fix: 恢复 rsync 误删的近期开发(agents/skills/mcp/knowledge/workflow/tenant/billing/storage/privacy/promotion/achievement/automation/code-exec/developer/distribution/im-bot/portal/casdoor 等后端模块 + 对应前端页面路由, 补回 main.py 挂载与 App.tsx 路由
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"""
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ACP Gateway - Agent Communication Protocol 内部实现
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Phase G3: 多 Agent 协作编排层
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将平台已有的 Agent（MarketApp type='agent'）封装为可编排调用的"Agent 节点"。
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每个 Agent 节点执行时，通过本 Gateway 创建会话、发送提示、接收流式响应。
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支持通过 Redis EventBus 发布节点进度通知，供 WebSocket 实时推送到前端。
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"""
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import json
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import uuid
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import time
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import logging
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import asyncio
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import threading
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from typing import Optional, Dict, Any, Callable, AsyncIterator
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from dataclasses import dataclass, field
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logger = logging.getLogger(__name__)
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# ============================================================
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# Data Models
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# ============================================================
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@dataclass
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class AcpSessionState:
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"""ACP 会话状态"""
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session_id: str
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agent_id: str # MarketApp id
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agent_name: str = ""
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status: str = "idle" # idle / running / completed / failed / cancelled
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prompt: str = ""
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created_at: float = field(default_factory=time.time)
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completed_at: Optional[float] = None
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output: str = ""
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error: Optional[str] = None
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tool_calls: list = field(default_factory=list)
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chain_of_thought: list = field(default_factory=list) # 思考过程
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def to_dict(self) -> dict:
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return {
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"session_id": self.session_id,
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"agent_id": self.agent_id,
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"agent_name": self.agent_name,
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"status": self.status,
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"prompt": self.prompt[:200],
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"created_at": self.created_at,
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"completed_at": self.completed_at,
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"output": self.output[:500] if self.output else "",
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"error": self.error,
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"tool_calls": self.tool_calls,
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"chain_of_thought": self.chain_of_thought,
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}
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@dataclass
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class AgentConfig:
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"""从 MarketApp.skill_content 解析出的 Agent 配置"""
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agent_id: str = ""
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agent_name: str = ""
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system_prompt: str = ""
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instructions: str = ""
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model_id: Optional[str] = None
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temperature: float = 0.7
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max_tokens: int = 4096
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skills: list = field(default_factory=list)
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knowledge_base_ids: list = field(default_factory=list)
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tools: list = field(default_factory=list)
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# ============================================================
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# Agent Resolver
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# ============================================================
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def resolve_agent_config(db, agent_id: str) -> Optional[AgentConfig]:
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"""从 MarketApp 解析 Agent 配置"""
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from app.models import MarketApp
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agent = db.query(MarketApp).filter(
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MarketApp.id == agent_id,
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MarketApp.type == "agent"
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).first()
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if not agent:
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return None
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config_json = {}
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if agent.skill_content:
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try:
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config_json = json.loads(agent.skill_content) if isinstance(agent.skill_content, str) else agent.skill_content
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except Exception:
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pass
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return AgentConfig(
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agent_id=agent.id,
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agent_name=agent.name,
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system_prompt=config_json.get("system_prompt", config_json.get("role", agent.description or "AI助手")),
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instructions=config_json.get("instructions", ""),
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model_id=config_json.get("model_id"),
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temperature=float(config_json.get("temperature", 0.7)),
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max_tokens=int(config_json.get("max_tokens", 4096)),
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skills=config_json.get("skills", []),
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knowledge_base_ids=config_json.get("knowledge_base_ids", []),
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tools=config_json.get("tools", []),
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)
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def resolve_model_config(db, model_id: str) -> dict:
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"""获取模型的 API 配置"""
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from app.models import AIModel
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model = db.query(AIModel).filter(AIModel.id == model_id).first()
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if not model:
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# Fallback to default model
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default_model = db.query(AIModel).filter(AIModel.is_enabled == True).first()
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if default_model:
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model = default_model
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else:
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return {
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"api_key": "",
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"api_base": "https://api.siliconflow.cn/v1",
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"model_name": "deepseek-ai/DeepSeek-V4-Flash",
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}
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return {
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"api_key": model.api_key or "",
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"api_base": model.api_base or "https://api.siliconflow.cn/v1",
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"model_name": model.model_id or model.name,
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}
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# ============================================================
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# ACP Gateway
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# ============================================================
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class AcpGateway:
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"""Agent 通信网关 — 平台内部多 Agent 编排核心"""
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def __init__(self):
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self._sessions: Dict[str, AcpSessionState] = {}
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self._lock = threading.Lock()
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self._on_notification: Optional[Callable] = None
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def set_notification_handler(self, handler: Callable[[dict], None]):
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"""设置通知回调 — 用于发布到 Redis EventBus"""
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self._on_notification = handler
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def _notify(self, event_type: str, data: dict):
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"""发送通知"""
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if self._on_notification:
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try:
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self._on_notification({
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"type": event_type,
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"timestamp": time.time(),
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**data,
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})
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except Exception as e:
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logger.error(f"[AcpGateway] 通知回调异常: {e}")
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# ---- Session Management ----
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def create_session(self, agent_id: str, agent_name: str = "") -> str:
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"""创建新的 Agent 会话"""
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session_id = f"acp-{uuid.uuid4().hex[:12]}"
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with self._lock:
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self._sessions[session_id] = AcpSessionState(
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session_id=session_id,
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agent_id=agent_id,
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agent_name=agent_name or agent_id,
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status="idle",
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)
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logger.info(f"[AcpGateway] 创建会话: {session_id} (agent={agent_name or agent_id})")
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return session_id
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def get_session(self, session_id: str) -> Optional[AcpSessionState]:
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"""获取会话"""
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with self._lock:
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return self._sessions.get(session_id)
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def cancel_session(self, session_id: str) -> bool:
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"""取消会话"""
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with self._lock:
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sess = self._sessions.get(session_id)
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if sess and sess.status == "running":
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sess.status = "cancelled"
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sess.completed_at = time.time()
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self._notify("agent_cancelled", sess.to_dict())
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return True
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return False
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# ---- Agent Invocation ----
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async def invoke_agent(
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self,
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agent_id: str,
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prompt: str,
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system_prompt: str = "",
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model_id: Optional[str] = None,
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temperature: float = 0.7,
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max_tokens: int = 4096,
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context: Optional[dict] = None,
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db_session=None,
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) -> dict:
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"""调用 Agent — 创建会话、发送提示、等待完成
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这是一个高级封装，内部自动创建和管理会话生命周期。
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返回完整结果字典，包含 output, tool_calls, chain_of_thought 等。
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"""
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import httpx
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# Resolve agent config from DB
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agent_name = agent_id
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if db_session:
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config = resolve_agent_config(db_session, agent_id)
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if config:
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agent_name = config.agent_name
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system_prompt = system_prompt or config.system_prompt
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model_id = model_id or config.model_id
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temperature = config.temperature
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max_tokens = config.max_tokens
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# Create session
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session_id = self.create_session(agent_id, agent_name)
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sess = self.get_session(session_id)
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# Resolve model
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if db_session and model_id:
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model_cfg = resolve_model_config(db_session, model_id)
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else:
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model_cfg = {"api_key": "", "api_base": "https://api.siliconflow.cn/v1", "model_name": "deepseek-ai/DeepSeek-V4-Flash"}
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# Update status
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with self._lock:
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sess.status = "running"
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sess.prompt = prompt
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self._notify("agent_running", {"session_id": session_id, "agent_name": agent_name, "prompt": prompt[:200]})
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try:
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# Build messages
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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# Call LLM
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headers = {
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"Authorization": f"Bearer {model_cfg['api_key']}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": model_cfg["model_name"],
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"stream": False,
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}
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async with httpx.AsyncClient(timeout=120.0) as client:
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resp = await client.post(
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f"{model_cfg['api_base']}/chat/completions",
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headers=headers,
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json=payload,
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)
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resp.raise_for_status()
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data = resp.json()
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output = data["choices"][0]["message"]["content"]
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usage = data.get("usage", {})
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with self._lock:
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sess.status = "completed"
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sess.output = output
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sess.completed_at = time.time()
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result = {
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"session_id": session_id,
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"status": "completed",
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"output": output,
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"agent_name": agent_name,
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"usage": usage,
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"tool_calls": sess.tool_calls,
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"chain_of_thought": sess.chain_of_thought,
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}
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self._notify("agent_completed", {"session_id": session_id, "output_preview": output[:200]})
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return result
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except Exception as e:
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error_msg = str(e)
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with self._lock:
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sess.status = "failed"
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sess.error = error_msg
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sess.completed_at = time.time()
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result = {
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"session_id": session_id,
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"status": "failed",
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"output": f"[Agent] 调用失败: {error_msg}",
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"agent_name": agent_name,
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"error": error_msg,
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}
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self._notify("agent_failed", {"session_id": session_id, "error": error_msg})
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return result
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async def invoke_agent_streaming(
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self,
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agent_id: str,
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prompt: str,
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system_prompt: str = "",
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model_id: Optional[str] = None,
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temperature: float = 0.7,
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max_tokens: int = 4096,
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context: Optional[dict] = None,
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db_session=None,
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) -> AsyncIterator[dict]:
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"""流式调用 Agent — 逐步返回 LLM 生成的 token chunks
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每个 yield 的 dict 格式:
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{"type": "token"|"done"|"error", "content": str, ...}
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"""
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import httpx
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# Resolve agent config
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agent_name = agent_id
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if db_session:
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config = resolve_agent_config(db_session, agent_id)
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if config:
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agent_name = config.agent_name
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system_prompt = system_prompt or config.system_prompt
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model_id = model_id or config.model_id
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temperature = config.temperature
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max_tokens = config.max_tokens
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session_id = self.create_session(agent_id, agent_name)
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sess = self.get_session(session_id)
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if db_session and model_id:
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model_cfg = resolve_model_config(db_session, model_id)
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else:
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model_cfg = {"api_key": "", "api_base": "https://api.siliconflow.cn/v1", "model_name": "deepseek-ai/DeepSeek-V4-Flash"}
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with self._lock:
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sess.status = "running"
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sess.prompt = prompt
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self._notify("agent_running", {"session_id": session_id, "agent_name": agent_name})
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try:
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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headers = {
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"Authorization": f"Bearer {model_cfg['api_key']}",
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"Content-Type": "application/json",
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||||
}
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payload = {
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"model": model_cfg["model_name"],
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"stream": True,
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}
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collected = []
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async with httpx.AsyncClient(timeout=120.0) as client:
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async with client.stream("POST", f"{model_cfg['api_base']}/chat/completions",
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headers=headers, json=payload) as resp:
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resp.raise_for_status()
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async for line in resp.aiter_lines():
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||||
if line.startswith("data: "):
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data_str = line[6:]
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||||
if data_str.strip() == "[DONE]":
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break
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||||
try:
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||||
chunk = json.loads(data_str)
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delta = chunk.get("choices", [{}])[0].get("delta", {})
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content = delta.get("content", "")
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if content:
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collected.append(content)
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yield {"type": "token", "content": content, "session_id": session_id}
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# Notify periodically (every ~20 tokens)
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if len(collected) % 20 == 0:
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self._notify("agent_token", {
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"session_id": session_id,
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"partial": "".join(collected[-20:]),
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})
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except json.JSONDecodeError:
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continue
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||||
full_output = "".join(collected)
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||||
with self._lock:
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sess.status = "completed"
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||||
sess.output = full_output
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sess.completed_at = time.time()
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||||
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self._notify("agent_completed", {"session_id": session_id, "output_preview": full_output[:200]})
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yield {"type": "done", "session_id": session_id, "output": full_output, "status": "completed"}
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||||
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||||
except Exception as e:
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||||
error_msg = str(e)
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with self._lock:
|
||||
sess.status = "failed"
|
||||
sess.error = error_msg
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||||
sess.completed_at = time.time()
|
||||
|
||||
self._notify("agent_failed", {"session_id": session_id, "error": error_msg})
|
||||
yield {"type": "error", "session_id": session_id, "error": error_msg, "status": "failed"}
|
||||
|
||||
# ---- Multi-Agent Parallel Execution ----
|
||||
|
||||
async def invoke_agents_parallel(
|
||||
self,
|
||||
tasks: list,
|
||||
db_session=None,
|
||||
) -> list:
|
||||
"""并行调用多个 Agent
|
||||
|
||||
tasks = [
|
||||
{"agent_id": "xxx", "prompt": "...", "system_prompt": "..."},
|
||||
{"agent_id": "yyy", "prompt": "...", "system_prompt": "..."},
|
||||
]
|
||||
|
||||
Returns list of results in same order.
|
||||
"""
|
||||
async def _invoke_one(task: dict) -> dict:
|
||||
try:
|
||||
return await self.invoke_agent(
|
||||
agent_id=task["agent_id"],
|
||||
prompt=task.get("prompt", ""),
|
||||
system_prompt=task.get("system_prompt", ""),
|
||||
model_id=task.get("model_id"),
|
||||
temperature=task.get("temperature", 0.7),
|
||||
max_tokens=task.get("max_tokens", 4096),
|
||||
context=task.get("context"),
|
||||
db_session=db_session,
|
||||
)
|
||||
except Exception as e:
|
||||
return {
|
||||
"status": "failed",
|
||||
"output": f"[Agent {task.get('agent_id', 'unknown')}] 执行异常: {str(e)}",
|
||||
"error": str(e),
|
||||
}
|
||||
|
||||
return await asyncio.gather(*[_invoke_one(t) for t in tasks])
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Global Singleton
|
||||
# ============================================================
|
||||
|
||||
_acp_gateway_instance: Optional[AcpGateway] = None
|
||||
_lock_gateway = threading.Lock()
|
||||
|
||||
|
||||
def get_acp_gateway() -> AcpGateway:
|
||||
"""获取全局 ACP Gateway 单例"""
|
||||
global _acp_gateway_instance
|
||||
if _acp_gateway_instance is None:
|
||||
with _lock_gateway:
|
||||
if _acp_gateway_instance is None:
|
||||
_acp_gateway_instance = AcpGateway()
|
||||
return _acp_gateway_instance
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Redis Notification Helper
|
||||
# ============================================================
|
||||
|
||||
def publish_workflow_event(workflow_id: str, node_id: str, event: dict):
|
||||
"""发布工作流执行事件到 Redis EventBus"""
|
||||
try:
|
||||
from app.event_bus import get_event_bus, MessageEvent
|
||||
|
||||
bus = get_event_bus()
|
||||
msg = MessageEvent(
|
||||
platform="workflow",
|
||||
channel_id=f"{workflow_id}:{node_id}",
|
||||
content=json.dumps(event, ensure_ascii=False),
|
||||
content_type="workflow_event",
|
||||
raw={
|
||||
"workflow_id": workflow_id,
|
||||
"node_id": node_id,
|
||||
**event,
|
||||
},
|
||||
)
|
||||
bus.publish(msg)
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"[AcpGateway] Redis 发布失败: {e}")
|
||||
return False
|
||||
Reference in new issue
Block a user