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How to Handle Models When Programmatically Creating Endpoints In Fastapi?

4 minutes read

When programmatically creating endpoints in FastAPI, you can handle models by defining them in Python classes and using them as parameters in your endpoint functions.


To do this, you first need to create a Pydantic model class that represents the structure of your data. You can then use this model class as a parameter in your endpoint function to automatically validate and convert incoming data to the correct type.


When defining your endpoint function, you can simply declare a parameter with the type of your model class. FastAPI will take care of parsing the incoming request data and converting it to an instance of your model class.


Using models in this way can help you ensure that your data is consistently formatted and validated across all your endpoints. This can also make your code more readable and maintainable, as you can easily see the structure of your data by looking at the model class.


How to handle polymorphism in FastAPI models?

Polymorphism in FastAPI models can be handled by using Pydantic's Union type. By defining a model with Union type, you can specify that a field can accept multiple types of values. This allows you to create models that can handle polymorphic data structures.


Here is an example of how to handle polymorphism in FastAPI models using Pydantic's Union type:

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from typing import Union
from pydantic import BaseModel

class Cat(BaseModel):
    name: str
    breed: str

class Dog(BaseModel):
    name: str
    age: int

class Animal(BaseModel):
    animal_type: str
    details: Union[Cat, Dog]

# Example Usage
cat_data = {"animal_type": "cat", "details": {"name": "Fluffy", "breed": "Persian"}}
dog_data = {"animal_type": "dog", "details": {"name": "Buddy", "age": 4}}

# Create an Animal model instance with cat data
cat_animal = Animal(**cat_data)
print(cat_animal.dict())

# Create an Animal model instance with dog data
dog_animal = Animal(**dog_data)
print(dog_animal.dict())


In the above example, the Animal model has a details field that accepts either a Cat or a Dog model. This allows you to create an instance of the Animal model with either cat or dog data.


By using Pydantic's Union type, you can handle polymorphism in FastAPI models in a type-safe and efficient manner.


How to handle error handling in FastAPI models?

Error handling in FastAPI models can be done using Pydantic's ValidationError class. When validating data against a Pydantic model in FastAPI, if there is an error in the data, Pydantic will raise a ValidationError.


To handle this error, you can catch the ValidationError using a try-except block and return a suitable response to the client. Here's an example of how you can handle error handling in FastAPI models:

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from fastapi import FastAPI
from pydantic import BaseModel, ValidationError

app = FastAPI()

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


@app.post("/items/")
async def create_item(item: Item):
    try:
        # Validate the incoming item data
        item_dict = item.dict()
        # Process the item data and return a response
        return {"name": item_dict["name"], "price": item_dict["price"]}
    except ValidationError as e:
        # Handle the validation error
        return {"error": "Validation error", "detail": e.errors()}


In this example, when a POST request is made to the "/items/" endpoint with invalid item data, a ValidationError will be raised by Pydantic. The try-except block will catch this error and return a response with the validation error details.


By handling errors in this way, you can ensure that the client receives informative error responses when there are issues with the data they provide.


How to handle model deserialization in FastAPI?

In FastAPI, you can handle model deserialization by defining a Pydantic model and using it as a parameter in your request handler function. This will automatically deserialize the request body JSON into an instance of your Pydantic model.


Here's an example:

  1. Define a Pydantic model for the data you want to deserialize. For example:
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from pydantic import BaseModel

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


  1. Use the Pydantic model as a parameter in your request handler function:
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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, "price": item.price}


  1. When you send a POST request to /items/ with JSON data in the request body, FastAPI will automatically deserialize the JSON data into an instance of the Item model and pass it to the create_item function.
  2. You can access the deserialized data from the Item instance in your request handler function.


By using Pydantic models as parameters in your request handler functions, you can easily handle model deserialization in FastAPI.


How to handle optional fields in FastAPI models?

In FastAPI, you can define optional fields in your models by using the typing module, specifically the Optional class. Here's an example of how to handle optional fields in FastAPI models:

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

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 the example above, the Item model has optional fields description and tax, which are set to None by default. This means that when creating an item, you can choose to provide a value for these fields or leave them empty.


When you send a POST request to the /items/ endpoint with a JSON payload that includes only the required fields (name and price) or includes the optional fields as well, FastAPI will automatically validate the data according to the model definition and handle the optional fields appropriately.

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