> A better solution (and, as far as I can tell, what every other RAG does) is to split the document into chunks that can actually fit the context of the embeddi

> A better solution (and, as far as I can tell, what every other RAG does) is to split the document into chunks that can actually fit the context of the embedding model, and then retrieve those chunks -- ideally with metadata about which part of the document it's from. Books have author provided logical chunking in chapters. You can further split/summarize smaller sections and then do a hierarchical search (naive chunking kind of sucks from my experience)