An application switches its embedding model but keeps old document vectors. Newly embedded queries return irrelevant matches although dimensions match. What is the best correction?

1. Change only the top-K limit
2. Convert all document identifiers to numbers
3. Re-embed the corpus using the query model and rebuild affected indexes
4. Normalize query text capitalization without changing vectors