Using Pydantic Properly in a Big FastAPI Application

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Pydantic is a data validation and settings management library for Python that uses Python 3.6+ type annotations to declare the expected type of input data and settings. FastAPI is a popular web framework for building APIs with Python. It is designed to be fast, easy to use, and scalable. Pydantic integrates seamlessly with FastAPI to provide automatic data validation and serialization for request and response data in API endpoints.

In this blog post, we will explore how to use Pydantic properly in a big FastAPI application. We will start with an overview of Pydantic and its benefits, then we will look at how to define Pydantic models and use them in FastAPI endpoints. Finally, we will explore Pydantic’s validation capabilities, including built-in validators and regular expression-based string constraints.

Introduction

Pydantic is a Python library that provides data validation and settings management capabilities to Python applications. It uses Python 3.6+ type annotations to declare the expected types of input data and settings, and it provides automatic data validation and error reporting for input data that does not conform to the expected structure.

FastAPI is a modern, fast web framework for building APIs with Python. It is designed to be easy to use, fast, and scalable. FastAPI integrates seamlessly with Pydantic to provide automatic data validation and serialization for request and response data in API endpoints.

What is Pydantic?

Pydantic is a data validation and settings management library for Python that uses Python 3.6+ type annotations to declare the expected type of input data and settings. Pydantic provides several features that make it a popular choice for validating data in Python applications:

1. Type validation: Pydantic uses Python’s type annotations to validate the type of input data at runtime. This ensures that the data conforms to the expected structure, and it provides informative error messages if the data is invalid.
2. Automatic data serialization: Pydantic can automatically serialize and deserialize data between Python objects and JSON or other formats. This makes it easy to work with data in different formats, without having to write custom serialization code.
3. Settings management: Pydantic can also be used to manage application configuration settings, including environment variables and configuration files. This makes it easy to configure an application in a consistent and maintainable way.

FastAPI uses Pydantic models to automatically validate and deserialize request data in API endpoints. To use a Pydantic model in a FastAPI endpoint, we simply declare it as a parameter to the endpoint function.

Using Pydantic in a FastAPI Application

Defining Pydantic Models

Pydantic models are defined using Python classes that inherit from pydantic.BaseModel. These models define the structure of data that will be validated and serialized by Pydantic.

Pydantic models use Python type annotations to define the expected types of data. These annotations can include built-in types like str, int, and bool, as well as custom types defined in the application.

Here is an example of a simple Pydantic model:

from pydantic import BaseModel

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

In this example, we define a Person model with two fields: name and age. The name field is defined as a str, and the age field is defined as an int.

Pydantic models can also include default values for fields, like this:

from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int = 18

In this example, we set a default value of 18 for the age field.

Using Pydantic Models in FastAPI Endpoints

FastAPI uses Pydantic models to automatically validate and deserialize request data in API endpoints. To use a Pydantic model in a FastAPI endpoint, we simply declare it as a parameter to the endpoint function.

Here is an example of a FastAPI endpoint that uses a Pydantic model:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

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

@app.post("/people/")
async def create_person(person: Person):
    return {"person": person}

In this example, we define a Person model and use it as a parameter to the create_person endpoint. When a request is made to this endpoint, FastAPI will automatically validate the request data against the Person model and deserialize it into a Person object.

Pydantic models can also be used to validate query parameters, path parameters, and headers in FastAPI endpoints. Here is an example of using a Pydantic model to validate a query parameter:

from fastapi import FastAPI
from pydantic import BaseModel, constr

app = FastAPI()

class PaginationParams(BaseModel):
    page: int = 1
    limit: int = 10

@app.get("/items/")
async def get_items(params: PaginationParams):
    # ...
    return {"items": items}

In this example, we define a PaginationParams model that includes two fields: page and limit. We use this model as a parameter to the get_items endpoint, which accepts a query parameter params.

When a request is made to this endpoint, FastAPI will automatically validate the query parameter against the PaginationParams model and deserialize it into a PaginationParams object.

Pydantic Validation

Pydantic provides several built-in validators that can be used to validate data within a Pydantic model. These validators allow you to define custom validation rules for fields in a Pydantic model.

Here are some examples of Pydantic’s built-in validators:

1. gt and ge: These validators check that a numeric value is greater than (gt) or greater than or equal to (ge) a specified value.
2. str_len: This validator checks that a string value is within a specified length range.
3. anystr_strip_whitespace: This validator strips whitespace from the beginning and end of a string value.

Pydantic’s constr type can be used to define regular expression-based string constraints.

Conclusion

Pydantic is a powerful library for data validation and settings management in Python applications. When used with FastAPI, it provides several benefits for building APIs with Python, including automatic data validation, serialization, and type hinting.

By using Pydantic in your FastAPI applications, you can write more robust and maintainable code, without having to write custom validation and serialization code.

In this blog post, we have explored how to use Pydantic properly in a big FastAPI application. We have looked at how to define Pydantic models and use them in FastAPI endpoints, as well as Pydantic’s validation capabilities, including built-in validators and regular expression-based string constraints.

Pydantic provides a simple and elegant solution to data validation in Python applications, and its seamless integration with FastAPI makes it an excellent choice for building robust and maintainable APIs.

We hope this blog post has been informative and helpful. If you have any questions or comments, please feel free to leave them below.

Further Learning

If you want to learn more about Pydantic and FastAPI, there are several resources available online. Here are a few suggestions:

1. The official FastAPI documentation provides detailed information on using Pydantic in FastAPI applications.
2. The Pydantic documentation provides comprehensive information on Pydantic’s features and capabilities.
3. The FastAPI course on Udemy provides a comprehensive introduction to FastAPI and Pydantic, with hands-on exercises and projects.
4. The FastAPI book by Sebastián Ramírez provides a deep dive into using FastAPI and Pydantic to build modern web applications with Python.