CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter

TL;DR

This paper introduces CFT-RAG, a novel retrieval-augmented generation method that leverages an entity tree structure and an improved Cuckoo Filter to significantly enhance retrieval efficiency without compromising generation quality.

Contribution

It proposes a new Tree-RAG acceleration technique using an improved Cuckoo Filter for faster entity retrieval in hierarchical knowledge bases.

Findings

Method is hundreds of times faster than naive Tree-RAG with similar quality.

Efficient entity localization improves retrieval speed in hierarchical structures.

Maintains high generative quality despite significant performance improvements.

Abstract

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.