Pydantic Compat 0.1.2 | GitLocker.com Product

Description:

pydanticcompat 0.1.2

pydantic-compat

Motivation
Pydantic 2 was a major release that completely changed the pydantic API.
For applications, this is not a big deal, as they can pin to whatever version of
pydantic they need. But for libraries that want to exist in a broader
environment, pinning to a specific version of pydantic is not always an option
(as it limits the ability to co-exist with other libraries).
This package provides (unofficial) compatibility mixins and function adaptors for pydantic
v1-v2 cross compatibility. It allows you to use either v1 or v2 API names,
regardless of the pydantic version installed. (Prefer using v2 names when possible).
Tests are run on Pydantic v1.8 and up
The API conversion is not exhaustive, but suffices for many of the use cases
I have come across. It is in use by the following libraries:

ome-types
psygnal
app-model
useq-schema

Feel free to open an issue or PR if you find it useful, but lacking features
you need.
What does it do?
Not much! :joy:
Mostly it serves to translate names from one API to another. It backports
the v2 API to v1 (so you can v2 names in a pydantic1 runtime),
and forwards the v1 API to v2 (so you can use v1 names in a v2 runtime
without deprecation warnings).

While pydantic2 does offer deprecated access to the v1 API, if you explicitly
wish to support pydantic1 without your users seeing deprecation warnings,
then you need to do a lot of name adaptation depending on the runtime
pydantic version. This package does that for you.

It does not do any significantly complex translation of API logic.
For custom types, you will still likely need to add class methods to
support both versions of pydantic.
It also does not prevent you from needing to know a what's changing
under the hood in pydantic 2. You should be running tests on both
versions of pydantic to ensure your library works as expected. This
library just makes it much easier to support both versions in a single
codebase without a lot of ugly conditionals and boilerplate.
Usage
from pydantic import BaseModel
from pydantic_compat import PydanticCompatMixin
from pydantic_compat import field_validator # or 'validator'
from pydantic_compat import model_validator # or 'root_validator'

class MyModel(PydanticCompatMixin, BaseModel):
x: int
y: int = 2

# prefer v2 dict, but v1 class Config is supported
model_config = {'frozen': True}

@field_validator('x', mode='after')
def _check_x(cls, v):
if v != 42:
raise ValueError("That's not the answer!")
return v

@model_validator('x', mode='after')
def _check_x(cls, v: MyModel):
# ...
return v

You can now use the following attributes and methods regardless of the
pydantic version installed (without deprecation warnings):

v1 name
v2 name

obj.dict()
obj.model_dump()

obj.json()
obj.model_dump_json()

obj.copy()
obj.model_copy()

Model.construct
Model.model_construct

Model.schema
Model.model_json_schema

Model.validate
Model.model_validate

Model.parse_obj
Model.model_validate

Model.parse_raw
Model.model_validate_json

Model.update_forward_refs
Model.model_rebuild

Model.__fields__
Model.model_fields

Model.__fields_set__
Model.model_fields_set

Field notes

pydantic_compat.Field will remove outdated fields (const) and translate
fields with new names:

v1 name
v2 name

min_items
min_length

max_items
max_length

regex
pattern

allow_mutation
not frozen

metadata
json_schema_extra

Don't use var = Field(..., const='val'), use var: Literal['val'] = 'val'
it works in both v1 and v2
No attempt is made to convert between v1's unique_items and v2's Set[]
semantics. See https://github.com/pydantic/pydantic-core/issues/296 for
discussion.

API rules

both V1 and V2 names may be used (regardless of pydantic version), but
usage of V2 names are strongly recommended.
But the API must match the pydantic version matching the name you are using.
For example, if you are using pydantic_compat.field_validator then the
signature must match the pydantic (v2) field_validator signature (regardless)
of the pydantic version installed. Similarly, if you choose to use
pydantic_compat.validator then the signature must match the pydantic
(v1) validator signature.

Notable differences

BaseModel.__fields__ in v1 is a dict of {'field_name' -> ModelField}
whereas in v2 BaseModel.model_fields is a dict of {'field_name' -> FieldInfo}. FieldInfo is a much simpler object that ModelField, so it is
difficult to directly support complicated v1 usage of __fields__.
pydantic-compat simply provides a name addaptor that lets you access many of
the attributes you may have accessed on ModelField in v1 while operating in
a v2 world, but ModelField methods will not be made available. You'll need
to update your usage accordingly.

in V2, pydantic.model_validator(..., mode='after') passes a model instance
to the validator function, whereas pydantic.v1.root_validator(..., pre=False) passes a dict of {'field_name' -> validated_value} to the
validator function. In pydantic-compat, both decorators follow the semantics
of their corresponding pydantic versions, but root_validator gains
parameter construct_object: bool=False that matches the model_validator
behavior (only when mode=='after'). If you want that behavior though, prefer
using model_validator directly.

TODO:

Serialization decorators