Comprehensive Guide to FastAPI

FastAPI has gained immense popularity among Python developers in recent years due to its high performance, ease of use, automatic interactive documentation generation, and its ability to scale for building modern APIs. In this blog post, we will explore the basics of FastAPI, cover a variety of useful APIs with examples, and finally show a generic application that uses them to create something practical.

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What is FastAPI?

FastAPI is a modern, fast (high-performance), and highly efficient Python web framework created by Sebastián Ramírez for building APIs. It is built on industry-standard Python tools, such as Starlette for web frameworks and Pydantic for data validation. FastAPI leverages Python’s type hints to provide:

• Automatic validation of request data.
• Auto-generation of API documentation using Swagger UI and ReDoc.
• Async support for modern Python asynchronous programming with asyncio.
• High performance thanks to the use of Starlette and Pydantic.

FastAPI can be an excellent choice for building APIs for microservices, serverless applications, ML/AI models, or APIs requiring real-time interactivity.

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FastAPI: Useful API Features with Code Snippets

Here, we’ll demonstrate at least 20 concepts and APIs in FastAPI with code snippets to highlight its rich functionality and unique features.

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1. Basic Hello World API

  from fastapi import FastAPI

  app = FastAPI()

  @app.get("/")
  def read_root():
      return {"message": "Welcome to FastAPI!"}

This is the foundational start for every FastAPI app. The @app.get("/") decorator creates a GET endpoint at the root URL.

2. Path Parameters

  @app.get("/items/{item_id}")
  def read_item(item_id: int):
      return {"item_id": item_id}

FastAPI automatically infers the type of item_id (here, an int) and validates the input.

3. Query Parameters

  @app.get("/search")
  def search(query: str, limit: int = 10):
      return {"query": query, "limit": limit}

Query parameters are extracted from the URL (e.g., /search?query=fastapi&limit=5). Setting a default value, like limit=10, allows optional parameters.

4. Request Body with Pydantic Models

  from pydantic import BaseModel

  class Item(BaseModel):
      name: str
      description: str = None
      price: float
      is_offer: bool = False

  @app.post("/items/")
  def create_item(item: Item):
      return {"item": item}

Pydantic makes request body data validation seamless. Input validation errors return a detailed JSON error message.

5. Response Models

  @app.post("/items/", response_model=Item)
  def create_and_return_item(item: Item):
      return item

The response_model allows you to control the structure of the API response.

6. Dependency Injection

  from fastapi import Depends

  def common_parameters(q: str = None, limit: int = 10):
      return {"q": q, "limit": limit}

  @app.get("/items/")
  def read_items(commons: dict = Depends(common_parameters)):
      return commons

FastAPI simplifies dependency injection for sharing common logic.

7. Request Validation

  @app.post("/users/")
  def create_user(username: str, password: str):
      if len(password) < 8:
          return {"error": "Password must be at least 8 characters"}
      return {"username": username}

Use simple Python logic for custom validations.

8. Handling Form Data

  from fastapi import Form

  @app.post("/login/")
  def login(username: str = Form(...), password: str = Form(...)):
      return {"username": username, "password": password}

FastAPI provides advanced support for form data with the Form dependency.

9. Using async for Endpoints

  from time import sleep

  @app.get("/async-example/")
  async def read_slow_data():
      await asyncio.sleep(2)
      return {"example": "This endpoint uses async"}

Asynchronous functions (async def) allow for handling multiple requests concurrently.

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