llm log

agent/deep_assistant.py

@@ -1,13 +1,84 @@
 import json
+import logging
+from typing import Any, Dict, Optional
 from langchain.chat_models import init_chat_model
 # from deepagents import create_deep_agent
 from langchain.agents import create_agent
 from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain_core.callbacks import BaseCallbackHandler
 from utils.fastapi_utils import detect_provider
 
 from .guideline_middleware import GuidelineMiddleware
 
 
+class LoggingCallbackHandler(BaseCallbackHandler):
+    """自定义的 CallbackHandler，使用项目的 logger 来记录日志"""
+
+    def __init__(self, logger_name: str = 'app'):
+        self.logger = logging.getLogger(logger_name)
+
+    # def on_llm_start(
+    #     self, serialized: Optional[Dict[str, Any]], prompts: list[str], **kwargs: Any
+    # ) -> None:
+    #     """当 LLM 开始时调用"""
+    #     self.logger.info("🤖 LLM Start - Input Messages:")
+    #     if prompts:
+    #         for i, prompt in enumerate(prompts):
+    #             self.logger.info(f"  Message {i+1}:\n{prompt}")
+    #     else:
+    #         self.logger.info("  No prompts")
+
+    def on_llm_end(self, response, **kwargs: Any) -> None:
+        """当 LLM 结束时调用"""
+        self.logger.info("✅ LLM End - Output:")
+
+        # 打印生成的文本
+        if hasattr(response, 'generations') and response.generations:
+            for gen_idx, generation_list in enumerate(response.generations):
+                for msg_idx, generation in enumerate(generation_list):
+                    if hasattr(generation, 'text'):
+                        output_list = generation.text.split("\n")
+                        for i, output in enumerate(output_list):
+                            if output.strip():
+                                self.logger.info(f"{output}")
+                    elif hasattr(generation, 'message'):
+                        output_list = generation.message.split("\n")
+                        for i, output in enumerate(output_list):
+                            if output.strip():
+                                self.logger.info(f"{output}")
+
+    def on_llm_error(
+        self, error: Exception, **kwargs: Any
+    ) -> None:
+        """当 LLM 出错时调用"""
+        self.logger.error(f"❌ LLM Error: {error}")
+
+    def on_tool_start(
+        self, serialized: Optional[Dict[str, Any]], input_str: str, **kwargs: Any
+    ) -> None:
+        """当工具开始调用时调用"""
+        if serialized is None:
+            tool_name = 'unknown_tool'
+        else:
+            tool_name = serialized.get('name', 'unknown_tool')
+        self.logger.info(f"🔧 Tool Start - {tool_name} with input: {str(input_str)[:100]}")
+
+    def on_tool_end(self, output: str, **kwargs: Any) -> None:
+        """当工具调用结束时调用"""
+        self.logger.info(f"✅ Tool End Output: {output}")
+
+    def on_tool_error(
+        self, error: Exception, **kwargs: Any
+    ) -> None:
+        """当工具调用出错时调用"""
+        self.logger.error(f"❌ Tool Error: {error}")
+
+
+    def on_agent_action(self, action, **kwargs: Any) -> None:
+        """当 Agent 执行动作时调用"""
+        self.logger.info(f"🎯 Agent Action: {action.log}")
+
+
 # Utility functions
 def read_system_prompt():
     """读取通用的无状态系统prompt"""
@@ -56,7 +127,7 @@ async def init_agent(bot_id: str, model_name="qwen3-next", api_key=None,
 
     # 检测或使用指定的提供商
     model_provider,base_url = detect_provider(model_name,model_server)
-    
+
     # 构建模型参数
     model_kwargs = {
         "model": model_name,
@@ -69,10 +140,17 @@ async def init_agent(bot_id: str, model_name="qwen3-next", api_key=None,
         model_kwargs.update(generate_cfg)
     llm_instance = init_chat_model(**model_kwargs)
 
+    # 创建自定义的日志处理器
+    logging_handler = LoggingCallbackHandler()
+
     agent = create_agent(
         model=llm_instance,
         system_prompt=system,
         tools=mcp_tools,
         middleware=[GuidelineMiddleware(bot_id, llm_instance, system, robot_type, language, user_identifier)]
     )
+
+    # 将 handler 存储在 agent 的属性中，方便在调用时使用
+    agent.logging_handler = logging_handler
+
     return agent

routes/chat.py

@@ -141,7 +141,8 @@ async def enhanced_generate_stream_response(
                 chunk_id = 0
                 message_tag = ""
                 
-                async for msg, metadata in agent.astream({"messages": messages}, stream_mode="messages"):
+                config = {"callbacks": [agent.logging_handler]} if hasattr(agent, 'logging_handler') else {}
+                async for msg, metadata in agent.astream({"messages": messages}, stream_mode="messages", config=config):
                     new_content = ""
                     
                     if isinstance(msg, AIMessageChunk):
@@ -311,7 +312,8 @@ async def create_agent_and_generate_response(
     final_messages = messages.copy()
 
     # 非流式响应
-    agent_responses = await agent.ainvoke({"messages": final_messages})
+    config = {"callbacks": [agent.logging_handler]} if hasattr(agent, 'logging_handler') else {}
+    agent_responses = await agent.ainvoke({"messages": final_messages}, config=config)
     append_messages = agent_responses["messages"][len(final_messages):]
     response_text = ""
     for msg in append_messages: