Azure Document Intelligence in Foundry Tools is a cloud-based Microsoft Foundry tool that uses machine-learning models to extract key-value pairs, text, and tables from your documents.

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

Azure Document Intelligence is a cloud-based Azure AI service that uses machine-learning models to extract key-value pairs, text, and tables from your documents. Document Intelligence analyzes your forms and documents, extracts text and data, ...

Check out all the Azure Document Intelligence documentation. ... Take the receipt analysis Microsoft Learn course. ... Check out sample code from Microsoft. ... Learn five key ways your organization can get started with AI to realize value quickly. ... Explore the Document Intelligence layout model—an advanced machine-learning based document analysis API available in the Document Intelligence cloud.

In this quickstart, learn to use the Document Intelligence REST API to analyze and extract data and values from documents: ... The cURL command line tool installed. PowerShell version 7.*+ (or a similar command-line application.): ... A Document Intelligence (single-service) or Foundry Tools (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.

Client library |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. Azure subscription - Create one for free.

You can find the endpoint for your Document Intelligence resource using the Azure Portal or the Azure CLI: # Get the endpoint for the Document Intelligence resource az cognitiveservices account show --name "<resource-name>" --resource-group "<resource-group-name>" --query "properties.endpoint" Either a regional endpoint or a custom subdomain can be used for authentication. They are formatted as follows: Regional endpoint: https://<region>.api.cognitive.microsoft.com/ Custom subdomain: https://<resource-name>.cognitiveservices.azure.com/

Document Intelligence returns extracted field values as strongly typed data. Each field in the extraction response carries a value type—such as string, number, integer, date, time, phoneNumber, currency, or address—that determines how the ...

Use a token credential from azure-identity to authenticate with Microsoft Entra ID. Here's where to find your Document Intelligence API key in the Azure portal:

You can find the endpoint for your Document Intelligence resource using the Azure Portal or Azure CLI: # Get the endpoint for the Document Intelligence resource az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "properties.endpoint" Either a regional endpoint or a custom subdomain can be used for authentication. They are formatted as follows: Regional endpoint: https://<region>.api.cognitive.microsoft.com/ Custom subdomain: https://<resource-name>.cognitiveservices.azure.com/

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.

Use a token credential from azure-identity to authenticate with Microsoft Entra ID. Here's where to find your Document Intelligence API key in the Azure portal:

For consumers of the SDK we recommend visiting our public developer docs at https://learn.microsoft.com/dotnet/azure/ or our versioned developer docs at https://azure.github.io/azure-sdk-for-net.

The SDK builds on the underlying Document Intelligence REST API allowing you to easily use those APIs within your programming language paradigm. Here's how you use the Document Intelligence SDK for your preferred language: dotnet add package Azure.AI.FormRecognizer --version 3.1.0

Use a Document Intelligence API key with AzureKeyCredential from azure.core.credentials.

To create a client instance to access the Document Intelligence API, you will need the endpoint of your Form Recognizer resource and a credential. The clients can use either an AzureKeyCredential with an API key of your resource or a TokenCredential that uses Azure Active Directory RBAC to authorize the client.

April 30, 2026 - Currently, Document Intelligence doesn't support reading order across page boundaries. Selection marks aren't positioned within the surrounding words. The top-level content property contains a concatenation of all content elements in reading order. All elements specify their position in the reader order via spans within this content string. The content of some elements isn't always contiguous. The Analyze response for each API returns different objects.

August 15, 2026 - Use a token credential from azure-identity to authenticate with Microsoft Entra ID. Here's where to find your Document Intelligence API key in the Azure portal:

The SDK builds on the underlying Document Intelligence REST API allowing you to easily use those APIs within your programming language paradigm. Here's how you use the Document Intelligence SDK for your preferred language: dotnet add package Azure.AI.FormRecognizer --version 4.1.0

March 22, 2026 - load_dotenv()endpoint = os.getenv("AZURE_DOC_INTELLIGENCE_ENDPOINT") model_id = os.getenv("AZURE_CUSTOM_MODEL_ID") async def submit_document(file_path: str) -> str: # Submit document for analysis and return the operation URL — don't wait for result. credential = DefaultAzureCredential() token = await credential.get_token("https://cognitiveservices.azure.com/.default") url = f"{endpoint}/documentintelligence/documentModels/{model_id}:analyze?api-version=2024-11-30" headers = { "Authorization": f"Bearer {token.token}", "Content-Type": "application/pdf" } with open(file_path, "rb") as f: file_b