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Description:

pydanticyaml 1.3.0

Pydantic-YAML

Pydantic-YAML adds YAML capabilities to Pydantic,
which is an excellent Python library for data validation and settings management.
If you aren't familiar with Pydantic, I would suggest you first check out their
docs.
Documentation on ReadTheDocs.org
Basic Usage
from enum import Enum
from pydantic import BaseModel, validator
from pydantic_yaml import parse_yaml_raw_as, to_yaml_str

class MyEnum(str, Enum):
"""A custom enumeration that is YAML-safe."""

a = "a"
b = "b"

class InnerModel(BaseModel):
"""A normal pydantic model that can be used as an inner class."""

fld: float = 1.0

class MyModel(BaseModel):
"""Our custom Pydantic model."""

x: int = 1
e: MyEnum = MyEnum.a
m: InnerModel = InnerModel()

@validator("x")
def _chk_x(cls, v: int) -> int: # noqa
"""You can add your normal pydantic validators, like this one."""
assert v > 0
return v

m1 = MyModel(x=2, e="b", m=InnerModel(fld=1.5))

# This dumps to YAML and JSON respectively
yml = to_yaml_str(m1)
jsn = m1.json()

# This parses YAML as the MyModel type
m2 = parse_yaml_raw_as(MyModel, yml)
assert m1 == m2

# JSON is also valid YAML, so this works too
m3 = parse_yaml_raw_as(MyModel, jsn)
assert m1 == m3

With Pydantic v2, you can also dump dataclasses:
from pydantic import RootModel
from pydantic.dataclasses import dataclass
from pydantic.version import VERSION as PYDANTIC_VERSION
from pydantic_yaml import to_yaml_str

assert PYDANTIC_VERSION >= "2"

@dataclass
class YourType:
foo: str = "bar"

obj = YourType(foo="wuz")
assert to_yaml_str(RootModel[YourType](obj)) == 'foo: wuz\n'

Configuration
Currently we use the JSON dumping of Pydantic to perform most of the magic.
This uses the Config inner class,
as in Pydantic:
class MyModel(BaseModel):
# ...
class Config:
# You can override these fields, which affect JSON and YAML:
json_dumps = my_custom_dumper
json_loads = lambda x: MyModel()
# As well as other Pydantic configuration:
allow_mutation = False

You can control some YAML-specfic options via the keyword options:
to_yaml_str(model, indent=4) # Makes it wider
to_yaml_str(model, map_indent=9, sequence_indent=7) # ... you monster.

You can additionally pass your own YAML instance:
from ruamel.yaml import YAML
my_writer = YAML(typ="safe")
my_writer.default_flow_style = True
to_yaml_file("foo.yaml", model, custom_yaml_writer=my_writer)

A separate configuration for YAML specifically will be added later, likely in v2.
Breaking Changes for pydantic-yaml V1
The API for pydantic-yaml version 1.0.0 has been greatly simplified!
Mixin Class
This functionality has currently been removed!
YamlModel and YamlModelMixin base classes are no longer needed.
The plan is to re-add it before v1 fully releases,
to allow the .yaml() or .parse_*() methods.
However, this will be availble only for pydantic<2.
Versioned Models
This functionality has been removed, as it's questionably useful for most users.
There is an example in the docs that's available.