Merge branch 'main' into release-azure-policy-versioning

Author: v-alje

.openpublishing.redirection.json

@@ -14,7 +14,7 @@ keywords: "qna maker, low code chat bots, multi-turn conversations"
 # What is QnA Maker?
 
 > [!NOTE]
-> [Azure Open AI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to QnA Maker. If you wish to migrate your QnA Maker project to Azure Open AI On Your Data, please check out our [guide](../How-To/migrate-to-openai.md).
+> [Azure OpenAI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to QnA Maker. If you wish to migrate your QnA Maker project to Azure OpenAI On Your Data, please check out our [guide](../How-To/migrate-to-openai.md).
 
 [!INCLUDE [Custom question answering](../includes/new-version.md)]
 

articles/ai-services/qnamaker/Overview/overview.md

@@ -80,11 +80,73 @@ For how to use Pronunciation Assessment in streaming mode in your own applicatio
 ## Set configuration parameters
 
 ::: zone pivot="programming-language-go"
+
 > [!NOTE]
 > Pronunciation assessment is not available with the Speech SDK for Go. You can read about the concepts in this guide. Select another programming language for your solution.
+
+::: zone-end
+
+In the `SpeechRecognizer`, you can specify the language to learn or practice improving pronunciation. The default locale is `en-US`. To learn how to specify the learning language for pronunciation assessment in your own application, you can use the following sample code.
+
+::: zone pivot="programming-language-csharp"
+
+```csharp
+var recognizer = new SpeechRecognizer(config, "en-US", audioInput);
+```
+
+::: zone-end  
+
+::: zone pivot="programming-language-cpp"
+
+```cpp
+auto recognizer = SpeechRecognizer::FromConfig(config, "en-US", audioConfig);
+```
+
+::: zone-end
+
+::: zone pivot="programming-language-java"
+
+```Java
+SpeechRecognizer recognizer = new SpeechRecognizer(config, "en-US", audioInput);
+```
+
 ::: zone-end
 
-In the `SpeechRecognizer`, you can specify the language to learn or practice improving pronunciation. The default locale is `en-US`. To learn how to specify the learning language for pronunciation assessment in your own application, see [sample code](https://github.com/Azure-Samples/cognitive-services-speech-sdk/blob/master/samples/csharp/sharedcontent/console/speech_recognition_samples.cs#LL1086C13-L1086C98).
+::: zone pivot="programming-language-python"
+
+```Python
+speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config, language="en-US", audio_config=audio_config)
+```
+
+::: zone-end
+
+::: zone pivot="programming-language-javascript"
+
+```JavaScript
+speechConfig.speechRecognitionLanguage = "en-US";
+```
+
+::: zone-end
+
+::: zone pivot="programming-language-objectivec"
+
+```ObjectiveC
+SPXSpeechRecognizer* speechRecognizer = [[SPXSpeechRecognizer alloc] initWithSpeechConfiguration:speechConfig language:@"en-US" audioConfiguration:pronAudioSource];
+```
+
+::: zone-end
+
+::: zone pivot="programming-language-swift"
+
+```swift
+let reco = try! SPXSpeechRecognizer(speechConfiguration: speechConfig, language: "en-US", audioConfiguration: audioInput)
+```
+
+::: zone-end
+
+::: zone pivot="programming-language-go"
+
+::: zone-end
 
 > [!TIP]
 > If you aren't sure which locale to set for a language that has multiple locales, try each locale separately. For instance, for Spanish, try `es-ES` and `es-MX`. Determine which locale scores higher for your scenario.

articles/ai-services/speech-service/how-to-pronunciation-assessment.md

@@ -8,20 +8,20 @@ ms.author: eur
 
 | Language | Locale (BCP-47) | 
 |--|--|
-|Arabic (Egypt)|`ar-EG`<sup>1</sup> |
+|Arabic (Egypt)|`ar-EG` |
 |Arabic (Saudi Arabia)|`ar-SA` |
 |Catalan|`ca-ES`<sup>1</sup>|
 |Chinese (Cantonese, Traditional)|`zh-HK`<sup>1</sup>|
 |Chinese (Mandarin, Simplified)|`zh-CN`|
-|Chinese (Taiwanese Mandarin, Traditional)|`zh-TW`<sup>1</sup>|
+|Chinese (Taiwanese Mandarin, Traditional)|`zh-TW`|
 |Danish (Denmark)|`da-DK`<sup>1</sup>|
 |Dutch (Netherlands)|`nl-NL`|
 |English (Australia)|`en-AU`|
 |English (Canada)|`en-CA` |
 |English (India)|`en-IN` |
 |English (United Kingdom)|`en-GB`|
 |English (United States)|`en-US`|  
-|Finnish (Finland)|`fi-FI`<sup>1</sup>|  
+|Finnish (Finland)|`fi-FI`|  
 |French (Canada)|`fr-CA`| 
 |French (France)|`fr-FR`|  
 |German (Germany)|`de-DE`|
@@ -31,14 +31,15 @@ ms.author: eur
 |Korean (Korea)|`ko-KR`|
 |Malay (Malaysia)|`ms-MY`|
 |Norwegian Bokmål (Norway)|`nb-NO`|
-|Polish (Poland)|`pl-PL`<sup>1</sup>|
+|Polish (Poland)|`pl-PL`|
 |Portuguese (Brazil)|`pt-BR`|
 |Portuguese (Portugal)|`pt-PT`|
 |Russian (Russia)|`ru-RU`|
 |Spanish (Mexico)|`es-MX` | 
 |Spanish (Spain)|`es-ES` | 
 |Swedish (Sweden)|`sv-SE`|
 |Tamil (India)|`ta-IN` | 
+|Thai (Thailand)|`th-TH`<sup>1</sup> | 
 |Vietnamese (Vietnam)|`vi-VN` |
 
 <sup>1</sup> The language is in public preview for pronunciation assessment.

articles/ai-services/speech-service/includes/language-support/pronunciation-assessment.md

@@ -118,7 +118,7 @@ With the cross-lingual feature, you can transfer your custom neural voice model
 
 # [Pronunciation assessment](#tab/pronunciation-assessment)
 
-The table in this section summarizes the 32 locales supported for pronunciation assessment, and each language is available on all [Speech to text regions](regions.md#speech-service). Latest update extends support from English to 31 more languages and quality enhancements to existing features, including accuracy, fluency and miscue assessment. You should specify the language that you're learning or practicing improving pronunciation. The default language is set as `en-US`. If you know your target learning language, [set the locale](how-to-pronunciation-assessment.md#get-pronunciation-assessment-results) accordingly. For example, if you're learning British English, you should specify the language as `en-GB`. If you're teaching a broader language, such as Spanish, and are uncertain about which locale to select, you can run various accent models (`es-ES`, `es-MX`) to determine the one that achieves the highest score to suit your specific scenario. If you're interested in languages not listed in the following table, fill out this [intake form](https://aka.ms/speechpa/intake) for further assistance.
+The table in this section summarizes the 33 locales supported for pronunciation assessment, and each language is available on all [Speech to text regions](regions.md#speech-service). Latest update extends support from English to 32 more languages and quality enhancements to existing features, including accuracy, fluency and miscue assessment. You should specify the language that you're learning or practicing improving pronunciation. The default language is set as `en-US`. If you know your target learning language, [set the locale](how-to-pronunciation-assessment.md#get-pronunciation-assessment-results) accordingly. For example, if you're learning British English, you should specify the language as `en-GB`. If you're teaching a broader language, such as Spanish, and are uncertain about which locale to select, you can run various accent models (`es-ES`, `es-MX`) to determine the one that achieves the highest score to suit your specific scenario. If you're interested in languages not listed in the following table, fill out this [intake form](https://aka.ms/speechpa/intake) for further assistance.
 
 [!INCLUDE [Language support include](includes/language-support/pronunciation-assessment.md)]
 

articles/ai-services/speech-service/language-support.md

@@ -18,12 +18,12 @@ ms.custom: references_regions, build-2024
 [!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
 
 In this article, you learn how to use Azure AI Studio to deploy the Mistral family of models as serverless APIs with pay-as-you-go token-based billing.
-Mistral AI offers two categories of models in [Azure AI Studio](https://ai.azure.com):
+Mistral AI offers two categories of models in the [Azure AI Studio](https://ai.azure.com). These models are available in the [model catalog](model-catalog-overview.md):
 
-* __Premium models__: Mistral Large and Mistral Small. These models are available as serverless APIs with pay-as-you-go token-based billing in the AI Studio model catalog. 
-* __Open models__: Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01. These models are also available in the AI Studio model catalog and can be deployed to managed compute in your own Azure subscription.
+* __Premium models__: Mistral Large and Mistral Small. These models can be deployed as serverless APIs with pay-as-you-go token-based billing.
+* __Open models__: Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01. These models can be deployed to managed computes in your own Azure subscription.
 
-You can browse the Mistral family of models in the [Model Catalog](model-catalog-overview.md) by filtering on the Mistral collection.
+You can browse the Mistral family of models in the model catalog by filtering on the Mistral collection.
 
 ## Mistral family of models
 
@@ -61,10 +61,18 @@ Certain models in the model catalog can be deployed as a serverless API with pay
 ### Prerequisites
 
 - An Azure subscription with a valid payment method. Free or trial Azure subscriptions won't work. If you don't have an Azure subscription, create a [paid Azure account](https://azure.microsoft.com/pricing/purchase-options/pay-as-you-go) to begin.
-- An [Azure AI Studio hub](../how-to/create-azure-ai-resource.md).
+- An [AI Studio hub](../how-to/create-azure-ai-resource.md). The serverless API model deployment offering for eligible models in the Mistral family is only available with hubs created in these regions:
+
+    - East US
+    - East US 2
+    - North Central US
+    - South Central US
+    - West US
+    - West US 3
+    - Sweden Central
+
+    For a list of  regions that are available for each of the models supporting serverless API endpoint deployments, see [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md).
 
-    > [!IMPORTANT]
-    > The serverless API model deployment offering for eligible models in the Mistral family is only available in hubs created in the **East US 2** and **Sweden Central** regions. 
 - An [Azure AI Studio project](../how-to/create-projects.md).
 - Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Studio. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, see [Role-based access control in Azure AI Studio](../concepts/rbac-ai-studio.md).
 
@@ -93,7 +101,7 @@ To create a deployment:
 
     :::image type="content" source="../media/deploy-monitor/mistral/mistral-large-deploy-pay-as-you-go.png" alt-text="A screenshot showing how to deploy a model as a serverless API." lightbox="../media/deploy-monitor/mistral/mistral-large-deploy-pay-as-you-go.png":::
 
-1. Select the project in which you want to deploy your model. To deploy the Mistral model, your project must be in the *EastUS2* or *Sweden Central* region.
+1. Select the project in which you want to deploy your model. To use the serverless API model deployment offering, your project must belong to one of the regions listed in the [prerequisites](#prerequisites).
 1. In the deployment wizard, select the link to **Azure Marketplace Terms** to learn more about the terms of use.
 1. Select the **Pricing and terms** tab to learn about pricing for the selected model.
 1. Select the **Subscribe and Deploy** button. If this is your first time deploying the model in the project, you have to subscribe your project for the particular offering. This step requires that your account has the **Azure AI Developer role** permissions on the resource group, as listed in the prerequisites. Each project has its own subscription to the particular Azure Marketplace offering of the model, which allows you to control and monitor spending. Currently, you can have only one deployment for each model within a project.
@@ -125,15 +133,15 @@ You can consume Mistral family models by using the chat API.
 
 For more information on using the APIs, see the [reference](#reference-for-mistral-family-of-models-deployed-as-a-service) section.
 
-### Reference for Mistral family of models deployed as a service
+## Reference for Mistral family of models deployed as a service
 
 Mistral models accept both the [Azure AI Model Inference API](../reference/reference-model-inference-api.md) on the route `/chat/completions` and the native [Mistral Chat API](#mistral-chat-api) on `/v1/chat/completions`. 
 
 ### Azure AI Model Inference API
 
 The [Azure AI Model Inference API](../reference/reference-model-inference-api.md) schema can be found in the [reference for Chat Completions](../reference/reference-model-inference-chat-completions.md) article and an [OpenAPI specification can be obtained from the endpoint itself](../reference/reference-model-inference-api.md?tabs=rest#getting-started).
 
-#### Mistral Chat API
+### Mistral Chat API
 
 Use the method `POST` to send the request to the `/v1/chat/completions` route:
 
@@ -168,7 +176,7 @@ The `messages` object has the following fields:
 | `role`    | `string` | The role of the message's author. One of `system`, `user`, or `assistant`. |
 
 
-#### Example
+#### Request example
 
 __Body__
 
@@ -234,7 +242,7 @@ The `logprobs` object is a dictionary with the following fields:
 | `tokens`         | `array` of `string`     | Selected tokens.   |
 | `top_logprobs`   | `array` of `dictionary` | Array of dictionary. In each dictionary, the key is the token and the value is the probability. |
 
-#### Example
+#### Response example
 
 The following JSON is an example response:
 
@@ -261,15 +269,16 @@ The following JSON is an example response:
     }
 }
 ```
+
 #### More inference examples
 
-| **Sample Type**       | **Sample Notebook**                             |
-|----------------|----------------------------------------|
-| CLI using CURL and Python web requests    | [webrequests.ipynb](https://aka.ms/mistral-large/webrequests-sample)|
-| OpenAI SDK (experimental)    | [openaisdk.ipynb](https://aka.ms/mistral-large/openaisdk)                                    |
-| LangChain      | [langchain.ipynb](https://aka.ms/mistral-large/langchain-sample)                                  |
-| Mistral AI     | [mistralai.ipynb](https://aka.ms/mistral-large/mistralai-sample)                                  |
-| LiteLLM        | [litellm.ipynb](https://aka.ms/mistral-large/litellm-sample) 
+| **Sample Type**                        | **Sample Notebook**                                                  |
+|----------------------------------------|----------------------------------------------------------------------|
+| CLI using CURL and Python web requests | [webrequests.ipynb](https://aka.ms/mistral-large/webrequests-sample) |
+| OpenAI SDK (experimental)              | [openaisdk.ipynb](https://aka.ms/mistral-large/openaisdk)            |
+| LangChain                              | [langchain.ipynb](https://aka.ms/mistral-large/langchain-sample)     |
+| Mistral AI                             | [mistralai.ipynb](https://aka.ms/mistral-large/mistralai-sample)     |
+| LiteLLM                                | [litellm.ipynb](https://aka.ms/mistral-large/litellm-sample)         |
 
 ## Cost and quotas
 
@@ -291,3 +300,4 @@ Models deployed as a serverless API with pay-as-you-go billing are protected by
 
 - [What is Azure AI Studio?](../what-is-ai-studio.md)
 - [Azure AI FAQ article](../faq.yml)
+- [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md)

articles/ai-studio/how-to/deploy-models-mistral.md

@@ -16,7 +16,7 @@ Your app uses DNS when making calls to dependent resources. Resources could be A
 
 If you aren't integrating your app with a virtual network and custom DNS servers aren't configured, your app uses [Azure DNS](../virtual-network/virtual-networks-name-resolution-for-vms-and-role-instances.md#azure-provided-name-resolution). If you integrate your app with a virtual network, your app uses the DNS configuration of the virtual network. The default for virtual network is also to use Azure DNS. Through the virtual network, it's also possible to link to [Azure DNS private zones](../dns/private-dns-overview.md) and use that for private endpoint resolution or private domain name resolution. 
 
-If you configured your virtual network with a list of custom DNS servers, name resolution uses these servers. If your virtual network is using custom DNS servers and you're using private endpoints, you should read [this article](../private-link/private-endpoint-dns.md) carefully. You also need to consider that your custom DNS servers are able to resolve any public DNS records used by your app. Your DNS configuration needs to either forward requests to a public DNS server, include a public DNS server like Azure DNS in the list of custom DNS servers or specify an alternative server at the app level.
+If you configured your virtual network with a list of custom DNS servers, name resolution in App Service will use up to five custom DNS servers. If your virtual network is using custom DNS servers and you're using private endpoints, you should read [this article](../private-link/private-endpoint-dns.md) carefully. You also need to consider that your custom DNS servers are able to resolve any public DNS records used by your app. Your DNS configuration needs to either forward requests to a public DNS server, include a public DNS server like Azure DNS in the list of custom DNS servers or specify an alternative server at the app level.
 
 When your app needs to resolve a domain name using DNS, the app sends a name resolution request to all configured DNS servers. If the first server in the list returns a response within the timeout limit, you get the result returned immediately. If not, the app waits for the other servers to respond within the timeout period and evaluates the DNS server responses in the order you configured the servers. If none of the servers respond within the timeout and you configured retry, you repeat the process.