Pgvector 0.5.0 got HNSW indexes that is much faster for AI application than IVF in most cases. We measured pgvector performance in Supabase and present recommendations on its efficient usage.
- HNSW preserves index quality even after massive table updates and doesn't need to be rebuilt
- HNSW search is several times faster than IVF for high accuracy. For lower accuracy likely even more.
- Pgvector/HNSW became faster than qdrant
- IVFflat build time decreased two-times in 0.5.0
- HNSW index could be built incrementally, you don't need to add all embeddings before index build
- HNSW preserves index quality even after massive table updates and doesn't need to be rebuilt
- HNSW search is several times faster than IVF for high accuracy. For lower accuracy likely even more.
- Pgvector/HNSW became faster than qdrant
- IVFflat build time decreased two-times in 0.5.0
- HNSW index could be built incrementally, you don't need to add all embeddings before index build