用图结构提升RAG在复杂数据中的检索与生成能力
Graphs RAG at Scale: Beyond Retrieval-Augmented Generation With Labeled Property Graphs and Resource Description Framework for Complex and Unknown Search Spaces
- 构建基于LPG和RDF的图式RAG框架，支持动态检索与实时查询
- 文本转Cypher准确率超90%，半结构化数据处理效率显著提升
- 适合需要复杂推理与未知搜索空间的智能问答系统
近年来，检索增强生成（RAG）在知识密集型任务中取得突破，但传统方法在搜索空间未知或文档为半结构化/结构化时表现不佳。本文提出一种端到端图式RAG框架，结合带标签属性图（LPG）与资源描述框架（RDF），克服上述局限。该方法无需预设文档数量，避免低效重排序。我们提出一种基于JSON键值对转换文档为RDF三元组的新方法，实现半结构化数据的无缝集成。此外，设计了文本到Cypher的转换框架，在实时查询生成上达到超过90%的准确率，适用于在线应用。实验表明，图式RAG在准确性、响应质量与推理能力上显著优于传统嵌入式RAG，尤其在复杂半结构化任务中表现突出。这些成果确立了图式RAG作为下一代检索增强系统的核心解决方案。
原文摘要 · Abstract (English)
Recent advances in Retrieval-Augmented Generation (RAG) have revolutionized knowledge-intensive tasks, yet traditional RAG methods struggle when the search space is unknown or when documents are semi-structured or structured. We introduce a novel end-to-end Graph RAG framework that leverages both Labeled Property Graph (LPG) and Resource Description Framework (RDF) architectures to overcome these limitations. Our approach enables dynamic document retrieval without the need to pre-specify the number of documents and eliminates inefficient reranking. We propose an innovative method for converting documents into RDF triplets using JSON key-value pairs, facilitating seamless integration of semi-structured data. Additionally, we present a text to Cypher framework for LPG, achieving over 90% accuracy in real-time translation of text queries to Cypher, enabling fast and reliable query generation suitable for online applications. Our empirical evaluation demonstrates that Graph RAG significantly outperforms traditional embedding-based RAG in accuracy, response quality, and reasoning, especially for complex, semi-structured tasks. These findings establish Graph RAG as a transformative solution for next-generation retrieval-augmented systems.
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