refactor: 整理文件夹结构，修复 create_serde 导入问题

- 移动 main_graph/tools/ 到 deprecated/main_graph_tools/（旧架构工具）
- 移动 rag_initializer.py 和 retry_utils.py 到 core/
- 清理 main_graph/nodes/ 里的旧节点到 deprecated/
- 修复 backend.py 中 create_serde 导入问题

backend/app/deprecated/routing.py

@@ -0,0 +1,220 @@
+"""
+路由与初始化模块
+包含状态初始化节点和条件路由函数
+
+三层统一循环防护：
+1. 全局步数硬上限（reasoning_step > max_steps）
+2. 路由模式检测（A→B→A→B 交替循环）
+3. 状态停滞检测（连续相同动作）
+"""
+
+from datetime import datetime
+
+from backend.app.core.intent import get_route_by_reasoning, ReasoningAction
+from ...main_graph.state import (
+    MainGraphState,
+    CurrentAction,
+    ReactReasoningState,
+    HybridRouterState,
+    FastPathState
+)
+from backend.app.logger import info
+
+
+# ========== 初始化状态节点 ==========
+def init_state_node(state: MainGraphState) -> MainGraphState:
+    """
+    初始化状态节点：在流程开始时设置初始值
+    
+    重置策略：
+    - 持久化字段（如 messages、turns_since_last_summary）不重置
+    - 临时字段（如 rag_context、final_result）重置为初始值
+    """
+    # 持久化字段保留原样
+    # - messages
+    # - turns_since_last_summary
+    # - user_id
+    
+    # ========== 重置临时字段 ==========
+    
+    # 主图控制字段
+    state.user_query = ""
+    state.current_action = CurrentAction.NONE
+    state.current_model = ""
+    state.intent_confidence = 0.0
+    
+    # React 推理专用字段
+    state.reasoning_step = 0
+    state.last_action = ""
+    state.reasoning_history = []
+    
+    # RAG 相关字段
+    state.rag_context = ""
+    state.rag_retrieved = False
+    state.rag_docs = []
+    state.rag_confidence = 0.0
+    state.rag_attempts = 0
+    
+    # 联网搜索相关字段
+    state.web_search_results = []
+    
+    # 错误处理字段
+    state.errors = []
+    state.current_error = None
+    state.retry_action = None
+    state.error_message = ""
+    
+    # 子图结果字段
+    state.news_result = None
+    state.dictionary_result = None
+    state.contact_result = None
+    
+    # 执行状态
+    state.current_phase = "initializing"
+    state.final_result = ""
+    state.success = False
+    
+    # 元数据
+    state.start_time = None
+    state.end_time = None
+    
+    # 结构化状态
+    state.react_reasoning = ReactReasoningState()
+    state.hybrid_router = HybridRouterState()
+    state.fast_path = FastPathState()
+    
+    # 统计字段
+    state.llm_calls = 0
+    state.last_token_usage = {}
+    state.last_elapsed_time = 0.0
+    state.memory_context = ""
+    
+    # 向后兼容字段
+    state.debug_info = {}
+    
+    # 设置初始值
+    state.current_phase = "initializing"
+    state.reasoning_step = 0
+    state.start_time = datetime.now().isoformat()
+    
+    # 从 messages 中提取 user_query（如果没有的话）
+    if not state.user_query and state.messages:
+        last_msg = state.messages[-1]
+        state.user_query = getattr(last_msg, "content", str(last_msg))
+    
+    return state
+
+
+# ========== 条件路由函数 ==========
+def route_by_reasoning(state: MainGraphState) -> str:
+    """
+    根据推理结果决定下一步路由，带三层统一循环防护
+
+    核心逻辑：
+    1. DIRECT_RESPONSE → 直接返回 llm_call
+    2. 子图完成/已有结果 → 直接返回 llm_call
+    3. 步数超限 → 直接返回 llm_call
+    4. 其他 → 正常路由
+    """
+    # 获取历史动作
+    previous_actions = [h.get("action") for h in state.reasoning_history]
+
+    info(f"[条件路由] step={state.reasoning_step}, phase={state.current_phase}, history={previous_actions}")
+
+    # ========== 获取推理结果 - 从新的结构化字段获取 ==========
+    reasoning_result = state.react_reasoning.reasoning_result
+    latest_action = reasoning_result.action.name if reasoning_result else None
+
+    # ========== 核心检查：DIRECT_RESPONSE 优先 ==========
+    # 从 reasoning_result 检查（最新）
+    if latest_action == "DIRECT_RESPONSE":
+        info(f"[条件路由] 推理结果为 DIRECT_RESPONSE，直接去 llm_call")
+        return "llm_call"
+
+    # 备用：从历史记录检查
+    if previous_actions and previous_actions[-1] == "DIRECT_RESPONSE":
+        info(f"[条件路由] 历史记录最新动作为 DIRECT_RESPONSE，直接去 llm_call")
+        return "llm_call"
+
+    # ========== 子图完成/已有结果 ==========
+    if "subgraph_completed" in previous_actions or state.final_result:
+        info("[条件路由] 子图已完成或已有结果，直接终止")
+        return "llm_call"
+
+    # ========== 步数超限 ==========
+    if state.reasoning_step > state.max_steps:
+        info(f"[条件路由] 步数超限 ({state.reasoning_step}/{state.max_steps})，强制终止")
+        return "llm_call"
+
+    # ========== 特殊阶段快速通道 ==========
+    if state.current_phase in ("max_steps_exceeded", "finalizing", "done"):
+        return "llm_call"
+    if state.current_phase == "error_handling" or state.current_error:
+        return "handle_error"
+
+    # ========== 无推理结果，默认终止 ==========
+    if not reasoning_result:
+        info("[条件路由] 无推理结果，默认去 llm_call")
+        return "llm_call"
+
+    # ========== 计算目标路由 ==========
+    route = get_route_by_reasoning(reasoning_result)
+
+    route_mapping = {
+        "direct_response": "llm_call",
+        "retrieve_rag": "rag_retrieve",
+        "re_retrieve_rag": "rag_retrieve",
+        "web_search": "web_search",
+        "clarify": "llm_call",
+        "call_tool": "llm_call",
+        "contact": "contact_subgraph",
+        "dictionary": "dictionary_subgraph",
+        "news_analysis": "news_analysis_subgraph",
+    }
+    target = route_mapping.get(route, "llm_call")
+
+    # ========== RAG 次数硬限制 ==========
+    rag_attempts = getattr(state, 'rag_attempts', 0)
+    if target == "rag_retrieve" and rag_attempts >= 2:
+        info(f"[条件路由] RAG已尝试{rag_attempts}次，强制走联网搜索")
+        target = "web_search"
+
+    # ========== 循环防护检测 ==========
+    # 1. 路由模式检测（A→B→A→B 交替）
+    if len(previous_actions) >= 4:
+        if (previous_actions[-4] == previous_actions[-2]
+                and previous_actions[-3] == previous_actions[-1]
+                and previous_actions[-2] != previous_actions[-1]):
+            info(f"[条件路由] 检测到路由循环: {previous_actions[-4:]}，强制终止")
+            return "llm_call"
+
+    # 2. 状态停滞检测（连续相同动作 TODO：本来应该是2）
+    if len(previous_actions) >= 3 and previous_actions[-1] == previous_actions[-2] and previous_actions[-2] == previous_actions[-3]:
+        info(f"[条件路由] 连续相同动作 '{previous_actions[-1]}'，强制终止")
+        return "llm_call"
+
+    # ========== 智能优化 ==========
+    if target == "rag_retrieve" and (state.rag_docs or state.rag_context):
+        info("[条件路由] RAG 结果已存在，跳过检索")
+        return "llm_call"
+
+    info(f"[条件路由] 动作={latest_action}, 目标={target}")
+    return target
+
+
+# ========== 完成阶段条件路由函数 ==========
+
+def should_summarize(state: MainGraphState) -> str:
+    """
+    检查是否需要总结对话（对话足够长时）
+
+    Args:
+        state: 当前图状态
+
+    Returns:
+        "summarize" 或 "finalize"
+    """
+    if state.turns_since_last_summary >= 5:  # 每5轮对话总结一次
+        return "summarize"
+    else:
+        return "finalize"