164 lines
6.1 KiB
Python
164 lines
6.1 KiB
Python
"""程序入口"""
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"""
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main.py
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智能体 Demo 程序入口
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组装所有模块，启动交互式对话循环
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"""
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import sys
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import argparse
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# ── 导入各模块 ─────────────────────────────────────────────────
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from client.agent_client import AgentClient
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from llm.llm_engine import LLMEngine
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from mcp.mcp_server import MCPServer
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from memory.memory_store import MemoryStore
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from tools.calculator import CalculatorTool
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from tools.code_executor import CodeExecutorTool
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from tools.file_reader import FileReaderTool
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from tools.web_search import WebSearchTool
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from tools.tool_generator import ToolGeneratorTool
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from utils.logger import get_logger
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logger = get_logger("SYSTEM")
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# ── 系统组装 ───────────────────────────────────────────────────
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def build_agent(agent_prompt) -> AgentClient:
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"""
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工厂函数：组装并返回完整的 Agent 实例
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组装顺序:
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1. 初始化 MCP Server，注册所有工具
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2. 初始化 LLM 引擎
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3. 初始化 Memory 模块
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4. 组装 AgentClient
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"""
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logger.info("🔧 开始组装 Agent 系统...")
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# 1. MCP Server：注册所有工具
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mcp_server = MCPServer(server_name="DemoMCPServer")
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mcp_server.register_tools(
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CalculatorTool,
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WebSearchTool,
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FileReaderTool,
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CodeExecutorTool,
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ToolGeneratorTool
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)
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# 2. LLM 引擎
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llm = LLMEngine(model_name="gpt-4o")
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# 3. 记忆模块
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memory = MemoryStore(max_history=20)
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# 4. 组装客户端
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client = AgentClient(llm=llm, mcp_server=mcp_server, memory=memory, prompt=agent_prompt)
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logger.info(f"✅ Agent 组装完成，已注册工具: {mcp_server.list_tools()}")
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return client
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# ── 演示场景 ───────────────────────────────────────────────────
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def run_demo(client: AgentClient) -> None:
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"""运行预设演示场景，展示各工具的完整调用链路"""
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demo_cases = [
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("🔢 数学计算", "计算 (100 + 200) × 3 等于多少"),
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("🌐 网络搜索", "搜索 Python 最新版本的新特性"),
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("📄 文件读取", "读取文件 config.json 的内容"),
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("🐍 代码执行", '执行代码 `print("Hello, Agent!")`'),
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]
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logger.info("\n" + "═" * 60)
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logger.info("🎬 开始演示模式，共 4 个场景")
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logger.info("═" * 60)
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for title, question in demo_cases:
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logger.info(f"\n{'─' * 55}")
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logger.info(f"📌 场景: {title}")
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logger.info(f"{'─' * 55}")
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response = client.chat(question)
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print(f"\n{'─' * 55}")
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print(f"👤 用户: {response.user_input}")
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if response.tool_used:
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print(f"🔧 工具: {response.tool_used}")
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print(f"📤 输出: {response.tool_output[:120]}...")
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print(f"🤖 回复:\n{response.final_reply}")
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print()
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# 打印记忆统计
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stats = client.get_memory_stats()
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logger.info(f"\n📊 Memory 统计: {stats}")
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# ── 交互式对话循环 ─────────────────────────────────────────────
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def run_interactive(client: AgentClient) -> None:
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"""启动交互式命令行对话"""
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print("\n" + "═" * 60)
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print(" 🤖 Agent Demo — 交互模式")
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print(" 输入 'quit' → 退出程序")
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print(" 输入 'clear' → 清空会话历史")
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print(" 输入 'stats' → 查看 Memory 统计")
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print(" 输入 'tools' → 查看已注册工具列表")
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print("═" * 60 + "\n")
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while True:
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try:
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user_input = input("👤 你: ").strip()
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except (KeyboardInterrupt, EOFError):
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print("\n👋 再见！")
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break
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if not user_input:
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continue
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# ── 内置命令 ──────────────────────────────────────────
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match user_input.lower():
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case "quit" | "exit":
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print("👋 再见！")
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break
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case "clear":
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client.clear_session()
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print("✅ 会话已清空\n")
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continue
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case "stats":
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print(f"📊 Memory 统计: {client.get_memory_stats()}\n")
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continue
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case "tools":
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tools = client.mcp_server.list_tools()
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print(f"🔧 已注册工具 ({len(tools)} 个): {', '.join(tools)}\n")
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continue
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# ── 执行 Agent 完整流程 ───────────────────────────────
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response = client.chat(user_input)
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print(f"\n{'─' * 55}")
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if response.tool_used:
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print(f" 🔧 调用工具: {response.tool_used}")
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print(f"🤖 Agent:\n{response.final_reply}")
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print(f"{'─' * 55}\n")
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# ── 主函数 ─────────────────────────────────────────────────────
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def main() -> None:
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"""
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主函数入口，支持两种运行模式:
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python main.py → 交互模式（默认）
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python main.py demo → 演示模式（自动执行预设场景）
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument("-d", "--daemon", help="服务模式", action="store_true")
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parser.add_argument("-p", "--prompt", default="你是一个通用智能体，非常擅长将用户指令分解成可以执行的任务进行执行。", help="智能体提示此词, 例如：你是一个XXXXX，非常擅长……")
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args = parser.parse_args(sys.argv[1:])
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client = build_agent(args.prompt)
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if args.daemon:
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run_demo(client)
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else:
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run_interactive(client)
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if __name__ == "__main__":
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main() |