Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep expertise. Retrieval-Augmented generation (RAG) has emerged as a promising solution to customize LLMs for professional fields by seamlessly integrating external knowledge bases, enabling real-time access to domain-specific expertise during inference. Despite its potential, traditional RAG systems, based on flat text retrieval, face three critical challenges: (i) complex query understanding in professional contexts, (ii) difficulties in knowledge integration across distributed sources, and (iii) system efficiency bottlenecks at scale. This survey presents a systematic analysis of Graph-based Retrieval-Augmented Generation (GraphRAG), a new paradigm that revolutionizes domain-specific LLM applications. GraphRAG addresses traditional RAG limitations through three key innovations: (i) graph-structured knowledge representation that explicitly captures entity relationships and domain hierarchies, (ii) efficient graph-based retrieval techniques that enable context-preserving knowledge retrieval with multihop reasoning ability, and (iii) structure-aware knowledge integration algorithms that leverage retrieved knowledge for accurate and logical coherent generation of LLMs. In this survey, we systematically analyze the technical foundations of GraphRAG and examine current implementations across various professional domains, identifying key technical challenges and promising research directions. All the related resources of GraphRAG, including research papers, open-source data, and projects, are collected for the community in https://github.com/DEEP-PolyU/Awesome-GraphRAG.

翻译：大语言模型（LLMs）已在广泛任务中展现出卓越能力，然而，由于其需要深厚的专业知识，在专业领域的应用仍面临挑战。检索增强生成（RAG）通过无缝集成外部知识库，使LLMs在推理过程中能够实时访问领域专业知识，已成为为专业领域定制LLMs的一种有前景的解决方案。尽管潜力巨大，基于扁平文本检索的传统RAG系统面临三个关键挑战：（i）专业语境下的复杂查询理解，（ii）跨分布式知识源整合的困难，以及（iii）规模化下的系统效率瓶颈。本综述系统分析了基于图的检索增强生成（GraphRAG）这一革新领域特定LLM应用的新范式。GraphRAG通过三项关键创新应对传统RAG的局限：（i）图结构知识表示，显式捕获实体关系与领域层级；（ii）高效的基于图的检索技术，实现具有多跳推理能力的上下文保持知识检索；（iii）结构感知的知识整合算法，利用检索到的知识实现LLMs准确且逻辑连贯的生成。在本综述中，我们系统分析了GraphRAG的技术基础，考察了其在不同专业领域的当前实现，并指出了关键技术挑战与有前景的研究方向。GraphRAG的所有相关资源，包括研究论文、开源数据与项目，已为社区收集于 https://github.com/DEEP-PolyU/Awesome-GraphRAG。