Migrating vector embeddings in production without downtime

In the fast-moving world of AI, models evolve rapidly. What was state-of-the-art six months ago is now being surpassed by newer models. For a RAG system, this presents a significant challenge: vector embeddings are tied to the specific model that generated them. If you want to upgrade your model, you can’t just start using the new one. Existing vectors in your database are incompatible with queries from the new model. A "naive" migration-shutting down the site, re-embedding everything, and restarting-means hours of potential downtime.