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Maybe I'm not getting it, but I see this as an indexing problem. The goal shouldn't be to fit the entire document in the prompt, we should include relevant part of the doc when we query it.

Edit: I'm thinking of something like LlamaIndex

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Embedding chunks and finding chunks based on similarity is definitely in use now. But if you can increase context size cheaply then the model can figure out what's relevant.

Yeah I get that, after all attension is all you need. But unless you want to spend a bunch of money on the 32k context version I don't think there are other options than embeddings and index.

What is the state of the art for running models locally in terms of context size?

I think local models SOTA is llama which has 2048 context[1].

[1] https://github.com/facebookresearch/llama/issues/16

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