Merge pull request #276388 from MicrosoftDocs/main

Publish to live, Sunday 4 AM PST, 5/26

Author: ttorble

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

@@ -240,5 +240,15 @@
             "redirect_url": "/azure/ai-services/content-moderator/term-lists-quickstart-dotnet",
             "redirect_document_id": false
           },
+          {
+            "source_path_from_root": "/articles/ai-services/content-safety/concepts/incident-response.md",
+            "redirect_url": "/azure/ai-services/content-safety/concepts/custom-categories-rapid",
+            "redirect_document_id": true
+          },
+          {
+            "source_path_from_root": "/articles/ai-services/content-safety/how-to/incident-response.md",
+            "redirect_url": "/azure/ai-services/content-safety/how-to/custom-categories-rapid",
+            "redirect_document_id": true
+          }
 	]
 }

articles/ai-services/content-safety/concepts/incident-response.md renamed to articles/ai-services/content-safety/concepts/custom-categories-rapid.md

@@ -1,5 +1,5 @@
 ---
-title: "Incident response in Azure AI Content Safety"
+title: "Custom categories (rapid) in Azure AI Content Safety"
 titleSuffix: Azure AI services
 description: Learn about content incidents and how you can use Azure AI Content Safety to handle them on your platform.
 #services: cognitive-services
@@ -12,15 +12,15 @@ ms.date: 04/11/2024
 ms.author: pafarley
 ---
 
-# Incident response
+# Custom categories (rapid)
 
-In content moderation scenarios, incident response is the process of identifying, analyzing, containing, eradicating, and recovering from cyber incidents that involve inappropriate or harmful content on online platforms. 
+In content moderation scenarios, custom categories (rapid) is the process of identifying, analyzing, containing, eradicating, and recovering from cyber incidents that involve inappropriate or harmful content on online platforms. 
 
 An incident may involve a set of emerging content patterns (text, image, or other modalities) that violate Microsoft community guidelines or the customers' own policies and expectations. These incidents need to be mitigated quickly and accurately to avoid potential live site issues or harm to users and communities. 
 
-## Incident response API features
+## Custom categories (rapid) API features
 
-One way to deal with emerging content incidents is to use [Blocklists](/azure/ai-services/content-safety/how-to/use-blocklist), but that only allows exact text matching and no image matching. The Azure AI Content Safety incident response API offers the following advanced capabilities:
+One way to deal with emerging content incidents is to use [Blocklists](/azure/ai-services/content-safety/how-to/use-blocklist), but that only allows exact text matching and no image matching. The Azure AI Content Safety custom categories (rapid) API offers the following advanced capabilities:
 - semantic text matching using embedding search with a lightweight classifier
 - image matching with a lightweight object-tracking model and embedding search.
 
@@ -34,11 +34,11 @@ Then, you can include your defined incident in a regular text analysis or image
 
 ### Language availability
 
-The text incident response API supports all languages that are supported by Content Safety text moderation. See [Language support](/azure/ai-services/content-safety/language-support).
+The text custom categories (rapid) API supports all languages that are supported by Content Safety text moderation. See [Language support](/azure/ai-services/content-safety/language-support).
 
 ### Input limitations
 
-See the following table for the input limitations of the incident response API:
+See the following table for the input limitations of the custom categories (rapid) API:
 
 | Object     | Limitation      |
 | :------------ | :----------- |
@@ -56,6 +56,6 @@ To use this API, you must create your Azure AI Content Safety resource in one of
 
 ## Next steps
 
-Follow the how-to guide to use the Azure AI Content Safety incident response API.
+Follow the how-to guide to use the Azure AI Content Safety custom categories (rapid) API.
 
-* [Use the incident response API](../how-to/incident-response.md)
+* [Use the custom categories (rapid) API](../how-to/custom-categories-rapid.md)

articles/ai-services/content-safety/faq.yml

@@ -27,7 +27,7 @@ sections:
 
           - Azure Content Moderator uses binary classification for each content type (such as `profanity` or `adult`), while Azure AI Content Safety uses multiple classes (such as `sexual`, `violent`, `hate`, and `self-harm`) with different severity levels.
           - Azure AI Content Safety supports multilingual content moderation in English, German, Japanese, Spanish, French, Italian, Portuguese, and Chinese, while Azure Content Moderator's AI classifiers only support English.
-          - Azure Content Moderator has a built-in term list and a custom term list feature, while Azure AI Content Safety does not have a built-in term list but relies on advanced language and vision models to detect harmful content. It also provides a custom term list feature for incident response and customization.
+          - Azure Content Moderator has a built-in term list and a custom term list feature, while Azure AI Content Safety does not have a built-in term list but relies on advanced language and vision models to detect harmful content. It also provides a custom term list feature for customization.
           - Azure AI Content Safety has an interactive studio for exploring and testing the service capabilities, while Azure Content Moderator does not.
       - question: |
           Why should I migrate from Azure Content Moderator to Azure AI Content Safety?

articles/ai-services/content-safety/how-to/incident-response.md renamed to articles/ai-services/content-safety/how-to/custom-categories-rapid.md

@@ -1,7 +1,7 @@
 ---
-title: "Use the incident response API"
+title: "Use the custom categories (rapid) API"
 titleSuffix: Azure AI services
-description: Learn how to use the incident response API to mitigate harmful content incidents quickly.
+description: Learn how to use the custom categories (rapid) API to mitigate harmful content incidents quickly.
 #services: cognitive-services
 author: PatrickFarley
 manager: nitinme
@@ -13,9 +13,9 @@ ms.author: pafarley
 ---
 
 
-# Use the incident response API
+# Use the custom categories (rapid) API
 
-The incident response API lets you quickly respond to emerging harmful content incidents. You can define an incident with a few examples in a specific topic, and the service will start detecting similar content.
+The custom categories (rapid) API lets you quickly respond to emerging harmful content incidents. You can define an incident with a few examples in a specific topic, and the service will start detecting similar content.
 
 Follow these steps to define an incident with a few examples of text content and then analyze new text content to see if it matches the incident.
 
@@ -37,7 +37,7 @@ Follow these steps to define an incident with a few examples of text content and
 
 <!--tbd env vars-->
 
-## Test the text incident response API
+## Test the text custom categories (rapid) API
 
 Use the sample code in this section to create a text incident, add samples to the incident, deploy the incident, and then detect text incidents.
 
@@ -231,7 +231,7 @@ print(response.text)
 ```
 ---
 
-## Test the image incident response API
+## Test the image custom categories (rapid) API
 
 Use the sample code in this section to create an image incident, add samples to the incident, deploy the incident, and then detect image incidents.
 
@@ -801,5 +801,5 @@ print(response.text)
 
 ## Related content
 
-- [Incident response concepts](../concepts/incident-response.md)
+- [Custom categories (rapid) concepts](../concepts/custom-categories-rapid.md)
 - [What is Azure AI Content Safety?](../overview.md)

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

@@ -50,7 +50,7 @@ There are different types of analysis available from this service. The following
 | Prompt Shields (preview) | Scans text for the risk of a [User input attack](./concepts/jailbreak-detection.md) on a Large Language Model. [Quickstart](./quickstart-jailbreak.md) |
 | Groundedness detection (preview) | Detects whether the text responses of large language models (LLMs) are grounded in the source materials provided by the users. [Quickstart](./quickstart-groundedness.md) |
 | Protected material text detection (preview) | Scans AI-generated text for known text content (for example, song lyrics, articles, recipes, selected web content). [Quickstart](./quickstart-protected-material.md)|
-| Incident response API (preview) | Lets you define [emerging harmful content patterns](./concepts/incident-response.md) and scan text and images for matches. [How-to guide](./how-to/incident-response.md) |
+| Custom categories (rapid) API (preview) | Lets you define [emerging harmful content patterns](./concepts/custom-categories-rapid.md) and scan text and images for matches. [How-to guide](./how-to/custom-categories-rapid.md) |
 
 ## Content Safety Studio
 

articles/ai-services/content-safety/toc.yml

@@ -47,7 +47,7 @@ items:
   - name: Use a blocklist
     href: how-to/use-blocklist.md
   - name: Handle harmful content incidents
-    href: how-to/incident-response.md
+    href: how-to/custom-categories-rapid.md
   - name: Encryption of data at rest
     href: how-to/encrypt-data-at-rest.md
   - name: Migrate from public preview to GA
@@ -64,8 +64,8 @@ items:
     href: concepts/jailbreak-detection.md
   - name: Groundedness detection
     href: concepts/groundedness.md
-  - name: Incident response
-    href: concepts/incident-response.md
+  - name: Custom categories (rapid)
+    href: concepts/custom-categories-rapid.md
 - name: Reference
   items:
   - name: Azure AI Content Safety REST API

articles/ai-services/content-safety/whats-new.md

@@ -19,9 +19,9 @@ Learn what's new in the service. These items might be release notes, videos, blo
 ## May 2024
 
 
-### Incident response API
+### Custom categories (rapid) API
 
-The incident response API lets you quickly define emerging harmful content patterns and scan text and images for matches. See [Incident response](./concepts/incident-response.md) to learn more.
+The custom categories (rapid) API lets you quickly define emerging harmful content patterns and scan text and images for matches. See [Custom categories (rapid)](./concepts/custom-categories-rapid.md) to learn more.
 
 ## March 2024
 

articles/ai-services/document-intelligence/quickstarts/includes/javascript-sdk.md

@@ -1,5 +1,5 @@
 ---
-title: "Quickstart: Document Intelligence (formerly Form Recognizer) JavaScript SDK (beta) | v3.1 | v3.0"
+title: "Quickstart: Document Intelligence (formerly Form Recognizer) JavaScript SDK"
 titleSuffix: Azure AI services
 description: Form and document processing, data extraction, and analysis using Document Intelligence JavaScript client library.
 author: laujan
@@ -73,8 +73,7 @@ In this quickstart, use the following features to analyze and extract data and v
  4. Install the `ai-document-intelligence` client library and `azure/identity` npm packages:
 
     ```console
-    npm i @azure-rest/[email protected] @azure/identity
-
+    npm i @azure-rest/[email protected] @azure/core-auth
     ```
 
     Your app's `package.json` file is updated with the dependencies.
@@ -119,7 +118,7 @@ To interact with the Document Intelligence service, you need to create an instan
 
 :::moniker range="doc-intel-3.1.0 || doc-intel-3.0.0"
 
-To interact with the Document Intelligence service, you need to create an instance of the `DocumentAnalysisClient` class. To do so, you create an `AzureKeyCredential` with your `key` from the Azure portal and a `DocumentAnalysisClient` instance with the `AzureKeyCredential` and your Form 
+To interact with the Document Intelligence service, you need to create an instance of the `DocumentAnalysisClient` class. To do so, you create an `AzureKeyCredential` with your `key` from the Azure portal and a `DocumentAnalysisClient` instance with the `AzureKeyCredential` and your Form
 Recognizer `endpoint`.
 
 :::moniker-end
@@ -146,7 +145,10 @@ Extract text, selection marks, text styles, table structures, and bounding regio
 :::moniker range="doc-intel-4.0.0"
 
 ```javascript
-    const DocumentIntelligenceClient = require("@azure-rest/ai-document-intelligence");
+    const DocumentIntelligence = require("@azure-rest/ai-document-intelligence").default,
+  { getLongRunningPoller, isUnexpected } = require("@azure-rest/ai-document-intelligence");
+
+  const { AzureKeyCredential } = require("@azure/core-auth");
 
     // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
     const key = "<your-key";
@@ -156,7 +158,7 @@ Extract text, selection marks, text styles, table structures, and bounding regio
     const formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"
 
    async function main() {
-    const client = DocumentIntelligenceClient(endpoint, {key:key},);
+    const client = DocumentIntelligence(endpoint, new AzureKeyCredential(key:key));
 
 
     const initialResponse = await client
@@ -168,6 +170,10 @@ Extract text, selection marks, text styles, table structures, and bounding regio
         },
        });
 
+       if (isUnexpected(initialResponse)) {
+       throw initialResponse.body.error;
+     }
+
     const poller = await getLongRunningPoller(client, initialResponse);
     const analyzeResult = (await poller.pollUntilDone()).body.analyzeResult;
 
@@ -309,19 +315,21 @@ In this example, we analyze an invoice using the **prebuilt-invoice** model.
 
 ```javascript
 
-const DocumentIntelligenceClient = require("@azure-rest/ai-document-intelligence");
+const DocumentIntelligence = require("@azure-rest/ai-document-intelligence").default,
+  { getLongRunningPoller, isUnexpected } = require("@azure-rest/ai-document-intelligence");
+
+const { AzureKeyCredential } = require("@azure/core-auth");
 
     // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
     const key = "<your-key>";
     const endpoint = "<your-endpoint>";
 
     // sample document
-    invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"
+    const invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"
 
 async function main() {
 
-    const client = DocumentIntelligenceClient(endpoint, {key: key},
-     );
+    const client = DocumentIntelligence(endpoint, new AzureKeyCredential(key:key));
 
     const initialResponse = await client
     .path("/documentModels/{modelId}:analyze", "prebuilt-invoice")
@@ -333,6 +341,9 @@ async function main() {
       },
     });
 
+    if (isUnexpected(initialResponse)) {
+       throw initialResponse.body.error;
+     }
 
     const poller = await getLongRunningPoller(client, initialResponse);
 

articles/aks/intro-aks-automatic.md

@@ -13,13 +13,13 @@ ms.author: asabbour
 
 **Applies to:** :heavy_check_mark: AKS Automatic (preview)
 
-In Azure Kubernetes Service (AKS) Automatic, Azure manages your cluster configuration, including your nodes, scaling, security, and other preconfigured settings. Automatic clusters are optimized to run most production workloads, and provision compute resources based on your workload needs. The streamlined configuration follows AKS best practices and recommendations for cluster and workload setup, scalability, and security.
+Azure Kubernetes Service (AKS) Automatic offers an experience that makes the most common tasks on Kubernetes fast and frictionless, while preserving the flexibility, extensibility, and consistency of Kubernetes. Azure takes care of your cluster setup, including node management, scaling, security, and preconfigured settings that follow AKS well-architected recommendations. Automatic clusters dynamically allocate compute resources based on your specific workload requirements and are tuned for running production applications.
 
-- **Optimal cluster configuration**: Clusters are preconfigured for optimal production use, suitable for most applications. They offer fully managed node pools that automatically allocate and scale resources based on your workload needs. Pods are bin packed efficiently, to maximize resource utilization.
+- **Production ready by default**: Clusters are preconfigured for optimal production use, suitable for most applications. They offer fully managed node pools that automatically allocate and scale resources based on your workload needs. Pods are bin packed efficiently, to maximize resource utilization.
+  
+- **Built-in best practices and safeguards**: AKS Automatic clusters have a hardened default configuration, with many cluster, application, and networking security settings enabled by default. AKS automatically patches your nodes and cluster components while adhering to any planned maintenance schedules.
 
-- **Streamlined application deployment**: Go from a container image to a deployed application that adheres to best practices patterns within minutes, with access to the comprehensive capabilities of the Kubernetes API and its rich ecosystem.
-
-- **Default security posture**: AKS Automatic clusters have a hardened default configuration, with many cluster, application, and networking security settings enabled by default. AKS automatically patches your nodes and cluster components while adhering to any planned maintenance schedules.
+- **Code to Kubernetes in minutes**: Go from a container image to a deployed application that adheres to best practices patterns within minutes, with access to the comprehensive capabilities of the Kubernetes API and its rich ecosystem.
 
 ## AKS Automatic and Standard feature comparison