diff --git a/src/chat/focus_chat/info_processors/relationship_processor.py b/src/chat/focus_chat/info_processors/relationship_processor.py index 7a0bdb03c..8407fc94c 100644 --- a/src/chat/focus_chat/info_processors/relationship_processor.py +++ b/src/chat/focus_chat/info_processors/relationship_processor.py @@ -536,14 +536,16 @@ class RelationshipProcessor(BaseProcessor): # 对于每个(person_id, info_type)组合，只保留最新的记录 latest_records = {} for info_fetching in self.info_fetching_cache: - key = (info_fetching['person_id'], info_fetching['info_type']) - if key not in latest_records or info_fetching['start_time'] > latest_records[key]['start_time']: + key = (info_fetching["person_id"], info_fetching["info_type"]) + if key not in latest_records or info_fetching["start_time"] > latest_records[key]["start_time"]: latest_records[key] = info_fetching - + # 按时间排序并生成显示文本 - sorted_records = sorted(latest_records.values(), key=lambda x: x['start_time']) + sorted_records = sorted(latest_records.values(), key=lambda x: x["start_time"]) for info_fetching in sorted_records: - info_cache_block += f"你已经调取了[{info_fetching['person_name']}]的[{info_fetching['info_type']}]信息\n" + info_cache_block += ( + f"你已经调取了[{info_fetching['person_name']}]的[{info_fetching['info_type']}]信息\n" + ) prompt = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format( name_block=name_block, @@ -569,13 +571,13 @@ class RelationshipProcessor(BaseProcessor): 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, @@ -625,7 +627,6 @@ class RelationshipProcessor(BaseProcessor): if person_infos_str: persons_infos_str += f"你对 {person_name} 的了解：{person_infos_str}\n" - return persons_infos_str # ================================ @@ -733,32 +734,31 @@ class RelationshipProcessor(BaseProcessor): 使用小模型提取单个信息类型 """ person_info_manager = get_person_info_manager() - + # 首先检查 info_list 缓存 info_list = await person_info_manager.get_value(person_id, "info_list") or [] cached_info = None - + print(f"info_list: {info_list}") - + # 查找对应的 info_type for info_item in info_list: if info_item.get("info_type") == info_type: cached_info = info_item.get("info_content") logger.info(f"{self.log_prefix} [缓存命中] 从 info_list 中找到 {info_type} 信息: {cached_info}") break - - + # 如果缓存中有信息，直接使用 - if cached_info : + if cached_info: person_name = await person_info_manager.get_value(person_id, "person_name") if person_id not in self.info_fetched_cache: self.info_fetched_cache[person_id] = {} - + if cached_info == "none": unknow = True else: unknow = False - + self.info_fetched_cache[person_id][info_type] = { "info": cached_info, "ttl": 8, @@ -768,26 +768,28 @@ class RelationshipProcessor(BaseProcessor): } logger.info(f"{self.log_prefix} [缓存使用] 直接使用缓存的 {person_name} 的 {info_type}: {cached_info}") return - + logger.info(f"{self.log_prefix} [缓存命中] 缓存中没有信息") - + try: nickname_str = ",".join(global_config.bot.alias_names) 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。" person_name = await person_info_manager.get_value(person_id, "person_name") person_impression = await person_info_manager.get_value(person_id, "impression") if person_impression: - person_impression_block = f"<对{person_name}的总体了解>\n{person_impression}\n" + person_impression_block = ( + f"<对{person_name}的总体了解>\n{person_impression}\n" + ) else: person_impression_block = "" - + points = await person_info_manager.get_value(person_id, "points") if points: points_text = "\n".join([f"{point[2]}:{point[0]}" for point in points]) points_text_block = f"<对{person_name}的近期了解>\n{points_text}\n" else: 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] = {} @@ -799,7 +801,7 @@ class RelationshipProcessor(BaseProcessor): "unknow": True, } return - + prompt = (await global_prompt_manager.get_prompt_async("fetch_person_info_prompt")).format( name_block=name_block, info_type=info_type, @@ -810,7 +812,7 @@ class RelationshipProcessor(BaseProcessor): ) except Exception: logger.error(traceback.format_exc()) - + print(prompt) try: @@ -824,7 +826,7 @@ class RelationshipProcessor(BaseProcessor): if info_type in content_json: info_content = content_json[info_type] is_unknown = info_content == "none" or not info_content - + # 保存到运行时缓存 if person_id not in self.info_fetched_cache: self.info_fetched_cache[person_id] = {} @@ -835,16 +837,20 @@ class RelationshipProcessor(BaseProcessor): "person_name": person_name, "unknow": is_unknown, } - + # 保存到持久化缓存 (info_list) await self._save_info_to_cache(person_id, info_type, info_content if not is_unknown else "none") - + if not is_unknown: - logger.info(f"{self.log_prefix} [LLM提取] 成功获取并缓存 {person_name} 的 {info_type}: {info_content}") + logger.info( + f"{self.log_prefix} [LLM提取] 成功获取并缓存 {person_name} 的 {info_type}: {info_content}" + ) else: logger.info(f"{self.log_prefix} [LLM提取] {person_name} 的 {info_type} 信息不明确") else: - logger.warning(f"{self.log_prefix} [LLM提取] 小模型返回空结果，获取 {person_name} 的 {info_type} 信息失败。") + logger.warning( + f"{self.log_prefix} [LLM提取] 小模型返回空结果，获取 {person_name} 的 {info_type} 信息失败。" + ) except Exception as e: logger.error(f"{self.log_prefix} [LLM提取] 执行小模型请求获取用户信息时出错: {e}") logger.error(traceback.format_exc()) @@ -852,7 +858,7 @@ class RelationshipProcessor(BaseProcessor): async def _save_info_to_cache(self, person_id: str, info_type: str, info_content: str): """ 将提取到的信息保存到 person_info 的 info_list 字段中 - + Args: person_id: 用户ID info_type: 信息类型 @@ -860,23 +866,23 @@ class RelationshipProcessor(BaseProcessor): """ try: person_info_manager = get_person_info_manager() - + # 获取现有的 info_list info_list = await person_info_manager.get_value(person_id, "info_list") or [] - + # 查找是否已存在相同 info_type 的记录 found_index = -1 for i, info_item in enumerate(info_list): if isinstance(info_item, dict) and info_item.get("info_type") == info_type: found_index = i break - + # 创建新的信息记录 new_info_item = { "info_type": info_type, "info_content": info_content, } - + if found_index >= 0: # 更新现有记录 info_list[found_index] = new_info_item @@ -885,14 +891,13 @@ class RelationshipProcessor(BaseProcessor): # 添加新记录 info_list.append(new_info_item) logger.info(f"{self.log_prefix} [缓存保存] 新增 {person_id} 的 {info_type} 信息缓存") - + # 保存更新后的 info_list await person_info_manager.update_one_field(person_id, "info_list", info_list) - + except Exception as e: logger.error(f"{self.log_prefix} [缓存保存] 保存信息到缓存失败: {e}") logger.error(traceback.format_exc()) - init_prompt() diff --git a/src/chat/focus_chat/info_processors/self_processor.py b/src/chat/focus_chat/info_processors/self_processor.py index 06d1b3c3b..61d607686 100644 --- a/src/chat/focus_chat/info_processors/self_processor.py +++ b/src/chat/focus_chat/info_processors/self_processor.py @@ -18,7 +18,6 @@ logger = get_logger("processor") def init_prompt(): - indentify_prompt = """ {time_now}，以下是正在进行的聊天内容： <聊天记录> @@ -46,7 +45,7 @@ class SelfProcessor(BaseProcessor): super().__init__() self.subheartflow_id = subheartflow_id - + self.info_fetched_cache: Dict[str, Dict[str, any]] = {} self.llm_model = LLMRequest( @@ -57,8 +56,6 @@ class SelfProcessor(BaseProcessor): name = get_chat_manager().get_stream_name(self.subheartflow_id) self.log_prefix = f"[{name}] " - - async def process_info(self, observations: List[Observation] = None, *infos) -> List[InfoBase]: """处理信息对象 @@ -123,22 +120,22 @@ class SelfProcessor(BaseProcessor): 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 + bot_name=global_config.bot.nickname, ) keyword = "" - + try: keyword, _ = await self.llm_model.generate_response_async(prompt=prompt) - + print(f"prompt: {prompt}\nkeyword: {keyword}") - + if not keyword: logger.warning(f"{self.log_prefix} LLM返回空结果，自我识别失败。") except Exception as e: @@ -146,7 +143,6 @@ class SelfProcessor(BaseProcessor): 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()) keyword = "我是谁，我从哪来，要到哪去" - # 解析关键词 keyword = keyword.strip() @@ -155,20 +151,20 @@ class SelfProcessor(BaseProcessor): else: # 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()] - + # 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(): @@ -176,18 +172,16 @@ class SelfProcessor(BaseProcessor): info["ttl"] -= 1 else: 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 = "" for keyword, info in self.info_fetched_cache.items(): fetched_info_str += f"你的：{keyword}信息是: {info['info']}\n" - + return fetched_info_str - init_prompt() diff --git a/src/chat/focus_chat/planners/planner_simple.py b/src/chat/focus_chat/planners/planner_simple.py index d34966c08..f7c59a896 100644 --- a/src/chat/focus_chat/planners/planner_simple.py +++ b/src/chat/focus_chat/planners/planner_simple.py @@ -342,11 +342,8 @@ class ActionPlanner(BasePlanner): else: chat_content_block = "你还未开始聊天" - action_options_block = "" for using_actions_name, using_actions_info in current_available_actions.items(): - - using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt") if using_actions_info["parameters"]: @@ -383,7 +380,7 @@ class ActionPlanner(BasePlanner): # 获取当前时间 time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" - + bot_name = global_config.bot.nickname if global_config.bot.alias_names: bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}" diff --git a/src/individuality/individuality.py b/src/individuality/individuality.py index 1a620c71c..9b0da2557 100644 --- a/src/individuality/individuality.py +++ b/src/individuality/individuality.py @@ -7,7 +7,6 @@ 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 @@ -19,11 +18,10 @@ 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。 @@ -38,7 +36,7 @@ def init_prompt(): 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} @@ -49,7 +47,6 @@ def init_prompt(): Prompt(fetch_info_prompt, "fetch_info_prompt") - class Individuality: """个体特征管理类""" @@ -60,7 +57,7 @@ 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" @@ -92,7 +89,7 @@ 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) @@ -265,18 +262,17 @@ class Individuality: 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() + 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: + 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}") @@ -288,7 +284,7 @@ class Individuality: 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: + 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}") @@ -297,30 +293,30 @@ class Individuality: """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") - + logger.info("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") + logger.info("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") + logger.info("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: + 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(): @@ -337,7 +333,7 @@ class Individuality: 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: + 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}") @@ -345,22 +341,21 @@ class Individuality: 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: @@ -369,7 +364,7 @@ class Individuality: 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: @@ -380,64 +375,64 @@ class Individuality: 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...") @@ -446,17 +441,17 @@ class Individuality: 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 + 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]: @@ -470,26 +465,29 @@ class Individuality: 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") + 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() @@ -499,32 +497,34 @@ class Individuality: 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 + 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 """ @@ -549,37 +549,38 @@ def get_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 + run_interval=3600, # 每小时运行一次 ) - + self.personality_sides = personality_sides - self.identity_detail = identity_detail + 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 @@ -589,17 +590,17 @@ class KeywordUpdateTask(AsyncTask): # 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() diff --git a/src/person_info/relationship_manager.py b/src/person_info/relationship_manager.py index dfcad63bc..584a902eb 100644 --- a/src/person_info/relationship_manager.py +++ b/src/person_info/relationship_manager.py @@ -283,13 +283,12 @@ class RelationshipManager: points_data = [points_data] # 添加可读时间到每个point points_list = [(item["point"], float(item["weight"]), current_time) for item in points_data] - - - logger_str=f"了解了有关{person_name}的新印象：\n" + + logger_str = f"了解了有关{person_name}的新印象：\n" for point in points_list: - logger_str+=f"{point[0]},重要性：{point[1]}\n\n" + logger_str += f"{point[0]},重要性：{point[1]}\n\n" logger.info("logger_str") - + except json.JSONDecodeError: logger.error(f"解析points JSON失败: {points}") return @@ -476,7 +475,6 @@ class RelationshipManager: person_id, "forgotten_points", json.dumps(forgotten_points, ensure_ascii=False, indent=None) ) - # 更新数据库 await person_info_manager.update_one_field( person_id, "points", json.dumps(current_points, ensure_ascii=False, indent=None)