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‎articles/ai-foundry/how-to/connections-add.md

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---
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title: How to add a new connection in Azure AI Foundry portal
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title: How to add a new connection in your project
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titleSuffix: Azure AI Foundry
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description: Learn how to add a new connection in Azure AI Foundry portal.
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description: Learn how to add a new connection in your hub based or Foundry project.
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manager: scottpolly
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ms.service: azure-ai-foundry
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ms.custom:
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- ignite-2023
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- build-2024
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- ignite-2024
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ms.topic: how-to
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ms.date: 07/08/2025
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ms.date: 08/05/2025
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ms.reviewer: sgilley
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ms.author: sgilley
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author: sdgilley
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zone_pivot_groups: project-type
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# Customer Intent: As an admin or developer, I want to understand how to add new connections in Azure AI Foundry portal.
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# Customer Intent: As an admin or developer, I want to understand how to add new connections in my project.
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---
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# How to add a new connection in Azure AI Foundry portal
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# How to add a new connection in your project
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[!INCLUDE [feature-preview](../includes/feature-preview.md)]
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::: zone-end
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## Create a new connection
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## Create a new connection in Azure AI Foundry portal
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[!INCLUDE [tip-left-pane](../includes/tip-left-pane.md)]
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1. After the service is connected, select __Close__.
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::: zone pivot="hub-project"
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## Create a new connection with Azure Machine Learning SDK
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[!INCLUDE [hub-connections](../includes/hub-connections.md)]
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::: zone-end
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## Network isolation
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::: zone pivot="hub-project"

‎articles/ai-foundry/includes/hub-connections.md

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---
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title: Include file
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description: Include file
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author: sdgilley
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ms.reviewer: sgilley
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ms.author: sgilley
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ms.service: azure-ai-foundry
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ms.topic: include
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ms.date: 08/05/2025
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ms.custom: include
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---
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### Set up your environment
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[!INCLUDE [SDK setup](../../includes/development-environment-config.md)]
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### Authenticating with Microsoft Entra ID
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There are various authentication methods for the different connection types. When you use Microsoft Entra ID, in addition to creating the connection you might also need to grant Azure role-based access control permissions before the connection can be used. For more information, visit [Role-based access control](../../concepts/rbac-azure-ai-foundry.md#scenario-connections-using-microsoft-entra-id-authentication).
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[!INCLUDE [Azure Key Vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/microsoft-entra-id-akv-expanded.md)]
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## [Azure OpenAI](#tab/aoai)
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The following example uses the [AzureOpenAIConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.azureopenaiconnection) class to create an Azure OpenAI in Azure AI Foundry Models connection.
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> [!TIP]
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> To connect to Azure OpenAI and more AI services with one connection, you can use the [AI services connection](#azure-ai-services) instead.
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```python
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from azure.ai.ml.entities import AzureOpenAIConnection
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name = "XXXXXXXXX"
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target = "https://XXXXXXXXX.cognitiveservices.azure.com/"
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resource_id= "Azure-resource-id"
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# Microsoft Entra ID
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credentials = None
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wps_connection = AzureOpenAIConnection(
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name=name,
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azure_endpoint=target,
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credentials=credentials,
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resource_id = resource_id,
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is_shared=False
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)
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ml_client.connections.create_or_update(wps_connection)
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```
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## [AI services](#tab/ai-services)
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The following example uses the [AzureAIServicesConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.azureaiservicesconnection) class to create an Azure AI services connection. This example creates one connection for the AI services documented in the [Connect to Azure AI services](../../../ai-services/connect-services-ai-foundry-portal.md) article. The same connection also supports Azure OpenAI.
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```python
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from azure.ai.ml.entities import AzureAIServicesConnection
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name = "my-ai-services"
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target = "https://my.cognitiveservices.azure.com/"
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resource_id=""
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# Microsoft Entra ID
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credentials = None
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wps_connection = AzureAIServicesConnection(
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name=name,
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endpoint=target,
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credentials=credentials,
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ai_services_resource_id=resource_id,
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)
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ml_client.connections.create_or_update(wps_connection)
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```
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## [Search](#tab/search)
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The following example uses the [AzureAISearchConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.azureaisearchconnection) class to create an Azure AI Search connection:
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```python
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from azure.ai.ml.entities import AzureAISearchConnection
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name = "my_aisearch_demo_connection"
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target = "https://my.search.windows.net"
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# Microsoft Entra ID
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credentials = None
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wps_connection = AzureAISearchConnection(
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name=name,
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endpoint=target,
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credentials=credentials,
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)
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ml_client.connections.create_or_update(wps_connection)
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```
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## [Content Safety](#tab/content-safety)
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The following example creates an Azure AI Content Safety connection:
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```python
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from azure.ai.ml.entities import AzureContentSafetyConnection, ApiKeyConfiguration
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name = "my_content_safety"
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target = "https://my.cognitiveservices.azure.com/"
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api_key = "XXXXXXXXX"
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wps_connection = AzureContentSafetyConnection(
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name=name,
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endpoint=target,
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credentials=ApiKeyConfiguration(key=api_key),
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#api_version="1234"
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)
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ml_client.connections.create_or_update(wps_connection)
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```
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## [Serverless model (preview)](#tab/serverless)
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The following example creates a serverless endpoint connection:
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```python
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from azure.ai.ml.entities import ServerlessConnection
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name = "my_maas_apk"
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endpoint = "https://my.eastus2.inference.ai.azure.com/"
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api_key = "XXXXXXXXX"
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wps_connection = ServerlessConnection(
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name=name,
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endpoint=endpoint,
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api_key=api_key,
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)
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ml_client.connections.create_or_update(wps_connection)
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```
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## [Blob Storage](#tab/blob)
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The following example uses the [AzureBlobStoreConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.azureblobstoreconnection) class to create an Azure Blob Storage connection. This connection is authenticated with an account key or a SAS token:
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```python
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from azure.ai.ml.entities import AzureBlobStoreConnection, SasTokenConfiguration,AccountKeyConfiguration
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name = "my_blobstore"
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url = "https://XXXXXXXXX.blob.core.windows.net/mycontainer/"
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wps_connection = AzureBlobStoreConnection(
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name=name,
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container_name="XXXXXXXXX",
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account_name="XXXXXXXXX",
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url=url,
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#credentials=None
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credentials=SasTokenConfiguration(sas_token="XXXXXXXXX")
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#credentials=AccountKeyConfiguration(account_key="XXXXXXXXX")
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)
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ml_client.connections.create_or_update(wps_connection)
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```
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## [Azure Data Lake Storage Gen 2](#tab/adl2)
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The following example creates Azure Data Lake Storage Gen 2 connection. This connection is authenticated with a Service Principal:
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```python
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from azure.ai.ml.entities import WorkspaceConnection
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from azure.ai.ml.entities import ServicePrincipalConfiguration
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sp_config = ServicePrincipalConfiguration(
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tenant_id="XXXXXXXXXXXX",
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client_id="XXXXXXXXXXXXX",
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client_secret="XXXXXXXXXXXXXXXk" # your-client-secret
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)
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name = "myadlsgen2"
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target = "https://ambadaladlsgen2.core.windows.net/dummycont"
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wps_connection = WorkspaceConnection(
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name=name,
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type="azure_data_lake_gen2",
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target=target,
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credentials=sp_config
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)
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ml_client.connections.create_or_update(workspace_connection=wps_connection)
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```
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## [Microsoft OneLake](#tab/onelake)
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The following example uses the [MicrosoftOneLakeWorkspaceConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.microsoftonelakeconnection) class to create a Microsoft OneLake connection. This connection is authenticated with a Service Principal:
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```python
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from azure.ai.ml.entities import MicrosoftOneLakeWorkspaceConnection, OneLakeArtifact
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from azure.ai.ml.entities import ServicePrincipalConfiguration
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sp_config = ServicePrincipalConfiguration(
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tenant_id="XXXXXXXXXXX",
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client_id="XXXXXXXXXXXXXXXXXX",
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client_secret="XXXXXXXXXXXXXXXX" # your-client-secret
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)
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name = "my_onelake_sp"
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artifact = OneLakeArtifact(
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name="XXXXXXXXX",
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type="lake_house"
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)
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wps_connection = MicrosoftOneLakeWorkspaceConnection(
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name=name,
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artifact=artifact,
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one_lake_workspace_name="XXXXXXXXXXXXXXXXX",
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endpoint="XXXXXXXXX.dfs.fabric.microsoft.com"
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credentials=sp_config
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)
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ml_client.connections.create_or_update(workspace_connection=wps_connection)
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```
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## [Serp](#tab/serp)
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The following example uses the [SerpConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.serpconnection) class:
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```python
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from azure.ai.ml.entities import SerpConnection
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name = "my_serp_apk"
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api_key = "XXXXXXXXX"
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wps_connection = SerpConnection(
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name=name,
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api_key=api_key,
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)
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ml_client.connections.create_or_update(wps_connection)
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```
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## [OpenAI](#tab/openai)
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The following example uses the [OpenAIConnection](/python/api/azure-ai-ml/azure.ai.ml.entities.openaiconnection) class to create an OpenAI (not Azure OpenAI) connection:
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```python
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from azure.ai.ml.entities import OpenAIConnection
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name = "my_oai_apk"
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api_key = "XXXXXXXX"
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wps_connection = OpenAIConnection(
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name=name,
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api_key=api_key,
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)
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ml_client.connections.create_or_update(wps_connection)
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```
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## [Custom](#tab/custom)
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The following example uses the [ApiKeyConfiguration](/python/api/azure-ai-ml/azure.ai.ml.entities.apikeyconnection) class to create custom connection:
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```python
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from azure.ai.ml.entities import WorkspaceConnection
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from azure.ai.ml.entities import ApiKeyConfiguration
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name = "my_custom"
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target = "https://XXXXXXXXX.core.windows.net/mycontainer"
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wps_connection = WorkspaceConnection(
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name=name,
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type="custom",
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target=target,
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credentials=ApiKeyConfiguration(key="XXXXXXXXX"),
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)
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ml_client.connections.create_or_update(workspace_connection=wps_connection)
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```
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---
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### List connections
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To list all connections, use the following example:
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```python
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from azure.ai.ml.entities import Connection, AzureOpenAIConnection, ApiKeyConfiguration
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connection_list = ml_client.connections.list()
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for conn in connection_list:
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print(conn)
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```
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### Delete connections
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To delete a connection, use the following example:
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```python
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name = "my-connection"
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ml_client.connections.delete(name)
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```

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