mirror of
https://github.com/microsoft/semantic-kernel.git
synced 2026-10-12 02:19:06 +00:00
Evan Mattson
ec89fe4a5e
### Motivation and Context When trying to do a model dump for `KernelFunction` models, they are failing with a message related to: ``` pydantic_core._pydantic_core.PydanticSerializationError: Unable to serialize unknown type: <class 'type'> ``` There are some class-level attributes that cannot be properly serialized. <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> ### Description Exclude dumping certain model attributes from the model dump, which allows the main KernelFunction metadata and its underlying parameters to be serialized. - Closes #11078 - Adds unit tests to ensure we have coverage and can dump Pydantic models. <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [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 😄
63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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from typing import Any
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from pydantic import Field, model_validator
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from semantic_kernel.kernel_pydantic import KernelBaseModel
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from semantic_kernel.schema.kernel_json_schema_builder import KernelJsonSchemaBuilder
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from semantic_kernel.utils.validation import FUNCTION_PARAM_NAME_REGEX
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class KernelParameterMetadata(KernelBaseModel):
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"""The kernel parameter metadata."""
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name: str | None = Field(..., pattern=FUNCTION_PARAM_NAME_REGEX)
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description: str | None = None
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default_value: Any | None = None
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type_: str | None = Field(default="str", alias="type")
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is_required: bool | None = False
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type_object: Any | None = Field(default=None, exclude=True)
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schema_data: dict[str, Any] | None = None
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include_in_function_choices: bool = True
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@model_validator(mode="before")
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@classmethod
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def form_schema(cls, data: Any) -> Any:
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"""Create a schema for the parameter metadata."""
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if isinstance(data, dict) and data.get("schema_data") is None:
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type_object = data.get("type_object", None)
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type_ = data.get("type_", None)
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default_value = data.get("default_value", None)
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description = data.get("description", None)
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inferred_schema = cls.infer_schema(type_object, type_, default_value, description)
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data["schema_data"] = inferred_schema
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return data
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@classmethod
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def infer_schema(
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cls,
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type_object: type | None = None,
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parameter_type: str | None = None,
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default_value: Any | None = None,
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description: str | None = None,
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structured_output: bool = False,
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) -> dict[str, Any] | None:
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"""Infer the schema for the parameter metadata."""
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schema = None
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if type_object is not None:
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schema = KernelJsonSchemaBuilder.build(type_object, description, structured_output)
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elif parameter_type is not None:
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string_default = str(default_value) if default_value is not None else None
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if string_default and string_default.strip():
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needs_space = bool(description and description.strip())
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description = (
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f"{description}{' ' if needs_space else ''}(default value: {string_default})"
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if description
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else f"(default value: {string_default})"
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)
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schema = KernelJsonSchemaBuilder.build_from_type_name(parameter_type, description)
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return schema
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