Update tool_processor.py

src/chat/focus_chat/info_processors/tool_processor.py

@@ -12,7 +12,7 @@ from .base_processor import BaseProcessor
 from typing import List, Optional, Dict
 from src.chat.heart_flow.observation.observation import Observation
 from src.chat.focus_chat.info.structured_info import StructuredInfo
-from src.chat.heart_flow.observation.structure_observation import StructureObservation
+from src.chat.focus_chat.info.info_base import InfoBase
 
 logger = get_logger("processor")
 
@@ -22,21 +22,21 @@ def init_prompt():
 
     # 添加工具执行器提示词
     tool_executor_prompt = """
-你是一个专门执行工具的助手。你的名字是{bot_name}。现在是{time_now}。
+You are a specialized tool execution assistant. Your name is {bot_name}. The current time is {time_now}.
 
-你当前的额外信息：
+Your current additional information:
 {memory_str}
 
-群里正在进行的聊天内容：
+Current chat content in the group:
 {chat_observe_info}
 
-请仔细分析聊天内容，考虑以下几点：
-1. 内容中是否包含需要查询信息的问题
-2. 是否需要执行特定操作
-3. 是否有明确的工具使用指令
-4. 考虑用户与你的关系以及当前的对话氛围
+Please carefully analyze the chat content and consider the following points:
+1. Does the content contain questions that require information queries?
+2. Are there specific operations that need to be executed?
+3. Are there explicit tool usage instructions?
+4. Consider your relationship with the user and the current conversation atmosphere.
 
-如果需要使用工具，请直接调用相应的工具函数。如果不需要使用工具，请简单输出"无需使用工具"。
+If you need to use tools, please directly call the corresponding tool functions. If no tools are needed, simply output "No tools needed".
 """
     Prompt(tool_executor_prompt, "tool_executor_prompt")
 
@@ -51,13 +51,13 @@ class ToolProcessor(BaseProcessor):
         self.llm_model = LLMRequest(
             model=global_config.model.focus_tool_use,
             max_tokens=500,
-            request_type="focus_tool",
+            request_type="tool_execution",
         )
         self.structured_info = []
 
     async def process_info(
         self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos
-    ) -> List[dict]:
+    ) -> List[InfoBase]:
         """处理信息对象
 
         Args:
@@ -67,36 +67,24 @@ class ToolProcessor(BaseProcessor):
             list: 处理后的结构化信息列表
         """
 
-        working_infos = []
+        result_infos = []
 
         if observations:
             for observation in observations:
                 if isinstance(observation, ChattingObservation):
-                    result, used_tools, prompt = await self.execute_tools(observation, running_memorys)
+                    # 执行工具调用
+                    tool_results, used_tools, _ = await self.execute_tools(observation, running_memorys)
                     
-            # 更新WorkingObservation中的结构化信息
-            logger.debug(f"工具调用结果: {result}")
+                    # 为每个工具调用结果创建StructuredInfo对象并返回
+                    for tool_result in tool_results:
+                        structured_info = StructuredInfo()
                         
-            for observation in observations:
-                if isinstance(observation, StructureObservation):
-                    for structured_info in result:
-                        # logger.debug(f"{self.log_prefix} 更新WorkingObservation中的结构化信息: {structured_info}")
-                        observation.add_structured_info(structured_info)
+                        structured_info.data[tool_result.get("type")] = tool_result.get("content")
+                        result_infos.append(structured_info)
+                        logger.info(f"{self.log_prefix} 工具调用成功: {tool_result.get('type')} - {tool_result.get('content')}")
         
-                    working_infos = observation.get_observe_info()
-                    logger.debug(f"{self.log_prefix} 获取更新后WorkingObservation中的结构化信息: {working_infos}")
-
-        structured_info = StructuredInfo()
-        if working_infos:
-            for working_info in working_infos:
-                # print(f"working_info: {working_info}")
-                # print(f"working_info.get('type'): {working_info.get('type')}")
-                # print(f"working_info.get('content'): {working_info.get('content')}")
-                structured_info.set_info(key=working_info.get("type"), value=working_info.get("content"))
-                # info = structured_info.get_processed_info()
-                # print(f"info: {info}")
-
-        return [structured_info]
+        logger.debug(f"result_infos: {result_infos}")
+        return result_infos
 
     async def execute_tools(self, observation: ChattingObservation, running_memorys: Optional[List[Dict]] = None):
         """
@@ -162,7 +150,7 @@ class ToolProcessor(BaseProcessor):
         )
 
         # 调用LLM，专注于工具使用
-        logger.debug(f"开始执行工具调用{prompt}")
+        # logger.debug(f"开始执行工具调用{prompt}")
         response, _, tool_calls = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools)
 
         logger.debug(f"获取到工具原始输出:\n{tool_calls}")