Embedding model migration zero-downtime

An e-commerce semantic-search system has 300M product embeddings serving live traffic, and the ML team wants to upgrade the embedding model to a better one. The new model produces vectors in a different space, so old and new embeddings are not comparable, you cannot mix them in one index. You must migrate 300M items to the new embedding without taking search down and without a window where results are garbage. Design the embedding-versioning and zero-downtime re-embedding migration.