Get started with Document Intelligence

Important

• Azure Cognitive Services Form Recognizer is now Azure AI Document Intelligence.
• Some platforms are still awaiting the renaming update.
• All mention of Form Recognizer or Document Intelligence in our documentation refers to the same Azure service.

This content applies to: v4.0 (preview) Earlier versions: v3.1 (GA) v3.0 (GA)

• Get started with Azure AI Document Intelligence latest preview version (2024-07-31-preview).

• Azure AI Document Intelligence / Form Recognizer is a cloud-based Azure AI service that uses machine learning to extract key-value pairs, text, tables, and key data from your documents.

• You can easily integrate document processing models into your workflows and applications by using a programming language SDK or calling the REST API.

• For this quickstart, we recommend that you use the free service while you're learning the technology. Remember that the number of free pages is limited to 500 per month.

To learn more about the API features and development options, visit our Overview page.

Client library | SDK reference | REST API reference | Package| Samples|Supported REST API version

In this quickstart, use the following features to analyze and extract data and values from forms and documents:

• Layout model—Analyze and extract tables, lines, words, and selection marks like radio buttons and check boxes in documents, without the need to train a model.

• Prebuilt model—Analyze and extract common fields from specific document types using a prebuilt model.

Prerequisites

• Azure subscription - Create one for free.

• The current version of Visual Studio IDE.

• An Azure AI services or Document Intelligence resource. Once you have your Azure subscription, create a single-service or Azure AI multi-service resource, in the Azure portal, to get your key and endpoint.

• You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

Tip

Create an Azure AI services resource if you plan to access multiple Azure AI services under a single endpoint/key. For Document Intelligence access only, create a Document Intelligence resource. Please note that you'll need a single-service resource if you intend to use Microsoft Entra authentication.

• After your resource deploys, select Go to resource. You need the key and endpoint from the resource you create to connect your application to the Document Intelligence API. You paste your key and endpoint into the code later in the quickstart:

  

Set up

1. Start Visual Studio.

2. On the start page, choose Create a new project.

   

3. On the Create a new project page, enter console in the search box. Choose the Console Application template, then choose Next.

   

4. In the Configure your new project dialog window, enter doc_intel_quickstart in the Project name box. Then choose Next.

5. In the Additional information dialog window, select .NET 8.0 (Long-term support), and then select Create.

   

Install the client library with NuGet

1. Right-click on your doc_intel_quickstart project and select Manage NuGet Packages... .

   

2. Select the Browse tab and type Azure.AI.FormRecognizer.

3. Select the Include prerelease checkbox.

   

4. Choose a version from the dropdown menu and install the package in your project.

Build your application

To interact with the Document Intelligence service, you need to create an instance of the DocumentIntelligenceClient class. To do so, you create an AzureKeyCredential with your key from the Azure portal and a DocumentIntelligenceClient instance with the AzureKeyCredential and your Document Intelligence endpoint.

Note

• Starting with .NET 6, new projects using the console template generate a new program style that differs from previous versions.
• The new output uses recent C# features that simplify the code you need to write.
• When you use the newer version, you only need to write the body of the Main method. You don't need to include top-level statements, global using directives, or implicit using directives.
• For more information, see New C# templates generate top-level statements.

1. Open the Program.cs file.

2. Delete the pre-existing code, including the line Console.Writeline("Hello World!"), and select one of the following code samples to copy and paste into your application's Program.cs file:

   • Layout model

   • Prebuilt model

Important

Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. For more information, see Azure AI services security.

Layout model

Extract text, selection marks, text styles, table structures, and bounding region coordinates from documents.

• For this example, you'll need a document file from a URI. You can use our sample document for this quickstart.
• We've added the file URI value to the Uri fileUri variable at the top of the script.
• To extract the layout from a given file at a URI, use the StartAnalyzeDocumentFromUri method and pass prebuilt-layout as the model ID. The returned value is an AnalyzeResult object containing data from the submitted document.

Add the following code sample to the Program.cs file. Make sure you update the key and endpoint variables with values from your Azure portal Document Intelligence instance:

using Azure;
using Azure.AI.DocumentIntelligence;

//set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal to create your `AzureKeyCredential` and `DocumentIntelligenceClient` instance
string endpoint = "<your-endpoint>";
string key = "<your-key>";
AzureKeyCredential credential = new AzureKeyCredential(key);
DocumentIntelligenceClient client = new DocumentIntelligenceClient(new Uri(endpoint), credential);

//sample document
Uri fileUri = new Uri ("https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf");

AnalyzeDocumentContent content = new AnalyzeDocumentContent()
{
    UrlSource= fileUri
};

Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync(WaitUntil.Completed, "prebuilt-layout", content);

AnalyzeResult result = operation.Value;

foreach (DocumentPage page in result.Pages)
{
    Console.WriteLine($"Document Page {page.PageNumber} has {page.Lines.Count} line(s), {page.Words.Count} word(s)," +
        $" and {page.SelectionMarks.Count} selection mark(s).");

    for (int i = 0; i < page.Lines.Count; i++)
    {
        DocumentLine line = page.Lines[i];

        Console.WriteLine($"  Line {i}:");
        Console.WriteLine($"    Content: '{line.Content}'");

        Console.Write("    Bounding polygon, with points ordered clockwise:");
        for (int j = 0; j < line.Polygon.Count; j += 2)
        {
            Console.Write($" ({line.Polygon[j]}, {line.Polygon[j + 1]})");
        }

        Console.WriteLine();
    }

    for (int i = 0; i < page.SelectionMarks.Count; i++)
    {
        DocumentSelectionMark selectionMark = page.SelectionMarks[i];

        Console.WriteLine($"  Selection Mark {i} is {selectionMark.State}.");
        Console.WriteLine($"    State: {selectionMark.State}");

        Console.Write("    Bounding polygon, with points ordered clockwise:");
        for (int j = 0; j < selectionMark.Polygon.Count; j++)
        {
            Console.Write($" ({selectionMark.Polygon[j]}, {selectionMark.Polygon[j + 1]})");
        }

        Console.WriteLine();
    }
}

for (int i = 0; i < result.Paragraphs.Count; i++)
{
    DocumentParagraph paragraph = result.Paragraphs[i];

    Console.WriteLine($"Paragraph {i}:");
    Console.WriteLine($"  Content: {paragraph.Content}");

    if (paragraph.Role != null)
    {
        Console.WriteLine($"  Role: {paragraph.Role}");
    }
}

foreach (DocumentStyle style in result.Styles)
{
    // Check the style and style confidence to see if text is handwritten.
    // Note that value '0.8' is used as an example.

    bool isHandwritten = style.IsHandwritten.HasValue && style.IsHandwritten == true;

    if (isHandwritten && style.Confidence > 0.8)
    {
        Console.WriteLine($"Handwritten content found:");

        foreach (DocumentSpan span in style.Spans)
        {
            var handwrittenContent = result.Content.Substring(span.Offset, span.Length);
            Console.WriteLine($"  {handwrittenContent}");
        }
    }
}

for (int i = 0; i < result.Tables.Count; i++)
{
    DocumentTable table = result.Tables[i];

    Console.WriteLine($"Table {i} has {table.RowCount} rows and {table.ColumnCount} columns.");

    foreach (DocumentTableCell cell in table.Cells)
    {
        Console.WriteLine($"  Cell ({cell.RowIndex}, {cell.ColumnIndex}) is a '{cell.Kind}' with content: {cell.Content}");
    }
}

Run your application

Once you add a code sample to your application, choose the green Start button next to formRecognizer_quickstart to build and run your program, or press F5.

Prebuilt model

Analyze and extract common fields from specific document types using a prebuilt model. In this example, we analyze an invoice using the prebuilt-invoice model.

Tip

You aren't limited to invoices—there are several prebuilt models to choose from, each of which has its own set of supported fields. The model to use for the analyze operation depends on the type of document to be analyzed. See model data extraction.

• Analyze an invoice using the prebuilt-invoice model. You can use our sample invoice document for this quickstart.
• We've added the file URI value to the Uri invoiceUri variable at the top of the Program.cs file.
• To analyze a given file at a URI, use the StartAnalyzeDocumentFromUri method and pass prebuilt-invoice as the model ID. The returned value is an AnalyzeResult object containing data from the submitted document.
• For simplicity, all the key-value pairs that the service returns are not shown here. To see the list of all supported fields and corresponding types, see our Invoice concept page.

Add the following code sample to your Program.cs file. Make sure you update the key and endpoint variables with values from your Azure portal Document Intelligence instance:

using Azure;
using Azure.AI.DocumentIntelligence;

//set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal to create your `AzureKeyCredential` and `DocumentIntelligenceClient` instance
string endpoint = "<your-endpoint>";
string key = "<your-key>";
AzureKeyCredential credential = new AzureKeyCredential(key);
DocumentIntelligenceClient client = new DocumentIntelligenceClient(new Uri(endpoint), credential);

//sample invoice document

Uri invoiceUri = new Uri ("https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf");

AnalyzeDocumentContent content = new AnalyzeDocumentContent()
{
    UrlSource = invoiceUri
};

Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync(WaitUntil.Completed, "prebuilt-invoice", content);

AnalyzeResult result = operation.Value;

for (int i = 0; i < result.Documents.Count; i++)
{
    Console.WriteLine($"Document {i}:");

    AnalyzedDocument document = result.Documents[i];

    if (document.Fields.TryGetValue("VendorName", out DocumentField vendorNameField)
        && vendorNameField.Type == DocumentFieldType.String)
    {
        string vendorName = vendorNameField.ValueString;
        Console.WriteLine($"Vendor Name: '{vendorName}', with confidence {vendorNameField.Confidence}");
    }

    if (document.Fields.TryGetValue("CustomerName", out DocumentField customerNameField)
        && customerNameField.Type == DocumentFieldType.String)
    {
        string customerName = customerNameField.ValueString;
        Console.WriteLine($"Customer Name: '{customerName}', with confidence {customerNameField.Confidence}");
    }

    if (document.Fields.TryGetValue("Items", out DocumentField itemsField)
        && itemsField.Type == DocumentFieldType.Array)
    {
        foreach (DocumentField itemField in itemsField.ValueArray)
        {
            Console.WriteLine("Item:");

            if (itemField.Type == DocumentFieldType.Object)
            {
                IReadOnlyDictionary<string, DocumentField> itemFields = itemField.ValueObject;

                if (itemFields.TryGetValue("Description", out DocumentField itemDescriptionField)
                    && itemDescriptionField.Type == DocumentFieldType.String)
                {
                    string itemDescription = itemDescriptionField.ValueString;
                    Console.WriteLine($"  Description: '{itemDescription}', with confidence {itemDescriptionField.Confidence}");
                }

                if (itemFields.TryGetValue("Amount", out DocumentField itemAmountField)
                    && itemAmountField.Type == DocumentFieldType.Currency)
                {
                    CurrencyValue itemAmount = itemAmountField.ValueCurrency;
                    Console.WriteLine($"  Amount: '{itemAmount.CurrencySymbol}{itemAmount.Amount}', with confidence {itemAmountField.Confidence}");
                }
            }
        }
    }

    if (document.Fields.TryGetValue("SubTotal", out DocumentField subTotalField)
        && subTotalField.Type == DocumentFieldType.Currency)
    {
        CurrencyValue subTotal = subTotalField.ValueCurrency;
        Console.WriteLine($"Sub Total: '{subTotal.CurrencySymbol}{subTotal.Amount}', with confidence {subTotalField.Confidence}");
    }

    if (document.Fields.TryGetValue("TotalTax", out DocumentField totalTaxField)
        && totalTaxField.Type == DocumentFieldType.Currency)
    {
        CurrencyValue totalTax = totalTaxField.ValueCurrency;
        Console.WriteLine($"Total Tax: '{totalTax.CurrencySymbol}{totalTax.Amount}', with confidence {totalTaxField.Confidence}");
    }

    if (document.Fields.TryGetValue("InvoiceTotal", out DocumentField invoiceTotalField)
        && invoiceTotalField.Type == DocumentFieldType.Currency)
    {
        CurrencyValue invoiceTotal = invoiceTotalField.ValueCurrency;
        Console.WriteLine($"Invoice Total: '{invoiceTotal.CurrencySymbol}{invoiceTotal.Amount}', with confidence {invoiceTotalField.Confidence}");
    }
}

Run your application

Once you add a code sample to your application, choose the green Start button next to formRecognizer_quickstart to build and run your program, or press F5.

Client library | SDK reference | REST API reference | Package (Maven) | Samples |Supported REST API version

In this quickstart, use the following features to analyze and extract data and values from forms and documents:

• Layout—Analyze and extract tables, lines, words, and selection marks like radio buttons and check boxes in documents, without the need to train a model.

• Prebuilt Invoice—Analyze and extract common fields from specific document types using a pretrained model.

Prerequisites

• Azure subscription - Create one for free.

• The latest version of Visual Studio Code or your preferred IDE. See Java in Visual Studio Code.

  Tip

  • Visual Studio Code offers a Coding Pack for Java for Windows and macOS.The coding pack is a bundle of VS Code, the Java Development Kit (JDK), and a collection of suggested extensions by Microsoft. The Coding Pack can also be used to fix an existing development environment.
  • If you are using VS Code and the Coding Pack For Java, install the Gradle for Java extension.

• If you aren't using Visual Studio Code, make sure you have the following installed in your development environment:

  • A Java Development Kit (JDK) version 8 or later. For more information, see Microsoft Build of OpenJDK.

  • Gradle, version 6.8 or later.

• An Azure AI services or Document Intelligence resource. Once you have your Azure subscription, create a single-service or multi-service Document Intelligence resource, in the Azure portal, to get your key and endpoint. You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

  Tip

  Create an Azure AI services resource if you plan to access multiple Azure AI services under a single endpoint/key. For Document Intelligence access only, create a Document Intelligence resource. Please note that you'll need a single-service resource if you intend to use Microsoft Entra authentication.

• After your resource deploys, select Go to resource. You need the key and endpoint from the resource you create to connect your application to the Document Intelligence API. Later, you paste your key and endpoint into the code:

  

Set up

Create a new Gradle project

1. In console window (such as cmd, PowerShell, or Bash), create a new directory for your app called doc-intel-app, and navigate to it.

   mkdir doc-intel-app && doc-intel-app
   

   mkdir doc-intel-app; cd doc-intel-app
   

2. Run the gradle init command from your working directory. This command creates essential build files for Gradle, including build.gradle.kts, which is used at runtime to create and configure your application.

   gradle init --type basic
   

3. When prompted to choose a DSL, select Kotlin.

4. Accept the default project name (doc-intel-app) by selecting Return or Enter.

Install the client library

This quickstart uses the Gradle dependency manager. You can find the client library and information for other dependency managers on the Maven Central Repository.

Open the project's build.gradle.kts file in your IDE. Copay and past the following code to include the client library as an implementation statement, along with the required plugins and settings.

   plugins {
       java
       application
   }
   application {
       mainClass.set("DocIntelligence")
   }
   repositories {
       mavenCentral()
   }
   dependencies {
       implementation group: 'com.azure', name: 'azure-ai-documentintelligence', version: '1.0.0-beta.4'

   }

Create a Java application

To interact with the Document Intelligence service, you need to create an instance of the DocumentIntelligenceClient class. To do so, you create an AzureKeyCredential with your key from the Azure portal and a DocumentIntelligenceClient instance with the AzureKeyCredential and your Document Intelligence endpoint.

1. From the doc-intel-app directory, run the following command:

   mkdir -p src/main/java
   

   You create the following directory structure:

   

2. Navigate to the java directory and create a file named DocIntelligence.java.

   Tip

   • You can create a new file using PowerShell.
   • Open a PowerShell window in your project directory by holding down the Shift key and right-clicking the folder.
   • Type the following command New-Item DocIntelligence.java.

3. Open the DocIntelligence.java file. Copy and paste one of the following code samples into your application:

   • Layout

   • Prebuilt Invoice

Important

Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. For more information, see Azure AI services security.

Layout model

Extract text, selection marks, text styles, table structures, and bounding region coordinates from documents.

• For this example, you'll need a document file at a URI. You can use our sample document for this quickstart.
• To analyze a given file at a URI, you'll use the beginAnalyzeDocumentFromUrl method and pass prebuilt-layout as the model Id. The returned value is an AnalyzeResult object containing data about the submitted document.
• We've added the file URI value to the documentUrl variable in the main method.

Add the following code sample to the DocIntelligence.java file. Make sure you update the key and endpoint variables with values from your Azure portal Document Intelligence instance:

import com.azure.ai.documentintelligence.models.AnalyzeDocumentRequest;
import com.azure.ai.documentintelligence.models.AnalyzeResult;
import com.azure.ai.documentintelligence.models.AnalyzeResultOperation;
import com.azure.ai.documentintelligence.models.DocumentTable;
import com.azure.core.credential.AzureKeyCredential;
import com.azure.core.util.polling.SyncPoller;

import java.util.List;

public class DocIntelligence {

  // set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
  private static final String endpoint = "<your-endpoint>";
  private static final String key = "<your-key>";

  public static void main(String[] args) {

    // create your `DocumentIntelligenceClient` instance and `AzureKeyCredential` variable
    DocumentIntelligenceClient client = new DocumentIntelligenceClientBuilder()
      .credential(new AzureKeyCredential(key))
      .endpoint(endpoint)
      .buildClient();

    // sample document
    String modelId = "prebuilt-layout";
    String documentUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf";

    SyncPoller <AnalyzeResultOperation, AnalyzeResultOperation> analyzeLayoutPoller =
      client.beginAnalyzeDocument(modelId,
          null,
          null,
          null,
          null,
          null,
          null,
          new AnalyzeDocumentRequest().setUrlSource(documentUrl));

    AnalyzeResult analyzeLayoutResult = analyzeLayoutPoller.getFinalResult().getAnalyzeResult();

    // pages
    analyzeLayoutResult.getPages().forEach(documentPage -> {
      System.out.printf("Page has width: %.2f and height: %.2f, measured with unit: %s%n",
        documentPage.getWidth(),
        documentPage.getHeight(),
        documentPage.getUnit());

      // lines
      documentPage.getLines().forEach(documentLine ->
        System.out.printf("Line '%s' is within a bounding polygon %s.%n",
          documentLine.getContent(),
          documentLine.getPolygon()));

      // words
      documentPage.getWords().forEach(documentWord ->
        System.out.printf("Word '%s' has a confidence score of %.2f.%n",
          documentWord.getContent(),
          documentWord.getConfidence()));

      // selection marks
      documentPage.getSelectionMarks().forEach(documentSelectionMark ->
        System.out.printf("Selection mark is '%s' and is within a bounding polygon %s with confidence %.2f.%n",
          documentSelectionMark.getState().toString(),
          documentSelectionMark.getPolygon(),
          documentSelectionMark.getConfidence()));
    });

    // tables
    List < DocumentTable > tables = analyzeLayoutResult.getTables();
    for (int i = 0; i < tables.size(); i++) {
      DocumentTable documentTable = tables.get(i);
      System.out.printf("Table %d has %d rows and %d columns.%n", i, documentTable.getRowCount(),
        documentTable.getColumnCount());
      documentTable.getCells().forEach(documentTableCell -> {
        System.out.printf("Cell '%s', has row index %d and column index %d.%n", documentTableCell.getContent(),
          documentTableCell.getRowIndex(), documentTableCell.getColumnIndex());
      });
      System.out.println();
    }

    // styles
    analyzeLayoutResult.getStyles().forEach(documentStyle -
      > System.out.printf("Document is handwritten %s.%n", documentStyle.isHandwritten()));
  }
}

Build and run the application

After you add a code sample to your application, navigate back to your main project directory—doc-intel-app.

1. Build your application with the build command:

   gradle build
   

2. Run your application with the run command:

   gradle run
   

Prebuilt model

Analyze and extract common fields from specific document types using a prebuilt model. In this example, we analyze an invoice using the prebuilt-invoice model.

Tip

You aren't limited to invoices—there are several prebuilt models to choose from, each of which has its own set of supported fields. The model to use for the analyze operation depends on the type of document to be analyzed. See model data extraction.

• Analyze an invoice using the prebuilt-invoice model. You can use our sample invoice document for this quickstart.
• We've added the file URL value to the invoiceUrl variable at the top of the file.
• To analyze a given file at a URI, you'll use the beginAnalyzeDocuments method and pass PrebuiltModels.Invoice as the model Id. The returned value is a result object containing data about the submitted document.
• For simplicity, all the key-value pairs that the service returns are not shown here. To see the list of all supported fields and corresponding types, see our Invoice concept page.

Add the following code sample to the DocIntelligence.java file. Make sure you update the key and endpoint variables with values from your Azure portal Document Intelligence instance:

import com.azure.ai.documentintelligence.models.AnalyzeDocumentRequest;
import com.azure.ai.documentintelligence.models.AnalyzeResult;
import com.azure.ai.documentintelligence.models.AnalyzeResultOperation;
import com.azure.ai.documentintelligence.models.Document;
import com.azure.ai.documentintelligence.models.DocumentField;
import com.azure.ai.documentintelligence.models.DocumentFieldType;
import com.azure.core.credential.AzureKeyCredential;
import com.azure.core.util.polling.SyncPoller;

import java.io.IOException;
import java.time.LocalDate;
import java.util.List;
import java.util.Map;

public class DocIntelligence {

  // set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
  private static final String endpoint = "<your-endpoint>";
  private static final String key = "<your-key>";

  public static void main(String[] args) {

    // sample document
    String modelId = "prebuilt-invoice";
    String invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf";

    public static void main(final String[] args) throws IOException {

      // Instantiate a client that will be used to call the service.
      DocumentIntelligenceClient client = new DocumentIntelligenceClientBuilder()
        .credential(new AzureKeyCredential(key))
        .endpoint(endpoint)
        .buildClient();

      SyncPoller<AnalyzeResultOperation, AnalyzeResultOperation > analyzeInvoicesPoller =
        client.beginAnalyzeDocument(modelId, 
            null,
            null,
            null,
            null,
            null,
            null,
            new AnalyzeDocumentRequest().setUrlSource(invoiceUrl));

      AnalyzeResult analyzeInvoiceResult = analyzeInvoicesPoller.getFinalResult().getAnalyzeResult();

      for (int i = 0; i < analyzeInvoiceResult.getDocuments().size(); i++) {
        Document analyzedInvoice = analyzeInvoiceResult.getDocuments().get(i);
        Map < String, DocumentField > invoiceFields = analyzedInvoice.getFields();
        System.out.printf("----------- Analyzing invoice  %d -----------%n", i);
        DocumentField vendorNameField = invoiceFields.get("VendorName");
        if (vendorNameField != null) {
          if (DocumentFieldType.STRING == vendorNameField.getType()) {
            String merchantName = vendorNameField.getValueString();
            System.out.printf("Vendor Name: %s, confidence: %.2f%n",
              merchantName, vendorNameField.getConfidence());
          }
        }

        DocumentField vendorAddressField = invoiceFields.get("VendorAddress");
        if (vendorAddressField != null) {
          if (DocumentFieldType.STRING == vendorAddressField.getType()) {
            String merchantAddress = vendorAddressField.getValueString();
            System.out.printf("Vendor address: %s, confidence: %.2f%n",
              merchantAddress, vendorAddressField.getConfidence());
          }
        }

        DocumentField customerNameField = invoiceFields.get("CustomerName");
        if (customerNameField != null) {
          if (DocumentFieldType.STRING == customerNameField.getType()) {
            String merchantAddress = customerNameField.getValueString();
            System.out.printf("Customer Name: %s, confidence: %.2f%n",
              merchantAddress, customerNameField.getConfidence());
          }
        }

        DocumentField customerAddressRecipientField = invoiceFields.get("CustomerAddressRecipient");
        if (customerAddressRecipientField != null) {
          if (DocumentFieldType.STRING == customerAddressRecipientField.getType()) {
            String customerAddr = customerAddressRecipientField.getValueString();
            System.out.printf("Customer Address Recipient: %s, confidence: %.2f%n",
              customerAddr, customerAddressRecipientField.getConfidence());
          }
        }

        DocumentField invoiceIdField = invoiceFields.get("InvoiceId");
        if (invoiceIdField != null) {
          if (DocumentFieldType.STRING == invoiceIdField.getType()) {
            String invoiceId = invoiceIdField.getValueString();
            System.out.printf("Invoice ID: %s, confidence: %.2f%n",
              invoiceId, invoiceIdField.getConfidence());
          }
        }

        DocumentField invoiceDateField = invoiceFields.get("InvoiceDate");
        if (customerNameField != null) {
          if (DocumentFieldType.DATE == invoiceDateField.getType()) {
            LocalDate invoiceDate = invoiceDateField.getValueDate();
            System.out.printf("Invoice Date: %s, confidence: %.2f%n",
              invoiceDate, invoiceDateField.getConfidence());
          }
        }

        DocumentField invoiceTotalField = invoiceFields.get("InvoiceTotal");
        if (customerAddressRecipientField != null) {
          if (DocumentFieldType.NUMBER == invoiceTotalField.getType()) {
            Double invoiceTotal = invoiceTotalField.getValueNumber();
            System.out.printf("Invoice Total: %.2f, confidence: %.2f%n",
              invoiceTotal, invoiceTotalField.getConfidence());
          }
        }

        DocumentField invoiceItemsField = invoiceFields.get("Items");
        if (invoiceItemsField != null) {
          System.out.printf("Invoice Items: %n");
          if (DocumentFieldType.ARRAY == invoiceItemsField.getType()) {
            List < DocumentField > invoiceItems = invoiceItemsField.getValueArray();
            invoiceItems.stream()
              .filter(invoiceItem -> DocumentFieldType.OBJECT == invoiceItem.getType())
              .map(documentField -> documentField.getValueObject())
              .forEach(documentFieldMap -> documentFieldMap.forEach((key, documentField) -> {

                // See a full list of fields found on an invoice here:
                // https://aka.ms/documentintelligence/invoicefields

                if ("Description".equals(key)) {
                  if (DocumentFieldType.STRING == documentField.getType()) {
                    String name = documentField.getValueString();
                    System.out.printf("Description: %s, confidence: %.2fs%n",
                      name, documentField.getConfidence());
                  }
                }
                if ("Quantity".equals(key)) {
                  if (DocumentFieldType.NUMBER == documentField.getType()) {
                    Double quantity = documentField.getValueNumber();
                    System.out.printf("Quantity: %f, confidence: %.2f%n",
                      quantity, documentField.getConfidence());
                  }
                }
                if ("UnitPrice".equals(key)) {
                  if (DocumentFieldType.NUMBER == documentField.getType()) {
                    Double unitPrice = documentField.getValueNumber();
                    System.out.printf("Unit Price: %f, confidence: %.2f%n",
                      unitPrice, documentField.getConfidence());
                  }
                }
                if ("ProductCode".equals(key)) {
                  if (DocumentFieldType.NUMBER == documentField.getType()) {
                    Double productCode = documentField.getValueNumber();
                    System.out.printf("Product Code: %f, confidence: %.2f%n",
                      productCode, documentField.getConfidence());
                  }
                }
              }));
          }
        }
      }
    }
  }
}

Build and run the application

After you add a code sample to your application, navigate back to your main project directory—doc-intel-app.

1. Build your application with the build command:

   gradle build
   

2. Run your application with the run command:

   gradle run
   

Client library | REST API reference | Package (npm) | Samples |Supported REST API version

In this quickstart, use the following features to analyze and extract data and values from forms and documents:

• Layout—Analyze and extract tables, lines, words, and selection marks like radio buttons and check boxes in documents, without the need to train a model.

• Prebuilt Invoice—Analyze and extract common fields from specific document types using a pretrained invoice model.

Prerequisites

• Azure subscription - Create one for free.

• The latest version of Visual Studio Code or your preferred IDE. For more information, see Node.js in Visual Studio Code.

• The latest LTS version of Node.js.

• An Azure AI services or Document Intelligence resource. Once you have your Azure subscription, create a single-service or multi-service Document Intelligence resource, in the Azure portal, to get your key and endpoint. You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

  Tip

  Create an Azure AI services resource if you plan to access multiple Azure AI services under a single endpoint/key. For Document Intelligence access only, create a Document Intelligence resource. Please note that you'll need a single-service resource if you intend to use Microsoft Entra authentication.

• After your resource deploys, select Go to resource. You need the key and endpoint from the resource you create to connect your application to the Document Intelligence API. You paste your key and endpoint into the code later in the quickstart:

  

Set up

1. Create a new Node.js Express application: In a console window (such as cmd, PowerShell, or Bash), create and navigate to a new directory for your app named doc-intel-app.

   mkdir doc-intel-app && cd doc-intel-app
   

2. Run the npm init command to initialize the application and scaffold your project.

   npm init
   

3. Specify your project's attributes using the prompts presented in the terminal.

   • The most important attributes are name, version number, and entry point.
   • We recommend keeping index.js for the entry point name. The description, test command, GitHub repository, keywords, author, and license information are optional attributes—they can be skipped for this project.
   • Accept the suggestions in parentheses by selecting Return or Enter.
   • After you complete the prompts, a package.json file will be created in your doc-intel-app directory.

4. Install the ai-document-intelligence client library and azure/identity npm packages:

   npm i @azure-rest/ai-document-intelligence@1.0.0-beta.3 @azure/core-auth
   

   Your app's package.json file is updated with the dependencies.

5. Create a file named index.js in the application directory.

   Tip

   • You can create a new file using PowerShell.
   • Open a PowerShell window in your project directory by holding down the Shift key and right-clicking the folder.
   • Type the following command New-Item index.js.

Build your application

To interact with the Document Intelligence service, you need to create an instance of the DocumentIntelligenceClient class. To do so, you create an AzureKeyCredential with your key from the Azure portal and a DocumentIntelligenceClient instance with the AzureKeyCredential and your Document Intelligence endpoint.

1. Open the index.js file in Visual Studio Code or your favorite IDE. Copy and paste one of the following code samples into your application:

   • Layout

   • Prebuilt Invoice

Important

Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. For more information, see Azure AI services security.

Layout model

Extract text, selection marks, text styles, table structures, and bounding region coordinates from documents.

• For this example, you'll need a document file from a URL. You can use our sample document for this quickstart.
• We've added the file URL value to the formUrl variable near the top of the file.
• To analyze a given file from a URL, you'll use the beginAnalyzeDocuments method and pass in prebuilt-layout as the model Id.

    const DocumentIntelligence = require("@azure-rest/ai-document-intelligence").default,
  { getLongRunningPoller, isUnexpected } = require("@azure-rest/ai-document-intelligence");

  const { AzureKeyCredential } = require("@azure/core-auth");

    // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
    const key = "<your-key>";
    const endpoint = "<your-endpoint>";

    // sample document
    const formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"

   async function main() {
    const client = DocumentIntelligence(endpoint, new AzureKeyCredential(key));


    const initialResponse = await client
      .path("/documentModels/{modelId}:analyze", "prebuilt-layout")
      .post({
        contentType: "application/json",
        body: {
          urlSource: formUrl
        },
       });

       if (isUnexpected(initialResponse)) {
       throw initialResponse.body.error;
     }

    const poller = await getLongRunningPoller(client, initialResponse);
    const analyzeResult = (await poller.pollUntilDone()).body.analyzeResult;

    const documents = analyzeResult?.documents;

    const document = documents && documents[0];
    if (!document) {
    throw new Error("Expected at least one document in the result.");
    }

    console.log(
    "Extracted document:",
    document.docType,
    `(confidence: ${document.confidence || "<undefined>"})`,
    );
    console.log("Fields:", document.fields);
}

main().catch((error) => {
    console.error("An error occurred:", error);
    process.exit(1);
});

Run your application

After you add a code sample to your application, run your program:

1. Navigate to the folder where you have your Document Intelligence application (doc-intel-app).

2. Type the following command in your terminal:

   node index.js
   

Prebuilt model

In this example, we analyze an invoice using the prebuilt-invoice model.

Tip

You aren't limited to invoices—there are several prebuilt models to choose from, each of which has its own set of supported fields. The model to use for the analyze operation depends on the type of document to be analyzed. See model data extraction.

• Analyze an invoice using the prebuilt-invoice model. You can use our sample invoice document for this quickstart.
• We've added the file URL value to the invoiceUrl variable at the top of the file.
• To analyze a given file at a URI, you'll use the beginAnalyzeDocuments method and pass PrebuiltModels.Invoice as the model Id. The returned value is a result object containing data about the submitted document.
• For simplicity, all the key-value pairs that the service returns are not shown here. To see the list of all supported fields and corresponding types, see our Invoice concept page.

const DocumentIntelligence = require("@azure-rest/ai-document-intelligence").default,
  { getLongRunningPoller, isUnexpected } = require("@azure-rest/ai-document-intelligence");

const { AzureKeyCredential } = require("@azure/core-auth");

    // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
    const key = "<your-key>";
    const endpoint = "<your-endpoint>";

    // sample document
    const invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"

async function main() {

    const client = DocumentIntelligence(endpoint, new AzureKeyCredential(key));

    const initialResponse = await client
    .path("/documentModels/{modelId}:analyze", "prebuilt-invoice")
    .post({
      contentType: "application/json",
      body: {
        // The Document Intelligence service will access the URL to the invoice image and extract data from it
        urlSource: invoiceUrl,
      },
    });

    if (isUnexpected(initialResponse)) {
       throw initialResponse.body.error;
     }

    const poller = await getLongRunningPoller(client, initialResponse);

    poller.onProgress((state) => console.log("Operation:", state.result, state.status));
    const analyzeResult = (await poller.pollUntilDone()).body.analyzeResult;

    const documents = analyzeResult?.documents;

    const result = documents && documents[0];
    if (result) {
      console.log(result.fields);
    } else {
      throw new Error("Expected at least one invoice in the result.");
    }

console.log(
    "Extracted invoice:",
    document.docType,
    `(confidence: ${document.confidence || "<undefined>"})`,
  );
  console.log("Fields:", document.fields);
}


main().catch((error) => {
    console.error("An error occurred:", error);
    process.exit(1);
});

Run your application

After you add a code sample to your application, run your program:

1. Navigate to the folder where you have your Document Intelligence application (doc-intel-app).

2. Type the following command in your terminal:

   node index.js
   

Client library |SDK reference | REST API reference | Package (PyPi) | Samples | Supported REST API version

In this quickstart, use the following features to analyze and extract data from forms and documents:

• Layout—Analyze and extract tables, lines, words, and selection marks like radio buttons and check boxes, and key-value pairs, without the need to train a model.

• Prebuilt Invoice—Analyze and extract common fields from specific document types using a pretrained model.

Prerequisites

• Azure subscription - Create one for free.

• Python 3.7 or later.

  • Your Python installation should include pip. You can check if you have pip installed by running pip --version on the command line. Get pip by installing the latest version of Python.

• The latest version of Visual Studio Code or your preferred IDE. For more information, see Getting Started with Python in Visual Studio Code.

• An Azure AI services or Document Intelligence resource. Once you have your Azure subscription, create a single-service or multi-service Document Intelligence resource, in the Azure portal, to get your key and endpoint. You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

Tip

Create an Azure AI services resource if you plan to access multiple Azure AI services under a single endpoint/key. For Document Intelligence access only, create a Document Intelligence resource. Please note that you'll need a single-service resource if you intend to use Microsoft Entra authentication.

• After your resource deploys, select Go to resource. You need the key and endpoint from the resource you create to connect your application to the Document Intelligence API. You paste your key and endpoint into the code later in the quickstart:

  

Set up

Open a terminal window in your local environment and install the Azure AI Document Intelligence client library for Python with pip:

pip install azure-ai-documentintelligence==1.0.0b4

Create your Python application

To interact with the Document Intelligence service, you need to create an instance of the DocumentIntelligenceClient class. To do so, you create an AzureKeyCredential with your key from the Azure portal and a DocumentIntelligenceClient instance with the AzureKeyCredential and your Document Intelligence endpoint.

1. Create a new Python file called doc_intel_quickstart.py in your preferred editor or IDE.

2. Open the doc_intel_quickstart.py file and select one of the following code samples to copy and paste into your application:

   • Layout

   • Prebuilt Invoice

Important

Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. For more information, see Azure AI services security.

Layout model

Extract text, selection marks, text styles, table structures, and bounding region coordinates from documents.

• For this example, you'll need a document file from a URL. You can use our sample document for this quickstart.
• We've added the file URL value to the formUrl variable in the analyze_layout function.

Add the following code sample to your doc_intel_quickstart.py application. Make sure you update the key and endpoint variables with values from your Azure portal Document Intelligence instance:

# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeResult
from azure.ai.documentintelligence.models import AnalyzeDocumentRequest

# set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
endpoint = "<your-endpoint>"
key = "<your-key>"

# helper functions

def get_words(page, line):
    result = []
    for word in page.words:
        if _in_span(word, line.spans):
            result.append(word)
    return result


def _in_span(word, spans):
    for span in spans:
        if word.span.offset >= span.offset and (
            word.span.offset + word.span.length
        ) <= (span.offset + span.length):
            return True
    return False


def analyze_layout():
    # sample document
    formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"

    document_intelligence_client = DocumentIntelligenceClient(
        endpoint=endpoint, credential=AzureKeyCredential(key)
    )

    poller = document_intelligence_client.begin_analyze_document(
        "prebuilt-layout", AnalyzeDocumentRequest(url_source=formUrl
    ))

    result: AnalyzeResult = poller.result()

    if result.styles and any([style.is_handwritten for style in result.styles]):
        print("Document contains handwritten content")
    else:
        print("Document does not contain handwritten content")

    for page in result.pages:
        print(f"----Analyzing layout from page #{page.page_number}----")
        print(
            f"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}"
        )

        if page.lines:
            for line_idx, line in enumerate(page.lines):
                words = get_words(page, line)
                print(
                    f"...Line # {line_idx} has word count {len(words)} and text '{line.content}' "
                    f"within bounding polygon '{line.polygon}'"
                )

                for word in words:
                    print(
                        f"......Word '{word.content}' has a confidence of {word.confidence}"
                    )

        if page.selection_marks:
            for selection_mark in page.selection_marks:
                print(
                    f"Selection mark is '{selection_mark.state}' within bounding polygon "
                    f"'{selection_mark.polygon}' and has a confidence of {selection_mark.confidence}"
                )

    if result.tables:
        for table_idx, table in enumerate(result.tables):
            print(
                f"Table # {table_idx} has {table.row_count} rows and "
                f"{table.column_count} columns"
            )
            if table.bounding_regions:
                for region in table.bounding_regions:
                    print(
                        f"Table # {table_idx} location on page: {region.page_number} is {region.polygon}"
                    )
            for cell in table.cells:
                print(
                    f"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'"
                )
                if cell.bounding_regions:
                    for region in cell.bounding_regions:
                        print(
                            f"...content on page {region.page_number} is within bounding polygon '{region.polygon}'"
                        )

    print("----------------------------------------")


if __name__ == "__main__":
    analyze_layout()

Run the application

After you add a code sample to your application, build and run your program:

1. Navigate to the folder where you have your doc_intel_quickstart.py file.

2. Type the following command in your terminal:

   python doc_intel_quickstart.py
   

---

Prebuilt model

Analyze and extract common fields from specific document types using a prebuilt model. In this example, we analyze an invoice using the prebuilt-invoice model.

Tip

You aren't limited to invoices—there are several prebuilt models to choose from, each of which has its own set of supported fields. The model to use for the analyze operation depends on the type of document to be analyzed. See model data extraction.

• Analyze an invoice using the prebuilt-invoice model. You can use our sample invoice document for this quickstart.
• We've added the file URL value to the invoiceUrl variable at the top of the file.
• For simplicity, all the key-value pairs that the service returns are not shown here. To see the list of all supported fields and corresponding types, see our Invoice concept page.

Add the following code sample to your doc_intel_quickstart.py application. Make sure you update the key and endpoint variables with values from your Azure portal Document Intelligence instance:

# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeResult
from azure.ai.documentintelligence.models import AnalyzeDocumentRequest



# set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
endpoint = "<your-endpoint>"
key = "<your-key>"

def analyze_invoice():
    # sample document

    invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"

    document_intelligence_client = DocumentIntelligenceClient(
        endpoint=endpoint, credential=AzureKeyCredential(key)
    )

    poller = document_intelligence_client.begin_analyze_document(
        "prebuilt-invoice", AnalyzeDocumentRequest(url_source=invoiceUrl)
    )
    invoices = poller.result()

    if invoices.documents:
        for idx, invoice in enumerate(invoices.documents):
            print(f"--------Analyzing invoice #{idx + 1}--------")
            vendor_name = invoice.fields.get("VendorName")
            if vendor_name:
                print(
                    f"Vendor Name: {vendor_name.get('content')} has confidence: {vendor_name.get('confidence')}"
                )
            vendor_address = invoice.fields.get("VendorAddress")
            if vendor_address:
                print(
                    f"Vendor Address: {vendor_address.get('content')} has confidence: {vendor_address.get('confidence')}"
                )
            vendor_address_recipient = invoice.fields.get("VendorAddressRecipient")
            if vendor_address_recipient:
                print(
                    f"Vendor Address Recipient: {vendor_address_recipient.get('content')} has confidence: {vendor_address_recipient.get('confidence')}"
                )
            customer_name = invoice.fields.get("CustomerName")
            if customer_name:
                print(
                    f"Customer Name: {customer_name.get('content')} has confidence: {customer_name.get('confidence')}"
                )
            customer_id = invoice.fields.get("CustomerId")
            if customer_id:
                print(
                    f"Customer Id: {customer_id.get('content')} has confidence: {customer_id.get('confidence')}"
                )
            customer_address = invoice.fields.get("CustomerAddress")
            if customer_address:
                print(
                    f"Customer Address: {customer_address.get('content')} has confidence: {customer_address.get('confidence')}"
                )
            customer_address_recipient = invoice.fields.get("CustomerAddressRecipient")
            if customer_address_recipient:
                print(
                    f"Customer Address Recipient: {customer_address_recipient.get('content')} has confidence: {customer_address_recipient.get('confidence')}"
                )
            invoice_id = invoice.fields.get("InvoiceId")
            if invoice_id:
                print(
                    f"Invoice Id: {invoice_id.get('content')} has confidence: {invoice_id.get('confidence')}"
                )
            invoice_date = invoice.fields.get("InvoiceDate")
            if invoice_date:
                print(
                    f"Invoice Date: {invoice_date.get('content')} has confidence: {invoice_date.get('confidence')}"
                )
            invoice_total = invoice.fields.get("InvoiceTotal")
            if invoice_total:
                print(
                    f"Invoice Total: {invoice_total.get('content')} has confidence: {invoice_total.get('confidence')}"
                )
            due_date = invoice.fields.get("DueDate")
            if due_date:
                print(
                    f"Due Date: {due_date.get('content')} has confidence: {due_date.get('confidence')}"
                )
            purchase_order = invoice.fields.get("PurchaseOrder")
            if purchase_order:
                print(
                    f"Purchase Order: {purchase_order.get('content')} has confidence: {purchase_order.get('confidence')}"
                )
            billing_address = invoice.fields.get("BillingAddress")
            if billing_address:
                print(
                    f"Billing Address: {billing_address.get('content')} has confidence: {billing_address.get('confidence')}"
                )
            billing_address_recipient = invoice.fields.get("BillingAddressRecipient")
            if billing_address_recipient:
                print(
                    f"Billing Address Recipient: {billing_address_recipient.get('content')} has confidence: {billing_address_recipient.get('confidence')}"
                )
            shipping_address = invoice.fields.get("ShippingAddress")
            if shipping_address:
                print(
                    f"Shipping Address: {shipping_address.get('content')} has confidence: {shipping_address.get('confidence')}"
                )
            shipping_address_recipient = invoice.fields.get("ShippingAddressRecipient")
            if shipping_address_recipient:
                print(
                    f"Shipping Address Recipient: {shipping_address_recipient.get('content')} has confidence: {shipping_address_recipient.get('confidence')}"
                )
            print("Invoice items:")
            for idx, item in enumerate(invoice.fields.get("Items").get("valueArray")):
                print(f"...Item #{idx + 1}")
                item_description = item.get("valueObject").get("Description")
                if item_description:
                    print(
                        f"......Description: {item_description.get('content')} has confidence: {item_description.get('confidence')}"
                    )
                item_quantity = item.get("valueObject").get("Quantity")
                if item_quantity:
                    print(
                        f"......Quantity: {item_quantity.get('content')} has confidence: {item_quantity.get('confidence')}"
                    )
                unit = item.get("valueObject").get("Unit")
                if unit:
                    print(
                        f"......Unit: {unit.get('content')} has confidence: {unit.get('confidence')}"
                    )
                unit_price = item.get("valueObject").get("UnitPrice")
                if unit_price:
                    unit_price_code = (
                        unit_price.get("valueCurrency").get("currencyCode")
                        if unit_price.get("valueCurrency").get("currencyCode")
                        else ""
                    )
                    print(
                        f"......Unit Price: {unit_price.get('content')}{unit_price_code} has confidence: {unit_price.get('confidence')}"
                    )
                product_code = item.get("valueObject").get("ProductCode")
                if product_code:
                    print(
                        f"......Product Code: {product_code.get('content')} has confidence: {product_code.get('confidence')}"
                    )
                item_date = item.get("valueObject").get("Date")
                if item_date:
                    print(
                        f"......Date: {item_date.get('content')} has confidence: {item_date.get('confidence')}"
                    )
                tax = item.get("valueObject").get("Tax")
                if tax:
                    print(
                        f"......Tax: {tax.get('content')} has confidence: {tax.get('confidence')}"
                    )
                amount = item.get("valueObject").get("Amount")
                if amount:
                    print(
                        f"......Amount: {amount.get('content')} has confidence: {amount.get('confidence')}"
                    )
            subtotal = invoice.fields.get("SubTotal")
            if subtotal:
                print(
                    f"Subtotal: {subtotal.get('content')} has confidence: {subtotal.get('confidence')}"
                )
            total_tax = invoice.fields.get("TotalTax")
            if total_tax:
                print(
                    f"Total Tax: {total_tax.get('content')} has confidence: {total_tax.get('confidence')}"
                )
            previous_unpaid_balance = invoice.fields.get("PreviousUnpaidBalance")
            if previous_unpaid_balance:
                print(
                    f"Previous Unpaid Balance: {previous_unpaid_balance.get('content')} has confidence: {previous_unpaid_balance.get('confidence')}"
                )
            amount_due = invoice.fields.get("AmountDue")
            if amount_due:
                print(
                    f"Amount Due: {amount_due.get('content')} has confidence: {amount_due.get('confidence')}"
                )
            service_start_date = invoice.fields.get("ServiceStartDate")
            if service_start_date:
                print(
                    f"Service Start Date: {service_start_date.get('content')} has confidence: {service_start_date.get('confidence')}"
                )
            service_end_date = invoice.fields.get("ServiceEndDate")
            if service_end_date:
                print(
                    f"Service End Date: {service_end_date.get('content')} has confidence: {service_end_date.get('confidence')}"
                )
            service_address = invoice.fields.get("ServiceAddress")
            if service_address:
                print(
                    f"Service Address: {service_address.get('content')} has confidence: {service_address.get('confidence')}"
                )
            service_address_recipient = invoice.fields.get("ServiceAddressRecipient")
            if service_address_recipient:
                print(
                    f"Service Address Recipient: {service_address_recipient.get('content')} has confidence: {service_address_recipient.get('confidence')}"
                )
            remittance_address = invoice.fields.get("RemittanceAddress")
            if remittance_address:
                print(
                    f"Remittance Address: {remittance_address.get('content')} has confidence: {remittance_address.get('confidence')}"
                )
            remittance_address_recipient = invoice.fields.get(
                "RemittanceAddressRecipient"
            )
            if remittance_address_recipient:
                print(
                    f"Remittance Address Recipient: {remittance_address_recipient.get('content')} has confidence: {remittance_address_recipient.get('confidence')}"
                )


          print("----------------------------------------")


if __name__ == "__main__":
    analyze_invoice()

Run the application

After you add a code sample to your application, build and run your program:

1. Navigate to the folder where you have your doc_intel_quickstart.py file.

2. Type the following command in your terminal:

   python doc_intel_quickstart.py
   

| Document Intelligence REST API | Supported Azure SDKS

In this quickstart, learn to use the Document Intelligence REST API to analyze and extract data and values from documents:

Prerequisites

• Azure subscription - Create one for free

• curl command line tool installed.

  • Windows
  • Mac or Linux

• PowerShell version 7.*+ (or a similar command-line application.):

  • Windows
  • macOS
  • Linux

• To check your PowerShell version, type the following command relative to your operating system:

  • Windows: Get-Host | Select-Object Version
  • macOS or Linux: $PSVersionTable

• A Document Intelligence (single-service) or Azure AI services (multi-service) resource. Once you have your Azure subscription, create a single-service or multi-service Document Intelligence resource, in the Azure portal, to get your key and endpoint. You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

Tip

Create an Azure AI services resource if you plan to access multiple Azure AI services under a single endpoint/key. For Document Intelligence access only, create a Document Intelligence resource. Please note that you'll need a single-service resource if you intend to use Microsoft Entra authentication.

• After your resource deploys, select Go to resource. You need the key and endpoint from the resource you create to connect your application to the Document Intelligence API. You paste your key and endpoint into the code later in the quickstart:

  

Analyze documents and get results

A POST request is used to analyze documents with a prebuilt or custom model. A GET request is used to retrieve the result of a document analysis call. The modelId is used with POST and resultId with GET operations.

Analyze document (POST Request)

Before you run the cURL command, make the following changes to the post request:

1. Replace {endpoint} with the endpoint value from your Azure portal Document Intelligence instance.

2. Replace {key} with the key value from your Azure portal Document Intelligence instance.

3. Using the following table as a reference, replace {modelID} and {your-document-url} with your desired values.

4. You need a document file at a URL. For this quickstart, you can use the sample forms provided in the following table for each feature:

Sample documents

Feature	{modelID}	{your-document-url}
Read	prebuilt-read	https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/read.png
Layout	prebuilt-layout	https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/layout.png
Health insurance card	prebuilt-healthInsuranceCard.us	https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/insurance-card.png
W-2	prebuilt-tax.us.w2	https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/w2.png
Invoice	prebuilt-invoice	https://github.com/Azure-Samples/cognitive-services-REST-api-samples/raw/master/curl/form-recognizer/rest-api/invoice.pdf
Receipt	prebuilt-receipt	https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/receipt.png
ID document	prebuilt-idDocument	https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/identity_documents.png

Important

Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. For more information, see Azure AI services security.

POST request

curl -v -i POST "{endpoint}/documentintelligence/documentModels/{modelId}:analyze?api-version=2024-07-31-preview" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {key}" --data-ascii "{'urlSource': '{your-document-url}'}"

POST response (resultID)

You receive a 202 (Success) response that includes a read-only Operation-Location header. The value of this header contains a resultID that can be queried to get the status of the asynchronous operation and retrieve the results using a GET request with your same resource subscription key:

Get analyze results (GET Request)

After you call the Analyze document API, call the Get analyze result API to get the status of the operation and the extracted data. Before you run the command, make these changes:

1. Replace {resultID} Operation-Location header from the POST response.

2. Replace {key} with the key value from your Document Intelligence instance in the Azure portal.

GET request

curl -v -X GET "{endpoint}/documentintelligence/documentModels/{modelId}/analyzeResults/{resultId}?api-version=2024-07-31-preview" -H "Ocp-Apim-Subscription-Key: {key}"

Examine the response

You receive a 200 (Success) response with JSON output. The first field, "status", indicates the status of the operation. If the operation isn't complete, the value of "status" is "running" or "notStarted", and you should call the API again, either manually or through a script. We recommend an interval of one second or more between calls.

Sample response for prebuilt-invoice

{
    "status": "succeeded",
    "createdDateTime": "2024-03-25T19:31:37Z",
    "lastUpdatedDateTime": "2024-03-25T19:31:43Z",
    "analyzeResult": {
        "apiVersion": "2024-07-31-preview",
        "modelId": "prebuilt-invoice",
        "stringIndexType": "textElements"...
    ..."pages": [
            {
                "pageNumber": 1,
                "angle": 0,
                "width": 8.5,
                "height": 11,
                "unit": "inch",
                "words": [
                    {
                        "content": "CONTOSO",
                        "boundingBox": [
                            0.5911,
                            0.6857,
                            1.7451,
                            0.6857,
                            1.7451,
                            0.8664,
                            0.5911,
                            0.8664
                        ],
                        "confidence": 1,
                        "span": {
                            "offset": 0,
                            "length": 7
                                }
                      }],
              }]
      }
}

Supported document fields

The prebuilt models extract predefined sets of document fields. See Model data extraction for extracted field names, types, descriptions, and examples.

That's it, congratulations!

In this quickstart, you used a document Intelligence model to analyze various forms and documents. Next, explore the Document Intelligence Studio and reference documentation to learn about Document Intelligence API in depth.

Next steps

• For an enhanced experience and advanced model quality, try Document Intelligence Studio

• For v3.1 to v4.0 migration, see Changelog Migration guides.

• Find more samples on GitHub.