chore: import upstream snapshot with attribution
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from enum import Enum
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from typing import TYPE_CHECKING
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from pydantic import Field
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from semantic_kernel.agents.strategies.termination.termination_strategy import TerminationStrategy
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from semantic_kernel.contents.chat_message_content import ChatMessageContent
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from semantic_kernel.kernel_pydantic import KernelBaseModel
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from semantic_kernel.utils.feature_stage_decorator import experimental
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if TYPE_CHECKING:
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from semantic_kernel.agents.agent import Agent
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@experimental
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class AggregateTerminationCondition(str, Enum):
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"""The condition for terminating the aggregation process."""
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ALL = "All"
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ANY = "Any"
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@experimental
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class AggregatorTerminationStrategy(KernelBaseModel):
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"""A strategy that aggregates multiple termination strategies."""
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strategies: list[TerminationStrategy]
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condition: AggregateTerminationCondition = Field(default=AggregateTerminationCondition.ALL)
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async def should_terminate_async(
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self,
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agent: "Agent",
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history: list[ChatMessageContent],
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) -> bool:
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"""Check if the agent should terminate.
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Args:
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agent: The agent to check.
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history: The history of messages in the conversation.
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Returns:
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True if the agent should terminate, False otherwise
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"""
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strategy_execution = [strategy.should_terminate(agent, history) for strategy in self.strategies]
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results = await asyncio.gather(*strategy_execution)
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if self.condition == AggregateTerminationCondition.ALL:
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return all(results)
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return any(results)
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# Copyright (c) Microsoft. All rights reserved.
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from typing import TYPE_CHECKING
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from pydantic import Field
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from semantic_kernel.agents.strategies.termination.termination_strategy import TerminationStrategy
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from semantic_kernel.utils.feature_stage_decorator import experimental
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if TYPE_CHECKING:
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from semantic_kernel.agents.agent import Agent
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from semantic_kernel.contents.chat_message_content import ChatMessageContent
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@experimental
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class DefaultTerminationStrategy(TerminationStrategy):
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"""A default termination strategy that never terminates."""
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maximum_iterations: int = Field(default=5, description="The maximum number of iterations to run the agent.")
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async def should_agent_terminate(self, agent: "Agent", history: list["ChatMessageContent"]) -> bool:
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"""Check if the agent should terminate.
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Args:
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agent: The agent to check.
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history: The history of messages in the conversation.
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Returns:
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Defaults to False for the default strategy
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"""
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return False
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+99
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# Copyright (c) Microsoft. All rights reserved.
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import logging
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from collections.abc import Callable
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from inspect import isawaitable
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from typing import TYPE_CHECKING, ClassVar
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from pydantic import Field
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from semantic_kernel.agents.strategies.termination.termination_strategy import TerminationStrategy
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from semantic_kernel.contents.chat_message_content import ChatMessageContent
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from semantic_kernel.contents.history_reducer.chat_history_reducer import ChatHistoryReducer
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from semantic_kernel.functions.kernel_arguments import KernelArguments
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from semantic_kernel.functions.kernel_function import KernelFunction
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from semantic_kernel.kernel import Kernel
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from semantic_kernel.utils.feature_stage_decorator import experimental
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if TYPE_CHECKING:
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from semantic_kernel.agents import Agent
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logger: logging.Logger = logging.getLogger(__name__)
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@experimental
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class KernelFunctionTerminationStrategy(TerminationStrategy):
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"""A termination strategy that uses a kernel function to determine termination."""
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DEFAULT_AGENT_VARIABLE_NAME: ClassVar[str] = "_agent_"
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DEFAULT_HISTORY_VARIABLE_NAME: ClassVar[str] = "_history_"
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agent_variable_name: str | None = Field(default=DEFAULT_AGENT_VARIABLE_NAME)
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history_variable_name: str | None = Field(default=DEFAULT_HISTORY_VARIABLE_NAME)
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arguments: KernelArguments | None = None
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function: KernelFunction
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kernel: Kernel
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result_parser: Callable[..., bool] = Field(default_factory=lambda: (lambda: True))
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history_reducer: ChatHistoryReducer | None = None
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async def should_agent_terminate(
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self,
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agent: "Agent",
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history: list[ChatMessageContent],
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) -> bool:
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"""Check if the agent should terminate.
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Args:
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agent: The agent to check.
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history: The history of messages in the conversation.
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Returns:
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True if the agent should terminate, False otherwise
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"""
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if self.history_reducer is not None:
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self.history_reducer.messages = history
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reduced_history = await self.history_reducer.reduce()
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if reduced_history is not None:
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history = reduced_history.messages
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original_arguments = self.arguments or KernelArguments()
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execution_settings = original_arguments.execution_settings or {}
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messages = [message.to_dict(role_key="role", content_key="content") for message in history]
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filtered_arguments = {
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self.agent_variable_name: agent.name or agent.id,
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self.history_variable_name: messages,
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}
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extracted_settings = {key: setting.model_dump() for key, setting in execution_settings.items()}
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combined_arguments = {
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**original_arguments,
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**extracted_settings,
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**{k: v for k, v in filtered_arguments.items()},
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}
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arguments = KernelArguments(
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**combined_arguments,
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)
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logger.info(f"should_agent_terminate, function invoking: `{self.function.fully_qualified_name}`")
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result = await self.function.invoke(kernel=self.kernel, arguments=arguments)
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if result is None:
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logger.info(
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f"should_agent_terminate, function `{self.function.fully_qualified_name}` invoked with result `None`",
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)
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return False
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logger.info(
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f"should_agent_terminate, function `{self.function.fully_qualified_name}` "
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f"invoked with result `{result.value if result.value else None}`",
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)
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result_parsed = self.result_parser(result)
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if isawaitable(result_parsed):
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result_parsed = await result_parsed
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return result_parsed
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@@ -0,0 +1,57 @@
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# Copyright (c) Microsoft. All rights reserved.
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import logging
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from typing import TYPE_CHECKING
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from pydantic import Field
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from semantic_kernel.agents.agent import Agent
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from semantic_kernel.kernel_pydantic import KernelBaseModel
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from semantic_kernel.utils.feature_stage_decorator import experimental
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if TYPE_CHECKING:
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from semantic_kernel.contents.chat_message_content import ChatMessageContent
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logger: logging.Logger = logging.getLogger(__name__)
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@experimental
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class TerminationStrategy(KernelBaseModel):
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"""A strategy for determining when an agent should terminate."""
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maximum_iterations: int = Field(default=99)
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automatic_reset: bool = False
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agents: list[Agent] = Field(default_factory=list)
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async def should_agent_terminate(self, agent: "Agent", history: list["ChatMessageContent"]) -> bool:
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"""Check if the agent should terminate.
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Args:
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agent: The agent to check.
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history: The history of messages in the conversation.
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Returns:
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True if the agent should terminate, False otherwise
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"""
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raise NotImplementedError("Subclasses should implement this method")
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async def should_terminate(self, agent: "Agent", history: list["ChatMessageContent"]) -> bool:
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"""Check if the agent should terminate.
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Args:
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agent: The agent to check.
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history: The history of messages in the conversation.
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Returns:
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True if the agent should terminate, False otherwise
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"""
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logger.info(f"Evaluating termination criteria for {agent.id}")
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if self.agents and not any(a.id == agent.id for a in self.agents):
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logger.info(f"Agent {agent.id} is out of scope")
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return False
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should_terminate = await self.should_agent_terminate(agent, history)
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logger.info(f"Evaluated criteria for {agent.id}, should terminate: {should_terminate}")
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return should_terminate
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