Foundations45 minAI Engineering Foundations

Build a Production HTTP API

Build a FastAPI endpoint that accepts JSON, validates input, handles errors gracefully and returns structured responses. The foundation of every AI service.

Scenario

You're building the service that will later host your LLM endpoints. Before any AI, build a robust, validated, well-error-handled HTTP API.

Objective

Implement a POST /api/echo endpoint that validates input, returns structured output, and handles errors with proper status codes.

Starter code

Implement the TODOs to complete the lab.

main.pypython

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI()

class EchoRequest(BaseModel):
    message: str
    uppercase: bool = False

class EchoResponse(BaseModel):
    original: str
    echoed: str
    length: int

@app.post("/api/echo")
def echo(req: EchoRequest):
    # TODO: implement echo logic
    # - if uppercase, transform message
    # - return EchoResponse
    pass

Solution hints

1Use req.uppercase to conditionally transform
2Return EchoResponse with original, echoed, length
3Add a try/except for unexpected errors
4Test with empty message — should it error?

Validation steps

Your implementation should pass these checks.

POST /api/echo with {message: 'hello'} returns echoed 'hello', length 5
POST /api/echo with {message: 'hello', uppercase: true} returns 'HELLO'
POST /api/echo with empty body returns 422 validation error
GET /api/echo returns 405 method not allowed

Run validation

This is a simulated validation environment. In production, this would run your code against the validation steps.