Traditional retrieval methods have been essential for assessing document similarity but struggle with capturing semantic nuances. Despite advancements in latent semantic analysis (LSA) and deep learning, achieving comprehensive semantic understanding and accurate retrieval remains challenging due to high dimensionality and semantic gaps. The above challenges call for new techniques to effectively reduce the dimensions and close the semantic gaps. To this end, we propose VectorSearch, which leverages advanced algorithms, embeddings, and indexing techniques for refined retrieval. By utilizing innovative multi-vector search operations and encoding searches with advanced language models, our approach significantly improves retrieval accuracy. Experiments on real-world datasets show that VectorSearch outperforms baseline metrics, demonstrating its efficacy for large-scale retrieval tasks.

翻译：传统检索方法在评估文档相似度方面至关重要，但难以捕捉语义上的细微差别。尽管潜在语义分析（LSA）和深度学习取得了进展，但由于高维度和语义鸿沟的存在，实现全面的语义理解和精确检索仍然具有挑战性。上述挑战要求开发能够有效降维并弥合语义鸿沟的新技术。为此，我们提出了VectorSearch，它利用先进的算法、嵌入和索引技术来实现精细化的检索。通过采用创新的多向量搜索操作，并利用先进的语言模型对搜索进行编码，我们的方法显著提升了检索准确率。在真实数据集上的实验表明，VectorSearch在各项基线指标上均表现优异，证明了其在大规模检索任务中的有效性。