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articles/ai-foundry/concepts/ai-resources.md

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## Create a hub-based project
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To start developing, [create a project](../how-to/create-projects.md). Hub-projects can be accessed in [AI Foundry Portal](https://ai.azure.com/?cid=learnDocs) to build with generative AI tools, and [ML Studio](https://ml.azure.com) to build with tools designed for custom machine learning model training.
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To start developing, [create a [!INCLUDE [hub-project-name](../includes/hub-project-name.md)]](../how-to/create-projects.md?pivots=hub-project). Hub-projects can be accessed in [AI Foundry Portal](https://ai.azure.com/?cid=learnDocs) to build with generative AI tools, and [ML Studio](https://ml.azure.com) to build with tools designed for custom machine learning model training.
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## Project concepts
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## Next steps
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- [Create a [!INCLUDE [hub-project-name](../includes/hub-project-name.md)]](../how-to/create-projects.md?pivots=hub-project)
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- [Quickstart: Analyze images and video in the chat playground](/azure/ai-services/openai/gpt-v-quickstart)
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- [Learn more about Azure AI Foundry](../what-is-azure-ai-foundry.md)
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- [Learn more about projects](../how-to/create-projects.md?pivots=hub-project)

articles/ai-services/agents/how-to/tools/azure-ai-search-samples.md

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```python
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from azure.ai.agents.models import AzureAISearchTool, AzureAISearchQueryType
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from azure.ai.projects.models import ConnectionType
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# Define the Azure AI Search connection ID and index name
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azure_ai_conn_id = os.environ["AZURE_AI_CONNECTION_ID"]
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azure_ai_conn_id = project_client.connections.get_default(ConnectionType.AZURE_AI_SEARCH).id
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# find the index name in your AI Search Azure resource page under Search Management -> Indexes
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index_name = "sample_index"
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# Initialize the Azure AI Search tool

articles/ai-services/agents/how-to/tools/logic-apps.md

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- `SUBSCRIPTION_ID`: Your Azure subscription ID.
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- `resource_group_name`: The name of your resource group.
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:::zone pivot="portal"
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## Add a Logic Apps workflow to an agent using the Azure AI Foundry portal
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1. Go to the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs). In the **Agents** screen for your agent, scroll down the **Setup** pane on the right to **Actions**. Then select **Add**.
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:::image type="content" source="../../media/tools/action-tools.png" alt-text="A screenshot showing the available tool categories in the Azure AI Foundry portal." lightbox="../../media/tools/action-tools.png":::
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1. Select **Azure Logic Apps** and follow the prompts to add the tool.
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:::image type="content" source="../../media/tools/action-tools-list.png" alt-text="A screenshot showing the available action tools in the Azure AI Foundry portal." lightbox="../../media/tools/action-tools-list.png":::
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You can choose to add Microsoft authored workflows, or add your own.
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> [!NOTE]
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> For your logic apps to appear in the Azure AI Foundry portal, they must:
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> * Be in the same subscription and resource group.
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> * Follow a request trigger with a description, and end with a response action.
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:::image type="content" source="../../media/tools/add-logic-apps.png" alt-text="A screenshot showing the screen to add Logic Apps." lightbox="../../media/tools/add-logic-apps.png":::
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:::zone-end
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:::zone pivot="python"
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## Create a project client
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Create a client object to connect to your AI project and other resources.
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```python
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import os
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from azure.ai.projects import AIProjectClient
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print("Deleted agent")
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```
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:::zone-end
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## Next steps
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[See the full sample for Logic Apps integration.](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_logic_apps.py)

articles/ai-services/agents/how-to/tools/sharepoint.md

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## Next steps
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[See the full sample for SharePoint.](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-projects/samples/agents/sample_agents_sharepoint.py)
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* [How to use the SharePoint tool](./sharepoint-samples.md)
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* Reference articles for content retrieval used by the tool:
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* [Overview of the Microsoft 365 Copilot Retrieval API](/microsoft-365-copilot/extensibility/api-reference/retrieval-api-overview).
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* [Semantic indexing for Microsoft 365 Copilot](/microsoftsearch/semantic-index-for-copilot)

articles/ai-services/agents/how-to/virtual-networks.md

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* A Microsoft-managed key vault is used by default.
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### Option 1: manually deploy the bicep template
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### Manually deploy the bicep template
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> [!NOTE]
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> Using the Bicep template is the only way to deploy a network secured environment for Azure AI Foundry Agent Service.
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1. To deploy and customize the bicep templates, [download the template from GitHub](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/microsoft/infrastructure-setup/15-private-network-standard-agent-setup). Download the following from the `private-network-standard-agent-setup` folder:
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1. `main-create.bicep`
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articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md

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description: Learn about provisioned throughput costs and billing in Azure AI Foundry.
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ms.service: azure-ai-openai
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ms.topic: conceptual
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ms.date: 05/28/2025
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ms.date: 06/13/2025
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manager: nitinme
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author: aahill
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|Regional provisioned minimum deployment|25| 50|25| 25 |50 | 25|25|50|25| NA|NA|
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|Regional provisioned scale increment|25| 50|25| 25 | 50 | 25|50|50|25|NA|NA|
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|Input TPM per PTU|5,400 | 3,000|14,900| 59,400 | 600 | 2,500|230|2,500|37,000|4,000|4,000|
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|Latency Target Value| 66 Tokens Per Second | 40 Tokens Per Second|50 Tokens Per Second| 60 Tokens Per Second | 40 Tokens Per Second | 66 Tokens Per Second |25 Tokens Per Second|25 Tokens Per Second|33 Tokens Per Second|50 Tokens Per Second|50 Tokens Per Second|
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|Latency Target Value| 99% > 66 Tokens Per Second\* | 99% > 40 Tokens Per Second\* | 99% > 50 Tokens Per Second\*| 99% > 60 Tokens Per Second\* | 99% > 40 Tokens Per Second\* | 99% > 66 Tokens Per Second\* | 99% > 25 Tokens Per Second\* | 99% > 25 Tokens Per Second\* | 99% > 33 Tokens Per Second\* | 99% > 50 Tokens Per Second\*| 99% > 50 Tokens Per Second\*|
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\* Calculated as the average request latency on a per-minute basis across the month.
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For a full list, see the [Azure AI Foundry calculator](https://ai.azure.com/resource/calculator).
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articles/machine-learning/how-to-assign-roles.md

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3. These scenarios don't include the permissions needed to create workspace dependent resources. For more information, see the write permissions for [Storage](https://learn.microsoft.com/azure/role-based-access-control/permissions/storage#microsoftstorage), [OperationalInsights](https://learn.microsoft.com/azure/role-based-access-control/permissions/monitor#microsoftoperationalinsights), [Key Vault](https://learn.microsoft.com/azure/role-based-access-control/permissions/security#microsoftkeyvault) and [Container Registry](https://learn.microsoft.com/azure/role-based-access-control/permissions/containers#microsoftcontainerregistry).
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4. When attaching user-managed identities, you also need to have `Microsoft.ManagedIdentity/userAssignedIdentities/assign/action` permission on the identities. For more information, see [Azure built-in roles for Identity](/azure/role-based-access-control/built-in-roles/identity).
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5. When specifying a serverless compute custom subnet, you also need to have `Microsoft.Network/virtualNetworks/subnets/join/action` on the virtual network. For more information, see [Azure permissions for Networking](/azure/role-based-access-control/permissions/networking).
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### Deploy into a virtual network or subnet
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[!INCLUDE [network-rbac](includes/network-rbac.md)]

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