It’s great that you’re diving into type annotations and exploring JSON serialization! This is an important topic, especially for APIs and configurations.

Creating a base class for JSON serializability is a solid approach. You could define a mixin class that requires subclasses to implement a method for returning a JSON-compatible representation of their instances. Something like this:

  class JSONSerializable:
      def to_dict(self) -> dict:
          raise NotImplementedError("Subclasses must implement to_dict method")
  

In your other classes, you’d then just need to implement this to_dict method to return a dictionary suitable for JSON serialization. For example:

  class MyDataClass(JSONSerializable):
      def __init__(self, name: str, age: int):
          self.name = name
          self.age = age
      
      def to_dict(self) -> dict:
          return {"name": self.name, "age": self.age}
  

This way, if you forget to implement to_dict, mypy will throw a type error.

Using Protocol from the typing module is also a cool idea if you’re looking to ensure that certain methods exist without strictly enforcing inheritance. You can define a protocol that requires the to_dict method and have your classes conform to it:

  from typing import Protocol, Dict

  class JSONProtocol(Protocol):
      def to_dict(self) -> Dict:
          ...
  
  def serialize_to_json(instance: JSONProtocol) -> str:
      import json
      return json.dumps(instance.to_dict())
  

Regarding TypedDict, it’s useful for defining dictionary-like objects with specific types for their fields. This is perfect for settings or configurations, but keep in mind it’s not the same as defining a full class with methods.

As for type annotations, yes, they help a lot in self-documenting your code and clarifying what types you expect for attributes and methods. It’s especially important for collaboration and maintenance.

The JSON serialization will often depend on the objects you are working with. Complex types can always cause issues, so make sure to handle those cases in your to_dict method.

In terms of mypy limitations, it can sometimes struggle with dynamic attributes or if you’re trying to get too clever with type hints. The more explicit and simple you keep your type structures, the better.

In real-world applications, having a clear, consistent strategy for serializing objects keeps things neat and manageable, not to mention easier to debug.

Hope that helps clear things up a bit!