aiagent/scripts/demo_digital_employee.py

"""
数字员工工厂 — 完整使用演示

用法:
  python scripts/demo_digital_employee.py

流程:
  1. 登录获取 Token
  2. 创建 Main Agent（数字员工 PM）
  3. 升级为 Main Agent（预置系统提示词 + 工具）
  4. 创建目标（Goal）
  5. 分解目标为子任务（decompose）
  6. 异步启动执行
  7. 查看任务树和进度
"""
import urllib.request
import json
import time
import sys

BASE = "http://localhost:8041"

# ── 登录 ──
def login():
    req = urllib.request.Request(
        f"{BASE}/api/v1/auth/login",
        data=b"username=admin&password=123456",
        headers={"Content-Type": "application/x-www-form-urlencoded"},
    )
    return json.loads(urllib.request.urlopen(req).read())["access_token"]

# ── API 助手 ──
def api(method, path, data=None, token=""):
    url = f"{BASE}{path}"
    body = json.dumps(data).encode("utf-8") if data else None
    req = urllib.request.Request(url, data=body, method=method,
        headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"})
    try:
        r = urllib.request.urlopen(req, timeout=30)
        if r.status == 204:
            return None
        return json.loads(r.read())
    except urllib.error.HTTPError as e:
        try:
            return json.loads(e.read())
        except:
            return {"detail": str(e), "status": e.code}

# ── 主流程 ──
def main():
    token = login()
    print("=" * 60)
    print("  数字员工工厂 — 完整使用演示")
    print("=" * 60)

    # ── Step 1: 创建 Main Agent ──
    print("\n[1] 创建 Main Agent（数字员工 PM）...")
    agent = api("POST", "/api/v1/agents", token=token, data={
        "name": "Demo 数字员工PM",
        "description": "我是数字员工项目经理，负责接收目标、分解任务、调度执行、追踪进度",
        "workflow_config": {
            "nodes": [
                {"id": "start-1", "data": {"label": "开始"}, "type": "start", "position": {"x": 80, "y": 200}},
                {"id": "agent-1", "data": {
                    "label": "项目经理",
                    "system_prompt": (
                        "你是一个数字员工项目经理。当用户提出目标时：\n"
                        "1. 理解目标的核心意图\n"
                        "2. 将目标分解为 2-5 个具体子任务\n"
                        "3. 调用 create_task 创建子任务\n"
                        "4. 调用 assign_task 分配 Agent\n"
                        "5. 调用 check_progress 追踪进度\n"
                        "6. 遇到问题调用 notify_user 通知用户"
                    ),
                    "model": "deepseek-v4-flash",
                    "provider": "deepseek",
                    "temperature": 0.7,
                    "max_iterations": 15,
                    "tools": ["web_search", "knowledge_graph_search"],
                    "memory": True,
                }, "type": "agent", "position": {"x": 350, "y": 200}},
                {"id": "end-1", "data": {"label": "结束"}, "type": "end", "position": {"x": 620, "y": 200}},
            ],
            "edges": [
                {"id": "e_start", "source": "start-1", "target": "agent-1", "sourceHandle": "right", "targetHandle": "left"},
                {"id": "e_end", "source": "agent-1", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"},
            ],
        },
    })
    agent_id = agent.get("id")
    if not agent_id:
        print(f"  [FAIL] 创建失败: {agent}")
        sys.exit(1)
    print(f"  [OK] Agent 已创建: {agent_id}")

    # ── Step 2: 升级为 Main Agent ──
    print("\n[2] 升级为 Main Agent（预置项目经理提示词 + 工具）...")
    try:
        upgraded = api("POST", f"/api/v1/agents/{agent_id}/create-main-agent", token=token)
        print(f"  [OK] Agent Type = {upgraded.get('agent_type', '?')}")
    except Exception as e:
        print(f"  [WARN] 升级失败 (后端未重启?): {e}")
        print("  → 手动设置 agent_type=main")
        try:
            api("PUT", f"/api/v1/agents/{agent_id}", token=token, data={
                "workflow_config": {
                    "nodes": [
                        {"id": "start-1", "data": {"label": "开始"}, "type": "start", "position": {"x": 80, "y": 200}},
                        {"id": "agent-1", "data": {
                            "label": "项目经理",
                            "system_prompt": (
                                "你是一个数字员工项目经理。当用户提出目标时：\n"
                                "1. 理解目标的核心意图\n"
                                "2. 将目标分解为 2-5 个具体子任务\n"
                                "3. 调用 create_task 创建子任务\n"
                                "4. 调用 assign_task 分配 Agent\n"
                                "5. 调用 check_progress 追踪进度\n"
                                "6. 遇到问题调用 notify_user 通知用户"
                            ),
                            "model": "deepseek-v4-flash",
                            "provider": "deepseek",
                            "temperature": 0.7,
                            "max_iterations": 15,
                            "tools": ["web_search", "knowledge_graph_search"],
                            "memory": True,
                        }, "type": "agent", "position": {"x": 350, "y": 200}},
                        {"id": "end-1", "data": {"label": "结束"}, "type": "end", "position": {"x": 620, "y": 200}},
                    ],
                    "edges": [
                        {"id": "e_start", "source": "start-1", "target": "agent-1", "sourceHandle": "right", "targetHandle": "left"},
                        {"id": "e_end", "source": "agent-1", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"},
                    ],
                },
                "description": "数字员工项目经理（Main Agent）",
            })
            print("  [OK] workflow_config 已更新")
        except Exception as e2:
            print(f"  [WARN] 手动更新也失败: {e2}")

    # ── Step 3: 创建目标 ──
    print("\n[3] 创建目标: 调研主流 AI Agent 平台...")
    goal = api("POST", "/api/v1/goals", token=token, data={
        "title": "调研主流 AI Agent 平台功能差异",
        "description": (
            "调研 Langchain、AutoGPT、CrewAI、Dify、Coze、字节方舟 "
            "等主流 AI Agent 平台的核心功能、优劣势、适用场景，"
            "输出一份对比分析报告"
        ),
        "priority": 3,
        "main_agent_id": agent_id,
        "autonomy_config": {
            "check_interval_minutes": 5,
            "auto_replan": True,
            "notify_on_progress": True,
        },
    })
    goal_id = goal.get("id")
    if not goal_id:
        print(f"  [FAIL] 创建目标失败: {goal}")
        sys.exit(1)
    print(f"  [OK] Goal 已创建: {goal_id}")
    print(f"    Title:     {goal['title']}")
    print(f"    Status:    {goal['status']}")
    print(f"    Priority:  P{goal['priority']}")

    # ── Step 4: 分解目标 ──
    print("\n[4] 分解目标为子任务 (decompose)...")
    try:
        decomp = api("POST", f"/api/v1/goals/{goal_id}/decompose", token=token)
        if decomp:
            print(f"  [OK] 分解完成: {decomp.get('tasks_count', '?')} 个任务")
    except Exception as e:
        print(f"  [INFO] 同步分解不可用 (可能 LLM 未配置): {e}")
        print("  → 任务将由 execute-async 异步创建")

    # ── Step 5: 启动异步执行 ──
    print("\n[5] 启动异步执行 (execute-async)...")
    try:
        exec_result = api("POST", f"/api/v1/goals/{goal_id}/execute-async", token=token)
        print(f"  [OK] {exec_result.get('message', '?')}")
    except Exception as e:
        print(f"  [WARN] 执行投递失败 (Celery Worker 未启动?): {e}")

    # ── Step 6: 等待并查看结果 ──
    print("\n[6] 等待 3 秒后查看任务树和进度...")
    time.sleep(3)

    tree = api("GET", f"/api/v1/goals/{goal_id}/tasks", token=token)
    tasks = tree.get("tasks", []) if tree else []
    print(f"  ┌─ 任务列表 ({len(tasks)} 个任务) " + "─" * 30)
    for t in tasks:
        deps = len(t.get("depends_on") or [])
        dep_str = f" [依赖 {deps} 项]" if deps else ""
        print(f"  │ [{t['status']:12s}] {t['title'][:55]}{dep_str}  P{t['priority']}")
    print(f"  └" + "─" * 50)

    goal_status = api("GET", f"/api/v1/goals/{goal_id}", token=token)
    print(f"  Status:   {goal_status.get('status', '?')}")
    print(f"  Progress: {int(goal_status.get('progress', 0) * 100)}%")

    # ── Step 7: 总结 ──
    print("\n" + "=" * 60)
    print("  演示完成！")
    print()
    print("  前端页面:")
    print(f"    目标列表: http://localhost:3001/digital-employees")
    print(f"    目标详情: http://localhost:3001/goals/{goal_id}")
    print(f"    Swagger:  http://localhost:8037/docs")
    print()
    print("  Goal ID:", goal_id)
    print("  Agent ID:", agent_id)
    print("=" * 60)


if __name__ == "__main__":
    main()