Merge pull request #280019 from MicrosoftDocs/main

7/3/2024 AM Publish

Author: Taojunshen

.openpublishing.redirection.json

@@ -4195,6 +4195,12 @@
         },
         {
 
+           "source_path_from_root": "/articles/cosmos-db/how-to-move-regions.md",
+           "redirect_url": "/azure/operational-excellence/relocation-cosmos-db",
+           "redirect_document_id": false
+
+        },
+        {
            "source_path_from_root": "/articles/site-recovery/move-vaults-across-regions.md",
             "redirect_url": "/azure/operational-excellence/relocation-site-recovery",
             "redirect_document_id": false

articles/active-directory-b2c/faq.yml

@@ -274,6 +274,12 @@ sections:
 
           * API connectors
           * Conditional Access
+
+      - question: |
+          I am using rolling refresh tokens for my application and I am getting an invalid_grant error on redeeming newly acquired refresh tokens well within their set validity period. Why does this happen? 
+        answer: |
+          While determining validity for rolling refresh tokens, B2C will consider the initial login time of the user in the application also to calculate the token validity skew. If the user haven't logged out of the application for a very long time, this skew value will exceed the validity period of the token and hence for security reasons the tokens will be considered as invalid. Hence the error. Inform the user to perform a proper logout and login back into the application and this should reset the skew. This scenario is not applicable if refresh token rolling is set as infinite rolling.
+             
           
       - question: |
           I've revoked the refresh token using Microsoft Graph invalidateAllRefreshTokens, or Microsoft Graph PowerShell,  Revoke-MgUserSignInSession. Why is Azure AD B2C still accepting the old refresh token?

articles/ai-services/openai/includes/chatgpt-java.md

@@ -8,10 +8,10 @@ ms.service: azure-ai-openai
 ms.topic: include
 author: mrbullwinkle
 ms.author: mbullwin
-ms.date: 07/26/2023
+ms.date: 07/03/2024
 ---
 
-[Source code](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/openai/azure-ai-openai) | [Artifact (Maven)](https://central.sonatype.com/artifact/com.azure/azure-ai-openai/1.0.0-beta.3) | [Samples](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/openai/azure-ai-openai/src/samples) | [Retrieval Augmented Generation (RAG) enterprise chat template](/azure/developer/java/quickstarts/get-started-app-chat-template) | [IntelliJ IDEA](/azure/developer/java/toolkit-for-intellij/chatgpt-intellij) 
+[Source code](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/openai/azure-ai-openai) | [Artifact (Maven)](https://central.sonatype.com/artifact/com.azure/azure-ai-openai/1.0.0-beta.10) | [Samples](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/openai/azure-ai-openai/src/samples) | [Retrieval Augmented Generation (RAG) enterprise chat template](/azure/developer/java/quickstarts/get-started-app-chat-template) | [IntelliJ IDEA](/azure/developer/java/toolkit-for-intellij/chatgpt-intellij) 
 
 ## Prerequisites
 
@@ -55,7 +55,7 @@ When prompted to choose a **DSL**, select **Kotlin**.
 
 ## Install the Java SDK
 
-This quickstart uses the Gradle dependency manager. You can find the client library and information for other dependency managers on the [Maven Central Repository](https://search.maven.org/artifact/com.microsoft.azure.cognitiveservices/azure-cognitiveservices-computervision).
+This quickstart uses the Gradle dependency manager. You can find the client library and information for other dependency managers on the [Maven Central Repository](https://central.sonatype.com/search?q=azure-ai-openai).
 
 Locate *build.gradle.kts* and open it with your preferred IDE or text editor. Then copy in the following build configuration. This configuration defines the project as a Java application whose entry point is the class **OpenAIQuickstart**. It imports the Azure AI Vision library.
 
@@ -71,7 +71,7 @@ repositories {
     mavenCentral()
 }
 dependencies {
-    implementation(group = "com.azure", name = "azure-ai-openai", version = "1.0.0-beta.3")
+    implementation(group = "com.azure", name = "azure-ai-openai", version = "1.0.0-beta.10")
     implementation("org.slf4j:slf4j-simple:1.7.9")
 }
 ```
@@ -91,43 +91,50 @@ dependencies {
 
 1. Open *OpenAIQuickstart.java* in your preferred editor or IDE and paste in the following code.
 
-    ```java    
+    ```java
+    package com.azure.ai.openai.usage;
+
     import com.azure.ai.openai.OpenAIClient;
     import com.azure.ai.openai.OpenAIClientBuilder;
     import com.azure.ai.openai.models.ChatChoice;
     import com.azure.ai.openai.models.ChatCompletions;
     import com.azure.ai.openai.models.ChatCompletionsOptions;
-    import com.azure.ai.openai.models.ChatMessage;
-    import com.azure.ai.openai.models.ChatRole;
+    import com.azure.ai.openai.models.ChatRequestAssistantMessage;
+    import com.azure.ai.openai.models.ChatRequestMessage;
+    import com.azure.ai.openai.models.ChatRequestSystemMessage;
+    import com.azure.ai.openai.models.ChatRequestUserMessage;
+    import com.azure.ai.openai.models.ChatResponseMessage;
     import com.azure.ai.openai.models.CompletionsUsage;
     import com.azure.core.credential.AzureKeyCredential;
+    import com.azure.core.util.Configuration;
     
     import java.util.ArrayList;
     import java.util.List;
     
-    public class GetChatCompletionsSample {
-    
+   
+    public class OpenAIQuickstart {
+
         public static void main(String[] args) {
-            String azureOpenaiKey = System.getenv("AZURE_OPENAI_API_KEY");;
-            String endpoint = System.getenv("AZURE_OPENAI_ENDPOINT");;
-            String deploymentOrModelId = "gpt-35-turbo"; //Change to match your deployment name
+            String azureOpenaiKey = Configuration.getGlobalConfiguration().get("AZURE_OPENAI_API_KEY");
+            String endpoint = Configuration.getGlobalConfiguration().get("AZURE_OPENAI_ENDPOINT");
+            String deploymentOrModelId = "{azure-open-ai-deployment-model-id}";
     
-          OpenAIClient client = new OpenAIClientBuilder()
+            OpenAIClient client = new OpenAIClientBuilder()
                 .endpoint(endpoint)
                 .credential(new AzureKeyCredential(azureOpenaiKey))
                 .buildClient();
     
-            List<ChatMessage> chatMessages = new ArrayList<>();
-            chatMessages.add(new ChatMessage(ChatRole.SYSTEM, "You are a helpful assistant"));
-            chatMessages.add(new ChatMessage(ChatRole.USER, "Does Azure OpenAI support customer managed keys?"));
-            chatMessages.add(new ChatMessage(ChatRole.ASSISTANT, "Yes, customer managed keys are supported by Azure OpenAI?"));
-            chatMessages.add(new ChatMessage(ChatRole.USER, "Do other Azure AI services support this too?"));
+            List<ChatRequestMessage> chatMessages = new ArrayList<>();
+            chatMessages.add(new ChatRequestSystemMessage("You are a helpful assistant."));
+            chatMessages.add(new ChatRequestUserMessage("Does Azure OpenAI support customer managed keys?"));
+            chatMessages.add(new ChatRequestAssistantMessage("Yes, customer managed keys are supported by Azure OpenAI?"));
+            chatMessages.add(new ChatRequestUserMessage("Do other Azure AI services support this too?"));    
     
             ChatCompletions chatCompletions = client.getChatCompletions(deploymentOrModelId, new ChatCompletionsOptions(chatMessages));
     
             System.out.printf("Model ID=%s is created at %s.%n", chatCompletions.getId(), chatCompletions.getCreatedAt());
             for (ChatChoice choice : chatCompletions.getChoices()) {
-                ChatMessage message = choice.getMessage();
+                ChatResponseMessage message = choice.getMessage();
                 System.out.printf("Index: %d, Chat Role: %s.%n", choice.getIndex(), message.getRole());
                 System.out.println("Message:");
                 System.out.println(message.getContent());
@@ -139,9 +146,10 @@ dependencies {
                     + "number of completion token is %d, and number of total tokens in request and response is %d.%n",
                 usage.getPromptTokens(), usage.getCompletionTokens(), usage.getTotalTokens());
         }
-    }  
+    }
     ```
 
+
     > [!IMPORTANT]
     > For production, use a secure way of storing and accessing your credentials like [Azure Key Vault](../../../key-vault/general/overview.md). For more information about credential security, see the Azure AI services [security](../../security-features.md) article.
 

articles/ai-studio/reference/reference-model-inference-api.md

@@ -136,7 +136,7 @@ Content-Type: application/json
 
 The Azure AI Model Inference API specifies a set of modalities and parameters that models can subscribe to. However, some models may have further capabilities that the ones the API indicates. On those cases, the API allows the developer to pass them as extra parameters in the payload.
 
-By setting a header `extra-parameters: allow`, the API will attempt to pass any unknown parameter directly to the underlying model. If the model can handle that parameter, the request completes.
+By setting a header `extra-parameters: pass-through`, the API will attempt to pass any unknown parameter directly to the underlying model. If the model can handle that parameter, the request completes.
 
 The following example shows a request passing the parameter `safe_prompt` supported by Mistral-Large, which isn't specified in the Azure AI Model Inference API:
 
@@ -163,6 +163,7 @@ var messages = [
 ];
 
 var response = await client.path("/chat/completions").post({
+    "extra-parameters": "pass-through",
     body: {
         messages: messages,
         safe_mode: true
@@ -178,7 +179,7 @@ __Request__
 POST /chat/completions?api-version=2024-04-01-preview
 Authorization: Bearer <bearer-token>
 Content-Type: application/json
-extra-parameters: allow
+extra-parameters: pass-through
 ```
 
 ```JSON
@@ -203,7 +204,7 @@ extra-parameters: allow
 ---
 
 > [!TIP]
-> Alternatively, you can set `extra-parameters: drop` to drop any unknown parameter in the request. Use this capability in case you happen to be sending requests with extra parameters that you know the model won't support but you want the request to completes anyway. A typical example of this is indicating `seed` parameter.
+> The default value for `extra-parameters` is `error` which returns an error if an extra parameter is indicated in the payload. Alternatively, you can set `extra-parameters: ignore` to drop any unknown parameter in the request. Use this capability in case you happen to be sending requests with extra parameters that you know the model won't support but you want the request to completes anyway. A typical example of this is indicating `seed` parameter.
 
 ### Models with disparate set of capabilities
 
@@ -426,3 +427,27 @@ __Response__
 ## Getting started
 
 The Azure AI Model Inference API is currently supported in certain models deployed as [Serverless API endpoints](../how-to/deploy-models-serverless.md) and Managed Online Endpoints. Deploy any of the [supported models](#availability) and use the exact same code to consume their predictions.
+
+# [Python](#tab/python)
+
+The client library `azure-ai-inference` does inference, including chat completions, for AI models deployed by Azure AI Studio and Azure Machine Learning Studio. It supports Serverless API endpoints and Managed Compute endpoints (formerly known as Managed Online Endpoints).
+
+Explore our [samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/ai/azure-ai-inference/samples) and read the [API reference documentation](https://aka.ms/azsdk/azure-ai-inference/python/reference) to get yourself started.
+
+# [JavaScript](#tab/javascript)
+
+The client library `@azure-rest/ai-inference` does inference, including chat completions, for AI models deployed by Azure AI Studio and Azure Machine Learning Studio. It supports Serverless API endpoints and Managed Compute endpoints (formerly known as Managed Online Endpoints).
+
+Explore our [samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples) and read the [API reference documentation](https://aka.ms/AAp1kxa) to get yourself started.
+
+# [REST](#tab/rest)
+
+Explore the reference section of the Azure AI model inference API to see parameters and options to consume models, including chat completions models, deployed by Azure AI Studio and Azure Machine Learning Studio. It supports Serverless API endpoints and Managed Compute endpoints (formerly known as Managed Online Endpoints).
+
+* [Get info](reference-model-inference-info.md): Returns the information about the model deployed under the endpoint.
+* [Text embeddings](reference-model-inference-embeddings.md): Creates an embedding vector representing the input text.
+* [Text completions](reference-model-inference-completions.md): Creates a completion for the provided prompt and parameters.
+* [Chat completions](reference-model-inference-chat-completions.md): Creates a model response for the given chat conversation.
+* [Image embeddings](reference-model-inference-images-embeddings.md): Creates an embedding vector representing the input text and image.
+
+---

articles/ai-studio/reference/reference-model-inference-chat-completions.md

@@ -35,7 +35,7 @@ POST /chat/completions?api-version=2024-04-01-preview
 
 | Name | Required | Type | Description |
 | --- | --- | --- | --- |
-| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `allow` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `drop` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
+| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `ignore` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
 | azureml-model-deployment |     | string | Name of the deployment you want to route the request to. Supported for endpoints that support multiple deployments. |
 
 ## Request Body

articles/ai-studio/reference/reference-model-inference-completions.md

@@ -28,6 +28,14 @@ POST /completions?api-version=2024-04-01-preview
 | --- | --- | --- | --- | --- |
 | api-version | query | True | string | The version of the API in the format "YYYY-MM-DD" or "YYYY-MM-DD-preview". |
 
+## Request Header
+
+
+| Name | Required | Type | Description |
+| --- | --- | --- | --- |
+| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `ignore` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
+| azureml-model-deployment |     | string | Name of the deployment you want to route the request to. Supported for endpoints that support multiple deployments. |
+
 
 ## Request Body
 
@@ -285,4 +293,4 @@ The object type, which is always "list".
 | detail | [Detail](#detail) |     |
 | error | string | The error description. |
 | message | string | The error message. |
-| status | integer | The HTTP status code. |
+| status | integer | The HTTP status code. |

articles/ai-studio/reference/reference-model-inference-embeddings.md

@@ -35,7 +35,7 @@ POST /embeddings?api-version=2024-04-01-preview
 
 | Name | Required | Type | Description |
 | --- | --- | --- | --- |
-| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `allow` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `drop` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
+| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `ignore` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
 | azureml-model-deployment |     | string | Name of the deployment you want to route the request to. Supported for endpoints that support multiple deployments. |
 
 ## Request Body

articles/ai-studio/reference/reference-model-inference-images-embeddings.md

@@ -35,7 +35,7 @@ POST /images/embeddings?api-version=2024-04-01-preview
 
 | Name | Required | Type | Description |
 | --- | --- | --- | --- |
-| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `allow` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `drop` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
+| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `ignore` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
 | azureml-model-deployment |     | string | Name of the deployment you want to route the request to. Supported for endpoints that support multiple deployments. |
 
 ## Request Body