FastRAG提升半结构化网络数据检索效率，速度快90%、成本降85%
FastRAG: Retrieval Augmented Generation for Semi-structured Data
- 通过模式与脚本学习提取结构化数据，避免全量传给大模型
- 结合文本搜索与知识图谱查询，准确率显著提升
- 适合需要高效处理网络日志等半结构化数据的工程师
高效处理与解析网络数据对日益复杂的网络运行至关重要。近年来，大语言模型（LLM）与检索增强生成（RAG）技术在网络管理数据处理中取得进展。然而，现有RAG方法如VectorRAG和GraphRAG在处理半结构化技术数据时面临复杂性和隐含性挑战，导致时间与成本效率低下。本文提出FastRAG，一种专为半结构化数据设计的新RAG方法。FastRAG通过模式学习与脚本学习提取并结构化数据，无需将整个数据源提交至LLM。它融合文本搜索与知识图谱（KG）查询，提升上下文丰富信息的检索准确性。评估结果表明，FastRAG在问答任务中表现准确，相比GraphRAG，时间效率提升最高达90%，成本降低最高达85%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。