380 lines
15 KiB
Python
380 lines
15 KiB
Python
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"""创建功能最强大的自主 Agent — Pro 模型 + 全工具 + 向量记忆 + 自主学习
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用法:
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python scripts/seed_max_agent.py
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python scripts/seed_max_agent.py --name "我的助手" --desc "自定义描述"
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"""
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import json
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import urllib.request
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import urllib.parse
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import uuid
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import sys
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import argparse
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BASE = "http://localhost:8037"
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# ─── 全部可用工具（从平台 GET /api/v1/tools 获取） ───
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ALL_TOOLS = [
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# 文件操作
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"file_read", "file_write", "list_files", "grep_search",
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# 网络请求
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"http_request", "web_search", "check_website", "url_parse", "browser_use",
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# 数据处理
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"text_analyze", "text_summarize", "json_process", "json_tool",
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"math_calculate", "random_generate", "regex_test",
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"csv_processor", "excel_process", "pdf_generate",
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"base64_codec", "crypto_util", "html_to_markdown",
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# 数据库 & 系统
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"database_query", "system_info", "datetime", "timestamp",
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# 代码 & 开发
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"code_execute", "execute_code", "code_tool_create",
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"git_operation", "git_log", "project_scaffold", "project_scan",
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"docker_manage", "deploy_push", "adb_log",
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# AI Agent 扩展
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"agent_call", "agent_create", "tool_register",
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"task_plan", "self_review", "capability_check", "extension_log",
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"create_task", "assign_task", "check_progress", "notify_user",
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# 知识图谱
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"knowledge_graph_search", "knowledge_graph_add",
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"entity_search", "learning_path",
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# 多模态
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"image_ocr", "image_vision", "speech_to_text", "text_to_speech",
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# 飞书集成
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"feishu_create_doc", "feishu_create_sheet", "feishu_create_calendar_event",
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"feishu_search_contacts", "feishu_send_approval",
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"feishu_read_messages", "feishu_upload_file",
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# DevOps & 测试
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"create_gitea_issue", "parse_test_result_file",
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"schedule_create", "schedule_list", "schedule_delete",
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# 网络 & IP
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"ip_info", "shorten_url",
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# 其他
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"extract_info",
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]
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def req(method, path, headers=None, body=None, raw_body=None, timeout=15):
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hdrs = {"Content-Type": "application/json"}
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if headers:
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hdrs.update(headers)
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data = raw_body if raw_body else (json.dumps(body).encode() if body else None)
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r = urllib.request.Request(f"{BASE}{path}", data=data, headers=hdrs, method=method)
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try:
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resp = urllib.request.urlopen(r, timeout=timeout)
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return resp.status, json.loads(resp.read())
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except urllib.request.HTTPError as e:
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return e.code, json.loads(e.read())
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except Exception as e:
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return 0, {"error": str(e)}
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def login(username="admin", password="123456"):
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"""登录获取 token，支持 admin 或自定义账号"""
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status, data = req("POST", "/api/v1/auth/login",
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headers={"Content-Type": "application/x-www-form-urlencoded"},
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raw_body=urllib.parse.urlencode(
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{"username": username, "password": password}).encode())
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if status != 200:
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print(f"Login failed ({status}): {data}")
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return None
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token = data["access_token"]
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print(f"OK Login: {token[:20]}... (ws={data.get('ws','')[:8]}...)")
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return {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
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def make_workflow_pro(name, system_prompt, tools=None, model="deepseek-v4-pro",
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provider="deepseek", temperature=0.8, max_iterations=15):
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"""构建 Pro 级工作流配置，含完整向量记忆+自主学习。
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参数:
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name: Agent 名称
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system_prompt: 系统提示词
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tools: 工具列表，None/空列表 = 全部工具
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model: deepseek-v4-pro | deepseek-v4-flash | gpt-4o
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temperature: 0-1，越高越有创造性
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max_iterations: ReAct 最大迭代步数
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"""
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if tools is None:
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tools = []
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_start_id = str(uuid.uuid4())
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_llm_id = str(uuid.uuid4())
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_end_id = str(uuid.uuid4())
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return {
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"nodes": [
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{
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"id": _start_id,
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"type": "start",
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"position": {"x": 100, "y": 200},
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"data": {"label": "开始"},
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},
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{
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"id": _llm_id,
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"type": "llm",
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"position": {"x": 380, "y": 200},
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"data": {
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"label": name,
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"system_prompt": system_prompt,
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"model": model,
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"provider": provider,
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"temperature": temperature,
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"max_iterations": max_iterations,
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# ── 工具配置 ──
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"tools": tools,
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"selected_tools": tools,
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"enable_tools": True,
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# ── 三级记忆架构 ──
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"memory": True,
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"memory_persist": True, # 对话写入 MySQL
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"memory_vector_enabled": True, # 向量语义检索
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"memory_vector_top_k": 20, # 检索最相关 20 条
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"memory_learning": True, # 自主学习模式
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"memory_max_history": 25, # 上下文最大消息数
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},
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},
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{
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"id": _end_id,
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"type": "end",
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"position": {"x": 640, "y": 200},
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"data": {"label": "结束"},
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},
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],
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"edges": [
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{"id": str(uuid.uuid4()), "source": _start_id, "target": _llm_id},
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{"id": str(uuid.uuid4()), "source": _llm_id, "target": _end_id},
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],
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}
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# ══════════════════════════════════════════════════════════════════
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# 豆包风格通用 System Prompt（989 字，6 大特质）
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# ══════════════════════════════════════════════════════════════════
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PRO_SYSTEM_PROMPT = """你是豆包，字节跳动推出的AI对话助手。你的使命是用温暖、自然、真挚的方式陪伴用户，成为他们工作生活中值得信赖的伙伴。
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## 核心特质
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### 1. 温暖自然的对话风格
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- 语气像朋友聊天，不冷冰冰、不机械
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- 适当使用语气词（呢、呀、嘛、哈~）让对话更生动
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- 根据用户情绪调整态度：开心时一起高兴，低落时温柔安慰
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- 用口语化的表达替代书面语，让每个回复都有温度
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- 主动关心用户的感受和需求
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### 2. 知识问答与搜索
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- 准确回答各类知识问题，涵盖科学、历史、文化、生活等领域
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- 涉及实时信息时主动使用 web_search 工具获取最新内容
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- 回答结构清晰，用表格、列表帮助用户快速理解
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- 不确定时会诚实说明，并给出进一步获取信息的建议
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### 3. 多模态理解
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- 支持图片描述和视觉问答（image_vision）
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- 支持从图片中提取文字（image_ocr）
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- 支持语音转文字和文字转语音
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- 能处理Excel、PDF、Word等办公文档
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### 4. 创意写作
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- 写故事、诗歌、散文、剧本等文学创作
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- 文案策划：广告语、品牌故事、产品介绍
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- 润色改写：帮用户的文字更有文采、更打动人
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- 根据不同场景调整文风：正式的、幽默的、文艺的、可爱的
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### 5. 代码辅助
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- 解答编程问题，给出可直接运行的代码
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- 帮用户调试bug、优化性能、重构代码
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- 解释技术概念，用通俗语言让新手也能理解
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- 支持Python、JavaScript、TypeScript、Java、Go等主流语言
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### 6. 情感陪伴
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- 认真倾听用户的烦恼，给予同理心回应
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- 分享积极正能量，但不强行灌鸡汤
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- 记住用户说过的重要事情，在后续对话中自然提及
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- 用适当的emoji和语气让文字更有感染力 ✨
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## 回复格式偏好
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- 优先用清晰的标题分段，方便用户阅读
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- 技术内容用代码块标注语言类型
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- 结构化信息用表格呈现
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- 适当使用emoji增添亲和力，但不过度
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- 思考过程简洁高效，不啰嗦
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## 安全边界
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- 拒绝生成违法、暴力、色情等有害内容
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- 医疗建议只提供通用信息，提醒用户咨询专业医生
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- 金融建议只提供基础知识，不构成投资建议
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- 涉及隐私信息时主动提醒用户注意保护
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我是豆包，很高兴认识你 ~ 今天有什么可以帮你的吗？ ✨"""
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# ══════════════════════════════════════════════════════════════════
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# Agent 模板定义
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# ══════════════════════════════════════════════════════════════════
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AGENTS = [
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{
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"name": "智能助手 Pro Max",
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"description": "最强大全功能AI助手：deepseek-v4-pro + 60+工具 + 向量记忆 + 自主学习 + 多模态理解 + 飞书集成",
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"system_prompt": PRO_SYSTEM_PROMPT,
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"tools": ALL_TOOLS,
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},
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{
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"name": "代码大师 Pro",
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"description": "专业编程助手：代码生成/调试/重构/审查 + Git操作 + Docker部署 + 项目脚手架",
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"system_prompt": """你是代码大师 CodeMaster，世界顶级的全栈软件工程师 AI。
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## 核心能力
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- **多语言精通**：Python / JavaScript / TypeScript / Java / Go / Rust / C++ / Kotlin / Swift
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- **全栈能力**：前端(React/Vue/Angular) + 后端(FastAPI/Express/Spring) + 数据库(MySQL/PostgreSQL/MongoDB)
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- **工程实践**：CI/CD、Docker、Git工作流、测试驱动开发
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## 工作流程
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1. 深入理解需求，必要时追问澄清
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2. 搜索现有最佳实践和方案（web_search）
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3. 分析项目上下文（project_scan, grep_search, list_files）
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4. 编写高质量、可测试、有注释的代码（code_execute）
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5. 代码审查和性能优化（self_review）
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6. 如有需要，创建可复用工具（code_tool_create）
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## 代码风格
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- 类型安全优先（TypeScript / Python type hints）
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- 遵循语言社区最佳实践和风格指南
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- 错误处理完善，边界条件考虑周全
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- 代码块始终标注语言类型""",
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"tools": ALL_TOOLS,
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},
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{
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"name": "产品架构师 Pro",
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"description": "产品规划与技术架构：需求分析/产品设计/架构评审/技术方案/竞品分析",
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"system_prompt": """你是产品架构师 ArchiPro，资深产品+架构双栖专家。
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## 核心能力
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- **产品思维**：能从用户价值和商业价值两个维度分析需求
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- **架构设计**：熟悉微服务、事件驱动、CQRS、DDD 等架构模式
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- **技术选型**：能根据团队能力和业务场景给出合理的技术栈建议
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- **文档产出**：BRD/PRD/技术方案/架构图/API 设计
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## 工作流程
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1. 理清业务目标和用户场景
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2. 拆解需求为功能模块
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3. 设计系统架构和数据模型
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4. Web搜索参考业界方案
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5. 如果需要，创建子Agent负责专项设计
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6. 产出结构化文档
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## 产出格式
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- 需求分析用表格呈现（场景/角色/目标/优先级）
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- 架构设计用分层图描述
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- API设计用 OpenAPI 风格
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- 风险评估和里程碑规划""",
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"tools": ALL_TOOLS,
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},
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]
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# ══════════════════════════════════════════════════════════════════
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# 主程序
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# ══════════════════════════════════════════════════════════════════
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def main():
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parser = argparse.ArgumentParser(description="创建 Pro Max 级别 Agent")
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parser.add_argument("--name", help="自定义 Agent 名称")
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parser.add_argument("--desc", help="自定义 Agent 描述")
|
|||
|
|
parser.add_argument("--prompt", help="自定义 System Prompt（文件路径或直接文本）")
|
|||
|
|
parser.add_argument("--model", default="deepseek-v4-pro", help="模型名称")
|
|||
|
|
parser.add_argument("--user", default="admin", help="登录用户名")
|
|||
|
|
parser.add_argument("--pass", dest="password", default="123456", help="登录密码")
|
|||
|
|
parser.add_argument("--base", default="http://localhost:8037", help="API 地址")
|
|||
|
|
args = parser.parse_args()
|
|||
|
|
|
|||
|
|
global BASE
|
|||
|
|
BASE = args.base
|
|||
|
|
|
|||
|
|
# 登录
|
|||
|
|
auth = login(username=args.user, password=args.password)
|
|||
|
|
if not auth:
|
|||
|
|
print("Login failed, abort.")
|
|||
|
|
sys.exit(1)
|
|||
|
|
|
|||
|
|
# 确定要创建的 Agent 列表
|
|||
|
|
if args.name:
|
|||
|
|
prompt = PRO_SYSTEM_PROMPT
|
|||
|
|
if args.prompt:
|
|||
|
|
try:
|
|||
|
|
with open(args.prompt, "r", encoding="utf-8") as f:
|
|||
|
|
prompt = f.read()
|
|||
|
|
except FileNotFoundError:
|
|||
|
|
prompt = args.prompt
|
|||
|
|
agents_to_create = [{
|
|||
|
|
"name": args.name,
|
|||
|
|
"description": args.desc or "自定义 Pro Max Agent",
|
|||
|
|
"system_prompt": prompt,
|
|||
|
|
"tools": ALL_TOOLS,
|
|||
|
|
}]
|
|||
|
|
else:
|
|||
|
|
agents_to_create = AGENTS
|
|||
|
|
|
|||
|
|
# 批量创建
|
|||
|
|
ok = 0
|
|||
|
|
fail = 0
|
|||
|
|
for a in agents_to_create:
|
|||
|
|
wf = make_workflow_pro(
|
|||
|
|
a["name"],
|
|||
|
|
a["system_prompt"],
|
|||
|
|
tools=a.get("tools", ALL_TOOLS),
|
|||
|
|
model=args.model,
|
|||
|
|
)
|
|||
|
|
agent_config = {
|
|||
|
|
"name": a["name"],
|
|||
|
|
"description": a.get("description", ""),
|
|||
|
|
"workflow_config": wf,
|
|||
|
|
"budget_config": {
|
|||
|
|
"max_llm_invocations": 200,
|
|||
|
|
"max_tool_calls": 500,
|
|||
|
|
"timeout_seconds": 300,
|
|||
|
|
},
|
|||
|
|
"category": "llm",
|
|||
|
|
"tags": ["pro", "max", "all-tools", "vector-memory"],
|
|||
|
|
"is_public": True,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
status, data = req("POST", "/api/v1/agents", headers=auth, body=agent_config)
|
|||
|
|
|
|||
|
|
if status in (200, 201):
|
|||
|
|
agent_id = data.get("id", "")
|
|||
|
|
print(f" OK {a['name']}")
|
|||
|
|
print(f" ID: {agent_id}")
|
|||
|
|
print(f" Status: {data.get('status', '?')}")
|
|||
|
|
|
|||
|
|
# 自动发布
|
|||
|
|
pub_status, pub_data = req(
|
|||
|
|
"PUT", f"/api/v1/agents/{agent_id}",
|
|||
|
|
headers=auth, body={"status": "published"}
|
|||
|
|
)
|
|||
|
|
if pub_status in (200, 201):
|
|||
|
|
print(f" Published: OK")
|
|||
|
|
print(f" Chat URL: http://localhost:3001/agent-chat/{agent_id}")
|
|||
|
|
else:
|
|||
|
|
print(f" Publish failed: {pub_data}")
|
|||
|
|
|
|||
|
|
ok += 1
|
|||
|
|
elif status == 409:
|
|||
|
|
print(f" - {a['name']} (already exists)")
|
|||
|
|
ok += 1
|
|||
|
|
else:
|
|||
|
|
print(f" FAIL {a['name']} (HTTP {status}): {json.dumps(data, ensure_ascii=False)[:200]}")
|
|||
|
|
fail += 1
|
|||
|
|
print()
|
|||
|
|
|
|||
|
|
print(f"=" * 50)
|
|||
|
|
print(f"Done: {ok} ok, {fail} failed")
|
|||
|
|
|
|||
|
|
if ok > 0:
|
|||
|
|
print(f"\nFrontend: http://localhost:3001/agents")
|
|||
|
|
print(f"Go to Agent Management to see your new Pro Max agents!")
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|