Version Embeddings for Safe Semantic Search Migrations
Semantic search systems often look simple from the outside: encode a document, store its vector, encode a query, and compare the vectors. The operational difficulty appears later, when the embedding model changes.
Two models can produce vectors with the same dimension and still define completely different coordinate spaces. Mixing vectors from model A with query vectors from model B can silently destroy ranking quality without producing an obvious error.
The safe approach is to treat an embedding model as a versioned data dependency, not a drop-in function.
