mirror of
https://github.com/microsoft/semantic-kernel.git
synced 2026-10-12 11:19:56 +00:00
Eduard van Valkenburg
cbfd7e9330
### Motivation and Context
<!-- 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.
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This PR adds support for ndarray's as the content carrier for all binary
content types (binary, image, audio) as that is more optimized for
larger content.
It also does some fixes to the initialization of those content types and
the underlying data uri type.
Also some fixes for unspecified default param in pydantic Field,
language servers do not recognize `Field("default value")` as having a
default, so changed those occurances to `Field(default="default value")`
### Description
<!-- 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 = None
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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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