Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter
This work addresses the dual challenges of retrieval accuracy and computational efficiency in RAG for LLMs, offering a practical improvement for real-time applications.
Bridge-RAG introduces an abstract bridge tree and improved Cuckoo Filter to enhance retrieval accuracy and efficiency in RAG, achieving ~15.65% accuracy improvement and 10x–500x retrieval time reduction over existing frameworks.
As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the two challenges regarding retrieval accuracy and computational efficiency. This paper presents a novel RAG framework called Bridge-RAG. To overcome the accuracy challenge, we introduce the concept of abstract to bridge query entities and document chunks, providing robust semantic understanding. We organize the abstracts into a tree structure and design a multi-level retrieval strategy to ensure the inclusion of sufficient contextual information. To overcome the efficiency challenge, we introduce the improved Cuckoo Filter, an efficient data structure supporting rapid membership queries and updates, to accelerate entity location during the retrieval process. We design a block linked list structure and an entity temperature-based sorting mechanism to improve efficiency from the aspects of spatial and temporal locality. Extensive experiments show that Bridge-RAG achieves around 15.65% accuracy improvement and reduces 10x to 500x retrieval time compared to other RAG frameworks.