1. RESTful API Gateway for LLM Request Handling


    To create a RESTful API Gateway that can handle requests for a Language Learning Model (LLM), we'll use AWS services through Pulumi's Python SDK. In this scenario, the focus will be on setting up the API Gateway which acts as the front door to our backend services, handling RESTful API requests, authenticating requests, and routing them to the appropriate Lambda function that interacts with our LLM.

    The main resources we'll create include:

    • An AWS API Gateway REST API that defines the entry point for the API.
    • Resources and methods for the API, specifying the paths and HTTP methods.
    • A Lambda function as a backend to handle the API requests.
    • Permissions for the API Gateway to invoke the Lambda function.

    Here's how each piece fits together:

    • The RestApi is the core AWS API Gateway resource that we'll define using our APIs' configuration.
    • Next, we'd define Resources inside our RestApi, representing different endpoints of our API.
    • For each Resource, we'd define Methods that represent how HTTP methods (like GET or POST) are handled.
    • We'll then create a Lambda function that will contain the logic for processing the LLM requests.
    • Lastly, we'll grant our API permission to invoke our Lambda function via a LambdaPermission.

    Let's dive into the Pulumi program that will create this setup:

    import pulumi import pulumi_aws as aws # Define the Lambda function that will handle the API requests. llm_handler = aws.lambda_.Function("llmHandler", runtime="python3.8", code=pulumi.FileArchive("./lambda"), # Assuming your Lambda code is zipped in the 'lambda' directory handler="handler.main", # Assuming your entry point is named 'main' in 'handler.py' role=pulumi_aws.iam.Role("lambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Principal": { "Service": "lambda.amazonaws.com" }, "Effect": "Allow", "Sid": "" }] }""").arn) # Create an API Gateway REST API. api = aws.apigateway.RestApi("api", description="API for LLM request handling") # Create a resource under the API for LLM. llm_resource = aws.apigateway.Resource("llmResource", rest_api=api.id, parent_id=api.root_resource_id, path_part="llm") # Create a GET method on the LLM resource. get_method = aws.apigateway.Method("getMethod", rest_api=api.id, resource_id=llm_resource.id, http_method="GET", authorization="NONE") # Create an Integration to connect the GET method to the Lambda function. integration = aws.apigateway.Integration("lambdaIntegration", rest_api=api.id, resource_id=llm_resource.id, http_method=get_method.http_method, integration_http_method=get_method.http_method, type="AWS_PROXY", uri=llm_handler.invoke_arn) # Grant the API Gateway permission to invoke the Lambda function. invoke_permission = aws.lambda_.Permission("invokePermission", action="lambda:InvokeFunction", function=llm_handler.name, principal="apigateway.amazonaws.com", source_arn=pulumi.Output.all(api.execution_arn, llm_resource.path_part, get_method.http_method).apply( lambda args: f"{args[0]}/*/{args[2]}/{args[1]}")) # Deploy the API to make it accessible. deployment = aws.apigateway.Deployment("apiDeployment", rest_api=api.id, description="Deploy LLM API", stage_name="v1") # Output the invoke URL which can be used to interact with the API. pulumi.export("invoke_url", deployment.invoke_url.apply(lambda url: f"{url}llm"))

    This program does the following:

    • It defines a Lambda function llm_handler, which is our backend service to process the LLM requests.
    • A new API Gateway RestApi is created to define the entry point for the API.
    • Inside the above RestApi, a Resource is defined as /llm, which would be appended to the base URL of the API.
    • A Method is associated with the /llm resource which responds to HTTP GET methods.
    • An Integration is set up to connect the GET method of the /llm resource with our Lambda function, enabling the Lambda function to receive HTTP requests through the API Gateway.
    • A Permission is granted for the API Gateway to invoke the Lambda function. For this example, we're constructing the ARN to match any path and method on the llm resource.
    • Finally, the program deploys the API Gateway with a Deployment and exports the invoke URL, which can be used to interact with the deployed API.

    Keep in mind that in a production setup, you should have additional configurations for logging, throttling, request and response transformations, and more. The `aws.lambda_.Permission