This study aims to optimize the existing retrieval-augmented generation model (RAG) by introducing a graph structure to improve the performance of the model in dealing with complex knowledge reasoning tasks. The traditional RAG model has the problem of insufficient processing efficiency when facing complex graph structure information (such as knowledge graphs, hierarchical relationships, etc.), which affects the quality and consistency of the generated results. This study proposes a scheme to process graph structure data by combining graph neural network (GNN), so that the model can capture the complex relationship between entities, thereby improving the knowledge consistency and reasoning ability of the generated text. The experiment used the Natural Questions (NQ) dataset and compared it with multiple existing generation models. The results show that the graph-based RAG model proposed in this paper is superior to the traditional generation model in terms of quality, knowledge consistency, and reasoning ability, especially when dealing with tasks that require multi-dimensional reasoning. Through the combination of the enhancement of the retrieval module and the graph neural network, the model in this study can better handle complex knowledge background information and has broad potential value in multiple practical application scenarios.

翻译：本研究旨在通过引入图结构优化现有检索增强生成模型（RAG），以提升模型在处理复杂知识推理任务时的性能。传统RAG模型在面对复杂图结构信息（如知识图谱、层次关系等）时存在处理效率不足的问题，影响了生成结果的质量与一致性。本研究提出结合图神经网络（GNN）处理图结构数据的方案，使模型能够捕捉实体间的复杂关联，从而提升生成文本的知识一致性与推理能力。实验采用Natural Questions（NQ）数据集，并与多种现有生成模型进行对比。结果表明，本文提出的基于图的RAG模型在生成质量、知识一致性和推理能力方面均优于传统生成模型，尤其在需要多维推理的任务处理中表现突出。通过检索模块增强与图神经网络的结合，本研究模型能够更好地处理复杂知识背景信息，在多种实际应用场景中具有广阔的潜在价值。