Vector Index Types

Inverted File Index vector search algorithm that partitions high-dimensional vectors into clusters using k-means, enabling efficient nearest neighbor search by restricting searches to relevant clusters and dramatically reducing search space.

Inverted File Index with flat vectors using K-means clustering to partition high-dimensional space into regions, enhancing search efficiency by narrowing search area through neighbor partitions.

Graph-based algorithm for approximate nearest neighbor search that maintains multi-layer graph structures for efficient vector similarity search with logarithmic complexity, widely used in modern vector databases.

Tree-based spatial data structure organizing vectors using spherical regions instead of axis-aligned splits, making it better suited for high-dimensional data compared to KD-trees.

Inverted File index with FLAT (uncompressed) vectors, partitioning the vector space into clusters with centroids, offering a balance between search speed and accuracy for approximate nearest neighbor search.