Vector search is a specific type of nearest-neighbor (NN) search where the goal is to find vectors in a dataset that are closest to a query vector based on a similarity metric (e.g., cosine similarity or Euclidean distance). In essence, vector search operates by comparing high-dimensional vectors, often representing features or embeddings, to identify the most similar vectors. This process is fundamental in applications like recommendation systems, search engines, and natural language processing.
Traditional search is exhaustive and compares the query to every other point in the dataset, making it computationally expensive, especially for high-dimensional data or large datasets. Vector search techniques often integrate optimizations to address these challenges. These include approximate nearest-neighbor (ANN) methods and specialized data structures that reduce search time while maintaining acceptable accuracy levels.
The connection lies in the shared principle of proximity-based retrieval. NN search provides a foundational approach, while vector search applies it to high-dimensional, semantically rich data. It enhances the practical usability of NN search in modern AI and ML systems by focusing on efficient and scalable processing.