What versioning strategy would you use so that switching to a new embedding model doesn't silently corrupt search quality for existing data?

The strategy is to treat the embedding model as a versioned property of the collection, not as a runtime detail. When you switch models, you create a new collection (or a new named vector within the same collection) with the new model's dimension and metric, and you dual-write new content to both the old and the new collections during the migration. You backfill the old collection's content into the new collection by re-embedding it with the new model, and you track progress so you know when the backfill is complete. Only when the new collection is fully populated and validated do you cut over the query path to it, typically by moving an alias. The old collection remains available as a rollback target until the new one has been stable for a defined period. This avoids the two failure modes of an in-place migration: mixing old and new vectors in the same collection, which produces a mix of representations that no query can rank correctly, and cutting over before the backfill is complete, which silently returns incomplete results.

The mechanism that makes this safe is that the old and new collections are separate vector spaces, and the query path always uses exactly one of them. During the migration, new writes go to both, and old data is backfilled into the new collection. If the backfill is slow, the old collection continues to serve queries correctly (it has all the data, just in the old space), and the new collection is incomplete but not yet used for queries. The migration state is therefore always consistent from the query path's perspective: it either uses the old collection (complete, old space) or the new collection (complete, new space), never a mix. The cutover is a metadata operation (moving an alias) and can be rolled back by moving the alias back. The validation before cutover compares recall on the new collection against a held-out query set, and compares the new collection's results with the old collection's results on a sample of queries to ensure that quality has not regressed. This is the standard pattern for any schema or model change that cannot be done in place.