1. Serverless API Endpoints for AI Services with Cloudflare Pages.


    To create serverless API endpoints for AI services using Cloudflare Pages, you would typically need to:

    1. Set up a Cloudflare Pages project to serve your front-end application and its static assets.
    2. Deploy serverless functions (using Cloudflare Workers) that will act as your API endpoints.
    3. Optionally, secure your endpoints using Cloudflare's security features and configure deployment settings.

    In our Pulumi program, we are going to:

    • Define a Cloudflare Pages project which will host the static assets and UI for our service.
    • Set up Cloudflare Workers as serverless functions to handle API requests and provide AI services.
    • Link these together within Cloudflare, such as having the Pages project call the Worker scripts for dynamic content.

    Here's how you can do it with Pulumi and Python:

    import pulumi import pulumi_cloudflare as cloudflare # Your account and project details account_id = 'your-cloudflare-account-id' project_name = 'your-project-name' domain = 'example.com' # The domain under which the Pages and Workers will be available # Configuring a new Cloudflare Pages Project pages_project = cloudflare.PagesProject('ai-service-pages', account_id=account_id, name=project_name, build_config=cloudflare.PagesProjectBuildConfigArgs( build_command='build', destination_dir='dist' ), source=cloudflare.PagesProjectSourceArgs( type='github', config=cloudflare.PagesProjectSourceConfigArgs( owner='your-github-username', repo_name='your-repo-name', production_branch='main', pr_comments_enabled=True, deployments_enabled=True ) ), ) # Configuring a Cloudflare Worker as an API endpoint worker_script = cloudflare.WorkerScript('ai-service-api', account_id=account_id, name='ai-service-api', content=""" addEventListener('fetch', event => { event.respondWith(handleRequest(event.request)) }) async function handleRequest(request) { // Your AI service logic here return new Response('Hello AI Service!', {status: 200}) } """) # Optionally, you can configure custom domains for your Pages project. pages_domain = cloudflare.PagesDomain("custom-domain", account_id=account_id, domain=domain, project_name=project_name ) # Output the URL of the Pages Project pulumi.export('pages_url', pages_project.default_domain) # Output the name of the Worker Script, which you can use to construct the API endpoint URL pulumi.export('worker_script_name', worker_script.name) # Construct the full API endpoint URL and export it api_url = pulumi.Output.concat('https://', worker_script.name, '.', pages_project.default_domain) pulumi.export('api_endpoint_url', api_url)


    • We create a PagesProject which represents the static site hosting, linked to your GitHub repository for continuous deployment.
    • We establish a WorkerScript that serves as an API endpoint. This is where you will place the code that interacts with AI services.
    • We configure a custom domain with PagesDomain for the Pages project if you own the domain and want to use it.
    • We export the URLs for the pages project and the API endpoint, which will be useful to know once the deployment is complete.

    Remember to replace placeholder values, such as account IDs, domain names, project names, GitHub owner, and repository name, with your actual Cloudflare and GitHub details. The workers' script's content should contain your AI service's logic, which will process incoming requests.

    This program assumes that your Pulumi and Cloudflare configurations are set up and that your Pulumi CLI is authenticated with Cloudflare. You need to define the content of the Cloudflare Worker according to your AI service logic. The response in the worker script is just a placeholder and doesn't represent an actual AI service.

    After deploying this Pulumi program, any push to the production_branch of the linked GitHub repository will trigger a new build and deployment on Cloudflare Pages, serving your static content. Requests to the API endpoints defined in the Cloudflare Worker will be processed by your serverless function code, enabling you to provide AI services serverlessly.