wehub-resource-sync
118 lines
5.0 KiB
Python
118 lines
5.0 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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from typing import Any
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from semantic_kernel.connectors.ai.function_call_choice_configuration import FunctionCallChoiceConfiguration
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from semantic_kernel.contents.function_call_content import FunctionCallContent
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from semantic_kernel.contents.function_result_content import FunctionResultContent
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from semantic_kernel.functions.kernel_function_metadata import KernelFunctionMetadata
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from semantic_kernel.functions.kernel_parameter_metadata import KernelParameterMetadata
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def kernel_function_to_bedrock_function_schema(
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function_choice_configuration: FunctionCallChoiceConfiguration,
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) -> dict[str, Any]:
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"""Convert the kernel function to bedrock function schema."""
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return {
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"functions": [
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kernel_function_metadata_to_bedrock_function_schema(function_metadata)
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for function_metadata in function_choice_configuration.available_functions or []
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]
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}
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def kernel_function_metadata_to_bedrock_function_schema(function_metadata: KernelFunctionMetadata) -> dict[str, Any]:
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"""Convert the kernel function metadata to bedrock function schema."""
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schema = {
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"description": function_metadata.description,
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"name": function_metadata.fully_qualified_name,
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"parameters": {
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parameter.name: kernel_function_parameter_to_bedrock_function_parameter(parameter)
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for parameter in function_metadata.parameters
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},
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# This field controls whether user confirmation is required to invoke the function.
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# If this is set to "ENABLED", the user will be prompted to confirm the function invocation.
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# Only after the user confirms, the function call request will be issued by the agent.
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# If the user denies the confirmation, the agent will act as if the function does not exist.
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# Currently, we do not support this feature, so we set it to "DISABLED".
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"requireConfirmation": "DISABLED",
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}
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# Remove None values from the schema
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return {key: value for key, value in schema.items() if value is not None}
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def kernel_function_parameter_to_bedrock_function_parameter(parameter: KernelParameterMetadata):
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"""Convert the kernel function parameters to bedrock function parameters."""
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schema = {
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"description": parameter.description,
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"type": kernel_function_parameter_type_to_bedrock_function_parameter_type(parameter.schema_data),
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"required": parameter.is_required,
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}
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# Remove None values from the schema
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return {key: value for key, value in schema.items() if value is not None}
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# These are the allowed parameter types in bedrock function.
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# https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent-runtime_ParameterDetail.html
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BEDROCK_FUNCTION_ALLOWED_PARAMETER_TYPES = {
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"string",
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"number",
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"integer",
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"boolean",
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"array",
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}
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def kernel_function_parameter_type_to_bedrock_function_parameter_type(schema_data: dict[str, Any] | None) -> str:
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"""Convert the kernel function parameter type to bedrock function parameter type."""
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if schema_data is None:
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raise ValueError(
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"Schema data is required to convert the kernel function parameter type to bedrock function parameter type."
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)
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type_ = schema_data.get("type")
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if type_ is None:
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raise ValueError(
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"Type is required to convert the kernel function parameter type to bedrock function parameter type."
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)
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if type_ not in BEDROCK_FUNCTION_ALLOWED_PARAMETER_TYPES:
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raise ValueError(
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f"Type {type_} is not allowed in bedrock function parameter type. "
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f"Allowed types are {BEDROCK_FUNCTION_ALLOWED_PARAMETER_TYPES}."
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)
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return type_
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def parse_return_control_payload(return_control_payload: dict[str, Any]) -> list[FunctionCallContent]:
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"""Parse the return control payload to a list of function call contents for the kernel."""
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return [
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FunctionCallContent(
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id=return_control_payload["invocationId"],
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name=invocation_input["functionInvocationInput"]["function"],
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arguments={
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parameter["name"]: parameter["value"]
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for parameter in invocation_input["functionInvocationInput"]["parameters"]
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},
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metadata=invocation_input,
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)
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for invocation_input in return_control_payload.get("invocationInputs", [])
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]
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def parse_function_result_contents(function_result_contents: list[FunctionResultContent]) -> list[dict[str, Any]]:
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"""Parse the function result contents to be returned to the agent in the session state."""
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return [
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{
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"functionResult": {
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"actionGroup": function_result_content.metadata["functionInvocationInput"]["actionGroup"],
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"function": function_result_content.name,
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"responseBody": {"TEXT": {"body": str(function_result_content.result)}},
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}
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}
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for function_result_content in function_result_contents
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]
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