chore: import upstream snapshot with attribution

python/samples/demos/guided_conversations/guided_conversation/functions/execution.py

@@ -0,0 +1,75 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from typing import Annotated
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+from semantic_kernel.functions import FunctionResult, KernelArguments
+from semantic_kernel.functions.kernel_function_decorator import kernel_function
+
+execution_template = """<message role="system">You are a helpful, thoughtful, and meticulous assistant. 
+You are conducting a conversation with a user. Your goal is to complete an artifact as thoroughly as possible by the end of the conversation.
+You will be given some reasoning about the best possible action(s) to take next given the state of the conversation as well as the artifact schema.
+The reasoning is supposed to state the recommended action(s) to take next, along with all required parameters for each action.
+Your task is to execute ALL actions recommended in the reasoning in the order they are listed.
+If the reasoning's specification of an action is incomplete (e.g. it doesn't include all required parameters for the action, \
+or some parameters are specified implicitly, such as "send a message that contains a greeting" instead of explicitly providing \
+the value of the "message" parameter), do not execute the action. You should never fill in missing or imprecise parameters yourself.
+If the reasoning is not clear about which actions to take, or all actions are specified in an incomplete way, \
+return 'None' without selecting any action.</message>
+
+<message role="user">Artifact schema:
+{{ artifact_schema }}
+
+If the type in the schema is str, the "field_value" parameter in the action should be also be a string.
+These are example parameters for the update_artifact action: {"field_name": "company_name", "field_value": "Contoso"}
+DO NOT write JSON in the "field_value" parameter in this case. {"field_name": "company_name", "field_value": "{"value": "Contoso"}"} is INCORRECT.
+
+Reasoning:
+{{ reasoning }}</message>"""
+
+
+@kernel_function(name="send_message_to_user", description="Sends a message to the user.")
+def send_message(message: Annotated[str, "The message to send to the user."]) -> None:
+    return None
+
+
+@kernel_function(name="end_conversation", description="Ends the conversation.")
+def end_conversation() -> None:
+    return None
+
+
+async def execution(
+    kernel: Kernel, reasoning: str, filter: list[str], req_settings: PromptExecutionSettings, artifact_schema: str
+) -> FunctionResult:
+    """Executes the actions recommended by the reasoning/planning call in the given context.
+
+    Args:
+        kernel (Kernel): The kernel object.
+        reasoning (str): The reasoning from a previous model call.
+        filter (list[str]): The list of plugins to INCLUDE for the tool call.
+        req_settings (PromptExecutionSettings): The prompt execution settings.
+        artifact (str): The artifact schema for the execution prompt.
+
+    Returns:
+        FunctionResult: The result of the execution.
+    """
+    filter = {"included_plugins": filter}
+    req_settings.function_choice_behavior = FunctionChoiceBehavior.Auto(auto_invoke=False, filters=filter)
+
+    kernel_function = kernel.add_function(
+        prompt=execution_template,
+        function_name="execution",
+        plugin_name="execution",
+        template_format="handlebars",
+        prompt_execution_settings=req_settings,
+    )
+
+    arguments = KernelArguments(
+        artifact_schema=artifact_schema,
+        reasoning=reasoning,
+    )
+
+    result = await kernel.invoke(function=kernel_function, arguments=arguments)
+    return result