Efficiently processing and interpreting network data is critical for the operation of increasingly complex networks. Recent advances in Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) techniques have improved data processing in network management. However, existing RAG methods like VectorRAG and GraphRAG struggle with the complexity and implicit nature of semi-structured technical data, leading to inefficiencies in time, cost, and retrieval. This paper introduces FastRAG, a novel RAG approach designed for semi-structured data. FastRAG employs schema learning and script learning to extract and structure data without needing to submit entire data sources to an LLM. It integrates text search with knowledge graph (KG) querying to improve accuracy in retrieving context-rich information. Evaluation results demonstrate that FastRAG provides accurate question answering, while improving up to 90% in time and 85% in cost compared to GraphRAG.

翻译：高效处理和解析网络数据对于日益复杂网络的运行至关重要。大型语言模型（LLM）与检索增强生成（RAG）技术的最新进展改善了网络管理中的数据处 理。然而，现有RAG方法（如VectorRAG和GraphRAG）难以应对半结构化技术数据的复杂性与隐含特性，导致时间、成本与检索效率低下。本文提出FastRAG，一种专为半结构化数据设计的新型RAG方法。FastRAG采用模式学习与脚本学习技术，无需将完整数据源提交给LLM即可实现数据提取与结构化。该方法融合文本搜索与知识图谱（KG）查询，以提升上下文丰富信息的检索准确度。评估结果表明，与GraphRAG相比，FastRAG在提供精确问答的同时，时间效率提升最高达90%，成本降低最高达85%。