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How does this differ from Azure Document Intelligence, or are they effectively the same thing?
You can use llmsherpa library - https://github.com/nlmatics/llmsherpa with this server to get nice layout friendly chunks for your LLM/RAG project.
How does this differ from Azure Document Intelligence, or are they effectively the same thing?
What this library, and something like fitz/pymupdf, allow you to do is extract the text straight from the pdf, using rules about how to parse & structure it. (Most modern pdfs you can extract text without ocr).
- much cheaper obviously but doesn’t scale (across dynamic layouts) well so you likely are using this when you can configure around a standard structure. I have found rule-based text extraction to work fairly dynamically though for things like scientific pdfs.
Do you ave any examples? There doesn't seem to be a single PDF file in the repo.
How does this differ from Azure Document Intelligence, or are they effectively the same thing?
Last I used it, Azure Document Intelligence wasn't all that smart about choosing split points. This seems to implement better heuristics.
All this is automated in the llmsherpa parser https://github.com/nlmatics/llmsherpa which you can use as an API over this library.
Earlier quoted context omitted.
I couldn't try this tool as it doesn't build on apple silicon (and there's no ARM docker image) However, I have a PDF parsing use-case that I tried those RAG tools for, but the output they give me is pretty low quality – it kinda works for RAG as the LLM can work around the issues but if you want to get higher quality responses with proper references and such I think the best way is to write your own rule-based parse…
For me, PyMuPDF/fitz has been the best way to retain natural reading order and set dynamic enough rules to extract text in complex layouts. None of the mentioned tools did this out of the box, none seemed easy to configured, all definitely hyped and marketed way beyond fitz though.
The only thing it doesn't do is tables detection (neither does pdfminer.six), but there are plenty of other ways to handle them.
Earlier quoted context omitted.
I couldn't try this tool as it doesn't build on apple silicon (and there's no ARM docker image) However, I have a PDF parsing use-case that I tried those RAG tools for, but the output they give me is pretty low quality – it kinda works for RAG as the LLM can work around the issues but if you want to get higher quality responses with proper references and such I think the best way is to write your own rule-based parse…
To run the docker image on apple silicon, you can use the following command to pull - it will be slower but works: docker pull --platform linux/x86_64 ghcr.io/nlmatics/nlm-ingestor:latest
The table parser in particular is really good. Is the trick that you draw some guide lines and rectangles around tables? I'm trying to understand the GraphicsStreamProcessor class as I'm not familiar with Tika, how does it know where to draw in the first place?
Overall, I believe there has to be some middle ground for identification and trust building over time, between "hidden group with no names on $CORP secure site" and other traditional means of introduction and trust building.
thanks for posting this interesting and relevant work