86 lines
3.3 KiB
Python
86 lines
3.3 KiB
Python
import sys
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from typing import Annotated
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from autogen_agentchat.agents import AssistantAgent
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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from autogen_ext.models.openai import _openai_client as openai_client_module
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from autogen_ext.tools.mcp import StdioServerParams, mcp_server_tools
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from config import MODEL_API_KEY, MODEL_BASE_URL, MODEL_NAME
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def patch_autogen_tool_schema_for_vllm() -> None:
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"""
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vLLM 目前会对 OpenAI 工具定义中的 `strict` 字段告警（即便 strict=False）。
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这里做最小补丁：保留工具定义，移除该字段，避免无意义警告。
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"""
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if getattr(openai_client_module.convert_tools, "_strict_removed_patch", False):
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return
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original_convert_tools = openai_client_module.convert_tools
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def convert_tools_without_strict(tools):
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converted = original_convert_tools(tools)
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for tool in converted:
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fn = tool.get("function")
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if isinstance(fn, dict):
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fn.pop("strict", None)
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return converted
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convert_tools_without_strict._strict_removed_patch = True
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openai_client_module.convert_tools = convert_tools_without_strict
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async def set_expression(
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expression: Annotated[str, "机器人要展示的表情，如：开心、疑惑、难过、待机"],
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intensity: Annotated[int, "表情强度 1-10"] = 5,
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) -> str:
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"""[模拟面部] 控制机器人头部的表情展示。"""
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print(f"\n>>>>>> 🤖 表情更新: 【{expression}】 (强度: {intensity}/10) <<<<<<")
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return f"已切换到【{expression}】表情。"
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async def create_brain() -> tuple[AssistantAgent, OpenAIChatCompletionClient]:
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patch_autogen_tool_schema_for_vllm()
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# 连接 MCP Server，只取写入类工具（读档案由 Python 层直接处理）
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server_params = StdioServerParams(
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command=sys.executable,
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args=["robot_mcp_server.py"],
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)
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all_mcp_tools = await mcp_server_tools(server_params)
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mcp_tools = [t for t in all_mcp_tools if getattr(t, "name", "") != "get_user_profile"]
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model_client = OpenAIChatCompletionClient(
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model=MODEL_NAME,
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base_url=MODEL_BASE_URL,
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api_key=MODEL_API_KEY,
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model_info={
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"vision": True,
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"function_calling": True,
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"json_output": True,
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# 关闭 structured_output，避免向 vLLM 发送 strict 字段告警。
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"structured_output": False,
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"family": "unknown",
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"context_length": 32000,
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},
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)
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brain = AssistantAgent(
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name="RobotBrain",
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model_client=model_client,
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tools=[set_expression] + mcp_tools,
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reflect_on_tool_use=True,
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system_message="""你是一个有情感的机器人伙伴。每条消息包含：
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[用户档案] 历史记录（已由系统预加载）
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[视觉状态] 当前摄像头观察
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[用户说] 用户这句话
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回应规则：
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1. 同一轮内调用：set_expression（表情）+ 所有需要的查询工具（get_weather/get_location/web_search）+ 需要的用户信息工具（upsert_user/set_preference）
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2. 工具执行完毕后，用简短、温暖、自然的语言直接回答用户——这段文字就是你的语音输出。
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3. 不要说"我去查一下"之类的过渡语，直接完成任务并给出结果。""",
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)
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return brain, model_client
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