eabf90c496
- Add AI学习助手 agent creation script with all 39 tools, 3-layer KG+RAG memory - Add renshenguo (人参果) feishu bot integration (app_service + ws_handler) - Register renshenguo WS client in main.py startup - Add RENSHENGUO_APP_ID / RENSHENGUO_APP_SECRET / RENSHENGUO_AGENT_ID config - Reorganize docs from root into docs/ subdirectories - Move startup scripts to scripts/startup/ - Various backend optimizations and tool improvements Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
308 lines
11 KiB
Python
308 lines
11 KiB
Python
"""
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Agent Runtime ⇄ WorkflowEngine 桥接。
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让 workflow_engine.execute_node() 通过寥寥几行调用 Agent Runtime。
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"""
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from __future__ import annotations
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import logging
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from typing import Any, Dict, Optional
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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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AgentBudgetConfig,
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AgentMemoryConfig,
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)
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logger = logging.getLogger(__name__)
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async def run_agent_node(
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node_data: Dict[str, Any],
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input_data: Dict[str, Any],
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execution_logger: Optional[Any] = None,
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user_id: Optional[str] = None,
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on_tool_executed: Optional[Any] = None,
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on_llm_invocation: Optional[Any] = None,
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budget_limits: Optional[Dict[str, int]] = None,
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) -> Dict[str, Any]:
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"""
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在工作流中执行 Agent 节点。
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node_data 支持的字段：
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system_prompt — Agent 人格/指令（支持 {{variable}} 模板）
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tools — 可选工具白名单，默认全部
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exclude_tools — 可选工具黑名单
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model — 模型名称
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provider — 提供商（openai/deepseek）
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temperature — 温度
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max_iterations — ReAct 最大步数
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memory — 是否启用长期记忆
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input_data 中的 "query" 或 "input" 字段作为用户输入。
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"""
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# 1. 解析配置
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query = (
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input_data.get("query")
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or input_data.get("input")
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or input_data.get("text", "")
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)
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if not isinstance(query, str):
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query = str(query) if query else ""
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if not query:
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return {"output": "错误：Agent 节点未收到用户输入", "status": "error"}
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# 2. 解析 system_prompt（支持模板变量）
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raw_prompt = node_data.get("system_prompt", "你是一个有用的AI助手。")
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try:
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formatted_prompt = raw_prompt.format(**input_data)
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except (KeyError, ValueError):
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formatted_prompt = raw_prompt
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# 3. 构建 Agent 配置
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llm_config = AgentLLMConfig(
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provider=node_data.get("provider", "openai"),
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model=node_data.get("model", "gpt-4o-mini"),
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temperature=float(node_data.get("temperature", 0.7)),
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max_iterations=int(node_data.get("max_iterations", 10)),
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)
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# 允许节点内联 api_key/base_url
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if node_data.get("api_key"):
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llm_config.api_key = node_data["api_key"]
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if node_data.get("base_url"):
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llm_config.base_url = node_data["base_url"]
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# 3a. 构建预算配置（接收工作流级预算限制）
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budget = AgentBudgetConfig()
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if budget_limits:
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if "max_llm_invocations" in budget_limits:
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budget.max_llm_invocations = max(1, int(budget_limits["max_llm_invocations"]))
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if "max_tool_calls" in budget_limits:
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budget.max_tool_calls = max(1, int(budget_limits["max_tool_calls"]))
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# 构建记忆配置（从 node_data 读取完整字段，兼容简化配置）
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mem_enabled = bool(node_data.get("memory", True))
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memory_config = AgentMemoryConfig(
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enabled=mem_enabled,
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max_history_messages=int(node_data.get("memory_max_history", 20)),
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persist_to_db=bool(node_data.get("memory_persist", mem_enabled)),
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vector_memory_enabled=bool(node_data.get("memory_vector_enabled", True)),
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vector_memory_top_k=int(node_data.get("memory_vector_top_k", 5)),
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learning_enabled=bool(node_data.get("memory_learning", True)),
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)
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# 构建工具审批配置
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tool_config = AgentToolConfig(
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include_tools=node_data.get("tools") or [],
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exclude_tools=node_data.get("exclude_tools") or [],
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require_approval=node_data.get("require_approval") or [],
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approval_timeout_ms=int(node_data.get("approval_timeout_ms", 60000)),
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approval_default=node_data.get("approval_default", "deny"),
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)
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agent_config = AgentConfig(
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name=node_data.get("label", "agent_node"),
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system_prompt=formatted_prompt,
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llm=llm_config,
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tools=tool_config,
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memory=memory_config,
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budget=budget,
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user_id=user_id,
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memory_scope_id=node_data.get("memory_scope_id") or node_data.get("agent_id", ""),
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self_review_enabled=node_data.get("self_review_enabled", False),
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)
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# 4. 执行 Agent
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runtime = AgentRuntime(
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config=agent_config,
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execution_logger=execution_logger,
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on_tool_executed=on_tool_executed,
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)
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# 注入 LLM 预算回调（使 Agent 内部 LLM 调用计入工作流预算）
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if on_llm_invocation:
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runtime.on_llm_invocation = on_llm_invocation
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result = await runtime.run(query)
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# 5. 返回结果（兼容工作流引擎的输出格式）
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if result.success:
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return {
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"output": result.content,
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"status": "success",
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"agent_meta": {
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"iterations": result.iterations_used,
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"tool_calls": result.tool_calls_made,
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"truncated": result.truncated,
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},
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}
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else:
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return {
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"output": result.content,
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"status": "error",
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"error": result.error,
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}
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async def run_orchestrator_node(
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node_data: Dict[str, Any],
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input_data: Dict[str, Any],
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execution_logger: Optional[Any] = None,
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user_id: Optional[str] = None,
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on_tool_executed: Optional[Any] = None,
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on_llm_invocation: Optional[Any] = None,
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) -> Dict[str, Any]:
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"""
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在工作流中执行多 Agent 编排节点。
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node_data 支持的字段：
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mode — "route" | "sequential" | "debate" | "pipeline"
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agents — Agent ID 列表（必填，至少 2 个）
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routing_prompt — route 模式的路由指令（可选）
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aggregation_prompt— debate 模式的汇总指令（可选）
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model — 覆盖各 Agent 的模型（可选）
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provider — 覆盖各 Agent 的提供商（可选）
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temperature — 覆盖各 Agent 的温度（可选）
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max_iterations — 覆盖各 Agent 的最大步数（可选）
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input_data 中的 "query" 或 "input" 字段作为用户输入。
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"""
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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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from app.agent_runtime.orchestrator import (
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AgentOrchestrator,
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OrchestratorAgentConfig,
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)
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# 1. 解析输入
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query = (
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input_data.get("query")
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or input_data.get("input")
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or input_data.get("text", "")
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)
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if not isinstance(query, str):
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query = str(query) if query else ""
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if not query:
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return {"output": "错误：Orchestrator 节点未收到用户输入", "status": "error"}
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# 2. 解析编排模式
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mode = node_data.get("mode", "debate").lower()
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if mode not in ("route", "sequential", "debate", "pipeline"):
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return {"output": f"错误：不支持的编排模式 '{mode}'，可选: route, sequential, debate, pipeline", "status": "error"}
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# 3. 解析 Agent 列表
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agent_ids = node_data.get("agents", [])
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if not agent_ids or not isinstance(agent_ids, list) or len(agent_ids) < 1:
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return {"output": "错误：Orchestrator 节点需要至少 1 个 Agent", "status": "error"}
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# 4. 从 DB 加载 Agent 配置
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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# 覆盖配置（可选）
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override_model = node_data.get("model")
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override_provider = node_data.get("provider")
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override_temperature = node_data.get("temperature")
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override_max_iterations = node_data.get("max_iterations")
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agent_configs: list = []
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for agent_id in agent_ids:
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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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logger.warning("Orchestrator: Agent %s 不存在，跳过", agent_id)
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continue
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# 从 workflow_config 提取 Agent 的 LLM 配置
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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 = override_model or "deepseek-v4-flash"
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provider = override_provider or "deepseek"
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temperature = float(override_temperature) if override_temperature else 0.7
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max_iterations = int(override_max_iterations) if override_max_iterations else 10
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tools_whitelist: list = []
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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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if not override_model:
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model = cfg.get("model", model)
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if not override_provider:
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provider = cfg.get("provider", provider)
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if not override_temperature:
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temperature = float(cfg.get("temperature", temperature))
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if not override_max_iterations:
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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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agent_configs.append(OrchestratorAgentConfig(
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id=agent.id,
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name=agent.name or "Agent",
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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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tools=tools_whitelist,
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description=agent.description or "",
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))
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if not agent_configs:
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return {"output": "错误：没有找到可用的 Agent", "status": "error"}
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# 5. 创建 Orchestrator 并执行
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orchestrator = AgentOrchestrator(
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default_llm_config=AgentLLMConfig(
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model=override_model or "deepseek-v4-flash",
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temperature=0.3,
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),
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)
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result = await orchestrator.run(
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mode=mode,
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question=query,
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agents=agent_configs,
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on_llm_call=on_llm_invocation,
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)
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# 6. 返回结构化结果
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return {
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"output": result.final_answer,
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"status": "success",
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"orchestrator_meta": {
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"mode": result.mode,
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"agent_count": len(agent_configs),
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"steps": [
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{
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"agent_id": s.agent_id,
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"agent_name": s.agent_name,
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"input": s.input[:200] if s.input else "",
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"output": s.output[:500] if s.output else "",
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"iterations_used": s.iterations_used,
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"tool_calls_made": s.tool_calls_made,
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"error": s.error,
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}
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for s in result.steps
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],
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},
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}
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except Exception as e:
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logger.error("Orchestrator 节点执行失败: %s", e, exc_info=True)
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return {
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"output": None,
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"status": "failed",
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"error": f"Orchestrator 执行失败: {e}",
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}
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finally:
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if db:
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db.close()
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