feat: Phase 4 - LLM/Agent fallback chain, cross-agent knowledge sharing, async agent execution
- 4.1 Fallback chain: LLM fallback_llm config in AgentLLMConfig, retry with alternate model on API failure; Agent fallback_agent in DAG nodes - 4.2 Knowledge sharing: GlobalKnowledge model with embedding-based semantic search, auto-extraction of tool names as tags after execution - 4.3 Async execution: execute_agent_task fully implemented with AgentRuntime, scheduler dual-path for workflow/non-workflow agents Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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renjianbo
@@ -1,5 +1,5 @@
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"""
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Agent任务
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Agent 异步执行任务 — Celery 任务，支持定时调度和手动触发。
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"""
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from celery import Task
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from app.core.tools_bootstrap import ensure_builtin_tools_registered
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@@ -7,10 +7,169 @@ from app.core.tools_bootstrap import ensure_builtin_tools_registered
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ensure_builtin_tools_registered()
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from app.core.celery_app import celery_app
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from app.core.database import SessionLocal
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from app.agent_runtime.core import AgentRuntime
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from app.agent_runtime.schemas import (
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AgentConfig,
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AgentLLMConfig,
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AgentToolConfig,
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AgentMemoryConfig,
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AgentBudgetConfig,
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)
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from app.models.agent import Agent
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from app.models.execution import Execution
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import asyncio
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import logging
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import time
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logger = logging.getLogger(__name__)
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def _build_agent_config_from_db(agent: Agent) -> AgentConfig:
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"""从 DB Agent 记录构建 AgentConfig。"""
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wf = agent.workflow_config or {}
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nodes = wf.get("nodes", [])
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edges = wf.get("edges", [])
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# 从工作流节点中查找 start 节点获取 system_prompt
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system_prompt = "你是一个有用的AI助手。"
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tools_include = []
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tools_exclude = []
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model_name = "gpt-4o-mini"
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provider = "openai"
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temperature = 0.7
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max_iterations = 10
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for node in nodes:
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nd = node.get("data", {}) if isinstance(node, dict) else {}
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node_type = node.get("type", "") if isinstance(node, dict) else ""
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if node_type == "start":
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system_prompt = nd.get("system_prompt", system_prompt)
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elif node_type == "agent":
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tools_include = nd.get("tools", tools_include)
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tools_exclude = nd.get("exclude_tools", tools_exclude)
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model_name = nd.get("model", model_name)
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provider = nd.get("provider", provider)
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temperature = float(nd.get("temperature", temperature))
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max_iterations = int(nd.get("max_iterations", max_iterations))
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return AgentConfig(
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name=agent.name,
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system_prompt=system_prompt,
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user_id=str(agent.user_id) if agent.user_id else None,
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llm=AgentLLMConfig(
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provider=provider,
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model=model_name,
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temperature=temperature,
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max_iterations=max_iterations,
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),
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tools=AgentToolConfig(
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include_tools=tools_include if tools_include else [],
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exclude_tools=tools_exclude if tools_exclude else [],
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),
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memory=AgentMemoryConfig(
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enabled=True,
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persist_to_db=True,
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learning_enabled=True,
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),
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budget=AgentBudgetConfig(),
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)
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@celery_app.task(bind=True)
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def execute_agent_task(self, agent_id: str, input_data: dict):
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"""执行Agent任务"""
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# TODO: 实现Agent执行逻辑
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return {"status": "pending", "agent_id": agent_id}
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"""异步执行 Agent 任务。
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由定时调度 (check_agent_schedules_task) 或手动 API 触发。
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创建 Execution 记录，运行 Agent，更新结果。
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Args:
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agent_id: Agent ID
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input_data: 输入数据，至少包含 "message" 字段
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"""
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db = SessionLocal()
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start_time = time.time()
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try:
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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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return {"status": "error", "detail": f"Agent {agent_id} 不存在"}
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user_message = input_data.get("message") or input_data.get("query") or ""
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if not user_message:
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return {"status": "error", "detail": "缺少 message 输入"}
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# 创建执行记录
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execution = Execution(
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agent_id=agent_id,
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input_data=input_data,
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status="running",
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)
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db.add(execution)
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db.flush()
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# 更新 Celery 任务状态
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self.update_state(
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state="PROGRESS",
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meta={
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"execution_id": str(execution.id),
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"agent_id": agent_id,
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"progress": 0,
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"status": "running",
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},
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)
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# 构建配置并执行
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config = _build_agent_config_from_db(agent)
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runtime = AgentRuntime(config)
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try:
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result = asyncio.run(runtime.run(user_message))
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execution_time = int((time.time() - start_time) * 1000)
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if result.success:
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execution.status = "completed"
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execution.output_data = {
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"content": result.content,
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"iterations_used": result.iterations_used,
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"tool_calls_made": result.tool_calls_made,
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}
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execution.execution_time = execution_time
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db.commit()
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logger.info(
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"Agent 异步执行完成: agent=%s execution=%s time=%dms",
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agent_id, execution.id, execution_time,
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)
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return {
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"status": "completed",
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"execution_id": str(execution.id),
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"content": result.content,
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"iterations_used": result.iterations_used,
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"tool_calls_made": result.tool_calls_made,
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"execution_time": execution_time,
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}
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else:
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execution.status = "failed"
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execution.error_message = result.error or "Agent 执行返回失败"
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execution.execution_time = int((time.time() - start_time) * 1000)
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db.commit()
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return {
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"status": "failed",
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"execution_id": str(execution.id),
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"error": result.error,
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}
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except Exception as run_e:
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execution_time = int((time.time() - start_time) * 1000)
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execution.status = "failed"
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execution.error_message = f"Agent 执行异常: {run_e!s}"
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execution.execution_time = execution_time
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db.commit()
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logger.error("Agent 异步执行异常: agent=%s error=%s", agent_id, run_e)
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raise
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except Exception as e:
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logger.error("execute_agent_task 失败: agent=%s error=%s", agent_id, e)
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raise
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finally:
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db.close()
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