Nvidia-Ingest: Multi-modal data extraction(github.com)
145 pointsby mihaid1508 days ago15 comments
  • hammersbald8 days ago
    Is there a OCR toolkit or a ML Model which is able to reliable extract tables from invoices?
    • CharlieDigital8 days ago
      By far the best one I've come across is Microsoft Azure Document Intelligence with the Layout Model[0].

      It's really, really good at tables.

      You have to use the Layout Model and not just the base Document Intelligence.

      A bit pricey, but if you're processing content one time and it's high value (my use case as clinical trial protocol documents and the trial will run anywhere from 6-24 months), then it's worth it, IMO.

      [0] https://learn.microsoft.com/en-us/azure/ai-services/document...

    • benpacker8 days ago
      All frontier multi modal LLMs can do this - there’s likely something lighter weight as well.

      In my experience, the latest Gemini is best at vision and OCR

      • michaelt8 days ago
        > All frontier multi modal LLMs can do this

        There's reliable, and there's reliable. For example [1] is a conversation where I ask ChatGPT 4o questions about a seven-page tabular PDF from [2] which contains a list of election polling stations.

        The results are simultaneously impressive and unimpressive. The document contains some repeated addresses, and the LLM correctly identifies all 11 of them... then says it found ten.

        It gracefully deals with the PDF table, and converts the all-caps input data into Title Case.

        The table is split across multiple pages, and the title row repeats each time. It deals with that easily.

        It correctly finds all five schools mentioned.

        When asked to extract an address that isn't in the document it correctly refuses, instead of hallucinating an answer.

        When asked to count churches, "Bunyan Baptist Church" gets missed out. Of two church halls, only one gets counted.

        The "Friends Meeting House" also doesn't get counted, but arguably that's not a church even if it is a place of worship.

        Longmeadow Evangelical Church has one address, three rows and two polling station numbers. When asked how many polling stations are in the table, the LLM counts that as two. A reasonable person might have expected one, two, three, or a warning. If I was writing an invoice parser, I would want this to be very predictable.

        So, it's a mixed bag. I've certainly seen worse attempts at parsing a PDF.

        [1] https://chatgpt.com/share/67812ad9-f2bc-8011-96be-faea40e48d... [2] https://www.stevenage.gov.uk/documents/elections/2024-pcc-el...

        • numba8887 days ago
          You can try to ask it to list all churches and assign them incremental number starting with 1. then print the last number. It's a variation of counting 'r' in 'raspberry' which works better than simple direct question.
        • dragonwriter7 days ago
          > There's reliable, and there's reliable. For example [1] is a conversation where I ask ChatGPT 4o questions about a seven-page tabular PDF from [2] which contains a list of election polling stations.

          From your description, it does perfectly at the task asked about upthread (extraction) and has mixed results on other, question-answering, tasks, that weren't the subject.

          • michaelt7 days ago
            > From your description, it does perfectly at the task asked about upthread (extraction) and has mixed results on other, question-answering, tasks, that weren't the subject.

            ¯\_(ツ)_/¯

            Which do you think was which?

        • NeedMoreTime4Me7 days ago
          Do I understand correctly that nearly all issues were related to counting (i.e. numerical operations)? that makes it still impressive because you can do that client-side with the structured data
          • michaelt7 days ago
            Some would say the numerical information is among the most important parts of an invoice.
        • philomath_mn8 days ago
          I wonder if performance would improve if you asked it to create csvs from the tables first, then fed the CSVs in to a new chat?
    • ttt3ts8 days ago
      https://github.com/microsoft/table-transformer

      This is much lighter weight and more reliable than vllm

      • serjester7 days ago
        As someone that spent quite a bit of time with table-transformers, I would definitely not recommend it. It was one of the first libraries we added for parsing tables into our chunking library [1] and the results were very underwhelming. This was a while back and at this point, it's just so much easier to use an LLM end to end for parsing docs (Gemini Flash can parse 20k pages per dollar) and I'm wary of any approach that stitches together different models.

        [1] https://github.com/Filimoa/open-parse/

        • ttt3ts6 days ago
          Do you have some benchmark results I can look at that compares results?
    • jonathan-adly7 days ago
      I would like to through our project in the ring. We use ColQwen2 over a ColPali implementation. Basically, search & extract pipeline: https://docs.colivara.com/guide/markdown
    • m_ke7 days ago
      Surya is a great open source toolkit for table parsing, layout analysis and OCR: https://github.com/VikParuchuri/surya
  • ixaxaar8 days ago
    Ah so like NIM is a set of microservices on top of various models, and this is another set of microservices using NIM microservices to do large scale OCR?

    and that too integrated with prometheus, 160GB VRAM requirement and so on?

    Looks like this is targeted for enterprises or maybe governments etc trying to digitalize at scale.

  • 8 days ago
    undefined
  • greatgib8 days ago
    I have hard time to understand what they mean by "early access micro services"...?

    Does it mean that it is yet another wrapper library to call they proprietary cloud api?

    Or that when you have the specific access right, you can retrieve a proprietary docker image with secret proprietary binary stuffs inside that will be the server used by the library available in GitHub?

    • theossuary8 days ago
      The latter. NIMs is Nvidia's umbrella branding for proprietary containerized AI models, which is being pushed hard by Jensen. They build models and containers, then push them to ngc.nvidia.com. They then provide reference architectures which rely on them. In this case the images are in an invite only org, so to use the helm chart you have to sign up, request access, then use an API key to pull the image.

      You can imagine how fun it is to debug.

  • joeevans10007 days ago
    How is this different than elasticsearch and solr? That’s not any kind of challenging question… I really don’t know that much about these different tools and I just want to know what this one is about.

    Also: I noticed that it mentioned images… does it do any kind of OCR or summary of them?

    • UltraSane7 days ago
      It is a method of extracting structured data from messy documents meant for human consumption that can then be indexed by tools like Elasticsearch and solr.
  • PeterStuer7 days ago
    Before you get too exited, this needs 2 A100 or H100's minimum.
  • OutOfHere7 days ago
    This requires Nvidia GPUs to run.

    The open question is whether to use rule-based parsing using simpler software or model-based parsing using this software.

  • lyime7 days ago
    So who is going to deploy this and turn this into a service/API?
  • UltraSane7 days ago
    What is the effective $/document of this method?
  • wiradikusuma7 days ago
    Is this like Nvidia version of MCP? (https://modelcontextprotocol.io/introduction)
  • joaquincabezas8 days ago
    lol, while checking which OCR is using (PaddleOCR) I found a line with the text: "TODO(Devin)" and was pretty excited thinking they were already using Devin AI...

    "Devin Robison" is the author of the package!! Funny, guess it will be similar with the name Alexa

    • 8 days ago
      undefined
  • vardump8 days ago
    Sounds pretty useful. What are the system requirements?

      Prerequisites
  Hardware
  GPU Family Memory # of GPUs (min.)
  H100 SXM or PCIe 80GB 2
  A100 SXM or PCIe 80GB 2
    Hmm, perhaps this is not for me.
    • neuroelectron7 days ago
      Seems pretty ridiculous to me to parse some PDFs. Almost like they made this as bloated as possible to justify buying $5,000+ GPUs for an office.
      • vardump7 days ago
        I think those GPUs cost between $25-40k each.
        • latchkey7 days ago
          Why even buy them at this point... just rent neocloud for $1-2... even at $2/hr, that's over a year of rental for $25k... by then you'd have made your money off the implementation.
          • vardump7 days ago
            Not sure whether I'd like to send potentially sensitive documents to a lesser known provider. Or even to a well known.
            • latchkey7 days ago
              Even at $3/hour (which is above the current market rate), that's roughly a year.

              I genuinely appreciate your perspective, but as a smaller, lesser-known provider, I’d like to understand your concerns better.

              Are you worried that I might misuse your data and compromise my entire business, by selling it to the highest bidder? Do you feel uncertain about the security of my systems? Or is it a belief that owning and managing the hardware yourself gives you greater control over security?

              What kind of validation or reassurance would help address these concerns?

              • neuroelectron6 days ago
                The main issue is a lot of smaller providers are clearly incentivized to get into the industry simply for the opportunity to eavesdrop into what other companies are doing. If you want to confuse or cloud this perceived motivation, also provide security services.
    • 8 days ago
      undefined
  • shutty8 days ago
    Wow, I perhaps need a kubernetes cluster just for a demo:

        CONTAINER ID   IMAGE                                                    
    0f2f86615ea5   nvcr.io/ohlfw0olaadg/ea-participants/nv-ingest:24.10     
    de44122c6ddc   otel/opentelemetry-collector-contrib:0.91.0              
    02c9ab8c6901   nvcr.io/ohlfw0olaadg/ea-participants/cached:0.2.0        
    d49369334398   nvcr.io/nim/nvidia/nv-embedqa-e5-v5:1.1.0                
    508715a24998   nvcr.io/ohlfw0olaadg/ea-participants/nv-yolox-structured-images-v1:0.2.0
    5b7a174a0a85   nvcr.io/ohlfw0olaadg/ea-participants/deplot:1.0.0                                                                     
    430045f98c02   nvcr.io/ohlfw0olaadg/ea-participants/paddleocr:0.2.0                                                                  
    8e587b45821b   grafana/grafana                                                         
    aa2c0ec387e2   redis/redis-stack                                                       
    bda9a2a9c8b5   openzipkin/zipkin                                                       
    ac27e5297d57   prom/prometheus:latest
  • foxhop7 days ago
    [dead]
  • jappgar8 days ago
    Nvidia getting in on the lucrative gpt-wrapper market.
    • dragonwriter7 days ago
      If it was a GPT wrapper, it wouldn't require an A100/H100 GPU; the container has a model wrapper, sure, but also it has the wrapped, standalone model, as well; its not calling OpenAI's model.