1. Connecting AI Services with Logic Apps


    To integrate AI services with Azure Logic Apps, you'll typically create a Logic App workflow that connects to the specific AI services you want to use. Let's suppose you want to create an Azure Logic App workflow that integrates with an Azure Cognitive Services API to analyze text.

    Azure Logic Apps provides a way to automate processes as a series of steps. The following Pulumi program will create a Logic App that connects to the Text Analytics API in Azure Cognitive Services. To achieve this, we will use the Workflow resource from the pulumi_azure_native.logic module. We will also create an IntegrationAccount to manage the resources for our Logic App.

    Here is a high-level overview of what our Pulumi program will do:

    1. Define the Azure resources required for our Logic App.
    2. Define the workflow for the Logic App using the Azure Logic App Designer's JSON definition format, which involves setting up a trigger and an action.
    3. Export any outputs we want to retrieve after deployment, such as the Logic App URL.

    Below is the program written in Python for Pulumi:

    import pulumi import pulumi_azure_native.logic as logic import pulumi_azure_native.resources as resources # Create a new Resource Group to contain our Logic App and associated resources. resource_group = resources.ResourceGroup("ai_logic_app_rg") # Define an Integration Account to manage the resources for a Logic App. integration_account = logic.IntegrationAccount("ai_int_account", resource_group_name=resource_group.name, location=resource_group.location, sku=logic.IntegrationAccountSkuArgs( name="Standard", ), ) # Define the Logic App workflow. workflow = logic.Workflow("ai_workflow", location=resource_group.location, resource_group_name=resource_group.name, definition={ # Replace with actual designer JSON definition content "definition": { "$schema": "https://schema.management.azure.com/providers/Microsoft.Logic/schemas/2016-06-01/workflowdefinition.json#", "contentVersion": "", "actions": { # You can define your actions here. For example, calling the Text Analytics API. }, "outputs": { # Define any outputs from your Logic App you wish to capture. }, "triggers": { # Define any triggers here. For example, Recurrence or HTTP Request. } }, "parameters": { # Define any parameters you need for your logic app's actions and triggers. } }, ) # Export the Logic App URL to view the workflow in the Azure portal after deployment. pulumi.export("logic_app_url", workflow.endpoint_configuration.apply( lambda endpoint: endpoint.workflow.default if endpoint and endpoint.workflow else None ))

    To flesh out this program further, you'd need to specify detailed actions in the definition attribute of the Workflow. The actions block would contain the steps your Logic App performs, such as connecting to an AI service, processing data, or responding to events.

    Additionally, you would define triggers, such as HTTP requests or periodic triggers, depending on how you want to start the workflow. You can design your Logic App in the Azure portal using the visual designer, which will generate this JSON for you, and then copy and paste the JSON into your Pulumi program.

    The pulumi.export line is important because it allows you to see the URL of your Logic App in Azure after it has been deployed by Pulumi. You can then navigate to this URL to check the status of your Logic App or to further modify it in the Azure portal.

    Remember that you will need the appropriate permissions and possibly subscription details set up with Pulumi's Azure provider to deploy this successfully. This program assumes that you've already handled authentication and configuration for Azure outside of the code shown here.

    By using Pulumi, you can automate the deployment of complex workflows in a way that is repeatable and version-controlled, leveraging infrastructure as code principles.