From 567179234194838a82b35dc2cadb2066a36a4844 Mon Sep 17 00:00:00 2001 From: 2829798842 <2829798842@qq.com> Date: Thu, 29 May 2025 20:52:00 +0800 Subject: [PATCH] Update mind_processor.py --- .../info_processors/mind_processor.py | 203 ++++++++++++++++++ 1 file changed, 203 insertions(+) diff --git a/src/chat/focus_chat/info_processors/mind_processor.py b/src/chat/focus_chat/info_processors/mind_processor.py index 2ac61a85..a85796b0 100644 --- a/src/chat/focus_chat/info_processors/mind_processor.py +++ b/src/chat/focus_chat/info_processors/mind_processor.py @@ -1,3 +1,27 @@ +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 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} @@ -38,3 +62,182 @@ 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_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() + + # ---------- 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, + 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()