GraphContainer：用于比较和调试图RAG方法的统一平台
GraphContainer: A Unified Platform for Comparing and Debugging Graph RAG Methods
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中文总结 AI 辅助
研究针对图RAG方法分散不兼容问题，提出GraphContainer平台，通过统一图表示层和图记录器两个关键组件，实现多格式图统一与检索过程可视化，助力可控比较不同图格式及检索策略，降低设计最优图RAG管道的难度。
中文摘要 AI 辅助
图RAG可减轻大语言模型中的幻觉和陈旧知识，尤其适用于多跳问答。然而，现有方法高度分散且不兼容。不同框架中图格式的结构异质性以及缺乏精细可视化工具，使得评估和比较检索行为极为困难。为弥合这一差距，我们提出了GraphContainer，一个旨在统一和可视化各种图RAG工作流程的新颖平台。GraphContainer有两个关键组件：统一图表示（UGR）层，可无缝标准化多格式图；图记录器，则跟踪并直观呈现逐步检索过程。通过交互式网络界面，展示了GraphContainer导入异构图并对图RAG方法进行实时、可追溯的可视化调试的能力。最终表明，GraphContainer能对各种图格式和检索策略进行可控比较，降低研究人员和从业者设计最优图RAG管道的难度。
英文摘要
Graph RAG mitigates hallucinations and stale knowledge in LLMs, particularly for multi-hop question answering. However, existing approaches remain highly fragmented and incompatible. The structural heterogeneity of graph formats across different frameworks and the lack of granular visualization tools make it exceedingly difficult to evaluate and compare retrieval behaviors. To bridge this gap, we propose GraphContainer, a novel platform designed to unify and visualize diverse graph RAG workflows. GraphContainer features two key components: (1) a Unified Graph Representation (UGR) layer that seamlessly standardizes multi-format graphs, and (2) a Graph Recorder that tracks and visually renders the step-by-step retrieval process. Through an interactive web interface, we demonstrate GraphContainer's ability to import heterogeneous graphs and perform live, traceable visual debugging of graph RAG methods. Ultimately, we show how GraphContainer enables controlled comparisons of various graph formats and retrieval strategies, lowering the barrier for researchers and practitioners to design optimal graph RAG pipelines. A demonstration video is available at https://youtu.be/O02eNJLwkU0.
发表机构
- KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
