Embeddings are indexed for efficient retrieval using specialized techniques such as Approximate Nearest Neighbor (ANN) algorithms. Common indexing methods include Hierarchical Navigable Small World (HNSW) graphs, Inverted File Systems (IVF), and LSH (Locality-Sensitive Hashing). These methods accelerate similarity searches in high-dimensional spaces by reducing the number of comparisons.
Indexing structures are designed to balance speed and accuracy. For example, HNSW builds graph-based indices where nearby vectors are directly connected, enabling fast traversal during queries. IVF groups vectors into clusters and searches only within relevant clusters for efficiency.
Indexing also supports updates, such as adding or removing embeddings, to keep the system dynamic. These methods are critical for applications like recommendation systems and search engines, where embeddings need to be retrieved quickly based on similarity metrics like cosine distance or Euclidean distance.