Handling POST Requests in FastAPI

Introduction to POST Operations

In the previous lesson, we explored GET operations in FastAPI. Now, we’ll dive into POST operations, which are essential for creating new objects on the server. Let’s explore how FastAPI makes it easy to handle POST requests.

GET vs. POST Operations

The primary differences between GET and POST operations are:

1. Purpose: GET is used to retrieve information, while POST is used to create new objects on the server.
2. Parameter Handling:
• GET request parameters are sent via the URL query string.
• POST request parameters can also be sent in the request body, allowing for more data to be transmitted.
3. Usage:
• GET requests can be entered directly into a browser’s URL bar.
• POST requests require an application or tool like cURL or Python’s requests module to send data.

HTTP Request Body

Both HTTP requests and responses can include a message body, which carries the data sent after the HTTP header. The header specifies how the body is encoded, ensuring proper decoding on the server side. FastAPI defaults to using JSON encoding for request bodies, making it ideal for modern APIs.

Here’s an example of a JSON-encoded request body for creating a movie review:

{
    "movie": "Inception",
    "review": {
        "num_stars": 5,
        "text": "An amazing sci-fi thriller!",
        "public": true
    }
}

The body supports nested data structures, which are ideal for complex API interactions.

Using Pydantic’s BaseModel

To handle the nested structure of request bodies, we use Pydantic’s BaseModel. Pydantic simplifies defining and managing nested model schemas in Python.

Here’s an example of defining models for a movie review:

from pydantic import BaseModel

class Review(BaseModel):
    num_stars: int
    text: str
    public: bool = False

class MovieReview(BaseModel):
    movie: str
    review: Review

• Review Model: Defines attributes like num_stars, text, and an optional public flag with a default value of False.
• MovieReview Model: References the Review model, enabling a nested body structure.

This modular design keeps models organized and allows for independent updates to each model.

Handling a POST Operation

Now, let’s create a POST endpoint to handle movie reviews:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Review(BaseModel):
    num_stars: int
    text: str
    public: bool = False

class MovieReview(BaseModel):
    movie: str
    review: Review

@app.post("/reviews")
def create_review(review: MovieReview):
    # Simulate database storage
    return {"message": "Review created successfully!", "data": review.dict()}

• Endpoint Path: The @app.post("/reviews") annotation defines the POST endpoint.
• Input Model: The create_review function uses the MovieReview Pydantic model as input.
• Response: The function returns a success message along with the review data in JSON format.

In a production setting, you would:

• Define CRUD utility functions in a `crud.py` file to interact with the database.
• Use these functions to create, read, update, and delete objects within your API endpoints.

For more details, refer to the FastAPI documentation on CRUD operations.

FastAPI’s robust support for POST operations, combined with Pydantic’s flexibility, makes it simple to create APIs that handle complex data structures. Whether you’re building basic endpoints or managing nested data, FastAPI ensures scalability and developer productivity.