Routing AI API Requests with Cloudflare Workers

Question

Pulumi · Accepted Answer

To route AI API requests with Cloudflare Workers using Pulumi, you'll need to define two main resources:

1. `cloudflare.WorkerScript`: This represents the Cloudflare Worker itself, which contains the logic for handling incoming HTTP requests and routing them to your AI API or performing computations on-the-fly before responding.

2. `cloudflare.WorkerRoute`: This resource creates a route pattern that determines which requests should be handled by your Worker.

Here's a step-by-step explanation of the program:

- **Import Required Modules**: We'll need to import `pulumi` for base functionality and `pulumi_cloudflare` for Cloudflare-specific resources.
- **Worker Script Logic**: While this example won't include the actual JavaScript or WASM for the Worker, you'll need to provide that in the `content` parameter of the `cloudflare.WorkerScript` resource. For example, it could be a simple fetch request to your AI API based on the request path or query parameters.
- **Worker Route**: A route pattern is created to match any requests to a specific URL pattern. When a request matches this pattern, Cloudflare will invoke the defined Worker script to handle the request.
- **Pulumi Exports**: The program will export the deployed worker's name and the pattern so that you can easily test and reference them.

Here's a Pulumi Python program that accomplishes this:

```python
import pulumi
import pulumi_cloudflare as cloudflare

# Setup the Cloudflare Worker script code (This should be your deployed JavaScript/WASM script)
# For this example, assume the Worker script code has already been 
# prepared and is available at `./worker.js`.
worker_script_content = open('./worker.js').read()

# Define the Worker script that will handle the requests
worker_script = cloudflare.WorkerScript("ai-api-worker-script",
    name="ai-api-worker",
    content=worker_script_content,
    # Additional bindings like KV storage, secrets, or environment variables can be added here.
    # Example for KV-Namespace binding (uncomment if needed):
    # kv_namespace_bindings=[cloudflare.WorkerScriptKvNamespaceBindingArgs(
    #     name="EXAMPLE_KV_NAMESPACE",
    #     namespace_id="replace-with-namespace-id"
    # )]
)

# Define the Worker route pattern that will trigger the worker script
# Replace `example.com` with your zone's domain and adjust the pattern as required.
worker_route = cloudflare.WorkerRoute("ai-api-worker-route",
    zone_id="replace-with-your-cloudflare-zone-id",  # The Zone ID of your domain in Cloudflare
    pattern="example.com/api/*",                      # The route pattern to match requests
    script_name=worker_script.name                    # The name of the worker script to run
)

# Exporting the worker script name and route pattern for easy access
pulumi.export("worker_script_name", worker_script.name)
pulumi.export("worker_route_pattern", worker_route.pattern)
```

Ensure that you have the `pulumi_cloudflare` package installed in your Python environment and replace placeholders like `replace-with-your-cloudflare-zone-id` and the script content (`./worker.js`) with your actual information.

Upload your AI API worker JavaScript or WASM code to a file (in this example, it's `worker.js`) and reference it in the `content` argument of your `cloudflare.WorkerScript`.

Adjust the `pattern` property of the `cloudflare.WorkerRoute` resource according to the URLs you expect to serve. The current pattern "`example.com/api/*`" will match any requests that go to "`api`" and send those requests to the Worker.

To deploy this program, you'll need to use the `pulumi up` command with the Pulumi CLI after setting up your Pulumi stack and Cloudflare access credentials. If you need to handle secrets or environment-sensitive configurations, consider using Pulumi's Config system which wasn't covered in this example for brevity.

After the deployment completes, any requests matching the route pattern will be managed by your Worker script, which can perform actions such as manipulating the request, performing computations, or proxying to an AI API as needed.