chore: import upstream snapshot with attribution

python/semantic_kernel/functions/kernel_parameter_metadata.py

@@ -0,0 +1,62 @@
+# 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