采用向量数据库实现长期记忆

app/nodes/llm_call.py

@@ -0,0 +1,139 @@
+"""
+LLM 调用节点模块
+负责调用大语言模型并处理响应
+"""
+
+import asyncio
+import time
+from typing import Any, Dict
+from langchain_core.language_models import BaseLLM
+from langchain_core.messages import AIMessage
+from langchain_core.runnables import RunnableLambda
+from langgraph.runtime import Runtime
+
+# 本地模块
+from app.state import MessagesState, GraphContext
+from app.prompts import create_system_prompt
+from app.utils.logging import log_state_change, print_llm_input
+from app.logger import debug, info, error
+
+
+def create_llm_call_node(llm: BaseLLM, 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()
+    llm_with_tools = llm.bind_tools(tools)
+    chain = prompt | RunnableLambda(print_llm_input) | llm_with_tools
+    
+    async def call_llm(state: MessagesState, runtime: Runtime[GraphContext]) -> Dict[str, Any]:
+        """
+        LLM 调用节点（异步方法）
+        
+        Args:
+            state: 当前对话状态
+            runtime: LangGraph 运行时上下文
+            
+        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")
+        loop = asyncio.get_event_loop()
+        start_time = time.time()
+        
+        try:
+            response = await loop.run_in_executor(
+                None, 
+                lambda: chain.invoke({
+                    "messages": state["messages"],
+                    "memory_context": memory_context
+                })
+            )
+            
+            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
+                if 'token_usage' in meta:
+                    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))
+            
+            # 打印响应统计信息
+            info(f"⏱️  [LLM统计] 调用耗时: {elapsed_time:.2f}秒")
+            info(f"📊 [LLM统计] Token用量: 输入={input_tokens}, 输出={output_tokens}, 总计={input_tokens + output_tokens}")
+            if token_usage:
+                debug(f"📋 [LLM统计] 详细用量: {token_usage}")
+            
+            # 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:")
+            debug(f"   消息类型: {response.type.upper()}")
+            debug(f"   内容长度: {len(str(response.content))} 字符")
+            debug("-"*80)
+            debug(f"{response.content}")
+            debug("="*80 + "\n")
+            
+            result = {
+                "messages": [response],
+                "llm_calls": state.get('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
+            }
+            
+            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)")
+            error(f"   错误类型: {type(e).__name__}")
+            error(f"   错误信息: {str(e)}")
+            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),
+                "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
+            }
+            
+            log_state_change("llm_call", state, "离开（异常）")
+            return error_result
+    
+    return call_llm