Merge pull request #5254 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-ai-docs (branch main)

Author: Taojunshen

articles/ai-foundry/includes/create-content-filter.md

@@ -35,7 +35,7 @@ Follow these steps to create a content filter:
 
     Now you can configure the input filters (for user prompts) and output filters (for model completion). 
 1. On the **Input filters** page, you can set the filter for the input prompt. For the first four content categories there are three severity levels that are configurable: Low, medium, and high. You can use the sliders to set the severity threshold if you determine that your application or usage scenario requires different filtering than the default values. 
-    Some filters, such as Prompt Shields and Protected material detection, enable you to determine if the model should annotate and/or block content. Selecting **Annotate only** runs the respective model and return annotations via API response, but it will not filter content. In addition to annotate, you can also choose to block content.
+    Some filters, such as Prompt Shields and Protected material detection, enable you to determine if the model should annotate and/or block content. Selecting **Annotate only** runs the respective model and returns annotations via API response, but it will not filter content. In addition to annotate, you can also choose to block content.
 
     If your use case was approved for modified content filters, you receive full control over content filtering configurations and can choose to turn filtering partially or fully off, or enable annotate only for the content harms categories (violence, hate, sexual and self-harm).
 
@@ -80,4 +80,4 @@ Follow these steps to apply a content filter to a deployment:
 Now, you can go to the playground to test whether the content filter works as expected.
 
 > [!TIP]
-> You can also create and update content filters using the REST APIs. For more information, see the [API reference](/rest/api/aiservices/accountmanagement/rai-policies/create-or-update). Content filters can be configured at the resource level. Once a new configuration is created, it can be associated with one or more deployments. For more information about model deployment, see the resource [deployment guide](../../ai-services/openai/how-to/create-resource.md). 
+> You can also create and update content filters using the REST APIs. For more information, see the [API reference](/rest/api/aiservices/accountmanagement/rai-policies/create-or-update). Content filters can be configured at the resource level. Once a new configuration is created, it can be associated with one or more deployments. For more information about model deployment, see the resource [deployment guide](../../ai-services/openai/how-to/create-resource.md). 

articles/ai-services/language-service/overview.md

@@ -23,9 +23,9 @@ The Language service also provides several new features as well, which can eithe
 * Preconfigured, which means the AI models that the feature uses aren't customizable. You just send your data, and use the feature's output in your applications.
 * Customizable, which means you train an AI model using our tools to fit your data specifically.
 
-Language features are also utilized in [agent templates](https://aka.ms/ai-agent-catalog):
-    * [Intent routing](https://aka.ms/intent-triage-agent-template) detects user intent and provides exact answering. Perfect for deterministically intent routing and exact question answering with human controls.
-    * [Exact question answering](https://aka.ms/exact-answer-agent-template) answers high-value predefined questions deterministically to ensure consistent and accurate responses.
+Language features are also utilized in [agent templates](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/agent-catalog):
+* [Intent routing agent](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/intent-routing-agent) detects user intent and provides exact answering. Perfect for deterministically intent routing and exact question answering with human controls.
+* [Exact question answering agent](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/exact-qna-agent) answers high-value predefined questions deterministically to ensure consistent and accurate responses.
 
 > [!TIP]
 > Unsure which feature to use? See [Which Language service feature should I use](#which-language-service-feature-should-i-use) to help you decide.

articles/ai-services/openai/how-to/responses.md

@@ -929,6 +929,7 @@ curl GET https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/responses/resp_12
 ```python
 from openai import AzureOpenAI
 from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+from time import sleep
 
 token_provider = get_bearer_token_provider(
     DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
@@ -947,7 +948,7 @@ response = client.responses.create(
 )
 
 while response.status in {"queued", "in_progress"}:
-    print(f"Current status: {resp.status}")
+    print(f"Current status: {response.status}")
     sleep(2)
     response = client.responses.retrieve(response.id)
 
@@ -1293,4 +1294,4 @@ For examples of how to use reasoning models with the responses API see the [reas
 
 ## Computer use
 
-Computer use with Playwright has moved to the [dedicated computer use model guide](./computer-use.md#playwright-integration)
+Computer use with Playwright has moved to the [dedicated computer use model guide](./computer-use.md#playwright-integration)

articles/ai-services/openai/references/elasticsearch.md

@@ -36,7 +36,11 @@ The configurable options for Elasticsearch when using Azure OpenAI On Your Data.
 | `strictness` | integer | False | The configured strictness of the search relevance filtering. The higher of strictness, the higher of the precision but lower recall of the answer. Default is `3`.| 
 | `top_n_documents` | integer | False | The configured top number of documents to feature for the configured query. Default is `5`. |
 
-## Key and key ID authentication options
+## Authentication Options
+
+Azure OpenAI On Your Data supports multiple authentication types:
+
+### Key and key ID authentication options
 
 The authentication options for Azure OpenAI On Your Data when using an API key.
 
@@ -46,7 +50,7 @@ The authentication options for Azure OpenAI On Your Data when using an API key.
 | `key_id`|string|True|The Elasticsearch key ID to use for authentication.|
 | `type`|string|True| Must be `key_and_key_id`.|
 
-## Encoded API key authentication options
+### Encoded API key authentication options
 
 The authentication options for Azure OpenAI On Your Data when using an Elasticsearch encoded API key.
 
@@ -74,23 +78,24 @@ The details of the vectorization source, used by Azure OpenAI On Your Data when
 | `authentication`| [ApiKeyAuthenticationOptions](#api-key-authentication-options)|True | Specifies the authentication options to use when retrieving embeddings from the specified endpoint.|
 | `type`|string|True| Must be `endpoint`.|
 
-## Model ID vectorization source
+### API key authentication options
 
-The details of the vectorization source, used by Azure OpenAI On Your Data when applying vector search. This vectorization source is based on Elasticsearch model ID.
+The authentication options for Azure OpenAI On Your Data when using an API key.
 
 |Name | Type | Required | Description |
 |--- | --- | --- | --- |
-| `model_id`|string|True| Specifies the model ID to use for vectorization. This model ID must be defined in Elasticsearch.|
-| `type`|string|True| Must be `model_id`.|
+| `key`|string|True|The API key to use for authentication.|
+| `type`|string|True| Must be `api_key`.|
 
-## API key authentication options
+## Model ID vectorization source
 
-The authentication options for Azure OpenAI On Your Data when using an API key.
+The details of the vectorization source, used by Azure OpenAI On Your Data when applying vector search. This vectorization source is based on Elasticsearch model ID.
 
 |Name | Type | Required | Description |
 |--- | --- | --- | --- |
-| `key`|string|True|The API key to use for authentication.|
-| `type`|string|True| Must be `api_key`.|
+| `model_id`|string|True| Specifies the model ID to use for vectorization. This model ID must be defined in Elasticsearch.|
+| `type`|string|True| Must be `model_id`.|
+
 
 ## Fields mapping options
 

articles/machine-learning/how-to-assign-roles.md

@@ -273,7 +273,7 @@ The following table is a summary of Azure Machine Learning activities and the pe
 
 | Activity | Subscription-level scope | Resource group-level scope | Workspace-level scope |
 | ----- | ----- | ----- | ----- |
-| Create new workspace <sub>1</sub> | Not required | Owner, contributor, or custom role allowing: `Microsoft.Resources/deployments/*` | N/A (becomes Owner or inherits higher scope role after creation) |
+| Create new workspace <sub>1</sub> | Not required | Owner, contributor, or custom role allowing: `Microsoft.Resources/deployments/*` and `Microsoft.MachineLearningServices/workspaces/write` | N/A (becomes Owner or inherits higher scope role after creation) |
 | Request subscription level Amlcompute quota or set workspace level quota | Owner, or contributor, or custom role <br>allowing `/locations/updateQuotas/action`<br> at subscription scope | Not authorized | Not authorized |
 | Create new compute cluster | Not required | Not required | Owner, contributor, or custom role allowing: `/workspaces/computes/write` |
 | Create new compute instance | Not required | Not required | Owner, contributor, or custom role allowing: `/workspaces/computes/write` |