diff --git a/src/do_tool/tool_use.py b/src/do_tool/tool_use.py deleted file mode 100644 index 23a3f652..00000000 --- a/src/do_tool/tool_use.py +++ /dev/null @@ -1,205 +0,0 @@ -from src.plugins.models.utils_model import LLMRequest -from src.config.config import global_config -from src.plugins.chat.chat_stream import ChatStream -import json -from src.common.logger import get_module_logger, TOOL_USE_STYLE_CONFIG, LogConfig -from src.do_tool.tool_can_use import get_all_tool_definitions, get_tool_instance -from src.heart_flow.sub_heartflow import SubHeartflow -import traceback - -tool_use_config = LogConfig( - # 使用消息发送专用样式 - console_format=TOOL_USE_STYLE_CONFIG["console_format"], - file_format=TOOL_USE_STYLE_CONFIG["file_format"], -) -logger = get_module_logger("tool_use", config=tool_use_config) - - -class ToolUser: - def __init__(self): - self.llm_model_tool = LLMRequest( - model=global_config.llm_tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use" - ) - - @staticmethod - async def _build_tool_prompt(message_txt: str, chat_stream: ChatStream, subheartflow: SubHeartflow = None): - """构建工具使用的提示词 - - Args: - message_txt: 用户消息文本 - chat_stream: 聊天流对象 - - Returns: - str: 构建好的提示词 - """ - if subheartflow: - mid_memory_info = subheartflow.observations[0].mid_memory_info - # print(f"intol111111111111111111111111111111111222222222222mid_memory_info：{mid_memory_info}") - else: - mid_memory_info = "" - - # stream_id = chat_stream.stream_id - # chat_talking_prompt = "" - # if stream_id: - # chat_talking_prompt = get_recent_group_detailed_plain_text( - # stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True - # ) - # new_messages = list( - # db.messages.find({"chat_id": chat_stream.stream_id, "time": {"$gt": time.time()}}).sort("time", 1).limit(15) - # ) - # new_messages_str = "" - # for msg in new_messages: - # if "detailed_plain_text" in msg: - # new_messages_str += f"{msg['detailed_plain_text']}" - - # 这些信息应该从调用者传入，而不是从self获取 - bot_name = global_config.BOT_NICKNAME - prompt = "" - prompt += mid_memory_info - prompt += "你正在思考如何回复群里的消息。\n" - prompt += "之前群里进行了如下讨论:\n" - 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 += "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。" - - - return prompt - - @staticmethod - def _define_tools(): - """获取所有已注册工具的定义 - - Returns: - list: 工具定义列表 - """ - return get_all_tool_definitions() - - @staticmethod - async def _execute_tool_call(tool_call, message_txt: str): - """执行特定的工具调用 - - Args: - tool_call: 工具调用对象 - message_txt: 原始消息文本 - - Returns: - dict: 工具调用结果 - """ - try: - function_name = tool_call["function"]["name"] - function_args = json.loads(tool_call["function"]["arguments"]) - - # 获取对应工具实例 - tool_instance = get_tool_instance(function_name) - if not tool_instance: - logger.warning(f"未知工具名称: {function_name}") - return None - - # 执行工具 - result = await tool_instance.execute(function_args, message_txt) - if result: - # 直接使用 function_name 作为 tool_type - tool_type = function_name - - return { - "tool_call_id": tool_call["id"], - "role": "tool", - "name": function_name, - "type": tool_type, - "content": result["content"], - } - return None - except Exception as e: - logger.error(f"执行工具调用时发生错误: {str(e)}") - return None - - async def use_tool(self, message_txt: str, chat_stream: ChatStream, sub_heartflow: SubHeartflow = None): - """使用工具辅助思考，判断是否需要额外信息 - - Args: - message_txt: 用户消息文本 - sender_name: 发送者名称 - chat_stream: 聊天流对象 - sub_heartflow: 子心流对象（可选） - - Returns: - dict: 工具使用结果，包含结构化的信息 - """ - try: - # 构建提示词 - prompt = await self._build_tool_prompt( - message_txt=message_txt, - chat_stream=chat_stream, - subheartflow=sub_heartflow, - ) - - # 定义可用工具 - tools = self._define_tools() - logger.trace(f"工具定义: {tools}") - - # 使用llm_model_tool发送带工具定义的请求 - payload = { - "model": self.llm_model_tool.model_name, - "messages": [{"role": "user", "content": prompt}], - "max_tokens": global_config.max_response_length, - "tools": tools, - "temperature": 0.2, - } - - logger.trace(f"发送工具调用请求，模型: {self.llm_model_tool.model_name}") - # 发送请求获取模型是否需要调用工具 - response = await self.llm_model_tool._execute_request( - endpoint="/chat/completions", payload=payload, prompt=prompt - ) - - # 根据返回值数量判断是否有工具调用 - if len(response) == 3: - content, reasoning_content, tool_calls = response - # logger.info(f"工具思考: {tool_calls}") - # logger.debug(f"工具思考: {content}") - - # 检查响应中工具调用是否有效 - if not tool_calls: - logger.debug("模型返回了空的tool_calls列表") - return {"used_tools": False} - - 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[0:100]}...\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) - if result: - tool_results.append(result) - # 使用工具名称作为键 - tool_name = result["name"] - if tool_name not in structured_info: - structured_info[tool_name] = [] - structured_info[tool_name].append({"name": result["name"], "content": result["content"]}) - - # 如果有工具结果，返回结构化的信息 - if structured_info: - logger.info(f"工具调用收集到结构化信息: {json.dumps(structured_info, ensure_ascii=False)}") - return {"used_tools": True, "structured_info": structured_info} - else: - # 没有工具调用 - content, reasoning_content = response - logger.debug("模型没有请求调用任何工具") - - # 如果没有工具调用或处理失败，直接返回原始思考 - return { - "used_tools": False, - } - - except Exception as e: - logger.error(f"工具调用过程中出错: {str(e)}") - logger.error(f"工具调用过程中出错: {traceback.format_exc()}") - return { - "used_tools": False, - "error": str(e), - }