Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into WI265756-DEASM

Author: ElazarK

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/language-service/conversational-language-understanding/glossary.md

@@ -38,7 +38,7 @@ A model is an object that's trained to do a certain task, in this case conversat
 
 ## Overfitting
 
-Overfitting happens when the model is fixated on the specific examples and is not able to generalize well.
+Overfitting happens when the model is fixated on the specific examples and isn't able to generalize well.
 
 ## Precision
 Measures how precise/accurate your model is. It's the ratio between the correctly identified positives (true positives) and all identified positives. The precision metric reveals how many of the predicted classes are correctly labeled.
@@ -53,14 +53,14 @@ Measures the model's ability to predict actual positive classes. It's the ratio
 A regular expression entity represents a regular expression. Regular expression entities are exact matches.
 
 ## Schema
-Schema is defined as the combination of intents and entities within your project. Schema design is a crucial part of your project's success. When creating a schema, you want think about which intents and entities should be included in your project
+Schema is defined as the combination of intents and entities within your project. Schema design is a crucial part of your project's success. When creating a schema, you want to think about which intents and entities should be included in your project.
 
 ## Training data
 Training data is the set of information that is needed to train a model.
 
 ## Utterance
 
-An utterance is user input that is short text representative of a sentence in a conversation. It is a natural language phrase such as "book 2 tickets to Seattle next Tuesday". Example utterances are added to train the model and the model predicts on new utterance at runtime
+An utterance is user input that is short text representative of a sentence in a conversation. It's a natural language phrase such as "book 2 tickets to Seattle next Tuesday". Example utterances are added to train the model and the model predicts on new utterance at runtime
 
 
 ## Next steps

articles/ai-services/language-service/question-answering/how-to/migrate-qnamaker-to-question-answering.md

@@ -9,7 +9,7 @@ ms.topic: how-to
 ms.date: 12/19/2023
 ms.custom: language-service-question-answering
 ---
-# Migrate from QnA Maker to custom question qnswering 
+# Migrate from QnA Maker to custom question answering 
 
 **Purpose of this document:** This article aims to provide information that can be used to successfully migrate applications that use QnA Maker to custom question answering. Using this article, we hope customers will gain clarity on the following: 
 
@@ -28,7 +28,7 @@ ms.custom: language-service-question-answering
 >  - Automatic RBAC to Language project (not resource)
 >  - Automatic enabling of analytics.
 
-You will also need to [re-enable analytics](analytics.md) for the language resource.
+You'll also need to [re-enable analytics](analytics.md) for the language resource.
 
 ## Comparison of features
 
@@ -42,7 +42,7 @@ In addition to a new set of features, custom question answering provides many te
 |Smart URL Refresh|➖|✔️|Custom question answering provides a means to refresh ingested content from public sources with a single click.|
 |Q&A over knowledge base (hierarchical extraction)|✔️|✔️| |
 |Active learning|✔️|✔️|Custom question answering has an improved active learning model.|
-|Alternate Questions|✔️|✔️|The improved models in custom question answering reduces the need to add alternate questions.|
+|Alternate Questions|✔️|✔️|The improved models in custom question answering reduce the need to add alternate questions.|
 |Synonyms|✔️|✔️| |
 |Metadata|✔️|✔️| |
 |Question Generation (private preview)|➖|✔️|This new feature will allow generation of questions over text.|
@@ -54,7 +54,7 @@ In addition to a new set of features, custom question answering provides many te
 
 ## Pricing
 
-When you are looking at migrating to custom question answering, please consider the following:
+When you're looking at migrating to custom question answering, please consider the following:
 
 |Component                      |QnA Maker|Custom question answering|Details                                                                                                  |
 |-------------------------------|---------|------------------|---------------------------------------------------------------------------------------------------------|
@@ -65,7 +65,7 @@ When you are looking at migrating to custom question answering, please consider
 
 - Users may select a higher tier with higher capacity, which will impact overall price they pay. It doesn’t impact the price on language component of custom question answering.
 
-- “Text Records” in custom question answering features refers to the query submitted by the user to the runtime, and it is a concept common to all features within Language service. Sometimes a query may have more text records when the query length is higher. 
+- “Text Records” in custom question answering features refers to the query submitted by the user to the runtime, and it's a concept common to all features within Language service. Sometimes a query may have more text records when the query length is higher. 
 
 **Example price estimations**
 
@@ -76,7 +76,7 @@ When you are looking at migrating to custom question answering, please consider
 |Medium|10                              |10(S1)                                    |800K                                |4x3(S1)                                  |Less expensive                     |
 |Low   |4                               |4(B1)                                     |100K                                |3x3(S1)                                  |Less expensive                     |
 
- Summary : Customers should save cost across the most common configurations as seen in the relative cost column.
+ Summary: Customers should save cost across the most common configurations as seen in the relative cost column.
 
 Here you can find the pricing details for [custom question answering](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/) and [QnA Maker](https://azure.microsoft.com/pricing/details/cognitive-services/qna-maker/).
 
@@ -101,7 +101,7 @@ Following are the broad migration phases to consider:
 
 ![A chart showing the phases of a successful migration](../media/migrate-qnamaker-to-question-answering/migration-phases.png)
 
-Additional links which can help you are given below:
+Additional links which can help you're given below:
 - [Authoring portal](https://language.cognitive.azure.com/home)
 - [API](authoring.md)
 - [SDK](/dotnet/api/microsoft.azure.cognitiveservices.knowledge.qnamaker)
@@ -115,7 +115,7 @@ This topic compares two hypothetical scenarios when migrating from QnA Maker to
 > An attempt has been made to ensure these scenarios are representative of real customer migrations, however, individual customer scenarios will of course differ. Also, this article doesn't include pricing details. Visit the [pricing](https://azure.microsoft.com/pricing/details/cognitive-services/language-service/) page for more information.
 
 > [!IMPORTANT] 
-> Each custom question answering project is equivalent to a knowledge base in QnA Maker. Resource level settings such as Role-based access control (RBAC) are not migrated to the new resource. These resource level settings would have to be reconfigured for the language resource post migration. You will also need to [re-enable analytics](analytics.md) for the language resource.
+> Each custom question answering project is equivalent to a knowledge base in QnA Maker. Resource level settings such as Role-based access control (RBAC) are not migrated to the new resource. These resource level settings would have to be reconfigured for the language resource post migration. You'll also need to [re-enable analytics](analytics.md) for the language resource.
 
 ### Migration scenario 1: No custom authoring portal
 

articles/ai-services/language-service/question-answering/how-to/migrate-qnamaker.md

@@ -9,7 +9,7 @@ ms.date: 12/19/2023
 ms.custom: language-service-question-answering
 ---
 
-# Migrate from QnA Maker to custom question answering
+# Migrate from QnA Maker knowledge bases to custom question answering
 
 > [!NOTE]
 > You can also migrate to [Azure OpenAI](../../../qnamaker/How-To/migrate-to-openai.md).
@@ -32,7 +32,7 @@ This [SDK Migration Guide](https://github.com/Azure/azure-sdk-for-net/blob/Azure
 
 You can follow the steps below to migrate knowledge bases:
 
-1. Create a [language resource](https://aka.ms/create-language-resource) with custom question answering enabled in advance. When you create the language resource in the Azure portal, you will see the option to enable custom question answering. When you select that option and proceed, you will be asked for Azure Search details to save the knowledge bases.
+1. Create a [language resource](https://aka.ms/create-language-resource) with custom question answering enabled in advance. When you create the language resource in the Azure portal, you'll see the option to enable custom question answering. When you select that option and proceed, you'll be asked for Azure Search details to save the knowledge bases.
 
 2. If you want to add knowledge bases in multiple languages to your language resource, visit [Language Studio](https://language.azure.com/) to create your first custom question answering project and select the first option as shown below. Language settings for the language resource can be specified only when creating a project. If you want to migrate existing knowledge bases in a single language to the language resource, you can skip this step.
 
@@ -53,12 +53,12 @@ You can follow the steps below to migrate knowledge bases:
    > [!div class="mx-imgBorder"]
    > ![Migrate QnAMaker with red selection box around the QnAMaker resource selection option](../media/migrate-qnamaker/select-resource.png)
 
-6. Select the language resource to which you want to migrate the knowledge bases. You will only be able to see those language resources that have custom question answering enabled. The language setting for the language resource is displayed in the options. You won’t be able to migrate knowledge bases in multiple languages from QnA Maker resources to a language resource if its language setting is not specified.
+6. Select the language resource to which you want to migrate the knowledge bases. You'll only be able to see those language resources that have custom question answering enabled. The language setting for the language resource is displayed in the options. You won’t be able to migrate knowledge bases in multiple languages from QnA Maker resources to a language resource if its language setting isn't specified.
 
    > [!div class="mx-imgBorder"]
    > ![Migrate QnAMaker with red selection box around the language resource option currently selected resource contains the information that language is unspecified](../media/migrate-qnamaker/language-setting.png)
 
-    If you want to migrate knowledge bases in multiple languages to the language resource, you must enable the multiple language setting when creating the first custom question answering project for the language resource. You can do so by following the instructions in step #2. **If the language setting for the language resource is not specified, it is assigned the language of the selected QnA Maker resource**.
+    If you want to migrate knowledge bases in multiple languages to the language resource, you must enable the multiple language setting when creating the first custom question answering project for the language resource. You can do so by following the instructions in step #2. **If the language setting for the language resource isn't specified, it is assigned the language of the selected QnA Maker resource**.
 
 7. Select all the knowledge bases that you wish to migrate > select **Next**.
 
@@ -71,26 +71,26 @@ You can follow the steps below to migrate knowledge bases:
     > If you migrate a knowledge base with the same name as a project that already exists in the target language resource, **the content of the project will be overridden** by the content of the selected knowledge base.
 
     > [!div class="mx-imgBorder"]
-    > ![Screenshot of an error message starting project names can't contain special characters](../media/migrate-qnamaker/migration-kb-name-validation.png)
+    > ![Screenshot of an error message starting project names can't contain special characters.](../media/migrate-qnamaker/migration-kb-name-validation.png)
 
 9. After resolving the validation errors, select **Start migration**
 
     > [!div class="mx-imgBorder"]
-    > ![Screenshot with special characters removed](../media/migrate-qnamaker/migration-kb-name-validation-success.png)
+    > ![Screenshot with special characters removed.](../media/migrate-qnamaker/migration-kb-name-validation-success.png)
 
-10. It will take a few minutes for the migration to occur. Do not cancel the migration while it is in progress. You can navigate to the migrated projects within the [Language Studio](https://language.azure.com/) post migration.
+10. It will take a few minutes for the migration to occur. Don't cancel the migration while it is in progress. You can navigate to the migrated projects within the [Language Studio](https://language.azure.com/) post migration.
 
     > [!div class="mx-imgBorder"]
-    > ![Screenshot of successfully migrated knowledge bases with information that you can publish by using Language Studio](../media/migrate-qnamaker/migration-success.png)
+    > ![Screenshot of successfully migrated knowledge bases with information that you can publish by using Language Studio.](../media/migrate-qnamaker/migration-success.png)
 
     If any knowledge bases fail to migrate to custom question answering projects, an error will be displayed. The most common migration errors occur when:
 
     - Your source and target resources are invalid.
-    - You are trying to migrate an empty knowledge base (KB).
-    - You have reached the limit for an Azure Search instance linked to your target resources.
+    - You're trying to migrate an empty knowledge base (KB).
+    - You've reached the limit for an Azure Search instance linked to your target resources.
 
     > [!div class="mx-imgBorder"]
-    > ![Screenshot of a failed migration with an example error](../media/migrate-qnamaker/migration-errors.png)
+    > ![Screenshot of a failed migration with an example error.](../media/migrate-qnamaker/migration-errors.png)
 
     Once you resolve these errors, you can rerun the migration.
 

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

@@ -66,9 +66,9 @@ These models are currently available for use in Azure OpenAI Service.
 | `gpt-35-turbo` | 0125 | No earlier than Feb 22, 2025 |
 | `gpt-4`<br>`gpt-4-32k` | 0314 | **Deprecation:** October 1, 2024 <br> **Retirement:** June 6, 2025 |
 | `gpt-4`<br>`gpt-4-32k` | 0613 | No earlier than Sep 30, 2024 |
-| `gpt-4` | 1106-preview | To be upgraded to `gpt-4` Version: `turbo-2024-04-09`, starting on July 15, 2024, or later **<sup>1</sup>** |
-| `gpt-4` | 0125-preview |To be upgraded to `gpt-4` Version: `turbo-2024-04-09`, starting on July 15, 2024, or later  **<sup>1</sup>**  |
-| `gpt-4` | vision-preview | To be upgraded to `gpt-4` Version: `turbo-2024-04-09`, starting on July 15, 2024, or later  **<sup>1</sup>** |
+| `gpt-4` | 1106-preview | To be upgraded to `gpt-4` Version: `turbo-2024-04-09`, starting on August 15, 2024, or later **<sup>1</sup>** |
+| `gpt-4` | 0125-preview |To be upgraded to `gpt-4` Version: `turbo-2024-04-09`, starting on August 15, 2024, or later  **<sup>1</sup>**  |
+| `gpt-4` | vision-preview | To be upgraded to `gpt-4` Version: `turbo-2024-04-09`, starting on August 15, 2024, or later  **<sup>1</sup>** |
 | `gpt-3.5-turbo-instruct` | 0914 | No earlier than Sep 14, 2025 |
 | `text-embedding-ada-002` | 2 | No earlier than April 3, 2025 |
 | `text-embedding-ada-002` | 1 | No earlier than April 3, 2025 |

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