Could you please provide some more info (or maybe links) about this, please?
What I meant was that at the time of indexing, you can add more information to any chunk. This[1] is a simple example by Anthropic where they add more relevant context. In our case, say you have two models, D1 and D2. At the time of creating a vector store, you can add which model is more suitable to a chunk, so that when you retrieve it, you use the same model for inference. This is custom built, very dependent on datasets, but would get you to the functionality described. I suggest this approach when there are linkages between various docs (eg: financial statements/earning calls etc.).
I try to explain this a bit better here: https://pa-mar.net/Study/AiKiDo/VirtualBudoPass.html
Btw, I notice only now that the link that was supposed to explain my question better is completely wrong.
https://news.ycombinator.com/item?id=42589014
(But you still provided much needed clarification).
The general idea is probably be better for the code use case too, since having the module's whole codebase in context likely allows for more precise edits. Whereas RAG is just search, not edit.
That said, probably code assistants must somewhat do this already, though it must be more ad-hoc. Obviously they wouldn't be able to do any completions if they don't have detailed context of the adjacent code.
One simple way is what Omar Khattab (ColBert) mentioned about scoring function instead of a simple vector.
Another is to use a classifier at the start directing queries to the right model. You will have to train the classifier though. (I mean a language model kind of does this implicitly, you are just taking more control by making it explicit.)
Another is how you index your docs. Today, most RAG approaches do not encode enough information. If you have defined domains/models already, you can encode the same in metadata for your docs at the time of indexing, and you pick the model based on the metadata.
These approaches would work pretty well, given a model as small as 100M size can regurgitate what is in your docs. And is faster compared to your larger models.
Benefit wise, I don't see a lot of benefit except preserving privacy and gaining more control.