[enragedcacti]

The only reason I can think of for the behavior you are describing is if one of the unioned types at some level of the hierarchy is equivalent to Dict[str, Any]. My understanding is that Pydantic will explore every option provided recursively and raise a ValidationError if none match but will never just give up and hand you a partially validated object.

Are you able to share a snippet that reproduces what you're seeing?

[jmugan]

That's an interesting idea. It's possible there's a Dict[str,Any] in there. And yeah, my assumption was that it tried everything recursively, but I just wasn't seeing that, and my LLM council said that it did not. But I'll check for a Dict[str,Any]. Unfortunately, I don't have a minimal example, but making one should be my next step.

[enragedcacti]

One thing to watch out for while you debug is that the default 'smart' mode for union discrimination can be very unintuitive. As you can see in this example, an int vs a string can cause a different model to be chosen two layers up even though both are valid. You may have perfectly valid uses of Dict within your model that are being chosen in error because they result in less type coercion. left_to_right mode (or ideally discriminated unions if your data has easy discriminators) will be much more consistent.

    >>> class A(BaseModel):
    >>>     a: int
    >>> class B(BaseModel):
    >>>     b: A
    >>> class C(BaseModel):
    >>>     c: B | Dict[str, Any]

    >>> C.model_validate({'c':{'b':{'a':1}}})
    
    C(c=B(b=A(a=1)))

    >>> C.model_validate({'c':{'b':{'a':"1"}}})

    C(c={'b': {'a': '1'}})

    >>> class C(BaseModel):
    >>>     c: B | Dict[str, Any] = Field(union_mode='left_to_right')
    
    >>> C.model_validate({'c':{'b':{'a':"1"}}})

    C(c=B(b=A(a=1)))