Approximate nearest-neighbor (ANN) search is a technique designed to find neighbors close to a query point in a dataset without guaranteeing exact proximity. ANN methods are used when exact NN search is computationally prohibitive due to the size of the dataset or the high dimensionality of the data. Instead, ANN algorithms provide results that are approximately correct but significantly faster.
ANN search achieves this speed-up by using data structures and algorithms optimized for specific scenarios. Techniques like locality-sensitive hashing (LSH) group similar vectors into buckets for fast retrieval, while tree-based structures like KD-trees and ball trees partition the dataset into manageable subsets. These methods balance accuracy and efficiency, making them suitable for real-world applications where slight inaccuracies are acceptable.
Common use cases for ANN search include recommendation systems, where it identifies similar user preferences, and image or audio recognition, where it matches features to known patterns. Its balance of speed and precision makes it invaluable for tasks requiring real-time or large-scale processing, such as retrieval-augmented generation (RAG) in LLMs.