Merge branch 'main' into release-2025-openai-march-latest

Author: v-alje

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

@@ -8,7 +8,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: how-to
-ms.date: 12/05/2024
+ms.date: 03/17/2025
 ms.reviewer: dantaylo
 ms.author: larryfr
 author: Blackmist

articles/ai-foundry/media/ai-services/endpoint/models-endpoints-azure-openai-deployments.png

@@ -7,7 +7,7 @@ author: msakande
 reviewer: santiagxf
 ms.service: azure-ai-model-inference
 ms.topic: how-to
-ms.date: 01/22/2025
+ms.date: 03/17/2025
 ms.author: mopeakande
 ms.reviewer: fasantia
 ms.custom: generated
@@ -41,7 +41,7 @@ import os
 from azure.ai.inference import ImageEmbeddingsClient
 from azure.core.credentials import AzureKeyCredential
 
-model = ImageEmbeddingsClient(
+client = ImageEmbeddingsClient(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=AzureKeyCredential(os.environ["AZURE_INFERENCE_CREDENTIAL"]),
     model="Cohere-embed-v3-english"
@@ -55,7 +55,7 @@ import os
 from azure.ai.inference import ImageEmbeddingsClient
 from azure.identity import DefaultAzureCredential
 
-model = ImageEmbeddingsClient(
+client = ImageEmbeddingsClient(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=DefaultAzureCredential(),
     model="Cohere-embed-v3-english"
@@ -70,7 +70,7 @@ To create image embeddings, you need to pass the image data as part of your requ
 from azure.ai.inference.models import ImageEmbeddingInput
 
 image_input= ImageEmbeddingInput.load(image_file="sample1.png", image_format="png")
-response = model.embed(
+response = client.embed(
     input=[ image_input ],
 )
 ```
@@ -102,7 +102,7 @@ Some models can generate embeddings from images and text pairs. In this case, yo
 ```python
 text_image_input= ImageEmbeddingInput.load(image_file="sample1.png", image_format="png")
 text_image_input.text = "A cute baby sea otter"
-response = model.embed(
+response = client.embed(
     input=[ text_image_input ],
 )
 ```
@@ -117,7 +117,7 @@ The following example shows how to create embeddings that are used to create an
 ```python
 from azure.ai.inference.models import EmbeddingInputType
 
-response = model.embed(
+response = client.embed(
     input=[ image_input ],
     input_type=EmbeddingInputType.DOCUMENT,
 )
@@ -129,7 +129,7 @@ When you work on a query to retrieve such a document, you can use the following
 ```python
 from azure.ai.inference.models import EmbeddingInputType
 
-response = model.embed(
+response = client.embed(
     input=[ image_input ],
     input_type=EmbeddingInputType.QUERY,
 )

articles/ai-foundry/model-inference/includes/use-image-embeddings/python.md

@@ -37,9 +37,9 @@ To see details for each model including, language, types, and capabilities, see
 | [Azure OpenAI](concepts/models.md#azure-openai) | - o3-mini <br /> - o1 <br /> - gpt-4o <br /> - o1-preview <br /> - o1-mini <br /> - gpt-4o-mini <br /> - text-embedding-3-large <br /> - text-embedding-3-small <br /> |
 | [Cohere](concepts/models.md#cohere) | - Cohere-embed-v3-english <br /> - Cohere-embed-v3-multilingual <br /> - Cohere-command-r-plus-08-2024 <br /> - Cohere-command-r-08-2024 <br /> - Cohere-command-r-plus <br /> - Cohere-command-r <br /> |
 | [Core42](concepts/models.md#core42) | - jais-30b-chat <br /> |
-| [DeepSeek](concepts/models.md#deepseek) | - DeepSeek-R1 |
+| [DeepSeek](concepts/models.md#deepseek) | - DeepSeek-V3 <br /> - DeepSeek-R1 <br /> |
 | [Meta](concepts/models.md#meta) | - Llama-3.3-70B-Instruct <br /> - Llama-3.2-11B-Vision-Instruct <br /> - Llama-3.2-90B-Vision-Instruct <br /> - Meta-Llama-3.1-405B-Instruct <br /> - Meta-Llama-3-8B-Instruct <br /> - Meta-Llama-3.1-70B-Instruct <br /> - Meta-Llama-3.1-8B-Instruct <br /> - Meta-Llama-3-70B-Instruct <br /> |
-| [Microsoft](concepts/models.md#microsoft) | - Phi-3-mini-128k-instruct <br /> - Phi-3-mini-4k-instruct <br /> - Phi-3-small-8k-instruct <br /> - Phi-3-medium-128k-instruct <br /> - Phi-3-medium-4k-instruct <br /> - Phi-3.5-vision-instruct <br /> - Phi-3.5-MoE-instruct <br /> - Phi-3-small-128k-instruct <br /> - Phi-3.5-mini-instruct <br /> - Phi-4 <br /> |
+| [Microsoft](concepts/models.md#microsoft) | - Phi-4-multimodal-instruct <br /> - Phi-4-mini-instruct <br />  - Phi-4 <br /> - Phi-3-mini-128k-instruct <br /> - Phi-3-mini-4k-instruct <br /> - Phi-3-small-8k-instruct <br /> - Phi-3-medium-128k-instruct <br /> - Phi-3-medium-4k-instruct <br /> - Phi-3.5-vision-instruct <br /> - Phi-3.5-MoE-instruct <br /> - Phi-3-small-128k-instruct <br /> - Phi-3.5-mini-instruct <br /> |
 | [Mistral AI](concepts/models.md#mistral-ai) | - Ministral-3B <br /> - Mistral-large <br /> - Mistral-small <br /> - Mistral-Nemo <br /> - Mistral-large-2407 <br /> - Mistral-Large-2411 <br /> - Codestral-2501 <br /> |
 | [NTT Data](concepts/models.md#ntt-data) | - Tsuzumi-7b |
 

articles/ai-foundry/model-inference/overview.md

@@ -48,7 +48,7 @@ To create an Azure AI services resource follow these instructions:
 
 ## Clean up resources
 
-If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources contained in the group.
+If you want to clean up and remove an Azure AI services resource, you can delete the resource or resource group. Deleting the resource group also deletes any other resources contained in the group.
 
 1. In the Azure portal, expand the menu on the left side to open the menu of services, and choose **Resource Groups** to display the list of your resource groups.
 1. Locate the resource group containing the resource to be deleted.

articles/ai-services/includes/quickstarts/management-azportal.md

@@ -67,11 +67,40 @@ The multi-service resource enables access to the following Azure AI services wit
 
 ::: zone-end
 
+## Azure AI services resource for Azure AI Search skills
+
+Azure AI Search skills don't support the multi-service resource as described previously in this article. You must create a different kind of Azure AI services resource for Azure AI Search skills. 
+
+The multi-service resource that you can use with Azure AI Search skills is listed under **Azure AI services** > **Azure AI services multi-service account** in the portal. Look for the logo as shown here:
+
+:::image type="content" source="./media/cognitive-services-resource-portal.png" alt-text="Screenshot of the Azure AI services multi-service account in the Azure portal." lightbox="./media/cognitive-services-resource-portal.png":::
+
+> [!IMPORTANT]
+> Azure provides more than one resource kinds named Azure AI services. Be sure to select the one that is listed under **Azure AI services** > **Azure AI services multi-service account** with the logo as shown previously.
+
+To create an Azure AI services resource follow these instructions:
+1. Select this link to create an **Azure AI services multi-service account** resource: [https://portal.azure.com/#create/Microsoft.CognitiveServicesAllInOne](https://portal.azure.com/#create/Microsoft.CognitiveServicesAllInOne)
+
+1. On the **Create** page, provide the following information:
+
+    |Project details| Description   |
+    |--|--|
+    | **Subscription** | Select one of your available Azure subscriptions. |
+    | **Resource group** | The Azure resource group that will contain your Azure AI services multi-service account resource. You can create a new group or add it to a pre-existing group. |
+    | **Region** | The location of your Azure AI services multi-service account instance. Different locations may introduce latency, but have no impact on the runtime availability of your resource. |
+    | **Name** | A descriptive name for your Azure AI services multi-service account resource. For example, *MyCognitiveServicesResource*. |
+    | **Pricing tier** | The cost of your Azure AI services multi-service account depends on the options you choose and your usage. For more information, see the API [pricing details](https://azure.microsoft.com/pricing/details/cognitive-services/). |
+
+1. Configure other settings for your resource as needed, read and accept the conditions (as applicable), and then select **Review + create**.
+
+> [!TIP]
+> If your subscription doesn't allow you to create an Azure AI services resource, you might need to enable the privilege of that [Azure resource provider](/azure/azure-resource-manager/management/resource-providers-and-types#register-resource-provider) using the [Azure portal](/azure/azure-resource-manager/management/resource-providers-and-types#azure-portal), [PowerShell command](/azure/azure-resource-manager/management/resource-providers-and-types#azure-powershell) or an [Azure CLI command](/azure/azure-resource-manager/management/resource-providers-and-types#azure-cli). If you are not the subscription owner, ask someone with the role of *Owner* or *Admin* to complete the registration for you or ask for the **/register/action** privileges to be granted to your account.
+
 ## Pricing
 
 [!INCLUDE [SKUs and pricing](./includes/quickstarts/sku-pricing.md)]
 
 ## Related content
 
-- Go to the [Azure AI services hub](../ai-services/index.yml).
+- Go to the [Azure AI services hub page](../ai-services/index.yml).
 - Try AI services in the [Azure AI Foundry portal](../ai-foundry/ai-services/how-to/connect-ai-services.md).

articles/ai-services/media/ai-services-resource-portal.png

@@ -56,7 +56,7 @@ deploy_data = {
     "properties": {
         "model": {
             "format": "OpenAI",
-            "name": <"fine_tuned_model">, #retrieve this value from the previous call, it will look like gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83
+            "name": <"fine_tuned_model">, #retrieve this value from the previous call, it will look like gpt-35-turbo-0125.ft-b044a9d3cf9c4228b5d393567f693b83
             "version": "1"
         }
     }
@@ -82,7 +82,7 @@ print(r.json())
 | resource_group | The resource group name for your Azure OpenAI resource. |
 | resource_name | The Azure OpenAI resource name. |
 | model_deployment_name | The custom name for your new fine-tuned model deployment. This is the name that will be referenced in your code when making chat completion calls. |
-| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83`. You will need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
+| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0125.ft-b044a9d3cf9c4228b5d393567f693b83`. You will need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
 
 ### Cross region deployment
 
@@ -122,7 +122,7 @@ deploy_data = {
     "properties": {
         "model": {
             "format": "OpenAI",
-            "name": <"FINE_TUNED_MODEL_NAME">, # This value will look like gpt-35-turbo-0613.ft-0ab3f80e4f2242929258fff45b56a9ce 
+            "name": <"FINE_TUNED_MODEL_NAME">, # This value will look like gpt-35-turbo-0125.ft-0ab3f80e4f2242929258fff45b56a9ce 
             "version": "1",
             "source": source
         }
@@ -220,7 +220,7 @@ curl -X POST "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resource
 | resource_group | The resource group name for your Azure OpenAI resource. |
 | resource_name | The Azure OpenAI resource name. |
 | model_deployment_name | The custom name for your new fine-tuned model deployment. This is the name that will be referenced in your code when making chat completion calls. |
-| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83`. You'll need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
+| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0125.ft-b044a9d3cf9c4228b5d393567f693b83`. You'll need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
 
 
 ### Cross region deployment

articles/ai-services/media/cognitive-services-resource-portal.png

@@ -15,7 +15,12 @@ ms.service: azure-ai-openai
 
 ## Dashboards
 
-Azure OpenAI provides out-of-box dashboards for each of your Azure OpenAI resources. To access the monitoring dashboards sign-in to [https://portal.azure.com](https://portal.azure.com) and select the overview pane for one of your Azure OpenAI resources.
+Azure OpenAI provides out-of-box dashboards for each of your Azure OpenAI resources. There are two key dashboards to monitor your resource: 
+
+- The metrics dashboard in the AI Foundry Azure OpenAI resource view 
+- The dashboard in the overview pane within the Azure portal 
+
+To access the monitoring dashboards, sign in to the [Azure portal](https://portal.azure.com) and then select the overview pane for one of your Azure OpenAI resources. To see the AI Foundry metrics dashboard from the Azure portal, select the overview pane and **Go to Azure AI Foundry portal**. Under tools, select the metrics dashboard.   
 
 :::image type="content" source="../media/monitoring/dashboard.png" alt-text="Screenshot that shows out-of-box dashboards for an Azure OpenAI resource in the Azure portal." lightbox="../media/monitoring/dashboard.png" border="false":::