长期记忆

app/agent.py

@@ -91,13 +91,6 @@ class AIAgentService:
             try:
                 info(f"🔄 正在初始化模型 '{model_name}'...")
                 llm = llm_creator()
-                
-                # 测试 LLM 连接（可选，用于调试）
-                if model_name == "local":
-                    debug(f"   测试 vLLM 连接: {os.getenv('VLLM_BASE_URL', '未设置')}")
-                elif model_name == "deepseek":
-                    debug(f"   测试 DeepSeek API 连接: https://api.deepseek.com")
-                
                 builder = GraphBuilder(llm, AVAILABLE_TOOLS, TOOLS_BY_NAME).build()
                 graph = builder.compile(checkpointer=self.checkpointer, store=self.store)
                 self.graphs[model_name] = graph

app/graph_builder.py

@@ -5,21 +5,27 @@ LangGraph 状态图构建模块 - 完全面向对象风格，无嵌套函数
 import operator
 import asyncio
 import time
+import os
 from typing import Literal, Annotated, Any
 from langchain_core.language_models import BaseLLM
 from langchain_core.messages import AnyMessage, AIMessage, ToolMessage, SystemMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables import RunnableLambda
 from langgraph.graph import StateGraph, START, END
 from typing_extensions import TypedDict
 from langgraph.store.postgres.aio import AsyncPostgresStore
 from langgraph.runtime import Runtime
 from dataclasses import dataclass
 import uuid
-
+from langchain_core.prompt_values import ChatPromptValue
 # 本地模块
 from app.logger import debug, info, warning, error
 
 
+# 是否启用 Graph 流转追踪（通过环境变量控制）
+ENABLE_GRAPH_TRACE = os.getenv("ENABLE_GRAPH_TRACE", "true").lower() == "true"
+
+
 class MessagesState(TypedDict):
     """对话状态类型定义"""
     messages: Annotated[list[AnyMessage], operator.add]
@@ -33,6 +39,53 @@ class GraphContext:
     user_id: str
     # 可扩展更多上下文信息
 
+def _log_state_change(node_name: str, state: MessagesState, prefix: str = "进入"):
+    """
+    通用辅助函数：打印节点状态变化
+    
+    Args:
+        node_name: 节点名称
+        state: 当前状态
+        prefix: 前缀（"进入" 或 "离开"）
+    """
+    if not ENABLE_GRAPH_TRACE:
+        return
+    
+    messages = state.get("messages", [])
+    msg_count = len(messages)
+    last_msg = messages[-1] if messages else None
+    last_info = ""
+    if last_msg:
+        content_preview = str(last_msg.content)[:100].replace("\n", " ")
+        last_info = f"{last_msg.type.upper()}: {content_preview}"
+    info(f"🔄 [{node_name}] {prefix} | 消息数:{msg_count} | 最后一条:{last_info}")
+
+def _print_llm_input(prompt_value: ChatPromptValue) -> ChatPromptValue:
+    """
+    RunnableLambda 回调函数：打印格式化后发送给 LLM 的完整消息
+    
+    Args:
+        prompt_value: ChatPromptValue 对象，包含格式化后的消息列表
+        
+    Returns:
+        原样返回 prompt_value，不影响链式调用
+    """
+    if not ENABLE_GRAPH_TRACE:
+        return prompt_value
+
+    messages = prompt_value.messages  # ChatPromptValue 提供 .messages 属性
+
+    debug("\n" + "=" * 80)
+    debug("📤 [LLM输入] 格式化后发送给大模型的完整消息:")
+    debug(f"   总消息数: {len(messages)}")
+    debug("-" * 80)
+    for i, msg in enumerate(messages):
+        content_preview = str(msg.content)  # 完整输出
+        debug(f"   [{i}] {msg.type.upper():10s}: {content_preview}")
+    debug( "\n"+"=" * 80 + "\n")
+
+    return prompt_value
+
 class GraphBuilder:
     """LangGraph 状态图构建器 - 所有节点均为类方法"""
 
@@ -50,13 +103,16 @@ class GraphBuilder:
         self.tools_by_name = tools_by_name
         self._llm_with_tools = llm.bind_tools(tools)
         self._prompt = self._create_prompt()
-        self._chain = self._prompt | self._llm_with_tools
+        self._chain = (
+            self._prompt 
+            | RunnableLambda(_print_llm_input) 
+            | self._llm_with_tools
+        )
 
     @staticmethod
     def _create_prompt() -> ChatPromptTemplate:
         """创建系统提示模板（静态方法，无需访问实例）"""
-        return ChatPromptTemplate.from_messages([
-            SystemMessage(content=(
+        system_template = (
             "你是一个个人生活助手和数据分析助手，请使用中文交流。\n\n"
             "【用户背景信息】\n"
             "以下是对当前用户的已知信息和长期记忆，你必须优先采纳并在回答中体现：\n"
@@ -73,7 +129,9 @@ class GraphBuilder:
             "1. 回答必须简洁、直接，禁止描述任何思考过程或内心活动。\n"
             "2. 优先利用已知用户信息进行个性化回复。\n"
             "3. 若无信息可依，礼貌询问或提供通用帮助。"
-            )),
+        )
+        return ChatPromptTemplate.from_messages([
+            ("system", system_template),
             MessagesPlaceholder(variable_name="messages")
         ])
 
@@ -82,26 +140,9 @@ class GraphBuilder:
         LLM 调用节点（异步方法）
         注意：因为 self._chain.invoke 是同步方法，使用 run_in_executor 避免阻塞事件循环
         """
+        _log_state_change("llm_call", state, "进入")
+        
         memory_context = state.get("memory_context", "暂无用户信息")
-        
-        # 构建完整的输入消息列表（用于调试打印）
-        system_prompt = self._prompt.messages[0]  # SystemMessage
-        if isinstance(system_prompt, SystemMessage):
-            system_content = system_prompt.content.format(memory_context=memory_context)
-        else:
-            system_content = str(system_prompt.content)
-        
-        input_messages = [SystemMessage(content=system_content)] + state["messages"]
-        
-        # 打印发送给大模型的最终输入
-        debug("\n" + "="*80)
-        debug("📤 [LLM输入] 发送给大模型的完整消息:")
-        debug(f"   总消息数: {len(input_messages)}")
-        for i, msg in enumerate(input_messages):
-            content_preview = str(msg.content)  # 不截断，完整输出
-            debug(f"   [{i}] {msg.type.upper():10s}: {content_preview}")
-        debug("="*80 + "\n")
-        
         loop = asyncio.get_event_loop()
         start_time = time.time()
         
@@ -155,13 +196,16 @@ class GraphBuilder:
             debug(f"{response.content}")
             debug("="*80 + "\n")
             
-            return {
+            result = {
                 "messages": [response],
                 "llm_calls": state.get('llm_calls', 0) + 1,
                 "last_token_usage": token_usage,
                 "last_elapsed_time": elapsed_time
             }
             
+            _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)")
@@ -175,33 +219,45 @@ class GraphBuilder:
             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。"
             )
-            return {
+            error_result = {
                 "messages": [error_response],
                 "llm_calls": state.get('llm_calls', 0),
                 "last_token_usage": {},
                 "last_elapsed_time": elapsed_time
             }
             
+            _log_state_change("llm_call", state, "离开（异常）")
+            return error_result
+
     async def call_tools(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
         """
         工具执行节点（异步方法）
         对于每个工具调用，在线程池中执行同步工具函数
         """
+        _log_state_change("tool_node", state, "进入")
+        
         last_message = state['messages'][-1]
         if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
+            _log_state_change("tool_node", state, "离开（无工具调用）")
             return {"messages": []}
 
         results = []
         loop = asyncio.get_event_loop()
         
+        info(f"🛠️  [工具调用] 准备执行 {len(last_message.tool_calls)} 个工具")
+        
         for tool_call in last_message.tool_calls:
             tool_name = tool_call["name"]
             tool_args = tool_call["args"]
             tool_id = tool_call["id"]
             tool_func = self.tools_by_name.get(tool_name)
 
+            debug(f"   ├─ 调用工具: {tool_name} 参数: {tool_args}")
+
             if tool_func is None:
-                results.append(ToolMessage(content=f"Tool {tool_name} not found", tool_call_id=tool_id))
+                err_msg = f"Tool {tool_name} not found"
+                debug(f"   └─ ❌ {err_msg}")
+                results.append(ToolMessage(content=err_msg, tool_call_id=tool_id))
                 continue
 
             try:
@@ -214,11 +270,20 @@ class GraphBuilder:
                         None,
                         lambda args=tool_args: tool_func.invoke(args)  # 默认参数捕获当前值
                     )
+                
+                # 字符打印
+                result_preview = str(observation).replace("\n", " ")
+                debug(f"   └─ ✅ 结果: {result_preview}")
                 results.append(ToolMessage(content=str(observation), tool_call_id=tool_id))
             except Exception as e:
+                debug(f"   └─ ❌ 异常: {e}")
                 results.append(ToolMessage(content=f"Error: {e}", tool_call_id=tool_id))
 
-        return {"messages": results}
+        info(f"🛠️  [工具调用] 执行完成，返回 {len(results)} 条 ToolMessage")
+        
+        result = {"messages": results}
+        _log_state_change("tool_node", {**state, **result}, "离开")
+        return result
 
     @staticmethod
     def should_continue(state: MessagesState) -> Literal['tool_node', 'save_memory', 'END']:
@@ -227,18 +292,26 @@ class GraphBuilder:
     
         # 1. 如果需要调用工具，优先进入工具节点
         if isinstance(last_message, AIMessage) and last_message.tool_calls:
+            if ENABLE_GRAPH_TRACE:
+                info(f"🔀 [路由决策] 检测到 {len(last_message.tool_calls)} 个工具调用 → 转向 'tool_node'")
             return 'tool_node'
     
         # 2. 如果是 AI 的最终回复，可以考虑进入记忆保存节点（可增加判断逻辑）
         #    这里简单处理：只要没有工具调用，且是 AI 消息，就尝试保存记忆。
         if isinstance(last_message, AIMessage):
+            if ENABLE_GRAPH_TRACE:
+                info(f"🔀 [路由决策] 收到 AI 最终回复（无工具调用） → 转向 'save_memory'")
             return 'save_memory'
     
         # 3. 其他情况（如只有用户消息）直接结束
+        if ENABLE_GRAPH_TRACE:
+            info(f"🔀 [路由决策] 非 AI 消息（如纯用户消息） → 结束流程")
         return 'END'
     
     async def retrieve_memory(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
         """搜索并返回长期记忆"""
+        _log_state_change("retrieve_memory", state, "进入")
+        
         user_id = runtime.context.user_id
         namespace = ("memories", user_id)
         query = str(state["messages"][-1].content)
@@ -260,24 +333,33 @@ class GraphBuilder:
                 for i, memory in enumerate(memories, 1):
                     debug(f"   [{i}] {memory.value['data']}")
                 debug(f"{'='*60}\n")
-                return {"memory_context": memory_text}
+                result = {"memory_context": memory_text}
+                _log_state_change("retrieve_memory", {**state, **result}, "离开")
+                return result
             else:
                 debug(f"⚠️  [记忆检索] 未找到相关记忆")
                 debug(f"{'='*60}\n")
-                return {"memory_context": ""}
+                result = {"memory_context": ""}
+                _log_state_change("retrieve_memory", {**state, **result}, "离开")
+                return result
                 
         except Exception as e:
             error(f"❌ [记忆检索] 检索失败: {e}")
             import traceback
             traceback.print_exc()
             debug(f"{'='*60}\n")
-            return {"memory_context": ""}
+            result = {"memory_context": ""}
+            _log_state_change("retrieve_memory", {**state, **result}, "离开（异常）")
+            return result
     
     async def save_memory(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
         """尝试从对话中提取并保存长期记忆"""
+        _log_state_change("save_memory", state, "进入")
+        
         # 获取最后一条用户消息（通常是要记住的内容的来源）
         user_messages = [msg for msg in state["messages"] if msg.type == "human"]
         if not user_messages:
+            _log_state_change("save_memory", state, "离开（无用户消息）")
             return {}
         
         last_user_msg = user_messages[-1].content.lower()
@@ -291,6 +373,7 @@ class GraphBuilder:
             await runtime.store.aput(namespace, str(uuid.uuid4()), {"data": memory_content})
             info(f"✅ 长期记忆已保存：{memory_content}")
         
+        _log_state_change("save_memory", state, "离开")
         return {}
 
     def build(self) -> StateGraph: