Given the model performance is thus affected by a k-nearest neighbor, but those algorithms are proving not great for baseline vector search, how well will this actually work?

It seems mostly like a vertically integrated vector DB + existing LLM call, but correct me if I'm wrong. There are of course some performance gains with that, but the holy grail of "understanding" at unlimited length still seems unsolved.