Approximate Nearest Neighbor (ANN) Search

Finding exact nearest neighbors in high-dimensional spaces becomes computationally prohibitive at scale. ANN algorithms make this practical:

Hierarchical Navigable Small World (HNSW):

• Creates multi-layer graphs connecting vectors by similarity
• Enables logarithmic-time search by navigating from distant to close neighbors
• Balances speed and accuracy through adjustable parameters

Inverted File Index with Product Quantization (IVF-PQ):

• Partitions vector space into clusters for coarse filtering
• Compresses vectors through product quantization to reduce memory requirements
• Enables billion-scale vector search on standard hardware

Other Common Approaches:

• Locality-Sensitive Hashing (LSH): Hash-based techniques for approximate matching
• Random Projection Trees: Tree structures for partitioning vector space

ANN algorithms involve trade-offs between search speed, memory usage, and result accuracy that must be tuned based on application requirements.