diff --git a/src/chat/focus_chat/info_processors/mind_processor.py b/src/chat/focus_chat/info_processors/mind_processor.py index 12671169..2ac61a85 100644 --- a/src/chat/focus_chat/info_processors/mind_processor.py +++ b/src/chat/focus_chat/info_processors/mind_processor.py @@ -1,38 +1,12 @@ -from src.chat.heart_flow.observation.chatting_observation import ChattingObservation -from src.chat.heart_flow.observation.observation import Observation -from src.llm_models.utils_model import LLMRequest -from src.config.config import global_config -import time -import traceback -from src.common.logger_manager import get_logger -from src.individuality.individuality import individuality -from src.chat.utils.prompt_builder import Prompt, global_prompt_manager -from src.chat.utils.json_utils import safe_json_dumps -from src.chat.message_receive.chat_stream import chat_manager -from src.person_info.relationship_manager import relationship_manager -from .base_processor import BaseProcessor -from src.chat.focus_chat.info.mind_info import MindInfo -from typing import List, Optional -from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation -from src.chat.heart_flow.observation.actions_observation import ActionObservation -from typing import Dict -from src.chat.focus_chat.info.info_base import InfoBase - -logger = get_logger("processor") - - -def init_prompt(): - group_prompt = """ 你的名字是{bot_name} {memory_str} -{extra_info} + {relation_prompt} {cycle_info_block} 现在是{time_now}，你正在上网，和qq群里的网友们聊天，以下是正在进行的聊天内容： {chat_observe_info} - -{action_observe_info} - +以下是你执行工具调用后的信息，结果较为准确： +{extra_info} 以下是你之前对聊天的观察和规划，你的名字是{bot_name}： {last_mind} @@ -47,14 +21,13 @@ def init_prompt(): private_prompt = """ 你的名字是{bot_name} {memory_str} -{extra_info} += {relation_prompt} {cycle_info_block} 现在是{time_now}，你正在上网，和qq群里的网友们聊天，以下是正在进行的聊天内容： {chat_observe_info} - -{action_observe_info} - +以下是你执行工具调用后的信息，结果较为准确： +{extra_info} 以下是你之前对聊天的观察和规划，你的名字是{bot_name}： {last_mind} @@ -65,185 +38,3 @@ def init_prompt(): 4. 注意群聊的时间线索，话题由谁发起，进展状况如何，思考聊天的时间线。 6. 语言简洁自然，不要分点，不要浮夸，不要修辞，仅输出思考内容就好""" Prompt(private_prompt, "sub_heartflow_prompt_private_before") - - -class MindProcessor(BaseProcessor): - log_prefix = "聊天思考" - - def __init__(self, subheartflow_id: str): - super().__init__() - - self.subheartflow_id = subheartflow_id - - self.llm_model = LLMRequest( - model=global_config.model.focus_chat_mind, - temperature=global_config.model.focus_chat_mind["temp"], - max_tokens=800, - request_type="focus.processor.chat_mind", - ) - - self.current_mind = "" - self.past_mind = [] - self.structured_info = [] - self.structured_info_str = "" - - name = chat_manager.get_stream_name(self.subheartflow_id) - self.log_prefix = f"[{name}] " - self._update_structured_info_str() - - def _update_structured_info_str(self): - """根据 structured_info 更新 structured_info_str""" - if not self.structured_info: - self.structured_info_str = "" - return - - lines = ["【信息】"] - for item in self.structured_info: - # 简化展示，突出内容和类型，包含TTL供调试 - type_str = item.get("type", "未知类型") - content_str = item.get("content", "") - - if type_str == "info": - lines.append(f"刚刚: {content_str}") - elif type_str == "memory": - lines.append(f"{content_str}") - elif type_str == "comparison_result": - lines.append(f"数字大小比较结果: {content_str}") - elif type_str == "time_info": - lines.append(f"{content_str}") - elif type_str == "lpmm_knowledge": - lines.append(f"你知道：{content_str}") - else: - lines.append(f"{type_str}的信息: {content_str}") - - self.structured_info_str = "\n".join(lines) - logger.debug(f"{self.log_prefix} 更新 structured_info_str: \n{self.structured_info_str}") - - async def process_info( - self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos - ) -> List[InfoBase]: - """处理信息对象 - - Args: - *infos: 可变数量的InfoBase类型的信息对象 - - Returns: - List[InfoBase]: 处理后的结构化信息列表 - """ - current_mind = await self.do_thinking_before_reply(observations, running_memorys) - - mind_info = MindInfo() - mind_info.set_current_mind(current_mind) - - return [mind_info] - - async def do_thinking_before_reply( - self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None - ): - """ - 在回复前进行思考，生成内心想法并收集工具调用结果 - - 参数: - observations: 观察信息 - - 返回: - 如果return_prompt为False: - tuple: (current_mind, past_mind) 当前想法和过去的想法列表 - 如果return_prompt为True: - tuple: (current_mind, past_mind, prompt) 当前想法、过去的想法列表和使用的prompt - """ - - # ---------- 0. 更新和清理 structured_info ---------- - if self.structured_info: - updated_info = [] - for item in self.structured_info: - item["ttl"] -= 1 - if item["ttl"] > 0: - updated_info.append(item) - else: - logger.debug(f"{self.log_prefix} 移除过期的 structured_info 项: {item['id']}") - self.structured_info = updated_info - self._update_structured_info_str() - logger.debug( - f"{self.log_prefix} 当前完整的 structured_info: {safe_json_dumps(self.structured_info, ensure_ascii=False)}" - ) - - 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" - - # ---------- 1. 准备基础数据 ---------- - # 获取现有想法和情绪状态 - previous_mind = self.current_mind if self.current_mind else "" - - if observations is None: - observations = [] - for observation in observations: - if isinstance(observation, ChattingObservation): - # 获取聊天元信息 - is_group_chat = observation.is_group_chat - chat_target_info = observation.chat_target_info - chat_target_name = "对方" # 私聊默认名称 - if not is_group_chat and chat_target_info: - # 优先使用person_name，其次user_nickname，最后回退到默认值 - chat_target_name = ( - chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or chat_target_name - ) - # 获取聊天内容 - chat_observe_info = observation.get_observe_info() - person_list = observation.person_list - if isinstance(observation, HFCloopObservation): - hfcloop_observe_info = observation.get_observe_info() - if isinstance(observation, ActionObservation): - action_observe_info = observation.get_observe_info() - - # ---------- 3. 准备个性化数据 ---------- - # 获取个性化信息 - - relation_prompt = "" - for person in person_list: - relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True) - - template_name = "sub_heartflow_prompt_before" if is_group_chat else "sub_heartflow_prompt_private_before" - logger.debug(f"{self.log_prefix} 使用{'群聊' if is_group_chat else '私聊'}思考模板") - - prompt = (await global_prompt_manager.get_prompt_async(template_name)).format( - bot_name=individuality.name, - memory_str=memory_str, - extra_info=self.structured_info_str, - relation_prompt=relation_prompt, - time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), - chat_observe_info=chat_observe_info, - last_mind=previous_mind, - cycle_info_block=hfcloop_observe_info, - action_observe_info=action_observe_info, - chat_target_name=chat_target_name, - ) - - content = "(不知道该想些什么...)" - try: - content, _ = await self.llm_model.generate_response_async(prompt=prompt) - if not content: - logger.warning(f"{self.log_prefix} LLM返回空结果，思考失败。") - except Exception as e: - # 处理总体异常 - logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}") - logger.error(traceback.format_exc()) - content = "思考过程中出现错误" - - # 记录初步思考结果 - logger.debug(f"{self.log_prefix} 思考prompt: \n{prompt}\n") - logger.info(f"{self.log_prefix} 聊天规划: {content}") - self.update_current_mind(content) - - return content - - def update_current_mind(self, response): - if self.current_mind: # 只有当 current_mind 非空时才添加到 past_mind - self.past_mind.append(self.current_mind) - self.current_mind = response - - -init_prompt()