统一可视化图RAG流程，让多跳问答更可调试。
GraphContainer: A Unified Platform for Comparing and Debugging Graph RAG Methods
- 用统一格式层标准化不同图结构数据
- 通过记录器追踪并可视化检索全过程
- 适合研究者快速对比不同图RAG方案
图RAG能缓解大模型的幻觉和知识过时问题，尤其在多跳问答任务中表现优异。但现有方法高度碎片化，不同框架间图格式差异大，缺乏细粒度可视化工具，导致评估与比较困难。为此，我们提出GraphContainer——一个统一的图RAG对比与调试平台。其包含两大核心组件：(1) 统一图表示（UGR）层，可无缝标准化多种格式的图数据；(2) 图记录器，可追踪并可视化检索过程的每一步。通过交互式网页界面，我们展示了该平台支持导入异构图数据，并实现对图RAG方法的实时、可追溯的视觉调试。最终，我们验证了GraphContainer能够支持不同图格式与检索策略的受控对比，显著降低研究人员与实践者构建最优图RAG流水线的门槛。演示视频见 https://youtu.be/O02eNJLwkU0。
原文摘要 · Abstract (English)
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.
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