1. Throttling and Caching for AI API Endpoints with Azure API Management

    Python

    To implement throttling and caching for AI API endpoints using Azure API Management (APIM), we'll use Pulumi to create and configure an Azure API Management service with the required policies. We will follow these steps:

    1. Create an Azure API Management Service: This manages and exposes the AI API endpoints.
    2. Configure a Product: We define a product in Azure APIM that will bundle our API and we can apply policies at the product level.
    3. Define an API: Here we describe our AI API endpoint within the Azure APIM service.
    4. Implement Policies: We will set up policies for caching and throttling at the API or operation level within the product to manage traffic and improve performance.

    We'll use the azure-native library as it provides the most up-to-date resources for working with Azure in Pulumi.

    Here's the Python program which setups the API Management Service, configures caching and throttling policies:

    import pulumi import pulumi_azure_native as azure_native # Initialize Azure API Management resource arguments api_management_args = azure_native.apimanagement.ServiceArgs( publisher_email="contact@yourcompany.com", publisher_name="Your Company", resource_group_name="your-resource-group", sku=azure_native.apimanagement.ServiceSkuPropertiesArgs( name=azure_native.apimanagement.SkuType.DEVELOPER, capacity=1 ) ) # Create Azure API Management Service api_management_service = azure_native.apimanagement.Service( "api-management-service", args=api_management_args ) # Define the Product in which the API will live product = azure_native.apimanagement.Product( "product", product_id="ai-product", display_name="AI Services", description="A bundle of AI service APIs", service_name=api_management_service.name, resource_group_name=api_management_args.resource_group_name ) # Define the API for the AI endpoint within the API Management Service api = azure_native.apimanagement.Api( "ai-api", api_id="ai-api-endpoint", display_name="AI API Endpoint", description="AI API Endpoint for various AI services", service_name=api_management_service.name, resource_group_name=api_management_args.resource_group_name, protocols=["https"], path="aiapi", subscription_required=True ) # Define the API operation, in this case, assuming a POST operation to the AI API api_operation = azure_native.apimanagement.ApiOperation( "post-operation", operation_id="ai-operation", method="POST", url_template="/analyze", request=azure_native.apimanagement.RequestContractArgs( description="Request to AI Analysis", query_parameters=[ azure_native.apimanagement.ParameterContractArgs( name="image", description="Image to analyze", required=True, type="string" ) ] ), responses=[azure_native.apimanagement.ResponseContractArgs( status=200, description="AI Analysis Response" )], display_name="Post AI Analysis", api_id=api.api_id, service_name=api_management_service.name, resource_group_name=api_management_args.resource_group_name ) # Apply caching policy to cache responses for 60 seconds cache_policy = azure_native.apimanagement.Policy( "cache-policy", policy_id="policy", value=""" <policies> <inbound> <cache-lookup vary-by-developer="false" vary-by-query-string="true" duration="60" /> </inbound> <backend> <forward-request /> </backend> <outbound> <cache-store duration="60" /> </outbound> </policies> """, format="xml", api_id=api.api_id, service_name=api_management_service.name, resource_group_name=api_management_args.resource_group_name ) # Apply rate limit (throttle) policy to limit calls to 5 per minute throttle_policy = azure_native.apimanagement.Policy( "throttle-policy", policy_id="policy", value=""" <policies> <inbound> <rate-limit calls="5" renewal-period="60" /> <quota-by-key calls="40" counter-key="@(context.Subscription.Id)" renewal-period="3600" /> </inbound> </policies> """, format="xml", api_id=api.api_id, service_name=api_management_service.name, resource_group_name=api_management_args.resource_group_name ) # Export the API endpoint for reference pulumi.export("api_management_service_endpoint", api_management_service.gateway_url)

    In the above code:

    • We established a new Azure API Management Service to manage our AI APIs.
    • A Product in API Management is defined, which acts like a grouping for our APIs.
    • An API representing our AI service is configured, along with a specific operation (such as a POST request).
    • XML-based policies for caching and quota rate limiting are applied to the API operation. These policies control how the response caching behaves and how the API Service throttles the number of incoming requests.
    • Lastly, we export the API Management Service endpoint for easy access outside of Pulumi.

    You can now take this program and adjust values like resource_group_name, publisher_email, publisher_name, and other parameters to suit your environment and requirements. Once you deploy this Pulumi program, your AI API endpoints will be managed by Azure API Management Service with the defined caching and throttling policies in place.


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