base_agent/main.py

"""
main.py
智能体 Demo 程序入口（OpenAI Function Calling 驱动）

运行模式:
    python main.py                           → 交互模式
    python main.py demo                      → 演示模式
    python main.py config                    → 打印当前配置
    python main.py health                    → 检测 OpenAI API 连通性
    LLM_API_KEY=sk-xxx python main.py        → 指定 API Key
    LLM_MODEL_NAME=gpt-4-turbo python main.py→ 指定模型
    AGENT_CONFIG_PATH=my.yaml python main.py → 指定配置文件
"""

import sys

from client.agent_client import AgentClient, AgentResponse
from config.settings import settings
from llm.llm_engine import LLMEngine
from mcp.mcp_server import MCPServer
from memory.memory_store import MemoryStore
from tools.calculator import CalculatorTool
from tools.code_executor import CodeExecutorTool
from tools.file_reader import FileReaderTool
from tools.web_search import WebSearchTool
from tools.static_analyzer import StaticAnalyzerTool
from tools.ssh_docker import SSHDockerTool
from utils.logger import get_logger

logger = get_logger("SYSTEM")

_ALL_TOOLS = {
    "calculator":    CalculatorTool,
    "web_search":    WebSearchTool,
    "file_reader":   FileReaderTool,
    "code_executor": CodeExecutorTool,
    "static_analyzer": StaticAnalyzerTool,
    "ssh_docker": SSHDockerTool
}


# ── 系统组装 ───────────────────────────────────────────────────
def build_agent() -> AgentClient:
    """工厂函数：由 settings 驱动的 Agent 组装"""
    logger.info("🔧 开始组装 Agent 系统（OpenAI Function Calling 模式）...")
    logger.info(settings.display())

    mcp_server = MCPServer()
    for tool_cls in _ALL_TOOLS.values():
        mcp_server.register_tool(tool_cls)

    llm    = LLMEngine()
    memory = MemoryStore(max_history=settings.memory.max_history)
    client = AgentClient(llm=llm, mcp_server=mcp_server, memory=memory)

    logger.info(f"✅ Agent 组装完成，已注册工具: {mcp_server.list_tools()}")
    return client


# ── 结果打印 ───────────────────────────────────────────────────
def print_response(response: AgentResponse) -> None:
    """格式化打印 AgentResponse"""
    print(f"\n{'═' * 62}")
    print(f"👤 用户:  {response.user_input}")
    print(f"{'─' * 62}")

    if response.chain_result:
        cr     = response.chain_result
        tag    = "🔗 多步串行" if response.is_multi_step else "🔧 单步调用"
        status = "✅ 全部成功" if cr.success else f"⚠️  步骤 {cr.failed_step} 失败"
        print(f"{tag}  |  {cr.completed_steps}/{cr.total_steps} 步  |  {status}")
        print()
        for r in cr.step_results:
            icon    = "✅" if r.success else "❌"
            preview = r.output.replace("\n", " ")[:90]
            print(f"  {icon} Step {r.step_id} [{r.tool_name}]")
            if r.success:
                print(f"     └─ {preview}...")
            else:
                print(f"     └─ 错误: {r.error}")
        print()

    print(f"🤖 Agent 回复:\n{response.final_reply}")
    print(f"{'═' * 62}\n")


# ── API 健康检测 ───────────────────────────────────────────────
def run_health_check() -> None:
    """检测 OpenAI API 连通性"""
    print(f"\n{'─' * 50}")
    print(f"  🏥 OpenAI API 健康检测")
    print(f"{'─' * 50}")
    print(f"  Provider  : {settings.llm.provider}")
    print(f"  Model     : {settings.llm.model_name}")
    print(f"  API Key   : {'***' + settings.llm.api_key[-4:] if len(settings.llm.api_key) > 4 else '(未设置)'}")
    print(f"  Base URL  : {settings.llm.api_base_url or 'https://api.openai.com/v1'}")
    print(f"{'─' * 50}")

    if not settings.llm.api_key:
        print("  ❌ API Key 未设置")
        print("  💡 请设置环境变量: export LLM_API_KEY=sk-...")
        print(f"{'─' * 50}\n")
        return

    print("  ⏳ 正在检测连通性...")
    llm = LLMEngine()
    ok  = llm.provider.health_check()

    if ok:
        print(f"  ✅ API 连通正常，模型 [{settings.llm.model_name}] 可用")
    else:
        print(f"  ❌ API 连接失败，请检查网络或 API Key")
        print(f"  💡 可尝试设置代理: export LLM_API_BASE_URL=https://your-proxy/v1")
    print(f"{'─' * 50}\n")


# ── 演示场景 ───────────────────────────────────────────────────
def run_demo(client: AgentClient) -> None:
    """运行预设演示场景"""
    demo_cases = [
        ("🔢 单步: 数学计算",
         "计算 (100 + 200) × 3 等于多少？"),

        ("🌐 单步: 网络搜索",
         "搜索 Python 3.12 的主要新特性"),

        ("🔗 两步: 搜索 + 计算",
         "搜索 Python 最新版本号，然后计算 3.12 × 100 的结果"),

        ("🔗 两步: 读取文件 + 执行代码",
         "读取 script.py 文件然后执行里面的代码"),

        ("💬 无工具: 直接问答",
         "你好，请介绍一下你自己"),
    ]

    logger.info("\n" + "═" * 62)
    logger.info(f"🎬 演示模式 | 模型: {settings.llm.model_name} | "
                f"Provider: {settings.llm.provider}")
    logger.info("═" * 62)

    for title, question in demo_cases:
        logger.info(f"\n📌 场景: {title}")
        response = client.chat(question)
        print_response(response)

    stats = client.get_memory_stats()
    print(f"📊 Memory 统计: {stats}\n")


# ── 交互模式 ───────────────────────────────────────────────────
def run_interactive(client: AgentClient) -> None:
    """启动交互式命令行对话"""
    print("\n" + "═" * 62)
    print(f"  🤖 Agent  |  {settings.llm.model_name}  |  {settings.llm.provider}")
    print(f"  Function Calling: {'✅ 开启' if settings.llm.function_calling else '❌ 关闭（规则引擎）'}")
    print(f"  Fallback Rules  : {'✅ 开启' if settings.agent.fallback_to_rules else '❌ 关闭'}")
    print("─" * 62)
    print("  💡 示例:")
    print("     计算 (100+200) × 3")
    print("     搜索 Python 新特性，然后计算 3.12 × 100")
    print("     读取 config.json 文件然后执行代码")
    print("─" * 62)
    print("  🛠  命令: config / health / tools / chains / stats / clear / quit")
    print("═" * 62 + "\n")

    while True:
        try:
            user_input = input("👤 你: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\n👋 再见！")
            break

        if not user_input:
            continue

        match user_input.lower():
            case "quit" | "exit":
                print("👋 再见！")
                break
            case "config":
                print(settings.display())
            case "health":
                run_health_check()
            case "clear":
                client.clear_session()
                print("✅ 会话已清空\n")
            case "stats":
                print(f"📊 {client.get_memory_stats()}\n")
            case "tools":
                schemas = client.mcp_server.get_tool_schemas()
                print(f"🔧 已注册工具 ({len(schemas)} 个):")
                for s in schemas:
                    print(f"   • [{s.name}]  {s.description}")
                print()
            case "chains":
                chains = client.memory.get_chain_history()
                if not chains:
                    print("🔗 暂无调用链历史\n")
                else:
                    print(f"🔗 调用链历史 ({len(chains)} 条):")
                    for i, c in enumerate(chains, 1):
                        steps  = " → ".join(s["tool_name"] for s in c["steps"])
                        ok_cnt = sum(1 for s in c["steps"] if s["success"])
                        total  = len(c["steps"])
                        print(f"  {i}. [{c['timestamp'][11:19]}] {c['goal'][:38]}...")
                        print(f"     链路: {steps}  ({ok_cnt}/{total} 步成功)")
                    print()
            case _:
                response = client.chat(user_input)
                print_response(response)


# ── 配置打印 ───────────────────────────────────────────────────
def run_show_config() -> None:
    print(settings.display())
    print("\n📁 配置文件查找路径（按优先级）:")
    print("   1. 环境变量 AGENT_CONFIG_PATH")
    print("   2. ./config/config.yaml")
    print("   3. ./config.yaml")
    print("\n🌍 支持的环境变量覆盖:")
    env_vars = [
        ("LLM_API_KEY",      "OpenAI API 密钥（sk-...）"),
        ("LLM_MODEL_NAME",   "模型名称，如 gpt-4o / gpt-4-turbo"),
        ("LLM_API_BASE_URL", "自定义 API 地址（兼容代理）"),
        ("LLM_MODEL_PATH",   "本地模型路径"),
        ("SEARCH_API_KEY",   "搜索 API 密钥"),
        ("LOG_LEVEL",        "日志级别 DEBUG/INFO/WARNING/ERROR"),
        ("AGENT_CONFIG_PATH","配置文件路径"),
    ]
    for var, desc in env_vars:
        print(f"   {var:<22} → {desc}")
    print()


# ── 主函数 ─────────────────────────────────────────────────────
def main() -> None:
    mode = sys.argv[1] if len(sys.argv) > 1 else "interactive"

    if mode == "config":
        run_show_config()
        return

    if mode == "health":
        run_health_check()
        return

    client = build_agent()

    if mode == "demo":
        run_demo(client)
    else:
        run_interactive(client)


if __name__ == "__main__":
    main()