Connect to an Azure AI multi-service resource with Service Connector

This page provides information on supported authentication methods and clients, along with sample code for connecting an Azure AI multi-service resource to other cloud services using Service Connector. This page also lists default environment variable names and values obtained when creating the service connection.

Supported compute services

Service Connector can be used to connect the following compute services to an Azure AI multi-service resource:

• Azure App Service
• Azure Container Apps
• Azure Functions
• Azure Kubernetes Service (AKS)
• Azure Spring Apps

Supported authentication types and client types

The table below indicates the authentication methods and clients supported for connecting your compute service to an Azure AI multi-service resource using Service Connector. A “Yes” indicates that the combination is supported, while a “No” indicates that it is not supported.

Client type	System-assigned managed identity	User-assigned managed identity	Secret/connection string	Service principal
.NET	Yes	Yes	Yes	Yes
Java	Yes	Yes	Yes	Yes
Node.js	Yes	Yes	Yes	Yes
Python	Yes	Yes	Yes	Yes
None	Yes	Yes	Yes	Yes

This table indicates that all combinations of client types and authentication methods in the table are supported. All client types can use any of the authentication methods to connect to an Azure AI multi-service resource using Service Connector.

Default environment variable names or application properties and sample code

Use the connection details below to connect compute services to an Azure AI multi-service resource. For more information about naming conventions, refer to the Service Connector internals article.

System-assigned managed identity (recommended)

Default environment variable name	Description	Sample value
AZURE_COGNITIVESERVICES_ENDPOINT	Azure Cognitive Services token provider service	https://<cognitive-service-name>.cognitiveservices.azure.com/

Sample code

Refer to the steps and code below to connect to an Azure AI multi-service resource using a system-assigned managed identity.

You can use the Azure client library to access various cognitive APIs that an Azure AI multi-service resource supports. We use Azure AI Text Analytics as an example in this sample. Refer to Authenticate requests to Azure AI services to call the cognitive APIs directly.

.NET

1. Install the following dependencies. We use Azure.AI.TextAnalytics as an example.

   dotnet add package Azure.AI.TextAnalytics
   dotnet add package Azure.Identity
   

2. Authenticate using Azure Identity library and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   using Azure.AI.TextAnalytics;
   using Azure.Identity;
   
   string endpoint = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_ENDPOINT");
   
   // Uncomment the following lines corresponding to the authentication type you want to use.
   // system-assigned managed identity
   // var credential = new DefaultAzureCredential();
   
   // user-assigned managed identity
   // var credential = new DefaultAzureCredential(
   //     new DefaultAzureCredentialOptions
   //     {
   //         ManagedIdentityClientId = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_CLIENTID");
   //     });
   
   // service principal 
   // var tenantId = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_TENANTID");
   // var clientId = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_CLIENTID");
   // var clientSecret = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_CLIENTSECRET");
   // var credential = new ClientSecretCredential(tenantId, clientId, clientSecret);
   
   TextAnalyticsClient languageServiceClient = new(
     new Uri(endpoint),
     credential);
   

Java

1. Add the following dependencies in your pom.xml file. We use azure-ai-textanalytics as an example.

   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-ai-textanalytics</artifactId>
       <version>5.1.12</version>
   </dependency>
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-identity</artifactId>
       <version>1.11.4</version>
   </dependency>
   

2. Authenticate using azure-identity and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   // Uncomment the following lines corresponding to the authentication type you want to use.
   // for system-managed identity
   // DefaultAzureCredential credential = new DefaultAzureCredentialBuilder().build();
   
   // for user-assigned managed identity
   // DefaultAzureCredential credential = new DefaultAzureCredentialBuilder()
   //     .managedIdentityClientId(System.getenv("AZURE_COGNITIVESERVICES_CLIENTID"))
   //     .build();
   
   // for service principal
   // ClientSecretCredential credential = new ClientSecretCredentialBuilder()
   //   .clientId(System.getenv("AZURE_COGNITIVESERVICES_CLIENTID"))
   //   .clientSecret(System.getenv("AZURE_COGNITIVESERVICES_CLIENTSECRET"))
   //   .tenantId(System.getenv("AZURE_COGNITIVESERVICES_TENANTID"))
   //   .build();
   
   String endpoint = System.getenv("AZURE_COGNITIVESERVICES_ENDPOINT");
   
   TextAnalyticsClient languageClient = new TextAnalyticsClientBuilder()
       .credential(credential)
       .endpoint(endpoint)
       .buildClient();
   

Python

1. Install the following dependencies. We use azure-ai-textanalytics as an example.

   pip install azure-ai-textanalytics==5.1.0
   pip install azure-identity
   

2. Authenticate using azure-identity and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   import os
   from azure.ai.textanalytics import TextAnalyticsClient
   from azure.identity import ManagedIdentityCredential, ClientSecretCredential
   
   # Uncomment the following lines corresponding to the authentication type you want to use.
   # system-assigned managed identity
   # cred = ManagedIdentityCredential()
   
   # user-assigned managed identity
   # managed_identity_client_id = os.getenv('AZURE_COGNITIVESERVICES_CLIENTID')
   # cred = ManagedIdentityCredential(client_id=managed_identity_client_id)
   
   # service principal
   # tenant_id = os.getenv('AZURE_COGNITIVESERVICES_TENANTID')
   # client_id = os.getenv('AZURE_COGNITIVESERVICES_CLIENTID')
   # client_secret = os.getenv('AZURE_COGNITIVESERVICES_CLIENTSECRET')
   # cred = ClientSecretCredential(tenant_id=tenant_id, client_id=client_id, client_secret=client_secret)
   endpoint = os.getenv('AZURE_COGNITIVESERVICES_ENDPOINT')
   
   language_service_client = TextAnalyticsClient(
     endpoint=endpoint, 
     credential=cred)
   

NodeJS

1. Install the following dependencies. We use ai-text-analytics as an example.

   npm install @azure/ai-text-analytics@5.1.0
   npm install @azure/identity
   

2. Authenticate using @azure/identity and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   import { DefaultAzureCredential,ClientSecretCredential } from "@azure/identity";
   const { TextAnalyticsClient } = require("@azure/ai-text-analytics");
   
   // Uncomment the following lines corresponding to the authentication type you want to use.
   // for system-assigned managed identity
   // const credential = new DefaultAzureCredential();
   
   // for user-assigned managed identity
   // const clientId = process.env.AZURE_COGNITIVESERVICES_CLIENTID;
   // const credential = new DefaultAzureCredential({
   //     managedIdentityClientId: clientId
   // });
   
   // for service principal
   // const tenantId = process.env.AZURE_COGNITIVESERVICES_TENANTID;
   // const clientId = process.env.AZURE_COGNITIVESERVICES_CLIENTID;
   // const clientSecret = process.env.AZURE_COGNITIVESERVICES_CLIENTSECRET;
   // const credential = new ClientSecretCredential(tenantId, clientId, clientSecret);
   
   const endpoint = process.env.AZURE_COGNITIVESERVICES_ENDPOINT;
   const languageClient = new TextAnalyticsClient(endpoint, credential);
   

Other

For other languages, you can use the connection information that Service Connector sets to the environment variables to connect to an Azure AI multi-service resource. For environment variable details, see Integrate an Azure AI multi-service resource with Service Connector.

User-assigned managed identity

Default environment variable name	Description	Sample value
AZURE_COGNITIVESERVICES_ENDPOINT	Azure Cognitive Services token provider service	https://<cognitive-service-name>.cognitiveservices.azure.com/
AZURE_COGNITIVESERVICES_CLIENTID	Your client ID	<client-ID>

Sample code

Refer to the steps and code below to connect to an Azure AI multi-service resource using a user-assigned managed identity.

You can use the Azure client library to access various cognitive APIs that an Azure AI multi-service resource supports. We use Azure AI Text Analytics as an example in this sample. Refer to Authenticate requests to Azure AI services to call the cognitive APIs directly.

.NET

1. Install the following dependencies. We use Azure.AI.TextAnalytics as an example.

   dotnet add package Azure.AI.TextAnalytics
   dotnet add package Azure.Identity
   

2. Authenticate using Azure Identity library and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   using Azure.AI.TextAnalytics;
   using Azure.Identity;
   
   string endpoint = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_ENDPOINT");
   
   // Uncomment the following lines corresponding to the authentication type you want to use.
   // system-assigned managed identity
   // var credential = new DefaultAzureCredential();
   
   // user-assigned managed identity
   // var credential = new DefaultAzureCredential(
   //     new DefaultAzureCredentialOptions
   //     {
   //         ManagedIdentityClientId = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_CLIENTID");
   //     });
   
   // service principal 
   // var tenantId = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_TENANTID");
   // var clientId = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_CLIENTID");
   // var clientSecret = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_CLIENTSECRET");
   // var credential = new ClientSecretCredential(tenantId, clientId, clientSecret);
   
   TextAnalyticsClient languageServiceClient = new(
     new Uri(endpoint),
     credential);
   

Java

1. Add the following dependencies in your pom.xml file. We use azure-ai-textanalytics as an example.

   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-ai-textanalytics</artifactId>
       <version>5.1.12</version>
   </dependency>
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-identity</artifactId>
       <version>1.11.4</version>
   </dependency>
   

2. Authenticate using azure-identity and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   // Uncomment the following lines corresponding to the authentication type you want to use.
   // for system-managed identity
   // DefaultAzureCredential credential = new DefaultAzureCredentialBuilder().build();
   
   // for user-assigned managed identity
   // DefaultAzureCredential credential = new DefaultAzureCredentialBuilder()
   //     .managedIdentityClientId(System.getenv("AZURE_COGNITIVESERVICES_CLIENTID"))
   //     .build();
   
   // for service principal
   // ClientSecretCredential credential = new ClientSecretCredentialBuilder()
   //   .clientId(System.getenv("AZURE_COGNITIVESERVICES_CLIENTID"))
   //   .clientSecret(System.getenv("AZURE_COGNITIVESERVICES_CLIENTSECRET"))
   //   .tenantId(System.getenv("AZURE_COGNITIVESERVICES_TENANTID"))
   //   .build();
   
   String endpoint = System.getenv("AZURE_COGNITIVESERVICES_ENDPOINT");
   
   TextAnalyticsClient languageClient = new TextAnalyticsClientBuilder()
       .credential(credential)
       .endpoint(endpoint)
       .buildClient();
   

Python

1. Install the following dependencies. We use azure-ai-textanalytics as an example.

   pip install azure-ai-textanalytics==5.1.0
   pip install azure-identity
   

2. Authenticate using azure-identity and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   import os
   from azure.ai.textanalytics import TextAnalyticsClient
   from azure.identity import ManagedIdentityCredential, ClientSecretCredential
   
   # Uncomment the following lines corresponding to the authentication type you want to use.
   # system-assigned managed identity
   # cred = ManagedIdentityCredential()
   
   # user-assigned managed identity
   # managed_identity_client_id = os.getenv('AZURE_COGNITIVESERVICES_CLIENTID')
   # cred = ManagedIdentityCredential(client_id=managed_identity_client_id)
   
   # service principal
   # tenant_id = os.getenv('AZURE_COGNITIVESERVICES_TENANTID')
   # client_id = os.getenv('AZURE_COGNITIVESERVICES_CLIENTID')
   # client_secret = os.getenv('AZURE_COGNITIVESERVICES_CLIENTSECRET')
   # cred = ClientSecretCredential(tenant_id=tenant_id, client_id=client_id, client_secret=client_secret)
   endpoint = os.getenv('AZURE_COGNITIVESERVICES_ENDPOINT')
   
   language_service_client = TextAnalyticsClient(
     endpoint=endpoint, 
     credential=cred)
   

NodeJS

1. Install the following dependencies. We use ai-text-analytics as an example.

   npm install @azure/ai-text-analytics@5.1.0
   npm install @azure/identity
   

2. Authenticate using @azure/identity and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   import { DefaultAzureCredential,ClientSecretCredential } from "@azure/identity";
   const { TextAnalyticsClient } = require("@azure/ai-text-analytics");
   
   // Uncomment the following lines corresponding to the authentication type you want to use.
   // for system-assigned managed identity
   // const credential = new DefaultAzureCredential();
   
   // for user-assigned managed identity
   // const clientId = process.env.AZURE_COGNITIVESERVICES_CLIENTID;
   // const credential = new DefaultAzureCredential({
   //     managedIdentityClientId: clientId
   // });
   
   // for service principal
   // const tenantId = process.env.AZURE_COGNITIVESERVICES_TENANTID;
   // const clientId = process.env.AZURE_COGNITIVESERVICES_CLIENTID;
   // const clientSecret = process.env.AZURE_COGNITIVESERVICES_CLIENTSECRET;
   // const credential = new ClientSecretCredential(tenantId, clientId, clientSecret);
   
   const endpoint = process.env.AZURE_COGNITIVESERVICES_ENDPOINT;
   const languageClient = new TextAnalyticsClient(endpoint, credential);
   

Other

For other languages, you can use the connection information that Service Connector sets to the environment variables to connect to an Azure AI multi-service resource. For environment variable details, see Integrate an Azure AI multi-service resource with Service Connector.

Connection string

Default environment variable name	Description	Sample value
AZURE_COGNITIVESERVICES_ENDPOINT	Azure Cognitive Services token provider service	https://<cognitive-service-name>.cognitiveservices.azure.com/
AZURE_COGNITIVESERVICES_KEY	API key of an Azure AI multi-service resource	<api-key>

Sample code

Refer to the steps and code below to connect to an Azure AI multi-service resource using a connection string.

You can use the Azure client library to access various cognitive APIs that an Azure AI multi-service resource supports. We use Azure AI Text Analytics as an example in this sample. Refer to Authenticate requests to Azure AI services to call the cognitive APIs directly.

.NET

1. Install the following dependencies. We use Azure.AI.TextAnalytics as an example.

   dotnet add package Azure.AI.TextAnalytics
   dotnet add package Azure.Core --version 1.40.0
   

2. Get the Azure AI multi-service resource endpoint and key from the environment variables added by Service Connector.

   using Azure.AI.TextAnalytics;
   
   string endpoint = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_ENDPOINT")
   string key = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_KEY");
   
   TextAnalyticsClient languageServiceClient = new(
     new Uri(endpoint),
     new AzureKeyCredential(key));
   

Java

1. Add the following dependencies in your pom.xml file. We use azure-ai-textanalytics as an example.

   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-core</artifactId>
       <version>1.49.1</version>
   </dependency>
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-ai-textanalytics</artifactId>
       <version>5.1.12</version>
   </dependency>
   

2. Get the Azure AI multi-service resource endpoint and key from the environment variables added by Service Connector.

   String endpoint = System.getenv("AZURE_COGNITIVESERVICES_ENDPOINT");
   String key = System.getenv("AZURE_COGNITIVESERVICES_KEY");
   TextAnalyticsClient languageClient = new TextAnalyticsClientBuilder()
       .credential(new AzureKeyCredential(key))
       .endpoint(endpoint)
       .buildClient();
   

Python

1. Install the following dependencies. We use azure-ai-textanalytics as an example.

   pip install azure-ai-textanalytics==5.1.0
   pip install azure-core
   

2. Get the Azure AI multi-service resource endpoint and key from the environment variables added by Service Connector.

   import os
   from azure.ai.textanalytics import TextAnalyticsClient
   from azure.core.credentials import AzureKeyCredential
   
   key = os.environ['AZURE_COGNITIVESERVICES_KEY']
   endpoint = os.environ['AZURE_COGNITIVESERVICES_ENDPOINT']
   language_service_client = TextAnalyticsClient(
     endpoint=retrieved_endpoint, 
     credential=AzureKeyCredential(key))
   

NodeJS

1. Install the following dependency. We use ai-text-analytics as an example.

   npm install @azure/ai-text-analytics@5.1.0
   

2. Get the Azure AI multi-service resource endpoint and key from the environment variables added by Service Connector.

   const { TextAnalyticsClient, AzureKeyCredential } = require("@azure/ai-text-analytics");
   
   const endpoint = process.env.AZURE_COGNITIVESERVICES_ENDPOINT;
   const credential = new AzureKeyCredential(process.env.AZURE_COGNITIVESERVICES_KEY);
   
   const languageClient = new TextAnalyticsClient(endpoint, credential);
   

Other

For other languages, you can use the connection information that Service Connector sets to the environment variables to connect to an Azure AI multi-service resource. For environment variable details, see Integrate an Azure AI multi-service resource with Service Connector.

Service principal

Default environment variable name	Description	Sample value
AZURE_COGNITIVESERVICES_ENDPOINT	Azure Cognitive Services token provider service	https://<cognitive-service-name>.cognitiveservices.azure.com/
AZURE_COGNITIVESERVICES_CLIENTID	Your client ID	<client-ID>
AZURE_COGNITIVESERVICES_CLIENTSECRET	Your client secret	<client-secret>
AZURE_COGNITIVESERVICES_TENANTID	Your tenant ID	<tenant-ID>

Sample code

Refer to the steps and code below to connect to an Azure AI multi-service resource using a service principaL.

You can use the Azure client library to access various cognitive APIs that an Azure AI multi-service resource supports. We use Azure AI Text Analytics as an example in this sample. Refer to Authenticate requests to Azure AI services to call the cognitive APIs directly.

.NET

1. Install the following dependencies. We use Azure.AI.TextAnalytics as an example.

   dotnet add package Azure.AI.TextAnalytics
   dotnet add package Azure.Identity
   

2. Authenticate using Azure Identity library and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   using Azure.AI.TextAnalytics;
   using Azure.Identity;
   
   string endpoint = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_ENDPOINT");
   
   // Uncomment the following lines corresponding to the authentication type you want to use.
   // system-assigned managed identity
   // var credential = new DefaultAzureCredential();
   
   // user-assigned managed identity
   // var credential = new DefaultAzureCredential(
   //     new DefaultAzureCredentialOptions
   //     {
   //         ManagedIdentityClientId = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_CLIENTID");
   //     });
   
   // service principal 
   // var tenantId = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_TENANTID");
   // var clientId = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_CLIENTID");
   // var clientSecret = Environment.GetEnvironmentVariable("AZURE_COGNITIVESERVICES_CLIENTSECRET");
   // var credential = new ClientSecretCredential(tenantId, clientId, clientSecret);
   
   TextAnalyticsClient languageServiceClient = new(
     new Uri(endpoint),
     credential);
   

Java

1. Add the following dependencies in your pom.xml file. We use azure-ai-textanalytics as an example.

   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-ai-textanalytics</artifactId>
       <version>5.1.12</version>
   </dependency>
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-identity</artifactId>
       <version>1.11.4</version>
   </dependency>
   

2. Authenticate using azure-identity and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   // Uncomment the following lines corresponding to the authentication type you want to use.
   // for system-managed identity
   // DefaultAzureCredential credential = new DefaultAzureCredentialBuilder().build();
   
   // for user-assigned managed identity
   // DefaultAzureCredential credential = new DefaultAzureCredentialBuilder()
   //     .managedIdentityClientId(System.getenv("AZURE_COGNITIVESERVICES_CLIENTID"))
   //     .build();
   
   // for service principal
   // ClientSecretCredential credential = new ClientSecretCredentialBuilder()
   //   .clientId(System.getenv("AZURE_COGNITIVESERVICES_CLIENTID"))
   //   .clientSecret(System.getenv("AZURE_COGNITIVESERVICES_CLIENTSECRET"))
   //   .tenantId(System.getenv("AZURE_COGNITIVESERVICES_TENANTID"))
   //   .build();
   
   String endpoint = System.getenv("AZURE_COGNITIVESERVICES_ENDPOINT");
   
   TextAnalyticsClient languageClient = new TextAnalyticsClientBuilder()
       .credential(credential)
       .endpoint(endpoint)
       .buildClient();
   

Python

1. Install the following dependencies. We use azure-ai-textanalytics as an example.

   pip install azure-ai-textanalytics==5.1.0
   pip install azure-identity
   

2. Authenticate using azure-identity and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   import os
   from azure.ai.textanalytics import TextAnalyticsClient
   from azure.identity import ManagedIdentityCredential, ClientSecretCredential
   
   # Uncomment the following lines corresponding to the authentication type you want to use.
   # system-assigned managed identity
   # cred = ManagedIdentityCredential()
   
   # user-assigned managed identity
   # managed_identity_client_id = os.getenv('AZURE_COGNITIVESERVICES_CLIENTID')
   # cred = ManagedIdentityCredential(client_id=managed_identity_client_id)
   
   # service principal
   # tenant_id = os.getenv('AZURE_COGNITIVESERVICES_TENANTID')
   # client_id = os.getenv('AZURE_COGNITIVESERVICES_CLIENTID')
   # client_secret = os.getenv('AZURE_COGNITIVESERVICES_CLIENTSECRET')
   # cred = ClientSecretCredential(tenant_id=tenant_id, client_id=client_id, client_secret=client_secret)
   endpoint = os.getenv('AZURE_COGNITIVESERVICES_ENDPOINT')
   
   language_service_client = TextAnalyticsClient(
     endpoint=endpoint, 
     credential=cred)
   

NodeJS

1. Install the following dependencies. We use ai-text-analytics as an example.

   npm install @azure/ai-text-analytics@5.1.0
   npm install @azure/identity
   

2. Authenticate using @azure/identity and get the Azure AI multi-service resource endpoint from the environment variables added by Service Connector. When using the code below, uncomment the part of the code snippet for the authentication type you want to use.

   import { DefaultAzureCredential,ClientSecretCredential } from "@azure/identity";
   const { TextAnalyticsClient } = require("@azure/ai-text-analytics");
   
   // Uncomment the following lines corresponding to the authentication type you want to use.
   // for system-assigned managed identity
   // const credential = new DefaultAzureCredential();
   
   // for user-assigned managed identity
   // const clientId = process.env.AZURE_COGNITIVESERVICES_CLIENTID;
   // const credential = new DefaultAzureCredential({
   //     managedIdentityClientId: clientId
   // });
   
   // for service principal
   // const tenantId = process.env.AZURE_COGNITIVESERVICES_TENANTID;
   // const clientId = process.env.AZURE_COGNITIVESERVICES_CLIENTID;
   // const clientSecret = process.env.AZURE_COGNITIVESERVICES_CLIENTSECRET;
   // const credential = new ClientSecretCredential(tenantId, clientId, clientSecret);
   
   const endpoint = process.env.AZURE_COGNITIVESERVICES_ENDPOINT;
   const languageClient = new TextAnalyticsClient(endpoint, credential);
   

Other

For other languages, you can use the connection information that Service Connector sets to the environment variables to connect to an Azure AI multi-service resource. For environment variable details, see Integrate an Azure AI multi-service resource with Service Connector.

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