Vector Search Algorithms: A Brief Survey

The rapid growth of high-dimensional data across various domains such as natural language processing, computer vision, and recommendation systems has created a need for efficient vector search algorithms. These algorithms power vector databases, which perform similarity searches to retrieve data points most relevant to a given query vector. This paper provides a comprehensive survey of vector search algorithms, categorizing them into brute-force, approximate nearest neighbor (ANN), tree-based, and graph-based methods. In this paper, we explore the underlying principles, trade-offs in accuracy and performance, and real-world applications for each algorithm. The survey further highlights the advantages and limitations of each individual approach, emphasizing their suitability for different scale, speed, and precision requirements in modern vector databases.