139 lines
4.9 KiB
Python
139 lines
4.9 KiB
Python
"""
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路由与初始化模块
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包含状态初始化节点和条件路由函数
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三层统一循环防护：
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1. 全局步数硬上限（reasoning_step > max_steps）
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2. 路由模式检测（A→B→A→B 交替循环）
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3. 状态停滞检测（连续相同动作）
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"""
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from datetime import datetime
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from ...core.intent import get_route_by_reasoning, ReasoningAction
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from ...main_graph.state import MainGraphState
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from ...logger import info
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# ========== 初始化状态节点 ==========
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def init_state_node(state: MainGraphState) -> MainGraphState:
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"""初始化状态节点：在流程开始时设置初始值"""
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state.current_phase = "initializing"
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state.reasoning_step = 0
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state.start_time = datetime.now().isoformat()
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if not state.user_query and state.messages:
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last_msg = state.messages[-1]
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state.user_query = getattr(last_msg, "content", str(last_msg))
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return state
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# ========== 条件路由函数 ==========
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def route_by_reasoning(state: MainGraphState) -> str:
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"""
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根据推理结果决定下一步路由，带三层统一循环防护
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核心逻辑：
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1. DIRECT_RESPONSE → 直接返回 llm_call
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2. 子图完成/已有结果 → 直接返回 llm_call
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3. 步数超限 → 直接返回 llm_call
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4. 其他 → 正常路由
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"""
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# 获取历史动作
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previous_actions = [h.get("action") for h in state.reasoning_history]
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info(f"[条件路由] step={state.reasoning_step}, phase={state.current_phase}, history={previous_actions}")
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# ========== 获取推理结果 ==========
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reasoning_result = state.debug_info.get("reasoning_result")
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latest_action = reasoning_result.action.name if reasoning_result else None
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# ========== 核心检查：DIRECT_RESPONSE 优先 ==========
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# 从 reasoning_result 检查（最新）
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if latest_action == "DIRECT_RESPONSE":
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info(f"[条件路由] 推理结果为 DIRECT_RESPONSE，直接去 llm_call")
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return "llm_call"
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# 备用：从历史记录检查
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if previous_actions and previous_actions[-1] == "DIRECT_RESPONSE":
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info(f"[条件路由] 历史记录最新动作为 DIRECT_RESPONSE，直接去 llm_call")
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return "llm_call"
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# ========== 子图完成/已有结果 ==========
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if "subgraph_completed" in previous_actions or state.final_result:
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info("[条件路由] 子图已完成或已有结果，直接终止")
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return "llm_call"
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# ========== 步数超限 ==========
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if state.reasoning_step > state.max_steps:
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info(f"[条件路由] 步数超限 ({state.reasoning_step}/{state.max_steps})，强制终止")
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return "llm_call"
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# ========== 特殊阶段快速通道 ==========
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if state.current_phase in ("max_steps_exceeded", "finalizing", "done"):
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return "llm_call"
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if state.current_phase == "error_handling" or state.current_error:
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return "handle_error"
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# ========== 无推理结果，默认终止 ==========
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if not reasoning_result:
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info("[条件路由] 无推理结果，默认去 llm_call")
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return "llm_call"
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# ========== 计算目标路由 ==========
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route = get_route_by_reasoning(reasoning_result)
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route_mapping = {
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"direct_response": "llm_call",
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"retrieve_rag": "rag_retrieve",
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"re_retrieve_rag": "rag_retrieve",
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"web_search": "web_search",
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"clarify": "llm_call",
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"call_tool": "llm_call",
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"contact": "contact_subgraph",
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"dictionary": "dictionary_subgraph",
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"news_analysis": "news_analysis_subgraph",
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}
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target = route_mapping.get(route, "llm_call")
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# ========== 循环防护检测 ==========
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# 1. 路由模式检测（A→B→A→B 交替）
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if len(previous_actions) >= 4:
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if (previous_actions[-4] == previous_actions[-2]
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and previous_actions[-3] == previous_actions[-1]
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and previous_actions[-2] != previous_actions[-1]):
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info(f"[条件路由] 检测到路由循环: {previous_actions[-4:]}，强制终止")
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return "llm_call"
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# 2. 状态停滞检测（连续相同动作）
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if len(previous_actions) >= 2 and previous_actions[-1] == previous_actions[-2]:
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info(f"[条件路由] 连续相同动作 '{previous_actions[-1]}'，强制终止")
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return "llm_call"
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# ========== 智能优化 ==========
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if target == "rag_retrieve" and (state.rag_docs or state.rag_context):
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info("[条件路由] RAG 结果已存在，跳过检索")
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return "llm_call"
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info(f"[条件路由] 动作={latest_action}, 目标={target}")
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return target
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# ========== 完成阶段条件路由函数 ==========
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def should_summarize(state: MainGraphState) -> str:
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"""
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检查是否需要总结对话（对话足够长时）
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Args:
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state: 当前图状态
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Returns:
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"summarize" 或 "finalize"
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"""
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if state.turns_since_last_summary >= 5: # 每5轮对话总结一次
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return "summarize"
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else:
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return "finalize"
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