DESKTOP-72TV0V4\caoxiaozhu
0d89325b09
Add sub-commander orchestration updates, align frontend integrations, and refine knowledge view behavior without including local data artifacts. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
402 lines
15 KiB
Python
402 lines
15 KiB
Python
"""
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Jarvis LangGraph Agent 主图定义
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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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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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)
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from app.agents.tools import ALL_TOOLS, SUB_COMMANDER_TOOLSETS
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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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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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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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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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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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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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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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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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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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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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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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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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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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)
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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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)
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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):
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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。"
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state["should_respond"] = True
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return state
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llm = _get_llm_for_state(state)
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system_msgs: list[BaseMessage] = [SystemMessage(content=MASTER_SYSTEM_PROMPT)]
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memory_ctx = state.get("memory_context")
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if memory_ctx:
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system_msgs.append(
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SystemMessage(content=f"\n\n【记忆上下文】\n{memory_ctx}\n\n---\n")
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)
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response: AIMessage = await _ainvoke(llm, system_msgs + messages)
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content = response.content.strip().lower()
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if any(kw in content for kw in ["搜索", "查找", "知识", "检索"]):
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next_agent = AgentRole.LIBRARIAN
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elif any(kw in content for kw in ["计划", "安排", "拆解", "规划"]):
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next_agent = AgentRole.PLANNER
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elif any(kw in content for kw in ["执行", "做", "操作", "创建", "更新"]):
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next_agent = AgentRole.EXECUTOR
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elif any(kw in content for kw in ["分析", "报告", "统计", "总结"]):
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next_agent = AgentRole.ANALYST
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else:
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state["final_response"] = response.content
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state["should_respond"] = True
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return state
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state["current_agent"] = next_agent
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state["current_sub_commander"] = None
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state["active_agents"] = state.get("active_agents", [AgentRole.MASTER]) + [next_agent]
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state["should_respond"] = True
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return state
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async def planner_node(state: AgentState) -> AgentState:
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"""规划Agent节点: 制定计划，拆解任务步骤"""
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user_msgs = _filter_user_messages(state["messages"])
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user_query = user_msgs[-1].content if user_msgs else ""
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return await _run_sub_commander(state, AgentRole.PLANNER, PLANNER_SYSTEM_PROMPT, user_query, use_tools=False)
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async def executor_node(state: AgentState) -> AgentState:
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"""执行Agent节点: 调用工具执行具体任务"""
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user_msgs = _filter_user_messages(state["messages"])
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user_query = user_msgs[-1].content if user_msgs else ""
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return await _run_sub_commander(state, AgentRole.EXECUTOR, EXECUTOR_SYSTEM_PROMPT, user_query, use_tools=True)
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async def librarian_node(state: AgentState) -> AgentState:
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"""知识管理员节点: 管理知识库和知识图谱"""
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user_msgs = _filter_user_messages(state["messages"])
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user_query = user_msgs[-1].content if user_msgs else ""
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return await _run_sub_commander(state, AgentRole.LIBRARIAN, LIBRARIAN_SYSTEM_PROMPT, user_query, use_tools=True, summary_target="knowledge_context")
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async def analyst_node(state: AgentState) -> AgentState:
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"""分析师节点: 分析工作数据，生成报告"""
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user_msgs = _filter_user_messages(state["messages"])
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user_query = user_msgs[-1].content if user_msgs else ""
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return await _run_sub_commander(state, AgentRole.ANALYST, ANALYST_SYSTEM_PROMPT, user_query, use_tools=True, summary_target="analysis_report")
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def route_agent(state: AgentState) -> str:
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"""路由函数: 决定下一个节点"""
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if state.get("final_response"):
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return END
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return state.get("current_agent", AgentRole.MASTER).value
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# ===================== 构建图 =====================
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def create_agent_graph(callbacks: list | None = None):
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graph = StateGraph(AgentState)
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graph.add_node(AgentRole.MASTER.value, master_node)
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graph.add_node(AgentRole.PLANNER.value, planner_node)
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graph.add_node(AgentRole.EXECUTOR.value, executor_node)
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graph.add_node(AgentRole.LIBRARIAN.value, librarian_node)
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graph.add_node(AgentRole.ANALYST.value, analyst_node)
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graph.set_entry_point(AgentRole.MASTER.value)
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graph.add_conditional_edges(
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AgentRole.MASTER.value,
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route_agent,
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{
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AgentRole.PLANNER.value: AgentRole.PLANNER.value,
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AgentRole.EXECUTOR.value: AgentRole.EXECUTOR.value,
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AgentRole.LIBRARIAN.value: AgentRole.LIBRARIAN.value,
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AgentRole.ANALYST.value: AgentRole.ANALYST.value,
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END: END,
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}
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)
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for role in [AgentRole.PLANNER, AgentRole.EXECUTOR, AgentRole.LIBRARIAN, AgentRole.ANALYST]:
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graph.add_edge(role.value, END)
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return _compile_graph(graph, callbacks=callbacks)
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_agent_graph = None
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def get_agent_graph(callbacks: list | None = None):
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"""
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获取编译好的 Agent 图（单例缓存）。
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Callbacks 在首次编译时固定注入，后续调用忽略 callbacks 参数。
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如需变更 Callbacks（如修改 LANGCHAIN_PROJECT），需重启服务。
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Args:
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callbacks: 可选的额外 Callbacks，会与全局 LangSmith Callbacks 合并
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"""
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global _agent_graph
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if _agent_graph is None:
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from app.config_tracing import get_langsmith_callbacks
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langsmith_callbacks = get_langsmith_callbacks()
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all_callbacks = (callbacks or []) + langsmith_callbacks
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_agent_graph = create_agent_graph(callbacks=all_callbacks or None)
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return _agent_graph
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