FastAPI Tutorial for Beginners: Build Your First API with Python

FastAPI Tutorial for Beginners: Build Your First API with Python

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It’s designed to be easy to use, highly performant, and provide automatic interactive documentation. This tutorial will guide you through building your first API with FastAPI, covering everything from installation and basic concepts to more advanced features like data validation, authentication, and deployment.

1. Introduction to FastAPI:

Why choose FastAPI? It offers several compelling advantages:

• Speed: Benchmark results place FastAPI among the fastest Python web frameworks, comparable to NodeJS and Go. This speed is achieved through the use of Starlette for the underlying ASGI (Asynchronous Server Gateway Interface) and Pydantic for data validation.
• Ease of Use: FastAPI’s intuitive syntax and reliance on standard Python type hints simplify development and reduce boilerplate code.
• Automatic Interactive Documentation: Swagger UI and ReDoc are automatically generated, providing interactive documentation that allows you to test your API directly from your browser.
• Data Validation: Pydantic handles data validation, ensuring that incoming requests conform to the expected data types. This reduces errors and improves code robustness.
• Dependency Injection: FastAPI’s built-in dependency injection system promotes clean code architecture and simplifies testing.
• Asynchronous Support: FastAPI fully supports asynchronous programming with async and await, allowing for highly concurrent applications.

2. Setting up the Environment:

Before we begin, you’ll need to set up your development environment.

• Install Python: Ensure you have Python 3.7 or later installed. You can download it from the official Python website.
• Create a Virtual Environment: It’s recommended to use a virtual environment to isolate your project’s dependencies.
  bash
python3 -m venv .venv # Create a virtual environment
source .venv/bin/activate # Activate the virtual environment (Linux/macOS)
.venv\Scripts\activate # Activate the virtual environment (Windows)
• Install FastAPI and Uvicorn: Uvicorn is an ASGI server that we’ll use to run our FastAPI application.
  bash
pip install fastapi uvicorn[standard]

3. Creating Your First FastAPI Application:

Let’s create a simple API that greets the user. Create a file named main.py:

“`python
from fastapi import FastAPI

app = FastAPI()

@app.get(“/”)
async def root():
return {“message”: “Hello World”}
“`

4. Running the Application:

Start the Uvicorn server using the following command in your terminal:

bash
uvicorn main:app --reload

This command tells Uvicorn to run the app instance found in the main.py file. The --reload flag enables automatic reloading of the server whenever you make changes to your code.

5. Interacting with the API:

Open your web browser and navigate to http://127.0.0.1:8000/. You should see a JSON response: {"message": "Hello World"}.

Navigate to http://127.0.0.1:8000/docs to access the automatic interactive Swagger UI documentation. You can also access the alternative ReDoc documentation at http://127.0.0.1:8000/redoc.

6. Path Parameters and Data Validation:

Let’s create an endpoint that greets a user by name:

“`python
from fastapi import FastAPI, Path

app = FastAPI()

@app.get(“/users/{user_id}”)
async def read_user(user_id: int = Path(…, title=”The ID of the user to retrieve”, ge=1)):
return {“user_id”: user_id}
“`

Here, {user_id} is a path parameter. We use Path(...) to define it and specify that it’s required. We also use type hints (int) for data validation and add metadata like title and ge (greater than or equal to) for validation and documentation.

7. Query Parameters:

Query parameters are added to the URL after a question mark (?). Let’s add a query parameter to our greeting endpoint:

“`python
from fastapi import FastAPI, Query

app = FastAPI()

@app.get(“/items/”)
async def read_items(q: str | None = Query(default=None, max_length=50)):
results = {“items”: [{“item_id”: “Foo”}, {“item_id”: “Bar”}]}
if q:
results.update({“q”: q})
return results
“`

Here, q is an optional query parameter of type str with a maximum length of 50 characters.

8. Request Body:

For more complex data, we can use request bodies. Let’s create an endpoint to create a new item:

“`python
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

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

@app.post(“/items/”)
async def create_item(item: Item):
return item
“`

We define a Pydantic model Item to specify the structure and data types of the request body.

9. Dependency Injection:

Dependency injection helps manage dependencies and promotes code reusability.

“`python
from fastapi import FastAPI, Depends

app = FastAPI()

async def common_parameters(q: str | None = None, skip: int = 0, limit: int = 100):
return {“q”: q, “skip”: skip, “limit”: limit}

@app.get(“/items/”)
async def read_items(commons: dict = Depends(common_parameters)):
return commons
“`

Here, common_parameters is a dependency function that provides common query parameters to multiple endpoints.

10. Authentication:

FastAPI supports various authentication mechanisms, including OAuth2, JWT, and API keys. You can use libraries like fastapi.security and third-party libraries like fastapi-users to implement authentication.

11. Deployment:

You can deploy your FastAPI application to various platforms like Uvicorn, Gunicorn, and Docker.

Example: Deploying with Uvicorn and Gunicorn:

For production deployments, it’s recommended to use a production-ready ASGI server like Gunicorn along with Uvicorn as a worker process:

bash
gunicorn -k uvicorn.workers.UvicornWorker main:app

12. Advanced Topics:

• Middleware: Middleware allows you to add functionality that runs before or after a request is processed. You can use middleware for logging, authentication, and other tasks.
• Background Tasks: Perform long-running tasks in the background without blocking the main thread.
• WebSockets: Build real-time applications with WebSocket support.
• Testing: FastAPI makes it easy to write tests using pytest and the TestClient.

This comprehensive tutorial covers the fundamental aspects of building APIs with FastAPI. By leveraging its speed, ease of use, and powerful features, you can create robust and efficient APIs for your web applications. Remember to consult the official FastAPI documentation for further exploration and more advanced concepts. With practice and further exploration, you’ll be able to harness the full power of FastAPI for your API development needs.