APigateway for LLM Inference Request Routing.

Question

Pulumi · Accepted Answer

To set up an API Gateway for routing inference requests to a Lambda function serving a language model (LLM), you'd use various AWS services such as Lambda, API Gateway and possibly IAM for permissions. Below is a detailed explanation and a Pulumi program in Python illustrating how you would provision these resources.

### Explanation

1. **Lambda Function**: We will create an AWS Lambda function that will serve your LLM inference requests. This function will handle incoming HTTP requests from the API Gateway, perform inference using your LLM, and return the results.

2. **API Gateway**: An Amazon API Gateway will be set up to expose your Lambda function over HTTP(s). This provides you with a URL to make inference requests to your LLM.

3. **IAM Role**: The Lambda function requires an execution role that provides permissions to run and to log to CloudWatch.

4. **Permissions**: You need to provide the API Gateway with permissions to invoke your Lambda function.

Here's the complete Pulumi program that creates these resources:

```python
import pulumi
import pulumi_aws as aws

# Create an IAM role that the Lambda function will assume
lambda_role = aws.iam.Role("lambdaRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            }
        }]
    }""")

# Attach the AWS managed LambdaBasicExecutionRole policy to the role
policy_attachment = aws.iam.RolePolicyAttachment("lambdaPolicyAttachment",
    role=lambda_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole")

# Define the Lambda function
llm_handler = aws.lambda_.Function("llmHandler",
    runtime="python3.8", # Replace with your desired runtime
    code=pulumi.FileArchive("./path-to-your-lambda-code.zip"), # Update with the path to your LLM model code
    timeout=300,  # Setting it to 5 minutes as inference can be a long-running task depending on the model
    handler="app.handler",  # Replace with the appropriate handler
    role=lambda_role.arn)

# Define an API Gateway to make the Lambda function accessible via HTTP
api = aws.apigatewayv2.Api("httpApi",
    protocol_type="HTTP")

# Define the integration between the API Gateway and Lambda function
integration = aws.apigatewayv2.Integration("lambdaIntegration",
    api_id=api.id,
    integration_type="AWS_PROXY",
    integration_uri=llm_handler.invoke_arn,
    payload_format_version="2.0")

# Define the route for the incoming HTTP requests
route = aws.apigatewayv2.Route("lambdaRoute",
    api_id=api.id,
    route_key="POST /inference",  # Adjust this depending on the endpoint you wish to expose
    target=pulumi.Output.concat("integrations/", integration.id))

# Deploy the API
deployment = aws.apigatewayv2.Deployment("apiDeployment",
    api_id=api.id,
    lifecycle={
        "create_before_destroy": True,
    })

# Define a stage, this is like an environment (e.g., prod, dev, staging)
stage = aws.apigatewayv2.Stage("apiStage",
    api_id=api.id,
    deployment_id=deployment.id,
    name="prod",  # Prod stage, you might want to parameterize this based on your environment
    auto_deploy=True)

# Lambda permission to allow invocation from the API Gateway
permission = aws.lambda_.Permission("apiGatewayPermission",
    action="lambda:InvokeFunction",
    principal="apigateway.amazonaws.com",
    function=llm_handler.name,
    source_arn=pulumi.Output.concat(api.execution_arn, "/*/*"))

# Output the HTTPS endpoint for the deployed API
pulumi.export("api_endpoint", api.api_endpoint)
```

In the program above:
- We create an IAM role that grants necessary permissions for the Lambda function.
- The IAM policy attached to this role allows logging to AWS CloudWatch.
- We create a Lambda function (`llm_handler`) with the necessary information, including the code packaging and the IAM role.
- An API Gateway is set up (`api`) for HTTP communication.
- We define an integration between our API and Lambda function (`integration`).
- A route (`route`) is created that the API Gateway will listen on for inference requests and forward to the Lambda.
- A deployment (`deployment`) and a stage (`stage`) are defined for the API, which effectively deploys the API so it's publicly accessible.
- We grant the API Gateway permission to invoke the Lambda function (`permission`).
- Finally, we export the URL (`api_endpoint`) of the deployed API Gateway to allow making inference requests.

Before running this Pulumi program, you should have the Lambda function code ready in a ZIP file located at the path specified in the `pulumi.FileArchive` and appropriate handler set in the `handler` property. This code assumes that your LLM model and inference logic are encapsulated within the Lambda function code.

Remember to replace `"./path-to-your-lambda-code.zip"` with the location of your actual Lambda function code and update the `handler` attribute to the entry point of your Lambda function within that code package.