Add GenericEncoder and Pydantic support #63
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Additional updates:
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| def _is_pydantic_model_type(typ: Type) -> bool: | ||
| return any( | ||
| base.__module__.startswith("pydantic") and base.__name__ == "BaseModel" |
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Pydantic also has a GenericModel do we need to consider that?
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You're right—Pydantic does have GenericModel, but that's only part of Pydantic v1. Fortunately, GenericModel also inherits from BaseModel, so any logic that targets BaseModel will still work seamlessly for GenericModel instances too.
On a related note, I wanted to raise a question about the is_allowed_type check. In my experience, this utility has become a bottleneck for extending zero with support for new types. While the encoder side is quite flexible and easy to extend, is_allowed_type feels a bit restrictive—it requires explicitly adding each new supported type, which slows down development.
I'd suggest either removing this check altogether or generalizing it—for example, by checking isinstance(obj, type)—so it can handle any class-based types, including dataclasses, msgspec, and Pydantic models, without special casing.
Curious to hear your thoughts on this!
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| class GenericEncoder(MsgspecEncoder): | ||
| def encode(self, data: Any) -> bytes: | ||
| if PYDANTIC_AVAILABLE and isinstance(data, BaseModel): |
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Nice and neat work! 🙌 |
This PR introduces built-in Pydantic support via a new
GenericEncoderthat is compatible with both Pydantic V1 and V2.Key changes:
GenericEncoderis now the default encoder.