混合GraphRAG架构在气候科学问答中显著优于传统RAG
原标题：Beyond Vector Search: Comparing Classical RAG with Hybrid GraphRAG for Climate Science Q\&A
AI 摘要
本研究提出一种混合架构，将向量搜索与GraphRAG、Leiden社区检测及交叉编码器重排序相结合，用于气候科学问答。相比传统RAG，该混合方法在上下文相关性和召回率上分别提升160%和177%，表明统一局部与全局检索优于文本片段孤立处理。实验基于Instituto Tecnológico Vale的科学文献数据集，使用7个LLM进行评估。
正文节选
Beyond Vector Search: Comparing Classical RAG with Hybrid GraphRAG for Climate Science Q&A Abstract Traditional Retrieval-Augmented Generation (RAG) systems treat documents in isolation, failing to capture hierarchical relationships between concepts in complex scientific corpora. This limitation compromises answer quality in specialized domains such as climatology, where conceptual dependencies frequently traverse multiple articles. We propose a hybrid architecture that integrates vector search