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This paper identifies 'vector search dilution' in RAG systems when scaling to large heterogeneous document collections, where accuracy dropped from 75% to 40% in a real-world deployment. The proposed MASDR-RAG method uses domain scoping via organizational metadata before retrieval, improving P@10 from 0.77 to 0.86 with low cost and easy deployment.
本文识别了RAG系统在扩展到大规模异构文档集合时出现的“向量搜索稀释”现象，并提出MASDR-RAG，一种利用组织元数据进行领域限定的检索方法，显著提升了检索准确率。