Merge pull request #2526 from MicrosoftDocs/main

Taojunshen · web-flow · commit 297866f96eb8 · 2025-01-28T02:00:18.000+08:00
1/27/2025 AM Publish
diff --git a/articles/ai-foundry/model-inference/includes/code-create-chat-client-entra.md b/articles/ai-foundry/model-inference/includes/code-create-chat-client-entra.md
@@ -23,11 +23,11 @@ Then, you can use the package to consume the model. The following example shows
 ```python
 import os
 from azure.ai.inference import ChatCompletionsClient
-from azure.identity import AzureDefaultCredential
+from azure.identity import DefaultAzureCredential
 
 model = ChatCompletionsClient(
     endpoint="https://<resource>.services.ai.azure.com/models",
-    credential=AzureDefaultCredential(),
+    credential=DefaultAzureCredential(),
     model="mistral-large-2407",
 )
 ```
@@ -45,11 +45,11 @@ Then, you can use the package to consume the model. The following example shows
 ```javascript
 import ModelClient from "@azure-rest/ai-inference";
 import { isUnexpected } from "@azure-rest/ai-inference";
-import { AzureDefaultCredential } from "@azure/identity";
+import { DefaultAzureCredential } from "@azure/identity";
 
 const client = new ModelClient(
     "https://<resource>.services.ai.azure.com/models", 
-    new AzureDefaultCredential(),
+    new DefaultAzureCredential(),
     "mistral-large-2407"
 );
 ```
@@ -81,7 +81,7 @@ Then, you can use the package to consume the model. The following example shows
 ```csharp
 ChatCompletionsClient client = new ChatCompletionsClient(
     new Uri("https://<resource>.services.ai.azure.com/models"),
-    new AzureDefaultCredential(includeInteractiveCredentials: true),
+    new DefaultAzureCredential(includeInteractiveCredentials: true),
     "mistral-large-2407"
 );
 ```
diff --git a/articles/ai-foundry/model-inference/includes/code-create-embeddings-client.md b/articles/ai-foundry/model-inference/includes/code-create-embeddings-client.md
@@ -36,11 +36,11 @@ If you are using an endpoint with support for Entra ID, you can create your clie
 ```python
 import os
 from azure.ai.inference import EmbeddingsClient
-from azure.identity import AzureDefaultCredential
+from azure.identity import DefaultAzureCredential
 
 client = EmbeddingsClient(
     endpoint="https://<resource>.services.ai.azure.com/models",
-    credential=AzureDefaultCredential(),
+    credential=DefaultAzureCredential(),
 )
 ```
 
@@ -72,11 +72,11 @@ For endpoint with support for Microsoft Entra ID, you can create your client as
 ```javascript
 import ModelClient from "@azure-rest/ai-inference";
 import { isUnexpected } from "@azure-rest/ai-inference";
-import { AzureDefaultCredential } from "@azure/identity";
+import { DefaultAzureCredential } from "@azure/identity";
 
 const client = new ModelClient(
     "https://<resource>.services.ai.azure.com/models", 
-    new AzureDefaultCredential()
+    new DefaultAzureCredential()
 );
 ```
 
diff --git a/articles/ai-services/openai/how-to/prompt-caching.md b/articles/ai-services/openai/how-to/prompt-caching.md
@@ -75,16 +75,16 @@ A single character difference in the first 1,024 tokens will result in a cache m
 
 ## What is cached?
 
-The o1-series models are text only and don't support system messages, images, tool use/function calling, or structured outputs. This limits the efficacy of prompt caching for these models to the user/assistant portions of the messages array which are less likely to have an identical 1024 token prefix.
+o1-series models feature support varies by model. For more details, see our dedicated [reasoning models guide](./reasoning.md). 
 
 Prompt caching is supported for:
 
 |**Caching supported**|**Description**|**Supported models**|
 |--------|--------|--------|
-| **Messages** | The complete messages array: system, user, and assistant content | `gpt-4o`<br/>`gpt-4o-mini`<br/>`gpt-4o-realtime-preview` (version 2024-12-17) |
-| **Images** | Images included in user messages, both as links or as base64-encoded data. The detail parameter must be set the same across requests. | `gpt-4o`<br/>`gpt-4o-mini` |
-| **Tool use** | Both the messages array and tool definitions. | `gpt-4o`<br/>`gpt-4o-mini`<br/>`gpt-4o-realtime-preview` (version 2024-12-17) |
-| **Structured outputs** | Structured output schema is appended as a prefix to the system message. | `gpt-4o`<br/>`gpt-4o-mini` |
+| **Messages** | The complete messages array: system, developer, user, and assistant content | `gpt-4o`<br/>`gpt-4o-mini`<br/>`gpt-4o-realtime-preview` (version 2024-12-17) <br> `o1` (version 2024-12-17) |
+| **Images** | Images included in user messages, both as links or as base64-encoded data. The detail parameter must be set the same across requests. | `gpt-4o`<br/>`gpt-4o-mini` <br> `o1` (version 2024-12-17)  |
+| **Tool use** | Both the messages array and tool definitions. | `gpt-4o`<br/>`gpt-4o-mini`<br/>`gpt-4o-realtime-preview` (version 2024-12-17) <br> `o1` (version 2024-12-17) |
+| **Structured outputs** | Structured output schema is appended as a prefix to the system message. | `gpt-4o`<br/>`gpt-4o-mini` <br> `o1` (version 2024-12-17) |
 
 To improve the likelihood of cache hits occurring, you should structure your requests such that repetitive content occurs at the beginning of the messages array.
 
diff --git a/articles/ai-studio/how-to/create-azure-ai-resource.md b/articles/ai-studio/how-to/create-azure-ai-resource.md
@@ -111,7 +111,7 @@ For hubs that use CMK encryption mode, you can update the encryption key to a ne
 
 ### Update Azure Application Insights and Azure Container Registry
 
-To use custom environments for Prompt Flow, you're required to configure an Azure Container Registry for your hub. To use Azure Application Insights for Prompt Flow deployments, a configured Azure Application Insights resource is required for your hub. Updating the workspace-attached Azure Container Registry or ApplicationInsights resources may break lineage of previous jobs, deployed inference endpoints, or your ability to rerun earlier jobs in the workspace. 
+To use custom environments for Prompt Flow, you're required to configure an Azure Container Registry for your hub. To use Azure Application Insights for Prompt Flow deployments, a configured Azure Application Insights resource is required for your hub. Updating the workspace-attached Azure Container Registry or Application Insights resources may break lineage of previous jobs, deployed inference endpoints, or your ability to rerun earlier jobs in the workspace. After association with an Azure AI Foundry hub, Azure Container Registry and Application Insights resources cannot be disassociated (set to null).
 
 You can use the Azure portal, Azure SDK/CLI options, or the infrastructure-as-code templates to update both Azure Application Insights and Azure Container Registry for the hub.
 
diff --git a/articles/ai-studio/reference/reference-model-inference-api.md b/articles/ai-studio/reference/reference-model-inference-api.md
@@ -115,11 +115,11 @@ If you are using an endpoint with support for Entra ID, you can create your clie
 ```python
 import os
 from azure.ai.inference import ChatCompletionsClient
-from azure.identity import AzureDefaultCredential
+from azure.identity import DefaultAzureCredential
 
 model = ChatCompletionsClient(
     endpoint=os.environ["AZUREAI_ENDPOINT_URL"],
-    credential=AzureDefaultCredential(),
+    credential=DefaultAzureCredential(),
 )
 ```
 
@@ -151,11 +151,11 @@ For endpoint with support for Microsoft Entra ID, you can create your client as
 ```javascript
 import ModelClient from "@azure-rest/ai-inference";
 import { isUnexpected } from "@azure-rest/ai-inference";
-import { AzureDefaultCredential } from "@azure/identity";
+import { DefaultAzureCredential } from "@azure/identity";
 
 const client = new ModelClient(
     process.env.AZUREAI_ENDPOINT_URL, 
-    new AzureDefaultCredential()
+    new DefaultAzureCredential()
 );
 ```
 
diff --git a/articles/machine-learning/reference-model-inference-api.md b/articles/machine-learning/reference-model-inference-api.md
@@ -108,11 +108,11 @@ If you are using an endpoint with support for Entra ID, you can create your clie
 ```python
 import os
 from azure.ai.inference import ChatCompletionsClient
-from azure.identity import AzureDefaultCredential
+from azure.identity import DefaultAzureCredential
 
 client = ChatCompletionsClient(
     endpoint=os.environ["AZUREAI_ENDPOINT_URL"],
-    credential=AzureDefaultCredential(),
+    credential=DefaultAzureCredential(),
 )
 ```
 
@@ -144,11 +144,11 @@ For endpoint with support for Microsoft Entra ID, you can create your client as
 ```javascript
 import ModelClient from "@azure-rest/ai-inference";
 import { isUnexpected } from "@azure-rest/ai-inference";
-import { AzureDefaultCredential } from "@azure/identity";
+import { DefaultAzureCredential } from "@azure/identity";
 
 const client = new ModelClient(
     process.env.AZUREAI_ENDPOINT_URL, 
-    new AzureDefaultCredential()
+    new DefaultAzureCredential()
 );
 ```
 
diff --git a/articles/search/monitor-azure-cognitive-search-data-reference.md b/articles/search/monitor-azure-cognitive-search-data-reference.md
@@ -1,7 +1,7 @@
 ---
 title: Monitoring data reference for Azure AI Search
 description: This article contains important reference material you need when you monitor Azure AI Search.
-ms.date: 02/15/2024
+ms.date: 01/27/2025
 ms.custom: horz-monitor
 ms.topic: reference
 author: HeidiSteen
diff --git a/articles/search/monitor-azure-cognitive-search.md b/articles/search/monitor-azure-cognitive-search.md
@@ -1,7 +1,7 @@
 ---
 title: Monitor Azure AI Search
 description: Start here to learn how to monitor Azure AI Search.
-ms.date: 02/15/2024
+ms.date: 01/27/2025
 ms.custom: horz-monitor
 ms.topic: conceptual
 author: HeidiSteen
diff --git a/articles/search/search-monitor-queries.md b/articles/search/search-monitor-queries.md
@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/21/2024
+ms.date: 01/27/2025
 ---
 
 # Monitor query requests in Azure AI Search
diff --git a/articles/search/search-region-support.md b/articles/search/search-region-support.md
@@ -9,7 +9,7 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
 ms.custom: references_regions
-ms.date: 11/19/2024
+ms.date: 01/27/2025
 
 ---
 
@@ -19,15 +19,17 @@ This article identifies the cloud regions in which Azure AI Search is available.
 
 ## Features subject to regional availability
 
-| Feature | Availability |
-|---------|--------------|
-| [Extra capacity](search-limits-quotas-capacity.md#service-limits) | Higher capacity partitions became available in selected regions starting in April 2024 with a second wave following in May 2024. If you're using an older search service, create a new search service to benefit from more capacity at the same billing rate. To check existing capacity, [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) and select the **Properties** tab in the middle of the Overview page. To check search service age, follow [these instructions](vector-search-index-size.md#how-to-check-service-creation-date).  Currently, there are just a few regions that *don't* offer higher capacity partitions. Regional support for extra capacity is noted in the footnotes of this article.|
-| [Availability zones](search-reliability.md#availability-zone-support) | Divides a region's data centers into distinct physical location groups, providing high-availability within the same geo. Regional support is noted in this article. |
-| [AI service integration](cognitive-search-concept-intro.md) | Refers to skills that make internal calls to Azure AI for enrichment and transformation during indexing. Integration requires that Azure AI Search coexists with an [Azure AI multi-service account](/azure/ai-services/multi-service-resource) in the same physical region. Regional support is noted in this article. |
-| [Azure OpenAI integration](vector-search-integrated-vectorization.md)  | Refers to skills and vectorizers that make internal calls to deployed embedding and chat models on Azure OpenAI. Check [Azure OpenAI model region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability) for the most current list of regions for each embedding and chat model. Specific Azure OpenAI models are in fewer regions, so be sure to check for joint regional availability before installing.|
-| [Azure AI Foundry integration](vector-search-integrated-vectorization-ai-studio.md) | Refers to skills and vectorizers that make internal calls to the models hosted in the model catalog. Check [Azure AI Foundry region availability](/azure/ai-studio/reference/region-support) for the most current list of regions. |
-| [Azure AI Vision 4.0 multimodal APIs for image vectorization](search-get-started-portal-image-search.md) | Refers to skills and vectorizers that call the multimodal embedding API. Check the [Azure AI Vision region list](/azure/ai-services/computer-vision/overview-image-analysis#region-availability) for joint regional availability. |
-| [Semantic ranker](semantic-search-overview.md) | Takes a dependency on Microsoft-hosted models in specific regions. Regional support is noted in this article. |
+Some features take a dependency on other Azure services or infrastructure that are subject to regional availability. If you need a specific feature, make sure it's available in the desired region.
+
+| Feature | Description | Availability |
+|---------|-------------|--------------|
+| [Extra capacity](search-limits-quotas-capacity.md#service-limits) | Higher capacity partitions became available in selected regions starting in April 2024 with a second wave following in May 2024. Currently, there are just a few regions that *don't* offer higher capacity partitions. If you're using an older search service, create a new search service to benefit from more capacity at the same billing rate. |  Regional support for extra capacity is noted in the footnotes of this article. <p>Check [service age](vector-search-index-size.md#how-to-check-service-creation-date) to see if your search service was created after high capacity partitions became available. <p>To check the capacity of an existing service, [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) and select the **Properties** tab in the middle of the Overview page.|
+| [Availability zones](search-reliability.md#availability-zone-support) | Divides a region's data centers into distinct physical location groups, providing high-availability within the same geo. | Regional support is noted in this article. |
+| [Semantic ranker](semantic-search-overview.md) | Takes a dependency on Microsoft-hosted models in specific regions. | Regional support is noted in this article. |
+| [AI service integration](cognitive-search-concept-intro.md) | Refers to [built-in skills](cognitive-search-predefined-skills.md) that make internal calls to Azure AI for enrichment and transformation during indexing. Integration requires that Azure AI Search coexists with an [Azure AI multi-service account](/azure/ai-services/multi-service-resource) in the same physical region. You can bypass region requirements if you use [identity-based connections](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection), currently in public review. | Regional support is noted in this article. |
+| [Azure OpenAI integration](vector-search-integrated-vectorization.md)  | Refers to the AzureOpenAIEmbedding skill and vectorizer that make internal calls to deployed embedding models on Azure OpenAI. | Check [Azure OpenAI model region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability) for the most current list of regions for each embedding and chat model. Specific Azure OpenAI models are in fewer regions, so check for model availability first, and then verify Azure AI Search is available in the same region.|
+| [Azure AI Foundry integration](vector-search-integrated-vectorization-ai-studio.md) | Refers to skills and vectorizers that make internal calls to the models hosted in the model catalog. | Check [Azure AI Foundry region availability](/azure/ai-studio/reference/region-support) for the most current list of regions. |
+| [Azure AI Vision 4.0 multimodal APIs](search-get-started-portal-image-search.md) | Refers to the Azure AI Vision multimodal embeddings skill and vectorizer that call the multimodal embedding API. | Check the [Azure AI Vision region list](/azure/ai-services/computer-vision/overview-image-analysis#region-availability) first, and then verify Azure AI Search is available in the same region.|
 
 ## Azure Public regions
 
diff --git a/articles/search/search-security-rbac.md b/articles/search/search-security-rbac.md
