Merge pull request #244391 from MicrosoftDocs/main

Publish to live, Sunday 4:00PM PDT, 7/9

Author: Stacyrch140

articles/azure-monitor/autoscale/autoscale-custom-metric.md

@@ -6,7 +6,7 @@ ms.author: edbaynash
 ms.service: azure-monitor
 ms.subservice: autoscale
 ms.topic: how-to
-ms.date: 06/22/2022
+ms.date: 07/09/2023
 ms.reviewer: akkumari
 
 # Customer intent: As a user or dev ops administrator, I want to use the portal to set up autoscale so I can scale my resources.
@@ -19,7 +19,7 @@ This article describes how to set up autoscale for a web app by using a custom m
 Autoscale allows you to add and remove resources to handle increases and decreases in load. In this article, we'll show you how to set up autoscale for a web app by using one of the Application Insights metrics to scale the web app in and out.
 
 > [!NOTE]
-> Autoscaling on custom metrics in Application Insights is supported only for metrics published to **Standard** and **Azure.ApplicationInsights** namespaces. If any other namespaces are used for custom metrics in Application Insights, it will return **Unsupported Metric** error.
+> Autoscaling on custom metrics in Application Insights is supported only for metrics published to **Standard** and **Azure.ApplicationInsights** namespaces. If any other namespaces are used for custom metrics in Application Insights, it returns an **Unsupported Metric** error.
 
 Azure Monitor autoscale applies to:
 
@@ -114,7 +114,7 @@ Set up a scale-out rule so that Azure spins up another instance of the web app w
 
 ### Set up a scale-in rule
 
-Set up a scale-in rule so that Azure spins down one of the instances when the number of sessions your web app is handling is less than 60 per instance. Azure will reduce the number of instances each time this rule is run until the minimum number of instances is reached.
+Set up a scale-in rule so that Azure spins down one of the instances when the number of sessions your web app is handling is less than 60 per instance. Azure reduces the number of instances each time this rule is run until the minimum number of instances is reached.
 
 1. In the **Rules** section of the default scale condition, select **Add a rule**.
 1. From the **Metric source** dropdown, select **Other resource**.
@@ -152,8 +152,8 @@ If you're not going to continue to use this application, delete resources.
 
 To learn more about autoscale, see the following articles:
 
-- [Use autoscale actions to send email and webhook alert notifications](./autoscale-webhook-email.md)
-- [Overview of autoscale](./autoscale-overview.md)
-- [Azure Monitor autoscale common metrics](./autoscale-common-metrics.md)
-- [Best practices for Azure Monitor autoscale](./autoscale-best-practices.md)
-- [Autoscale REST API](/rest/api/monitor/autoscalesettings)
++ [Use autoscale actions to send email and webhook alert notifications](./autoscale-webhook-email.md)
++ [Overview of autoscale](./autoscale-overview.md)
++ [Azure Monitor autoscale common metrics](./autoscale-common-metrics.md)
++ [Best practices for Azure Monitor autoscale](./autoscale-best-practices.md)
++ [Autoscale REST API](/rest/api/monitor/autoscalesettings)

articles/azure-vmware/disaster-recovery-using-vmware-site-recovery-manager.md

@@ -17,6 +17,8 @@ VMware vSphere Replication is VMware's hypervisor-based replication technology f
 In this article, you'll implement disaster recovery for on-premises VMware vSphere virtual machines (VMs) or Azure VMware Solution-based VMs.
 
 
+> [!NOTE]
+> The current version of VMware Site Recovery Manager (SRM) in Azure VMware Solution is 8.5.0.3.
 ## Supported scenarios
 
 VMware SRM helps you plan, test, and run the recovery of VMs between a protected VMware vCenter Server site and a recovery VMware vCenter Server site. You can use VMware SRM with Azure VMware Solution with the following two DR scenarios: 
@@ -88,15 +90,18 @@ Make sure you've explicitly provided the remote user the VMware VRM administrato
 
 1. In your on-premises data center, install VMware SRM and vSphere Replication.
 
-   >[!NOTE]
-   >Use the [Two-site Topology with one vCenter Server instance per PSC](https://docs.vmware.com/en/Site-Recovery-Manager/8.4/com.vmware.srm.install_config.doc/GUID-F474543A-88C5-4030-BB86-F7CC51DADE22.html) deployment model. Also, make sure that the [required vSphere Replication Network ports](https://kb.VMware.com/s/article/2087769) are opened.
-
+   > [!NOTE]
+   > Use the [Two-site Topology with one vCenter Server instance per PSC](https://docs.vmware.com/en/Site-Recovery-Manager/8.4/com.vmware.srm.install_config.doc/GUID-F474543A-88C5-4030-BB86-F7CC51DADE22.html) deployment model. Also, make sure that the [required vSphere Replication Network ports](https://kb.VMware.com/s/article/2087769) are opened.
 1. In your Azure VMware Solution private cloud, under **Manage**, select **Add-ons** > **Disaster recovery**.
 
-   The default CloudAdmin user in the Azure VMware Solution private cloud doesn't have sufficient privileges to install VMware SRM or vSphere Replication. The installation process involves multiple steps outlined in the [Prerequisites](#prerequisites) section. Instead, you can install VMware SRM with vSphere Replication as an add-on service from your Azure VMware Solution private cloud.
+1. The default CloudAdmin user in the Azure VMware Solution private cloud doesn't have sufficient privileges to install VMware SRM or vSphere Replication. The installation process involves multiple steps outlined in the [Prerequisites](#prerequisites) section. Instead, you can install VMware SRM with vSphere Replication as an add-on service from your Azure VMware Solution private cloud.
+
 
-   :::image type="content" source="media/VMware-srm-vsphere-replication/disaster-recovery-add-ons.png" alt-text="Screenshot of Azure VMware Solution private cloud to install VMware SRM with vSphere Replication as an add-on" border="true" lightbox="media/VMware-srm-vsphere-replication/disaster-recovery-add-ons.png":::
+1. :::image type="content" source="media/VMware-srm-vsphere-replication/disaster-recovery-add-ons.png" alt-text="Screenshot of Azure VMware Solution private cloud to install VMware SRM with vSphere Replication as an add-on" border="true" lightbox="media/VMware-srm-vsphere-replication/disaster-recovery-add-ons.png":::
 
+
+> [!NOTE]
+> The current version of VMware Site Recovery Manager (SRM) in Azure VMware Solution is 8.5.0.3.
 1. From the **Disaster Recovery Solution** drop-down, select **VMware Site Recovery Manager (SRM) – vSphere Replication**. 
 
    :::image type="content" source="media/VMware-srm-vsphere-replication/disaster-recovery-solution-srm-add-on.png" alt-text="Screenshot showing the Disaster recovery tab under Add-ons with VMware Site Recovery Manager (SRM) - vSphere replication selected." border="true" lightbox="media/VMware-srm-vsphere-replication/disaster-recovery-solution-srm-add-on.png":::
@@ -304,3 +309,5 @@ VMware and Microsoft support teams will engage each other as needed to troublesh
 - [Pre-requisites and Best Practices for SRM installation](https://docs.vmware.com/en/Site-Recovery-Manager/8.3/com.vmware.srm.install_config.doc/GUID-BB0C03E4-72BE-4C74-96C3-97AC6911B6B8.html)
 - [Network ports for SRM](https://docs.vmware.com/en/Site-Recovery-Manager/8.3/com.vmware.srm.install_config.doc/GUID-499D3C83-B8FD-4D4C-AE3D-19F518A13C98.html)
 - [Network ports for vSphere Replication](https://kb.vmware.com/s/article/2087769)
+
+

articles/machine-learning/prompt-flow/how-to-deploy-for-real-time-inference.md

@@ -9,7 +9,7 @@ ms.topic: how-to
 author: likebupt
 ms.author: keli19
 ms.reviewer: lagayhar
-ms.date: 06/30/2023
+ms.date: 07/07/2023
 ---
 
 
@@ -93,6 +93,12 @@ You can continue to configure the endpoint in wizard, as the endpoint creation w
 
 If the checkbox is selected, the first row of your input data will be used as sample input data for testing the endpoint later.
 
+### Outputs
+
+In this step, you can view all flow outputs, and specify which outputs will be included in the response of the endpoint you deploy.
+
+:::image type="content" source="./media/how-to-deploy-for-real-time-inference/deploy-wizard-outputs.png" alt-text="Screenshot of the outputs step in the deploy wizard." lightbox = "./media/how-to-deploy-for-real-time-inference/deploy-wizard-outputs.png":::
+
 ### Connections
 
 In this step, you can view all connections within your flow, and change connections used by the endpoint when it performs inference later.

articles/machine-learning/prompt-flow/media/how-to-deploy-for-real-time-inference/deploy-wizard-outputs.png

@@ -16,29 +16,26 @@ ms.date: 06/30/2023
 
 Faiss Index Lookup is a tool tailored for querying within a user-provided Faiss-based vector store. In combination with our Large Language Model (LLM) tool, it empowers users to extract contextually relevant information from a domain knowledge base.
 
-> [!IMPORTANT]
-> Prompt flow is currently in public preview. This preview is provided without a service-level agreement, and is not recommended for production workloads. Certain features might not be supported or might have constrained capabilities.
-> For more information, see [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
-
 ## Requirements
-
-- embeddingstore==0.0.93026209 --extra-index-url https://azuremlsdktestpypi.azureedge.net/embeddingstore
+- embeddingstore --extra-index-url https://azuremlsdktestpypi.azureedge.net/embeddingstore
 
 ## Prerequisites
-
 - Prepare an accessible path on Azure Blob Storage. Here's the guide if a new storage account needs to be created:  [Azure Storage Account](../../../storage/common/storage-account-create.md).
-- Create related Faiss-based index files on Azure Blob Storage. We support the LangChain format (index.faiss + index.pkl) for the index files, which can be prepared either by employing our EmbeddingStore SDK or following the quick guide from [LangChain documentation](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss). Refer to the instructions of `Vector DB QnA Step 1` for building index using EmbeddingStore SDK.
-- The identity used by the Prompt flow runtime should be granted with certain roles to access the vector store, based on which kind of path you provide. Refer to [Steps to assign an Azure role](../../../role-based-access-control/role-assignments-steps.md):
-  - Workspace relative path, blob url on workspace default storage, and AML Datastore url: `AzureML Data Scientist`
-  - Other blob urls: `Storage Blob Data Reader role`
+- Create related Faiss-based index files on Azure Blob Storage. We support the LangChain format (index.faiss + index.pkl) for the index files, which can be prepared either by employing our EmbeddingStore SDK or following the quick guide from [LangChain documentation](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss). Please refer to [the sample notebook for creating Faiss index](https://aka.ms/pf-sample-build-faiss-index) for building index using EmbeddingStore SDK.
+- Based on where you put your own index files, the identity used by the promptflow runtime should be granted with certain roles. Please refer to [Steps to assign an Azure role](../../../role-based-access-control/role-assignments-steps.md):
+
+    | Location | Role |
+    | ---- | ---- |
+    | workspace datastores or workspace default blob | AzureML Data Scientist |
+    | other blobs | Storage Blob Data Reader |
 
 ## Inputs
 
 The tool accepts the following inputs:
 
 | Name | Type | Description | Required |
 | ---- | ---- | ----------- | -------- |
-| path | string | blob/datastore url or workspace relative path for the vector store.<br><br>blob url format:<br>https://`<account_name>`.blob.core.windows.net/`<container_name>`/`<path_and_folder_name>`.<br><br>AML datastore url format:<br>azureml://subscriptions/`<your_subscription>`/resourcegroups/`<your_resource_group>`/workspaces/`<your_workspace>`/data/`<data_path>`<br><br>relative path to workspace datastore `workspaceblobstore`:<br>`<path_and_folder_name>` | Yes |
+| path | string | URL or path for the vector store.<br><br>blob URL format:<br>https://`<account_name>`.blob.core.windows.net/`<container_name>`/`<path_and_folder_name>`.<br><br>AML datastore URL format:<br>azureml://subscriptions/`<your_subscription>`/resourcegroups/`<your_resource_group>`/workspaces/`<your_workspace>`/data/`<data_path>`<br><br>relative path to workspace datastore `workspaceblobstore`:<br>`<path_and_folder_name>`<br><br> public http/https URL (for public demonstration):<br>http(s)://`<path_and_folder_name>` | Yes |
 | vector | list[float] | The target vector to be queried, which can be generated by the LLM tool. | Yes |
 | top_k | integer | The count of top-scored entities to return. Default value is 3. | No |
 
@@ -52,7 +49,6 @@ The following is an example for JSON format response returned by the tool, which
 | score | float |  Distance between the entity and the query vector |
 | metadata | dict | Customized key-value pairs provided by user when creating the index |
 
-
 ```json
 [
   {

articles/machine-learning/prompt-flow/tools-reference/faiss-index-lookup-tool.md

@@ -28,15 +28,15 @@ This tool adds support for more vector databases, including Pinecone, Weaviete,
 
 ## Requirements
 
-- embeddingstore==0.0.93026209 --extra-index-url https://azuremlsdktestpypi.azureedge.net/embeddingstore
+- embeddingstore --extra-index-url https://azuremlsdktestpypi.azureedge.net/embeddingstore
 
 ## Prerequisites
 
 The tool searches data from a third-party vector database. To use it, you should create resources in advance and establish connections between the tool and the resource.
 
   - **Azure Cognitive Search:**
     - Create resource [Azure Cognitive Search](../../../search/search-create-service-portal.md).
-    - Add "CognitiveSearchConnection" connection. Fill "API key" field with "Primary admin key" from "Keys" section of created resource, and fill "Api Base" field with the Url, the Url format is `https://{your_serive_name}.search.windows.net`.
+    - Add "CognitiveSearchConnection" connection. Fill "API key" field with "Primary admin key" from "Keys" section of created resource, and fill "Api Base" field with the URL, the URL format is `https://{your_serive_name}.search.windows.net`.
 
 ## Inputs
 

articles/machine-learning/prompt-flow/tools-reference/vector-db-lookup-tool.md

@@ -22,22 +22,25 @@ Vector index lookup is a tool tailored for querying within an Azure Machine Lear
 
 ## Requirements
 
-- embeddingstore==0.0.93026209 --extra-index-url https://azuremlsdktestpypi.azureedge.net/embeddingstore
+- embeddingstore --extra-index-url https://azuremlsdktestpypi.azureedge.net/embeddingstore
 
 ## Prerequisites
 
 - Follow the instructions from sample flow `Bring your own Data QnA` to prepare a Vector Index as an input.
-- The identity used by the Prompt flow runtime should be granted with certain roles to access the Vector Index, based on which kind of path you provide. Refer to [Steps to assign an Azure role](../../../role-based-access-control/role-assignments-steps.md):
-    - blob url on workspace default storage, AML asset url, and AML Datastore url: `AzureML Data Scientist`
-    - other blob urls: `Storage Blob Data Reader role`
+- Based on where you put your Vector Index, the identity used by the promptflow runtime should be granted with certain roles. Please refer to [Steps to assign an Azure role](../../../role-based-access-control/role-assignments-steps.md):
+
+    | Location | Role |
+    | ---- | ---- |
+    | workspace datastores or workspace default blob | AzureML Data Scientist |
+    | other blobs | Storage Blob Data Reader |
 
 ## Inputs
 
 The tool accepts the following inputs:
 
 | Name | Type | Description | Required |
 | ---- | ---- | ----------- | -------- |
-| path | string | blob/AML asset/datastore url for the VectorIndex.<br><br>blob url format:<br>https://`<account_name>`.blob.core.windows.net/`<container_name>`/`<path_and_folder_name>`.<br><br>AML asset url format:<br>azureml://subscriptions/`<your_subscription>`/resourcegroups/`<your_resource_group>>`/workspaces/`<your_workspace>`/datastores/`<your_datastore>`/paths/`<asset_name and optional version/label>`<br><br>AML datastore url format:<br>azureml://subscriptions/`<your_subscription>`/resourcegroups/`<your_resource_group>`/workspaces/`<your_workspace>`/data/`<data_path>` | Yes |
+| path | string | blob/AML asset/datastore URL for the VectorIndex.<br><br>blob URL format:<br>https://`<account_name>`.blob.core.windows.net/`<container_name>`/`<path_and_folder_name>`.<br><br>AML asset URL format:<br>azureml://subscriptions/`<your_subscription>`/resourcegroups/`<your_resource_group>>`/workspaces/`<your_workspace>`/datastores/`<your_datastore>`/paths/`<asset_name and optional version/label>`<br><br>AML datastore URL format:<br>azureml://subscriptions/`<your_subscription>`/resourcegroups/`<your_resource_group>`/workspaces/`<your_workspace>`/data/`<data_path>` | Yes |
 | query | string, list[float] | The text to be queried.<br>or<br>The target vector to be queried, which can be generated by the LLM tool. | Yes |
 | top_k | integer | The count of top-scored entities to return. Default value is 3. | No |
 

articles/machine-learning/prompt-flow/tools-reference/vector-index-lookup-tool.md

@@ -369,7 +369,7 @@ This example collects events for:
 1. To capture messages sent from a logger or a connected device, run this command in the background:
 
     ```
-    tcpdump -I any port 514 -A vv &
+    tcpdump -i any port 514 -A vv &
     ```
 1. After you complete the validation, we recommend that you stop the `tcpdump`: Type `fg` and then select <kbd>Ctrl</kbd>+<kbd>C</kbd>.
 1. To send demo messages, do one of the following: