RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs
This work addresses scalability and adaptability issues in graph-based RAG systems for researchers and practitioners, though it is incremental by building on existing graph learning and RAG advancements.
The authors tackled the problem of inefficient and inflexible retrieval-augmented generation (RAG) systems on graphs by introducing RGL, a modular framework that integrates the entire RAG pipeline, achieving speedups of up to 143x compared to conventional methods.
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.