wehub-resource-sync
101 lines
4.0 KiB
Python
101 lines
4.0 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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from typing import TYPE_CHECKING, ClassVar
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from opentelemetry import trace
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from pydantic import Field
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from semantic_kernel.agents.strategies.selection.selection_strategy import SelectionStrategy
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from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
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from semantic_kernel.connectors.ai.open_ai import (
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AzureChatPromptExecutionSettings,
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OpenAIChatCompletion,
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)
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from semantic_kernel.contents import ChatHistory
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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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from semantic_kernel.contents.chat_message_content import ChatMessageContent
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NEWLINE = "\n"
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@experimental
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class CustomSelectionStrategy(SelectionStrategy):
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"""A selection strategy that selects the next agent intelligently."""
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NUM_OF_RETRIES: ClassVar[int] = 3
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chat_completion_service: ChatCompletionClientBase = Field(default_factory=lambda: OpenAIChatCompletion())
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async def next(self, agents: list["Agent"], history: list["ChatMessageContent"]) -> "Agent":
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"""Select the next agent to interact with.
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Args:
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agents: The list of agents to select from.
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history: The history of messages in the conversation.
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Returns:
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The next agent to interact with.
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"""
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if len(agents) == 0:
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raise ValueError("No agents to select from")
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tracer = trace.get_tracer(__name__)
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with tracer.start_as_current_span("selection_strategy"):
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chat_history = ChatHistory(system_message=self.get_system_message(agents).strip())
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for message in history:
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content = message.content
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# We don't want to add messages whose text content is empty.
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# Those messages are likely messages from function calls and function results.
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if content:
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chat_history.add_message(message)
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chat_history.add_user_message("Now follow the rules and select the next agent by typing the agent's index.")
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for _ in range(self.NUM_OF_RETRIES):
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completion = await self.chat_completion_service.get_chat_message_content(
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chat_history,
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AzureChatPromptExecutionSettings(),
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)
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if completion is None:
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continue
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try:
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return agents[int(completion.content)]
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except ValueError as ex:
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chat_history.add_message(completion)
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chat_history.add_user_message(str(ex))
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chat_history.add_user_message(f"You must only say a number between 0 and {len(agents) - 1}.")
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raise ValueError("Failed to select an agent since the model did not return a valid index")
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def get_system_message(self, agents: list["Agent"]) -> str:
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return f"""
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You are in a multi-agent chat to create a document.
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Each message in the chat history contains the agent's name and the message content.
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Initially, the chat history may be empty.
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Here are the agents with their indices, names, and descriptions:
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{NEWLINE.join(f"[{index}] {agent.name}:{NEWLINE}{agent.description}" for index, agent in enumerate(agents))}
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Your task is to select the next agent based on the conversation history.
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The conversation must follow these steps:
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1. The content creation agent writes a draft.
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2. The code validation agent checks the code in the draft.
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3. The content creation agent updates the draft based on the feedback.
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4. The code validation agent checks the updated code.
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...
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If the code validation agent approves the code, the user agent can ask the user for final feedback.
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N: The user agent provides feedback.
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(If the feedback is not positive, the conversation goes back to the content creation agent.)
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Respond with a single number between 0 and {len(agents) - 1}, representing the agent's index.
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Only return the index as an integer.
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"""
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