diff --git a/README.md b/README.md index c14ac646e..30e41a368 100644 --- a/README.md +++ b/README.md @@ -43,6 +43,7 @@ - [一群](https://qm.qq.com/q/VQ3XZrWgMs) 766798517 ，建议加下面的（开发和建议相关讨论）不一定有空回复，会优先写文档和代码 - [二群](https://qm.qq.com/q/RzmCiRtHEW) 571780722 （开发和建议相关讨论）不一定有空回复，会优先写文档和代码 - [三群](https://qm.qq.com/q/wlH5eT8OmQ) 1035228475（开发和建议相关讨论）不一定有空回复，会优先写文档和代码 +- [四群](https://qm.qq.com/q/wlH5eT8OmQ) 729957033（开发和建议相关讨论）不一定有空回复，会优先写文档和代码 @@ -57,7 +58,7 @@

📚 文档 ⬇️ 快速开始使用麦麦 ⬇️

-### 部署方式 +### 部署方式(忙于开发，部分内容可能过时) - 📦 **Windows 一键傻瓜式部署**：请运行项目根目录中的 `run.bat`，部署完成后请参照后续配置指南进行配置 diff --git a/bot.py b/bot.py index a3a844a15..acc7990ed 100644 --- a/bot.py +++ b/bot.py @@ -8,14 +8,21 @@ import time import uvicorn from dotenv import load_dotenv -from loguru import logger from nonebot.adapters.onebot.v11 import Adapter import platform +from src.plugins.utils.logger_config import setup_logger + +from loguru import logger + +# 配置日志格式 # 获取没有加载env时的环境变量 env_mask = {key: os.getenv(key) for key in os.environ} uvicorn_server = None +driver = None +app = None +loop = None def easter_egg(): @@ -95,43 +102,7 @@ def load_env(): def load_logger(): - logger.remove() - - # 配置日志基础路径 - log_path = os.path.join(os.getcwd(), "logs") - if not os.path.exists(log_path): - os.makedirs(log_path) - - current_env = os.getenv("ENVIRONMENT", "dev") - - # 公共配置参数 - log_level = os.getenv("LOG_LEVEL", "INFO" if current_env == "prod" else "DEBUG") - log_filter = lambda record: ( - ("nonebot" not in record["name"] or record["level"].no >= logger.level("ERROR").no) - if current_env == "prod" - else True - ) - log_format = ( - "{time:YYYY-MM-DD HH:mm:ss.SSS} " - "| {level: <7} " - "| {name:.<8}:{function:.<8}:{line: >4} " - "- {message}" - ) - - # 日志文件储存至/logs - logger.add( - os.path.join(log_path, "maimbot_{time:YYYY-MM-DD}.log"), - rotation="00:00", - retention="30 days", - format=log_format, - colorize=False, - level=log_level, - filter=log_filter, - encoding="utf-8", - ) - - # 终端输出 - logger.add(sys.stderr, format=log_format, colorize=True, level=log_level, filter=log_filter) + setup_logger() def scan_provider(env_config: dict): @@ -203,11 +174,14 @@ def raw_main(): if platform.system().lower() != "windows": time.tzset() + # 配置日志 + load_logger() easter_egg() init_config() init_env() load_env() - load_logger() + + # load_logger() env_config = {key: os.getenv(key) for key in os.environ} scan_provider(env_config) @@ -235,17 +209,21 @@ if __name__ == "__main__": try: raw_main() - global app app = nonebot.get_asgi() - loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) - loop.run_until_complete(uvicorn_main()) - except KeyboardInterrupt: - logger.warning("麦麦会努力做的更好的！正在停止中......") + + try: + loop.run_until_complete(uvicorn_main()) + except KeyboardInterrupt: + logger.warning("收到中断信号，正在优雅关闭...") + loop.run_until_complete(graceful_shutdown()) + finally: + loop.close() + except Exception as e: - logger.error(f"主程序异常: {e}") - finally: - loop.run_until_complete(graceful_shutdown()) - loop.close() - logger.info("进程终止完毕，麦麦开始休眠......下次再见哦！") + logger.error(f"主程序异常: {str(e)}") + if loop and not loop.is_closed(): + loop.run_until_complete(graceful_shutdown()) + loop.close() + sys.exit(1) diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py index b90b3d0f3..4290b1f40 100644 --- a/src/plugins/chat/bot.py +++ b/src/plugins/chat/bot.py @@ -1,7 +1,6 @@ import re import time from random import random -from loguru import logger from nonebot.adapters.onebot.v11 import ( Bot, GroupMessageEvent, @@ -30,6 +29,10 @@ from .utils_image import image_path_to_base64 from .utils_user import get_user_nickname, get_user_cardname, get_groupname from .willing_manager import willing_manager # 导入意愿管理器 from .message_base import UserInfo, GroupInfo, Seg +from ..utils.logger_config import setup_logger, LogModule + +# 配置日志 +logger = setup_logger(LogModule.CHAT) class ChatBot: diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py index 76437f8f2..5d6b0bca0 100644 --- a/src/plugins/chat/emoji_manager.py +++ b/src/plugins/chat/emoji_manager.py @@ -18,11 +18,17 @@ from ..chat.utils import get_embedding from ..chat.utils_image import ImageManager, image_path_to_base64 from ..models.utils_model import LLM_request +from ..utils.logger_config import setup_logger, LogModule + +# 配置日志 +logger = setup_logger(LogModule.EMOJI) + driver = get_driver() config = driver.config image_manager = ImageManager() + class EmojiManager: _instance = None EMOJI_DIR = "data/emoji" # 表情包存储目录 @@ -154,20 +160,20 @@ class EmojiManager: # 更新使用次数 db.emoji.update_one({"_id": selected_emoji["_id"]}, {"$inc": {"usage_count": 1}}) - logger.success( - f"找到匹配的表情包: {selected_emoji.get('description', '无描述')} (相似度: {similarity:.4f})" + logger.info( + f"[匹配] 找到表情包: {selected_emoji.get('description', '无描述')} (相似度: {similarity:.4f})" ) # 稍微改一下文本描述，不然容易产生幻觉，描述已经包含 表情包 了 return selected_emoji["path"], "[ %s ]" % selected_emoji.get("description", "无描述") except Exception as search_error: - logger.error(f"搜索表情包失败: {str(search_error)}") + logger.error(f"[错误] 搜索表情包失败: {str(search_error)}") return None return None except Exception as e: - logger.error(f"获取表情包失败: {str(e)}") + logger.error(f"[错误] 获取表情包失败: {str(e)}") return None async def _get_emoji_discription(self, image_base64: str) -> str: @@ -181,7 +187,7 @@ class EmojiManager: return description except Exception as e: - logger.error(f"获取标签失败: {str(e)}") + logger.error(f"[错误] 获取表情包描述失败: {str(e)}") return None async def _check_emoji(self, image_base64: str, image_format: str) -> str: @@ -189,11 +195,11 @@ class EmojiManager: prompt = f'这是一个表情包，请回答这个表情包是否满足"{global_config.EMOJI_CHECK_PROMPT}"的要求，是则回答是，否则回答否，不要出现任何其他内容' content, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format) - logger.debug(f"输出描述: {content}") + logger.debug(f"[检查] 表情包检查结果: {content}") return content except Exception as e: - logger.error(f"获取标签失败: {str(e)}") + logger.error(f"[错误] 表情包检查失败: {str(e)}") return None async def _get_kimoji_for_text(self, text: str): @@ -201,11 +207,11 @@ class EmojiManager: prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对消息内容的分析内容，只输出"一种什么样的感觉"中间的形容词部分。' content, _ = await self.llm_emotion_judge.generate_response_async(prompt, temperature=1.5) - logger.info(f"输出描述: {content}") + logger.info(f"[情感] 表情包情感描述: {content}") return content except Exception as e: - logger.error(f"获取标签失败: {str(e)}") + logger.error(f"[错误] 获取表情包情感失败: {str(e)}") return None async def scan_new_emojis(self): @@ -252,7 +258,7 @@ class EmojiManager: db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True) # 保存描述到image_descriptions集合 image_manager._save_description_to_db(image_hash, description, "emoji") - logger.success(f"同步已存在的表情包到images集合: {filename}") + logger.success(f"[同步] 已同步表情包到images集合: {filename}") continue # 检查是否在images集合中已有描述 @@ -268,15 +274,10 @@ class EmojiManager: check = await self._check_emoji(image_base64, image_format) if "是" not in check: os.remove(image_path) - logger.info(f"描述: {description}") - - logger.info(f"描述: {description}") - logger.info(f"其不满足过滤规则，被剔除 {check}") + logger.info(f"[过滤] 表情包描述: {description}") + logger.info(f"[过滤] 表情包不满足规则，已移除: {check}") continue - logger.info(f"check通过 {check}") - - if description is not None: - embedding = await get_embedding(description) + logger.info(f"[检查] 表情包检查通过: {check}") if description is not None: embedding = await get_embedding(description) @@ -293,8 +294,8 @@ class EmojiManager: # 保存到emoji数据库 db["emoji"].insert_one(emoji_record) - logger.success(f"注册新表情包: {filename}") - logger.info(f"描述: {description}") + logger.success(f"[注册] 新表情包: {filename}") + logger.info(f"[描述] {description}") # 保存到images数据库 image_doc = { @@ -307,17 +308,17 @@ class EmojiManager: db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True) # 保存描述到image_descriptions集合 image_manager._save_description_to_db(image_hash, description, "emoji") - logger.success(f"同步保存到images集合: {filename}") + logger.success(f"[同步] 已保存到images集合: {filename}") else: - logger.warning(f"跳过表情包: {filename}") + logger.warning(f"[跳过] 表情包: {filename}") except Exception: - logger.exception("扫描表情包失败") + logger.exception("[错误] 扫描表情包失败") async def _periodic_scan(self, interval_MINS: int = 10): """定期扫描新表情包""" while True: - logger.info("开始扫描新表情包...") + logger.info("[扫描] 开始扫描新表情包...") await self.scan_new_emojis() await asyncio.sleep(interval_MINS * 60) # 每600秒扫描一次 @@ -335,48 +336,48 @@ class EmojiManager: for emoji in all_emojis: try: if "path" not in emoji: - logger.warning(f"发现无效记录（缺少path字段），ID: {emoji.get('_id', 'unknown')}") + logger.warning(f"[检查] 发现无效记录（缺少path字段），ID: {emoji.get('_id', 'unknown')}") db.emoji.delete_one({"_id": emoji["_id"]}) removed_count += 1 continue if "embedding" not in emoji: - logger.warning(f"发现过时记录（缺少embedding字段），ID: {emoji.get('_id', 'unknown')}") + logger.warning(f"[检查] 发现过时记录（缺少embedding字段），ID: {emoji.get('_id', 'unknown')}") db.emoji.delete_one({"_id": emoji["_id"]}) removed_count += 1 continue # 检查文件是否存在 if not os.path.exists(emoji["path"]): - logger.warning(f"表情包文件已被删除: {emoji['path']}") + logger.warning(f"[检查] 表情包文件已被删除: {emoji['path']}") # 从数据库中删除记录 result = db.emoji.delete_one({"_id": emoji["_id"]}) if result.deleted_count > 0: - logger.debug(f"成功删除数据库记录: {emoji['_id']}") + logger.debug(f"[清理] 成功删除数据库记录: {emoji['_id']}") removed_count += 1 else: - logger.error(f"删除数据库记录失败: {emoji['_id']}") + logger.error(f"[错误] 删除数据库记录失败: {emoji['_id']}") continue if "hash" not in emoji: - logger.warning(f"发现缺失记录（缺少hash字段），ID: {emoji.get('_id', 'unknown')}") + logger.warning(f"[检查] 发现缺失记录（缺少hash字段），ID: {emoji.get('_id', 'unknown')}") hash = hashlib.md5(open(emoji["path"], "rb").read()).hexdigest() db.emoji.update_one({"_id": emoji["_id"]}, {"$set": {"hash": hash}}) except Exception as item_error: - logger.error(f"处理表情包记录时出错: {str(item_error)}") + logger.error(f"[错误] 处理表情包记录时出错: {str(item_error)}") continue # 验证清理结果 remaining_count = db.emoji.count_documents({}) if removed_count > 0: - logger.success(f"已清理 {removed_count} 个失效的表情包记录") - logger.info(f"清理前总数: {total_count} | 清理后总数: {remaining_count}") + logger.success(f"[清理] 已清理 {removed_count} 个失效的表情包记录") + logger.info(f"[统计] 清理前: {total_count} | 清理后: {remaining_count}") else: - logger.info(f"已检查 {total_count} 个表情包记录") + logger.info(f"[检查] 已检查 {total_count} 个表情包记录") except Exception as e: - logger.error(f"检查表情包完整性失败: {str(e)}") + logger.error(f"[错误] 检查表情包完整性失败: {str(e)}") logger.error(traceback.format_exc()) async def start_periodic_check(self, interval_MINS: int = 120): diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index f87f037d5..f5c7181b3 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -8,9 +8,8 @@ import os import jieba import networkx as nx -from loguru import logger from nonebot import get_driver -from ...common.database import db # 使用正确的导入语法 +from ...common.database import db from ..chat.config import global_config from ..chat.utils import ( calculate_information_content, @@ -20,6 +19,13 @@ from ..chat.utils import ( ) from ..models.utils_model import LLM_request +from ..utils.logger_config import setup_logger, LogModule + +# 配置日志 +logger = setup_logger(LogModule.MEMORY) + +logger.info("初始化记忆系统") + class Memory_graph: def __init__(self): self.G = nx.Graph() # 使用 networkx 的图结构 @@ -471,7 +477,7 @@ class Hippocampus: {'concept': concept}, {'$set': update_data} ) - logger.info(f"为节点 {concept} 添加缺失的时间字段") + logger.info(f"[时间更新] 节点 {concept} 添加缺失的时间字段") # 获取时间信息(如果不存在则使用当前时间) created_time = node.get('created_time', current_time) @@ -504,7 +510,7 @@ class Hippocampus: {'source': source, 'target': target}, {'$set': update_data} ) - logger.info(f"为边 {source} - {target} 添加缺失的时间字段") + logger.info(f"[时间更新] 边 {source} - {target} 添加缺失的时间字段") # 获取时间信息(如果不存在则使用当前时间) created_time = edge.get('created_time', current_time) @@ -518,16 +524,27 @@ class Hippocampus: last_modified=last_modified) if need_update: - logger.success("已为缺失的时间字段进行补充") + logger.success("[数据库] 已为缺失的时间字段进行补充") async def operation_forget_topic(self, percentage=0.1): """随机选择图中一定比例的节点和边进行检查,根据时间条件决定是否遗忘""" # 检查数据库是否为空 + # logger.remove() + + logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:") + # logger.info(f"- Logger名称: {logger.name}") + logger.info(f"- Logger等级: {logger.level}") + # logger.info(f"- Logger处理器: {[handler.__class__.__name__ for handler in logger.handlers]}") + + # logger2 = setup_logger(LogModule.MEMORY) + # logger2.info(f"[遗忘] 开始检查数据库... 当前Logger信息:") + # logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:") + all_nodes = list(self.memory_graph.G.nodes()) all_edges = list(self.memory_graph.G.edges()) if not all_nodes and not all_edges: - logger.info("记忆图为空,无需进行遗忘操作") + logger.info("[遗忘] 记忆图为空,无需进行遗忘操作") return check_nodes_count = max(1, int(len(all_nodes) * percentage)) @@ -542,35 +559,32 @@ class Hippocampus: current_time = datetime.datetime.now().timestamp() # 检查并遗忘连接 - logger.info("开始检查连接...") + logger.info("[遗忘] 开始检查连接...") for source, target in edges_to_check: edge_data = self.memory_graph.G[source][target] last_modified = edge_data.get('last_modified') - # print(source,target) - # print(f"float(last_modified):{float(last_modified)}" ) - # print(f"current_time:{current_time}") - # print(f"current_time - last_modified:{current_time - last_modified}") - if current_time - last_modified > 3600*global_config.memory_forget_time: # test + + if current_time - last_modified > 3600*global_config.memory_forget_time: current_strength = edge_data.get('strength', 1) new_strength = current_strength - 1 if new_strength <= 0: self.memory_graph.G.remove_edge(source, target) edge_changes['removed'] += 1 - logger.info(f"\033[1;31m[连接移除]\033[0m {source} - {target}") + logger.info(f"[遗忘] 连接移除: {source} -> {target}") else: edge_data['strength'] = new_strength edge_data['last_modified'] = current_time edge_changes['weakened'] += 1 - logger.info(f"\033[1;34m[连接减弱]\033[0m {source} - {target} (强度: {current_strength} -> {new_strength})") + logger.info(f"[遗忘] 连接减弱: {source} -> {target} (强度: {current_strength} -> {new_strength})") # 检查并遗忘话题 - logger.info("开始检查节点...") + logger.info("[遗忘] 开始检查节点...") for node in nodes_to_check: node_data = self.memory_graph.G.nodes[node] last_modified = node_data.get('last_modified', current_time) - if current_time - last_modified > 3600*24: # test + if current_time - last_modified > 3600*24: memory_items = node_data.get('memory_items', []) if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] @@ -584,27 +598,22 @@ class Hippocampus: self.memory_graph.G.nodes[node]['memory_items'] = memory_items self.memory_graph.G.nodes[node]['last_modified'] = current_time node_changes['reduced'] += 1 - logger.info(f"\033[1;33m[记忆减少]\033[0m {node} (记忆数量: {current_count} -> {len(memory_items)})") + logger.info(f"[遗忘] 记忆减少: {node} (数量: {current_count} -> {len(memory_items)})") else: self.memory_graph.G.remove_node(node) node_changes['removed'] += 1 - logger.info(f"\033[1;31m[节点移除]\033[0m {node}") + logger.info(f"[遗忘] 节点移除: {node}") if any(count > 0 for count in edge_changes.values()) or any(count > 0 for count in node_changes.values()): self.sync_memory_to_db() - logger.info("\n遗忘操作统计:") - logger.info(f"连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除") - logger.info(f"节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除") + logger.info("[遗忘] 统计信息:") + logger.info(f"[遗忘] 连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除") + logger.info(f"[遗忘] 节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除") else: - logger.info("\n本次检查没有节点或连接满足遗忘条件") + logger.info("[遗忘] 本次检查没有节点或连接满足遗忘条件") async def merge_memory(self, topic): - """ - 对指定话题的记忆进行合并压缩 - - Args: - topic: 要合并的话题节点 - """ + """对指定话题的记忆进行合并压缩""" # 获取节点的记忆项 memory_items = self.memory_graph.G.nodes[topic].get('memory_items', []) if not isinstance(memory_items, list): @@ -619,8 +628,8 @@ class Hippocampus: # 拼接成文本 merged_text = "\n".join(selected_memories) - logger.debug(f"\n[合并记忆] 话题: {topic}") - logger.debug(f"选择的记忆:\n{merged_text}") + logger.debug(f"[合并] 话题: {topic}") + logger.debug(f"[合并] 选择的记忆:\n{merged_text}") # 使用memory_compress生成新的压缩记忆 compressed_memories, _ = await self.memory_compress(selected_memories, 0.1) @@ -632,11 +641,11 @@ class Hippocampus: # 添加新的压缩记忆 for _, compressed_memory in compressed_memories: memory_items.append(compressed_memory) - logger.info(f"添加压缩记忆: {compressed_memory}") + logger.info(f"[合并] 添加压缩记忆: {compressed_memory}") # 更新节点的记忆项 self.memory_graph.G.nodes[topic]['memory_items'] = memory_items - logger.debug(f"完成记忆合并，当前记忆数量: {len(memory_items)}") + logger.debug(f"[合并] 完成记忆合并，当前记忆数量: {len(memory_items)}") async def operation_merge_memory(self, percentage=0.1): """ @@ -766,7 +775,7 @@ class Hippocampus: async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int: """计算输入文本对记忆的激活程度""" - logger.info(f"识别主题: {await self._identify_topics(text)}") + logger.info(f"[激活] 识别主题: {await self._identify_topics(text)}") # 识别主题 identified_topics = await self._identify_topics(text) @@ -777,7 +786,7 @@ class Hippocampus: all_similar_topics = self._find_similar_topics( identified_topics, similarity_threshold=similarity_threshold, - debug_info="记忆激活" + debug_info="激活" ) if not all_similar_topics: @@ -798,7 +807,7 @@ class Hippocampus: activation = int(score * 50 * penalty) logger.info( - f"[记忆激活]单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}") + f"[激活] 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}") return activation # 计算关键词匹配率，同时考虑内容数量 @@ -825,8 +834,8 @@ class Hippocampus: matched_topics.add(input_topic) adjusted_sim = sim * penalty topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim) - logger.info( - f"[记忆激活]主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})") + logger.debug( + f"[激活] 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})") # 计算主题匹配率和平均相似度 topic_match = len(matched_topics) / len(identified_topics) @@ -835,7 +844,7 @@ class Hippocampus: # 计算最终激活值 activation = int((topic_match + average_similarities) / 2 * 100) logger.info( - f"[记忆激活]匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}") + f"[激活] 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}") return activation diff --git a/src/plugins/utils/logger_config.py b/src/plugins/utils/logger_config.py new file mode 100644 index 000000000..cc15d53a4 --- /dev/null +++ b/src/plugins/utils/logger_config.py @@ -0,0 +1,71 @@ +import sys +from loguru import logger +from enum import Enum + +class LogModule(Enum): + BASE = "base" + MEMORY = "memory" + EMOJI = "emoji" + CHAT = "chat" + +def setup_logger(log_type: LogModule = LogModule.BASE): + """配置日志格式 + + Args: + log_type: 日志类型，可选值：BASE(基础日志)、MEMORY(记忆系统日志)、EMOJI(表情包系统日志) + """ + # 移除默认的处理器 + logger.remove() + + # 基础日志格式 + base_format = "{time:HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}" + + chat_format = "{time:HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}" + + # 记忆系统日志格式 + memory_format = "{time:HH:mm} | {level: <8} | 海马体 | {message}" + + # 表情包系统日志格式 + emoji_format = "{time:HH:mm} | {level: <8} | 表情包 | {function}:{line} - {message}" + # 根据日志类型选择日志格式和输出 + if log_type == LogModule.CHAT: + logger.add( + sys.stderr, + format=chat_format, + # level="INFO" + ) + elif log_type == LogModule.MEMORY: + # 同时输出到控制台和文件 + logger.add( + sys.stderr, + format=memory_format, + # level="INFO" + ) + logger.add( + "logs/memory.log", + format=memory_format, + level="INFO", + rotation="1 day", + retention="7 days" + ) + elif log_type == LogModule.EMOJI: + logger.add( + sys.stderr, + format=emoji_format, + # level="INFO" + ) + logger.add( + "logs/emoji.log", + format=emoji_format, + level="INFO", + rotation="1 day", + retention="7 days" + ) + else: # BASE + logger.add( + sys.stderr, + format=base_format, + level="INFO" + ) + + return logger