Use prebuilt Document Intelligence models

In this exercise, you’ll set up an Azure AI Document Intelligence resource in your Azure subscription. You’ll use both the Azure AI Document Intelligence Studio and C# or Python to submit forms to that resource for analysis.

Create an Azure AI Document Intelligence resource

Before you can call the Azure AI Document Intelligence service, you must create a resource to host that service in Azure:

In a browser tab, open the Azure portal at https://portal.azure.com, signing in with the Microsoft account associated with your Azure subscription.
On the Azure portal home page, navigate to the top search box and type Document Intelligence and then press Enter.
On the Document Intelligence page, select Create.
On the Create Document Intelligence page, use the following to configure your resource:
- Subscription: Your Azure subscription.
- Resource group: Select or create a resource group with a unique name such as DocIntelligenceResources.
- Region: select a region near you.
- Name: Enter a globally unique name.
- Pricing tier: select Free F0 (if you don’t have a Free tier available, select Standard S0).
Then select Review + create, and Create. Wait while Azure creates the Azure AI Document Intelligence resource.
When the deployment is complete, select Go to resource. Keep this page open for the rest of this exercise.

Use the Read model

Let’s start by using the Azure AI Document Intelligence Studio and the Read model to analyze a document with multiple languages. You’ll connect Azure AI Document Intelligence Studio to the resource you just created to perform the analysis:

Open a new browser tab and go to the Azure AI Document Intelligence Studio at https://documentintelligence.ai.azure.com/studio.
Under Document Analysis, select the Read tile.
If you are asked to sign into your account, use your Azure credentials.
If you are asked which Azure AI Document Intelligence resource to use, select the subscription and resource name you used when you created the Azure AI Document Intelligence resource.
In the list of documents on the left, select read-german.pdf.
At the top-left, select Analyze options, then enable the Language check-box (under Optional detection) in the Analyze options pane and click on Save.
At the top-left, select Run Analysis.
When the analysis is complete, the text extracted from the image is shown on the right in the Content tab. Review this text and compare it to the text in the original image for accuracy.
Select the Result tab. This tab displays the extracted JSON code.
Scroll to the bottom of the JSON code in the Result tab. Notice that the read model has detected the language of each span. Most spans are in German (language code de) but you can find other language codes in the spans (e.g. English - language code en - in one of the last span).

Prepare to develop an app in Visual Studio Code

Now let’s explore the app that uses the Azure Document Intelligence service SDK. You’ll develop your app using Visual Studio Code. The code files for your app have been provided in a GitHub repo.

Tip: If you have already cloned the mslearn-ai-document-intelligence repo, open it in Visual Studio code. Otherwise, follow these steps to clone it to your development environment.

Start Visual Studio Code.
Open the palette (SHIFT+CTRL+P) and run a Git: Clone command to clone the https://github.com/MicrosoftLearning/mslearn-ai-document-intelligence repository to a local folder (it doesn’t matter which folder).
When the repository has been cloned, open the folder in Visual Studio Code.
Wait while additional files are installed to support the C# code projects in the repo.

Note: If you are prompted to add required assets to build and debug, select Not Now. If you are prompted with the Message Detected an Azure Function Project in folder, you can safely close that message.

Configure your application

Applications for both C# and Python have been provided, as well as a sample pdf file you’ll use to test Document Intelligence. Both apps feature the same functionality. First, you’ll complete some key parts of the application to enable using your Azure Document Intelligence resource.

Examine the following invoice and note some of its fields and values. This is the invoice that your code will analyze.
In Visual Studio Code, in the Explorer pane, browse to the Labfiles/01-prebuild-models folder and expand the CSharp or Python folder depending on your language preference. Each folder contains the language-specific files for an app into which you’re you’re going to integrate Azure Document Intelligence functionality.
Right-click the CSharp or Python folder containing your code files and select Open an integrated terminal. Then install the Azure Form Recognizer (the previous name for Document Intelligence) SDK package by running the appropriate command for your language preference:

C#:
```
 dotnet add package Azure.AI.FormRecognizer --version 4.1.0
```
Python:
```
 pip install azure-ai-formrecognizer==3.3.0
```

Add code to use the Azure Document Intelligence service

Now you’re ready to use the SDK to evaluate the pdf file.

Switch to the browser tab that displays the Azure AI Document Intelligence overview in the Azure portal. On the left pane, under Resource Management, select Keys and Endpoint. To the right of the Endpoint value, click the Copy to clipboard button.
In the Explorer pane, in the CSharp or Python folder, open the code file for your preferred language, and replace <Endpoint URL> with the string you just copied:

C#: Program.cs
```
 string endpoint = "<Endpoint URL>";
```
Python: document-analysis.py
```
 endpoint = "<Endpoint URL>"
```
Switch to the browser tab that displays the Azure AI Document Intelligence Keys and Endpoint in the Azure portal. To the right of the KEY 1 value, click the *Copy to clipboard** button.
In the code file in Visual Studio Code, locate this line and replace <API Key> with the string you just copied:

C#
```
 string apiKey = "<API Key>";
```
Python
```
 key = "<API Key>"
```

Locate the comment Create the client. Following that, on new lines, enter the following code:

 var cred = new AzureKeyCredential(apiKey);
 var client = new DocumentAnalysisClient(new Uri(endpoint), cred);

Python

 document_analysis_client = DocumentAnalysisClient(
     endpoint=endpoint, credential=AzureKeyCredential(key)
 )

Locate the comment Analyze the invoice. Following that, on new lines, enter the following code:

 AnalyzeDocumentOperation operation = await client.AnalyzeDocumentFromUriAsync(WaitUntil.Completed, "prebuilt-invoice", fileUri);

Python

 poller = document_analysis_client.begin_analyze_document_from_url(
     fileModelId, fileUri, locale=fileLocale
 )

Locate the comment Display invoice information to the user. Following that, on news lines, enter the following code:

 AnalyzeResult result = operation.Value;
    
 foreach (AnalyzedDocument invoice in result.Documents)
 {
     if (invoice.Fields.TryGetValue("VendorName", out DocumentField? vendorNameField))
     {
         if (vendorNameField.FieldType == DocumentFieldType.String)
         {
             string vendorName = vendorNameField.Value.AsString();
             Console.WriteLine($"Vendor Name: '{vendorName}', with confidence {vendorNameField.Confidence}.");
         }
     }

Python

 receipts = poller.result()
    
 for idx, receipt in enumerate(receipts.documents):
    
     vendor_name = receipt.fields.get("VendorName")
     if vendor_name:
         print(f"\nVendor Name: {vendor_name.value}, with confidence {vendor_name.confidence}.")

[!NOTE] You’ve added code to display the vendor name. The starter project also includes code to display the customer name and invoice total.

Save the changes to the code file.
In the interactive terminal pane, ensure the folder context is the folder for your preferred language. Then enter the following command to run the application.
For C# only, to build your project, enter this command:

C#:
```
 dotnet build
```
To run your code, enter this command:

C#:
```
 dotnet run
```
Python:
```
 python document-analysis.py
```

The program displays the vendor name, customer name, and invoice total with confidence levels. Compare the values it reports with the sample invoice you opened at the start of this section.

Clean up

If you’re done with your Azure resource, remember to delete the resource in the Azure portal to avoid further charges.

More information

For more information about the Document Intelligence service, see the Document Intelligence documentation.