1. Custom API Workflows for AI Services with Azure API Management

    Python

    To create custom API workflows for AI services with Azure API Management, you would typically define and configure several components within the Azure API Management service. These components include APIs, operations, policies, products, and possibly gateways or backends, depending on the complexity and requirements of your workflow. Using Pulumi, you can programmatically define these resources, which allows for repeatability, version control, and more sophisticated infrastructure management.

    In the Python program below, I'll guide you through a basic setup of Azure API Management using Pulumi. We'll:

    1. Create an API Management service instance.
    2. Define a new API that connects to an AI service (for illustration purposes, let's assume a generic AI service with an HTTP endpoint).
    3. Set up a product, which is a logical grouping of one or more APIs.
    4. Configure a policy to enforce certain behaviors, like rate limits or transformation of incoming requests.

    Here is a detailed Pulumi Python program to set up an API Management service with these components:

    import pulumi from pulumi_azure_native import apimanagement as api_management # Replace 'resource_group_name' with your Azure Resource Group name resource_group_name = 'my-resource-group' # Create an instance of the API Management service api_management_service = api_management.Service("apim-service", resource_group_name=resource_group_name, sku_name="Developer", publisher_name="MyCompany", publisher_email="contact@mycompany.com") # Define a new API linked to an AI service # Replace 'ai_service_url' with the endpoint URL of your AI service ai_service_api = api_management.Api("ai-service-api", resource_group_name=resource_group_name, service_name=api_management_service.name, display_name="AI Service API", path="ai-service", service_url="https://ai-service.example.com") # Set up a product to group the AI service API ai_service_product = api_management.Product("ai-service-product", resource_group_name=resource_group_name, service_name=api_management_service.name, display_name="AI Service Product", published=True) # Associate the API with the product api_product_association = api_management.ApiProduct("api-product-association", api_id=ai_service_api.name, product_id=ai_service_product.product_id, service_name=api_management_service.name, resource_group_name=resource_group_name) # Configure a policy on the API # For demo purposes, this policy sets a rate limit of 5 calls per minute per subscription policy_content = """<policies> <inbound> <rate-limit calls="5" renewal-period="60" /> <base /> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <base /> </on-error> </policies>""" api_policy = api_management.Policy("ai-service-api-policy", resource_group_name=resource_group_name, service_name=api_management_service.name, api_id=ai_service_api.name, value=policy_content, format="xml") # Export the API Management service endpoint pulumi.export("api_management_service_endpoint", api_management_service.gateway_url)

    In this program:

    • We create an Azure API Management service instance (api_management.Service), which will host our APIs and the related configuration.
    • We define a new API (api_management.Api) that serves as a proxy to an external AI service. You need to input the actual URL of your AI service in place of ai_service_url.
    • We establish a product (api_management.Product) to logically group this API, making it easier to manage access and subscriptions.
    • We set up a rate limit policy (api_management.Policy) to restrict the number of calls to 5 per minute, which is a common requirement to protect backend services and control usage.
    • We export the endpoint URL of the API Management service, which can be used to access the managed APIs.

    This program uses pulumi_azure_native, a Pulumi package for interacting with Azure native resources.

    Remember, the above code is a starting point demonstrating a simple API setup. You will likely need to further refine policies, operations, and security according to your specific AI service's requirements and the goals of your API workflows.


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