Merge pull request #285438 from MicrosoftDocs/repo_sync_working_branch

thomps23 · web-flow · commit 3910c53ce2df · 2024-08-26T08:27:52.000-07:00
Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)
diff --git a/articles/ai-services/openai/how-to/use-your-data-securely.md b/articles/ai-services/openai/how-to/use-your-data-securely.md
@@ -144,7 +144,7 @@ The [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains)
 
 ### Enable managed identity
 
-To allow your Azure AI Search and Storage Account to recognize your Azure OpenAI service via Microsoft Entra ID authentication, you need to assign a managed identity for your Azure OpenAI service. The easiest way is to toggle on system assigned managed identity on Azure portal.
+To allow your Azure AI Search and Storage Account to recognize your Azure OpenAI Service via Microsoft Entra ID authentication, you need to assign a managed identity for your Azure OpenAI Service. The easiest way is to toggle on system assigned managed identity on Azure portal.
 :::image type="content" source="../media/use-your-data/openai-managed-identity.png" alt-text="A screenshot showing the system assigned managed identity option in the Azure portal." lightbox="../media/use-your-data/openai-managed-identity.png":::
 
 To set the managed identities via the management API, see [the management API reference documentation](/rest/api/aiservices/accountmanagement/accounts/update#identity).
@@ -175,7 +175,7 @@ This step can be skipped only if you have a [shared private link](#create-shared
 
 You can disable public network access of your Azure OpenAI resource in the Azure portal. 
 
-To allow access to your Azure OpenAI service from your client machines, like using Azure OpenAI Studio, you need to create [private endpoint connections](/azure/ai-services/cognitive-services-virtual-networks?tabs=portal#use-private-endpoints) that connect to your Azure OpenAI resource.
+To allow access to your Azure OpenAI Service from your client machines, like using Azure OpenAI Studio, you need to create [private endpoint connections](/azure/ai-services/cognitive-services-virtual-networks?tabs=portal#use-private-endpoints) that connect to your Azure OpenAI resource.
 
 
 ## Configure Azure AI Search
@@ -290,7 +290,7 @@ To enable the developers to use these resources to build applications, the admin
 
 ## Configure gateway and client
 
-To access the Azure OpenAI service from your on-premises client machines, one of the approaches is to configure Azure VPN Gateway and Azure VPN Client.
+To access the Azure OpenAI Service from your on-premises client machines, one of the approaches is to configure Azure VPN Gateway and Azure VPN Client.
 
 Follow [this guideline](/azure/vpn-gateway/tutorial-create-gateway-portal#VNetGateway) to create virtual network gateway for your virtual network.
 
diff --git a/articles/ai-services/openai/includes/dall-e-go.md b/articles/ai-services/openai/includes/dall-e-go.md
@@ -61,6 +61,7 @@ import (
 	"os"
 
 	"github.com/Azure/azure-sdk-for-go/sdk/ai/azopenai"
+	"github.com/Azure/azure-sdk-for-go/sdk/azcore"
 	"github.com/Azure/azure-sdk-for-go/sdk/azcore/to"
 )
 
@@ -75,19 +76,15 @@ func main() {
 		return
 	}
 
-	keyCredential, err := azopenai.NewKeyCredential(azureOpenAIKey)
-
-	if err != nil {
-		// handle error
-	}
+	keyCredential := azcore.NewKeyCredential(azureOpenAIKey)
 
 	client, err := azopenai.NewClientWithKeyCredential(azureOpenAIEndpoint, keyCredential, nil)
 
 	if err != nil {
 		// handle error
 	}
 
-	resp, err := client.CreateImage(context.TODO(), azopenai.ImageGenerationOptions{
+	resp, err := client.GetImageGenerations(context.TODO(), azopenai.ImageGenerationOptions{
 		Prompt:         to.Ptr("a painting of a cat in the style of Dali"),
 		ResponseFormat: to.Ptr(azopenai.ImageGenerationResponseFormatURL),
 	}, nil)
diff --git a/articles/ai-services/openai/includes/go.md b/articles/ai-services/openai/includes/go.md
@@ -38,6 +38,7 @@ import (
 	"os"
 
 	"github.com/Azure/azure-sdk-for-go/sdk/ai/azopenai"
+	"github.com/Azure/azure-sdk-for-go/sdk/azcore"
 	"github.com/Azure/azure-sdk-for-go/sdk/azcore/to"
 )
 
@@ -52,11 +53,7 @@ func main() {
 		return
 	}
 
-	keyCredential, err := azopenai.NewKeyCredential(azureOpenAIKey)
-
-	if err != nil {
-		// TODO: handle error
-	}
+	keyCredential := azcore.NewKeyCredential(azureOpenAIKey)
 
 	client, err := azopenai.NewClientWithKeyCredential(azureOpenAIEndpoint, keyCredential, nil)
 
@@ -65,10 +62,10 @@ func main() {
 	}
 
 	resp, err := client.GetCompletions(context.TODO(), azopenai.CompletionsOptions{
-		Prompt:       []string{"What is Azure OpenAI, in 20 words or less"},
-		MaxTokens:    to.Ptr(int32(2048)),
-		Temperature:  to.Ptr(float32(0.0)),
-		Deployment: modelDeploymentID,
+		Prompt:         []string{"What is Azure OpenAI, in 20 words or less"},
+		MaxTokens:      to.Ptr(int32(2048)),
+		Temperature:    to.Ptr(float32(0.0)),
+		DeploymentName: &modelDeploymentID,
 	}, nil)
 
 	if err != nil {
diff --git a/articles/ai-services/openai/includes/use-your-data-go.md b/articles/ai-services/openai/includes/use-your-data-go.md
@@ -80,15 +80,15 @@ ms.date: 03/07/2024
    		},
    		MaxTokens: to.Ptr[int32](512),
    		AzureExtensionsOptions: []azopenai.AzureChatExtensionConfigurationClassification{
-   			&azopenai.AzureCognitiveSearchChatExtensionConfiguration{
+   			&azopenai.AzureSearchChatExtensionConfiguration{
    				// This allows Azure OpenAI to use an Azure AI Search index.
    				//
    				// > Because the model has access to, and can reference specific sources to support its responses, answers are not only based on its pretrained knowledge
    				// > but also on the latest information available in the designated data source. This grounding data also helps the model avoid generating responses
    				// > based on outdated or incorrect information.
    				//
    				// Quote from here: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/use-your-data
-   				Parameters: &azopenai.AzureCognitiveSearchChatExtensionParameters{
+   				Parameters: &azopenai.AzureSearchChatExtensionParameters{
    					Endpoint:  &searchEndpoint,
    					IndexName: &searchIndex,
    					Authentication: &azopenai.OnYourDataAPIKeyAuthenticationOptions{
@@ -105,12 +105,9 @@ ms.date: 03/07/2024
    		log.Fatalf("ERROR: %s", err)
    	}
    
-   	// Contains contextual information from your Azure chat completion extensions, configured above in `AzureExtensionsOptions`
-   	msgContext := resp.Choices[0].Message.Context
-   
    	fmt.Fprintf(os.Stderr, "Extensions Context Role: %s\nExtensions Context (length): %d\n",
-   		*msgContext.Messages[0].Role,
-   		len(*msgContext.Messages[0].Content))
+   		*resp.Choices[0].Message.Role,
+   		len(*resp.Choices[0].Message.Content))
    
    	fmt.Fprintf(os.Stderr, "ChatRole: %s\nChat content: %s\n",
    		*resp.Choices[0].Message.Role,
diff --git a/articles/azure-monitor/agents/azure-monitor-agent-migration.md b/articles/azure-monitor/agents/azure-monitor-agent-migration.md
@@ -25,6 +25,10 @@ Migration is a complex task. Start planning your migration to Azure Monitor Agen
 > - **Installation:** The ability to install the legacy agents will be removed from the Azure Portal and installation policies for legacy agents will be removed. You can still install the MMA agents extension as well as perform offline installations.
 > - **Customer Support:** You will not be able to get support for legacy agent issues.
 > - **OS Support:** Support for new Linux or Windows distros, including service packs, won't be added after the deprecation of the legacy agents.
+> - Log Analytics Agent will continue to function but not be able to connect Log Analytics workspaces.
+> - Log Analytics Agent can coexist with Azure Monitor Agent. Expect to see duplicate data if both agents are collecting the same data.
+
+ 
 
 ## Benefits
 Using Azure Monitor agent, you get immediate benefits as shown below:
diff --git a/articles/azure-monitor/essentials/prometheus-remote-write-virtual-machines.md b/articles/azure-monitor/essentials/prometheus-remote-write-virtual-machines.md
@@ -362,14 +362,14 @@ remote_write:
   - url: "<metrics ingestion endpoint for your Azure Monitor workspace>"
 # AzureAD configuration.
 # The Azure Cloud. Options are 'AzurePublic', 'AzureChina', or 'AzureGovernment'.
-  azuread:
-    cloud: 'AzurePublic'
-    managed_identity:
-      client_id: "<client-id of the managed identity>"
-    oauth:
-      client_id: "<client-id from the Entra app>"
-      client_secret: "<client secret from the Entra app>"
-      tenant_id: "<Azure subscription tenant Id>"
+    azuread:
+      cloud: 'AzurePublic'
+      managed_identity:
+        client_id: "<client-id of the managed identity>"
+      oauth:
+        client_id: "<client-id from the Entra app>"
+        client_secret: "<client secret from the Entra app>"
+        tenant_id: "<Azure subscription tenant Id>"
 ```
 
 ---
diff --git a/articles/machine-learning/concept-compute-target.md b/articles/machine-learning/concept-compute-target.md
@@ -145,7 +145,7 @@ While Azure Machine Learning supports these VM series, they might not be availab
 :::moniker-end
 :::moniker range="azureml-api-2"
 > [!NOTE]
-> Azure Machine Learning doesn't support all VM sizes that Azure Compute supports. To list the available VM sizes, use one of the following methods:
+> Azure Machine Learning doesn't support all VM sizes that Azure Compute supports. To list the available VM sizes supported by specific compute VM types, use one of the following methods:
 > * [REST API](/rest/api/azureml/virtual-machine-sizes/list)
 > * The [Azure CLI extension 2.0 for machine learning](how-to-configure-cli.md) command, [az ml compute list-sizes](/cli/azure/ml/compute#az-ml-compute-list-sizes).
 :::moniker-end
