mlsanityData to 14 Oct 2026

FastRAG: Retrieval Augmented Generation for Semi-structured Data

arXiv 2411.13773 ↗First posted 21 Nov 2024cs.NI cs.AIcited by 3

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 13 of its 23 references that are in the map. First posted on arXiv 21 Nov 2024.

Builds on

13 of 23 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr

Other references · 12

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  3. 2024HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extractioncited by 93
  4. 2020Loghub: A Large Collection of System Log Datasets for AI-driven Log Analyticscited by 70
  5. 2023Log Parsing with Prompt-based Few-shot Learningcited by 29
  6. 2023What do LLMs need to Synthesize Correct Router Configurations?cited by 29
  7. 2024Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunicationscited by 26
  8. 2023RAGLog: Log Anomaly Detection using Retrieval Augmented Generationcited by 19
  9. 2024Research Trends for the Interplay between Large Language Models and Knowledge Graphscited by 15
  10. 2023Making Network Configuration Human Friendlycited by 13
  11. 2024Can LLMs Understand Computer Networks? Towards a Virtual System Administratorcited by 11
  12. 2024GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configurationcited by 6

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 88
  2. 2025Graph-Guided Concept Selection for Efficient Retrieval-Augmented Generationcited by 0
  3. 2025LGM: Enhancing Large Language Models with Conceptual Meta-Relations and Iterative Retrievalcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
  2. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
  3. 2024Graph Retrieval-Augmented Generation: A Survey
  4. 2025PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths
  5. 2024LightRAG: Simple and Fast Retrieval-Augmented Generation
  6. 2025MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation
  7. 2024AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline
  8. 2024EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations