HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) systems often struggle with imperfect retrieval, as traditional retrievers focus on lexical or semantic similarity rather than logical relevance. To address this, we propose HopRAG, a novel RAG framework that augments retrieval with logical reasoning through graph-structured knowledge exploration. During indexing, HopRAG constructs a passage graph, with text chunks as vertices and logical connections established via LLM-generated pseudo-queries as edges. During retrieval, it employs a retrieve-reason-prune mechanism: starting with lexically or semantically similar passages, the system explores multi-hop neighbors guided by pseudo-queries and LLM reasoning to identify truly relevant ones. Extensive experiments demonstrate HopRAG's superiority, achieving 76.78\% higher answer accuracy and 65.07\% improved retrieval F1 score compared to conventional methods. The repository is available atthis https URL.

@article{liu2025_2502.12442,
  title={ HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation },
  author={ Hao Liu and Zhengren Wang and Xi Chen and Zhiyu Li and Feiyu Xiong and Qinhan Yu and Wentao Zhang },
  journal={arXiv preprint arXiv:2502.12442},
  year={ 2025 }
}