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Python: Renames SK prefix to Kernel (#4716)
### Motivation and Context The .NET SDK uses the `Kernel` prefix instead of `SK` for the context, function, model, etc. classes. This PR aligns the Python package with that policy. Fixes #4561 ### Contribution Checklist - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone 😄
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@@ -170,7 +170,7 @@ from this field is sufficient to have these types of classes as valid Pydantic f
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any class using them as attributes to be serialized.
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```python
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from semantic_kernel.sk_pydantic import PydanticField
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from semantic_kernel.kernel_pydantic import PydanticField
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class B(PydanticField): ... # correct, B is still an ABC because PydanticField subclasses ABC
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class B(PydanticField, ABC): ... # Also correct
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@@ -223,13 +223,13 @@ class A:
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self.d = d
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```
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You would convert this to a Pydantic class by subclassing from the `SKBaseModel` class.
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You would convert this to a Pydantic class by subclassing from the `KernelBaseModel` class.
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```python
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from pydantic import Field
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from semantic_kernel.sk_pydantic import SKBaseModel
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from semantic_kernel.kernel_pydantic import KernelBaseModel
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class A(SKBaseModel):
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class A(KernelBaseModel):
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# The notation for the fields is similar to dataclasses.
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a: int
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b: float
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@@ -255,14 +255,14 @@ class A:
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self.c = c
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```
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You can uses the `SKGenericModel` to convert these to pydantic serializable classes.
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You can use the `KernelBaseModel` to convert these to pydantic serializable classes.
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```python
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from typing import Generic
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from semantic_kernel.sk_pydantic import SKGenericModel
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from semantic_kernel.kernel_pydantic import KernelBaseModel
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class A(SKGenericModel, Generic[T1, T2]):
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class A(KernelBaseModel, Generic[T1, T2]):
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# T1 and T2 must be specified in the Generic argument otherwise, pydantic will
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# NOT be able to serialize this class
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a: int
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