python-devtools integration with Pydantic
Pydantic integrates with python-devtools by implementing the __pretty__ method on most public classes. This allows devtools to format Pydantic models in a readable way during development.
Pydantic · Integrations · all subjects
devtools
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Using debug() to inspect Pydantic models
The debug() function from python-devtools is useful when inspecting Pydantic models. It formats output in a way that is easier to read than print(), and provides information about which file/line the statement is on and what value was printed.
devtools installation
Install devtools using pip: pip install devtools
Example of debug() with Pydantic BaseModel
The following example demonstrates using debug() to inspect a Pydantic model with nested models and various field types: ```python from datetime import datetime from devtools import debug from pydantic import BaseModel class Address(BaseModel): street: str country: str lat: float lng: float class User(BaseModel): id: int name: str signup_ts: datetime friends: list[int] address: Address user = User( id='123', name='John Doe', signup_ts='2019-06-01 12:22', friends=[1234, 4567, 7890], address=dict(street='Testing', country='uk', lat=51.5, lng=0), ) debug(user) print('\nshould be much easier read than:\n') print('user:', user) ``` The debug() call produces formatted output that is much easier to read than the standard print() output.
debug() is a development-time tool
debug() is a development-time tool that prints to the terminal of a process you're watching. For deployed applications, the closest equivalent is Logfire, where models and validations show up as structured output you can browse and query.