mkdocs-with-pdf plugin)You need a plugin to convert MkDocs to PDF.
pip install mkdocs-with-pdf
Now, edit mkdocs.yml in the root directory. Add:
plugins:
- search
- with-pdf:
author: "Sebastián Ramírez"
copyright: "Copyright (c) FastAPI"
cover_title: "FastAPI Tutorial"
cover_subtitle: "Official Documentation"
output_path: "fastapi-official.pdf"
enabled_if_env: "ENABLE_PDF_EXPORT"
Install: pip install pytest httpx
Test file test_main.py:
from fastapi.testclient import TestClient from main import appclient = TestClient(app)
def test_root(): response = client.get("/") assert response.status_code == 200 assert response.json() == "message": "Hello World"
def test_create_item(): response = client.post("/items/", json="name": "Apple", "price": 1.99) assert response.status_code == 200 data = response.json() assert data["item_name"] == "Apple" assert data["price_with_tax"] == 2.189
Run: pytest -v
Target Audience: Python developers (beginner to intermediate) Prerequisites: Basic Python (functions, classes, decorators, type hints)
Mastering FastAPI is one of the best investments a Python developer can make in 2025 and beyond. Its combination of speed, type safety, and developer happiness is unmatched.
To continue your journey:
Remember: the best tutorial is the one you actively use. Keep your FastAPI PDF open as a reference, but write code every day.
Happy building, and may your APIs be fast and your bugs be few.
This article is optimized for the keyword "FastAPI tutorial PDF" and serves as a comprehensive guide. For an actual downloadable PDF version, use your browser’s print-to-PDF function on this page.
The rain drummed against the window of the "Coffee & Code" café, but
barely noticed. Spread out on his screen was the holy grail he’d been searching for: fastapi_tutorial_final_v2.pdf
Just two weeks ago, Leo was a junior developer drowning in slow, clunky legacy frameworks. He had heard whispers of
—the sleek, high-performance Python framework used by giants like fastapi tutorial pdf
. But to master it, he needed a roadmap he could carry with him, offline and focused. He opened the PDF. Chapter One was titled "The Need for Speed."
It didn't just teach him syntax; it told the story of an API that could handle thousands of requests without breaking a sweat. As Leo sipped his espresso, he followed the guide's steps: The Foundation : He typed app = FastAPI() , feeling like he was building the engine of a supercar. : He added his first "path operation," a simple @app.get("/") that returned a world of possibilities. The Validation
: The PDF explained Pydantic models—automatic data validation that felt like having a personal assistant check his work for errors before he even made them.
By the time the café lights dimmed, Leo hadn't just read a tutorial; he had built a microservice. The PDF wasn't just a document anymore; it was the blueprint for his career upgrade. He closed his laptop, the "tutorial" now a living, breathing application on his local server, ready for the world. code snippet for the first steps mentioned in the story? First Steps - FastAPI
FastAPI Tutorial: Building High-Performance APIs with Python
Introduction
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. In this tutorial, we'll explore the basics of FastAPI and build a simple API to demonstrate its capabilities.
Why FastAPI?
Installing FastAPI
To get started with FastAPI, you'll need to install it using pip:
pip install fastapi
Basic FastAPI Application
Create a new file called main.py and add the following code:
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
def read_root():
return "message": "Welcome to FastAPI"
This code creates a basic FastAPI application with a single endpoint at /.
Running the Application
To run the application, use the following command:
uvicorn main:app --host 0.0.0.0 --port 8000
This will start the development server, and you can access your API at http://localhost:8000.
API Endpoints
Let's create a few more endpoints to demonstrate FastAPI's capabilities. Update the main.py file with the following code: The Ultimate FastAPI Tutorial PDF: From Zero to
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
# Define a Pydantic model for our data
class Item(BaseModel):
id: int
name: str
description: str
# Create a list to store our items
items = [
"id": 1, "name": "Item 1", "description": "This is item 1",
"id": 2, "name": "Item 2", "description": "This is item 2",
]
# GET endpoint to retrieve all items
@app.get("/items/")
def read_items():
return items
# GET endpoint to retrieve a single item by ID
@app.get("/items/item_id")
def read_item(item_id: int):
for item in items:
if item["id"] == item_id:
return item
return "error": "Item not found"
# POST endpoint to create a new item
@app.post("/items/")
def create_item(item: Item):
items.append(item.dict())
return item
# PUT endpoint to update an existing item
@app.put("/items/item_id")
def update_item(item_id: int, item: Item):
for existing_item in items:
if existing_item["id"] == item_id:
existing_item["name"] = item.name
existing_item["description"] = item.description
return existing_item
return "error": "Item not found"
# DELETE endpoint to delete an item
@app.delete("/items/item_id")
def delete_item(item_id: int):
for item in items:
if item["id"] == item_id:
items.remove(item)
return "message": "Item deleted"
return "error": "Item not found"
This code defines a few endpoints for creating, reading, updating, and deleting items.
Testing the API
You can test the API using tools like curl or a REST client like Postman. Here are some examples:
curl http://localhost:8000/items/curl http://localhost:8000/items/1curl -X POST -H "Content-Type: application/json" -d '"id": 3, "name": "Item 3", "description": "This is item 3"' http://localhost:8000/items/curl -X PUT -H "Content-Type: application/json" -d '"id": 1, "name": "Updated Item 1", "description": "This is updated item 1"' http://localhost:8000/items/1curl -X DELETE http://localhost:8000/items/1Conclusion
In this tutorial, we've built a simple API using FastAPI to demonstrate its capabilities. FastAPI provides a lot of features out of the box, including support for asynchronous programming, automatic API documentation, and strong typing.
Further Reading
PDF Version
You can download a PDF version of this tutorial [here](insert link to PDF).
FastAPI is a modern, high-performance web framework for building APIs with Python 3.8+ based on standard Python type hints. Its speed, ease of use, and automatic documentation have made it a favorite among developers looking to move beyond traditional frameworks like Flask or Django for RESTful services.
This tutorial serves as a comprehensive guide for those looking to master FastAPI, whether you are reading this online or saving it as a PDF for offline study. Introduction to FastAPI
FastAPI is built on top of Starlette for the web parts and Pydantic for the data parts. It is designed to be easy to use for developers while providing production-grade performance. Key features include:
High Performance: On par with NodeJS and Go, thanks to Starlette and pydantic. Fast Coding: Increases development speed by 200% to 300%. Fewer Bugs: Reduces human-induced errors by about 40%. Intuitive: Great editor support with completion everywhere.
Standards-based: Fully compatible with OpenAPI and JSON Schema. Setting Up Your Environment
To get started with FastAPI, you need Python installed on your machine. It is highly recommended to use a virtual environment to manage your dependencies.
First, create a directory for your project and navigate into it: mkdir fastapi-projectcd fastapi-project Next, create and activate a virtual environment:
python -m venv venvsource venv/bin/activate # On Windows use venv\Scripts\activate
Now, install FastAPI and Uvicorn, an ASGI server that will run your application: pip install fastapi uvicorn Creating Your First API Create a file named main.py and add the following code: from fastapi import FastAPI app = FastAPI() @app.get("/")def read_root():return "Hello": "World"
@app.get("/items/item_id")def read_item(item_id: int, q: str = None):return "item_id": item_id, "q": q To run the application, use the following command: uvicorn main:app --reload Now, edit mkdocs
The --reload flag makes the server restart after code changes, which is perfect for development. You can now access your API at http://127.0.0.1:8000. Automatic Documentation
One of the most powerful features of FastAPI is its automatic interactive API documentation. Once your server is running, you can visit:
/docs: Interactive API documentation provided by Swagger UI. You can call your API endpoints directly from the browser. /redoc: Alternative API documentation provided by ReDoc. Path Parameters and Query Parameters
In the example above, we saw both path and query parameters.
Path Parameters: Used to identify a specific resource. In /items/item_id, item_id is a path parameter. FastAPI uses Python type hints to validate the data type.
Query Parameters: Used to filter or modify the request. In the read_item function, q is an optional query parameter because it has a default value of None. Request Body and Pydantic Models
When you need to send data from a client to your API, you use a request body. FastAPI uses Pydantic models to define the structure of the data you expect. from pydantic import BaseModel
class Item(BaseModel):name: strdescription: str = Noneprice: floattax: float = None @app.post("/items/")def create_item(item: Item):return item
By declaring the item parameter as an Item model, FastAPI will: Read the request body as JSON. Convert the types if necessary. Validate the data. Give you the resulting object in the item parameter. Dependency Injection
FastAPI has a powerful Dependency Injection system. This allows you to share logic, enforce security, or handle database connections easily. from fastapi import Depends
def common_parameters(q: str = None, skip: int = 0, limit: int = 10):return "q": q, "skip": skip, "limit": limit
@app.get("/users/")def read_users(commons: dict = Depends(common_parameters)):return commons Database Integration
FastAPI does not require a specific database, but it works seamlessly with SQLAlchemy, Tortoise ORM, and databases like PostgreSQL, MySQL, and SQLite. Using an asynchronous database driver is recommended to leverage FastAPI's performance. Summary and PDF Export
FastAPI is a robust framework that simplifies the process of building modern APIs. Its reliance on standard Python types makes it intuitive, while its performance keeps it competitive for high-traffic applications.
To save this tutorial as a PDF, you can use your browser's "Print" function (Ctrl+P or Cmd+P) and select "Save as PDF" as the destination. This will allow you to keep this guide as a handy reference for your future FastAPI projects.
Create main.py:
from fastapi import FastAPIapp = FastAPI()
@app.get("/") async def root(): return "message": "Hello World"
Run: uvicorn main:app --reload