when a model has many optional field, it will raise unwanted validation type error

[brightgems]

Bug

it will raise unwanted validation type error when a model has many optional field;
pydantic won't raise any error

from odmantic import Model,Field
from pydantic import BaseModel
from typing import Optional, Dict

class DummpyEmbedModel(Model):
"""item url and title, thumbnail
treat as task queue
"""
infos: Optional[Dict] = None
# all image text in item page
pso: Optional[str] = None
gptTags: Optional[Dict] = None

Current Behavior

self = DummpyEmbedModel(), data = {'infos': {}, 'pso': 'xx'}

    def __init__(self, **data: Any):
>       super().__init__(**data)
E       pydantic_core._pydantic_core.ValidationError: 1 validation error for DummpyEmbedModel
E       pso
E         Input should be a valid dictionary [type=dict_type, input_value='xx', input_type=str]
E           For further information visit https://errors.pydantic.dev/2.5/v/dict_type

Expected behavior

should no raise any error as pydantic

Environment

• ODMantic version: ...
• MongoDB version: ...
• Pydantic infos (output of python -c "import pydantic.utils; print(pydantic.utils.version_info())):

class DummpyEmbedModel(Model):
    """item url and title, thumbnail
    treat as task queue
    """
    infos: Optional[Dict] = None
    # all image text in item page
    pso: Optional[str] = None
    gptTags: Optional[Dict] = None
    
...

• Version of additional modules (if relevant):
  • ...

Additional context

v1.0.0

[laveolus]

In v1.0.0, all optional fields are aliased to the (first) field type, see #379.

[francisdeh]

@laveolus and @brightgems and @Jibola, until this is addressed, have you found any temporary workaround to this? I also encountered same.

[pr0gg3d]

We are also plenty of Optional[] fields with different types, and this makes odmantic 1.0.0 pretty much unusable.

Anyone found a workaround for this? Thanks!!

[Jibola]

@laveolus and @brightgems and @Jibola, until this is addressed, have you found any temporary workaround to this? I also encountered same.

Currently, I have just leveraged the Pydantic BaseModel class as my workaround for now. If it's unavoidable, I've settled for removing the Optional field usage.