Reporting the Status of Group-Based License Assignments
This article describes how to create a report about group-based licensing assignments and any errors that might have occured. The code uses the Microsoft Graph PowerShell SDK to fetch information about the groups used for licensing assignments, interpret the assignments, find users with assignment errors, and send email to inform administrators about what's been found. https://practical365.com/group-based-licensing-report-email/Practical Graph: Nag Users to Upgrade to a Strong Authentication Method
Convincing people to use MFA is one challenge. Convincing them to use a stronger authentication method than SMS is another. This article explains how to use PowerShell to find people still using SMS for MFA and send email to ask them to upgrade their authentication method. https://practical365.com/upgrade-stronger-authentication-method-mfa/
Importing JSON as a list in SharePoint Online via pnp
I'm trying to import a JSON list file to my Sharepoint online website. But it seems impossible to import it directly. Does anyone know a possibility via pnp because I saw a method on pnp named "fromJson" but I don't know how to use it to import my files to my SharePoint website. Thanks in advance!Importing JSON as a list in SharePoint Online via pnp
I'm trying to import a JSON list file to my Sharepoint online website. But it seems impossible to import it directly. Does anyone know a possibility via pnp because I saw a method on pnp named "fromJson" but I don't know how to use it to import my files to my SharePoint website. Thanks in advance!
Automatic Version History in SharePoint Online
Hi, I want to turn on Automatic Version History to reduce storage. Has anyone implemented this on existing Large Libraries? What I want to know is, does it use the current meta data and delete versions straight away and free up space quickly? Or will it only start analysing from when you change the setting and i'll have to wait a longer time to reduce versions? Many thanks! ChrisUsing OpenAI on Azure Web App
TOC Introduction to OpenAI System Architecture Architecture Focus of This Tutorial Setup Azure Resources File and Directory Structure ARM Template ARM Template From Azure Portal Running Locally Training Models and Training Data Predicting with the Model Publishing the Project to Azure Running on Azure Web App Training the Model Using the Model for Prediction Troubleshooting Startup Command Issue App Becomes Unresponsive After a Period az cli command for Linux webjobs fail Others Conclusion References 1. Introduction to OpenAI OpenAI is a leading artificial intelligence research and deployment company founded in December 2015. Its mission is to ensure that artificial general intelligence (AGI)—highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. OpenAI focuses on developing safe and scalable AI technologies and ensuring equitable access to these innovations. Known for its groundbreaking advancements in natural language processing, OpenAI has developed models like GPT (Generative Pre-trained Transformer), which powers applications for text generation, summarization, translation, and more. GPT models have revolutionized fields like conversational AI, creative writing, and programming assistance. OpenAI has also released models like Codex, designed to understand and generate computer code, and DALL·E, which creates images from textual descriptions. OpenAI operates with a unique hybrid structure: a for-profit company governed by a nonprofit entity to balance the development of AI technology with ethical considerations. The organization emphasizes safety, research transparency, and alignment to human values. By providing access to its models through APIs and fostering partnerships, OpenAI empowers developers, businesses, and researchers to leverage AI for innovative solutions across diverse industries. Its long-term goal is to ensure AI advances benefit humanity as a whole. 2. System Architecture Architecture Development Environment OS: Ubuntu Version: Ubuntu 18.04 Bionic Beaver Python Version: 3.7.3 Azure Resources App Service Plan: SKU - Premium Plan 0 V3 App Service: Platform - Linux (Python 3.9, Version 3.9.19) Storage Account: SKU - General Purpose V2 File Share: No backup plan Focus of This Tutorial This tutorial walks you through the following stages: Setting up Azure resources Running the project locally Publishing the project to Azure Running the application on Azure Troubleshooting common issues Each of the mentioned aspects has numerous corresponding tools and solutions. The relevant information for this session is listed in the table below. Local OS Windows Linux Mac V How to setup Azure resources Portal (i.e., REST api) ARM Bicep Terraform V V How to deploy project to Azure VSCode CLI Azure DevOps GitHub Action V 3. Setup Azure Resources File and Directory Structure Please open a bash terminal and enter the following commands: git clone https://github.com/theringe/azure-appservice-ai.git cd azure-appservice-ai bash ./openai/tools/add-venv.sh If you are using a Windows platform, use the following alternative PowerShell commands instead: git clone https://github.com/theringe/azure-appservice-ai.git cd azure-appservice-ai .\openai\tools\add-venv.cmd After completing the execution, you should see the following directory structure: File and Path Purpose openai/tools/add-venv.* The script executed in the previous step (cmd for Windows, sh for Linux/Mac) to create all Python virtual environments required for this tutorial. .venv/openai-webjob/ A virtual environment specifically used for training models (i.e., calculating embedding vectors indeed). openai/webjob/requirements.txt The list of packages (with exact versions) required for the openai-webjob virtual environment. .venv/openai/ A virtual environment specifically used for the Flask application, enabling API endpoint access for querying predictions (i.e., suggestion). openai/requirements.txt The list of packages (with exact versions) required for the openai virtual environment. openai/ The main folder for this tutorial. openai/tools/arm-template.json The ARM template to setup all the Azure resources related to this tutorial, including an App Service Plan, a Web App, and a Storage Account. openai/tools/create-folder.* A script to create all directories required for this tutorial in the File Share, including train, model, and test. openai/tools/download-sample-training-set.* A script to download a sample training set from News-Headlines-Dataset-For-Sarcasm-Detection, containing headlines data from TheOnion and HuffPost, into the train directory of the File Share. openai/webjob/cal_embeddings.py A script for calculating embedding vectors from headlines. It loads the training set, applies the transformation on OpenAI API, and saves the embedding vectors in the model directory of the File Share. openai/App_Data/jobs/triggered/cal-embeddings/cal_embeddings.sh A shell script for Azure App Service web jobs. It activates the openai-webjob virtual environment and starts the cal_embeddings.py script. openai/api/app.py Code for the Flask application, including routes, port configuration, input parsing, vectors loading, predictions, and output generation. openai/start.sh A script executed after deployment (as specified in the ARM template startup command I will introduce it later). It sets up the virtual environment and starts the Flask application to handle web requests. ARM Template We need to create the following resources or services: Manual Creation Required Resource/Service App Service Plan No Resource (plan) App Service Yes Resource (app) Storage Account Yes Resource (storageAccount) File Share Yes Service Let’s take a look at the openai/tools/arm-template.json file. Refer to the configuration section for all the resources. Since most of the configuration values don’t require changes, I’ve placed them in the variables section of the ARM template rather than the parameters section. This helps keep the configuration simpler. However, I’d still like to briefly explain some of the more critical settings. As you can see, I’ve adopted a camelCase naming convention, which combines the [Resource Type] with [Setting Name and Hierarchy]. This makes it easier to understand where each setting will be used. The configurations in the diagram are sorted by resource name, but the following list is categorized by functionality for better clarity. Configuration Name Value Purpose storageAccountFileShareName data-and-model [Purpose 1: Link File Share to Web App] Use this fixed name for File Share storageAccountFileShareShareQuota 5120 [Purpose 1: Link File Share to Web App] The value is in GB storageAccountFileShareEnabledProtocols SMB [Purpose 1: Link File Share to Web App] appSiteConfigAzureStorageAccountsType AzureFiles [Purpose 1: Link File Share to Web App] appSiteConfigAzureStorageAccountsProtocol Smb [Purpose 1: Link File Share to Web App] planKind linux [Purpose 2: Specify platform and stack runtime] Select Linux (default if Python stack is chosen) planSkuTier Premium0V3 [Purpose 2: Specify platform and stack runtime] Choose at least Premium Plan to ensure enough memory for your AI workloads planSkuName P0v3 [Purpose 2: Specify platform and stack runtime] Same as above appKind app,linux [Purpose 2: Specify platform and stack runtime] Same as above appSiteConfigLinuxFxVersion PYTHON|3.9 [Purpose 2: Specify platform and stack runtime] Select Python 3.9 to avoid dependency issues appSiteConfigAppSettingsWEBSITES_CONTAINER_START_TIME_LIMIT 600 [Purpose 3: Deploying] The value is in seconds, ensuring the Startup Command can continue execution beyond the default timeout of 230 seconds. This tutorial’s Startup Command typically takes around 300 seconds, so setting it to 600 seconds provides a safety margin and accommodates future project expansion (e.g., adding more packages) appSiteConfigAppCommandLine [ -f /home/site/wwwroot/start.sh ] && bash /home/site/wwwroot/start.sh || GUNICORN_CMD_ARGS=\"--timeout 600 --access-logfile '-' --error-logfile '-' -c /opt/startup/gunicorn.conf.py --chdir=/opt/defaultsite\" gunicorn application:app [Purpose 3: Deploying] This is the Startup Command, which can be break down into 3 parts: First (-f /home/site/wwwroot/start.sh): Checks whether start.sh exists. This is used to determine whether the app is in its initial state (just created) or has already been deployed. Second (bash /home/site/wwwroot/start.sh): If the file exists, it means the app has already been deployed. The start.sh script will be executed, which installs the necessary packages and starts the Flask application. Third (GUNICORN_CMD_ARGS=\"--timeout 600 --access-logfile '-' --error-logfile '-' -c /opt/startup/gunicorn.conf.py --chdir=/opt/defaultsite\" gunicorn application:app): If the file does not exist, the command falls back to the default HTTP server (gunicorn) to start the web app. Since the command is enclosed in double quotes within the ARM template, during actual execution, replace \" with " appSiteConfigAppSettingsSCM_DO_BUILD_DURING_DEPLOYMENT false [Purpose 3: Deploying] Since we have already defined the handling for different virtual environments in start.sh, we do not need to initiate the default build process of the Web App appSiteConfigAppSettingsWEBSITES_ENABLE_APP_SERVICE_STORAGE true [Purpose 4: Webjobs] This setting is required to enable the App Service storage feature, which is necessary for using web jobs (e.g., for model training) storageAccountPropertiesAllowSharedKeyAccess true [Purpose 5: Troubleshooting] This setting is enabled by default. The reason for highlighting it is that certain enterprise IT policies may enforce changes to this configuration after a period, potentially causing a series of issues. For more details, please refer to the Troubleshooting section below. Return to bash terminal and execute the following commands (their purpose has been described earlier). # Please change <ResourceGroupName> to your prefer name, for example: azure-appservice-ai # Please change <RegionName> to your prefer region, for example: eastus2 # Please change <ResourcesPrefixName> to your prefer naming pattern, for example: openai-arm (it will create openai-arm-asp as App Service Plan, openai-arm-app for web app, and openaiarmsa for Storage Account) az group create --name <ResourceGroupName> --location <RegionName> az deployment group create --resource-group <ResourceGroupName> --template-file ./openai/tools/arm-template.json --parameters resourcePrefix=<ResourcesPrefixName> If you are using a Windows platform, use the following alternative PowerShell commands instead: # Please change <ResourceGroupName> to your prefer name, for example: azure-appservice-ai # Please change <RegionName> to your prefer region, for example: eastus2 # Please change <ResourcesPrefixName> to your prefer naming pattern, for example: openai-arm (it will create openai-arm-asp as App Service Plan, openai-arm-app for web app, and openaiarmsa for Storage Account) az group create --name <ResourceGroupName> --location <RegionName> az deployment group create --resource-group <ResourceGroupName> --template-file .\openai\tools\arm-template.json --parameters resourcePrefix=<ResourcesPrefixName> After execution, please copy the output section containing 3 key-value pairs from the result like this. Return to bash terminal and execute the following commands: # Please setup 3 variables you've got from the previous step OUTPUT_STORAGE_NAME="<outputStorageName>" OUTPUT_STORAGE_KEY="<outputStorageKey>" OUTPUT_SHARE_NAME="<outputShareName>" sudo mkdir -p /mnt/$OUTPUT_SHARE_NAME if [ ! -d "/etc/smbcredentials" ]; then sudo mkdir /etc/smbcredentials fi CREDENTIALS_FILE="/etc/smbcredentials/$OUTPUT_STORAGE_NAME.cred" if [ ! -f "$CREDENTIALS_FILE" ]; then sudo bash -c "echo \"username=$OUTPUT_STORAGE_NAME\" >> $CREDENTIALS_FILE" sudo bash -c "echo \"password=$OUTPUT_STORAGE_KEY\" >> $CREDENTIALS_FILE" fi sudo chmod 600 $CREDENTIALS_FILE sudo bash -c "echo \"//$OUTPUT_STORAGE_NAME.file.core.windows.net/$OUTPUT_SHARE_NAME /mnt/$OUTPUT_SHARE_NAME cifs nofail,credentials=$CREDENTIALS_FILE,dir_mode=0777,file_mode=0777,serverino,nosharesock,actimeo=30\" >> /etc/fstab" sudo mount -t cifs //$OUTPUT_STORAGE_NAME.file.core.windows.net/$OUTPUT_SHARE_NAME /mnt/$OUTPUT_SHARE_NAME -o credentials=$CREDENTIALS_FILE,dir_mode=0777,file_mode=0777,serverino,nosharesock,actimeo=30 Or you could simply go to Azure Portal, navigate to the File Share you just created, and refer to the diagram below to copy the required command. You can choose Windows or Mac if you are using such OS in your dev environment. After executing the command, the network drive will be successfully mounted. You can use df to verify, as illustrated in the diagram. ARM Template From Azure Portal In addition to using az cli to invoke ARM Templates, if the JSON file is hosted on a public network URL, you can also load its configuration directly into the Azure Portal by following the method described in the article [Deploy to Azure button - Azure Resource Manager]. This is my example. Click Me After filling in all the required information, click Create. Once the creation process is complete, click Outputs on the left menu to retrieve the connection information for the File Share. 4. Running Locally Training Models and Training Data In the next steps, you will need to use OpenAI services. Please ensure that you have registered as a member and added credits to your account (Billing overview - OpenAI API). For this example, adding $10 USD will be sufficient. Additionally, you will need to generate a new API key (API keys - OpenAI API), you may choose to create a project as well for future project organization, depending on your needs (Projects - OpenAI API). After getting the API key, create a text file named apikey.txt in the openai/tools/ folder. Paste the key you just copied into the file and save it. Return to bash terminal and execute the following commands (their purpose has been described earlier). source .venv/openai-webjob/bin/activate bash ./openai/tools/create-folder.sh bash ./openai/tools/download-sample-training-set.sh python ./openai/webjob/cal_embeddings.py --sampling_ratio 0.002 If you are using a Windows platform, use the following alternative PowerShell commands instead: .\.venv\openai-webjob\Scripts\Activate.ps1 .\openai\tools\create-folder.cmd .\openai\tools\download-sample-training-set.cmd python .\openai\webjob\cal_embeddings.py --sampling_ratio 0.002 After execution, the File Share will now include the following directories and files. Let’s take a brief detour to examine the structure of the training data downloaded from the GitHub. The right side of the image explains each field of the data. This dataset was originally used to detect whether news headlines contain sarcasm. However, I am repurposing it for another application. In this example, I will use the "headline" field to create embeddings. The left side displays the raw data, where each line is a standalone JSON string containing the necessary fields. In the code, I first extract the "headline" field from each record and send it to OpenAI to compute the embedding vector for the text. This embedding represents the position of the text in a semantic space (akin to coordinates in a multi-dimensional space). After the computation, I obtain an embedding vector for each headline. Moving forward, I will refer to these simply as embeddings. By the way, the sampling_ratio parameter in the command is something I configured to speed up the training process. The original dataset contains nearly 30,000 records, which would result in a training time of around 8 hours. To simplify the tutorial, you can specify a relatively low sampling_ratio value (ranging from 0 to 1, representing 0% to 100% sampling from the original records). For example, a value of 0.01 corresponds to a 1% sample, allowing you to accelerate the experiment. In this semantic space, vectors that are closer to each other often have similar values, which corresponds to similar meanings. In this context, the distance between vectors will serve as our metric to evaluate the semantic similarity between pieces of text. For this, we will use a method called cosine similarity. In the subsequent tutorial, we will construct some test texts. These test texts will also be converted into embeddings using the same method. Each test embedding will then be compared against the previously computed headline embeddings. The comparison will identify the nearest headline embeddings in the multi-dimensional vector space, and their original text will be returned. Additionally, we will leverage OpenAI's well-known generative AI capabilities to provide a textual explanation. This explanation will describe why the constructed test text is related to the recommended headline. Predicting with the Model Return to terminal and execute the following commands. First, deactivate the virtual environment used for calculating the embeddings, then activate the virtual environment for the Flask application, and finally, start the Flask app. Commands for Linux or Mac: deactivate source .venv/openai/bin/activate python ./openai/api/app.py Commands for Windows: deactivate .\.venv\openai\Scripts\Activate.ps1 python .\openai\api\app.py When you see a screen similar to the following, it means the server has started successfully. Press Ctrl+C to stop the server if needed. Before conducting the actual test, let’s construct some sample query data: education Next, open a terminal and use the following curl commands to send requests to the app: curl -X GET http://127.0.0.1:8000/api/detect?text=education You should see the calculation results, confirming that the embeddings and Gen AI is working as expected. PS: Your results may differ from mine due to variations in the sampling of your training dataset compared to mine. Additionally, OpenAI's generative content can produce different outputs depending on the timing and context. Please keep this in mind. 5. Publishing the Project to Azure Return to terminal and execute the following commands. Commands for Linux or Mac: # Please change <resourcegroup_name> and <webapp_name> to your own # Create the Zip file from project zip -r openai/app.zip openai/* # Deploy the App az webapp deploy --resource-group <resourcegroup_name> --name <webapp_name> --src-path openai/app.zip --type zip # Delete the Zip file rm openai/app.zip Commands for Windows: # Please change <resourcegroup_name> and <webapp_name> to your own # Create the Zip file from project Compress-Archive -Path openai\* -DestinationPath openai\app.zip # Deploy the App az webapp deploy --resource-group <resourcegroup_name> --name <webapp_name> --src-path openai\app.zip --type zip # Delete the Zip file del openai\app.zip PS: WebJobs follow the directory structure of App_Data/jobs/triggered/<webjob_name>/. As a result, once the Web App is deployed, the WebJob is automatically deployed along with it, requiring no additional configuration. 6. Running on Azure Web App Training the Model Return to terminal and execute the following commands to invoke the WebJobs. Commands for Linux or Mac: # Please change <subscription_id> <resourcegroup_name> and <webapp_name> to your own token=$(az account get-access-token --resource https://management.azure.com --query accessToken -o tsv) ; curl -X POST -H "Authorization: Bearer $token" -H "Content-Type: application/json" -d '{}' "https://management.azure.com/subscriptions/<subscription_id>/resourceGroups/<resourcegroup_name>/providers/Microsoft.Web/sites/<webapp_name>/triggeredwebjobs/cal-embeddings/run?api-version=2024-04-01" Commands for Windows: # Please change <subscription_id> <resourcegroup_name> and <webapp_name> to your own $token=$(az account get-access-token --resource https://management.azure.com --query accessToken -o tsv) ; Invoke-RestMethod -Uri "https://management.azure.com/subscriptions/<subscription_id>/resourceGroups/<resourcegroup_name>/providers/Microsoft.Web/sites/<webapp_name>/triggeredwebjobs/cal-embeddings/run?api-version=2024-04-01" -Headers @{Authorization = "Bearer $token"; "Content-type" = "application/json"} -Method POST -Body '{}' You could see the training status by execute the following commands. Commands for Linux or Mac: # Please change <subscription_id> <resourcegroup_name> and <webapp_name> to your own token=$(az account get-access-token --resource https://management.azure.com --query accessToken -o tsv) ; response=$(curl -s -H "Authorization: Bearer $token" "https://management.azure.com/subscriptions/<subscription_id>/resourceGroups/<resourcegroup_name>/providers/Microsoft.Web/sites/<webapp_name>/webjobs?api-version=2024-04-01") ; echo "$response" | jq Commands for Windows: # Please change <subscription_id> <resourcegroup_name> and <webapp_name> to your own $token=$(az account get-access-token --resource https://management.azure.com --query accessToken -o tsv); $response = Invoke-RestMethod -Uri "https://management.azure.com/subscriptions/<subscription_id>/resourceGroups/<resourcegroup_name>/providers/Microsoft.Web/sites/<webapp_name>/webjobs?api-version=2024-04-01" -Headers @{Authorization = "Bearer $token"} -Method GET ; $response | ConvertTo-Json -Depth 10 Processing Complete And you can get the latest detail log by execute the following commands. Commands for Linux or Mac: # Please change <subscription_id> <resourcegroup_name> and <webapp_name> to your own token=$(az account get-access-token --resource https://management.azure.com --query accessToken -o tsv) ; history_id=$(az webapp webjob triggered log --resource-group <resourcegroup_name> --name <webapp_name> --webjob-name cal-embeddings --query "[0].id" -o tsv | sed 's|.*/history/||') ; response=$(curl -X GET -H "Authorization: Bearer $token" -H "Content-Type: application/json" "https://management.azure.com/subscriptions/<subscription_id>/resourceGroups/<resourcegroup_name>/providers/Microsoft.Web/sites/<webapp_name>/triggeredwebjobs/cal-embeddings/history/$history_id/?api-version=2024-04-01") ; log_url=$(echo "$response" | jq -r '.properties.output_url') ; curl -X GET -H "Authorization: Bearer $token" "$log_url" Commands for Windows: # Please change <subscription_id> <resourcegroup_name> and <webapp_name> to your own $token = az account get-access-token --resource https://management.azure.com --query accessToken -o tsv ; $history_id = az webapp webjob triggered log --resource-group <resourcegroup_name> --name <webapp_name> --webjob-name cal-embeddings --query "[0].id" -o tsv | ForEach-Object { ($_ -split "/history/")[-1] } ; $response = Invoke-RestMethod -Uri "https://management.azure.com/subscriptions/<subscription_id>/resourceGroups/<resourcegroup_name>/providers/Microsoft.Web/sites/<webapp_name>/triggeredwebjobs/cal-embeddings/history/$history_id/?api-version=2024-04-01" -Headers @{ Authorization = "Bearer $token" } -Method GET ; $log_url = $response.properties.output_url ; Invoke-RestMethod -Uri $log_url -Headers @{ Authorization = "Bearer $token" } -Method GET Once you see the report in the Logs, it indicates that the embeddings calculation is complete, and the Flask app is ready for predictions. You can also find the newly calculated embeddings in the File Share mounted in your local environment. Using the Model for Prediction Just like in local testing, open a bash terminal and use the following curl commands to send requests to the app: # Please change <webapp_name> to your own curl -X GET https://<webapp_name>.azurewebsites.net/api/detect?text=education As with the local environment, you should see the expected results. 7. Troubleshooting Startup Command Issue Symptom: Without any code changes and when the app was previously functioning, updating the Startup Command causes the app to stop working. The related default_docker.log shows multiple attempts to run the container without errors in a short time, but the container does not respond on port 8000 as seen in docker.log. Cause: Since Linux Web Apps actually run in containers, the final command in the Startup Command must function similarly to the CMD instruction in a Dockerfile. CMD ["/usr/sbin/sshd", "-D", "-o", "ListenAddress=0.0.0.0"] This command must ensure it runs in the foreground (i.e., not in daemon mode) and cannot exit the process unless manually interrupted. Resolution: Check the final command in the Startup Command to ensure it does not include a daemon execution mode. Alternatively, use the Web SSH interface to execute and verify these commands directly. App Becomes Unresponsive After a Period Symptom: An app that runs normally becomes unresponsive after some time. Both the front-end webpage and the Kudu page display an "Application Error," and the deployment log shows "Too many requests." Additionally, the local environment cannot connect to the associated File Share. Cause: Clicking on "diagnostic resources" in the initial error screen provides more detailed error information. In this example, the issue is caused by internal enterprise Policies or Automations (e.g., enterprise applications) that periodically or randomly scan storage account settings created by employees. If the settings are deemed non-compliant with security standards, they are automatically adjusted. For instance, the allowSharedKeyAccess parameter may be forcibly set to false, preventing both the Web App and the local development environment from connecting to the File Share under the Storage Account. Modification history for such settings can be checked via the Activity Log of the Storage Account (note that only the last 90 days of data are retained). Resolution: The proper approach is to work offline with the enterprise IT team to coordinate and request the necessary permissions. As a temporary workaround, modify the affected settings to Enable during testing periods and revert them to Disabled afterward. You can find the setting for allowSharedKeyAccess here. Note: Azure Storage Mount currently does not support access via Managed Identity. az cli command for Linux webjobs fail Symptom: Got "Operation returned an invalid status 'Unauthorized'" message from different platforms even in Azure CloudShell with latest az version Cause: After using "--debug --verbose" from the command I can see the actual error occurred on which REST API, for example, I'm using this command (az webapp webjob triggered): az webapp webjob triggered list --resource-group azure-appservice-ai --name openai-arm-app --debug --verbose Which represent that the operation has invoked under this API: /Microsoft.Web/sites/{app_name}/triggeredwebjobs (Web Apps - List Triggered Web Jobs) After I directly test that API from the official doc, I still get such the error, which means this preview feature is still under construction, and we cannot use it currently. Resolution: I found a related API endpoint via Azure Portal: /Microsoft.Web/sites/{app_name}/webjobs (Web Apps - List Web Jobs) After I directly test that API from the official doc, I can get the trigger list now. So I have modified the original command: az webapp webjob triggered list --resource-group azure-appservice-ai --name openai-arm-app To the following command (please note the differences between Linux/Mac and Windows commands). Make sure to replace <subscription_id>, <resourcegroup_name>, and <webapp_name> with your specific values. Commands for Linux or Mac: token=$(az account get-access-token --resource https://management.azure.com --query accessToken -o tsv) ; response=$(curl -s -H "Authorization: Bearer $token" "https://management.azure.com/subscriptions/<subscription_id>/resourceGroups/<resourcegroup_name>/providers/Microsoft.Web/sites/<webapp_name>/webjobs?api-version=2024-04-01") ; echo "$response" | jq Commands for Windows: $token=$(az account get-access-token --resource https://management.azure.com --query accessToken -o tsv); $response = Invoke-RestMethod -Uri "https://management.azure.com/subscriptions/<subscription_id>/resourceGroups/<resourcegroup_name>/providers/Microsoft.Web/sites/<webapp_name>/webjobs?api-version=2024-04-01" -Headers @{Authorization = "Bearer $token"} -Method GET ; $response | ConvertTo-Json -Depth 10 For "run" commands, due to the same issue when invoking the problematic API, so I also modify the operation. Commands for Linux or Mac: token=$(az account get-access-token --resource https://management.azure.com --query accessToken -o tsv) ; curl -X POST -H "Authorization: Bearer $token" -H "Content-Type: application/json" -d '{}' "https://management.azure.com/subscriptions/<subscription_id>/resourceGroups/<resourcegroup_name>/providers/Microsoft.Web/sites/<webapp_name>/triggeredwebjobs/cal-embeddings/run?api-version=2024-04-01" Commands for Windows: $token=$(az account get-access-token --resource https://management.azure.com --query accessToken -o tsv) ; Invoke-RestMethod -Uri "https://management.azure.com/subscriptions/029b4739-1f55-4cab-bf84-a9393f8ac8fe/resourceGroups/azure-appservice-ai/providers/Microsoft.Web/sites/openai-arm-app/triggeredwebjobs/cal-embeddings/run?api-version=2024-04-01" -Headers @{Authorization = "Bearer $token"; "Content-type" = "application/json"} -Method POST -Body '{}' Others Using Scikit-learn on Azure Web App 8. Conclusion Beyond simple embedding vector calculations, OpenAI's most notable strength is generative AI. You can provide instructions to the GPT model through natural language (as a prompt), clearly specifying the format you need in the instruction. You can then parse the returned content easily. While PaaS products are not ideal for heavy vector calculations, they are well-suited for acting as intermediaries to forward commands to generative AI. These outputs can even be used for various applications, such as patent infringement detection, plagiarism detection in research papers, or trending news analysis. I believe that in the future, we will see more similar applications on Azure Web Apps. 9. References Overview - OpenAI API News-Headlines-Dataset-For-Sarcasm-Detection Quickstart: Deploy a Python (Django, Flask, or FastAPI) web app to Azure - Azure App Service Configure a custom startup file for Python apps on Azure App Service on Linux - Python on Azure Mount Azure Storage as a local share - Azure App Service Deploy to Azure button - Azure Resource Manager Using Scikit-learn on Azure Web AppHow to Retrieve Loop Workspaces Data with PowerShell
A previous attempt to write a script to report all Loop workspaces in a tenant was flawed because it only retrieved the first 200 workspaces. I hadn’t realized that the Get-SPOContainer cmdlet supported an odd form of pagination to retrieve workspace data. In any case, I figured out how to page top find all available workspaces and updated the script. It’s just another example of oddness in the SharePoint Online PowerShell module. https://office365itpros.com/2024/04/08/loop-workspaces-report-ps/Microsoft Graph PowerShell SDK Runs into Choppy Waters
A bunch of problems with V2.26 of the Microsoft Graph PowerShell SDK V2.26 make the software unusable. Not only did Microsoft do a horrible job to test the new release before making it available to customers, but they also failed to communicate the level of change in the new SDK and how it could impact Azure Automation runbooks. https://office365itpros.com/2025/02/25/graph-sdk-v2-26-issues/Practical Graph: Creating SharePoint News Items from an RSS Feed
This article explains how to use the SharePoint Pages API to create new site pages with the Microsoft Graph PowerShell SDK after reading information from an RSS feed. Parsing the RSS feed to find article details is just one of the challenges encountered and solved to accomplish the goal. https://practical365.com/sharepoint-pages-api-rss/EOL - DDG - EQ operator wildcard bug fix
EOL > MSFT recently announced (i/t Health - Message center - MC912176) a bug fix related to the -eq operator and use of wildcards ('*') to compile the member list for Dynamic Distribution Groups. When the update takes effect, the * character will be treated as a literal value and not a wildcard. Therefore, expressions using the -eq operator will only evaluate to true if the right-hand side value matches the property values exactly. For example, the expression (WindowsLiveID -eq '*@microsoft.com') will only return true if the recipient’s WindowsLiveID property is exactly "*@microsoft.com". The current behavior will no longer function after the fix, and this may impact any processes that rely on this behavior. To distinguish the DLs (for corporate communication) membership of internal employees only and external consultants/contractors, we've created several DDGs using PowerShell to define the Recipient filter options based on our internal naming conventions (FTEs get FN.LN@emaildomain whereas consultants/contractors get FN.LN.ext@emaildomain). The formula was based on -eq operator combined with '*' wildcard > formula extract > -RecipientFilter {(( RecipientType -eq 'UserMailbox') -and ((WindowsLiveID -eq '*.*.ext@emaildomain') ... When the bug fix is corrected the formulas will no longer return the expected members. We've tried already several alternatives w/t -like operator but were not able to retrieve the same list. Other operators as -contains and -match are not supported i/t context of EOL. Are there other operators or other parameters to compile a filtered list using wildcards?
