diff --git a/src/chat/focus_chat/info_processors/relationship_processor.py b/src/chat/focus_chat/info_processors/relationship_processor.py index c42c99790..7a0bdb03c 100644 --- a/src/chat/focus_chat/info_processors/relationship_processor.py +++ b/src/chat/focus_chat/info_processors/relationship_processor.py @@ -566,25 +566,30 @@ class RelationshipProcessor(BaseProcessor): person_info_manager = get_person_info_manager() for person_name, info_type in content_json.items(): person_id = person_info_manager.get_person_id_by_person_name(person_name) - if person_id: - self.info_fetching_cache.append( - { - "person_id": person_id, - "person_name": person_name, - "info_type": info_type, - "start_time": time.time(), - "forget": False, - } - ) - if len(self.info_fetching_cache) > 20: - self.info_fetching_cache.pop(0) - else: + if not person_id: logger.warning(f"{self.log_prefix} 未找到用户 {person_name} 的ID，跳过调取信息。") continue + + # 检查是否是bot自己，如果是则跳过 + user_id = person_info_manager.get_value_sync(person_id, "user_id") + if user_id == global_config.bot.qq_account: + logger.info(f"{self.log_prefix} 跳过调取bot自己({person_name})的信息。") + continue + + self.info_fetching_cache.append( + { + "person_id": person_id, + "person_name": person_name, + "info_type": info_type, + "start_time": time.time(), + "forget": False, + } + ) + if len(self.info_fetching_cache) > 20: + self.info_fetching_cache.pop(0) logger.info(f"{self.log_prefix} 调取用户 {person_name} 的 {info_type} 信息。") - # 收集即时提取任务 instant_tasks.append((person_id, info_type, time.time())) @@ -784,6 +789,8 @@ class RelationshipProcessor(BaseProcessor): points_text_block = "" if not points_text_block and not person_impression_block: + if person_id not in self.info_fetched_cache: + self.info_fetched_cache[person_id] = {} self.info_fetched_cache[person_id][info_type] = { "info": "none", "ttl": 8, @@ -791,6 +798,7 @@ class RelationshipProcessor(BaseProcessor): "person_name": person_name, "unknow": True, } + return prompt = (await global_prompt_manager.get_prompt_async("fetch_person_info_prompt")).format( name_block=name_block, diff --git a/src/chat/focus_chat/info_processors/self_processor.py b/src/chat/focus_chat/info_processors/self_processor.py index 7d8f63476..06d1b3c3b 100644 --- a/src/chat/focus_chat/info_processors/self_processor.py +++ b/src/chat/focus_chat/info_processors/self_processor.py @@ -4,9 +4,6 @@ from src.llm_models.utils_model import LLMRequest from src.config.config import global_config import time import traceback -import json -import os -import hashlib from src.common.logger import get_logger from src.chat.utils.prompt_builder import Prompt, global_prompt_manager from src.chat.message_receive.chat_stream import get_chat_manager @@ -15,6 +12,7 @@ from typing import List, Dict from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation from src.chat.focus_chat.info.info_base import InfoBase from src.chat.focus_chat.info.self_info import SelfInfo +from src.individuality.individuality import get_individuality logger = get_logger("processor") @@ -28,23 +26,17 @@ def init_prompt(): {name_block} -请你根据以上聊天记录，思考聊天记录中是否有人提到你自己相关的信息，或者有人询问你的相关信息，例如你的性格，身高，喜好，外貌，身份，兴趣，爱好，习惯，等等。 -然后请你根据你的聊天需要，输出关键词属性在数据库中进行查询，数据库包含了关于你的所有信息，你需要直接输出你要查询的关键词，如果要输出多个，请用逗号隔开 -如果没有需要查询的内容，请输出none -现在请输出关键词，注意只输出关键词就好，不要输出其他内容： +请你根据以上聊天记录，思考聊天记录中是否有人提到你自己相关的信息，或者有人询问你的相关信息。 + +数据库中包含以下关键词的信息： +{available_keywords} + +请从上述关键词中选择你需要查询的关键词来回答聊天中的问题。如果需要多个关键词，请用逗号隔开。 +如果聊天中没有涉及任何关于你的问题，请输出none。 + +现在请输出你要查询的关键词，注意只输出关键词就好，不要输出其他内容： """ Prompt(indentify_prompt, "indentify_prompt") - - - fetch_info_prompt = """ - -{name_block}，你的性格是： -{prompt_personality} -{indentify_block} - -请从中提取有关你的有关"{keyword}"信息，请输出原始内容，如果{bot_name}没有涉及"{keyword}"相关信息，请输出none： -""" - Prompt(fetch_info_prompt, "fetch_info_prompt") class SelfProcessor(BaseProcessor): @@ -56,99 +48,16 @@ class SelfProcessor(BaseProcessor): self.subheartflow_id = subheartflow_id self.info_fetched_cache: Dict[str, Dict[str, any]] = {} - - self.fetch_info_file_path = "data/personality/fetch_info.json" - self.meta_info_file_path = "data/personality/meta_info.json" self.llm_model = LLMRequest( model=global_config.model.utils_small, request_type="focus.processor.self_identify", ) - name = get_chat_manager().get_stream_name(self.subheartflow_id) self.log_prefix = f"[{name}] " - - # 在初始化时检查配置是否发生变化 - self._check_config_change_and_clear() - def _get_config_hash(self) -> str: - """获取当前personality和identity配置的哈希值""" - personality_sides = list(global_config.personality.personality_sides) - identity_detail = list(global_config.identity.identity_detail) - - # 将配置转换为字符串并排序，确保一致性 - config_str = json.dumps({ - "personality_sides": sorted(personality_sides), - "identity_detail": sorted(identity_detail) - }, sort_keys=True) - - return hashlib.md5(config_str.encode('utf-8')).hexdigest() - def _load_meta_info(self) -> Dict[str, str]: - """从JSON文件中加载元信息""" - if os.path.exists(self.meta_info_file_path): - try: - with open(self.meta_info_file_path, 'r', encoding='utf-8') as f: - return json.load(f) - except Exception as e: - logger.warning(f"{self.log_prefix} 读取meta_info文件失败: {e}") - return {} - return {} - - def _save_meta_info(self, meta_info: Dict[str, str]): - """将元信息保存到JSON文件""" - try: - # 确保目录存在 - os.makedirs(os.path.dirname(self.meta_info_file_path), exist_ok=True) - with open(self.meta_info_file_path, 'w', encoding='utf-8') as f: - json.dump(meta_info, f, ensure_ascii=False, indent=2) - except Exception as e: - logger.error(f"{self.log_prefix} 保存meta_info文件失败: {e}") - - def _check_config_change_and_clear(self): - """检查配置是否发生变化，如果变化则清空fetch_info.json""" - current_config_hash = self._get_config_hash() - meta_info = self._load_meta_info() - - stored_config_hash = meta_info.get("config_hash", "") - - if current_config_hash != stored_config_hash: - logger.info(f"{self.log_prefix} 检测到personality或identity配置发生变化，清空fetch_info数据") - - # 清空fetch_info文件 - if os.path.exists(self.fetch_info_file_path): - try: - os.remove(self.fetch_info_file_path) - logger.info(f"{self.log_prefix} 已清空fetch_info文件") - except Exception as e: - logger.error(f"{self.log_prefix} 清空fetch_info文件失败: {e}") - - # 更新元信息 - meta_info["config_hash"] = current_config_hash - self._save_meta_info(meta_info) - logger.info(f"{self.log_prefix} 已更新配置哈希值") - - def _load_fetch_info_from_file(self) -> Dict[str, str]: - """从JSON文件中加载已保存的fetch_info数据""" - if os.path.exists(self.fetch_info_file_path): - try: - with open(self.fetch_info_file_path, 'r', encoding='utf-8') as f: - return json.load(f) - except Exception as e: - logger.warning(f"{self.log_prefix} 读取fetch_info文件失败: {e}") - return {} - return {} - - def _save_fetch_info_to_file(self, fetch_info_data: Dict[str, str]): - """将fetch_info数据保存到JSON文件""" - try: - # 确保目录存在 - os.makedirs(os.path.dirname(self.fetch_info_file_path), exist_ok=True) - with open(self.fetch_info_file_path, 'w', encoding='utf-8') as f: - json.dump(fetch_info_data, f, ensure_ascii=False, indent=2) - except Exception as e: - logger.error(f"{self.log_prefix} 保存fetch_info文件失败: {e}") async def process_info(self, observations: List[Observation] = None, *infos) -> List[InfoBase]: """处理信息对象 @@ -210,19 +119,16 @@ class SelfProcessor(BaseProcessor): 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_sides_str = "你" - identity_detail_str = "你" - for personality_side in global_config.personality.personality_sides: - personality_sides_str += f"{personality_side}," - - for identity_detail in global_config.identity.identity_detail: - identity_detail_str += f"{identity_detail}," - + # 获取所有可用的关键词 + individuality = get_individuality() + available_keywords = individuality.get_all_keywords() + available_keywords_str = "、".join(available_keywords) if available_keywords else "暂无关键词" prompt = (await global_prompt_manager.get_prompt_async("indentify_prompt")).format( name_block=name_block, time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), chat_observe_info=chat_observe_info[-200:], + available_keywords=available_keywords_str, bot_name = global_config.bot.nickname ) @@ -242,8 +148,7 @@ class SelfProcessor(BaseProcessor): keyword = "我是谁，我从哪来，要到哪去" - # keyword_json = json.loads(repair_json(keyword)) - # 根据逗号分割为list + # 解析关键词 keyword = keyword.strip() if not keyword or keyword == "none": keyword_set = [] @@ -251,50 +156,30 @@ class SelfProcessor(BaseProcessor): # 只保留非空关键词，去除多余空格 keyword_set = [k.strip() for k in keyword.split(",") if k.strip()] - for keyword in keyword_set: - if keyword not in self.info_fetched_cache: - # 首先尝试从文件中读取 - fetch_info_data = self._load_fetch_info_from_file() - - if keyword in fetch_info_data: - # 从文件中获取已保存的信息 - fetched_info = fetch_info_data[keyword] - logger.info(f"{self.log_prefix} 从文件中读取到关键词 '{keyword}' 的信息") - else: - # 文件中没有，使用LLM生成 - prompt = (await global_prompt_manager.get_prompt_async("fetch_info_prompt")).format( - name_block=name_block, - prompt_personality=personality_sides_str, - indentify_block=identity_detail_str, - keyword=keyword, - bot_name = global_config.bot.nickname - ) - - print(prompt) - - fetched_info, _ = await self.llm_model.generate_response_async(prompt=prompt) - if not fetched_info: - logger.warning(f"{self.log_prefix} LLM返回空结果，自我识别失败。") - fetched_info = "" - elif fetched_info == "none": - fetched_info = "" - else: - # 保存新生成的信息到文件 - fetch_info_data[keyword] = fetched_info - self._save_fetch_info_to_file(fetch_info_data) - logger.info(f"{self.log_prefix} 新生成的关键词 '{keyword}' 信息已保存到文件") - - if fetched_info: - self.info_fetched_cache[keyword] = { - "info": fetched_info, - "ttl": 5, - } + # 从individuality缓存中查询关键词信息 + for keyword in keyword_set: + if keyword not in self.info_fetched_cache: + # 直接从individuality的json缓存中获取关键词信息 + fetched_info = individuality.get_keyword_info(keyword) + + if fetched_info: + self.info_fetched_cache[keyword] = { + "info": fetched_info, + "ttl": 5, + } + logger.info(f"{self.log_prefix} 从个体特征缓存中获取关键词 '{keyword}' 的信息") + # 管理TTL（生存时间） + expired_keywords = [] for fetched_keyword, info in self.info_fetched_cache.items(): if info["ttl"] > 0: info["ttl"] -= 1 else: - del self.info_fetched_cache[fetched_keyword] + expired_keywords.append(fetched_keyword) + + # 删除过期的关键词 + for expired_keyword in expired_keywords: + del self.info_fetched_cache[expired_keyword] fetched_info_str = "" diff --git a/src/chat/focus_chat/replyer/default_generator.py b/src/chat/focus_chat/replyer/default_generator.py index f229922f2..df8d08bae 100644 --- a/src/chat/focus_chat/replyer/default_generator.py +++ b/src/chat/focus_chat/replyer/default_generator.py @@ -402,7 +402,7 @@ class DefaultReplyer: time_block=time_block, reply_target_block=reply_target_block, keywords_reaction_prompt=keywords_reaction_prompt, - indentify_block=indentify_block, + identity=indentify_block, target_message=target, sender_name=sender, config_expression_style=global_config.expression.expression_style, @@ -416,12 +416,9 @@ class DefaultReplyer: chat_target=chat_target_1, chat_info=chat_talking_prompt, extra_info_block=extra_info_block, - relation_info_block=relation_info_block, - self_info_block=self_info_block, time_block=time_block, - reply_target_block=reply_target_block, keywords_reaction_prompt=keywords_reaction_prompt, - indentify_block=indentify_block, + identity=indentify_block, target_message=target, sender_name=sender, config_expression_style=global_config.expression.expression_style, diff --git a/src/individuality/individuality.py b/src/individuality/individuality.py index d1f728fb4..1a620c71c 100644 --- a/src/individuality/individuality.py +++ b/src/individuality/individuality.py @@ -3,10 +3,52 @@ from .personality import Personality from .identity import Identity from .expression_style import PersonalityExpression import random +import json +import os +import hashlib +import traceback +import time from rich.traceback import install +from src.chat.utils.prompt_builder import Prompt, global_prompt_manager +from src.manager.async_task_manager import AsyncTask +from src.llm_models.utils_model import LLMRequest +from src.config.config import global_config +from src.common.logger import get_logger install(extra_lines=3) +logger = get_logger("individuality") + +def init_prompt(): + """初始化用于关键词提取的prompts""" + + + + extract_keywords_prompt = """ +请分析以下对某人的描述，提取出其中的独立关键词。每个关键词应该是可以用来从某一角度概括的方面：性格，身高，喜好，外貌，身份，兴趣，爱好，习惯，等等。 + +描述内容： +{personality_sides} + +要求： +1. 提取独立的关键词，不要使用句子或短语 +2. 每个关键词用逗号分隔 +3. 只输出关键词，不要输出任何解释或其他内容 + +请输出关键词： +""" + Prompt(extract_keywords_prompt, "extract_keywords_prompt") + + fetch_info_prompt = """ +{name_block}，你的性格的特征是： +{prompt_personality} +{indentify_block} + +请从中提取有关你的有关"{keyword}"信息，请输出原始内容，如果{bot_name}没有涉及"{keyword}"相关信息，请输出none： +""" + Prompt(fetch_info_prompt, "fetch_info_prompt") + + class Individuality: """个体特征管理类""" @@ -18,6 +60,11 @@ class Individuality: self.express_style: PersonalityExpression = PersonalityExpression() self.name = "" + + # 关键词缓存相关 + self.keyword_info_cache: dict = {} # {keyword: [info_list]} + self.fetch_info_file_path = "data/personality/fetch_info.json" + self.meta_info_file_path = "data/personality/meta_info.json" async def initialize( self, @@ -45,6 +92,9 @@ class Individuality: await self.express_style.extract_and_store_personality_expressions() self.name = bot_nickname + + # 预处理关键词和生成信息缓存 + await self._preprocess_personality_keywords(personality_sides, identity_detail) def to_dict(self) -> dict: """将个体特征转换为字典格式""" @@ -212,6 +262,280 @@ class Individuality: return self.personality.neuroticism return None + def _get_config_hash(self, personality_sides: list, identity_detail: list) -> str: + """获取当前personality和identity配置的哈希值""" + # 将配置转换为字符串并排序，确保一致性 + config_str = json.dumps({ + "personality_sides": sorted(personality_sides), + "identity_detail": sorted(identity_detail) + }, sort_keys=True) + + return hashlib.md5(config_str.encode('utf-8')).hexdigest() + + def _load_meta_info(self) -> dict: + """从JSON文件中加载元信息""" + if os.path.exists(self.meta_info_file_path): + try: + with open(self.meta_info_file_path, 'r', encoding='utf-8') as f: + return json.load(f) + except Exception as e: + print(f"读取meta_info文件失败: {e}") + return {} + return {} + + def _save_meta_info(self, meta_info: dict): + """将元信息保存到JSON文件""" + try: + # 确保目录存在 + os.makedirs(os.path.dirname(self.meta_info_file_path), exist_ok=True) + with open(self.meta_info_file_path, 'w', encoding='utf-8') as f: + json.dump(meta_info, f, ensure_ascii=False, indent=2) + except Exception as e: + print(f"保存meta_info文件失败: {e}") + + def _check_config_change_and_clear(self, personality_sides: list, identity_detail: list): + """检查配置是否发生变化，如果变化则清空fetch_info.json""" + current_config_hash = self._get_config_hash(personality_sides, identity_detail) + meta_info = self._load_meta_info() + + stored_config_hash = meta_info.get("config_hash", "") + + if current_config_hash != stored_config_hash: + logger.info(f"检测到personality或identity配置发生变化，清空fetch_info数据") + + # 清空fetch_info文件 + if os.path.exists(self.fetch_info_file_path): + try: + os.remove(self.fetch_info_file_path) + logger.info(f"已清空fetch_info文件") + except Exception as e: + logger.error(f"清空fetch_info文件失败: {e}") + + # 更新元信息 + meta_info["config_hash"] = current_config_hash + self._save_meta_info(meta_info) + logger.info(f"已更新配置哈希值") + + def _load_fetch_info_from_file(self) -> dict: + """从JSON文件中加载已保存的fetch_info数据""" + if os.path.exists(self.fetch_info_file_path): + try: + with open(self.fetch_info_file_path, 'r', encoding='utf-8') as f: + data = json.load(f) + # 兼容旧格式：如果是字符串则转换为列表 + for keyword, value in data.items(): + if isinstance(value, str): + data[keyword] = [value] + return data + except Exception as e: + logger.error(f"读取fetch_info文件失败: {e}") + return {} + return {} + + def _save_fetch_info_to_file(self, fetch_info_data: dict): + """将fetch_info数据保存到JSON文件""" + try: + # 确保目录存在 + os.makedirs(os.path.dirname(self.fetch_info_file_path), exist_ok=True) + with open(self.fetch_info_file_path, 'w', encoding='utf-8') as f: + json.dump(fetch_info_data, f, ensure_ascii=False, indent=2) + except Exception as e: + logger.error(f"保存fetch_info文件失败: {e}") + + async def _preprocess_personality_keywords(self, personality_sides: list, identity_detail: list): + """预处理personality关键词，提取关键词并生成缓存""" + try: + + logger.info("开始预处理personality关键词...") + + # 检查配置变化 + self._check_config_change_and_clear(personality_sides, identity_detail) + + # 加载已有的预处理数据（如果存在） + fetch_info_data = self._load_fetch_info_from_file() + logger.info(f"加载已有数据，现有关键词数量: {len(fetch_info_data)}") + + # 检查并清理错误分割的关键词（包含逗号的键） + keys_to_fix = [] + for key in fetch_info_data.keys(): + if "," in key: + keys_to_fix.append(key) + + if keys_to_fix: + logger.info(f"发现 {len(keys_to_fix)} 个需要重新分割的关键词") + for bad_key in keys_to_fix: + logger.info(f"重新分割关键词: '{bad_key}'") + # 获取对应的信息 + info_list = fetch_info_data[bad_key] + # 删除旧的错误键 + del fetch_info_data[bad_key] + + # 按逗号分割并重新添加 + split_keywords = [k.strip() for k in bad_key.split(",") if k.strip()] + for split_keyword in split_keywords: + if split_keyword not in fetch_info_data: + fetch_info_data[split_keyword] = [] + # 将信息添加到分割后的关键词中 + for info in info_list: + if info not in fetch_info_data[split_keyword]: + fetch_info_data[split_keyword].append(info) + logger.info(f"已将信息分配给关键词: '{split_keyword}'") + + # 保存清理后的数据 + self._save_fetch_info_to_file(fetch_info_data) + logger.info(f"清理完成，现在共有 {len(fetch_info_data)} 个关键词") + + # 构建完整描述（personality + identity） + personality_sides_str = "" + for personality_side in personality_sides: + personality_sides_str += f"{personality_side}，" + + # 添加identity内容 + for detail in identity_detail: + personality_sides_str += f"{detail}，" + + if not personality_sides_str: + logger.info("没有personality和identity配置，跳过预处理") + return + + # 提取关键词 + extract_prompt = (await global_prompt_manager.get_prompt_async("extract_keywords_prompt")).format( + personality_sides=personality_sides_str + ) + + llm_model = LLMRequest( + model=global_config.model.utils_small, + request_type="individuality.keyword_extract", + ) + + keywords_result, _ = await llm_model.generate_response_async(prompt=extract_prompt) + logger.info(f"LLM返回的原始关键词结果: '{keywords_result}'") + + if not keywords_result or keywords_result.strip() == "none": + logger.info("未提取到有效关键词") + return + + # 解析关键词 + keyword_set = [k.strip() for k in keywords_result.split(",") if k.strip()] + logger.info(f"分割后的关键词列表: {keyword_set}") + logger.info(f"共提取到 {len(keyword_set)} 个关键词") + + # 构建名称块和身份信息 + nickname_str = "" + for nickname in global_config.bot.alias_names: + nickname_str += f"{nickname}," + name_block = f"你的名字是{self.name},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。" + + identity_detail_str = "你" + for detail in identity_detail: + identity_detail_str += f"{detail}," + + # 为每个关键词生成fetched_info，添加到现有数据中 + updated_count = 0 + new_count = 0 + + for keyword in keyword_set: + try: + logger.info(f"正在处理关键词: '{keyword}' (长度: {len(keyword)})") + + # 检查是否已存在该关键词 + if keyword in fetch_info_data: + logger.info(f"关键词 '{keyword}' 已存在，将添加新信息...") + action_type = "追加" + else: + logger.info(f"正在为新关键词 '{keyword}' 生成信息...") + action_type = "新增" + fetch_info_data[keyword] = [] # 初始化为空列表 + + fetch_prompt = (await global_prompt_manager.get_prompt_async("fetch_info_prompt")).format( + name_block=name_block, + prompt_personality=personality_sides_str, + indentify_block=identity_detail_str, + keyword=keyword, + bot_name=self.name + ) + + fetched_info, _ = await llm_model.generate_response_async(prompt=fetch_prompt) + + if fetched_info and fetched_info.strip() != "none": + # 添加到列表中，避免重复 + if fetched_info not in fetch_info_data[keyword]: + fetch_info_data[keyword].append(fetched_info) + if action_type == "追加": + updated_count += 1 + else: + new_count += 1 + logger.info(f"{action_type}关键词 '{keyword}' 的信息成功") + else: + logger.info(f"关键词 '{keyword}' 的信息已存在，跳过重复添加") + else: + logger.info(f"关键词 '{keyword}' 没有相关信息") + + except Exception as e: + logger.error(f"为关键词 '{keyword}' 生成信息时出错: {e}") + continue + + # 保存合并后的数据到文件和内存缓存 + if updated_count > 0 or new_count > 0: + self._save_fetch_info_to_file(fetch_info_data) + logger.info(f"预处理完成，新增 {new_count} 个关键词，追加 {updated_count} 个关键词信息，总计 {len(fetch_info_data)} 个关键词") + else: + logger.info("预处理完成，但没有生成任何新的有效信息") + + # 将数据加载到内存缓存 + self.keyword_info_cache = fetch_info_data + logger.info(f"关键词缓存已加载，共 {len(self.keyword_info_cache)} 个关键词") + + # 注册定时任务（延迟执行，避免阻塞初始化） + import asyncio + asyncio.create_task(self._register_keyword_update_task_delayed()) + + except Exception as e: + logger.error(f"预处理personality关键词时出错: {e}") + traceback.print_exc() + + async def _register_keyword_update_task_delayed(self): + """延迟注册关键词更新定时任务""" + try: + # 等待一小段时间确保系统完全初始化 + import asyncio + await asyncio.sleep(5) + + from src.manager.async_task_manager import async_task_manager + logger = get_logger("individuality") + + # 创建定时任务 + task = KeywordUpdateTask( + personality_sides=list(global_config.personality.personality_sides), + identity_detail=list(global_config.identity.identity_detail), + individuality_instance=self + ) + + # 注册任务 + await async_task_manager.add_task(task) + logger.info("关键词更新定时任务已注册") + + except Exception as e: + logger.error(f"注册关键词更新定时任务失败: {e}") + traceback.print_exc() + + def get_keyword_info(self, keyword: str) -> str: + """获取指定关键词的信息 + + Args: + keyword: 关键词 + + Returns: + str: 随机选择的一条信息，如果没有则返回空字符串 + """ + if keyword in self.keyword_info_cache and self.keyword_info_cache[keyword]: + return random.choice(self.keyword_info_cache[keyword]) + return "" + + def get_all_keywords(self) -> list: + """获取所有已缓存的关键词列表""" + return list(self.keyword_info_cache.keys()) + individuality = None @@ -221,3 +545,65 @@ def get_individuality(): if individuality is None: individuality = Individuality() return individuality + + +class KeywordUpdateTask(AsyncTask): + """关键词更新定时任务""" + + def __init__(self, personality_sides: list, identity_detail: list, individuality_instance): + # 调用父类构造函数 + super().__init__( + task_name="keyword_update_task", + wait_before_start=3600, # 1小时后开始 + run_interval=3600 # 每小时运行一次 + ) + + self.personality_sides = personality_sides + self.identity_detail = identity_detail + self.individuality_instance = individuality_instance + + # 任务控制参数 + self.max_runs = 20 + self.current_runs = 0 + self.original_config_hash = individuality_instance._get_config_hash(personality_sides, identity_detail) + + async def run(self): + """执行任务""" + try: + from src.common.logger import get_logger + logger = get_logger("individuality.task") + + # 检查是否超过最大运行次数 + if self.current_runs >= self.max_runs: + logger.info(f"关键词更新任务已达到最大运行次数({self.max_runs})，停止执行") + # 设置为0间隔来停止循环任务 + self.run_interval = 0 + return + + # 检查配置是否发生变化 + current_config_hash = self.individuality_instance._get_config_hash( + self.personality_sides, self.identity_detail + ) + if current_config_hash != self.original_config_hash: + logger.info("检测到personality或identity配置发生变化，停止定时任务") + # 设置为0间隔来停止循环任务 + self.run_interval = 0 + return + + self.current_runs += 1 + logger.info(f"开始执行关键词更新任务 (第{self.current_runs}/{self.max_runs}次)") + + # 执行关键词预处理 + await self.individuality_instance._preprocess_personality_keywords( + self.personality_sides, self.identity_detail + ) + + logger.info(f"关键词更新任务完成 (第{self.current_runs}/{self.max_runs}次)") + + except Exception as e: + logger.error(f"关键词更新任务执行失败: {e}") + traceback.print_exc() + + +# 初始化prompt模板 +init_prompt() diff --git a/src/person_info/relationship_manager.py b/src/person_info/relationship_manager.py index 55a41f48d..dfcad63bc 100644 --- a/src/person_info/relationship_manager.py +++ b/src/person_info/relationship_manager.py @@ -468,14 +468,14 @@ class RelationshipManager: forgotten_points = [] info_list = [] + await person_info_manager.update_one_field( + person_id, "info_list", json.dumps(info_list, ensure_ascii=False, indent=None) + ) - # 这句代码的作用是：将更新后的 forgotten_points（遗忘的记忆点）列表，序列化为 JSON 字符串后，写回到数据库中的 forgotten_points 字段 await person_info_manager.update_one_field( person_id, "forgotten_points", json.dumps(forgotten_points, ensure_ascii=False, indent=None) ) - await person_info_manager.update_one_field( - person_id, "info_list", json.dumps(info_list, ensure_ascii=False, indent=None) - ) + # 更新数据库 await person_info_manager.update_one_field(