Although retrieval-augmented generation(RAG) significantly improves generation quality by retrieving external knowledge bases and integrating generated content, it faces computational efficiency bottlenecks, particularly in knowledge retrieval tasks involving hierarchical structures for Tree-RAG. This paper proposes a Tree-RAG acceleration method based on the improved Cuckoo Filter, which optimizes entity localization during the retrieval process to achieve significant performance improvements. Tree-RAG effectively organizes entities through the introduction of a hierarchical tree structure, while the Cuckoo Filter serves as an efficient data structure that supports rapid membership queries and dynamic updates. The experiment results demonstrate that our method is much faster than naive Tree-RAG while maintaining high levels of generative quality. When the number of trees is large, our method is hundreds of times faster than naive Tree-RAG. Our work is available at https://github.com/TUPYP7180/CFT-RAG-2025.

翻译：尽管检索增强生成（RAG）通过检索外部知识库并整合生成内容，显著提升了生成质量，但其面临计算效率瓶颈，尤其是在涉及层次化结构的Tree-RAG知识检索任务中。本文提出了一种基于改进布谷鸟过滤器的Tree-RAG加速方法，通过优化检索过程中的实体定位，实现了显著的性能提升。Tree-RAG通过引入层次化树结构有效组织实体，而布谷鸟过滤器作为一种高效的数据结构，支持快速的成员查询与动态更新。实验结果表明，我们的方法在保持高水平生成质量的同时，速度远优于朴素Tree-RAG。当树的数量较大时，我们的方法比朴素Tree-RAG快数百倍。我们的工作可在 https://github.com/TUPYP7180/CFT-RAG-2025 获取。