Vectorsearch: Enhancing Document Retrieval With Semantic Embeddings And Optimized Search

Abstract

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keymonir2024vectorsearch

Related papers

• Semantic Vector Encoding And Similarity Search Using Fulltext Search Engines (2017)6.77
• Gleanvec: Accelerating Vector Search With Minimalist Nonlinear Dimensionality Reduction (2024)0.00
• Leanvec: Searching Vectors Faster By Making Them Fit (2023)0.00
• Zoom: Ssd-based Vector Search For Optimizing Accuracy, Latency And Memory (2018)0.00
• Neural Vector Spaces For Unsupervised Information Retrieval (2017)12.93
• Optimization Of Latent-space Compression Using Game-theoretic Techniques For Transformer-based Vector Search (2025)0.00
• Lucene For Approximate Nearest-neighbors Search On Arbitrary Dense Vectors (2019)0.00
• Hybrid-vector Retrieval For Visually Rich Documents: Combining Single-vector Efficiency And Multi-vector Accuracy (2025)2.23