From d998202cc6b1ca4c54b135fa6016778acf804462 Mon Sep 17 00:00:00 2001
From: 2829798842 <2829798842@qq.com>
Date: Wed, 28 May 2025 22:07:23 +0800
Subject: [PATCH] Update tool_processor.py

---
 .../info_processors/tool_processor.py         | 49 ++++++++++++-------
 1 file changed, 30 insertions(+), 19 deletions(-)

diff --git a/src/chat/focus_chat/info_processors/tool_processor.py b/src/chat/focus_chat/info_processors/tool_processor.py
index e82dbb85..f41b1f68 100644
--- a/src/chat/focus_chat/info_processors/tool_processor.py
+++ b/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.focus_chat.info.info_base import InfoBase
+from src.chat.heart_flow.observation.structure_observation import StructureObservation
 
 logger = get_logger("processor")
 
@@ -40,7 +40,6 @@ If you need to use tools, please directly call the corresponding tool functions.
 """
     Prompt(tool_executor_prompt, "tool_executor_prompt")
 
-
 class ToolProcessor(BaseProcessor):
     log_prefix = "工具执行器"
 
@@ -51,13 +50,13 @@ class ToolProcessor(BaseProcessor):
         self.llm_model = LLMRequest(
             model=global_config.model.focus_tool_use,
             max_tokens=500,
-            request_type="tool_execution",
+            request_type="focus_tool",
         )
         self.structured_info = []
 
     async def process_info(
         self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos
-    ) -> List[InfoBase]:
+    ) -> List[dict]:
         """处理信息对象
 
         Args:
@@ -67,24 +66,36 @@ class ToolProcessor(BaseProcessor):
             list: 处理后的结构化信息列表
         """
 
-        result_infos = []
+        working_infos = []
 
         if observations:
             for observation in observations:
                 if isinstance(observation, ChattingObservation):
-                    # 执行工具调用
-                    tool_results, used_tools, _ = await self.execute_tools(observation, running_memorys)
-                    
-                    # 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()
-                        
-                        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')}")
-        
-        logger.debug(f"result_infos: {result_infos}")
-        return result_infos
+                    result, used_tools, prompt = 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}")
+
+            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)
+
+                    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]
 
     async def execute_tools(self, observation: ChattingObservation, running_memorys: Optional[List[Dict]] = None):
         """
@@ -150,7 +161,7 @@ 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}")