text segments lack complete source attribution, RAG models may struggle to clearly indicate the origin of the information in their responses, thereby affecting the credibility of the generated content. To address these challenges, this study proposes a method for extracting semi-structured data to enhance the quality of document parsing. By implementing this approach, the final output of the RAG system can generate high-quality responses, precisely annotate interface locations and procedural steps, and assist field personnel in utilizing management systems more efficiently.