mlsanityData to 7 Oct 2026

A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph

arXiv 2407.19994 ↗First posted 29 Jul 2024cs.AIcited by 8

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

Abstract (arXiv)

This study aims to improve knowledge-based question-answering (QA) systems by overcoming the limitations of existing Retrieval-Augmented Generation (RAG) models and implementing an advanced RAG system based on Graph technology to develop high-quality generative AI services. While existing RAG models demonstrate high accuracy and fluency by utilizing retrieved information, they may suffer from accuracy degradation as they generate responses using pre-loaded knowledge without reprocessing. Additionally, they cannot incorporate real-time data after the RAG configuration stage, leading to issues with contextual understanding and biased information. To address these limitations, this study implemented an enhanced RAG system utilizing Graph technology. This system is designed to efficiently search and utilize information. Specifically, it employs LangGraph to evaluate the reliability of retrieved information and synthesizes diverse data to generate more accurate and enhanced responses. Furthermore, the study provides a detailed explanation of the system's operation, key implementation steps, and examples through implementation code and validation results, thereby enhancing the understanding of advanced RAG technology. This approach offers practical guidelines for implementing advanced RAG systems in corporate services, making it a valuable resource for practical application.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 7 of its 32 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

7 of 32 references.

References · 7

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  3. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  4. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  5. 2024Corrective Retrieval Augmented Generationcited by 187
  6. 2024RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculturecited by 80
  7. 2023A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecturecited by 28

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 2.

8 papers.

  1. 2024Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAIcited by 35
  2. 2025HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generationcited by 31
  3. 2025CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Executioncited by 14
  4. 2025LLMs Working in Harmony: A Survey on the Technological Aspects of Building Effective LLM-Based Multi Agent Systemscited by 10
  5. 2025MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systemscited by 0
  6. 2025Conversational No-code, Multi-agentic Disease Module Identification and Drug Repurposing Prediction with ChatDRexcited by 0
  7. 2026LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns & Paradigmscited by 0
  8. 2026Toward Agentic Optical Networks: A Vision of LLM Agent-Driven Autonomous Lifecycle Managementcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations
  2. 2025In-depth Analysis of Graph-based RAG in a Unified Framework
  3. 2026Augmenting Question Answering with A Hybrid RAG Approach
  4. 2025A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
  5. 2025From Search to Reasoning: A Five-Level RAG Capability Framework for Enterprise Data
  6. 2026Agent-Enhanced Heterogeneous Graph RAG for Academic Question Answering
  7. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
  8. 2024WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs