It correctly identifies tables that even paid models like the AWS Textract Document Analysis API fails to – for instance tables with one column which often confuse AWS even if they have a clear header and are labelled "Table" in the text.
I would however love to know broadly what kind of document it was trained on, as my results could be pure luck, hard to say without a proper benchmark
Very nice layout recognition, although I can't quite comment on the RAG performance itself – I think some of the architecture decisions are odd, it mixes a bunch of different PDF parsers for example which will all result in different quality and it's not clear to me which one it defaults to as it seems to be different in different places in the code (the simple parser defaults to pypdf2 which is not a great option)