refactor: 将 RAG 节点拆分为独立模块

- 新增 rag_nodes.py: 独立的 RAG 检索节点
- 从 react_nodes.py 移除 RAG 相关代码
- 更新导入和导出
- rag_nodes.py 包含 rag_retrieve_node 和 rag_re_retrieve_node
- 添加 inject_rag_tool_to_state 工具函数

backend/app/graph/rag_nodes.py

@@ -0,0 +1,204 @@
+"""
+RAG 节点模块 - 独立的 RAG 检索节点
+包含：
+- rag_retrieve_node: RAG 检索节点（带超时重试）
+- rag_re_retrieve_node: 重新检索节点
+- 相关的 RAG 工具集成
+"""
+
+import time
+from typing import Dict, Any, Optional
+from datetime import datetime
+
+from .state import MainGraphState, ErrorRecord, ErrorSeverity
+from .retry_utils import (
+    RetryConfig,
+    RAG_RETRY_CONFIG,
+    create_retry_wrapper_for_node
+)
+
+# 尝试导入现有的 RAG 工具
+try:
+    from ..rag.tools import create_rag_tool_sync
+    from ..rag.pipeline import RAGPipeline
+    HAS_RAG = True
+except ImportError:
+    HAS_RAG = False
+
+
+def get_rag_tool_from_state(state: MainGraphState) -> Optional[Any]:
+    """
+    从状态中获取 RAG 工具（如果有）
+    
+    Args:
+        state: 主图状态
+    
+    Returns:
+        RAG 工具实例或 None
+    """
+    if "rag_tool" in state.debug_info:
+        return state.debug_info["rag_tool"]
+    return None
+
+
+# ========== RAG 检索核心逻辑 ==========
+def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
+    """
+    RAG 检索核心逻辑（不带重试）
+    
+    Args:
+        state: 主图状态
+    
+    Returns:
+        更新后的状态
+    """
+    # 获取检索查询（优先使用推理结果中的优化查询）
+    retrieval_query = state.user_query
+    if "reasoning_result" in state.debug_info:
+        reasoning_result = state.debug_info["reasoning_result"]
+        if hasattr(reasoning_result, "retrieval_config"):
+            cfg = reasoning_result.retrieval_config
+            if cfg and cfg.retrieval_query:
+                retrieval_query = cfg.retrieval_query
+    
+    # 尝试获取 RAG 工具
+    rag_tool = get_rag_tool_from_state(state)
+    
+    if rag_tool and HAS_RAG:
+        # 使用真实的 RAG 工具
+        try:
+            rag_context = rag_tool.invoke(retrieval_query)
+            state.rag_context = rag_context
+            state.rag_docs = [
+                {"source": "rag_doc", "content": rag_context}
+            ]
+            state.rag_retrieved = True
+            state.success = True
+            return state
+        except Exception as e:
+            raise RuntimeError(f"RAG 调用失败: {str(e)}") from e
+    else:
+        # 没有 RAG 工具，使用模拟数据（演示用）
+        state.rag_context = (
+            f"[RAG 检索结果]\n"
+            f"查询: {retrieval_query}\n"
+            f"这是来自知识库的相关信息。"
+        )
+        state.rag_docs = [
+            {"source": "doc1.txt", "content": "LangGraph 是一个用于构建 Agent 的框架"},
+            {"source": "doc2.txt", "content": "React 模式是 '思考→行动→观察' 循环"}
+        ]
+        state.rag_retrieved = True
+        state.success = True
+        return state
+
+
+# ========== RAG 检索节点（带超时和重试） ==========
+def rag_retrieve_node(state: MainGraphState) -> MainGraphState:
+    """
+    RAG 检索节点：带超时和重试
+    
+    Args:
+        state: 主图状态
+    
+    Returns:
+        更新后的状态
+    """
+    state.current_phase = "rag_retrieving"
+    
+    start_time = time.time()
+    last_error = None
+    
+    for attempt in range(RAG_RETRY_CONFIG.max_retries + 1):
+        try:
+            # 执行核心逻辑
+            result = _rag_retrieve_core(state)
+            
+            # 成功
+            state.debug_info["rag_retrieval"] = {
+                "attempt": attempt + 1,
+                "success": True,
+                "time": time.time() - start_time
+            }
+            return result
+            
+        except Exception as e:
+            last_error = e
+            
+            if attempt >= RAG_RETRY_CONFIG.max_retries:
+                break
+            
+            # 指数退避等待
+            delay = RAG_RETRY_CONFIG.base_delay * (2 ** attempt)
+            time.sleep(min(delay, RAG_RETRY_CONFIG.max_delay))
+    
+    # 所有重试都失败，记录结构化错误
+    error_record = ErrorRecord(
+        error_type="RAGRetrievalError",
+        error_message=str(last_error) if last_error else "RAG 检索超时",
+        severity=ErrorSeverity.WARNING,
+        source="rag_retrieve_node",
+        timestamp=datetime.now().isoformat(),
+        retry_count=RAG_RETRY_CONFIG.max_retries,
+        max_retries=RAG_RETRY_CONFIG.max_retries,
+        context={
+            "query": state.user_query,
+            "total_time": time.time() - start_time,
+            "timeout": RAG_RETRY_CONFIG.timeout
+        }
+    )
+    
+    state.errors.append(error_record)
+    state.current_error = error_record
+    state.current_phase = "error_handling"
+    
+    return state
+
+
+# ========== 重新检索节点 ==========
+def rag_re_retrieve_node(state: MainGraphState) -> MainGraphState:
+    """
+    重新检索节点：用于第二次检索（不同的参数）
+    
+    Args:
+        state: 主图状态
+    
+    Returns:
+        更新后的状态
+    """
+    state.current_phase = "rag_re_retrieving"
+    
+    # 可以在这里修改检索参数（例如：扩大范围、调整查询）
+    state.debug_info["rag_re_retrieve"] = {
+        "original_retrieved": state.rag_retrieved,
+        "original_docs_count": len(state.rag_docs)
+    }
+    
+    # 使用相同的检索逻辑
+    return rag_retrieve_node(state)
+
+
+# ========== 工具：将 RAG 工具注入到状态 ==========
+def inject_rag_tool_to_state(state: MainGraphState, rag_tool: Any) -> MainGraphState:
+    """
+    将 RAG 工具注入到状态中，供后续节点使用
+    
+    Args:
+        state: 主图状态
+        rag_tool: RAG 工具实例
+    
+    Returns:
+        更新后的状态
+    """
+    state.debug_info["rag_tool"] = rag_tool
+    state.debug_info["rag_tool_injected"] = datetime.now().isoformat()
+    return state
+
+
+# ========== 导出 ==========
+__all__ = [
+    "rag_retrieve_node",
+    "rag_re_retrieve_node",
+    "inject_rag_tool_to_state",
+    "get_rag_tool_from_state"
+]