217 lines
9.1 KiB
Python
217 lines
9.1 KiB
Python
"""
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数字员工工厂 — 完整使用演示
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用法:
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python scripts/demo_digital_employee.py
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流程:
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1. 登录获取 Token
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2. 创建 Main Agent（数字员工 PM）
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3. 升级为 Main Agent（预置系统提示词 + 工具）
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4. 创建目标（Goal）
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5. 分解目标为子任务（decompose）
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6. 异步启动执行
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7. 查看任务树和进度
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"""
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import urllib.request
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import json
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import time
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import sys
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BASE = "http://localhost:8037"
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# ── 登录 ──
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def login():
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req = urllib.request.Request(
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f"{BASE}/api/v1/auth/login",
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data=b"username=admin&password=123456",
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headers={"Content-Type": "application/x-www-form-urlencoded"},
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)
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return json.loads(urllib.request.urlopen(req).read())["access_token"]
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# ── API 助手 ──
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def api(method, path, data=None, token=""):
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url = f"{BASE}{path}"
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body = json.dumps(data).encode("utf-8") if data else None
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req = urllib.request.Request(url, data=body, method=method,
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headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"})
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try:
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r = urllib.request.urlopen(req, timeout=30)
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if r.status == 204:
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return None
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return json.loads(r.read())
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except urllib.error.HTTPError as e:
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try:
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return json.loads(e.read())
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except:
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return {"detail": str(e), "status": e.code}
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# ── 主流程 ──
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def main():
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token = login()
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print("=" * 60)
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print(" 数字员工工厂 — 完整使用演示")
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print("=" * 60)
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# ── Step 1: 创建 Main Agent ──
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print("\n[1] 创建 Main Agent（数字员工 PM）...")
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agent = api("POST", "/api/v1/agents", token=token, data={
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"name": "Demo 数字员工PM",
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"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",
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"workflow_config": {
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"nodes": [
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{"id": "start-1", "data": {"label": "开始"}, "type": "start", "position": {"x": 80, "y": 200}},
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{"id": "agent-1", "data": {
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"label": "项目经理",
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"system_prompt": (
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"你是一个数字员工项目经理。当用户提出目标时：\n"
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"1. 理解目标的核心意图\n"
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"2. 将目标分解为 2-5 个具体子任务\n"
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"3. 调用 create_task 创建子任务\n"
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"4. 调用 assign_task 分配 Agent\n"
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"5. 调用 check_progress 追踪进度\n"
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"6. 遇到问题调用 notify_user 通知用户"
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),
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"model": "deepseek-v4-flash",
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"provider": "deepseek",
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"temperature": 0.7,
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"max_iterations": 15,
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"tools": ["web_search", "knowledge_graph_search"],
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"memory": True,
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}, "type": "agent", "position": {"x": 350, "y": 200}},
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{"id": "end-1", "data": {"label": "结束"}, "type": "end", "position": {"x": 620, "y": 200}},
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],
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"edges": [
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{"id": "e_start", "source": "start-1", "target": "agent-1", "sourceHandle": "right", "targetHandle": "left"},
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{"id": "e_end", "source": "agent-1", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"},
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],
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},
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})
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agent_id = agent.get("id")
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if not agent_id:
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print(f" [FAIL] 创建失败: {agent}")
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sys.exit(1)
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print(f" [OK] Agent 已创建: {agent_id}")
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# ── Step 2: 升级为 Main Agent ──
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print("\n[2] 升级为 Main Agent（预置项目经理提示词 + 工具）...")
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try:
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upgraded = api("POST", f"/api/v1/agents/{agent_id}/create-main-agent", token=token)
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print(f" [OK] Agent Type = {upgraded.get('agent_type', '?')}")
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except Exception as e:
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print(f" [WARN] 升级失败 (后端未重启?): {e}")
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print(" → 手动设置 agent_type=main")
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try:
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api("PUT", f"/api/v1/agents/{agent_id}", token=token, data={
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"workflow_config": {
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"nodes": [
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{"id": "start-1", "data": {"label": "开始"}, "type": "start", "position": {"x": 80, "y": 200}},
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{"id": "agent-1", "data": {
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"label": "项目经理",
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"system_prompt": (
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"你是一个数字员工项目经理。当用户提出目标时：\n"
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"1. 理解目标的核心意图\n"
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"2. 将目标分解为 2-5 个具体子任务\n"
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"3. 调用 create_task 创建子任务\n"
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"4. 调用 assign_task 分配 Agent\n"
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"5. 调用 check_progress 追踪进度\n"
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"6. 遇到问题调用 notify_user 通知用户"
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),
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"model": "deepseek-v4-flash",
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"provider": "deepseek",
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"temperature": 0.7,
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"max_iterations": 15,
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"tools": ["web_search", "knowledge_graph_search"],
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"memory": True,
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}, "type": "agent", "position": {"x": 350, "y": 200}},
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{"id": "end-1", "data": {"label": "结束"}, "type": "end", "position": {"x": 620, "y": 200}},
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],
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"edges": [
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{"id": "e_start", "source": "start-1", "target": "agent-1", "sourceHandle": "right", "targetHandle": "left"},
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{"id": "e_end", "source": "agent-1", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"},
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],
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},
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"description": "数字员工项目经理（Main Agent）",
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})
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print(" [OK] workflow_config 已更新")
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except Exception as e2:
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print(f" [WARN] 手动更新也失败: {e2}")
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# ── Step 3: 创建目标 ──
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print("\n[3] 创建目标: 调研主流 AI Agent 平台...")
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goal = api("POST", "/api/v1/goals", token=token, data={
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"title": "调研主流 AI Agent 平台功能差异",
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"description": (
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"调研 Langchain、AutoGPT、CrewAI、Dify、Coze、字节方舟 "
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"等主流 AI Agent 平台的核心功能、优劣势、适用场景，"
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"输出一份对比分析报告"
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),
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"priority": 3,
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"main_agent_id": agent_id,
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"autonomy_config": {
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"check_interval_minutes": 5,
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"auto_replan": True,
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"notify_on_progress": True,
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},
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})
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goal_id = goal.get("id")
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if not goal_id:
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print(f" [FAIL] 创建目标失败: {goal}")
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sys.exit(1)
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print(f" [OK] Goal 已创建: {goal_id}")
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print(f" Title: {goal['title']}")
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print(f" Status: {goal['status']}")
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print(f" Priority: P{goal['priority']}")
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# ── Step 4: 分解目标 ──
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print("\n[4] 分解目标为子任务 (decompose)...")
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try:
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decomp = api("POST", f"/api/v1/goals/{goal_id}/decompose", token=token)
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if decomp:
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print(f" [OK] 分解完成: {decomp.get('tasks_count', '?')} 个任务")
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except Exception as e:
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print(f" [INFO] 同步分解不可用 (可能 LLM 未配置): {e}")
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print(" → 任务将由 execute-async 异步创建")
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# ── Step 5: 启动异步执行 ──
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print("\n[5] 启动异步执行 (execute-async)...")
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try:
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exec_result = api("POST", f"/api/v1/goals/{goal_id}/execute-async", token=token)
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print(f" [OK] {exec_result.get('message', '?')}")
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except Exception as e:
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print(f" [WARN] 执行投递失败 (Celery Worker 未启动?): {e}")
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# ── Step 6: 等待并查看结果 ──
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print("\n[6] 等待 3 秒后查看任务树和进度...")
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time.sleep(3)
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tree = api("GET", f"/api/v1/goals/{goal_id}/tasks", token=token)
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tasks = tree.get("tasks", []) if tree else []
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print(f" ┌─ 任务列表 ({len(tasks)} 个任务) " + "─" * 30)
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for t in tasks:
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deps = len(t.get("depends_on") or [])
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dep_str = f" [依赖 {deps} 项]" if deps else ""
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print(f" │ [{t['status']:12s}] {t['title'][:55]}{dep_str} P{t['priority']}")
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print(f" └" + "─" * 50)
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goal_status = api("GET", f"/api/v1/goals/{goal_id}", token=token)
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print(f" Status: {goal_status.get('status', '?')}")
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print(f" Progress: {int(goal_status.get('progress', 0) * 100)}%")
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# ── Step 7: 总结 ──
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print("\n" + "=" * 60)
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print(" 演示完成！")
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print()
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print(" 前端页面:")
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print(f" 目标列表: http://localhost:3001/digital-employees")
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print(f" 目标详情: http://localhost:3001/goals/{goal_id}")
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print(f" Swagger: http://localhost:8037/docs")
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print()
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print(" Goal ID:", goal_id)
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print(" Agent ID:", agent_id)
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print("=" * 60)
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if __name__ == "__main__":
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main()
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