RGL让图数据的检索生成更快更灵活，提速最高达143倍。
RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs
- 模块化框架整合图索引、动态节点检索到生成全流程。
- 相比传统方法速度提升最高143倍，减少令牌消耗。
- 适合快速构建高效图增强生成系统的研究者与工程师。
图学习的进展推动了利用图数据内在关系结构的检索增强生成（RAG）系统发展。然而，现有方法常受限于固定设置和高工程成本，难以适应与扩展。此外，RAG领域长期忽视图数据库研究中关于大规模图上高效检索有趣子结构的成果。本文提出图上的检索增强生成库（RGL），一个模块化框架，将完整的RAG流程——从高效的图索引、动态节点检索、子图构建、分词到最终生成——无缝集成到统一系统中。RGL通过支持多种图格式并集成关键组件的优化实现，相比传统方法最快提速143倍。其灵活工具如动态节点过滤，可快速提取相关子图，降低令牌消耗。大量实验表明，RGL不仅加速原型开发，还提升了各类任务下基于图的RAG系统的性能与适用性。
原文摘要 · Abstract (English)
Recent advances in graph learning have paved the way for innovative retrieval-augmented generation (RAG) systems that leverage the inherent relational structures in graph data. However, many existing approaches suffer from rigid, fixed settings and significant engineering overhead, limiting their adaptability and scalability. Additionally, the RAG community has largely overlooked the decades of research in the graph database community regarding the efficient retrieval of interesting substructures on large-scale graphs. In this work, we introduce the RAG-on-Graphs Library (RGL), a modular framework that seamlessly integrates the complete RAG pipeline-from efficient graph indexing and dynamic node retrieval to subgraph construction, tokenization, and final generation-into a unified system. RGL addresses key challenges by supporting a variety of graph formats and integrating optimized implementations for essential components, achieving speedups of up to 143x compared to conventional methods. Moreover, its flexible utilities, such as dynamic node filtering, allow for rapid extraction of pertinent subgraphs while reducing token consumption. Our extensive evaluations demonstrate that RGL not only accelerates the prototyping process but also enhances the performance and applicability of graph-based RAG systems across a range of tasks.
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