This study proposes a novel hybrid retrieval strategy for Retrieval-Augmented Generation (RAG) that integrates cosine similarity and cosine distance measures to improve retrieval performance, particularly for sparse data. The traditional cosine similarity measure is widely used to capture the similarity between vectors in high-dimensional spaces. However, it has been shown that this measure can yield arbitrary results in certain scenarios. To address this limitation, we incorporate cosine distance measures to provide a complementary perspective by quantifying the dissimilarity between vectors. Our approach is experimented on proprietary data, unlike recent publications that have used open-source datasets. The proposed method demonstrates enhanced retrieval performance and provides a more comprehensive understanding of the semantic relationships between documents or items. This hybrid strategy offers a promising solution for efficiently and accurately retrieving relevant information in knowledge-intensive applications, leveraging techniques such as BM25 (sparse) retrieval , vector (Dense) retrieval, and cosine distance based retrieval to facilitate efficient information retrieval.

翻译：本研究提出了一种新颖的检索增强生成（RAG）混合检索策略，该策略整合了余弦相似度与余弦距离度量，旨在提升检索性能，尤其在稀疏数据场景下。传统的余弦相似度度量被广泛用于捕捉高维空间中向量间的相似性。然而，研究表明，该度量在某些情况下可能产生任意性结果。为应对这一局限，我们引入余弦距离度量，通过量化向量间的相异性提供互补视角。与近期使用开源数据集的研究不同，我们的方法在专有数据上进行了实验验证。所提出的方法展现出增强的检索性能，并对文档或项目间的语义关系提供了更全面的理解。该混合策略通过利用BM25（稀疏）检索、向量（稠密）检索及基于余弦距离的检索等技术，为知识密集型应用中高效、准确地检索相关信息提供了一种有前景的解决方案。