Merge pull request #279950 from PatrickFarley/gh-issues-address

Gh issues address

Author: prmerger-automator[bot]

articles/ai-services/computer-vision/concept-model-customization.md

@@ -80,8 +80,8 @@ The following table describes the limits on the scale of your custom model proje
 | Max # training images               | 1,000,000                       | 200,000                         |
 | Max # evaluation images             | 100,000                        | 100,000                         |
 | Min # training images per category  | 2                               | 2                               |
-| Max # tags per image                | multiclass: 1                   | NA                              |
-| Max # regions per image             | NA                              | 1,000                           |
+| Max # tags per image                | 1                               | N/A                            |
+| Max # regions per image             | N/A                              | 1,000                           |
 | Max # categories                    | 2,500                           | 1,000                           |
 | Min # categories                    | 2                               | 1                               |
 | Max image size (Training)           | 20 MB                            | 20 MB                            |

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

@@ -30,6 +30,8 @@ This documentation contains the following article types:
 
 The following are a few scenarios in which a software developer or team would require a content moderation service:
 
+- User prompts submitted to a generative AI service. 
+- Content produced by generative AI models.
 - Online marketplaces that moderate product catalogs and other user-generated content.
 - Gaming companies that moderate user-generated game artifacts and chat rooms.
 - Social messaging platforms that moderate images and text added by their users.

articles/ai-services/openai/how-to/gpt-with-vision.md

@@ -30,7 +30,7 @@ The following command shows the most basic way to use the GPT-4 Turbo with Visio
 
 #### [REST](#tab/rest)
 
-Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2023-12-01-preview` where 
+Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where 
 
 - RESOURCE_NAME is the name of your Azure OpenAI resource 
 - DEPLOYMENT_NAME is the name of your GPT-4 Turbo with Vision model deployment 
@@ -88,12 +88,12 @@ The following is a sample request body. The format is the same as the chat compl
     api_base = '<your_azure_openai_endpoint>' # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
     api_key="<your_azure_openai_key>"
     deployment_name = '<your_deployment_name>'
-    api_version = '2023-12-01-preview' # this might change in the future
+    api_version = '2024-02-15-preview' # this might change in the future
 
     client = AzureOpenAI(
         api_key=api_key,  
         api_version=api_version,
-        base_url=f"{api_base}openai/deployments/{deployment_name}/extensions",
+        base_url=f"{api_base}openai/deployments/{deployment_name}",
     )
     ```
 
@@ -265,7 +265,7 @@ The **object grounding** integration brings a new layer to data analysis and use
 
 #### [REST](#tab/rest)
 
-Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/extensions/chat/completions?api-version=2023-12-01-preview` where 
+Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where 
 
 - RESOURCE_NAME is the name of your Azure OpenAI resource 
 - DEPLOYMENT_NAME is the name of your GPT-4 Turbo with Vision model deployment 
@@ -577,7 +577,7 @@ To use a User assigned identity on your Azure AI Services resource, follow these
 
 #### [REST](#tab/rest)
 
-1. Prepare a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/extensions/chat/completions?api-version=2023-12-01-preview` where 
+1. Prepare a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where 
 
     - RESOURCE_NAME is the name of your Azure OpenAI resource 
     - DEPLOYMENT_NAME is the name of your GPT-4 Vision model deployment