Calling Azure AI Document Intelligence using the REST API
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- Опубликовано: 13 май 2024
- Azure AI Document Intelligence can read images and PDF scans of forms, extracting data for later use in data solutions. While various language SDKs are available, it's also possible to call these services directly using the REST API. This tutorial walks through the REST API process.
For links to source code and similar articles, visit the blog version of this post:
robkerr.ai/azure-ai-document-... 
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Hey Rob, this was really helpful. I was looking to apply information from a form like this onto a prebuilt data entry tool with certain sections for each of the matching sections on the form. Would this be possible?Could you possibly make a video on this? Thanks
Rob, David here, Developer here from Australia. Thanks for the great video. Exactly what I needed.
I have an encoded Bas64 file ready to go, an attachment extracted from Outlook Exchange, so it makes a lot more sense to send that Base64 string directly, rather than store the file to a disk then pass its location.
All microsoft documentation shows examples of passing a string to the documents location. This doesn't make much sense to me, since we are using a REST API rather than an SDK, and the service most likely will not have access to the file location.
Ps. Subbed and notified. In just 20 videos on your channel, you have provided so much great content in this space. Please keep making videos! If you make courses anywhere, I would also happily buy. Great stuff.
Glad the videos are helpful! The feedback is encouraging.
In your scenario I tend to agree, you probably don't have a good way to give AzureAI access to your data via a URL since it's the content of an email. You're right that the examples usually give a URL that's publicly available on the Internet (not very likely in an enterprise scenario).
However the passed URL approach could work in an enterprise solution if, for example, you had a file in Azure Blob store and generated a short-lived SAS token to append to the URL passed to AI Services. I prefer Base64 for "small" input payloads, but I would go for a SAS token if the input file was large to reduce the size of the request to Azure.
This was so helpful. The step by step guide helped me solve an issue that has stumped our developers for weeks. This was the breakthrough I needed.
Glad it helped!
Thank you for this tutorial, sir!
Glad it was helpful!
you the real mwp in ocr
Thanks, this has been really useful. One issue I found was that the model was showing as ‘resource not found’ on the initial post request. I had to change the ‘project settings’ to ‘endpoint and key’ and then create a new model. Not sure if I had done something wrong but maybe other people have been puzzled by this too! Thanks again for the video, really helpful!
Good point!
Hey Rob!
Thanks for the introduction.
I have made some really great experiences with the layout API usage. This document AI service is kind of groundbreaking in my opinion. It didn’t really hit mainstream yet really.
What is your take on the model?
These models are most commonly part of enterprise deployments, and don't get a lot of PR coverage. Microsoft published a case study where Volvo uses Doc Intel to speed processing of supply chain documents you can probably find via a web search. These types of models can make a lot of impact on cost savings.
Nice video
Thanks