Is there a decent way of converting to a structure with a very constrained vocabulary? For example, given some input text, converting it to something like {"OI

Is there a decent way of converting to a structure with a very constrained vocabulary? For example, given some input text, converting it to something like {"OID-189": "QQID-378", "OID-478":"QQID-678"}. Where OID and QQID dictionaries can be e.g. millions of different items defined by a description. The rules for mapping could be essentially what looks closest in semantic space to the descriptions given in a dictionary. I know this should be able to be solvable by local LLMs and bert cosine similarity (it isn't exactly, but it's a start on the idea), but is there a way to do this with decoder models rather than encoder models with other logic?