TL;DR: To build a REST API with FastAPI, install the library, create a Python file with `FastAPI()` instance, and define endpoints using decorators like `@app.get()` and `@app.post()`. Use Pydantic models for data validation and run the application with the Uvicorn ASGI server to generate automatic interactive documentation.
Step 1: Install FastAPI and Uvicorn
Before coding, ensure your Python environment is ready. Open your terminal or command prompt and execute the pip install command. FastAPI handles the core routing and logic, while Uvicorn is an ASGI web server that runs your application. You must have Python 3.7 or higher installed on your system. Run the command `pip install fastapi uvicorn` to download the necessary packages. This step is crucial because Uvicorn provides the performance benefits of asynchronous Python code, which is a key feature of FastAPI. Verify the installation by checking your site-packages directory or running `python -m fastapi –version` in the terminal.
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Step 2: Create the Application Instance
Create a new Python file named `main.py`. Import the `FastAPI` class from the `fastapi` module. Instantiate the `FastAPI` class by calling `app = FastAPI()`. This creates the main application object that will handle all incoming requests. You can add optional parameters to this instance, such as `title`, `version`, and `description`, which will appear in the automatic documentation. For example, use `app = FastAPI(title=”My API”, version=”1.0.0″)`. This metadata helps clients understand the purpose and current status of your API without needing to read external documentation.
Step 3: Define Pydantic Models
Define the structure of your data using Pydantic models. Import `BaseModel` from `pydantic`. Create a class that inherits from `BaseModel`, such as `Item`. Inside this class, define fields with their expected data types, for example, `name: str` and `price: float`. You can also set default values or make fields optional using `Optional` types. These models ensure that any data sent to your API is validated before it reaches your business logic. If the data does not match the defined types, FastAPI automatically returns a 422 error response with detailed information about what went wrong.
Step 4: Create API Endpoints
Decorate your functions with `@app.get(“/path”)` for GET requests or `@app.post(“/path”)` for POST requests. Define a function, such as `def read_item(item_id: int)`. The function should return a dictionary or a Pydantic model instance. For POST endpoints, include the Pydantic model as a parameter in the function signature, such as `def create_item(item: Item)`. FastAPI automatically parses the JSON body of the request and validates it against your model. You can also add path parameters, query parameters, and headers directly in the function arguments. FastAPI infers their types and validates them automatically.
Step 5: Run the Server
Open your terminal and navigate to the directory containing your `main.py` file. Run the command `uvicorn main:app –reload`. The `–reload` flag is useful during development because it restarts the server automatically whenever you save changes to your code. Once the server starts, you will see a message indicating the application is running. Open your web browser and navigate to `http://127.0.0.1:8000/docs`. This URL displays the Swagger UI, an interactive interface where you can test your endpoints directly from the browser.
Tips for Success
Use type hints consistently to help FastAPI understand your code better. Structure your project with separate modules for models, routes, and utilities as it grows. Implement middleware for logging and error handling to make your API robust. Always test your endpoints using the automatic documentation or tools like Postman. Keep your response models simple and clear to avoid
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