SDK target: REST API 2022–08–31 (GA)

REST API version 2022–08–31 (GA)

Azure AI Document Intelligence is a cloud service that uses machine learning to analyze text and structured data from documents. The Document Intelligence software development kit (SDK) is a set of libraries and tools that enable you to easily integrate Document Intelligence models and capabilities into your applications. Document Intelligence SDK is available across platforms in C#/.NET, Java, JavaScript, and Python programming languages.

Supported programming languages

Document Intelligence SDK supports the following languages and platforms:

Language → Document Intelligence SDK version	Package	Supported API version	Platform support
.NET/C# → 4.0.0 (GA)	NuGet	v3.0 v2.1 v2.0	Windows, macOS, Linux, Docker
Java → 4.0.6 (GA)	MVN repository	v3.0 v2.1 v2.0	Windows, macOS, Linux
JavaScript → 4.0.0 (GA)	npm	v3.0 v2.1 v2.0	Browser, Windows, macOS, Linux
Python → 3.2.0 (GA)	PyPI	v3.0 v2.1 v2.0	Windows, macOS, Linux

Supported Clients

Language	SDK version	API version	Supported clients
.NET/C# Java JavaScript	4.0.0 (GA)	v3.0:2022-08-31 (default)	DocumentAnalysisClient DocumentModelAdministrationClient
.NET/C# Java JavaScript	3.1.x	v2.1 (default) v2.0	FormRecognizerClient FormTrainingClient
.NET/C# Java JavaScript	3.0.x	v2.0	FormRecognizerClient FormTrainingClient
Python	3.2.x (GA)	v3.0:2022-08-31 (default)	DocumentAnalysisClient DocumentModelAdministrationClient
Python	3.1.x	v2.1 (default) v2.0	FormRecognizerClient FormTrainingClient
Python	3.0.0	v2.0	FormRecognizerClient FormTrainingClient

Use Document Intelligence SDK in your applications

The Document Intelligence SDK enables the use and management of the Document Intelligence service in your application. 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:

1. Install the SDK client library

C#/.NET

dotnet add package Azure.AI.FormRecognizer --version 4.0.0

Install-Package Azure.AI.FormRecognizer -Version 4.0.0

Java

<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-formrecognizer</artifactId>
<version>4.0.6</version>
</dependency>

implementation("com.azure:azure-ai-formrecognizer:4.0.6")

JavaScript

npm i @azure/ai-form-recognizer@4.0.0

Python

pip install azure-ai-formrecognizer==3.2.0

2. Import the SDK client library into your application

C#/.NET

using Azure;
using Azure.AI.FormRecognizer.DocumentAnalysis;

Java

import com.azure.ai.formrecognizer.*;
import com.azure.ai.formrecognizer.models.*;
import com.azure.ai.formrecognizer.DocumentAnalysisClient.*;

import com.azure.core.credential.AzureKeyCredential;

JavaScript

const { AzureKeyCredential, DocumentAnalysisClient } = require("@azure/ai-form-recognizer");

Python

from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential

3. Set up authentication

There are two supported methods for authentication

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

• Use a token credential from azure-identity to authenticate with Microsoft Entra ID.

Use your API key

Here's where to find your Document Intelligence API key in the Azure portal:

C#/.NET

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

Java

// create your `DocumentAnalysisClient` instance and `AzureKeyCredential` variable
DocumentAnalysisClient client = new DocumentAnalysisClientBuilder()
            .credential(new AzureKeyCredential("<your-key>"))
            .endpoint("<your-endpoint>")
            .buildClient();

JavaScript

// create your `DocumentAnalysisClient` instance and `AzureKeyCredential` variable
async function main() {
    const client = new DocumentAnalysisClient("<your-endpoint>", new AzureKeyCredential("<your-key>"));

Python

# create your `DocumentAnalysisClient` instance and `AzureKeyCredential` variable
    document_analysis_client = DocumentAnalysisClient(endpoint="<your-endpoint>", credential=AzureKeyCredential("<your-key>"))

Use a Microsoft Entra token credential

Note

Regional endpoints do not support Microsoft Entra authentication. Create a custom subdomain for your resource in order to use this type of authentication.

Authorization is easiest using the DefaultAzureCredential. It provides a default token credential, based upon the running environment, capable of handling most Azure authentication scenarios.

C#/.NET

Here's how to acquire and use the DefaultAzureCredential for .NET applications:

1. Install the Azure Identity library for .NET:

       dotnet add package Azure.Identity
   

       Install-Package Azure.Identity
   

2. Register a Microsoft Entra application and create a new service principal.

3. Grant access to Document Intelligence by assigning the Cognitive Services User role to your service principal.

4. Set the values of the client ID, tenant ID, and client secret in the Microsoft Entra application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, and AZURE_CLIENT_SECRET, respectively.

5. Create your DocumentAnalysisClient instance including the DefaultAzureCredential:

   string endpoint = "<your-endpoint>";
   var client = new DocumentAnalysisClient(new Uri(endpoint), new DefaultAzureCredential());
   

For more information, see Authenticate the client

Java

Here's how to acquire and use the DefaultAzureCredential for Java applications:

1. Install the Azure Identity library for Java:

   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-identity</artifactId>
       <version>1.5.3</version>
   </dependency>
   

2. Register a Microsoft Entra application and create a new service principal.

3. Grant access to Document Intelligence by assigning the Cognitive Services User role to your service principal.

4. Set the values of the client ID, tenant ID, and client secret of the Microsoft Entra application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, and AZURE_CLIENT_SECRET, respectively.

5. Create your DocumentAnalysisClient instance and TokenCredential variable:

   TokenCredential credential = new DefaultAzureCredentialBuilder().build();
   DocumentAnalysisClient documentAnalysisClient = new DocumentAnalysisClientBuilder()
       .endpoint("{your-endpoint}")
       .credential(credential)
       .buildClient();
   

For more information, see Authenticate the client

JavaScript

Here's how to acquire and use the DefaultAzureCredential for JavaScript applications:

1. Install the Azure Identity library for JavaScript:

   npm install @azure/identity
   

2. Register a Microsoft Entra application and create a new service principal.

3. Grant access to Document Intelligence by assigning the Cognitive Services User role to your service principal.

4. Set the values of the client ID, tenant ID, and client secret of the Microsoft Entra application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, and AZURE_CLIENT_SECRET, respectively.

5. Create your DocumentAnalysisClient instance including the DefaultAzureCredential:

   const { DocumentAnalysisClient } = require("@azure/ai-form-recognizer");
   const { DefaultAzureCredential } = require("@azure/identity");
   
   const client = new DocumentAnalysisClient("<your-endpoint>", new DefaultAzureCredential());
   

For more information, see Create and authenticate a client.

Python

Here's how to acquire and use the DefaultAzureCredential for Python applications.

1. Install the Azure Identity library for Python:

   pip install azure-identity
   

2. Register a Microsoft Entra application and create a new service principal.

3. Grant access to Document Intelligence by assigning the Cognitive Services User role to your service principal.

4. Set the values of the client ID, tenant ID, and client secret of the Microsoft Entra application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, and AZURE_CLIENT_SECRET, respectively.

5. Create your DocumentAnalysisClient instance including the DefaultAzureCredential:

   from azure.identity import DefaultAzureCredential
   from azure.ai.formrecognizer import DocumentAnalysisClient
   
   credential = DefaultAzureCredential()
   document_analysis_client = DocumentAnalysisClient(
       endpoint="https://<my-custom-subdomain>.cognitiveservices.azure.com/",
       credential=credential
   )
   

For more information, see Authenticate the client

4. Build your application

Create a client object to interact with the Document Intelligence SDK, and then call methods on that client object to interact with the service. The SDKs provide both synchronous and asynchronous methods. For more insight, try a quickstart in a language of your choice.

Help options

The Microsoft Q&A and Stack Overflow forums are available for the developer community to ask and answer questions about Azure AI Document Intelligence and other services. Microsoft monitors the forums and replies to questions that the community has yet to answer. To make sure that we see your question, tag it with azure-form-recognizer.

Next steps

Explore Document Intelligence REST API v3.0

Try a Document Intelligence quickstart