204 lines
8.0 KiB
Python
204 lines
8.0 KiB
Python
import asyncio
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import json
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import sqlite3
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import sys
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from pathlib import Path
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from typing import Annotated
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from autogen_agentchat.agents import AssistantAgent
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from autogen_agentchat.messages import TextMessage
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from autogen_core import CancellationToken
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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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BASE_DIR = Path(__file__).resolve().parent
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USER_DB_PATH = BASE_DIR / "users.db"
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MODEL_CALL_TIMEOUT_SECONDS = 45
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# --- 第一部分：本地工具（面部 + 语音，以后接硬件）---
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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 _async_console_input(prompt: str) -> str:
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"""在线程中执行阻塞 input，避免阻塞事件循环。"""
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return await asyncio.to_thread(input, prompt)
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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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# --- 第二部分：直接读取用户档案（不经过 MCP，避免多轮工具调用）---
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def _load_user_profile(user_name: str, db_path: str | Path = USER_DB_PATH) -> str:
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"""在 Python 层直接读档案，注入到消息上下文，模型无需主动调用 get_user_profile。"""
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try:
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with sqlite3.connect(db_path) as conn:
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conn.row_factory = sqlite3.Row
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user = conn.execute(
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"SELECT * FROM users WHERE name = ?", (user_name,)
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).fetchone()
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if not user:
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return f"用户 {user_name} 尚无历史记录，这是第一次见面。"
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prefs = conn.execute(
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"SELECT category, content FROM preferences WHERE user_name = ?",
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(user_name,)
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).fetchall()
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conn.execute(
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"UPDATE users SET last_seen = datetime('now') WHERE name = ?",
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(user_name,)
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)
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return json.dumps({
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"基本信息": {"姓名": user["name"], "年龄": user["age"], "上次见面": user["last_seen"]},
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"偏好习惯": {p["category"]: p["content"] for p in prefs},
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}, ensure_ascii=False)
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except Exception as e:
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return f"档案读取失败（{e}），当作第一次见面。"
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# --- 第三部分：启动大脑 ---
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async def start_simulated_head():
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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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# 过滤掉 get_user_profile，模型无需主动调用它
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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="Qwen/Qwen3-VL-8B-Instruct",
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base_url="http://localhost:8000/v1",
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api_key="EMPTY",
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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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# --- 第四部分：交互循环 ---
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print("=" * 50)
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print(" 机器人已上线！输入 'quit' 退出")
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print("=" * 50)
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try:
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user_name = (await _async_console_input("请输入你的名字: ")).strip() or "用户"
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except (EOFError, KeyboardInterrupt):
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print("\n机器人下线，再见！")
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return
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visual_context = "视觉输入：用户坐在电脑前，表情平静，看着屏幕。"
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print(f"\n[当前视觉状态]: {visual_context}")
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print("提示：输入 'v <描述>' 可以更新视觉状态，例如: v 用户在笑\n")
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history = []
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try:
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while True:
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try:
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user_input = (await _async_console_input("你说: ")).strip()
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except (EOFError, KeyboardInterrupt):
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print("\n机器人下线，再见！")
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break
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if not user_input:
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continue
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if user_input.lower() in ("quit", "exit", "退出"):
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print("机器人下线，再见！")
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break
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if user_input.lower().startswith("v "):
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visual_context = f"视觉输入：{user_input[2:].strip()}。"
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print(f"[视觉状态已更新]: {visual_context}\n")
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continue
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# Python 层直接读取档案并注入消息，模型无需发起额外工具调用
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profile = _load_user_profile(user_name)
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combined_input = (
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f"[用户档案]\n{profile}\n\n"
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f"[视觉状态] {visual_context}\n"
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f"[用户说] {user_input}"
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)
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history.append(TextMessage(content=combined_input, source="user"))
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# 只保留最近 6 条消息（3轮对话），防止超出 token 上限
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# 用户档案每轮从数据库重新注入，不依赖长历史
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if len(history) > 6:
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history = history[-6:]
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try:
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response = await asyncio.wait_for(
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brain.on_messages(history, CancellationToken()),
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timeout=MODEL_CALL_TIMEOUT_SECONDS,
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)
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except asyncio.TimeoutError:
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print(">>>>>> ⚠️ 请求超时，请稍后重试或简化问题。 <<<<<<\n")
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continue
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except Exception as e:
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print(f">>>>>> ⚠️ 本轮处理失败：{e} <<<<<<\n")
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continue
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# 模型的文字回复就是语音输出（reflect_on_tool_use=True 保证这里是 TextMessage）
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speech = response.chat_message.content
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if speech and isinstance(speech, str):
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print(f">>>>>> 🔊 机器人说: {speech} <<<<<<\n")
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# 只把最终回复加入历史，inner_messages 是事件对象不能序列化回模型
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history.append(response.chat_message)
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finally:
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model_client.close()
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if __name__ == "__main__":
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asyncio.run(start_simulated_head())
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