FastRAG: Retrieval Augmented Generation for Semi-structured Data

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 for semi-structured data. FastRAG proposes chunk sampling, schema learning, and script learning to extract and structure data without submitting entire data sources to the LLM. It integrates text search with knowledge graph (KG) querying to improve accuracy. The evaluation results demonstrate that FastRAG provides accurate question answering while improving up to $90 \%$ in time and $85 \%$ in cost compared to GraphRAG.