FastAPI Minimal

FastAPI reads your Python type hints at runtime. If you say a field is a str with minimum length 1, it validates every request automatically and generates OpenAPI docs for free.

from fastapi import FastAPI, HTTPException from pydantic import BaseModel, Field app = FastAPI() class TodoItem(BaseModel): title: str = Field(min_length=1, max_length=120) done: bool = False TODOS: dict[int, TodoItem] = {} _next_id = 1 @app.post("/todos", status_code=201) async def create_todo(item: TodoItem) -> dict: global _next_id tid = _next_id _next_id += 1 TODOS[tid] = item return {"id": tid, **item.model_dump()} @app.get("/todos/{tid}") async def get_todo(tid: int) -> dict: if tid not in TODOS: raise HTTPException(status_code=404, detail="not found") return {"id": tid, **TODOS[tid].model_dump()}

Dependency injection for auth

from fastapi import Depends, Header async def require_api_key(x_api_key: str = Header()) -> str: if x_api_key != "secret123": raise HTTPException(status_code=401, detail="bad api key") return x_api_key @app.get("/secure") async def secure(api_key: str = Depends(require_api_key)) -> dict: return {"ok": True}

Understanding "FastAPI Minimal" in practice: AI-assisted coding shifts work from syntax recall to design thinking — models handle boilerplate so you focus on architecture. FastAPI is Python's modern web framework. Type hints become schema. Docs auto-generate. Ship an API in 20 lines — and knowing how to apply this gives you a concrete advantage.

• Apply FastAPI in your ai-coding workflow to get better results
• Apply Pydantic in your ai-coding workflow to get better results
• Apply dependency in your ai-coding workflow to get better results
• Apply async in your ai-coding workflow to get better results

1. 1Use AI to generate unit tests for an existing function
2. 2Ask AI to refactor a messy function and explain the changes
3. 3Have AI suggest a code review for a recent pull request

The big idea: types define your contract, FastAPI enforces it, and dependencies keep your routes clean.