Ivfflat Inverted File Approximate Indexing With Clustering Based Partitioning

via “ivfflat inverted-file approximate indexing with clustering-based partitioning”

Vector search for PostgreSQL — HNSW indexes, similarity queries in SQL, use existing Postgres.

Unique: Uses k-means clustering to partition vectors into inverted lists, then performs exact distance calculation only within top-k nearest clusters. This approach trades recall for memory efficiency and index build speed, making it suitable for billion-scale deployments where HNSW memory overhead is prohibitive.

vs others: More memory-efficient than HNSW for 10M+ vectors (1-2x vs 8-12x overhead), and faster to build (O(n) vs O(n log n)), making it better for cost-sensitive cloud deployments where storage is the primary constraint.