README.md

Azure Durable Functions Pipeline - Form Recognizer

Python support for Durable Functions is currently in public preview.

This example pipeline utilises the function chaining functionality from Durable Functions. In the function chaining pattern, a sequence of functions executes in a specific order. In this pattern, the output of one function is applied to the input of another function.

Features

This example has the following functionality built-in, but could easily be extended by adding your own Activity functions.

• Triggered by new files on a blob storage
• Remove character boxes using OpenCV
• Call Form Recognizer service via a temporary SAS token
• Post processing to clean output

Prerequisites

• Durable Functions require an Azure storage account. You need an Azure subscription.
• Form Recognizer instance and a model trained

How to develop and test locally

The easiest way of development is to use Dev Containers in Visual Studio Code. This will guarantee that you have the latest Azure Function core tools and related dependencies. When you open the project the first time, it will ask you to create a virtual environment for Python.

You can also run the Azure Function on your local system, following this quickstart.

• Create a blob storage with 3 containers, name them input, input-cleaned and output.
• Create a Form Recognizer instance and train a model

Create a new file in the root, named local.settings.json.

{
  "IsEncrypted": false,
  "Values": {
    "AzureWebJobsStorage": "Connection string from your Blob Storage",
    "FUNCTIONS_WORKER_RUNTIME": "python",
    "StorageAccount": "Connection string from your Blob Storage with the input / input-cleaned / output container",
    "FormRecognizer_Endpoint": "https://xxxxxx.cognitiveservices.azure.com",
    "FormRecognizer_SubscriptionKey": "",
    "FormRecognizer_ModelId": ""
  }
}

Run your project by selecting Run -> Start Debugging in VSCode.

How to deploy

The easiest is to use Visual Studio Code to publish your project to Azure, following this quickstart. Make sure you add the values from your local.settings.json as environment variables to your Azure Function app.

Architecture