Merge branch 'main' of https://github.com/MicrosoftDocs/azure-ai-docs-pr into deploy-and-monitor-flows

Author: paulth1

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

@@ -48,6 +48,7 @@ Azure AI Agent Service offers a standard agent configuration with private networ
     * `Microsoft.MachineLearningServices`
     * `Microsoft.Search`
     * `Microsoft.Network`
+    * `Microsoft.App`
     * To use Bing Search tool: `Microsoft.Bing`
 
     ```console
@@ -109,7 +110,7 @@ Azure AI Agent Service offers a standard agent configuration with private networ
 
 | Template | Description   | Autodeploy |
 | ------------------- | -----------------------------------------------| -----------------------|
-| `network-secured-agent.bicep`  | Deploy a network secured agent setup that uses user-managed identity authentication on the Agent connections. | [![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Frefs%2Fheads%2Fmaster%2Fquickstarts%2Fmicrosoft.azure-ai-agent-service%2Fnetwork-secured-agent%2Fazuredeploy.json)
+| `network-secured-agent.bicep`  | Deploy a network secured agent setup that uses user-managed identity authentication on the Agent connections. | [![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure-Samples%2Fazureai-samples%2Fmain%2Fscenarios%2FAgents%2Fsetup%2Fnetwork-secured-agent%2Fazuredeploy.json)
 
 ### Option 2: manually deploy the bicep template
 
@@ -131,7 +132,7 @@ Azure AI Agent Service offers a standard agent configuration with private networ
 
     Make sure you have the Azure AI Developer role for the resource group you created. 
 
-1. Using the resource group you created in the previous step and one of the template files (either `basic-agent-keys.bicep` or `basic-agent-identity.bicep`), run one of the following commands: 
+1. Using the resource group you created in the previous step and one of the template files (`network-secured-agent`), run one of the following commands: 
 
     1. To use default resource names, run:
 

articles/ai-services/openai/concepts/content-filter.md

@@ -6,7 +6,7 @@ author: PatrickFarley
 ms.author: pafarley
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 02/20/2025
+ms.date: 02/27/2025
 ms.custom: template-concept, devx-track-python
 manager: nitinme
 ---
@@ -820,9 +820,7 @@ This is an example message array:
 In addition to detection on last user content, Azure OpenAI also supports the detection of specific risks inside context documents via Prompt Shields – Indirect Prompt Attack Detection. You should identify parts of the input that are a document (for example, retrieved website, email, etc.) with the following document delimiter.  
 
 ```
-<documents> 
-*insert your document content here* 
-</documents>
+\"\"\" <documents> *insert your document content here* </documents> \"\"\" 
 ```
 
 When you do so, the following options are available for detection on tagged documents: 
@@ -1051,4 +1049,4 @@ As part of your application design, consider the following best practices to del
 - Apply for modified content filters via [this form](https://ncv.microsoft.com/uEfCgnITdR).
 - Azure OpenAI content filtering is powered by [Azure AI Content Safety](https://azure.microsoft.com/products/cognitive-services/ai-content-safety).
 - Learn more about understanding and mitigating risks associated with your application: [Overview of Responsible AI practices for Azure OpenAI models](/legal/cognitive-services/openai/overview?context=/azure/ai-services/openai/context/context).
-- Learn more about how data is processed in connection with content filtering and abuse monitoring: [Data, privacy, and security for Azure OpenAI Service](/legal/cognitive-services/openai/data-privacy?context=/azure/ai-services/openai/context/context#preventing-abuse-and-harmful-content-generation).
+- Learn more about how data is processed in connection with content filtering and abuse monitoring: [Data, privacy, and security for Azure OpenAI Service](/legal/cognitive-services/openai/data-privacy?context=/azure/ai-services/openai/context/context#preventing-abuse-and-harmful-content-generation).

articles/ai-services/openai/concepts/models.md

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI
 description: Learn about the different model capabilities that are available with Azure OpenAI.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 2/19/2025
+ms.date: 2/27/2025
 ms.custom: references_regions, build-2023, build-2023-dataai, refefences_regions
 manager: nitinme
 author: mrbullwinkle #ChrisHMSFT
@@ -18,6 +18,7 @@ Azure OpenAI Service is powered by a diverse set of models with different capabi
 
 | Models | Description |
 |--|--|
+| [GPT-4.5 Preview](#gpt-45-preview) |The latest GPT model that excels at diverse text and image tasks.  |
 | [o-series models](#o-series-models) |[Reasoning models](../how-to/reasoning.md) with advanced problem-solving and increased focus and capability.  |
 | [GPT-4o & GPT-4o mini & GPT-4 Turbo](#gpt-4o-and-gpt-4-turbo) | The latest most capable Azure OpenAI models with multimodal versions, which can accept both text and images as input. |
 | [GPT-4o audio](#gpt-4o-audio) | GPT-4o audio models that support either low-latency, "speech in, speech out" conversational interactions or audio generation. |
@@ -28,6 +29,31 @@ Azure OpenAI Service is powered by a diverse set of models with different capabi
 | [Whisper](#whisper-models) | A series of models in preview that can transcribe and translate speech to text. |
 | [Text to speech](#text-to-speech-models-preview) (Preview) | A series of models in preview that can synthesize text to speech. |
 
+## GPT-4.5 Preview
+
+### Availability
+
+**For access to `gpt-4.5-preview` registration is required, and access will be granted based on Microsoft's eligibility criteria**. Customers who have access to other limited access models will still need to request access for this model.
+
+Request access: [GPT-4.5-preview limited access model application](https://aka.ms/oai/gptaccess)
+
+Once access has been granted, you will need to create a deployment for the model
+
+### Region Availability
+
+| Model | Region |
+|---|---|
+| `gpt-4.5-preview` | East US 2 (Global Standard) <br> Sweden Central (Global Standard) |
+
+### Capabilities
+
+|  Model ID  | Description | Context Window | Max Output Tokens | Training Data (up to)  |
+|  --- |  :--- |:--- |:---|:---: |
+| `gpt-4.5-preview` (2025-02-27) <br> **GPT-4.5 Preview**  | The **latest GPT model** that excels at diverse text and image tasks. <br>-Structured outputs <br>-Prompt caching <br>-Tools <br>-Streaming<br>-Text(input/output)<br>- Image(input)   | 128,000 | 16,384 | Oct 2023 |
+
+> [!NOTE]
+> It is expected behavior that the model cannot answer questions about itself. If you want to know when the knowledge cutoff for the model's training data is, or other details about the model you should refer to the model documentation above.
+
 ## o-series models
 
 The Azure OpenAI o<sup>&#42;</sup> series models are specifically designed to tackle reasoning and problem-solving tasks with increased focus and capability. These models spend more time processing and understanding the user's request, making them exceptionally strong in areas like science, coding, and math compared to previous iterations.
@@ -64,6 +90,9 @@ The GPT 4o audio models are part of the GPT-4o model family and support either l
 - GPT-4o real-time audio is designed to handle real-time, low-latency conversational interactions, making it a great fit for support agents, assistants, translators, and other use cases that need highly responsive back-and-forth with a user. For more information on how to use GPT-4o real-time audio, see the [GPT-4o real-time audio quickstart](../realtime-audio-quickstart.md) and [how to use GPT-4o audio](../how-to/realtime-audio.md).
 - GPT-4o audio completion is designed to generate audio from audio or text prompts, making it a great fit for generating audio books, audio content, and other use cases that require audio generation. The GPT-4o audio completions model introduces the audio modality into the existing `/chat/completions` API. For more information on how to use GPT-4o audio completions, see the [audio generation quickstart](../audio-completions-quickstart.md).
 
+> [!CAUTION]
+> We don't recommend using preview models in production. We will upgrade all deployments of preview models to either future preview versions or to the latest stable GA version. Models that are designated preview don't follow the standard Azure OpenAI model lifecycle.
+
 To use GPT-4o audio, you need [an Azure OpenAI resource](../how-to/create-resource.md) in one of the [supported regions](#global-standard-model-availability).
 
 When your resource is created, you can [deploy](../how-to/create-resource.md#deploy-a-model) the GPT-4o audio model. 

articles/ai-services/openai/quotas-limits.md

@@ -10,7 +10,7 @@ ms.custom:
   - ignite-2023
   - references_regions
 ms.topic: conceptual
-ms.date: 2/14/2025
+ms.date: 2/27/2025
 ms.author: mbullwin
 ---
 
@@ -62,6 +62,13 @@ The following sections provide you with a quick guide to the default quotas and
 
 [!INCLUDE [Quota](./includes/global-batch-limits.md)]
 
+### GPT-4.5 Preview global standard
+
+| Model|Tier| Quota Limit in tokens per minute (TPM) | Requests per minute |
+|---|---|:---:|:---:|
+| `gpt-4.5` | Enterprise Tier | 50 K | 1 |
+| `gpt-4.5` | Default | 50 K | 1 |
+
 ## `o-series` rate limits
 
 > [!IMPORTANT]

articles/ai-services/openai/whats-new.md

@@ -11,7 +11,7 @@ ms.custom:
   - references_regions
   - ignite-2024
 ms.topic: whats-new
-ms.date: 2/19/2025
+ms.date: 2/27/2025
 recommendations: false
 ---
 
@@ -21,9 +21,23 @@ This article provides a summary of the latest releases and major documentation u
 
 ## February 2025
 
+### GPT-4.5 Preview
+
+The latest GPT model that excels at diverse text and image tasks is now available on Azure OpenAI.
+
+**For access to `gpt-4.5-preview` registration is required, and access will be granted based on Microsoft's eligibility criteria**. Customers who have access to other limited access models will still need to request access for this model.
+
+Request access: [GPT-4.5-preview limited access model application](https://aka.ms/oai/gptaccess)
+
+For more information on model capabilities, and region availability see the [models documentation](./concepts/models.md#gpt-45-preview).
+
+### Stored completions API
+
+[Stored completions](./how-to/stored-completions.md#stored-completions-api) allow you to capture the conversation history from chat completions sessions to use as datasets for evaluations and fine-tuning.
+
 ### o3-mini datazone standard deployments
 
-`o3-mini` is now available for global standard, and data zone standard deployments for registered limited access customers. Data standard deployment regions are currently United States regions only.
+`o3-mini` is now available for global standard, and data zone standard deployments for registered limited access customers.
 
 For more information, see our [reasoning model guide](./how-to/reasoning.md). 
 

articles/ai-studio/concepts/connections.md

@@ -9,8 +9,8 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: conceptual
-ms.date: 11/21/2024
-ms.reviewer: sgilley
+ms.date: 02/21/2025
+ms.reviewer: meerakurup
 ms.author: sgilley
 author: sdgilley
 ---
@@ -31,30 +31,30 @@ As another example, you can [create a connection](../how-to/connections-add.md)
 
 ## Connections to non-Microsoft services
 
-Azure AI Foundry supports connections to non-Microsoft services, including the following:
-- The [API key connection](../how-to/connections-add.md) handles authentication to your specified target on an individual basis. This is the most common non-Microsoft connection type.
-- The [custom connection](../how-to/connections-add.md) allows you to securely store and access keys while storing related properties, such as targets and versions. Custom connections are useful when you have many targets that or cases where you wouldn't need a credential to access. LangChain scenarios are a good example where you would use custom service connections. Custom connections don't manage authentication, so you'll have to manage authentication on your own.
+Azure AI Foundry supports connections to non-Microsoft services, including:
+- The [API key connection](../how-to/connections-add.md) handles authentication to your specified target on an individual basis. API key is the most common non-Microsoft connection type.
+- The [custom connection](../how-to/connections-add.md) allows you to securely store and access keys while storing related properties, such as targets and versions. Custom connections are useful when you have many targets that or cases where you wouldn't need a credential to access. LangChain scenarios are a good example where you would use custom service connections. Custom connections don't manage authentication, so you have to manage authentication on your own.
 
 ## Connections to datastores
 
 > [!IMPORTANT]
-> Data connections cannot be shared across projects. They are created exclusively in the context of one project. 
+> Data connections can't be shared across projects. They're created exclusively in the context of one project. 
 
 Creating a data connection allows you to access external data without copying it to your project. Instead, the connection provides a reference to the data source.
 
 A data connection offers these benefits:
 
 - A common, easy-to-use API that interacts with different storage types including Microsoft OneLake, Azure Blob, and Azure Data Lake Gen2.
 - Easier discovery of useful connections in team operations.
-- For credential-based access (service principal/SAS/key), Azure AI Foundry connection secures credential information. This way, you won't need to place that information in your scripts.
+- Credential-based access (service principal/SAS/key). Azure AI Foundry connection secures credential information so you don't need to place that information in your scripts.
 
 When you create a connection with an existing Azure storage account, you can choose between two different authentication methods:
 
 - **Credential-based**: Authenticate data access with a service principal, shared access signature (SAS) token, or account key. Users with *Reader* project permissions can access the credentials.
 - **Identity-based**: Use your Microsoft Entra ID or managed identity to authenticate data access.
 
     > [!TIP]
-    > When using an identity-based connection, Azure role-based access control (Azure RBAC) is used to determine who can access the connection. You must assign the correct Azure RBAC roles to your developers before they can use the connection. For more information, see [Scenario: Connections using Microsoft Entra ID](rbac-ai-studio.md#scenario-connections-using-microsoft-entra-id-authentication).
+    > When you use an identity-based connection, Azure role-based access control (Azure RBAC) determines who can access the connection. You must assign the correct Azure RBAC roles to your developers before they can use the connection. For more information, see [Scenario: Connections using Microsoft Entra ID](rbac-ai-studio.md#scenario-connections-using-microsoft-entra-id-authentication).
 
 
 The following table shows the supported Azure cloud-based storage services and authentication methods:
@@ -82,7 +82,7 @@ A Uniform Resource Identifier (URI) represents a storage location on your local
 
 ## Key vaults and secrets
 
-Connections allow you to securely store credentials, authenticate access, and consume data and information.  Secrets associated with connections are securely persisted in the corresponding Azure Key Vault, adhering to robust security and compliance standards. As an administrator, you can audit both shared and project-scoped connections on a hub level (link to connection rbac). 
+Connections allow you to securely store credentials, authenticate access, and consume data and information.  Secrets associated with connections are securely persisted in the corresponding Azure Key Vault, adhering to robust security and compliance standards. As an administrator, you can audit both shared and project-scoped connections on a hub level. 
 
 Azure connections serve as key vault proxies, and interactions with connections are direct interactions with an Azure key vault. Azure AI Foundry connections store API keys securely, as secrets, in a key vault. The key vault [Azure role-based access control (Azure RBAC)](./rbac-ai-studio.md) controls access to these connection resources. A connection references the credentials from the key vault storage location for further use. You won't need to directly deal with the credentials after they're stored in the hub's key vault. You have the option to store the credentials in the YAML file. A CLI command or SDK can override them. We recommend that you avoid credential storage in a YAML file, because a security breach could lead to a credential leak.