feat:合并工具调用模型和心流模型

src/do_tool/tool_can_use/base_tool.py

@@ -41,12 +41,11 @@ class BaseTool:
             "function": {"name": cls.name, "description": cls.description, "parameters": cls.parameters},
         }
 
-    async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
+    async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
         """执行工具函数
 
         Args:
             function_args: 工具调用参数
-            message_txt: 原始消息文本
 
         Returns:
             Dict: 工具执行结果

src/do_tool/tool_can_use/compare_numbers_tool.py

@@ -19,7 +19,7 @@ class CompareNumbersTool(BaseTool):
         "required": ["num1", "num2"],
     }
 
-    async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
+    async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
         """执行比较两个数的大小
 
         Args:

src/do_tool/tool_can_use/get_knowledge.py

@@ -21,7 +21,7 @@ class SearchKnowledgeTool(BaseTool):
         "required": ["query"],
     }
 
-    async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
+    async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
         """执行知识库搜索
 
         Args:
@@ -32,7 +32,7 @@ class SearchKnowledgeTool(BaseTool):
             Dict: 工具执行结果
         """
         try:
-            query = function_args.get("query", message_txt)
+            query = function_args.get("query")
             threshold = function_args.get("threshold", 0.4)
 
             # 调用知识库搜索

src/do_tool/tool_can_use/get_memory.py

@@ -20,7 +20,7 @@ class GetMemoryTool(BaseTool):
         "required": ["topic"],
     }
 
-    async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
+    async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
         """执行记忆获取
 
         Args:
@@ -31,7 +31,7 @@ class GetMemoryTool(BaseTool):
             Dict: 工具执行结果
         """
         try:
-            topic = function_args.get("topic", message_txt)
+            topic = function_args.get("topic")
             max_memory_num = function_args.get("max_memory_num", 2)
 
             # 将主题字符串转换为列表

src/do_tool/tool_can_use/get_time_date.py

@@ -17,7 +17,7 @@ class GetCurrentDateTimeTool(BaseTool):
         "required": [],
     }
 
-    async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
+    async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
         """执行获取当前时间、日期、年份和星期
 
         Args:

src/do_tool/tool_can_use/lpmm_get_knowledge.py

@@ -24,7 +24,7 @@ class SearchKnowledgeFromLPMMTool(BaseTool):
         "required": ["query"],
     }
 
-    async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
+    async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
         """执行知识库搜索
 
         Args:
@@ -35,7 +35,7 @@ class SearchKnowledgeFromLPMMTool(BaseTool):
             Dict: 工具执行结果
         """
         try:
-            query = function_args.get("query", message_txt)
+            query = function_args.get("query")
             # threshold = function_args.get("threshold", 0.4)
 
             # 调用知识库搜索

src/do_tool/tool_use.py

@@ -50,8 +50,8 @@ class ToolUser:
         prompt += message_txt
         # prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
         prompt += f"注意你就是{bot_name}，{bot_name}是你的名字。根据之前的聊天记录补充问题信息，搜索时避开你的名字。\n"
-        prompt += "必须调用 'lpmm_get_knowledge' 工具来获取知识。\n"
-        prompt += "你现在需要对群里的聊天内容进行回复，现在选择工具来对消息和你的回复进行处理，你是否需要额外的信息，比如回忆或者搜寻已有的知识，改变关系和情感，或者了解你现在正在做什么。"
+        # prompt += "必须调用 'lpmm_get_knowledge' 工具来获取知识。\n"
+        prompt += "你现在需要对群里的聊天内容进行回复，请你思考应该使用什么工具，然后选择工具来对消息和你的回复进行处理，你是否需要额外的信息，比如回忆或者搜寻已有的知识，改变关系和情感，或者了解你现在正在做什么。"
 
         prompt = await relationship_manager.convert_all_person_sign_to_person_name(prompt)
         prompt = parse_text_timestamps(prompt, mode="lite")
@@ -68,7 +68,7 @@ class ToolUser:
         return get_all_tool_definitions()
 
     @staticmethod
-    async def _execute_tool_call(tool_call, message_txt: str):
+    async def _execute_tool_call(tool_call):
         """执行特定的工具调用
 
         Args:
@@ -89,7 +89,7 @@ class ToolUser:
                 return None
 
             # 执行工具
-            result = await tool_instance.execute(function_args, message_txt)
+            result = await tool_instance.execute(function_args)
             if result:
                 # 直接使用 function_name 作为 tool_type
                 tool_type = function_name
@@ -159,13 +159,13 @@ class ToolUser:
                 tool_calls_str = ""
                 for tool_call in tool_calls:
                     tool_calls_str += f"{tool_call['function']['name']}\n"
-                logger.info(f"根据:\n{prompt}\n模型请求调用{len(tool_calls)}个工具: {tool_calls_str}")
+                logger.info(f"根据:\n{prompt}\n\n内容：{content}\n\n模型请求调用{len(tool_calls)}个工具: {tool_calls_str}")
                 tool_results = []
                 structured_info = {}  # 动态生成键
 
                 # 执行所有工具调用
                 for tool_call in tool_calls:
-                    result = await self._execute_tool_call(tool_call, message_txt)
+                    result = await self._execute_tool_call(tool_call)
                     if result:
                         tool_results.append(result)
                         # 使用工具名称作为键