Merge branch 'MicrosoftDocs:main' into patch-3

Author: Joey Chen

articles/advisor/advisor-reference-cost-recommendations.md

@@ -261,8 +261,17 @@ We noticed that your virtual network gateway has been idle for over 90 days. Thi
 
 Learn more about [Virtual network gateway - IdleVNetGateway (Repurpose or delete idle virtual network gateways)](https://aka.ms/aa_idlevpngateway_learnmore).
 
+### Consider migrating to Front Door Standard/Premium
 
+Your Front Door Classic tier contains a large number of domains or routing rules, which adds extra charges. Front Door Standard or Premium do not charge per additional domain or routing rule. Consider migrating to save costs.
 
+Learn more about [Front Door pricing](https://aka.ms/afd-pricing).
+
+### Consider using multiple endpoints under one single Front Door Standard/Premium profile
+
+We detected your subscription contains multiple Front Door Standard/Premium profiles with a small number of endpoints on them. You can save costs in base fees by using multiple endpoints within one profile. You can use a maximum of 10 endpoints with Standard tier and 25 endpoints with Premium tier.
+
+Learn more about [Front Door endpoints](https://aka.ms/afd-endpoints).
 
 ## Reserved instances
 

articles/ai-services/document-intelligence/concept-retrieval-augumented-generation.md

@@ -19,9 +19,9 @@ monikerRange: '>=doc-intel-3.1.0'
 
 ## Introduction
 
-Retrieval-Augmented Generation (RAG) is a document generative AI solution that combines a pretrained Large Language Model (LLM) like ChatGPT with an external data retrieval system to generate an enhanced response incorporating new data outside of the original training data. Adding an information retrieval system to your applications enables you to chat with your documents, generate captivating content, and access the power of Azure OpenAI models for your data. You also have more control over the data used by the LLM as it formulates a response.
+Retrieval-Augmented Generation (RAG) is a design pattern that combines a pretrained Large Language Model (LLM) like ChatGPT with an external data retrieval system to generate an enhanced response incorporating new data outside of the original training data. Adding an information retrieval system to your applications enables you to chat with your documents, generate captivating content, and access the power of Azure OpenAI models for your data. You also have more control over the data used by the LLM as it formulates a response.
 
-The Document Intelligence [Layout model](concept-layout.md) is an advanced machine-learning based document analysis API. With semantic chunking, the Layout model offers a comprehensive solution for advanced content extraction and document structure analysis capabilities. With the Layout model, you can easily extract text and structural to divide large bodies of text into smaller, meaningful chunks based on semantic content rather than arbitrary splits. The extracted information can be conveniently outputted to Markdown format, enabling you to define your semantic chunking strategy based on the provided building blocks.
+The Document Intelligence [Layout model](concept-layout.md) is an advanced machine-learning based document analysis API. The Layout model offers a comprehensive solution for advanced content extraction and document structure analysis capabilities. With the Layout model, you can easily extract text and structural to divide large bodies of text into smaller, meaningful chunks based on semantic content rather than arbitrary splits. The extracted information can be conveniently outputted to Markdown format, enabling you to define your semantic chunking strategy based on the provided building blocks.
 
 :::image type="content" source="media/rag/azure-rag-processing.png" alt-text="Screenshot depicting semantic chunking with RAG using Azure AI Document Intelligence.":::
 
@@ -47,7 +47,7 @@ Markdown is a structured and formatted markup language and a popular input for e
 
 * **Large learning model (LLM) compatibility**. The Layout model Markdown formatted output is LLM friendly and facilitates seamless integration into your workflows. You can turn any table in a document into Markdown format and avoid extensive effort parsing the documents for greater LLM understanding.
 
-**Text image processed with Document Intelligence Studio using Layout model**
+**Text image processed with Document Intelligence Studio and output to markdown using Layout model**
 
   :::image type="content" source="media/rag/markdown-text-output.png" alt-text="Screenshot of newspaper article processed by Layout model and outputted to Markdown.":::
 
@@ -103,13 +103,15 @@ You can follow the [Document Intelligence Studio quickstart](quickstarts/try-doc
 
   * [Java](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/documentintelligence/azure-ai-documentintelligence/src/samples/java/com/azure/ai/documentintelligence/AnalyzeLayoutMarkdownOutput.java)
 
+  * [.NET](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/documentintelligence/Azure.AI.DocumentIntelligence/samples/Sample_ExtractLayout.md)
+
 ## Build document chat with semantic chunking
 
 * [Azure OpenAI on your data](../openai/concepts/use-your-data.md) enables you to run supported chat on your documents.  Azure OpenAI on your data applies the Document Intelligence Layout model to extract and parse document data by chunking long text based on tables and paragraphs. You can also customize your chunking strategy using [Azure OpenAI sample scripts](https://github.com/microsoft/sample-app-aoai-chatGPT/tree/main/scripts) located in our GitHub repo.
 
-* Azure AI Document Intelligence is now integrated with [LangChain](https://python.langchain.com/docs/integrations/document_loaders/azure_document_intelligence) as one of its document loaders. You can use it to easily load the data and output to Markdown format. This [notebook](https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/AI%20Services/Quickstart%20-%20Document%20Question%20and%20Answering%20with%20PDFs/) shows a simple demo for RAG pattern with Azure AI Document Intelligence as document loader and Azure Search as retriever in LangChain.
+* Azure AI Document Intelligence is now integrated with [LangChain](https://python.langchain.com/docs/integrations/document_loaders/azure_document_intelligence) as one of its document loaders. You can use it to easily load the data and output to Markdown format. This [notebook](https://github.com/microsoft/Form-Recognizer-Toolkit/blob/main/SampleCode/Python/sample_rag_langchain.ipynb) shows a simple demo for RAG pattern with Azure AI Document Intelligence as document loader and Azure Search as retriever in LangChain.
 
-* The chat with your data solution accelerator[code sample](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator) demonstrates an end-to-end baseline RAG pattern sample. It uses Azure AI Search as a retriever and Azure AI Document Intelligence for document loading and semantic chunking.
+* The chat with your data solution accelerator [code sample](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator) demonstrates an end-to-end baseline RAG pattern sample. It uses Azure AI Search as a retriever and Azure AI Document Intelligence for document loading and semantic chunking.
 
 ## Use case
 
@@ -122,20 +124,15 @@ If you're looking for a specific section in a document, you can use semantic chu
 # pip install langchain langchain-community azure-ai-documentintelligence
 
 from azure.ai.documentintelligence import DocumentIntelligenceClient
- from azure.core.credentials import AzureKeyCredential
 
- endpoint = "https://<my-custom-subdomain>.cognitiveservices.azure.com/"
- credential = AzureKeyCredential("<api_key>")
+endpoint = "https://<my-custom-subdomain>.cognitiveservices.azure.com/"
+key = "<api_key>"
 
- document_intelligence_client = DocumentIntelligenceClient(
-     endpoint, credential)
- 
 from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader
- 
 from langchain.text_splitter import MarkdownHeaderTextSplitter
 
 # Initiate Azure AI Document Intelligence to load the document. You can either specify file_path or url_path to load the document.
-loader = AzureAIDocumentIntelligenceLoader(file_path="<path to your file>", api_key = doc_intelligence_key, api_endpoint = doc_intelligence_endpoint, api_model="prebuilt-layout")
+loader = AzureAIDocumentIntelligenceLoader(file_path="<path to your file>", api_key = key, api_endpoint = endpoint, api_model="prebuilt-layout")
 docs = loader.load()
 
 # Split the document into chunks base on markdown headers.

articles/ai-services/document-intelligence/toc.yml

@@ -172,6 +172,7 @@ items:
     href: concept-layout.md
   - name: 🆕 Add-on capabilities
     displayName: extract, formula, font, styles, fontStyle, ocr.highResolution, ocr.formula, high resolution, background color, inline, display
+    href: concept-add-on-capabilities.md
   - name: 🆕 Query field extraction
     displayName: queries, fields, OpenAI, chat
     href: concept-query-fields.md

articles/app-service/overview-managed-identity.md

@@ -432,9 +432,9 @@ The **IDENTITY_ENDPOINT** is a local URL from which your app can request tokens.
 > | resource          | Query  | The Microsoft Entra resource URI of the resource for which a token should be obtained. This could be one of the [Azure services that support Microsoft Entra authentication](../active-directory/managed-identities-azure-resources/services-support-managed-identities.md#azure-services-that-support-azure-ad-authentication) or any other resource URI.    |
 > | api-version       | Query  | The version of the token API to be used. Use `2019-08-01`.                                                                                                                                                                                                                                                                 |
 > | X-IDENTITY-HEADER | Header | The value of the IDENTITY_HEADER environment variable. This header is used to help mitigate server-side request forgery (SSRF) attacks.                                                                                                                                                                                                    |
-> | client_id         | Query  | (Optional) The client ID of the user-assigned identity to be used. Cannot be used on a request that includes `principal_id`, `mi_res_id`, or `object_id`. If all ID parameters  (`client_id`, `principal_id`, `object_id`, and `mi_res_id`) are omitted, the system-assigned identity is used.                                             |
-> | principal_id      | Query  | (Optional) The principal ID of the user-assigned identity to be used. `object_id` is an alias that may be used instead. Cannot be used on a request that includes client_id, mi_res_id, or object_id. If all ID parameters (`client_id`, `principal_id`, `object_id`, and `mi_res_id`)  are omitted, the system-assigned identity is used. |
-> | mi_res_id         | Query  | (Optional) The Azure resource ID of the user-assigned identity to be used. Cannot be used on a request that includes `principal_id`, `client_id`, or `object_id`. If all ID parameters (`client_id`, `principal_id`, `object_id`, and `mi_res_id`) are omitted, the system-assigned identity is used.                                      |
+> | client_id         | Query  | (Optional) The client ID of the user-assigned identity to be used. Cannot be used on a request that includes `principal_id`, `msi_res_id`, or `object_id`. If all ID parameters  (`client_id`, `principal_id`, `object_id`, and `msi_res_id`) are omitted, the system-assigned identity is used.                                             |
+> | principal_id      | Query  | (Optional) The principal ID of the user-assigned identity to be used. `object_id` is an alias that may be used instead. Cannot be used on a request that includes client_id, msi_res_id, or object_id. If all ID parameters (`client_id`, `principal_id`, `object_id`, and `msi_res_id`)  are omitted, the system-assigned identity is used. |
+> | msi_res_id         | Query  | (Optional) The Azure resource ID of the user-assigned identity to be used. Cannot be used on a request that includes `principal_id`, `client_id`, or `object_id`. If all ID parameters (`client_id`, `principal_id`, `object_id`, and `msi_res_id`) are omitted, the system-assigned identity is used.                                      |
 
 > [!IMPORTANT]
 > If you are attempting to obtain tokens for user-assigned identities, you must include one of the optional properties. Otherwise the token service will attempt to obtain a token for a system-assigned identity, which may or may not exist.

articles/azure-arc/system-center-virtual-machine-manager/disaster-recovery.md

@@ -3,7 +3,7 @@ title: Recover from accidental deletion of resource bridge VM
 description: Learn how to perform recovery operations for the Azure Arc resource bridge VM in Azure Arc-enabled System Center Virtual Machine Manager disaster scenarios.
 ms.topic: how-to 
 ms.custom:
-ms.date: 12/06/2023
+ms.date: 12/28/2023
 ms.services: azure-arc
 ms.subservice: azure-arc-scvmm
 author: Farha-Bano
@@ -27,12 +27,14 @@ To recover from Arc resource bridge VM deletion, you need to deploy a new resour
 > DHCP-based Arc Resource Bridge deployment is no longer supported.<br><br>
 If you had deployed Arc Resource Bridge earlier using DHCP, you must clean up your deployment by removing your resources from Azure and do a [fresh onboarding](./quickstart-connect-system-center-virtual-machine-manager-to-arc.md).
 
-1. Copy the Azure region and resource IDs of the Arc resource bridge, custom location, and SCVMM Azure resources.
+### Recover Arc resource bridge from a Windows machine
 
-2. Download the [onboarding script](/azure/azure-arc/system-center-virtual-machine-manager/quickstart-connect-system-center-virtual-machine-manager-to-arc#download-the-onboarding-script) from the Azure portal and update the following section in the script, using the same information as the original resources in Azure.
+1.	Copy the Azure region and resource IDs of the Arc resource bridge, custom location, and SCVMM management server Azure resources.
+
+2.	Download [this script](https://download.microsoft.com/download/a/a/8/aa8687e4-1a30-485f-9de4-4f15fc576724/arcvmm-windows-dr.ps1) and update the following section in the script using the same information as the original resources in Azure. 
 
     ```powershell
-    $location = <Azure region of the resources>
+    $location = <Azure region of the original Arc resource bridge>
     $applianceSubscriptionId = <subscription-id>
     $applianceResourceGroupName = <resource-group-name>
     $applianceName = <resource-bridge-name>
@@ -45,20 +47,10 @@ If you had deployed Arc Resource Bridge earlier using DHCP, you must clean up yo
     $vmmserverResourceGroupName = <resource-group-name>
     $vmmserverName= <SCVMM-name-in-azure>
     ```
+ 
+3.	Run the updated script from the same location where the config YAML files are stored after the initial onboarding. This is most likely the same folder from where you ran the initial onboarding script unless the config files were moved later to a different location. [Provide the inputs](quickstart-connect-system-center-virtual-machine-manager-to-arc.md#script-runtime) as prompted. 
 
-3. [Run the onboarding script](/azure/azure-arc/system-center-virtual-machine-manager/quickstart-connect-system-center-virtual-machine-manager-to-arc#download-the-onboarding-script) again with the `-Force` parameter.
-
-    ``` powershell-interactive
-    ./resource-bridge-onboarding-script.ps1 -Force
-    ```
-
-4. [Provide the inputs](/azure/azure-arc/system-center-virtual-machine-manager/quickstart-connect-system-center-virtual-machine-manager-to-arc#script-runtime) as prompted.
-
-5. In the same machine, run the following scripts, as applicable:
-     - [Download the script](https://download.microsoft.com/download/6/b/4/6b4a5009-fed8-46c2-b22b-b24a4d0a06e3/arcvmm-appliance-dr.ps1) if you're running the script from a Windows machine
-     - [Download the script](https://download.microsoft.com/download/0/5/c/05c2bcb8-87f8-4ead-9757-a87a0759071c/arcvmm-appliance-dr.sh) if you're running the script from a Linux machine
-
-6. Once the script is run successfully, the old Resource Bridge is recovered and the connection is re-established to the existing Azure-enabled SCVMM resources.
+4.	Once the script is run successfully, the old Resource Bridge is recovered, and the connection is re-established to the existing Azure-enabled SCVMM resources.
 
 ## Next steps
 

articles/azure-monitor/essentials/data-collection-endpoint-overview.md

@@ -26,8 +26,8 @@ This table describes the components of a data collection endpoint, related regio
 
 | Component | Description | Regionality considerations |Data collection rule configuration |
 |:---|:---|:---|
-| 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 **Basics** tab when you create a data collection rule using the portal. | 
-| 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 **Resources** tab when you create a data collection rule using the portal.|
+| 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. |
+| 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.| 
 
 
 ## How to set up data collection endpoints based on your deployment

articles/azure-monitor/logs/cross-workspace-query.md

@@ -4,7 +4,7 @@ description: Query and correlated data from multiple Log Analytics workspaces, a
 ms.topic: how-to
 author: guywi-ms
 ms.author: guywild
-ms.date: 05/30/2023
+ms.date: 12/28/2023
 # Customer intent: As a data analyst, I want to write KQL queries that correlate data from multiple Log Analytics workspaces, applications, or resources, to enable my analysis.
 
 ---

articles/azure-monitor/logs/ingest-logs-event-hub.md

@@ -6,7 +6,7 @@ author: guywi-ms
 ms.author: guywild
 ms.reviewer: ilanawaitser
 ms.topic: tutorial 
-ms.date: 09/20/2022
+ms.date: 12/28/2023
 ms.custom: references_regions 
 
 # customer-intent: As a DevOps engineer, I want to ingest data from an event hub into a Log Analytics workspace so that I can monitor logs that I send to Azure Event Hubs.

articles/azure-monitor/logs/log-analytics-overview.md

@@ -2,7 +2,7 @@
 title: Overview of Log Analytics in Azure Monitor
 description: This overview describes Log Analytics, which is a tool in the Azure portal used to edit and run log queries for analyzing data in Azure Monitor logs.
 ms.topic: conceptual
-ms.date: 06/28/2022
+ms.date: 12/28/2023
 
 ---
 

articles/azure-monitor/logs/log-analytics-workspace-insights-overview.md

@@ -5,8 +5,8 @@ services: azure-monitor
 ms.topic: conceptual
 author: guywild
 ms.author: guywild
-ms.reviewer: noakuper
-ms.date: 06/27/2022
+ms.reviewer: osalzberg
+ms.date: 12/28/2023
 
 ---