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, Ho-pRAG 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 retrievereason-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.Experiments on multiple multi-hop benchmarks demonstrate that HopRAG's retrieve-reason-prune mechanism can expand the retrieval scope based on logical connections and improve final answer quality.