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How to Validate Request Body In Fastapi?

5 minutes read

In FastAPI, you can validate the request body by defining a Pydantic model that represents the structure of the request body. This model will automatically validate the incoming request data against the specified schema. If the data does not match the schema, FastAPI will return a 422 Unprocessable Entity response with details about the validation errors.


To validate the request body, you need to declare a Pydantic model and use it as a parameter in your endpoint function. FastAPI will take care of validating the request data against the model schema before calling your endpoint function. You can also specify additional validation rules in the Pydantic model using Pydantic's built-in validators.


For example, let's say you have a POST endpoint that expects a JSON request body with an "name" field that should be a string. You can define a Pydantic model like this:

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from pydantic import BaseModel

class Item(BaseModel):
    name: str


And in your endpoint function, you can use this model as a parameter:

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from fastapi import FastAPI
from models import Item

app = FastAPI()

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


When a request is made to the /items/ endpoint with a JSON body that does not have a "name" field or has a "name" field that is not a string, FastAPI will automatically return a 422 response with details about the validation error.


Overall, using Pydantic models for request body validation in FastAPI helps you ensure that your API endpoints receive the expected data format and types, improving the reliability and robustness of your application.


How to handle invalid request bodies in FastAPI?

In FastAPI, you can handle invalid request bodies by using Pydantic models for request validation. Pydantic is a data validation library that allows you to define the structure and types of your request bodies in a declarative way.


You can create a Pydantic model for your request body and use it with FastAPI's dependency injection system to automatically validate incoming requests. If the request body does not match the structure defined in the Pydantic model, FastAPI will automatically return a 422 Unprocessable Entity response with details about the validation errors.


Here's an example of how you can handle invalid request bodies in FastAPI:

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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

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

@app.exception_handler(HTTPException)
async def http_exception_handler(request, exc):
    return JSONResponse(status_code=exc.status_code, content={"detail": exc.detail})


In this example, we have defined a Pydantic model called Item to validate the structure of the request body. The create_item endpoint accepts a POST request with a JSON body that should match the structure of the Item model. If the request body is invalid, FastAPI will automatically return a 422 Unprocessable Entity response with details about the validation errors.


Additionally, we have defined an exception handler for HTTPException to customize the error response. You can customize the error response further by providing your own implementation of the exception handler.


By using Pydantic models for request validation and exception handlers, you can easily handle invalid request bodies in FastAPI and provide informative error responses to clients.


How to handle optional fields in request body validation in FastAPI?

To handle optional fields in request body validation in FastAPI, you can use Pydantic models with Optional from the typing module. Here's an example:

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from fastapi import FastAPI
from pydantic import BaseModel
from typing import Optional

app = FastAPI()

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

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


In this example, the Item Pydantic model has optional fields description and tax. The Optional type hint from the typing module indicates that these fields are not required. When a request is made to create an item, the validation will check that the required fields (name and price) are present, but allow the optional fields to be omitted.


You can now test the API endpoint using a tool like curl or a GUI client like Postman:

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curl -X POST "http://localhost:8000/items/" -H "Content-Type: application/json" -d '{"name": "item1", "price": 10.5}'


This request will be valid even though the description and tax fields are not included.


What is the relation between request body validation and input validation in FastAPI?

Request body validation and input validation are closely related in FastAPI as both involve validating the data that is being passed to an API endpoint.


Request body validation specifically refers to validating the data that is sent in the body of an HTTP request (such as a POST or PUT request). FastAPI allows you to define the structure of the request body using Pydantic models, and it will automatically validate and deserialize the incoming data into the specified model.


Input validation, on the other hand, is a broader concept that encompasses all forms of data validation in an API, not just the request body. This includes validating query parameters, path parameters, request headers, and any other form of input that the API might receive.


In FastAPI, input validation can be implemented using Pydantic models for request bodies as well as query and path parameters. By defining Pydantic models for each type of input, you can ensure that all incoming data is properly formatted and validated before being processed by the API endpoint.


Overall, both request body validation and input validation are essential components of building a robust and secure API in FastAPI, and they work together to ensure that the API only receives and processes valid data.

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