Week 12 of 16

FastAPI Fundamentals

From Flask to production-grade APIs — automatic validation, automatic docs, and speed that scales.

Day 56 75 minutes Watch

Day 56 of 80

Why FastAPI?

Flask taught you to think like a web developer. FastAPI teaches you to think like an API developer — and that's a different skill. Flask is excellent for learning and for rendering HTML pages. FastAPI is what production Python APIs look like in 2026.

Three things make FastAPI stand out:

Flask vs FastAPI Side by Side

If you've been writing Flask routes, FastAPI will feel immediately familiar — but cleaner in every dimension.

What you're doing Flask FastAPI
Define a GET route @app.route("/prompts", methods=["GET"]) @app.get("/prompts")
Read form/request data request.form or request.json Typed function parameters — no import needed
Validate incoming data Manual — you write the checks yourself Auto-validation via Pydantic — declare the shape, FastAPI enforces it
API documentation None built in — you'd write it separately Auto-generated interactive UI at /docs
Type hints Optional / cosmetic Functional — used for validation and docs generation
Async routes Via extensions (Flask-Async) Native — just use async def

Installation

Two packages: fastapi (the framework) and uvicorn (the server that runs it). Flask uses a built-in dev server; FastAPI uses uvicorn, which is production-grade from day one.

terminal bash
# Install both packages
pip install fastapi uvicorn

# Run your app (fastapi_test.py, app = FastAPI(...))
uvicorn fastapi_test:app --reload

# --reload means the server restarts when you save changes
# The server starts at http://localhost:8000
The fastapi_test:app syntax means "in the file fastapi_test.py, find the variable named app." The --reload flag is for development — never use it in production.

Watch First

Before writing any code today, watch this full tutorial. Tech With Tim covers everything from a blank file to a working API with path parameters and Pydantic models. The runtime is about 60 minutes — worth every minute.

Tech With Tim — FastAPI Full Tutorial
~60 minutes • YouTube
Watch on YouTube →

After watching, also read the official FastAPI First Steps page. It's short, well-written, and shows you the minimal viable app:

FastAPI Official Tutorial — First Steps →

Your First FastAPI App

Here's the smallest possible FastAPI application. Compare this to what Flask requires — FastAPI is more concise and does more automatically.

main.py python
from fastapi import FastAPI

app = FastAPI(title="DVP Prompt Vault API")

# @app.get means: respond to GET requests at this path
@app.get("/")
def read_root():
    return {"message": "DVP Prompt Vault API is running"}

# Path parameter: {item_id} in the URL → item_id in the function
@app.get("/items/{item_id}")
def read_item(item_id: int):
    return {"item_id": item_id}
Notice item_id: int — that type hint isn't just documentation. FastAPI uses it to validate the URL. If someone visits /items/abc, FastAPI returns a 422 error automatically. You wrote zero validation code.
HTTP Methods — The Four Verbs

Every API operation maps to an HTTP method. FastAPI has a decorator for each one:

A well-designed REST API uses these four verbs consistently. You don't need a route called /delete_prompt — that's what DELETE /prompts/{id} is for.

The Free Gift: /docs

This is one of the most useful things about FastAPI. Run your app and visit http://localhost:8000/docs. You'll see a full interactive UI for every route you've defined. You can:

Zero Code Required

You didn't write any of the /docs UI. FastAPI generates it by reading your type hints and function signatures. Every time you add a route, the docs update automatically. This is the power of making type hints functional rather than cosmetic.

There's also a second docs UI at /redoc — same data, different visual style. Use whichever you prefer.

Coming Up: Pydantic Models

On Day 58 you'll use Pydantic to define exactly what shape your data should have. Here's a preview of the concept so it doesn't feel foreign when you get there:

preview — not for today python
from pydantic import BaseModel

# Define what a valid prompt looks like
class PromptCreate(BaseModel):
    platform: str
    shot: str
    prompt_text: str

# FastAPI validates the request body against this model
@app.post("/prompts")
def create_prompt(prompt: PromptCreate):
    # If the body doesn't match PromptCreate, FastAPI rejects it
    return {"message": "Created", "data": prompt}
Pydantic is installed automatically with FastAPI. You define a class, FastAPI does the rest — parsing the JSON body, validating each field, returning a clear error if anything is wrong.
Today's Goal

Watch the Tech With Tim tutorial in full and read the FastAPI First Steps page. Don't write code today — absorb the concepts. Tomorrow you'll experiment with a real working app.

End of Day Checklist

Tomorrow — Day 57: Read + Experiment

You'll build fastapi_test.py — a minimal FastAPI app that serves real prompt data with GET routes and query parameters. You'll run it with uvicorn and test every endpoint in /docs.