Merge pull request #278802 from MicrosoftDocs/main

6/20/2024 AM Publish

Author: Taojunshen

articles/ai-studio/concepts/retrieval-augmented-generation.md

@@ -22,18 +22,18 @@ This article talks about the importance and need for Retrieval Augmented Generat
 
 ## What is RAG?
 
-Some basics first. Large language models (LLMs) like ChatGPT are trained on public internet data which was available at the point in time when they were trained. They can answer questions related to the data they were trained on. This public data might not be sufficient to meet all your needs. You might want questions answered based on your private data. Or, the public data might simply have gotten out of date. The solution to this problem is Retrieval Augmented Generation (RAG), a pattern used in AI which uses an LLM to generate answers with your own data.
+Some basics first. Large language models (LLMs) like ChatGPT are trained on public internet data that was available at the point in time when they were trained. They can answer questions related to the data they were trained on. This public data might not be sufficient to meet all your needs. You might want questions answered based on your private data. Or, the public data might simply have gotten out of date. The solution to this problem is Retrieval Augmented Generation (RAG), a pattern used in AI that uses an LLM to generate answers with your own data.
 
 ## How does RAG work?
 
-RAG is a pattern which uses your data with an LLM to generate answers specific to your data. When a user asks a question, the data store is searched based on user input. The user question is then combined with the matching results and sent to the LLM using a prompt (explicit instructions to an AI or machine learning model) to generate the desired answer. This can be illustrated as follows.
+RAG is a pattern that uses your data with an LLM to generate answers specific to your data. When a user asks a question, the data store is searched based on user input. The user question is then combined with the matching results and sent to the LLM using a prompt (explicit instructions to an AI or machine learning model) to generate the desired answer. This can be illustrated as follows.
 
 :::image type="content" source="../media/index-retrieve/rag-pattern.png" alt-text="Screenshot of the RAG pattern." lightbox="../media/index-retrieve/rag-pattern.png":::
 
 
 ## What is an index and why do I need it?
 
-RAG uses your data to generate answers to the user question. For RAG to work well, we need to find a way to search and send your data in an easy and cost efficient manner to the LLMs. This is achieved by using an index. An index is a data store which allows you to search data efficiently. This is very useful in RAG. An index can be optimized for LLMs by creating vectors (text data converted to number sequences using an embedding model). A good index usually has efficient search capabilities like keyword searches, semantic searches, vector searches or a combination of these. This optimized RAG pattern can be illustrated as follows.
+RAG uses your data to generate answers to the user question. For RAG to work well, we need to find a way to search and send your data in an easy and cost efficient manner to the LLMs. This is achieved by using an index. An index is a data store that allows you to search data efficiently. This is very useful in RAG. An index can be optimized for LLMs by creating vectors (text data converted to number sequences using an embedding model). A good index usually has efficient search capabilities like keyword searches, semantic searches, vector searches or a combination of these. This optimized RAG pattern can be illustrated as follows.
 
 :::image type="content" source="../media/index-retrieve/rag-pattern-with-index.png" alt-text="Screenshot of the RAG pattern with index." lightbox="../media/index-retrieve/rag-pattern-with-index.png":::
 

articles/ai-studio/media/index-retrieve/rag-pattern-with-index.png

@@ -20,10 +20,10 @@ In this article, we use Azure Pipelines to deploy a Windows container applicatio
 - A working Windows app with Dockerfile hosted on [Azure Repos](https://docs.github.com/get-started/quickstart/create-a-repo). 
 
 ## Add a Service Connection
-Before you create your pipeline, you should first create your Service Connection since you'll be asked to choose and verify your connection when creating your template. A Service Connection allows you to connect to your registry of choice (ACR or Docker Hub) when using the task templates. When adding a new service connection, choose the Docker Registry option. The following form asks you to choose Docker Hub or Azure Container Registry along with pertaining information. To follow along with this tutorial, use Azure Container Registry. You can create a new Service Connection following the directions [here](https://learn.microsoft.com/azure/devops/pipelines/library/service-endpoints?view=azure-devops&tabs=yaml#create-new).
+Before you create your pipeline, you should first create your Service Connection since you'll be asked to choose and verify your connection when creating your template. A Service Connection allows you to connect to your registry of choice (ACR or Docker Hub) when using the task templates. When adding a new service connection, choose the Docker Registry option. The following form asks you to choose Docker Hub or Azure Container Registry along with pertaining information. To follow along with this tutorial, use Azure Container Registry. You can create a new Service Connection following the directions [here](/azure/devops/pipelines/library/service-endpoints).
 
 ## Secure your secrets
-Since we're using sensitive information that you don't want others to access, we use variables to protect our information. Create a variable by following the directions [here](https://learn.microsoft.com/azure/devops/pipelines/process/variables?view=azure-devops&tabs=yaml%2Cbatch#secret-variables).
+Since we're using sensitive information that you don't want others to access, we use variables to protect our information. Create a variable by following the directions [here](/azure/devops/pipelines/process/variables).
 
 To add a Variable, you click the **Variables** button next to the Save button in the top-right of the editing view for your pipeline. Select the **New Variable** button and enter your information. Add the variables below with your own secrets appropriate from each resource.
 

articles/ai-studio/media/index-retrieve/rag-pattern.png

@@ -4,7 +4,7 @@
   items:
   - name: About App Service Environment
     href: overview.md
-  - name: App Service offering comparison
+  - name: Compare with App Service
     href: ase-multi-tenant-comparison.md
   - name: Version comparison
     href: version-comparison.md
@@ -142,4 +142,4 @@
       - name: Open-source technologies FAQ
         href: ../faq-open-source-technologies.yml?toc=%2fazure%2fapp-service%2fenvironment%2ftoc.json
       - name: Configuration and management FAQ
-        href: ../faq-configuration-and-management.yml?toc=%2fazure%2fapp-service%2fenvironment%2ftoc.json
+        href: ../faq-configuration-and-management.yml?toc=%2fazure%2fapp-service%2fenvironment%2ftoc.json

articles/app-service/deploy-container-azure-pipelines.md

@@ -73,7 +73,7 @@ The following image shows a standard configuration for a new Automation account.
 
 ### Networking
 
-On the **Networking** tab, you can configure connectivity to Automation Account - either publicly via public IP addresses or privately using a [Azure Automation Private Link](./how-to/private-link-security.md). Azure Automation Private Link connects one or more private endpoints (and therefore the virtual networks they are contained in) to your Automation Account resource. 
+On the **Networking** tab, you can configure connectivity to Automation Account - either publicly via public IP addresses or privately using an [Azure Automation Private Link](./how-to/private-link-security.md). Azure Automation Private Link connects one or more private endpoints (and therefore the virtual networks they are contained in) to your Automation Account resource. 
 
 The following image shows a standard configuration for a new Automation account.
 

articles/app-service/environment/toc.yml

@@ -2,7 +2,7 @@
 title: What's new with Azure Connected Machine agent
 description: This article has release notes for Azure Connected Machine agent. For many of the summarized issues, there are links to more details.
 ms.topic: overview
-ms.date: 04/09/2024
+ms.date: 06/19/2024
 ms.custom: references_regions
 ---
 
@@ -20,6 +20,16 @@ This page is updated monthly, so revisit it regularly. If you're looking for ite
 > Only Connected Machine agent versions within the last 1 year are officially supported by the product group. Customers should update to an agent version within this window.
 > 
 
+## Version 1.43 - June 2024 
+
+Download for [Windows](https://aka.ms/AzureConnectedMachineAgent) or [Linux](manage-agent.md#installing-a-specific-version-of-the-agent)
+
+### Fixed
+
+- Fix for OpenSSL Vulnerability for Linux (Upgrading OpenSSL version from 3.0.13 to 3.014)
+- Added Server Name Indicator (SNI) to our service calls, fixing Proxy and Firewall scenarios
+- Skipped lockdown policy on the downloads directory under Guest Configuration
+
 ## Version 1.42 - May 2024 (Second Release)
 
 Download for [Windows](https://download.microsoft.com/download/9/6/0/9600825a-e532-4e50-a2d5-7f07e400afc1/AzureConnectedMachineAgent.msi) or [Linux](manage-agent.md#installing-a-specific-version-of-the-agent)

articles/automation/automation-create-standalone-account.md

@@ -561,7 +561,7 @@ If you use the webhook action, your target webhook endpoint must be able to proc
 
 > [!NOTE]
 >
->Pre-requisites: https://learn.microsoft.com/powershell/microsoftgraph/installation?view=graph-powershell-1.0
+>Pre-requisites: [Install the Microsoft Graph PowerShell SDK](/powershell/microsoftgraph/installation?view=graph-powershell-1.0&preserve-view=true)
 
 #### How to run?
 

articles/azure-arc/servers/agent-release-notes.md

@@ -90,12 +90,10 @@ Use the [New-AzDiagnosticSetting](/powershell/module/az.monitor/new-azdiagnostic
 The following example PowerShell cmdlet creates a diagnostic setting for all logs, or for audit logs, and metrics for a key vault by using Log Analytics Workspace.
 
 ```powershell
-$KV= Get-AzKeyVault -ResourceGroupName <resource group name> -VaultName <key vault name>
-$Law= Get-AzOperationalInsightsWorkspace -ResourceGroupName <resource group name> -Name <workspace name>  #LAW name is case sensitive
+$KV = Get-AzKeyVault -ResourceGroupName <resource group name> -VaultName <key vault name>
+$Law = Get-AzOperationalInsightsWorkspace -ResourceGroupName <resource group name> -Name <workspace name>  # LAW name is case sensitive
 
-$metric = @()
-$log = @()
-$metric += New-AzDiagnosticSettingMetricSettingsObject -Enabled $true -Category AllMetrics
+$metric = New-AzDiagnosticSettingMetricSettingsObject -Enabled $true -Category AllMetrics
 # For all available logs, use:
 $log = New-AzDiagnosticSettingLogSettingsObject -Enabled $true -CategoryGroup allLogs  
 # or, for audit logs, use:

articles/azure-monitor/alerts/action-groups.md

@@ -35,6 +35,7 @@ This table describes the components of a data collection endpoint, related regio
 | Component | Description | Regionality considerations |Data collection rule configuration |
 |:---|:---|:---|:---|
 | Logs ingestion endpoint | The endpoint that ingests logs into the data ingestion pipeline. Azure Monitor transforms the data and sends it to the defined destination Log Analytics workspace and table based on a DCR ID sent with the collected data.<br>Example: `<unique-dce-identifier>.<regionname>-1.ingest`. |Same region as the destination Log Analytics workspace. |Set on the **Basics** tab when you create a data collection rule using the portal. |
+| Metrics ingestion endpoint | The endpoint that ingests metrics into the data ingestion pipeline. Azure Monitor transforms the data and sends it to the defined destination Azure Monitor workspace and table based on a DCR ID sent with the collected data.<br>Example: `<unique-dce-identifier>.<regionname>-1.metrics.ingest`. |Same region as the destination Azure Monitor workspace. |Set on the **Basics** tab when you create a data collection rule using the portal. |
 | Configuration access endpoint | The endpoint from which Azure Monitor Agent retrieves data collection rules (DCRs).<br>Example: `<unique-dce-identifier>.<regionname>-1.handler.control`. | Same region as the monitored resources. | Set on the **Resources** tab when you create a data collection rule using the portal.| 
 
 
@@ -109,6 +110,9 @@ The sample data collection endpoint (DCE) below is for virtual machines with Azu
     "logsIngestion": {
       "endpoint": "https://mycollectionendpoint-abcd.eastus-1.ingest.monitor.azure.com"
     },
+    "metricsIngestion": {
+      "endpoint": "https://mycollectionendpoint-abcd.eastus-1.metrics.ingest.monitor.azure.com"
+    },
     "networkAcls": {
       "publicNetworkAccess": "Disabled"
     }
@@ -127,7 +131,7 @@ The sample data collection endpoint (DCE) below is for virtual machines with Azu
 
 ## Limitations
 
-- Data collection endpoints only support Log Analytics workspaces as a destination for collected data. [Custom metrics (preview)](../essentials/metrics-custom-overview.md) collected and uploaded via Azure Monitor Agent aren't currently controlled by DCEs.
+- Data collection endpoints only support Log Analytics workspaces and Azure Monitor Workspace as destinations for collected data. [Custom metrics (preview)](../essentials/metrics-custom-overview.md) collected and uploaded via Azure Monitor Agent aren't currently controlled by DCEs.
 
 
 ## Next steps