Tutorial on Building RESTful APIs using FastAPI in Python

FastAPI is a modern web framework for building high-performance APIs with Python. It is based on standard Python type hints which make it easy to write clear and concise code. In this tutorial, we will learn how to build RESTful APIs using FastAPI.

Prerequisites:

• Basic knowledge of Python programming
• Understanding of RESTful APIs

Installation:
First, we need to install FastAPI and uvicorn, which is an ASGI server that can run FastAPI applications. We can install them using pip:

pip install fastapi uvicorn

Creating a new FastAPI project:
To create a new FastAPI project, first, create a new Python file (e.g., main.py). In this file, we will define our API endpoints.

from fastapi import FastAPI

app = FastAPI()

@app.get('/')
def read_root():
    return {"Hello": "World"}

In this code snippet, we have created a new FastAPI app and defined a single endpoint (/) that returns a simple JSON response.

Running the API:
To run the API, we need to use the uvicorn server. We can start the server by running the following command:

uvicorn main:app --reload

This command will start the uvicorn server and reload the app automatically whenever we make changes to the code.

Visiting http://127.0.0.1:8000/ in your web browser should display the JSON response {"Hello": "World"}.

Adding more endpoints:
We can add more endpoints to our API by defining new functions with the @app.get or @app.post decorators. For example:

@app.get('/items/{item_id}')
def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "q": q}

This endpoint takes an item_id parameter and an optional q query parameter, and returns a JSON response with these parameters.

API documentation:
FastAPI provides automatic generation of API documentation using Swagger UI. This documentation is available at http://127.0.0.1:8000/docs by default. It displays information about the API endpoints, parameters, and responses.

Validation and serialization:
FastAPI supports automatic validation and serialization of request parameters using Python type hints. For example, we can define a request body with specific data types:

from pydantic import BaseModel

class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None

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

In this code snippet, we define a Item class with specific data types for each attribute. When a POST request is made to the endpoint /items/, FastAPI will automatically validate the request body and deserialize it into an Item object.

Conclusion:
In this tutorial, we have learned how to build RESTful APIs with FastAPI in Python. FastAPI provides a powerful and efficient way to create APIs using Python type hints. We have covered the basics of creating endpoints, running the API, adding documentation, and handling request validation and serialization. FastAPI is a great choice for building high-performance APIs with minimal boilerplate code.