Beyond Vector Search: Comparing Classical RAG with Hybrid GraphRAG for Climate Science Q\&A

2026-08-28

AI Summary

论文提出一种结合向量搜索、GraphRAG、Leiden社区检测和交叉编码重排序的混合架构，以解决传统RAG系统在处理气候科学领域复杂文献时无法捕捉概念间层级关系的问题。

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 with GraphRAG, Leiden community detection, and cross-encoder re-ranking, achieving gains of 160\% in contextual relevance and 177\% in contextual recall compared to classical RAG. These results demonstrate that unifying local and global retrieval significantly outperforms text-span isolation, paving the way for more effective question-answering systems over dispersed scientific literature.

Problem

• Retrieval-Augmented Generation
• conceptual relationships
• climate science
• answer quality

Innovation

• GraphRAG
• Leiden community detection
• cross-encoder re-ranking