Merge branch 'main' into ft-ptum-pup

Author: voutilad

.openpublishing.redirection.json

@@ -15,6 +15,11 @@
       "redirect_url": "/azure/search/search-how-to-dotnet-sdk",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/ai-services/agents/how-to/tools/overview.md",
+      "redirect_url": "/azure/ai-services/agents/overview",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/search/search-howto-index-csv-blobs.md",
       "redirect_url": "/azure/search/search-how-to-index-csv-blobs",
@@ -25,6 +30,11 @@
       "redirect_url": "/azure/search/search-how-to-large-index",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/ai-services/agents/concepts/agents.md",
+      "redirect_url": "/azure/ai-services/agents/overview",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/ai-services/openai/how-to/use-your-data-securely.md",
       "redirect_url": "/azure/ai-services/openai/how-to/on-your-data-configuration",

articles/ai-foundry/model-inference/concepts/models.md

@@ -17,9 +17,6 @@ ms.custom: references_regions, tool_generated
 
 Azure AI model inference in Azure AI Foundry gives you access to flagship models in Azure AI to consume them as APIs without hosting them on your infrastructure.
 
-> [!TIP]
-> DeepSeek-R1 (preview) is available for deployment as [Serverless API endpoint](../../../ai-studio/how-to/deploy-models-deepseek.md).
-
 :::image type="content" source="../media/models/models-catalog.gif" alt-text="An animation showing Azure AI studio model catalog section and the models available." lightbox="../media/models/models-catalog.gif":::
 
 Model availability varies by model provider, deployment SKU, and cloud. All models available in Azure AI Model Inference support the [Global standard](deployment-types.md#global-standard) deployment type which uses global capacity to guarantee throughput. [Azure OpenAI models](#azure-openai) also support regional deployments and [sovereign clouds](/entra/identity-platform/authentication-national-cloud)—Azure Government, Azure Germany, and Azure China 21Vianet.
@@ -52,10 +49,11 @@ Azure OpenAI Service offers a diverse set of models with different capabilities
 - Models that can transcribe and translate speech to text
 
 | Model  | Type | Tier | Capabilities |
-| ------ | ---- | --- | ------------ |
+| ------ | ---- | ---- | ------------ |
+| [o3-mini](https://ai.azure.com/explore/models/o3-mini/version/2025-01-31/registry/azure-openai) | chat-completion | Global standard | - **Input:** text and image (200,000 tokens) <br /> - **Output:** text (100,000 tokens) <br /> - **Languages:** `en`, `it`, `af`, `es`, `de`, `fr`, `id`, `ru`, `pl`, `uk`, `el`, `lv`, `zh`, `ar`, `tr`, `ja`, `sw`, `cy`, `ko`, `is`, `bn`, `ur`, `ne`, `th`, `pa`, `mr`, and `te`. <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON, structured outputs |
 | [o1](https://ai.azure.com/explore/models/o1/version/2024-12-17/registry/azure-openai) | chat-completion | Global standard | - **Input:** text and image (200,000 tokens) <br /> - **Output:** text (100,000 tokens) <br /> - **Languages:** `en`, `it`, `af`, `es`, `de`, `fr`, `id`, `ru`, `pl`, `uk`, `el`, `lv`, `zh`, `ar`, `tr`, `ja`, `sw`, `cy`, `ko`, `is`, `bn`, `ur`, `ne`, `th`, `pa`, `mr`, and `te`. <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON, structured outputs |
 | [o1-preview](https://ai.azure.com/explore/models/o1-preview/version/1/registry/azure-openai) | chat-completion | Global standard <br />Standard<br /> | - **Input:** text (128,000 tokens) <br /> - **Output:**  (32,768 tokens) <br /> - **Languages:** `en`, `it`, `af`, `es`, `de`, `fr`, `id`, `ru`, `pl`, `uk`, `el`, `lv`, `zh`, `ar`, `tr`, `ja`, `sw`, `cy`, `ko`, `is`, `bn`, `ur`, `ne`, `th`, `pa`, `mr`, and `te`. <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON, structured outputs |
-| [o1-mini](https://ai.azure.com/explore/models/o1-mini/version/1/registry/azure-openai) | chat-completion | Global standard <br />Standard | - **Input:** text (128,000 tokens) <br /> - **Output:**  (65,536 tokens) <br /> - **Languages:** `en`, `it`, `af`, `es`, `de`, `fr`, `id`, `ru`, `pl`, `uk`, `el`, `lv`, `zh`, `ar`, `tr`, `ja`, `sw`, `cy`, `ko`, `is`, `bn`, `ur`, `ne`, `th`, `pa`, `mr`, and `te`. <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON, structured outputs |
+| [o1-mini](https://ai.azure.com/explore/models/o1-mini/version/1/registry/azure-openai) | chat-completion | Global standard <br />Standard | - **Input:** text (128,000 tokens) <br /> - **Output:**  (65,536 tokens) <br /> - **Languages:** `en`, `it`, `af`, `es`, `de`, `fr`, `id`, `ru`, `pl`, `uk`, `el`, `lv`, `zh`, `ar`, `tr`, `ja`, `sw`, `cy`, `ko`, `is`, `bn`, `ur`, `ne`, `th`, `pa`, `mr`, and `te`. <br />  - **Tool calling:** No <br /> - **Response formats:** Text |
 | [gpt-4o-realtime-preview](https://ai.azure.com/explore/models/gpt-4o-realtime-preview/version/2024-10-01/registry/azure-openai) | real-time | Global standard | - **Input:** control, text, and audio (131,072 tokens) <br /> - **Output:** text and audio (16,384 tokens) <br /> - **Languages:** en <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
 | [gpt-4o](https://ai.azure.com/explore/models/gpt-4o/version/2024-11-20/registry/azure-openai) | chat-completion | Global standard <br />Standard<br />Batch<br />Provisioned<br />Global provisioned<br />Data Zone | - **Input:** text and image (131,072 tokens) <br /> - **Output:** text (16,384 tokens) <br /> - **Languages:** `en`, `it`, `af`, `es`, `de`, `fr`, `id`, `ru`, `pl`, `uk`, `el`, `lv`, `zh`, `ar`, `tr`, `ja`, `sw`, `cy`, `ko`, `is`, `bn`, `ur`, `ne`, `th`, `pa`, `mr`, and `te`. <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON, structured outputs |
 | [gpt-4o-mini](https://ai.azure.com/explore/models/gpt-4o-mini/version/2024-07-18/registry/azure-openai) | chat-completion | Global standard <br />Standard<br />Batch<br />Provisioned<br />Global provisioned<br />Data Zone | - **Input:** text, image, and audio (131,072 tokens) <br /> - **Output:**  (16,384 tokens) <br /> - **Languages:** `en`, `it`, `af`, `es`, `de`, `fr`, `id`, `ru`, `pl`, `uk`, `el`, `lv`, `zh`, `ar`, `tr`, `ja`, `sw`, `cy`, `ko`, `is`, `bn`, `ur`, `ne`, `th`, `pa`, `mr`, and `te`. <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON, structured outputs |
@@ -93,6 +91,16 @@ Core42 includes autoregressive bi-lingual LLMs for Arabic & English with state-o
 
 See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=core42).
 
+### DeepSeek
+
+DeepSeek family of models include DeepSeek-R1, which excels at reasoning tasks using a step-by-step training process, such as language, scientific reasoning, and coding tasks.
+
+| Model  | Type | Tier | Capabilities |
+| ------ | ---- | --- | ------------ |
+| [DeekSeek-R1](https://ai.azure.com/explore/models/deepseek-r1/version/1/registry/azureml-deepseek) | chat-completion <br /> [(with reasoning content)](../how-to/use-chat-reasoning.md) | Global standard | - **Input:** text (16,384 tokens) <br /> - **Output:**  (163,840 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text. |
+
+See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=deepseek).
+
 ### Meta
 
 Meta Llama models and tools are a collection of pretrained and fine-tuned generative AI text and image reasoning models. Meta models range is scale to include:
@@ -143,10 +151,10 @@ Mistral AI offers two categories of models: premium models including Mistral Lar
 | Model  | Type | Tier | Capabilities |
 | ------ | ---- | --- | ------------ |
 | [Ministral-3B](https://ai.azure.com/explore/models/Ministral-3B/version/1/registry/azureml-mistral) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** text (4,096 tokens) <br /> - **Languages:** fr, de, es, it, and en <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
-| [Mistral-large](https://ai.azure.com/explore/models/Mistral-large/version/1/registry/azureml-mistral) | chat-completion | Global standard | - **Input:** text (32,768 tokens) <br /> - **Output:**  (4,096 tokens) <br /> - **Languages:** fr, de, es, it, and en <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
+| [Mistral-large](https://ai.azure.com/explore/models/Mistral-large/version/1/registry/azureml-mistral) <br /> (deprecated) | chat-completion | Global standard | - **Input:** text (32,768 tokens) <br /> - **Output:**  (4,096 tokens) <br /> - **Languages:** fr, de, es, it, and en <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
 | [Mistral-small](https://ai.azure.com/explore/models/Mistral-small/version/1/registry/azureml-mistral) | chat-completion | Global standard | - **Input:** text (32,768 tokens) <br /> - **Output:** text (4,096 tokens) <br /> - **Languages:** fr, de, es, it, and en <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
 | [Mistral-Nemo](https://ai.azure.com/explore/models/Mistral-Nemo/version/1/registry/azureml-mistral) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** text (4,096 tokens) <br /> - **Languages:** en, fr, de, es, it, zh, ja, ko, pt, nl, and pl <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
-| [Mistral-large-2407](https://ai.azure.com/explore/models/Mistral-large-2407/version/1/registry/azureml-mistral) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:**  (4,096 tokens) <br /> - **Languages:** en, fr, de, es, it, zh, ja, ko, pt, nl, and pl <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
+| [Mistral-large-2407](https://ai.azure.com/explore/models/Mistral-large-2407/version/1/registry/azureml-mistral) <br /> (legacy) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:**  (4,096 tokens) <br /> - **Languages:** en, fr, de, es, it, zh, ja, ko, pt, nl, and pl <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
 | [Mistral-Large-2411](https://ai.azure.com/explore/models/Mistral-Large-2411/version/2/registry/azureml-mistral) | chat-completion | Global standard | - **Input:** text (128,000 tokens) <br /> - **Output:** text (4,096 tokens) <br /> - **Languages:** en, fr, de, es, it, zh, ja, ko, pt, nl, and pl <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
 | [Codestral-2501](https://ai.azure.com/explore/models/Codestral-2501/version/2/registry/azureml-mistral) | chat-completion | Global standard | - **Input:** text (262,144 tokens) <br /> - **Output:** text (4,096 tokens) <br /> - **Languages:** en <br />  - **Tool calling:** No <br /> - **Response formats:** Text |
 

articles/ai-foundry/model-inference/how-to/inference.md

@@ -48,6 +48,14 @@ For a chat model, you can create a request as follows:
 
 If you specify a model name that doesn't match any given model deployment, you get an error that the model doesn't exist. You can control which models are available for users by creating model deployments as explained at [add and configure model deployments](create-model-deployments.md).
 
+## Key-less authentication
+
+Models deployed to Azure AI model inference in Azure AI Services support key-less authorization using Microsoft Entra ID. Key-less authorization enhances security, simplifies the user experience, reduces operational complexity, and provides robust compliance support for modern development. It makes it a strong choice for organizations adopting secure and scalable identity management solutions.
+
+To use key-less authentication, [configure your resource and grant access to users](configure-entra-id.md) to perform inference. Once configured, then you can authenticate as follows:
+
+[!INCLUDE [code-create-chat-client-entra](../includes/code-create-chat-client-entra.md)]
+
 ## Limitations
 
 * Azure OpenAI Batch can't be used with the Azure AI model inference endpoint. You have to use the dedicated deployment URL as explained at [Batch API support in Azure OpenAI documentation](../../../ai-services/openai/how-to/batch.md#api-support).
@@ -56,4 +64,4 @@ If you specify a model name that doesn't match any given model deployment, you g
 ## Next steps
 
 * [Use embedding models](use-embeddings.md)
-* [Use chat completion models](use-chat-completions.md)
+* [Use chat completion models](use-chat-completions.md)

articles/ai-foundry/model-inference/how-to/quickstart-github-models.md

@@ -85,6 +85,7 @@ Use the parameter `model="<deployment-name>` to route your request to this deplo
 Azure AI model inference supports additional features not available in GitHub Models, including:
 
 * [Explore the model catalog](https://ai.azure.com/github/models) to see additional models not available in GitHub Models.
+* Configure [key-less authentication](configure-entra-id.md).
 * Configure [content filtering](configure-content-filters.md).
 * Configure rate limiting (for specific models).
 * Explore additional [deployment SKUs (for specific models)](../concepts/deployment-types.md).
@@ -97,4 +98,4 @@ See the [FAQ section](../faq.yml) to explore more help.
 ## Next steps
 
 * [Explore the model catalog](https://ai.azure.com/github/models) in Azure AI studio.
-* [Add more models](create-model-deployments.md) to your endpoint.
+* [Add more models](create-model-deployments.md) to your endpoint.

articles/ai-foundry/model-inference/how-to/use-chat-reasoning.md

@@ -0,0 +1,53 @@
+---
+title: How to use reasoning models with Azure AI model inference
+titleSuffix: Azure AI Foundry
+description: Learn how to use reasoning capabilities from models with Azure AI model inference
+manager: scottpolly
+author: msakande
+reviewer: santiagxf
+ms.service: azure-ai-model-inference
+ms.topic: how-to
+ms.date: 1/31/2025
+ms.author: mopeakande
+ms.reviewer: fasantia
+ms.custom: generated
+zone_pivot_groups: azure-ai-inference-samples
+---
+
+# How to use reasoning models with Azure AI model inference
+
+
+::: zone pivot="programming-language-python"
+
+[!INCLUDE [python](../includes/use-chat-reasoning/python.md)]
+::: zone-end
+
+
+::: zone pivot="programming-language-javascript"
+
+[!INCLUDE [javascript](../includes/use-chat-reasoning/javascript.md)]
+::: zone-end
+
+
+::: zone pivot="programming-language-java"
+
+[!INCLUDE [java](../includes/use-chat-reasoning/java.md)]
+::: zone-end
+
+
+::: zone pivot="programming-language-csharp"
+
+[!INCLUDE [csharp](../includes/use-chat-reasoning/csharp.md)]
+::: zone-end
+
+
+::: zone pivot="programming-language-rest"
+
+[!INCLUDE [rest](../includes/use-chat-reasoning/rest.md)]
+::: zone-end
+
+## Related content
+
+* [Use embeddings models](use-embeddings.md)
+* [Use image embeddings models](use-image-embeddings.md)
+* [Azure AI Model Inference API](.././reference/reference-model-inference-api.md)

articles/ai-foundry/model-inference/includes/code-create-chat-client-entra.md

@@ -12,12 +12,9 @@ author: santiagxf
 Install the package `azure-ai-inference` using your package manager, like pip:
 
 ```bash
-pip install azure-ai-inference>=1.0.0b5
+pip install azure-ai-inference
 ```
 
-> [!WARNING]
-> Azure AI Services resource requires the version `azure-ai-inference>=1.0.0b5` for Python.
-
 Then, you can use the package to consume the model. The following example shows how to create a client to consume chat completions with Entra ID:
 
 ```python

articles/ai-foundry/model-inference/includes/code-create-chat-client.md

@@ -12,12 +12,9 @@ author: santiagxf
 Install the package `azure-ai-inference` using your package manager, like pip:
 
 ```bash
-pip install azure-ai-inference>=1.0.0b5
+pip install azure-ai-inference
 ```
 
-> [!WARNING]
-> Azure AI Services resource requires the version `azure-ai-inference>=1.0.0b5` for Python.
-
 Then, you can use the package to consume the model. The following example shows how to create a client to consume chat completions:
 
 ```python
@@ -115,7 +112,7 @@ __Request__
 
 ```HTTP/1.1
 POST https://<resource>.services.ai.azure.com/models/chat/completions?api-version=2024-05-01-preview
-Authorization: Bearer <bearer-token>
+api-key: <api-key>
 Content-Type: application/json
 ```
----
+---

articles/ai-foundry/model-inference/includes/code-create-chat-completion.md

@@ -78,7 +78,7 @@ __Request__
 
 ```HTTP/1.1
 POST https://<resource>.services.ai.azure.com/models/chat/completions?api-version=2024-05-01-preview
-Authorization: Bearer <bearer-token>
+api-key: <api-key>
 Content-Type: application/json
 ```
 
@@ -98,4 +98,4 @@ Content-Type: application/json
 }
 ```
 
----
+---

articles/ai-foundry/model-inference/includes/code-create-embeddings-client.md

@@ -12,12 +12,9 @@ author: santiagxf
 Install the package `azure-ai-inference` using your package manager, like pip:
 
 ```bash
-pip install azure-ai-inference>=1.0.0b5
+pip install azure-ai-inference
 ```
 
-> [!WARNING]
-> Azure AI Services resource requires the version `azure-ai-inference>=1.0.0b5` for Python.
-
 Then, you can use the package to consume the model. The following example shows how to create a client to consume chat completions:
 
 ```python
@@ -132,7 +129,7 @@ __Request__
 
 ```HTTP/1.1
 POST https://<resource>.services.ai.azure.com/models/embeddings?api-version=2024-05-01-preview
-Authorization: Bearer <bearer-token>
+api-key: <api-key>
 Content-Type: application/json
 ```
----
+---

articles/ai-foundry/model-inference/includes/code-create-embeddings.md

@@ -54,7 +54,7 @@ __Request__
 
 ```HTTP/1.1
 POST https://<resource>.services.ai.azure.com/models/embeddings?api-version=2024-05-01-preview
-Authorization: Bearer <bearer-token>
+api-key: <api-key>
 Content-Type: application/json
 ```
 
@@ -100,4 +100,4 @@ __Response__
 }
 ```
 
----
+---