feat: enhance agent orchestration, knowledge flow and UI refinements
This commit is contained in:
@@ -1,397 +1,354 @@
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"""
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Jarvis LangGraph Agent 主图定义
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Jarvis LangGraph Agent 主图定义 - 优化重构版
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"""
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import json
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import logging
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import re
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from typing import Literal, Union, List, Any
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from langchain_core.messages import (
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BaseMessage,
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HumanMessage,
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AIMessage,
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SystemMessage,
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ToolMessage
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)
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from langgraph.graph import StateGraph, END
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from langchain_core.messages import HumanMessage, AIMessage, SystemMessage, BaseMessage
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from app.agents.state import AgentState, AgentRole
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from app.agents.prompts import (
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MASTER_SYSTEM_PROMPT,
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PLANNER_SYSTEM_PROMPT,
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SCHEDULE_PLANNER_SYSTEM_PROMPT,
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EXECUTOR_SYSTEM_PROMPT,
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LIBRARIAN_SYSTEM_PROMPT,
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ANALYST_SYSTEM_PROMPT,
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PLANNER_SCOPE_PROMPT,
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PLANNER_STEPS_PROMPT,
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EXECUTOR_TASKS_PROMPT,
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EXECUTOR_FORUM_PROMPT,
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LIBRARIAN_RETRIEVAL_PROMPT,
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LIBRARIAN_GRAPH_PROMPT,
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ANALYST_PROGRESS_PROMPT,
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ANALYST_INSIGHTS_PROMPT,
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JSON_ACTION_FALLBACK_PROMPT,
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)
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from app.agents.tools import ALL_TOOLS, SUB_COMMANDER_TOOLSETS
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from app.agents.tools.time_reasoning import normalize_tool_time_arguments
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from app.agents.skill_registry import build_skill_context
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from app.services.llm_service import get_llm
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from langchain_openai import ChatOpenAI
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from langchain_anthropic import ChatAnthropic
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from langchain_ollama import ChatOllama
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import httpx
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from app.services.llm_service import (
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get_llm,
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create_llm_from_config,
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resolve_provider_capabilities,
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default_provider_capabilities
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)
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from app.logging_utils import summarize_llm_config
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logger = logging.getLogger("jarvis.agent")
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SUB_COMMANDER_PROMPTS = {
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"planner_scope": PLANNER_SCOPE_PROMPT,
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"planner_steps": PLANNER_STEPS_PROMPT,
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"executor_tasks": EXECUTOR_TASKS_PROMPT,
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"executor_forum": EXECUTOR_FORUM_PROMPT,
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"librarian_retrieval": LIBRARIAN_RETRIEVAL_PROMPT,
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"librarian_graph": LIBRARIAN_GRAPH_PROMPT,
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"analyst_progress": ANALYST_PROGRESS_PROMPT,
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"analyst_insights": ANALYST_INSIGHTS_PROMPT,
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}
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ROLE_SUB_COMMANDERS = {
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AgentRole.PLANNER: ["planner_scope", "planner_steps"],
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AgentRole.EXECUTOR: ["executor_tasks", "executor_forum"],
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AgentRole.LIBRARIAN: ["librarian_retrieval", "librarian_graph"],
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AgentRole.ANALYST: ["analyst_progress", "analyst_insights"],
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}
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ROLE_SKILL_CONTEXT = {
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AgentRole.PLANNER: "planner",
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AgentRole.EXECUTOR: "executor",
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AgentRole.LIBRARIAN: "librarian",
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AgentRole.ANALYST: "analyst",
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}
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def _create_llm_from_config(config: dict):
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"""根据用户模型配置创建 LLM 实例"""
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provider = config.get("provider", "openai")
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model = config.get("model", "")
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api_key = config.get("api_key", "")
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base_url = config.get("base_url", "")
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if provider == "openai" or provider == "deepseek" or provider == "custom":
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return ChatOpenAI(
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api_key=api_key,
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model=model,
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base_url=base_url or None,
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timeout=httpx.Timeout(60.0, connect=10.0),
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)
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elif provider == "claude":
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return ChatAnthropic(
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api_key=api_key,
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model=model,
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timeout=httpx.Timeout(60.0, connect=10.0),
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)
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elif provider == "ollama":
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return ChatOllama(
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base_url=base_url or "http://localhost:11434",
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model=model,
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timeout=httpx.Timeout(120.0, connect=10.0),
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)
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else:
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return ChatOpenAI(
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api_key=api_key,
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model=model,
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base_url=base_url or None,
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timeout=httpx.Timeout(60.0, connect=10.0),
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)
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# ===================== 工具辅助函数 =====================
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def _get_llm_for_state(state: AgentState):
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"""从 state 获取 LLM 实例，优先使用用户配置的模型"""
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"""获取配置好的 LLM 实例"""
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user_llm_config = state.get("user_llm_config")
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if user_llm_config:
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return _create_llm_from_config(user_llm_config)
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return get_llm()
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llm = create_llm_from_config(user_llm_config) if user_llm_config else get_llm()
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# 注入解析到的能力
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capabilities = getattr(llm, "_jarvis_provider_capabilities", None)
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if capabilities is None:
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capabilities = resolve_provider_capabilities(user_llm_config) if user_llm_config else default_provider_capabilities()
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state["provider_capabilities"] = {
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"provider": capabilities.provider,
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"supports_native_tools": capabilities.supports_native_tools,
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"preferred_tool_strategy": capabilities.preferred_tool_strategy,
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}
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return llm, capabilities
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async def _ainvoke(llm, messages: list[BaseMessage]):
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ainvoke = getattr(llm, "ainvoke", None)
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if callable(ainvoke):
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return await ainvoke(messages)
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return await llm.invoke(messages)
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def _filter_user_messages(messages: list[BaseMessage]) -> list[BaseMessage]:
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return [m for m in messages if m.type in ("human", "user")]
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async def _ainvoke_with_tools(llm, messages: list[BaseMessage], tools=None):
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toolset = tools if tools is not None else ALL_TOOLS
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bound_llm = llm.bind_tools(toolset)
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if hasattr(bound_llm, "ainvoke"):
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return await bound_llm.ainvoke(messages)
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return await bound_llm.invoke(messages)
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def _compile_graph(graph: StateGraph, callbacks: list | None = None):
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if callbacks:
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try:
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return graph.compile(callbacks=callbacks)
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except TypeError as exc:
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if "callbacks" not in str(exc):
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raise
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return graph.compile()
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def _msg_type(msg: BaseMessage) -> str:
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"""Get message type, handles both .type (new) and .role (old) attribute names."""
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return getattr(msg, "type", None) or getattr(msg, "role", "human")
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def _filter_user_messages(messages: list) -> list[BaseMessage]:
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return [m for m in messages if _msg_type(m) in ("human", "user")]
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def _normalize_user_text(text: str) -> str:
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return (text or "").strip().lower()
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def _is_simple_greeting(text: str) -> bool:
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normalized = _normalize_user_text(text)
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return normalized in {"你好", "您好", "早", "早上好", "在吗", "嗨", "hi", "hello"}
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def _is_identity_question(text: str) -> bool:
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normalized = _normalize_user_text(text)
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return normalized in {"你是谁", "你是誰"}
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def _is_capability_question(text: str) -> bool:
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normalized = _normalize_user_text(text)
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return normalized in {"你能做什么", "你可以做什么", "你会做什么"}
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def _choose_sub_commander(role: AgentRole, user_query: str) -> str:
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text = _normalize_user_text(user_query)
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if role == AgentRole.PLANNER:
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if any(keyword in text for keyword in ["步骤", "计划", "拆解", "排期", "优先级", "路线"]):
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return "planner_steps"
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return "planner_scope"
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def _get_role_tools(role: AgentRole) -> list:
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"""获取角色对应的所有可用工具集"""
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if role == AgentRole.SCHEDULE_PLANNER:
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# 合并分析和规划工具
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return list(set(SUB_COMMANDER_TOOLSETS["schedule_analysis"] + SUB_COMMANDER_TOOLSETS["schedule_planning"]))
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if role == AgentRole.EXECUTOR:
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if any(keyword in text for keyword in ["论坛", "帖子", "发帖", "指令", "discussion", "instruction"]):
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return "executor_forum"
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return "executor_tasks"
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return list(set(SUB_COMMANDER_TOOLSETS["executor_tasks"] + SUB_COMMANDER_TOOLSETS["executor_forum"]))
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if role == AgentRole.LIBRARIAN:
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if any(keyword in text for keyword in ["图谱", "关系", "构建", "沉淀", "节点", "graph"]):
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return "librarian_graph"
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return "librarian_retrieval"
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return list(set(SUB_COMMANDER_TOOLSETS["librarian_retrieval"] + SUB_COMMANDER_TOOLSETS["librarian_graph"]))
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if role == AgentRole.ANALYST:
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if any(keyword in text for keyword in ["趋势", "风险", "洞察", "建议", "机会", "insight"]):
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return "analyst_insights"
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return "analyst_progress"
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return ROLE_SUB_COMMANDERS[role][0]
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return list(set(SUB_COMMANDER_TOOLSETS["analyst_progress"] + SUB_COMMANDER_TOOLSETS["analyst_insights"]))
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return []
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def _record_sub_commander(state: AgentState, sub_commander: str, user_query: str):
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state["current_sub_commander"] = sub_commander
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state["active_sub_commanders"] = state.get("active_sub_commanders", []) + [sub_commander]
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state["sub_commander_trace"] = state.get("sub_commander_trace", []) + [{
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"agent": state.get("current_agent", AgentRole.MASTER).value,
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"sub_commander": sub_commander,
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"query": user_query,
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}]
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# ===================== 核心执行逻辑 (ReAct) =====================
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def _build_system_messages(state: AgentState, system_prompt: str, role: AgentRole):
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system_msgs: list[BaseMessage] = [SystemMessage(content=system_prompt)]
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skill_ctx = build_skill_context(ROLE_SKILL_CONTEXT[role])
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async def call_agent_llm(state: AgentState, role: AgentRole, system_prompt: str) -> dict:
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"""通用的 LLM 调用节点逻辑"""
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llm, capabilities = _get_llm_for_state(state)
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tools = _get_role_tools(role)
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# 构建消息序列
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messages = []
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# 1. 系统提示词
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messages.append(SystemMessage(content=system_prompt))
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# 2. 环境上下文 (时间、记忆等)
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if state.get("current_datetime_context"):
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messages.append(SystemMessage(content=f"当前时间上下文: {state['current_datetime_context']}"))
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if state.get("memory_context"):
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messages.append(SystemMessage(content=f"长期记忆上下文: {state['memory_context']}"))
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# 3. 技能增强
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role_skill_key = role.value.replace("agent_", "")
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skill_ctx = build_skill_context(role_skill_key)
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if skill_ctx:
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system_msgs.append(SystemMessage(content=skill_ctx))
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return system_msgs
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messages.append(SystemMessage(content=skill_ctx))
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# 4. 历史对话 (add_messages 已经处理好了)
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messages.extend(state["messages"])
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# 绑定工具
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if tools and capabilities.supports_native_tools:
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llm_with_tools = llm.bind_tools(tools)
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else:
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llm_with_tools = llm
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if tools: # 如果有工具但不支持原生，注入 JSON Fallback 提示
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messages.append(SystemMessage(content=JSON_ACTION_FALLBACK_PROMPT))
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tool_names = [t.name for t in tools]
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messages.append(SystemMessage(content=f"本次可用工具列表: {', '.join(tool_names)}"))
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logger.info(
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f"agent_node_started",
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extra={
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"details": {
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"role": role.value,
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"message_count": len(messages),
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"tool_count": len(tools),
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"provider": capabilities.provider
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}
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}
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)
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# 执行调用
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response = await llm_with_tools.ainvoke(messages)
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logger.info(
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f"agent_node_finished",
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extra={
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"details": {
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"role": role.value,
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"has_tool_calls": bool(getattr(response, "tool_calls", None)),
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"content_length": len(response.content) if response.content else 0
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}
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}
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)
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return {"messages": [response]}
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async def _run_sub_commander(
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state: AgentState,
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role: AgentRole,
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manager_prompt: str,
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user_query: str,
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*,
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use_tools: bool,
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summary_target: str | None = None,
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):
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llm = _get_llm_for_state(state)
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sub_commander = _choose_sub_commander(role, user_query)
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_record_sub_commander(state, sub_commander, user_query)
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toolset = SUB_COMMANDER_TOOLSETS.get(sub_commander, [])
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system_msgs = _build_system_messages(state, manager_prompt, role)
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system_msgs.append(SystemMessage(content=f"本次应由子指挥官 `{sub_commander}` 接手。请严格按该角色职责输出。"))
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system_msgs.append(SystemMessage(content=SUB_COMMANDER_PROMPTS[sub_commander]))
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if use_tools and toolset:
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response = await _ainvoke_with_tools(
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llm,
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system_msgs + [HumanMessage(content=f"用户请求: {user_query}")],
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toolset,
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async def execute_tools_node(state: AgentState) -> dict:
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"""执行工具调用并返回 ToolMessage 的通用节点"""
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last_message = state["messages"][-1]
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if not hasattr(last_message, "tool_calls") or not last_message.tool_calls:
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return {"messages": []}
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tool_map = {t.name: t for t in ALL_TOOLS}
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tool_messages = []
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created_entities = []
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for tool_call in last_message.tool_calls:
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tool_name = tool_call["name"]
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tool_args = tool_call["args"]
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tool_id = tool_call.get("id")
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logger.info(
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f"tool_execution_started",
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extra={
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"details": {
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"tool_name": tool_name,
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"tool_args": tool_args,
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"tool_id": tool_id
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}
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}
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)
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tool_calls = getattr(response, "tool_calls", None) or []
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if tool_calls:
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results = []
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for tc in tool_calls:
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tool_name = tc.get("name")
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args = tc.get("args", {})
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for tool in toolset:
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if tool.name == tool_name:
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try:
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result = tool.invoke(args)
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results.append(f"[{tool_name}] {result}")
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except Exception as e:
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results.append(f"[{tool_name}] 执行失败: {e}")
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break
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state["tool_calls"] = tool_calls
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state["last_tool_result"] = "\n".join(results)
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follow_up = await _ainvoke(
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llm,
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[
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SystemMessage(content=SUB_COMMANDER_PROMPTS[sub_commander]),
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HumanMessage(content=f"工具执行结果:\n{state['last_tool_result']}")
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]
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try:
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# 时间参数归一化
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normalized_args = normalize_tool_time_arguments(
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tool_name,
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tool_args,
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state.get("current_datetime_context")
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)
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state["final_response"] = follow_up.content
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else:
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state["final_response"] = response.content
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else:
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response = await _ainvoke(
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llm,
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system_msgs + [HumanMessage(content=f"用户请求: {user_query}")],
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)
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state["final_response"] = response.content
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if summary_target:
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state[summary_target] = state.get("final_response", "")
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state["should_respond"] = True
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return state
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# ===================== 节点定义 (async) =====================
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async def master_node(state: AgentState) -> AgentState:
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"""主Agent节点: 理解用户意图，决定调用哪个子Agent"""
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messages: list[BaseMessage] = state["messages"]
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user_msgs = _filter_user_messages(messages)
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user_query = user_msgs[-1].content.strip() if user_msgs else ""
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if _is_simple_greeting(user_query):
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state["final_response"] = "您好。我在。\n\n您把问题给我，我先帮您收束重点，再往下推。"
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state["should_respond"] = True
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return state
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if _is_identity_question(user_query):
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state["final_response"] = "我是 Jarvis。\n\n比起做一个泛泛的助手，我更像您的判断型协作伙伴：帮您看清问题、压缩路径、把事情往前推进。"
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state["should_respond"] = True
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return state
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if _is_capability_question(user_query):
|
||||
state["final_response"] = "主要做三件事。\n- 帮您判断：看问题本质、梳理取舍、给出方向\n- 帮您收束：把复杂内容理顺，把重点拎出来\n- 帮您推进：拆任务、定步骤、把下一步变清楚\n\n如果您现在有具体目标，我可以直接进入处理。"
|
||||
state["should_respond"] = True
|
||||
return state
|
||||
|
||||
llm = _get_llm_for_state(state)
|
||||
system_msgs: list[BaseMessage] = [SystemMessage(content=MASTER_SYSTEM_PROMPT)]
|
||||
|
||||
memory_ctx = state.get("memory_context")
|
||||
if memory_ctx:
|
||||
system_msgs.append(
|
||||
SystemMessage(content=f"\n\n【记忆上下文】\n{memory_ctx}\n\n---\n")
|
||||
|
||||
tool = tool_map.get(tool_name)
|
||||
if not tool:
|
||||
result = f"Error: Tool {tool_name} not found."
|
||||
else:
|
||||
result = await tool.ainvoke(normalized_args) if hasattr(tool, "ainvoke") else tool.invoke(normalized_args)
|
||||
|
||||
# 实体识别（用于业务追踪）
|
||||
if any(k in tool_name for k in ["create", "add", "new"]):
|
||||
created_entities.append({"tool": tool_name, "result": str(result)})
|
||||
|
||||
status = "success"
|
||||
except Exception as e:
|
||||
logger.exception(f"tool_execution_failed: {tool_name}")
|
||||
result = f"Error executing tool {tool_name}: {str(e)}"
|
||||
status = "failed"
|
||||
|
||||
tool_messages.append(ToolMessage(
|
||||
tool_call_id=tool_id,
|
||||
content=str(result),
|
||||
name=tool_name
|
||||
))
|
||||
|
||||
logger.info(
|
||||
f"tool_execution_finished",
|
||||
extra={
|
||||
"details": {
|
||||
"tool_name": tool_name,
|
||||
"status": status,
|
||||
"result_preview": str(result)[:200]
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
response: AIMessage = await _ainvoke(llm, system_msgs + messages)
|
||||
return {
|
||||
"messages": tool_messages,
|
||||
"created_entities": state.get("created_entities", []) + created_entities
|
||||
}
|
||||
|
||||
|
||||
# ===================== 各角色节点定义 =====================
|
||||
|
||||
async def master_node(state: AgentState) -> dict:
|
||||
"""主控节点：负责意图识别与初步分发"""
|
||||
user_messages = _filter_user_messages(state["messages"])
|
||||
if not user_messages:
|
||||
return {"final_response": "未收到有效输入。"}
|
||||
|
||||
query = user_messages[-1].content.strip()
|
||||
|
||||
# 快捷回复逻辑 (保留原有的人性化设计)
|
||||
if re.match(r"^(你好|早|在吗|嗨|hi|hello)", query.lower()):
|
||||
return {"final_response": "您好。我在。\n\n您把问题给我，我先帮您收束重点，再往下推。", "messages": [AIMessage(content="您好。我在。")]}
|
||||
|
||||
llm, capabilities = _get_llm_for_state(state)
|
||||
|
||||
# 路由判断：让 LLM 决定跳转到哪个角色，或者直接回答
|
||||
# 这里我们使用一个简洁的提示词让 LLM 输出角色名称或直接回答
|
||||
system_msg = SystemMessage(content=MASTER_SYSTEM_PROMPT + "\n\n请直接输出接下来该由哪个 Agent 接手(role_name)，如果直接回答，请正常输出。")
|
||||
|
||||
response = await llm.ainvoke([system_msg] + state["messages"])
|
||||
content = response.content.strip().lower()
|
||||
|
||||
if any(kw in content for kw in ["搜索", "查找", "知识", "检索"]):
|
||||
next_agent = AgentRole.LIBRARIAN
|
||||
elif any(kw in content for kw in ["计划", "安排", "拆解", "规划"]):
|
||||
next_agent = AgentRole.PLANNER
|
||||
elif any(kw in content for kw in ["执行", "做", "操作", "创建", "更新"]):
|
||||
next_agent = AgentRole.EXECUTOR
|
||||
elif any(kw in content for kw in ["分析", "报告", "统计", "总结"]):
|
||||
next_agent = AgentRole.ANALYST
|
||||
else:
|
||||
state["final_response"] = response.content
|
||||
state["should_respond"] = True
|
||||
return state
|
||||
|
||||
state["current_agent"] = next_agent
|
||||
state["current_sub_commander"] = None
|
||||
state["active_agents"] = state.get("active_agents", [AgentRole.MASTER]) + [next_agent]
|
||||
state["should_respond"] = True
|
||||
return state
|
||||
|
||||
# 简单的角色映射识别
|
||||
roles = {r.value: r for r in AgentRole}
|
||||
target_role = None
|
||||
for r_val, r_enum in roles.items():
|
||||
if r_val in content and len(content) < 50: # 如果内容很短且包含角色名，视为路由
|
||||
target_role = r_enum
|
||||
break
|
||||
|
||||
if target_role and target_role != AgentRole.MASTER:
|
||||
logger.info(f"master_routing_decided: {target_role.value}")
|
||||
return {
|
||||
"current_agent": target_role.value,
|
||||
"agent_trace": state.get("agent_trace", []) + [target_role.value],
|
||||
"messages": [AIMessage(content=f"已分发至 {target_role.value} 处理。")]
|
||||
}
|
||||
|
||||
return {"final_response": response.content, "messages": [response]}
|
||||
|
||||
|
||||
async def planner_node(state: AgentState) -> AgentState:
|
||||
"""规划Agent节点: 制定计划，拆解任务步骤"""
|
||||
user_msgs = _filter_user_messages(state["messages"])
|
||||
user_query = user_msgs[-1].content if user_msgs else ""
|
||||
return await _run_sub_commander(state, AgentRole.PLANNER, PLANNER_SYSTEM_PROMPT, user_query, use_tools=False)
|
||||
async def planner_node(state: AgentState) -> dict:
|
||||
return await call_agent_llm(state, AgentRole.SCHEDULE_PLANNER, SCHEDULE_PLANNER_SYSTEM_PROMPT)
|
||||
|
||||
async def executor_node(state: AgentState) -> dict:
|
||||
return await call_agent_llm(state, AgentRole.EXECUTOR, EXECUTOR_SYSTEM_PROMPT)
|
||||
|
||||
async def librarian_node(state: AgentState) -> dict:
|
||||
return await call_agent_llm(state, AgentRole.LIBRARIAN, LIBRARIAN_SYSTEM_PROMPT)
|
||||
|
||||
async def analyst_node(state: AgentState) -> dict:
|
||||
return await call_agent_llm(state, AgentRole.ANALYST, ANALYST_SYSTEM_PROMPT)
|
||||
|
||||
|
||||
async def executor_node(state: AgentState) -> AgentState:
|
||||
"""执行Agent节点: 调用工具执行具体任务"""
|
||||
user_msgs = _filter_user_messages(state["messages"])
|
||||
user_query = user_msgs[-1].content if user_msgs else ""
|
||||
return await _run_sub_commander(state, AgentRole.EXECUTOR, EXECUTOR_SYSTEM_PROMPT, user_query, use_tools=True)
|
||||
# ===================== 路由逻辑 =====================
|
||||
|
||||
def route_after_agent(state: AgentState) -> Literal["tools", "__end__"]:
|
||||
"""判断 Agent 执行后是该走工具节点还是结束"""
|
||||
last_message = state["messages"][-1]
|
||||
if hasattr(last_message, "tool_calls") and last_message.tool_calls:
|
||||
return "tools"
|
||||
return END
|
||||
|
||||
async def librarian_node(state: AgentState) -> AgentState:
|
||||
"""知识管理员节点: 管理知识库和知识图谱"""
|
||||
user_msgs = _filter_user_messages(state["messages"])
|
||||
user_query = user_msgs[-1].content if user_msgs else ""
|
||||
return await _run_sub_commander(state, AgentRole.LIBRARIAN, LIBRARIAN_SYSTEM_PROMPT, user_query, use_tools=True, summary_target="knowledge_context")
|
||||
|
||||
|
||||
async def analyst_node(state: AgentState) -> AgentState:
|
||||
"""分析师节点: 分析工作数据，生成报告"""
|
||||
user_msgs = _filter_user_messages(state["messages"])
|
||||
user_query = user_msgs[-1].content if user_msgs else ""
|
||||
return await _run_sub_commander(state, AgentRole.ANALYST, ANALYST_SYSTEM_PROMPT, user_query, use_tools=True, summary_target="analysis_report")
|
||||
|
||||
|
||||
def route_agent(state: AgentState) -> str:
|
||||
"""路由函数: 决定下一个节点"""
|
||||
def route_master(state: AgentState) -> str:
|
||||
"""主控路由逻辑"""
|
||||
if state.get("final_response"):
|
||||
return END
|
||||
return state.get("current_agent", AgentRole.MASTER).value
|
||||
return state.get("current_agent", END)
|
||||
|
||||
|
||||
# ===================== 构建图 =====================
|
||||
# ===================== 图构建 =====================
|
||||
|
||||
def create_agent_graph(callbacks: list | None = None):
|
||||
graph = StateGraph(AgentState)
|
||||
workflow = StateGraph(AgentState)
|
||||
|
||||
graph.add_node(AgentRole.MASTER.value, master_node)
|
||||
graph.add_node(AgentRole.PLANNER.value, planner_node)
|
||||
graph.add_node(AgentRole.EXECUTOR.value, executor_node)
|
||||
graph.add_node(AgentRole.LIBRARIAN.value, librarian_node)
|
||||
graph.add_node(AgentRole.ANALYST.value, analyst_node)
|
||||
# 添加节点
|
||||
workflow.add_node(AgentRole.MASTER.value, master_node)
|
||||
workflow.add_node(AgentRole.SCHEDULE_PLANNER.value, planner_node)
|
||||
workflow.add_node(AgentRole.EXECUTOR.value, executor_node)
|
||||
workflow.add_node(AgentRole.LIBRARIAN.value, librarian_node)
|
||||
workflow.add_node(AgentRole.ANALYST.value, analyst_node)
|
||||
workflow.add_node("tools", execute_tools_node)
|
||||
|
||||
graph.set_entry_point(AgentRole.MASTER.value)
|
||||
# 设置入口
|
||||
workflow.set_entry_point(AgentRole.MASTER.value)
|
||||
|
||||
graph.add_conditional_edges(
|
||||
# 主控分发逻辑
|
||||
workflow.add_conditional_edges(
|
||||
AgentRole.MASTER.value,
|
||||
route_agent,
|
||||
route_master,
|
||||
{
|
||||
AgentRole.PLANNER.value: AgentRole.PLANNER.value,
|
||||
AgentRole.SCHEDULE_PLANNER.value: AgentRole.SCHEDULE_PLANNER.value,
|
||||
AgentRole.EXECUTOR.value: AgentRole.EXECUTOR.value,
|
||||
AgentRole.LIBRARIAN.value: AgentRole.LIBRARIAN.value,
|
||||
AgentRole.ANALYST.value: AgentRole.ANALYST.value,
|
||||
END: END,
|
||||
END: END
|
||||
}
|
||||
)
|
||||
|
||||
for role in [AgentRole.PLANNER, AgentRole.EXECUTOR, AgentRole.LIBRARIAN, AgentRole.ANALYST]:
|
||||
graph.add_edge(role.value, END)
|
||||
# 各角色节点的 ReAct 循环
|
||||
for role in [AgentRole.SCHEDULE_PLANNER, AgentRole.EXECUTOR, AgentRole.LIBRARIAN, AgentRole.ANALYST]:
|
||||
workflow.add_conditional_edges(
|
||||
role.value,
|
||||
route_after_agent,
|
||||
{
|
||||
"tools": "tools",
|
||||
END: END
|
||||
}
|
||||
)
|
||||
|
||||
# 工具执行完后回到当前 Agent 角色继续处理
|
||||
workflow.add_conditional_edges(
|
||||
"tools",
|
||||
lambda s: s.get("current_agent", AgentRole.MASTER.value),
|
||||
{
|
||||
AgentRole.SCHEDULE_PLANNER.value: AgentRole.SCHEDULE_PLANNER.value,
|
||||
AgentRole.EXECUTOR.value: AgentRole.EXECUTOR.value,
|
||||
AgentRole.LIBRARIAN.value: AgentRole.LIBRARIAN.value,
|
||||
AgentRole.ANALYST.value: AgentRole.ANALYST.value,
|
||||
}
|
||||
)
|
||||
|
||||
return _compile_graph(graph, callbacks=callbacks)
|
||||
# 编译
|
||||
if callbacks:
|
||||
return workflow.compile(callbacks=callbacks)
|
||||
return workflow.compile()
|
||||
|
||||
|
||||
_agent_graph = None
|
||||
|
||||
|
||||
def get_agent_graph(callbacks: list | None = None):
|
||||
"""
|
||||
获取编译好的 Agent 图（单例缓存）。
|
||||
|
||||
Callbacks 在首次编译时固定注入，后续调用忽略 callbacks 参数。
|
||||
如需变更 Callbacks（如修改 LANGCHAIN_PROJECT），需重启服务。
|
||||
|
||||
Args:
|
||||
callbacks: 可选的额外 Callbacks，会与全局 LangSmith Callbacks 合并
|
||||
"""
|
||||
global _agent_graph
|
||||
if _agent_graph is None:
|
||||
from app.config_tracing import get_langsmith_callbacks
|
||||
|
||||
Reference in New Issue
Block a user