We switched our semantic search to a cheaper 1024-dimension embedding model and every insert and query failed against the 1536-dimension column. Worse, the half-finished fix would have mixed incompatible vectors. How we migrated with versioned columns, a backfill and dual reads.
Our docs assistant answered v4 questions with v3 settings, fluently and with citations. Fixed-size chunking split tables from their headings, chunks carried no version metadata and we had no retrieval eval. How we rebuilt chunking, added filters and hybrid search, and measured it.
Small tenants in our RAG product got 2 or 3 chunks back instead of 10, and sometimes none. HNSW finds ef_search candidates first and applies the tenant filter afterwards. Iterative index scans in pgvector 0.8 plus partial indexes for big tenants fixed recall.
Semantic search in our docs assistant took 1.9 seconds and returned odd rankings. The query sorted by L2 distance while the HNSW index was built for cosine, so Postgres scanned every row. Matching the operator to the opclass brought it to 14 ms.