Nvidia-Ingest: Multi-modal data extraction
50 comments
·January 10, 2025hammersbald
CharlieDigital
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...
benpacker
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
michaelt
> 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...
numba888
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.
dragonwriter
> 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.
NeedMoreTime4Me
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
philomath_mn
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?
ttt3ts
https://github.com/microsoft/table-transformer
This is much lighter weight and more reliable than vllm
serjester
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.
ttt3ts
Do you have some benchmark results I can look at that compares results?
jonathan-adly
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_ke
Surya is a great open source toolkit for table parsing, layout analysis and OCR: https://github.com/VikParuchuri/surya
ixaxaar
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.
null
greatgib
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?
theossuary
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.
joeevans1000
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?
UltraSane
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.
PeterStuer
Before you get too exited, this needs 2 A100 or H100's minimum.
alecco
GH200 $1.49 / GPU / hr
numba888
yes, that's the whole idea, they want you to rent.
OutOfHere
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.
lyime
So who is going to deploy this and turn this into a service/API?
UltraSane
What is the effective $/document of this method?
wiradikusuma
Is this like Nvidia version of MCP? (https://modelcontextprotocol.io/introduction)
OutOfHere
No relation.
joaquincabezas
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
null
vardump
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
neuroelectron
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.
null
Is there a OCR toolkit or a ML Model which is able to reliable extract tables from invoices?