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* feat: add discriminated union * feat: add OpenAPI spec schema * test: add basic example for generated schema * test: add validation tests * docs: add basic documentation * fix: support ForwardRef * test: add ForwardRef case * fix: false positive lint error https://github.com/PyCQA/pyflakes/pull/600 * improve error * add schema/schema_json utils * fix tests after merge * refactor: add `discriminator` attribute to `FieldInfo` * refactor: @cybojenix remarks * fix schema with forward ref * start nested * feat: add allowed values in error message * fix wrong check Same example with ``` class FooDomainA(BaseModel): __root__: Union[FooDomainAA, FooDomainAB] ``` (without a discriminator) should obviously be valid * test: add nested examples * remove uncovered code as we don't need it * docs: add nested example * fix: support properly Annotated Field syntax * support naked annotated * fix: handle TypeError * make error loc more explicit * fix behaviour with basemodel instance as value * support schema for dataclasses * tweak examples * refactor: context manager just around code that fails * refactor: add docstring + tweak on `get_sub_types` * refactor: move `get_discriminator_values` in `utils.py` * refactor: create `MissingDiscriminator` and `InvalidDiscriminator` * refactor: move logic in `_validate_discriminated_union` * refactor: remove `DiscriminatedUnionConfig` * docs: schema/schema_json * tests: add tests with other `Literal` types * update 3.10 * add schema docstring * weird bug on 3.8 with `Literal[None]` * bump to view docs & coverage * bump to prompt tests * move tests in dedicated file * chore: rewording * refactor: replace property by direct slot * refactor: faster check * refactor: missing discriminator * refactor: change error to ConfigError * refactor: use display_as_type * fix: mypy * fix: duplicate * feat: handle alias * feat: handle nested unions * tweak first example Co-authored-by: Samuel Colvin <s@muelcolvin.com>
pydantic
Data validation and settings management using Python type hinting.
Fast and extensible, pydantic plays nicely with your linters/IDE/brain. Define how data should be in pure, canonical Python 3.6+; validate it with pydantic.
Help
See documentation for more details.
Installation
Install using pip install -U pydantic or conda install pydantic -c conda-forge.
For more installation options to make pydantic even faster,
see the Install section in the documentation.
A Simple Example
from datetime import datetime
from typing import List, Optional
from pydantic import BaseModel
class User(BaseModel):
id: int
name = 'John Doe'
signup_ts: Optional[datetime] = None
friends: List[int] = []
external_data = {'id': '123', 'signup_ts': '2017-06-01 12:22', 'friends': [1, '2', b'3']}
user = User(**external_data)
print(user)
#> User id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3]
print(user.id)
#> 123
Contributing
For guidance on setting up a development environment and how to make a contribution to pydantic, see Contributing to Pydantic.
Reporting a Security Vulnerability
See our security policy.
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