Graph Retrieval-Augmented Generation: A Survey

Original Paper: https://arxiv.org/abs/2408.08921

By: Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, Siliang Tang

Abstract

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an external knowledge base, RAG refines LLM outputs, effectively mitigating issues such as ``hallucination'', lack of domain-specific knowledge, and outdated information. However, the complex structure of relationships among different entities in databases presents challenges for RAG systems. In response, GraphRAG leverages structural information across entities to enable more precise and comprehensive retrieval, capturing relational knowledge and facilitating more accurate, context-aware responses. Given the novelty and potential of GraphRAG, a systematic review of current technologies is imperative. This paper provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation. We then outline the core technologies and training methods at each stage. Additionally, we examine downstream tasks, application domains, evaluation methodologies, and industrial use cases of GraphRAG. Finally, we explore future research directions to inspire further inquiries and advance progress in the field.

Summary Notes

Figure 1.Comparision between Direct LLM, RAG, and GraphRAG. Given a user query, direct answering by LLMs may suffer from shallow responses or lack of specificity. RAG addresses this by retrieving relevant textual information, somewhat alleviating the issue. However, due to the text’s length and flexible natural language expressions of entity relationships, RAG struggles to emphasize “influence” relations, which is the core of the question. While, GraphRAG methods leverage explicit entity and relationship representations in graph data, enabling precise answers by retrieving relevant structured information.

Figure 2.The overview of the GraphRAG framework for question answering task. In this survey, we divide GraphRAG into three stages: G-Indexing, G-Retrieval, and G-Generation. We categorize the retrieval sources into open-source knowledge graphs and self-constructed graph data. Various enhancing techniques like query enhancement and knowledge enhancement may be adopted to boost the relevance of the results. Unlike RAG, which uses retrieved text directly for generation, GraphRAG requires converting the retrieved graph information into patterns acceptable to generators to enhance the task performance.

Introduction

In recent years, the advent of Large Language Models (LLMs) like GPT-4 and LLaMA has fundamentally transformed the landscape of natural language processing.

These models, trained on vast datasets, have demonstrated remarkable capabilities in understanding and generating human language.

However, LLMs are not without their limitations, such as a lack of domain-specific knowledge and the phenomenon of "hallucination," where models generate inaccurate or fabricated information.

To address these challenges, Retrieval-Augmented Generation (RAG) has emerged as a significant advancement. RAG enhances the quality and relevance of generated content by integrating a retrieval component within the generation process.

However, traditional RAG systems struggle with capturing the complex relationships among entities in databases.

Enter Graph Retrieval-Augmented Generation (GraphRAG), a novel approach that leverages the structural information in graphs to enable more precise and comprehensive retrieval, facilitating context-aware responses.

Key Methodologies in GraphRAG

GraphRAG comprises three primary stages: Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation.

Graph-Based Indexing (G-Indexing)

The initial phase involves identifying or constructing a graph database that aligns with downstream tasks and establishing indices for efficient retrieval.

Graph data can originate from public knowledge graphs like Wikidata and Freebase or be constructed from proprietary data sources. Indexing methods range from graph indexing, which preserves the entire graph structure, to text and vector indexing, which optimize retrieval processes.

Graph-Guided Retrieval (G-Retrieval)

Following indexing, the retrieval phase focuses on extracting relevant information from the graph database in response to user queries.

Various retrievers are employed, including non-parametric retrievers, LM-based retrievers, and GNN-based retrievers. Retrieval paradigms can be single-stage or multi-stage, with granularity ranging from nodes and triplets to paths and subgraphs.

Graph-Enhanced Generation (G-Generation)

In the generation phase, the retrieved graph data is combined with the query to produce the final response. Generators can be GNNs, language models, or hybrid models.

The retrieved graph data is transformed into formats compatible with the generators, such as natural language descriptions or node sequences. Generation enhancement techniques are employed to further improve output quality.

Main Findings and Results

GraphRAG has shown significant improvements over traditional RAG systems, particularly in terms of accuracy and contextual relevance.

By leveraging structured graph data, GraphRAG systems can capture complex relational knowledge and provide more precise answers.

For instance, in the biomedical field, GraphRAG systems have demonstrated advanced performance in medical decision-making by integrating comprehensive medical knowledge graphs.

Example: Enhancing Biomedical QA Systems

In a biomedical question-answering system, GraphRAG can utilize a knowledge graph that includes diseases, symptoms, treatments, and medications.

By retrieving relevant subgraphs and integrating them with the query, the system can provide detailed and accurate responses, significantly improving the quality of medical consultations.

Implications and Potential Applications

The implications of GraphRAG extend across various domains, including e-commerce, healthcare, finance, and legal services.

In e-commerce, GraphRAG can enhance customer service and recommendation systems by leveraging historical interaction data. In healthcare, it can support medical diagnosis and personalized treatment plans by integrating medical literature and patient histories.

Application in Legal Services

In the legal domain extensive, citation connections exist between cases and judicial opinions, forming a structured graph.

GraphRAG can enhance legal research and case analysis by retrieving relevant legal precedents and providing comprehensive insights.

Conclusion

GraphRAG represents a significant advancement in the field of information retrieval and generation, addressing the limitations of traditional RAG systems.

By leveraging the structural information in graphs, GraphRAG enables more precise and context-aware responses, enhancing the capabilities of LLMs across various domains.

The future of GraphRAG looks promising, with ongoing research focusing on dynamic and adaptive graphs, multi-modality information integration, and scalable retrieval mechanisms.

As the field continues to evolve, GraphRAG is poised to revolutionize the way we retrieve and generate information, paving the way for more intelligent and accurate AI systems.