Interesting. But if you are upgrading your embedding model, wouldn't you want to re-embed to get its benefits on old entries?
Interesting. But if you are upgrading your embedding model, wouldn't you want to re-embed to get its benefits on old entries?
You are right that the adapter doesn't upgrade your old vectors — it aligns the two spaces, so retrieval stays bounded by what the old model encoded. If the goal is better representations on existing data, re-embed(btw my solutioning offers this dual-write path in migrateion as well :)
The adapter is for the more common case: the upgrade is forced (ada-002 gets deprecated) or a reindex is too big to eat at once — a billion vectors is ~$6k and a stale index for most of a day. Instead you cut over instantly: the new model serves new writes and queries through the rotation, the old index keeps serving untouched, and you re-embed in the background at your own pace. It's a zero-downtime transition, not a free quality bump. Small index? Just re-embed.