From ebdf2de581455306bbf00d41b4987ef6881a992b Mon Sep 17 00:00:00 2001 From: 2829798842 <2829798842@qq.com> Date: Wed, 28 May 2025 21:10:55 +0800 Subject: [PATCH] Update self_processor.py --- .../info_processors/self_processor.py | 176 ++++++++++++------ 1 file changed, 123 insertions(+), 53 deletions(-) diff --git a/src/chat/focus_chat/info_processors/self_processor.py b/src/chat/focus_chat/info_processors/self_processor.py index 38b9c757..2a1642ad 100644 --- a/src/chat/focus_chat/info_processors/self_processor.py +++ b/src/chat/focus_chat/info_processors/self_processor.py @@ -7,45 +7,63 @@ 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 -from src.chat.focus_chat.info.self_info import SelfInfo logger = get_logger("processor") def init_prompt(): - indentify_prompt = """ -{name_block} -You are an AI, but you disguise yourself as a human. Your personality is: {prompt_personality}. -{indentify_block} - -{relation_prompt} + group_prompt = """ +你的名字是{bot_name} {memory_str} - -It is now {time_now}, you are online, chatting with netizens in the QQ group. The following is the ongoing chat content: +{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} -Now please think about your self-identity based on the existing information: -1. What kind of person are you, and what is your relationship with people in the group? -2. What is your image? -3. Think about whether anyone mentioned you, or if any images are related to you. -4. Does your self-identity help with your response? If you need self-related information to help you participate in the chat, please output it; otherwise, please output a brief self-identity of more than ten words. -5. Generally, you don't need to output self-identity, just output a brief self-identity of more than ten words, unless there is an obvious scenario that requires self-identity. +以下是你之前对聊天的观察和规划，你的名字是{bot_name}： +{last_mind} -The output content should be plain, speak in Chinese, don't be exaggerated, be plain. -Please do not output redundant content (including prefixes and suffixes, colons and quotation marks, parentheses (), emoticons, @ or @ etc.). Only output self-identity content, remember to clearly state that this is your self-identity. -""" - Prompt(indentify_prompt, "indentify_prompt") +现在请你继续输出观察和规划，输出要求： +1. 先关注未读新消息的内容和近期回复历史 +2. 根据新信息，修改和删除之前的观察和规划 +3. 根据聊天内容继续输出观察和规划 +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(group_prompt, "sub_heartflow_prompt_before") + + 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} + +以下是你之前对聊天的观察和规划，你的名字是{bot_name}： +{last_mind} + +现在请你继续输出观察和规划，输出要求： +1. 先关注未读新消息的内容和近期回复历史 +2. 根据新信息，修改和删除之前的观察和规划 +3. 根据聊天内容继续输出观察和规划 +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 SelfProcessor(BaseProcessor): - log_prefix = "自我认同" +class MindProcessor(BaseProcessor): + log_prefix = "聊天思考" def __init__(self, subheartflow_id: str): super().__init__() @@ -53,14 +71,48 @@ class SelfProcessor(BaseProcessor): self.subheartflow_id = subheartflow_id self.llm_model = LLMRequest( - model=global_config.model.focus_self_recognize, - temperature=global_config.model.focus_self_recognize["temp"], + model=global_config.model.focus_chat_mind, + temperature=global_config.model.focus_chat_mind["temp"], max_tokens=800, - request_type="self_identify", + 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 @@ -73,18 +125,14 @@ class SelfProcessor(BaseProcessor): Returns: List[InfoBase]: 处理后的结构化信息列表 """ - self_info_str = await self.self_indentify(observations, running_memorys) + current_mind = await self.do_thinking_before_reply(observations, running_memorys) - if self_info_str: - self_info = SelfInfo() - self_info.set_self_info(self_info_str) - else: - self_info = None - return None + mind_info = MindInfo() + mind_info.set_current_mind(current_mind) - return [self_info] + return [mind_info] - async def self_indentify( + async def do_thinking_before_reply( self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None ): """ @@ -100,12 +148,31 @@ class SelfProcessor(BaseProcessor): 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: @@ -123,49 +190,52 @@ class SelfProcessor(BaseProcessor): chat_observe_info = observation.get_observe_info() person_list = observation.person_list if isinstance(observation, HFCloopObservation): - # hfcloop_observe_info = observation.get_observe_info() - pass + hfcloop_observe_info = observation.get_observe_info() - nickname_str = "" - for nicknames in global_config.bot.alias_names: - nickname_str += f"{nicknames}," - name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。" - - personality_block = individuality.get_personality_prompt(x_person=2, level=2) - identity_block = individuality.get_identity_prompt(x_person=2, level=2) + # ---------- 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) - prompt = (await global_prompt_manager.get_prompt_async("indentify_prompt")).format( - name_block=name_block, - prompt_personality=personality_block, - indentify_block=identity_block, + 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 = "" + 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，自我识别失败。") + 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 = "自我识别过程中出现错误" + content = "思考过程中出现错误" - if content == "None": - content = "" # 记录初步思考结果 - logger.debug(f"{self.log_prefix} 自我识别prompt: \n{prompt}\n") - logger.info(f"{self.log_prefix} 自我识别结果: {content}") + logger.debug(f"{self.log_prefix} 思考prompt: \n{prompt}\n") + logger.info(f"{self.log_prefix} 思考结果: {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()