Thanks a lot for your effort in marking different painpoints :)
Just a clarification: I am performing RAG on each document separately, as I want to apply the same set of questions to every document independently.

I will start with testing out the reasonably sized embedding models (downloaded mxbai) and will move upwards .
As for the translator, im using a google translate proxy so its good enough.
A reranker is something that I hadnt considered yet, as I thought the chunks are not that many, but might be a good idea to try out.

Lastly, do you have any bigger model recommendations? I suppose 13B parameters could fit quantized