feat: 添加联网搜索、地理位置查询、天气查询
This commit is contained in:
JiajunLI
203
main.py
203
main.py
@@ -1,12 +1,50 @@
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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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# --- 第一部分：工具定义 ---
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# 以后接上机器人时，把 print 替换成串口指令 / TTS 调用即可
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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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@@ -16,15 +54,47 @@ async def set_expression(
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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 speak(
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text: Annotated[str, "机器人要说的话，简短自然"]
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) -> str:
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"""[模拟 TTS] 机器人开口说话。以后接 TTS 引擎播放语音。"""
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print(f">>>>>> 🔊 机器人说: {text} <<<<<<\n")
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return "语音已播放。"
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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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# --- 第二部分：配置 VLM 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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@@ -33,70 +103,101 @@ async def start_simulated_head():
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"vision": True,
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"function_calling": True,
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"json_output": True,
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"structured_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": 4096,
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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, speak],
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system_message="""你是一个有情感的机器人伙伴，能感知用户状态并进行语言交流。
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每次收到输入时，你必须：
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1. 综合视觉信息和用户说的话，理解当前情境和用户的情绪/需求。
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2. 调用 set_expression 展示合适的表情。
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3. 调用 speak 用简短、温暖、自然的语言回应用户。
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回应风格：像和老朋友聊天，不要太正式，有点个性和幽默感。"""
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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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# --- 第三部分：交互循环 ---
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# 模拟视觉上下文（真实项目中由摄像头实时提供）
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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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# --- 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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print(f"[当前视觉状态]: {visual_context}")
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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 = [] # 维护完整对话历史，让机器人记住上下文
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history = []
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while True:
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try:
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user_input = 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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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 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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await brain.on_messages(
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[*history, TextMessage(content=f"{visual_context}\n用户说：「再见」", source="user")],
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CancellationToken()
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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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print("\n机器人下线，再见！")
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break
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history.append(TextMessage(content=combined_input, source="user"))
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# 支持临时更新视觉状态
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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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# 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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# 合并视觉 + 语言输入
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combined_input = f"{visual_context}\n用户说：「{user_input}」"
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history.append(TextMessage(content=combined_input, source="user"))
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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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response = await brain.on_messages(history, CancellationToken())
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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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# 把本轮所有消息（工具调用、工具结果、最终回复）加入历史
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if response.inner_messages:
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history.extend(response.inner_messages)
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history.append(response.chat_message)
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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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358
robot_mcp_server.py
Normal file
358
robot_mcp_server.py
Normal file
@@ -0,0 +1,358 @@
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"""
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机器人用户档案 MCP Server
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存储并维护用户基本信息、偏好习惯。
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"""
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import json
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import logging
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import sqlite3
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from pathlib import Path
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# 压制 mcp 库的 INFO 日志，只保留 WARNING 及以上
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logging.basicConfig(level=logging.WARNING)
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from mcp.server.fastmcp import FastMCP
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DB_PATH = Path(__file__).parent / "users.db"
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mcp = FastMCP("robot-user-db")
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def _db_connect() -> sqlite3.Connection:
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"""统一数据库连接入口，确保启用外键约束。"""
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conn = sqlite3.connect(DB_PATH)
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conn.execute("PRAGMA foreign_keys = ON")
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return conn
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def _create_preferences_table(conn: sqlite3.Connection) -> None:
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conn.execute("""
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CREATE TABLE preferences (
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user_name TEXT NOT NULL,
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category TEXT NOT NULL,
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content TEXT NOT NULL,
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updated_at TEXT DEFAULT (datetime('now')),
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PRIMARY KEY (user_name, category),
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FOREIGN KEY (user_name) REFERENCES users(name)
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ON UPDATE CASCADE
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ON DELETE CASCADE
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)
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""")
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# --- 初始化数据库 ---
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def _init_db():
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with _db_connect() as conn:
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conn.execute("""
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CREATE TABLE IF NOT EXISTS users (
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name TEXT PRIMARY KEY,
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age INTEGER,
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created_at TEXT DEFAULT (datetime('now')),
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last_seen TEXT
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)
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""")
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preferences_exists = conn.execute(
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"SELECT 1 FROM sqlite_master WHERE type='table' AND name='preferences'"
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).fetchone() is not None
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if not preferences_exists:
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_create_preferences_table(conn)
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_init_db()
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# --- MCP 工具定义 ---
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@mcp.tool()
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def get_user_profile(user_name: str) -> str:
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"""
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获取用户档案（基本信息、所有偏好）。
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在每次对话的第一轮调用，用于了解用户背景。
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"""
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with _db_connect() as conn:
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conn.row_factory = sqlite3.Row
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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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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 json.dumps(
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{"found": False, "message": f"用户 {user_name} 尚无档案，这是第一次见面。"},
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ensure_ascii=False
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)
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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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return json.dumps({
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"found": True,
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"basic": {
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"name": user["name"],
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"age": user["age"],
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"last_seen": user["last_seen"],
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},
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"preferences": {p["category"]: p["content"] for p in prefs},
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}, ensure_ascii=False, indent=2)
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@mcp.tool()
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def upsert_user(user_name: str, age: int = None) -> str:
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"""
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创建或更新用户基本信息。
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当得知用户姓名、年龄等基本信息时调用。
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"""
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with _db_connect() as conn:
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existing = conn.execute(
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"SELECT 1 FROM users WHERE name = ?",
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(user_name,),
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).fetchone() is not None
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conn.execute(
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"""INSERT INTO users (name, age, last_seen)
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VALUES (?, ?, datetime('now'))
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ON CONFLICT(name) DO UPDATE SET
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age = COALESCE(excluded.age, users.age),
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last_seen = datetime('now')""",
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(user_name, age),
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)
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if existing:
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return f"已更新用户 {user_name} 的档案。"
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return f"已为 {user_name} 创建新档案。"
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@mcp.tool()
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def set_preference(user_name: str, category: str, content: str) -> str:
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"""
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更新用户的某项偏好或习惯（同一 category 只保留最新值）。
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category 示例：'话题喜好'、'沟通风格'、'工作习惯'、'忌讳'、'饮食偏好'。
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在对话中发现新偏好时调用。
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"""
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with _db_connect() as conn:
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# 保证 users 中存在主档，满足 preferences 外键约束。
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conn.execute(
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"""INSERT INTO users (name, last_seen)
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VALUES (?, datetime('now'))
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ON CONFLICT(name) DO UPDATE SET last_seen = datetime('now')""",
|
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(user_name,),
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)
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conn.execute(
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"""INSERT INTO preferences (user_name, category, content)
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VALUES (?, ?, ?)
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ON CONFLICT(user_name, category)
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DO UPDATE SET content = excluded.content, updated_at = datetime('now')""",
|
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(user_name, category, content)
|
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)
|
||||
return f"已更新 {user_name} 的偏好 [{category}]：{content}。"
|
||||
|
||||
# ============================================================
|
||||
# 联网工具：定位 / 天气 / 搜索
|
||||
# ============================================================
|
||||
|
||||
import requests
|
||||
|
||||
# WMO 天气代码 → 中文描述
|
||||
_WMO = {
|
||||
0: "晴天", 1: "基本晴朗", 2: "局部多云", 3: "阴天",
|
||||
45: "雾", 48: "冻雾",
|
||||
51: "轻微毛毛雨", 53: "毛毛雨", 55: "密集毛毛雨",
|
||||
61: "小雨", 63: "中雨", 65: "大雨",
|
||||
71: "小雪", 73: "中雪", 75: "大雪", 77: "冰粒",
|
||||
80: "阵雨", 81: "中等阵雨", 82: "强阵雨",
|
||||
85: "阵雪", 86: "强阵雪",
|
||||
95: "雷阵雨", 96: "伴有冰雹的雷阵雨", 99: "强雷阵雨",
|
||||
}
|
||||
|
||||
|
||||
def _lookup_location_cn(ip: str = None) -> dict | None:
|
||||
"""
|
||||
使用国内 IP 归属地接口查询地理信息。
|
||||
优先返回城市/省份；该接口不提供经纬度。
|
||||
"""
|
||||
params = {"json": "true"}
|
||||
if ip:
|
||||
params["ip"] = ip
|
||||
resp = requests.get(
|
||||
"https://whois.pconline.com.cn/ipJson.jsp",
|
||||
params=params,
|
||||
timeout=8,
|
||||
)
|
||||
# 该接口常见返回为 gbk 编码
|
||||
resp.encoding = resp.apparent_encoding or "gbk"
|
||||
raw = resp.text.strip()
|
||||
if raw.startswith("var returnCitySN"):
|
||||
raw = raw.split("=", 1)[-1].strip().rstrip(";")
|
||||
data = json.loads(raw)
|
||||
|
||||
city = (data.get("city") or "").strip()
|
||||
region = (data.get("pro") or "").strip()
|
||||
ip_value = (data.get("ip") or ip or "").strip()
|
||||
if not (city or region):
|
||||
return None
|
||||
return {
|
||||
"city": city or region,
|
||||
"region": region,
|
||||
"country": "中国",
|
||||
"lat": None,
|
||||
"lon": None,
|
||||
"ip": ip_value,
|
||||
}
|
||||
|
||||
|
||||
def _lookup_location_ipapi() -> dict | None:
|
||||
"""回退定位：使用 ip-api。"""
|
||||
data = requests.get(
|
||||
"http://ip-api.com/json/",
|
||||
params={"lang": "zh-CN", "fields": "status,city,regionName,country,lat,lon,query"},
|
||||
timeout=8,
|
||||
).json()
|
||||
if data.get("status") != "success":
|
||||
return None
|
||||
return {
|
||||
"city": data.get("city") or "",
|
||||
"region": data.get("regionName") or "",
|
||||
"country": data.get("country") or "",
|
||||
"lat": data.get("lat"),
|
||||
"lon": data.get("lon"),
|
||||
"ip": data.get("query") or "",
|
||||
}
|
||||
|
||||
|
||||
def _lookup_location() -> dict | None:
|
||||
"""统一定位入口：中国 IP 接口优先，失败回退 ip-api。"""
|
||||
return _lookup_location_cn() or _lookup_location_ipapi()
|
||||
|
||||
|
||||
def _geocode_city(city: str) -> tuple[float | None, float | None, str]:
|
||||
"""根据城市名查经纬度，供天气查询使用。"""
|
||||
geo = requests.get(
|
||||
"https://geocoding-api.open-meteo.com/v1/search",
|
||||
params={"name": city, "count": 1, "language": "zh"},
|
||||
timeout=8,
|
||||
).json()
|
||||
results = geo.get("results")
|
||||
if not results:
|
||||
return None, None, city
|
||||
return (
|
||||
results[0].get("latitude"),
|
||||
results[0].get("longitude"),
|
||||
results[0].get("name", city),
|
||||
)
|
||||
|
||||
|
||||
def _resolve_weather_target(
|
||||
city: str | None, lat: float | None, lon: float | None
|
||||
) -> tuple[float | None, float | None, str | None, str | None]:
|
||||
"""统一解析天气查询目标，减少重复分支。"""
|
||||
auto_locate_error = "自动定位失败，请手动传入城市名。"
|
||||
if lat is not None and lon is not None:
|
||||
return lat, lon, city, None
|
||||
|
||||
if city:
|
||||
lat, lon, city = _geocode_city(city)
|
||||
if lat is None or lon is None:
|
||||
return None, None, city, f"找不到城市：{city}"
|
||||
return lat, lon, city, None
|
||||
|
||||
loc = _lookup_location()
|
||||
if not loc:
|
||||
return None, None, None, auto_locate_error
|
||||
city = loc["city"] or city
|
||||
lat, lon = loc.get("lat"), loc.get("lon")
|
||||
if lat is None or lon is None:
|
||||
if not city:
|
||||
return None, None, None, auto_locate_error
|
||||
lat, lon, city = _geocode_city(city)
|
||||
if lat is None or lon is None:
|
||||
return None, None, None, auto_locate_error
|
||||
return lat, lon, city, None
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
def get_location() -> str:
|
||||
"""
|
||||
通过 IP 地址获取当前地理位置（城市、省份、国家、经纬度）。
|
||||
在查询天气前，或需要了解用户所在城市时调用。
|
||||
"""
|
||||
try:
|
||||
loc = _lookup_location()
|
||||
if not loc:
|
||||
return "定位失败，请稍后再试。"
|
||||
return json.dumps({
|
||||
"城市": loc["city"],
|
||||
"省份": loc["region"],
|
||||
"国家": loc["country"],
|
||||
"纬度": loc["lat"],
|
||||
"经度": loc["lon"],
|
||||
"IP": loc["ip"],
|
||||
}, ensure_ascii=False)
|
||||
except Exception as e:
|
||||
return f"定位失败：{e}"
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
def get_weather(city: str = None, lat: float = None, lon: float = None) -> str:
|
||||
"""
|
||||
获取实时天气信息。
|
||||
可以传入城市名（city），或经纬度（lat/lon）；若都不传则自动定位。
|
||||
返回温度、天气状况、风速。
|
||||
"""
|
||||
try:
|
||||
lat, lon, city, err = _resolve_weather_target(city, lat, lon)
|
||||
if err:
|
||||
return err
|
||||
|
||||
# 查询天气
|
||||
resp = requests.get(
|
||||
"https://api.open-meteo.com/v1/forecast",
|
||||
params={
|
||||
"latitude": lat, "longitude": lon,
|
||||
"current": "temperature_2m,apparent_temperature,weather_code,wind_speed_10m,relative_humidity_2m",
|
||||
"timezone": "auto",
|
||||
},
|
||||
timeout=10,
|
||||
).json()
|
||||
cur = resp.get("current", {})
|
||||
code = cur.get("weather_code", -1)
|
||||
return json.dumps({
|
||||
"城市": city or f"{lat},{lon}",
|
||||
"天气": _WMO.get(code, f"未知(code={code})"),
|
||||
"温度": f"{cur.get('temperature_2m', '?')}°C",
|
||||
"体感温度": f"{cur.get('apparent_temperature', '?')}°C",
|
||||
"湿度": f"{cur.get('relative_humidity_2m', '?')}%",
|
||||
"风速": f"{cur.get('wind_speed_10m', '?')} km/h",
|
||||
}, ensure_ascii=False)
|
||||
except Exception as e:
|
||||
return f"天气查询失败：{e}"
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
def web_search(query: str, max_results: int = 5) -> str:
|
||||
"""
|
||||
联网搜索，获取实时信息（新闻、百科、价格等）。
|
||||
返回最多 max_results 条结果（标题 + 摘要 + 链接）。
|
||||
"""
|
||||
try:
|
||||
query = query.strip()
|
||||
if not query:
|
||||
return "搜索关键词不能为空。"
|
||||
max_results = max(1, min(max_results, 10))
|
||||
|
||||
from ddgs import DDGS
|
||||
results = DDGS().text(query, max_results=max_results)
|
||||
if not results:
|
||||
return "搜索无结果。"
|
||||
output = []
|
||||
for i, r in enumerate(results, 1):
|
||||
output.append(f"{i}. {r['title']}\n {r['body'][:150]}\n {r['href']}")
|
||||
return "\n\n".join(output)
|
||||
except Exception as e:
|
||||
return f"搜索失败：{e}"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
mcp.run()
|
||||
13
start_vllm.sh
Normal file
13
start_vllm.sh
Normal file
@@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
# 启动 vLLM 服务器脚本
|
||||
# 用法: bash start_vllm.sh
|
||||
|
||||
python -m vllm.entrypoints.openai.api_server \
|
||||
--model Qwen/Qwen3-VL-8B-Instruct \
|
||||
--trust-remote-code \
|
||||
--port 8000 \
|
||||
--gpu-memory-utilization 0.85 \
|
||||
--max-model-len 32000 \
|
||||
--enable-auto-tool-choice \
|
||||
--tool-call-parser hermes \
|
||||
--uvicorn-log-level warning
|
||||
Reference in New Issue
Block a user