Files
semantic-kernel/python/semantic_kernel/functions/kernel_parameter_metadata.py
T
Evan Mattson ec89fe4a5e Python: Exclude certain fields from KernelFunction model dump (#11081)
### 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.

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### 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 😄
2025-03-20 02:16:30 +00:00

63 lines
2.5 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
from typing import Any
from pydantic import Field, model_validator
from semantic_kernel.kernel_pydantic import KernelBaseModel
from semantic_kernel.schema.kernel_json_schema_builder import KernelJsonSchemaBuilder
from semantic_kernel.utils.validation import FUNCTION_PARAM_NAME_REGEX
class KernelParameterMetadata(KernelBaseModel):
"""The kernel parameter metadata."""
name: str | None = Field(..., pattern=FUNCTION_PARAM_NAME_REGEX)
description: str | None = None
default_value: Any | None = None
type_: str | None = Field(default="str", alias="type")
is_required: bool | None = False
type_object: Any | None = Field(default=None, exclude=True)
schema_data: dict[str, Any] | None = None
include_in_function_choices: bool = True
@model_validator(mode="before")
@classmethod
def form_schema(cls, data: Any) -> Any:
"""Create a schema for the parameter metadata."""
if isinstance(data, dict) and data.get("schema_data") is None:
type_object = data.get("type_object", None)
type_ = data.get("type_", None)
default_value = data.get("default_value", None)
description = data.get("description", None)
inferred_schema = cls.infer_schema(type_object, type_, default_value, description)
data["schema_data"] = inferred_schema
return data
@classmethod
def infer_schema(
cls,
type_object: type | None = None,
parameter_type: str | None = None,
default_value: Any | None = None,
description: str | None = None,
structured_output: bool = False,
) -> dict[str, Any] | None:
"""Infer the schema for the parameter metadata."""
schema = None
if type_object is not None:
schema = KernelJsonSchemaBuilder.build(type_object, description, structured_output)
elif parameter_type is not None:
string_default = str(default_value) if default_value is not None else None
if string_default and string_default.strip():
needs_space = bool(description and description.strip())
description = (
f"{description}{' ' if needs_space else ''}(default value: {string_default})"
if description
else f"(default value: {string_default})"
)
schema = KernelJsonSchemaBuilder.build_from_type_name(parameter_type, description)
return schema