重构架构：恢复统一的 llm_call 节点，移除错误的 final_response 节点

backend/app/main_graph/nodes/llm_call.py

@@ -9,54 +9,68 @@ from langchain_core.language_models import BaseLLM
 from langchain_core.messages import AIMessage
 
 # 本地模块
-from app.main_graph.state import MessagesState
+from app.main_graph.state import MainGraphState
 from app.agent.prompts import create_system_prompt
 from app.utils.logging import log_state_change
 from app.logger import debug, info, error
 
-def create_llm_call_node(llm: BaseLLM, tools: list):
+def create_llm_call_node(llm, tools: list):
     """
     工厂函数：创建 LLM 调用节点
-    
+
     Args:
         llm: LangChain LLM 实例
         tools: 工具列表
-        
+
     Returns:
         异步节点函数
     """
     # 构建调用链
     prompt = create_system_prompt(tools)
     llm_with_tools = llm.bind_tools(tools)
-    
+
     # 恢复带 RunnableLambda 的链，并在下方使用 astream 遍历
     chain = prompt  | llm_with_tools
-    
+
     from langchain_core.runnables.config import RunnableConfig
-    
-    async def call_llm(state: MessagesState, config: RunnableConfig) -> Dict[str, Any]:
+
+    async def call_llm(state: MainGraphState, config: RunnableConfig) -> Dict[str, Any]:
         """
         LLM 调用节点（异步方法）
-        
+
         Args:
             state: 当前对话状态
             config: LangChain/LangGraph 自动注入的配置，包含 callbacks 等信息
-            
+
         Returns:
             更新后的状态字典
         """
         log_state_change("llm_call", state, "进入")
-        
-        memory_context = state.get("memory_context", "暂无用户信息")
+
+        memory_context = getattr(state, "memory_context", "暂无用户信息")
         start_time = time.time()
-        
+
         try:
+            # 添加 RAG 上下文到消息
+            messages_with_context = list(state.messages)
+            if state.rag_context:
+                from langchain_core.messages import SystemMessage
+                rag_system_msg = SystemMessage(content=f"以下是检索到的相关信息：\n{state.rag_context}")
+                inserted = False
+                for i, msg in enumerate(messages_with_context):
+                    if msg.type == "human":
+                        messages_with_context.insert(i, rag_system_msg)
+                        inserted = True
+                        break
+                if not inserted:
+                    messages_with_context.insert(0, rag_system_msg)
+
             # 恢复为：手动进行 astream，并将所有的 chunk 拼接成最终的 response 返回。
             # LangGraph 会自动监听这期间产生的所有 token。
             chunks = []
             async for chunk in chain.astream(
                 {
-                    "messages": state["messages"],
+                    "messages": messages_with_context,
                     "memory_context": memory_context
                 },
                 config=config
@@ -70,14 +84,14 @@ def create_llm_call_node(llm: BaseLLM, tools: list):
                     response = response + chunk
             else:
                 response = AIMessage(content="")
-            
+
             elapsed_time = time.time() - start_time
-            
+
             # 提取 token 用量（兼容不同 LLM 提供商的元数据格式）
             token_usage = {}
             input_tokens = 0
             output_tokens = 0
-            
+
             # 尝试从 response_metadata 中提取
             if hasattr(response, 'response_metadata') and response.response_metadata:
                 meta = response.response_metadata
@@ -85,18 +99,18 @@ def create_llm_call_node(llm: BaseLLM, tools: list):
                     token_usage = meta['token_usage']
                 elif 'usage' in meta:
                     token_usage = meta['usage']
-            
+
             # 尝试从 additional_kwargs 中提取
             if not token_usage and hasattr(response, 'additional_kwargs'):
                 add_kwargs = response.additional_kwargs
                 if 'llm_output' in add_kwargs and 'token_usage' in add_kwargs['llm_output']:
                     token_usage = add_kwargs['llm_output']['token_usage']
-            
+
             # 提取具体的 token 数值
             if token_usage:
                 input_tokens = token_usage.get('prompt_tokens', token_usage.get('input_tokens', 0))
                 output_tokens = token_usage.get('completion_tokens', token_usage.get('output_tokens', 0))
-           
+
             # 打印 LLM 的完整输出
             debug("\n" + "="*80)
             debug("📥 [LLM输出] 大模型返回的完整响应:")
@@ -111,18 +125,21 @@ def create_llm_call_node(llm: BaseLLM, tools: list):
             if token_usage:
                 debug(f"📋 [LLM统计] 详细用量: {token_usage}")
             debug("="*80 + "\n")
-            
+
             result = {
                 "messages": [response],
-                "llm_calls": state.get('llm_calls', 0) + 1,
+                "llm_calls": getattr(state, 'llm_calls', 0) + 1,
                 "last_token_usage": token_usage,
                 "last_elapsed_time": elapsed_time,
-                "turns_since_last_summary": state.get('turns_since_last_summary', 0) + 1  # 递增计数器
+                "turns_since_last_summary": getattr(state, 'turns_since_last_summary', 0) + 1,
+                "final_result": response.content,
+                "success": True,
+                "current_phase": "done"
             }
-            
+
             log_state_change("llm_call", {**state, **result}, "离开")
             return result
-            
+
         except Exception as e:
             elapsed_time = time.time() - start_time
             error(f"\n❌ [LLM错误] 调用失败 (耗时: {elapsed_time:.2f}秒)")
@@ -131,20 +148,23 @@ def create_llm_call_node(llm: BaseLLM, tools: list):
             import traceback
             traceback.print_exc()
             debug("="*80 + "\n")
-            
+
             # 返回一个友好的错误消息
             error_response = AIMessage(
                 content="抱歉，模型暂时无法响应，可能是网络超时或服务繁忙，请稍后再试。"
             )
             error_result = {
                 "messages": [error_response],
-                "llm_calls": state.get('llm_calls', 0),
+                "llm_calls": getattr(state, 'llm_calls', 0),
                 "last_token_usage": {},
                 "last_elapsed_time": elapsed_time,
-                "turns_since_last_summary": state.get('turns_since_last_summary', 0) + 1  # 即使出错也递增计数器
+                "turns_since_last_summary": getattr(state, 'turns_since_last_summary', 0) + 1,
+                "final_result": "抱歉，模型暂时无法响应，可能是网络超时或服务繁忙，请稍后再试。",
+                "success": False,
+                "current_phase": "done"
             }
-            
+
             log_state_change("llm_call", state, "离开（异常）")
             return error_result
-    
+
     return call_llm