diff --git a/src/chat/focus_chat/info_processors/tool_processor.py b/src/chat/focus_chat/info_processors/tool_processor.py index aa56ca18..359f7bcf 100644 --- a/src/chat/focus_chat/info_processors/tool_processor.py +++ b/src/chat/focus_chat/info_processors/tool_processor.py @@ -182,4 +182,190 @@ class ToolProcessor(BaseProcessor): return new_structured_items, used_tools, prompt -init_prompt() \ No newline at end of file +init_prompt()init_prompt() +======= +from src.chat.heart_flow.observation.chatting_observation import ChattingObservation +from src.llm_models.utils_model import LLMRequest +from src.config.config import global_config +import time +from src.common.logger import get_logger +from src.individuality.individuality import get_individuality +from src.chat.utils.prompt_builder import Prompt, global_prompt_manager +from src.tools.tool_use import ToolUser +from src.chat.utils.json_utils import process_llm_tool_calls +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 + +logger = get_logger("processor") + + +def init_prompt(): + # ... 原有代码 ... + + # 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}。 +群里正在进行的聊天内容： +{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. 是否有明确的工具使用指令 + +If you need to use a tool, please directly call the corresponding tool function. If you do not need to use any tool, simply output "No tool needed". +""" + Prompt(tool_executor_prompt, "tool_executor_prompt") + + +class ToolProcessor(BaseProcessor): + log_prefix = "工具执行器" + + def __init__(self, subheartflow_id: str): + super().__init__() + self.subheartflow_id = subheartflow_id + self.log_prefix = f"[{subheartflow_id}:ToolExecutor] " + self.llm_model = LLMRequest( + model=global_config.model.focus_tool_use, + request_type="focus.processor.tool", + ) + self.structured_info = [] + + async def process_info( + self, observations: Optional[List[Observation]] = None, running_memories: Optional[List[Dict]] = None, *infos + ) -> List[StructuredInfo]: + """处理信息对象 + + Args: + observations: 可选的观察列表，包含ChattingObservation和StructureObservation类型 + running_memories: 可选的运行时记忆列表，包含字典类型的记忆信息 + *infos: 可变数量的InfoBase类型的信息对象 + + Returns: + list: 处理后的结构化信息列表 + """ + + working_infos = [] + result = [] + + if observations: + for observation in observations: + if isinstance(observation, ChattingObservation): + result, used_tools, prompt = await self.execute_tools(observation, running_memories) + + logger.debug(f"工具调用结果: {result}") + # 更新WorkingObservation中的结构化信息 + 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: + structured_info.set_info(key=working_info.get("type"), value=working_info.get("content")) + + return [structured_info] + + async def execute_tools(self, observation: ChattingObservation, running_memorys: Optional[List[Dict]] = None): + """ + 并行执行工具，返回结构化信息 + + 参数: + sub_mind: 子思维对象 + chat_target_name: 聊天目标名称，默认为"对方" + is_group_chat: 是否为群聊，默认为False + return_details: 是否返回详细信息，默认为False + cycle_info: 循环信息对象，可用于记录详细执行信息 + + 返回: + 如果return_details为False: + List[Dict]: 工具执行结果的结构化信息列表 + 如果return_details为True: + Tuple[List[Dict], List[str], str]: (工具执行结果列表, 使用的工具列表, 工具执行提示词) + """ + tool_instance = ToolUser() + tools = tool_instance._define_tools() + + # logger.debug(f"observation: {observation}") + # logger.debug(f"observation.chat_target_info: {observation.chat_target_info}") + # logger.debug(f"observation.is_group_chat: {observation.is_group_chat}") + # logger.debug(f"observation.person_list: {observation.person_list}") + + is_group_chat = observation.is_group_chat + + chat_observe_info = observation.get_observe_info() + # person_list = observation.person_list + + memory_str = "" + if running_memorys: + memory_str = "以下是当前在聊天中，你回忆起的记忆：\n" + for running_memory in running_memorys: + memory_str += f"{running_memory['topic']}: {running_memory['content']}\n" + + # 获取时间信息 + time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + + # 构建专用于工具调用的提示词 + prompt = await global_prompt_manager.format_prompt( + "tool_executor_prompt", + memory_str=memory_str, + chat_observe_info=chat_observe_info, + is_group_chat=is_group_chat, + bot_name=get_individuality().name, + time_now=time_now, + ) + + # 调用LLM，专注于工具使用 + # logger.info(f"开始执行工具调用{prompt}") + response, other_info = await self.llm_model.generate_response_async(prompt=prompt, tools=tools) + + if len(other_info) == 3: + reasoning_content, model_name, tool_calls = other_info + else: + reasoning_content, model_name = other_info + tool_calls = None + + # print("tooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltool") + if tool_calls: + logger.info(f"获取到工具原始输出:\n{tool_calls}") + # 处理工具调用和结果收集，类似于SubMind中的逻辑 + new_structured_items = [] + used_tools = [] # 记录使用了哪些工具 + + if tool_calls: + success, valid_tool_calls, error_msg = process_llm_tool_calls(tool_calls) + if success and valid_tool_calls: + for tool_call in valid_tool_calls: + try: + # 记录使用的工具名称 + tool_name = tool_call.get("name", "unknown_tool") + used_tools.append(tool_name) + + result = await tool_instance._execute_tool_call(tool_call) + + name = result.get("type", "unknown_type") + content = result.get("content", "") + + logger.info(f"工具{name}，获得信息:{content}") + if result: + new_item = { + "type": result.get("type", "unknown_type"), + "id": result.get("id", f"tool_exec_{time.time()}"), + "content": result.get("content", ""), + "ttl": 3, + } + new_structured_items.append(new_item) + except Exception as e: + logger.error(f"{self.log_prefix}工具执行失败: {e}") + + return new_structured_items, used_tools, prompt + + +init_prompt()