Merge pull request #1785 from MicrosoftDocs/main

12/2/2024 PM Publish

Author: Taojunshen

articles/ai-services/content-understanding/overview.md

@@ -46,10 +46,10 @@ Common applications for Content Understanding include:
 
 |Application|Description|Quickstart|
 |:---------|:----------|:----------|
-|Post-call analytics| Businesses and call centers can generate insights from call recordings to track key KPIs, improve product experience, generate business insights, create differentiated customer experiences, and answer queries faster and more accurately.| [**Post-call analytics quickstart**](quickstart/use-ai-foundry.md#audio-analyzer-templates) |
-|Media asset management| Software and media vendors can use Content Understanding to extract richer, targeted information from videos for media asset management solutions.| [**Media asset management quickstart**](quickstart/use-ai-foundry.md#video-analyzer-templates) |
-|Tax automation| Tax preparation companies can use Content Understanding to generate a unified view of information from various documents and create comprehensive tax returns.| [**Tax automation quickstart**](quickstart/use-ai-foundry.md#document-analyzer-templates) | 
-|Chart understanding| Businesses can enhance chart understanding by automating the analysis and interpretation of various types of charts and diagrams using Content Understanding.| [**Chart understanding quickstart**](quickstart/use-ai-foundry.md#image-analyzer-templates) |
+|Post-call analytics| Businesses and call centers can generate insights from call recordings to track key KPIs, improve product experience, generate business insights, create differentiated customer experiences, and answer queries faster and more accurately.| [**Post-call analytics quickstart**](quickstart/use-ai-foundry.md?tabs=audio#analyzer-templates) |
+|Media asset management| Software and media vendors can use Content Understanding to extract richer, targeted information from videos for media asset management solutions.| [**Media asset management quickstart**](quickstart/use-ai-foundry.md?tabs=video#analyzer-templates) |
+|Tax automation| Tax preparation companies can use Content Understanding to generate a unified view of information from various documents and create comprehensive tax returns.| [**Tax automation quickstart**](quickstart/use-ai-foundry.md?tabs=document#analyzer-templates) | 
+|Chart understanding| Businesses can enhance chart understanding by automating the analysis and interpretation of various types of charts and diagrams using Content Understanding.| [**Chart understanding quickstart**](quickstart/use-ai-foundry.md?tabs=image#analyzer-templates) |
 
 See [Quickstart](quickstart/use-ai-foundry.md) for more examples.
 

articles/ai-services/content-understanding/quickstart/use-ai-foundry.md

@@ -71,9 +71,7 @@ Content Understanding analyzer templates give you a head start by allowing you t
 
 The following analyzer templates are available for use in the [Azure AI Foundry Content Understanding experience](https://ai.azure.com/).
 
-### Document analyzer templates
-
-To follow are several of our document analyzer templates.
+# [Document](#tab/document)
 
 |Template| Description|
 | ----|----|----|
@@ -82,9 +80,7 @@ To follow are several of our document analyzer templates.
 
    :::image type="content" source="../media/analyzer-template/image-templates.png" alt-text="Screenshot of document analyzer template.":::
 
-### Image analyzer templates
-
-To follow are several of our image analyzer templates.
+# [Image](#tab/image)
 
 |Template| Description|
 | ----|----|----|
@@ -94,9 +90,7 @@ To follow are several of our image analyzer templates.
 
    :::image type="content" source="../media/analyzer-template/image-templates.png" alt-text="Screenshot of image analyzer template.":::
 
-### Audio analyzer templates
-
-To follow are several of our audio analyzer templates.
+# [Audio](#tab/audio)
 
 |Template| Description|
 | ----|----|----|
@@ -107,9 +101,7 @@ To follow are several of our audio analyzer templates.
 
    :::image type="content" source="../media/analyzer-template/audio-templates.png" alt-text="Screenshot of audio analyzer template.":::
 
-### Video analyzer templates
-
-To follow are several of our video analyzer templates.
+# [Video](#tab/video)
 
 |Template| Description|
 | ----|----|----|
@@ -120,6 +112,8 @@ To follow are several of our video analyzer templates.
 
    :::image type="content" source="../media/analyzer-template/video-templates.png" alt-text="Screenshot of video analyzer template.":::
 
+---
+
 ## Next steps
 
 * In this quickstart, you learned how to create an analyzer in Azure AI Foundry. To use [REST API](/rest/api/contentunderstanding/operation-groups?view=rest-contentunderstanding-2024-12-01-preview&preserve-view=true), *see* the [REST API quickstart](use-rest-api.md).

articles/ai-services/content-understanding/quickstart/use-rest-api.md

@@ -239,13 +239,13 @@ Before running the cURL command, make the following changes to the HTTP request:
 
 ### POST request
 ```bash
-curl -i -X POST "{endpoint}/contentunderstanding/analyzers/{analyzerId}:analyze?stringEncoding=codePoint&api-version=2024-12-01-preview" \
+curl -i -X POST "{endpoint}/contentunderstanding/analyzers/{analyzerId}:analyze?api-version=2024-12-01-preview" \
   -H "Ocp-Apim-Subscription-Key: {key}" \
   -H "Content-Type: application/json" \
   -d "{\"url\":\"{fileUrl}\"}"
 ```
 
-### POST responseH
+### POST response
 
 The 202 (`Accepted`) response includes an `Operation-Location` header containing a URL that you can use to track the status of this asynchronous analyze operation.
 

articles/ai-services/content-understanding/video/overview.md

@@ -48,7 +48,7 @@ Content extraction for video includes transcription, shot detection, key frame e
 * **Shot detection**: Identifies segments of the video aligned with shot boundaries where possible, allowing for precise editing and repackaging of content with breaks exactly on shot boundaries.
 * **Key frame extraction**: Extracts key frames from videos to represent each shot completely, ensuring each shot has enough key frames to enable Field Extraction to work effectively.
 * **Face grouping**: Grouped faces appearing in a video to extract one representative face image for each person and provides segments where each one is present. The grouped face data is available as metadata and can be used to generate customized metadata fields.
-* This feature is limited access and involves face identification and grouping; customers need to register for access at [Face Recognition](https://aka.ms/facerecognition).
+  * This feature is limited access and involves face identification and grouping; customers need to register for access at [Face Recognition](https://aka.ms/facerecognition).
 
 ### Field extraction 
 

articles/ai-services/openai/concepts/model-retirements.md

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI
 description: Learn about the model deprecations and retirements in Azure OpenAI.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 11/22/2024
+ms.date: 12/02/2024
 ms.custom: 
 manager: nitinme
 author: mrbullwinkle
@@ -106,7 +106,7 @@ These models are currently available for use in Azure OpenAI Service.
 | `gpt-4` | vision-preview | To be upgraded to `gpt-4` version: `turbo-2024-04-09`, starting no sooner than January 27, 2025  **<sup>1</sup>** | `gpt-4o`|
 | `gpt-4o` | 2024-05-13 | No earlier than May 20, 2025 <br><br>Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `2024-08-06`, starting on February 13, 2025. | |
 | `gpt-4o-mini` | 2024-07-18 | No earlier than July 18, 2025  | |
-| `gpt-3.5-turbo-instruct` | 0914 | No earlier than Sep 14, 2025 |  |
+| `gpt-3.5-turbo-instruct` | 0914 | No earlier than February 1, 2025 |  |
 | `text-embedding-ada-002` | 2 | No earlier than April 3, 2025 | `text-embedding-3-small` or `text-embedding-3-large` |
 | `text-embedding-ada-002` | 1 | No earlier than April 3, 2025 | `text-embedding-3-small` or `text-embedding-3-large` |
 | `text-embedding-3-small` | | No earlier than April 3, 2025 | |
@@ -162,9 +162,13 @@ If you're an existing customer looking for information about these models, see [
 
 ## Retirement and deprecation history
 
+## December 2, 2024
+
+`gpt-3.5-turbo-instruct` updated to no earlier than February 1, 2025.
+
 ## November 22, 2024
 
-`gpt-35-turbo` 1106 retirement date updated to no earlier than March 31, 2025. 
+`gpt-35-turbo` 1106 retirement date updated to no earlier than March 31, 2025.
 
 ## November 11, 2024
 

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

@@ -8,8 +8,8 @@ ms.service: azure-ai-openai
 ms.custom: references_regions
 ms.topic: how-to
 ms.date: 05/20/2024
-author: mrbullwinkle
-ms.author: mbullwin
+author: aahill
+ms.author: aahi
 recommendations: false
 
 ---

articles/ai-services/openai/how-to/content-filters.md

@@ -43,40 +43,7 @@ You can configure the following filter categories in addition to the default har
 | Protected material - text | GA| On | Completion | Identifies and blocks known text content from being displayed in the model output (for example, song lyrics, recipes, and selected web content).  |
 | Groundedness* | Preview |Off | Completion |Detects whether the text responses of large language models (LLMs) are grounded in the source materials provided by the users. Ungroundedness refers to instances where the LLMs produce information that is non-factual or inaccurate from what was present in the source materials. Requires: [Document embedding and formatting](/azure/ai-services/openai/concepts/content-filter?tabs=warning%2Cuser-prompt%2Cpython-new#embedding-documents-in-your-prompt).|
 
-
-
-## Configure content filters with Azure AI Foundry
-
-The following steps show how to set up a customized content filtering configuration for your Azure OpenAI resource within AI Foundry portal. For guidance with content filters in your Azure AI Foundry project, you can read more at [Azure AI Foundry content filtering](/azure/ai-studio/concepts/content-filtering).
-
-1. Go to Azure AI Foundry and navigate to the **Safety + security** page on the left menu.
-1. Proceed to the **Content filters** tab and create a new customized content filtering configuration. 
-
-    This leads to the following configuration view, where you can choose a name for the custom content filtering configuration. After entering a name, you can configure the **input filters** (for user prompts) and **output filters** (for model completion). 
-
-    :::image type="content" source="../media/content-filters/input-filter.png" alt-text="Screenshot of input filter screen.":::
-
-    :::image type="content" source="../media/content-filters/output-filter.png" alt-text="Screenshot of output filter screen.":::
-
-    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.
-
-    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).
-
-
-1. You can create multiple content filtering configurations as per your requirements.
-
-    :::image type="content" source="../media/content-filters/multiple.png" alt-text="Screenshot of multiple content configurations in the Azure portal." lightbox="../media/content-filters/multiple.png":::
-
-1. Next, to use a custom content filtering configuration, assign it to one or more deployments in your resource. To do this, go to the **Deployments** tab and select your deployment. Then select **Edit**.
-1. In the **Update deployment** window that appears, select your custom filter from the **Content filter** dropdown menu. Then select **Save and close** to apply the selected configuration to the deployment.
-
-    :::image type="content" source="../media/content-filters/select-filter.png" alt-text="Screenshot of edit deployment configuration with content filter selected." lightbox="../media/content-filters/select-filter.png":::
-
-    You can also edit and delete a content filter configuration if required.
-    
-    Before you delete a content filtering configuration, you will need to unassign and replace it from any deployment in the **Deployments** tab.
+[!INCLUDE [create-content-filter](../../../ai-studio/includes/create-content-filter.md)]
 
 ## Report content filtering feedback
 

articles/ai-services/openai/includes/assistants-ai-studio.md

@@ -9,8 +9,8 @@ ms.custom:
   - ignite-2024
 ms.topic: include
 ms.date: 03/04/2024
-ms.author: mbullwin
-author: mrbullwinkle
+author: aahill
+ms.author: aahi
 ---
 
 [!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]

articles/ai-services/openai/includes/assistants-env-var-key.md

@@ -1,7 +1,7 @@
 ---
 manager: nitinme
-author: mrbullwinkle
-ms.author: mbullwin
+author: aahill
+ms.author: aahi
 ms.service: azure-ai-openai
 ms.topic: include
 ms.date: 10/09/2024

articles/ai-services/openai/includes/assistants-env-var-without-key.md

@@ -1,7 +1,7 @@
 ---
 manager: nitinme
-author: mrbullwinkle
-ms.author: mbullwin
+author: aahill
+ms.author: aahi
 ms.service: azure-ai-openai
 ms.topic: include
 ms.date: 10/0//2024