I’m currently trying to build a template for the whole document understanding process / life cycle and came across a few problems regarding best practises when combining document understanding with the REFramework.
This is the basic process of document understanding according to UiPath:
In my case, I’ve added a 3.5 as well, which is another validation station as in step 5, for documents where the classifier is below a certain confidence level.
So my process basically looks like this:
My big question is, how to split up the process in order to maximize parallelism. I cannot wait with the process until a user has finished their classification or extraction tasks because I might have to extract data from hundreds or thousands of documents.
So I was thinking about using queues between a lot of the steps and having several different bots (or rather processes).
Example: Once the document is classified, the classification results get put into a queue for further processing. This happens when either the bot or a person does the classification. Same for the data extraction, results get put into a queue for further processing. I’ll probably also need queues for retraining the models.
This would mean I’d have to split my process into 3 different parts connected by queues: Digitizing + Classification, Data Extraction and further processing of the extracted data.
Each of those parts would then use the REFramework.
Another idea would be to create an Orchestration Process and run this for every single file which needs to be processed. This is definitely easier to implement but there might be a time with hundreds or more processes waiting for user input and I’m not sure how this is handled.
The first approach is definitely scaleable with simply adding more robots, not sure about the second way.
I’d be great if we could discuss those approaches or you could even share your own way of tackling the whole document understanding process.