Sadly, re-embed. Or if you're really, really stuck, you could take the source embeddings, some of the sources, get destination new embeddings, then train a sma

Sadly, re-embed. Or if you're really, really stuck, you could take the source embeddings, some of the sources, get destination new embeddings, then train a small model to update. It's unlikely this will do anything but lower your quality compared to re-embedding everything. This lock in effect is one reason I've avoided using OpenAI's embedding models; at least if it's open source, you'll be able to embed everything on an open model you have control of. The idea of committing to a large datastore using embedding APIs makes me feel very uncomfortable.