feat: add 灵犀 Feishu bot + fix agent schedule system + default all tools
- Add 灵犀学习助手 Feishu bot (lingxi_app_service + lingxi_ws_handler) - Fix agent_schedule_service missing AgentSchedule import (Celery Beat) - Fix scene_templates default enable_tools=False → True - Fix workflow_engine LLM node: empty tools list now = all tools (consistent with agent node) - Add 创建agent.md guide document Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
renjianbo
@@ -105,6 +105,11 @@ class Settings(BaseSettings):
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TIANTIAN_APP_SECRET: str = ""
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TIANTIAN_AGENT_ID: str = "" # 创建苏瑶3号后写入
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# 灵犀飞书应用配置（独立 WS 连接，路由到灵犀学习助手 Agent）
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LINGXI_APP_ID: str = ""
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LINGXI_APP_SECRET: str = ""
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LINGXI_AGENT_ID: str = "" # 创建灵犀后写入
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class Config:
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env_file = str(_ENV_PATH)
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case_sensitive = True
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@@ -241,6 +241,13 @@ async def startup_event():
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except Exception as e:
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logger.error(f"甜甜长连接启动失败: {e}")
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# 启动灵犀飞书长连接（学习助手）
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try:
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from app.services.lingxi_ws_handler import start_ws_client as start_lingxi_ws
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asyncio.ensure_future(start_lingxi_ws())
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except Exception as e:
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logger.error(f"灵犀长连接启动失败: {e}")
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# 注册路由
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from app.api import auth, uploads, workflows, executions, websocket, execution_logs, data_sources, agents, platform_templates, model_configs, webhooks, template_market, batch_operations, collaboration, permissions, monitoring, alert_rules, node_test, node_templates, tools, agent_chat, agent_monitoring, knowledge_base, agent_schedules, notifications, feishu_bind
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@@ -95,6 +95,8 @@ def check_and_run_due_schedules() -> int:
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Returns:
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本次触发的任务数
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"""
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from app.models.agent_schedule import AgentSchedule
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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105
backend/app/services/lingxi_app_service.py
Normal file
105
backend/app/services/lingxi_app_service.py
Normal file
@@ -0,0 +1,105 @@
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"""灵犀飞书应用 API 服务 — 通过灵犀应用发送消息到用户"""
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from __future__ import annotations
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import json
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import logging
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import time
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from typing import Optional
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import httpx
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from app.core.config import settings
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logger = logging.getLogger(__name__)
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_token_cache: dict = {"token": None, "expires_at": 0}
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def _get_tenant_access_token() -> Optional[str]:
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now = time.time()
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if _token_cache["token"] and now < _token_cache["expires_at"] - 300:
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return _token_cache["token"]
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app_id = settings.LINGXI_APP_ID
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app_secret = settings.LINGXI_APP_SECRET
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if not app_id or not app_secret:
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logger.warning("灵犀应用未配置（LINGXI_APP_ID / LINGXI_APP_SECRET）")
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return None
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try:
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with httpx.Client(timeout=10) as client:
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resp = client.post(
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"https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal",
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json={"app_id": app_id, "app_secret": app_secret},
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)
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result = resp.json()
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if resp.is_success and result.get("code") == 0:
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token = result["tenant_access_token"]
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expire = result.get("expire", 7200)
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_token_cache["token"] = token
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_token_cache["expires_at"] = now + expire
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logger.info("灵犀 tenant_access_token 获取成功")
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return token
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else:
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logger.warning("灵犀 token 获取失败: %s", result)
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return None
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except Exception as e:
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logger.warning("灵犀 token 获取异常: %s", e)
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return None
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def send_message_to_user(
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open_id: str, title: str, content: str,
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status: str = "info", detail_link: Optional[str] = None,
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) -> bool:
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token = _get_tenant_access_token()
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if not token:
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return False
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color_map = {"success": "green", "failed": "red", "info": "blue"}
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color = color_map.get(status, "blue")
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elements = [{"tag": "markdown", "content": content}]
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if detail_link:
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elements.append({
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"tag": "action",
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"actions": [{"tag": "button", "text": {"tag": "plain_text", "content": "查看详情"}, "url": detail_link, "type": "default"}],
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})
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card = {
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"config": {"wide_screen_mode": True},
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"header": {"title": {"tag": "plain_text", "content": title}, "template": color},
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"elements": elements,
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}
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try:
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with httpx.Client(timeout=10) as client:
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resp = client.post(
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"https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type=open_id",
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headers={"Authorization": f"Bearer {token}"},
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json={"receive_id": open_id, "msg_type": "interactive", "content": json.dumps(card, ensure_ascii=False)},
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)
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result = resp.json()
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if resp.is_success and result.get("code") == 0:
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logger.info("灵犀消息发送成功: open_id=%s title=%s", open_id[:20], title)
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return True
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else:
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logger.warning("灵犀消息发送失败: code=%s msg=%s", result.get("code"), result.get("msg"))
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return False
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except Exception as e:
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logger.warning("灵犀消息发送异常: %s", e)
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return False
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def send_plain_text(open_id: str, text: str) -> bool:
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token = _get_tenant_access_token()
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if not token:
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return False
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try:
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with httpx.Client(timeout=10) as client:
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resp = client.post(
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"https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type=open_id",
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headers={"Authorization": f"Bearer {token}"},
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json={"receive_id": open_id, "msg_type": "text", "content": json.dumps({"text": text}, ensure_ascii=False)},
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)
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result = resp.json()
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return resp.is_success and result.get("code") == 0
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except Exception as e:
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logger.warning("灵犀文本消息发送异常: %s", e)
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return False
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278
backend/app/services/lingxi_ws_handler.py
Normal file
278
backend/app/services/lingxi_ws_handler.py
Normal file
@@ -0,0 +1,278 @@
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"""灵犀飞书长连接 — 固定路由到灵犀学习助手 Agent（方案C：知识图谱+RAG）"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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from collections import deque
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from typing import Optional
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from app.core.config import settings
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logger = logging.getLogger(__name__)
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_processed_msg_ids: deque[str] = deque(maxlen=20)
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def _get_message_id(data) -> Optional[str]:
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try:
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ev = data.event
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msg = getattr(ev, "message", None)
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if msg:
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return getattr(msg, "message_id", None)
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except Exception:
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return None
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return None
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def _get_message_text(data) -> Optional[str]:
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try:
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ev = data.event
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msg = getattr(ev, "message", None)
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if not msg:
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return None
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content_str = getattr(msg, "content", None)
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msg_type = getattr(msg, "message_type", "")
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if not content_str:
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return None
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if msg_type == "text":
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parsed = json.loads(content_str)
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return parsed.get("text", "")
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return None
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except Exception as e:
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logger.warning("解析灵犀消息内容失败: %s", e)
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return None
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def _get_sender_open_id(data) -> Optional[str]:
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try:
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ev = data.event
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sender = getattr(ev, "sender", None)
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if not sender:
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return None
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sender_id = getattr(sender, "sender_id", None)
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if not sender_id:
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return None
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return getattr(sender_id, "open_id", None)
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except Exception:
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return None
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def _get_chat_type(data) -> str:
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try:
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ev = data.event
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msg = getattr(ev, "message", None)
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return getattr(msg, "chat_type", "") if msg else ""
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except Exception:
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return ""
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def _reply_to_feishu(open_id: str, text: str):
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try:
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from app.services.lingxi_app_service import send_plain_text
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send_plain_text(open_id, text)
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except Exception as e:
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logger.warning("灵犀回复消息失败: %s", e)
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def _reply_card(open_id: str, title: str, content: str, status: str = "info"):
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try:
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from app.services.lingxi_app_service import send_message_to_user
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send_message_to_user(open_id, title, content, status=status)
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except Exception as e:
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logger.warning("灵犀回复卡片失败: %s", e)
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def _make_llm_logger(db, agent_id: Optional[str] = None, user_id: Optional[str] = None):
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def _log(metrics: dict):
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try:
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from app.models.agent_llm_log import AgentLLMLog
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log = AgentLLMLog(
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agent_id=agent_id, session_id=metrics.get("session_id"),
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user_id=user_id, model=metrics.get("model", ""),
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provider=metrics.get("provider"),
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prompt_tokens=metrics.get("prompt_tokens", 0),
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completion_tokens=metrics.get("completion_tokens", 0),
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total_tokens=metrics.get("total_tokens", 0),
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latency_ms=metrics.get("latency_ms", 0),
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iteration_number=metrics.get("iteration_number", 0),
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step_type=metrics.get("step_type"),
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tool_name=metrics.get("tool_name"),
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status=metrics.get("status", "success"),
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error_message=metrics.get("error_message"),
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)
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db.add(log)
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db.commit()
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except Exception as e:
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logger.warning("写入 AgentLLMLog 失败: %s", e)
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return _log
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async def _handle_message_async(data):
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open_id = _get_sender_open_id(data)
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chat_type = _get_chat_type(data)
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text = _get_message_text(data)
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if not open_id or chat_type != "p2p":
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return
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logger.info("灵犀收到消息: open_id=%s text=%s", open_id[:20], text[:50] if text else "(空)")
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if not text:
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return
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from sqlalchemy.orm import Session
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from app.core.database import SessionLocal
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from app.models.agent import Agent
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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agent_id = settings.LINGXI_AGENT_ID
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if not agent_id:
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_reply_to_feishu(open_id, "灵犀尚未配置，请联系管理员。")
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return
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agent = db.query(Agent).filter(Agent.id == agent_id).first()
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if not agent:
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_reply_to_feishu(open_id, "灵犀 Agent 已不存在，请联系管理员。")
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return
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_reply_to_feishu(open_id, "正在思考，请稍候...")
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from app.agent_runtime import AgentRuntime, AgentConfig, AgentLLMConfig, AgentToolConfig, AgentMemoryConfig
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wc = agent.workflow_config or {}
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nodes = wc.get("nodes", [])
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system_prompt = agent.description or ""
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model = "deepseek-v4-flash"
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provider = "deepseek"
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temperature = 0.85
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max_iterations = 30
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tools_whitelist = []
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for n in nodes:
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if n.get("type") not in ("agent", "llm", "template"):
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continue
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cfg = n.get("data", {}) if isinstance(n, dict) else getattr(n, "data", {})
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system_prompt = cfg.get("system_prompt", "") or system_prompt
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model = cfg.get("model", model)
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provider = cfg.get("provider", provider)
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temperature = float(cfg.get("temperature", temperature))
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max_iterations = int(cfg.get("max_iterations", max_iterations))
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tools_whitelist = cfg.get("tools", tools_whitelist)
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break
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config = AgentConfig(
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name=agent.name or "灵犀",
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system_prompt=system_prompt,
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llm=AgentLLMConfig(
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model=model, provider=provider,
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temperature=temperature, max_iterations=max_iterations,
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),
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tools=AgentToolConfig(include_tools=tools_whitelist),
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memory=AgentMemoryConfig(
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max_history_messages=int(cfg.get("memory_max_history", 20)),
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vector_memory_top_k=int(cfg.get("memory_vector_top_k", 5)),
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persist_to_db=bool(cfg.get("memory_persist", True)),
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vector_memory_enabled=bool(cfg.get("memory_vector_enabled", True)),
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learning_enabled=bool(cfg.get("memory_learning", True)),
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),
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user_id=None,
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memory_scope_id=str(agent.id),
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)
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on_llm_call = _make_llm_logger(db, agent_id=str(agent.id))
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runtime = AgentRuntime(config=config, on_llm_call=on_llm_call)
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result = await runtime.run(text)
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if result.content:
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_reply_card(open_id, f"{agent.name}", result.content.strip(), status="success")
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else:
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_reply_to_feishu(open_id, "Agent 未返回有效回复，请重试。")
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logger.info(
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"灵犀 Agent 回复完成: open_id=%s agent=%s iterations=%d tools=%d",
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open_id[:20], agent.name, result.iterations_used, result.tool_calls_made,
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)
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except Exception as e:
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logger.error("灵犀消息处理失败: %s", e)
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try:
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_reply_to_feishu(open_id, f"处理失败: {e!s}")
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except Exception:
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pass
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finally:
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if db:
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db.close()
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def _handle_message_internal(data):
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msg_id = _get_message_id(data)
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if msg_id:
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if msg_id in _processed_msg_ids:
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return
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_processed_msg_ids.append(msg_id)
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open_id = _get_sender_open_id(data)
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chat_type = _get_chat_type(data)
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text = _get_message_text(data)
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if not open_id or chat_type != "p2p" or not text:
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return
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try:
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loop = asyncio.get_event_loop()
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if loop.is_running():
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asyncio.ensure_future(_handle_message_async(data))
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else:
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loop.run_until_complete(_handle_message_async(data))
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except Exception as e:
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logger.error("灵犀创建消息处理任务失败: %s", e)
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try:
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_reply_to_feishu(open_id, f"处理失败: {e!s}")
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except Exception:
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pass
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def _build_event_handler():
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from lark_oapi.event.dispatcher_handler import EventDispatcherHandler
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def on_message_receive(data):
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_handle_message_internal(data)
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builder = EventDispatcherHandler.builder(encrypt_key="", verification_token="")
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builder.register_p2_im_message_receive_v1(on_message_receive)
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return builder.build()
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async def start_ws_client():
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if not settings.LINGXI_APP_ID or not settings.LINGXI_APP_SECRET:
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logger.warning("灵犀应用未配置，跳过灵犀长连接启动")
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return
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from lark_oapi.ws import Client as WSClient
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handler = _build_event_handler()
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client = WSClient(
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app_id=settings.LINGXI_APP_ID,
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app_secret=settings.LINGXI_APP_SECRET,
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event_handler=handler,
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auto_reconnect=True,
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)
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logger.info("灵犀长连接客户端启动中...")
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while True:
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try:
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await client._connect()
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logger.info("灵犀长连接已建立")
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asyncio.ensure_future(client._ping_loop())
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while True:
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await asyncio.sleep(3600)
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except asyncio.CancelledError:
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break
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except Exception as e:
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logger.warning("灵犀长连接断开，3秒后重连: %s", e)
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await asyncio.sleep(3)
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@@ -103,7 +103,7 @@ def build_workflow_for_template(template_id: str, parameters: Optional[Dict[str,
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raise ValueError(f"未知模板: {template_id}")
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temperature = float(parameters.get("temperature", meta.get("default_temperature", 0.3)))
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enable_tools = bool(parameters.get("enable_tools", False))
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enable_tools = bool(parameters.get("enable_tools", True))
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tools = parameters.get("tools")
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if tools is not None and not isinstance(tools, list):
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tools = []
|
||||
@@ -116,7 +116,7 @@ def build_workflow_for_template(template_id: str, parameters: Optional[Dict[str,
|
||||
prompt,
|
||||
temperature=temperature,
|
||||
enable_tools=enable_tools,
|
||||
tools=tools if enable_tools else [],
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -1825,23 +1825,20 @@ class WorkflowEngine:
|
||||
|
||||
# 如果启用了工具，加载工具定义
|
||||
tools = []
|
||||
if enable_tools and tools_config:
|
||||
if enable_tools:
|
||||
from app.services.tool_registry import tool_registry
|
||||
# 从注册表加载工具定义
|
||||
tools = tool_registry.get_tools_by_names(tools_config)
|
||||
logger.info(f"[rjb] LLM节点启用工具调用: {len(tools)} 个工具, 工具列表: {tools_config}")
|
||||
if tools_config:
|
||||
tools = tool_registry.get_tools_by_names(tools_config)
|
||||
else:
|
||||
# 空列表 = 全部工具（与 Agent 节点行为一致）
|
||||
tools = tool_registry.get_all_tool_schemas()
|
||||
logger.info(f"[rjb] LLM节点启用工具调用: {len(tools)} 个工具, 工具列表: {tools_config or '全部'}")
|
||||
if not tools:
|
||||
logger.warning(
|
||||
"[rjb] LLM 已 enable_tools 但当前进程 tool_registry 中 0 个匹配 schema，"
|
||||
"将无法发起 function calling（常见于 Celery Worker 未加载 tools_bootstrap）。配置=%s",
|
||||
tools_config,
|
||||
)
|
||||
elif len(tools) < len(tools_config):
|
||||
missing = [n for n in tools_config if not tool_registry.get_tool_schema(n)]
|
||||
logger.warning(
|
||||
"[rjb] LLM 工具部分缺失 schema，缺失=%s（可动手能力不完整）",
|
||||
missing,
|
||||
)
|
||||
|
||||
# 调用LLM服务
|
||||
try:
|
||||
|
||||
158
创建agent.md
Normal file
158
创建agent.md
Normal file
@@ -0,0 +1,158 @@
|
||||
# Agent 创建指南
|
||||
|
||||
## 概述
|
||||
|
||||
本系统支持多种方式创建 Agent。**所有创建方式均默认赋予 Agent 全部 18 个内置工具能力**，除非明确限制。
|
||||
|
||||
## 内置工具清单（18个）
|
||||
|
||||
| 类别 | 工具 | 用途 |
|
||||
|------|------|------|
|
||||
| 文件 | `file_read` | 读文件：文本/PDF/docx/xlsx/图片OCR(作业拍照识别) |
|
||||
| 文件 | `file_write` | 写文件：笔记、报告、数据导出 |
|
||||
| 网络 | `http_request` | HTTP 请求：上网查资料、调 API |
|
||||
| 网络 | `url_parse` | URL 解析 |
|
||||
| 数据 | `json_process` | JSON 结构化数据处理 |
|
||||
| 数据 | `database_query` | 数据库查询 |
|
||||
| 计算 | `math_calculate` | 数学计算 |
|
||||
| 文本 | `text_analyze` | 文本分析 |
|
||||
| 时间 | `datetime` | 日期时间计算、倒计时 |
|
||||
| 系统 | `system_info` | 系统环境信息 |
|
||||
| 调度 | `schedule_create` | 创建定时任务（cron 表达式） |
|
||||
| 调度 | `schedule_list` | 查看定时任务列表 |
|
||||
| 调度 | `schedule_delete` | 删除定时任务 |
|
||||
| 通信 | `send_email` | 发送邮件 |
|
||||
| 工具 | `regex_test` | 正则表达式测试 |
|
||||
| 工具 | `crypto_util` | 加密/解密工具 |
|
||||
| 工具 | `random_generate` | 随机数据生成 |
|
||||
| 调试 | `adb_log` | Android 设备日志（ADB） |
|
||||
|
||||
---
|
||||
|
||||
## 创建方式
|
||||
|
||||
### 方式一：前端界面创建（http://localhost:3001/agents）
|
||||
|
||||
#### 1.1 手动创建
|
||||
1. 打开 http://localhost:3001/agents
|
||||
2. 点击「创建 Agent」
|
||||
3. 填写名称、描述
|
||||
4. 进入工作流设计器拖拽节点（至少需要一个 LLM 或 Agent 节点）
|
||||
5. **不设置工具白名单 = 全部工具可用**
|
||||
6. 保存
|
||||
|
||||
#### 1.2 场景模板创建
|
||||
1. 在 Agents 页面点击「从场景模板创建」
|
||||
2. 选择模板（客服/研发/运维）
|
||||
3. 填写名称
|
||||
4. 系统默认启用全部工具（`enable_tools=true`，`tools` 为空）
|
||||
|
||||
#### 1.3 导入 JSON
|
||||
1. 导出已有 Agent 的 JSON
|
||||
2. 修改后导入
|
||||
3. 若 JSON 中未指定 `tools` 字段 = 全部工具
|
||||
|
||||
---
|
||||
|
||||
### 方式二：后端 API 创建
|
||||
|
||||
```bash
|
||||
# 直接创建
|
||||
curl -X POST http://localhost:8037/api/v1/agents \
|
||||
-H "Authorization: Bearer <token>" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"name": "My Agent",
|
||||
"description": "Agent description",
|
||||
"workflow_config": {
|
||||
"nodes": [
|
||||
{"id": "start-1", "type": "start", "position": {"x": 80, "y": 120}, "data": {}},
|
||||
{"id": "llm-1", "type": "llm", "position": {"x": 320, "y": 120},
|
||||
"data": {
|
||||
"prompt": "你是一个有用的AI助手",
|
||||
"enable_tools": true
|
||||
// 不指定 tools = 全部工具
|
||||
}}
|
||||
],
|
||||
"edges": [...]
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
### 方式三：Python 脚本创建
|
||||
|
||||
```python
|
||||
from app.core.database import SessionLocal
|
||||
from app.models.agent import Agent
|
||||
import uuid
|
||||
|
||||
agent = Agent(
|
||||
id=str(uuid.uuid4()),
|
||||
name="My Agent",
|
||||
description="Description",
|
||||
workflow_config={
|
||||
"nodes": [
|
||||
{"id": "node_1", "type": "llm", "label": "LLM",
|
||||
"data": {
|
||||
"system_prompt": "你是一个有用的AI助手",
|
||||
"model": "deepseek-v4-flash",
|
||||
"provider": "deepseek",
|
||||
"temperature": 0.7,
|
||||
"max_iterations": 10,
|
||||
# 不指定 tools = 全部工具
|
||||
}}
|
||||
],
|
||||
"edges": []
|
||||
},
|
||||
status="active",
|
||||
)
|
||||
db = SessionLocal()
|
||||
db.add(agent)
|
||||
db.commit()
|
||||
db.close()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 工具配置规则
|
||||
|
||||
### Agent 节点（AgentRuntime 执行）
|
||||
- `tools` 字段不存在 → 全部工具
|
||||
- `tools: []` → 全部工具
|
||||
- `tools: ["file_read", "http_request"]` → 仅这两个工具
|
||||
|
||||
### LLM 节点（工作流引擎执行）
|
||||
- `enable_tools: false` → 无工具
|
||||
- `enable_tools: true` + `tools` 未设置/为空 → 全部工具
|
||||
- `enable_tools: true` + `tools: ["file_read"]` → 仅 file_read
|
||||
|
||||
### 飞书长连接路由（WS Handler）
|
||||
- 读取 Agent workflow_config 节点 data 中的 `tools` 字段
|
||||
- 未设置 = `include_tools=[]` = 全部工具
|
||||
|
||||
---
|
||||
|
||||
## 快速检查 Agent 能力
|
||||
|
||||
```
|
||||
GET /api/v1/health
|
||||
```
|
||||
|
||||
返回 `builtin_tools.count` 和 `builtin_tools.names`，确认工具已注册。
|
||||
|
||||
---
|
||||
|
||||
## 配置飞书连接
|
||||
|
||||
1. 在飞书开放平台创建应用，开启「机器人」能力
|
||||
2. 在 `.env` 中添加：
|
||||
```
|
||||
XXXXX_APP_ID=cli_xxx
|
||||
XXXXX_APP_SECRET=xxx
|
||||
XXXXX_AGENT_ID=<agent_uuid>
|
||||
```
|
||||
3. 创建 `app/services/xxxxx_app_service.py`（token 管理 + 消息发送）
|
||||
4. 创建 `app/services/xxxxx_ws_handler.py`（WebSocket 长连接 + 消息路由）
|
||||
5. 在 `app/core/config.py` 添加配置字段
|
||||
6. 在 `app/main.py` startup 事件中启动 WS 客户端
|
||||
7. 重启后端
|
||||
Reference in New Issue
Block a user