Merge pull request #1137 from MicrosoftDocs/main

Merge main to live, 4 AM

Author: v-ccolin

articles/ai-services/.openpublishing.redirection.ai-services.json

@@ -769,6 +769,16 @@
       "source_path_from_root": "/articles/ai-services/speech-service/video-translation-studio.md",
       "redirect_url": "/azure/ai-services/speech-service/video-translation-get-started",
       "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/qnamaker/how-to/migrate-to-openai.md",
+      "redirect_url": "/azure/ai-services/qnamaker/overview/overview",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/language-service/question-answering/how-to/azure-openai-integration.md",
+      "redirect_url": "/azure/ai-services/language-service/question-answering/overview",
+      "redirect_document_id": true
     }
   ]
 }

articles/ai-services/language-service/custom-text-analytics-for-health/overview.md

@@ -12,7 +12,10 @@ ms.author: jboback
 ms.custom: language-service-custom-ta4h
 ---
 
-# What is custom Text Analytics for health? 
+# What is custom Text Analytics for health?
+
+> [!NOTE]
+> Custom text analytics for health (preview) will be retired on 10 January 2025, please transition to other custom model training services, such as custom named entity recognition in Azure AI Language, by that date. From now to 10 January 2025, you can continue to use custom text analytics for health (preview) in your existing projects without disruption. You can’t create new projects. On 10 January 2025 – workloads running on custom text analytics for health (preview) will be deleted and associated project data will be lost.
 
 Custom Text Analytics for health is one of the custom features offered by [Azure AI Language](../overview.md). It is a cloud-based API service that applies machine-learning intelligence to enable you to build custom models on top of [Text Analytics for health](../text-analytics-for-health/overview.md) for custom healthcare entity recognition tasks.
 

articles/ai-services/language-service/custom-text-analytics-for-health/quickstart.md

@@ -15,6 +15,9 @@ zone_pivot_groups: usage-custom-language-features
 
 # Quickstart: custom Text Analytics for health
 
+> [!NOTE]
+> Custom text analytics for health (preview) will be retired on 10 January 2025, please transition to other custom model training services, such as custom named entity recognition in Azure AI Language, by that date. From now to 10 January 2025, you can continue to use custom text analytics for health (preview) in your existing projects without disruption. You can’t create new projects. On 10 January 2025 – workloads running on custom text analytics for health (preview) will be deleted and associated project data will be lost.
+
 Use this article to get started with creating a custom Text Analytics for health project where you can train custom models on top of Text Analytics for health for custom entity recognition. A model is artificial intelligence software that's trained to do a certain task. For this system, the models extract healthcare related named entities and are trained by learning from labeled data.
 
 In this article, we use Language Studio to demonstrate key concepts of custom Text Analytics for health. As an example we’ll build a custom Text Analytics for health model to extract the Facility or treatment location from short discharge notes.

articles/ai-services/language-service/question-answering/how-to/azure-openai-integration.md

@@ -11,9 +11,6 @@ ms.custom: language-service-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).
-
 Custom question answering, a feature of Azure AI Language was introduced in May 2021 with several new capabilities including enhanced relevance using a deep learning ranker, precise answers, and end-to-end region support. Each custom question answering project is equivalent to a knowledge base in QnA Maker. You can easily migrate knowledge bases from a QnA Maker resource to custom question answering projects within a [language resource](https://aka.ms/create-language-resource). You can also choose to migrate knowledge bases from multiple QnA Maker resources to a specific language resource.
 
 To successfully migrate knowledge bases, **the account performing the migration needs contributor access to the selected QnA Maker and language resource**. When a knowledge base is migrated, the following details are copied to the new custom question answering project:

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

@@ -13,9 +13,6 @@ ms.custom: language-service-question-answering
 
 # What is custom question answering?
 
-> [!NOTE]
-> [Azure OpenAI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to Custom Question Answering. If you wish to connect an existing Custom Question Answering project to Azure OpenAI On Your Data, please check out our [guide]( how-to/azure-openai-integration.md).
-
 Custom question answering provides cloud-based Natural Language Processing (NLP) that allows you to create a natural conversational layer over your data. It is used to find appropriate answers from customer input or from a project.
 
 Custom question answering is commonly used to build conversational client applications, which include social media applications, chat bots, and speech-enabled desktop applications. This offering includes features like enhanced relevance using a deep learning ranker, precise answers, and end-to-end region support.

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

@@ -15,9 +15,6 @@ zone_pivot_groups: custom-qna-quickstart
 
 # Quickstart: custom question answering
 
-> [!NOTE]
-> [Azure OpenAI On Your Data](../../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to Custom Question Answering. If you wish to connect an existing Custom Question Answering project to Azure OpenAI On Your Data, please check out our [guide](../how-to/azure-openai-integration.md).
-
 > [!NOTE]
 > Are you looking to migrate your workloads from QnA Maker? See our [migration guide](../how-to/migrate-qnamaker-to-question-answering.md) for information on feature comparisons and migration steps.
 

articles/ai-services/language-service/question-answering/quickstart/sdk.md

@@ -15,6 +15,9 @@ zone_pivot_groups: usage-custom-language-features
 
 # Quickstart: Custom sentiment analysis (preview)
 
+> [NOTE]
+> Custom sentiment analysis (preview) will be retired on 10 January 2025, please transition to other custom model training services, such as custom text classification in Azure AI Language, by that date. From now to 10 January 2025, you can continue to use custom sentiment analysis (preview) in your existing projects without disruption. You can’t create new projects. On 10 January 2025 – workloads running on custom sentiment analysis (preview) will be deleted and associated project data will be lost. 
+
 Use this article to get started with creating a Custom sentiment analysis project where you can train custom models for detecting the sentiment of text. A model is artificial intelligence software that's trained to do a certain task. For this system, the models classify text, and are trained by learning from tagged data.
 
 ::: zone pivot="language-studio"

articles/ai-services/language-service/sentiment-opinion-mining/custom/quickstart.md

@@ -14,24 +14,12 @@ recommendations: false
 
 ## Prerequisites
 
-#### [JavaScript](#tab/javascript)
-
-- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
-- [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
-- An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
-
-
-#### [TypeScript](#tab/typescript)
-
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
 - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
-- [TypeScript](https://www.typescriptlang.org/download/)
+- [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 
 
-
----
-
 ## Set up
 
 ### Retrieve key and endpoint
@@ -104,32 +92,35 @@ Your app's _package.json_ file will be updated with the dependencies.
 
 ## Create a speech file
 
+    
 
-
-#### [JavaScript](#tab/javascript)
+#### [Microsoft Entra ID](#tab/javascript-keyless)
 
 1. Create a new file named _Text-to-speech.js_ and open it in your preferred code editor. Copy the following code into the _Text-to-speech.js_ file:
 
     ```javascript
-    require("dotenv/config");
     const { writeFile } = require("fs/promises");
     const { AzureOpenAI } = require("openai");
+    const { DefaultAzureCredential, getBearerTokenProvider } = require("@azure/identity");
     require("openai/shims/node");
 
     // You will need to set these environment variables or edit the following values
     const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || "<endpoint>";
-    const apiKey = process.env["AZURE_OPENAI_API_KEY"] || "<api key>";
-    const speechFilePath =
-      process.env["SPEECH_FILE_PATH"] || "<path to save the speech file>";
+    const speechFilePath = "<path to save the speech file>";
 
     // Required Azure OpenAI deployment name and API version
     const deploymentName = "tts";
     const apiVersion = "2024-08-01-preview";
 
+    // keyless authentication    
+    const credential = new DefaultAzureCredential();
+    const scope = "https://cognitiveservices.azure.com/.default";
+    const azureADTokenProvider = getBearerTokenProvider(credential, scope);
+
     function getClient() {
       return new AzureOpenAI({
         endpoint,
-        apiKey,
+        azureADTokenProvider,
         apiVersion,
         deployment: deploymentName,
       });
@@ -169,30 +160,26 @@ Your app's _package.json_ file will be updated with the dependencies.
     ```console
     node Text-to-speech.js
     ```
-    
 
-#### [TypeScript](#tab/typescript)
+#### [API key](#tab/javascript-key)
 
-1. Create a new file named _Text-to-speech.ts_ and open it in your preferred code editor. Copy the following code into the _Text-to-speech.ts_ file:
+1. Create a new file named _Text-to-speech.js_ and open it in your preferred code editor. Copy the following code into the _Text-to-speech.js_ file:
 
-    ```typescript
-    import "dotenv/config";
-    import { writeFile } from "fs/promises";
-    import { AzureOpenAI } from "openai";
-    import type { SpeechCreateParams } from "openai/resources/audio/speech";
-    import "openai/shims/node";
+    ```javascript
+    const { writeFile } = require("fs/promises");
+    const { AzureOpenAI } = require("openai");
+    require("openai/shims/node");
     
     // You will need to set these environment variables or edit the following values
     const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || "<endpoint>";
     const apiKey = process.env["AZURE_OPENAI_API_KEY"] || "<api key>";
-    const speechFilePath =
-      process.env["SPEECH_FILE_PATH"] || "<path to save the speech file>";
+    const speechFilePath = "<path to save the speech file>";
     
     // Required Azure OpenAI deployment name and API version
     const deploymentName = "tts";
     const apiVersion = "2024-08-01-preview";
     
-    function getClient(): AzureOpenAI {
+    function getClient() {
       return new AzureOpenAI({
         endpoint,
         apiKey,
@@ -202,9 +189,9 @@ Your app's _package.json_ file will be updated with the dependencies.
     }
     
     async function generateAudioStream(
-      client: AzureOpenAI,
-      params: SpeechCreateParams
-    ): Promise<NodeJS.ReadableStream> {
+      client,
+      params
+    ) {
       const response = await client.audio.speech.create(params);
       if (response.ok) return response.body;
       throw new Error(`Failed to generate audio stream: ${response.statusText}`);
@@ -229,19 +216,12 @@ Your app's _package.json_ file will be updated with the dependencies.
     });
     
     ```
-    
-   The import of `"openai/shims/node"` is necessary when running the code in a Node.js environment. It ensures that the output type of the `client.audio.speech.create` method is correctly set to `NodeJS.ReadableStream`.
-
-1. Build the application with the following command:
-
-    ```console
-    tsc
-    ```
 
-1. Run the application with the following command:
+1. Run the script with the following command:
 
     ```console
     node Text-to-speech.js
     ```
+    
 
 ---