Use Jev for reranking retrieval results, not as an embedding model
The author argues Jev was not trained to produce embeddings for downstream search, and suggests using a bi-encoder for retrieval then reranking the top-k documents with Jev.
There's a reason we use higher dimensional embeddings trained on search related tasks. you are losing all the information from these strings and the results are gonna be less accurate. Jev wasn't trained to produce embeddings that are accurate for downstream search. use any bi-encoder search embedding models for retrieval and then rank the top-k documents using jev. This is gonna be much more accurate. Just using embeddings is already gonna be much cheaper and more accurate than what you proposed.