From 021e7f1a971d677a5c2498efef64cb94350dccd8 Mon Sep 17 00:00:00 2001 From: Oct-autumn Date: Fri, 16 May 2025 16:50:53 +0800 Subject: [PATCH] =?UTF-8?q?refactor:=20=E9=87=8D=E6=9E=84=E9=85=8D?= =?UTF-8?q?=E7=BD=AE=E6=A8=A1=E5=9D=97?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/api/reload_config.py | 6 +- src/chat/emoji_system/emoji_manager.py | 23 +- .../expressors/default_expressor.py | 16 +- .../expressors/exprssion_learner.py | 5 +- src/chat/focus_chat/heartflow_processor.py | 4 +- .../focus_chat/heartflow_prompt_builder.py | 40 +- .../info_processors/chattinginfo_processor.py | 11 +- .../info_processors/mind_processor.py | 4 +- .../info_processors/tool_processor.py | 2 +- src/chat/focus_chat/memory_activator.py | 3 +- src/chat/heart_flow/heartflow.py | 3 +- src/chat/heart_flow/interest_chatting.py | 4 +- src/chat/heart_flow/mai_state_manager.py | 17 +- .../observation/chatting_observation.py | 19 +- src/chat/heart_flow/subheartflow_manager.py | 7 +- src/chat/memory_system/Hippocampus.py | 56 +- src/chat/memory_system/debug_memory.py | 3 +- src/chat/memory_system/memory_config.py | 48 -- src/chat/message_receive/bot.py | 10 +- src/chat/message_receive/message_buffer.py | 4 +- src/chat/message_receive/message_sender.py | 2 +- src/chat/models/utils_model.py | 14 +- src/chat/normal_chat/normal_chat.py | 20 +- src/chat/normal_chat/normal_chat_generator.py | 15 +- .../normal_chat/willing/mode_classical.py | 28 +- src/chat/normal_chat/willing/mode_mxp.py | 9 +- .../normal_chat/willing/willing_manager.py | 5 +- src/chat/person_info/person_info.py | 3 +- src/chat/utils/chat_message_builder.py | 8 +- src/chat/utils/info_catcher.py | 3 +- src/chat/utils/utils.py | 37 +- src/chat/utils/utils_image.py | 6 +- src/common/remote.py | 11 +- src/config/config.py | 745 +++--------------- src/config/config_base.py | 116 +++ src/config/official_configs.py | 399 ++++++++++ src/experimental/PFC/action_planner.py | 4 +- src/experimental/PFC/chat_observer.py | 2 +- src/experimental/PFC/message_sender.py | 4 +- src/experimental/PFC/pfc.py | 7 +- src/experimental/PFC/pfc_KnowledgeFetcher.py | 5 +- src/experimental/PFC/reply_checker.py | 4 +- src/experimental/PFC/reply_generator.py | 2 +- src/experimental/PFC/waiter.py | 2 +- src/experimental/only_message_process.py | 4 +- src/main.py | 32 +- src/manager/mood_manager.py | 8 +- src/tools/not_used/change_mood.py | 2 +- src/tools/tool_use.py | 4 +- template/bot_config_meta.toml | 104 --- template/bot_config_template.toml | 107 ++- tests/test_config.py | 7 + 52 files changed, 902 insertions(+), 1102 deletions(-) delete mode 100644 src/chat/memory_system/memory_config.py create mode 100644 src/config/config_base.py create mode 100644 src/config/official_configs.py delete mode 100644 template/bot_config_meta.toml create mode 100644 tests/test_config.py diff --git a/src/api/reload_config.py b/src/api/reload_config.py index a5f36e3db..1772800b6 100644 --- a/src/api/reload_config.py +++ b/src/api/reload_config.py @@ -1,6 +1,6 @@ from fastapi import HTTPException from rich.traceback import install -from src.config.config import BotConfig +from src.config.config import Config from src.common.logger_manager import get_logger import os @@ -14,8 +14,8 @@ async def reload_config(): from src.config import config as config_module logger.debug("正在重载配置文件...") - bot_config_path = os.path.join(BotConfig.get_config_dir(), "bot_config.toml") - config_module.global_config = BotConfig.load_config(config_path=bot_config_path) + bot_config_path = os.path.join(Config.get_config_dir(), "bot_config.toml") + config_module.global_config = Config.load_config(config_path=bot_config_path) logger.debug("配置文件重载成功") return {"status": "reloaded"} except FileNotFoundError as e: diff --git a/src/chat/emoji_system/emoji_manager.py b/src/chat/emoji_system/emoji_manager.py index 5d800866f..52a7288ec 100644 --- a/src/chat/emoji_system/emoji_manager.py +++ b/src/chat/emoji_system/emoji_manager.py @@ -369,14 +369,15 @@ class EmojiManager: def __init__(self): self._initialized = None self._scan_task = None - self.vlm = LLMRequest(model=global_config.vlm, temperature=0.3, max_tokens=1000, request_type="emoji") + + self.vlm = LLMRequest(model=global_config.model.vlm, temperature=0.3, max_tokens=1000, request_type="emoji") self.llm_emotion_judge = LLMRequest( - model=global_config.llm_normal, max_tokens=600, request_type="emoji" + model=global_config.model.normal, max_tokens=600, request_type="emoji" ) # 更高的温度，更少的token（后续可以根据情绪来调整温度） self.emoji_num = 0 - self.emoji_num_max = global_config.max_emoji_num - self.emoji_num_max_reach_deletion = global_config.max_reach_deletion + self.emoji_num_max = global_config.emoji.max_reg_num + self.emoji_num_max_reach_deletion = global_config.emoji.do_replace self.emoji_objects: list[MaiEmoji] = [] # 存储MaiEmoji对象的列表，使用类型注解明确列表元素类型 logger.info("启动表情包管理器") @@ -613,18 +614,18 @@ class EmojiManager: logger.warning(f"[警告] 表情包目录不存在: {EMOJI_DIR}") os.makedirs(EMOJI_DIR, exist_ok=True) logger.info(f"[创建] 已创建表情包目录: {EMOJI_DIR}") - await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60) + await asyncio.sleep(global_config.emoji.check_interval * 60) continue # 检查目录是否为空 files = os.listdir(EMOJI_DIR) if not files: logger.warning(f"[警告] 表情包目录为空: {EMOJI_DIR}") - await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60) + await asyncio.sleep(global_config.emoji.check_interval * 60) continue # 检查是否需要处理表情包(数量超过最大值或不足) - if (self.emoji_num > self.emoji_num_max and global_config.max_reach_deletion) or ( + if (self.emoji_num > self.emoji_num_max and global_config.emoji.do_replace) or ( self.emoji_num < self.emoji_num_max ): try: @@ -651,7 +652,7 @@ class EmojiManager: except Exception as e: logger.error(f"[错误] 扫描表情包目录失败: {str(e)}") - await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60) + await asyncio.sleep(global_config.emoji.check_interval * 60) async def get_all_emoji_from_db(self): """获取所有表情包并初始化为MaiEmoji类对象，更新 self.emoji_objects""" @@ -788,7 +789,7 @@ class EmojiManager: # 构建提示词 prompt = ( - f"{global_config.BOT_NICKNAME}的表情包存储已满({self.emoji_num}/{self.emoji_num_max})，" + f"{global_config.bot.nickname}的表情包存储已满({self.emoji_num}/{self.emoji_num_max})，" f"需要决定是否删除一个旧表情包来为新表情包腾出空间。\n\n" f"新表情包信息：\n" f"描述: {new_emoji.description}\n\n" @@ -871,10 +872,10 @@ class EmojiManager: description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format) # 审核表情包 - if global_config.EMOJI_CHECK: + if global_config.emoji.content_filtration: prompt = f''' 这是一个表情包，请对这个表情包进行审核，标准如下： - 1. 必须符合"{global_config.EMOJI_CHECK_PROMPT}"的要求 + 1. 必须符合"{global_config.emoji.filtration_prompt}"的要求 2. 不能是色情、暴力、等违法违规内容，必须符合公序良俗 3. 不能是任何形式的截图，聊天记录或视频截图 4. 不要出现5个以上文字 diff --git a/src/chat/focus_chat/expressors/default_expressor.py b/src/chat/focus_chat/expressors/default_expressor.py index 37c50c0dc..c5aa5f9a4 100644 --- a/src/chat/focus_chat/expressors/default_expressor.py +++ b/src/chat/focus_chat/expressors/default_expressor.py @@ -25,9 +25,10 @@ logger = get_logger("expressor") class DefaultExpressor: def __init__(self, chat_id: str): self.log_prefix = "expressor" + # TODO: API-Adapter修改标记 self.express_model = LLMRequest( - model=global_config.llm_normal, - temperature=global_config.llm_normal["temp"], + model=global_config.model.normal, + temperature=global_config.model.normal["temp"], max_tokens=256, request_type="response_heartflow", ) @@ -51,8 +52,8 @@ class DefaultExpressor: messageinfo = anchor_message.message_info thinking_time_point = parse_thinking_id_to_timestamp(thinking_id) bot_user_info = UserInfo( - user_id=global_config.BOT_QQ, - user_nickname=global_config.BOT_NICKNAME, + user_id=global_config.bot.qq_account, + user_nickname=global_config.bot.nickname, platform=messageinfo.platform, ) # logger.debug(f"创建思考消息：{anchor_message}") @@ -141,7 +142,7 @@ class DefaultExpressor: try: # 1. 获取情绪影响因子并调整模型温度 arousal_multiplier = mood_manager.get_arousal_multiplier() - current_temp = float(global_config.llm_normal["temp"]) * arousal_multiplier + current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier self.express_model.params["temperature"] = current_temp # 动态调整温度 # 2. 获取信息捕捉器 @@ -183,6 +184,7 @@ class DefaultExpressor: try: with Timer("LLM生成", {}): # 内部计时器，可选保留 + # TODO: API-Adapter修改标记 # logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n") content, reasoning_content, model_name = await self.express_model.generate_response(prompt) @@ -330,8 +332,8 @@ class DefaultExpressor: thinking_start_time = await self.heart_fc_sender.get_thinking_start_time(self.chat_id, thinking_id) bot_user_info = UserInfo( - user_id=global_config.BOT_QQ, - user_nickname=global_config.BOT_NICKNAME, + user_id=global_config.bot.qq_account, + user_nickname=global_config.bot.nickname, platform=self.chat_stream.platform, ) diff --git a/src/chat/focus_chat/expressors/exprssion_learner.py b/src/chat/focus_chat/expressors/exprssion_learner.py index 942162bc8..7766fde56 100644 --- a/src/chat/focus_chat/expressors/exprssion_learner.py +++ b/src/chat/focus_chat/expressors/exprssion_learner.py @@ -77,8 +77,9 @@ def init_prompt() -> None: class ExpressionLearner: def __init__(self) -> None: + # TODO: API-Adapter修改标记 self.express_learn_model: LLMRequest = LLMRequest( - model=global_config.llm_normal, + model=global_config.model.normal, temperature=0.1, max_tokens=256, request_type="response_heartflow", @@ -289,7 +290,7 @@ class ExpressionLearner: # 构建prompt prompt = await global_prompt_manager.format_prompt( "personality_expression_prompt", - personality=global_config.expression_style, + personality=global_config.personality.expression_style, ) # logger.info(f"个性表达方式提取prompt: {prompt}") diff --git a/src/chat/focus_chat/heartflow_processor.py b/src/chat/focus_chat/heartflow_processor.py index bbfa4ce46..a4cf360a5 100644 --- a/src/chat/focus_chat/heartflow_processor.py +++ b/src/chat/focus_chat/heartflow_processor.py @@ -112,7 +112,7 @@ def _check_ban_words(text: str, chat, userinfo) -> bool: Returns: bool: 是否包含过滤词 """ - for word in global_config.ban_words: + for word in global_config.chat.ban_words: if word in text: chat_name = chat.group_info.group_name if chat.group_info else "私聊" logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}") @@ -132,7 +132,7 @@ def _check_ban_regex(text: str, chat, userinfo) -> bool: Returns: bool: 是否匹配过滤正则 """ - for pattern in global_config.ban_msgs_regex: + for pattern in global_config.chat.ban_msgs_regex: if pattern.search(text): chat_name = chat.group_info.group_name if chat.group_info else "私聊" logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}") diff --git a/src/chat/focus_chat/heartflow_prompt_builder.py b/src/chat/focus_chat/heartflow_prompt_builder.py index 55fb79b46..fae00a9db 100644 --- a/src/chat/focus_chat/heartflow_prompt_builder.py +++ b/src/chat/focus_chat/heartflow_prompt_builder.py @@ -6,14 +6,13 @@ from src.chat.utils.chat_message_builder import build_readable_messages, get_raw from src.chat.person_info.relationship_manager import relationship_manager from src.chat.utils.utils import get_embedding import time -from typing import Union, Optional, Dict, Any +from typing import Union, Optional from src.common.database import db from src.chat.utils.utils import get_recent_group_speaker from src.manager.mood_manager import mood_manager from src.chat.memory_system.Hippocampus import HippocampusManager from src.chat.knowledge.knowledge_lib import qa_manager from src.chat.focus_chat.expressors.exprssion_learner import expression_learner -import traceback import random @@ -142,7 +141,7 @@ async def _build_prompt_focus( message_list_before_now = get_raw_msg_before_timestamp_with_chat( chat_id=chat_stream.stream_id, timestamp=time.time(), - limit=global_config.observation_context_size, + limit=global_config.chat.observation_context_size, ) chat_talking_prompt = await build_readable_messages( message_list_before_now, @@ -209,7 +208,7 @@ async def _build_prompt_focus( chat_target=chat_target_1, # Used in group template # chat_talking_prompt=chat_talking_prompt, chat_info=chat_talking_prompt, - bot_name=global_config.BOT_NICKNAME, + bot_name=global_config.bot.nickname, # prompt_personality=prompt_personality, prompt_personality="", reason=reason, @@ -225,7 +224,7 @@ async def _build_prompt_focus( info_from_tools=structured_info_prompt, sender_name=effective_sender_name, # Used in private template chat_talking_prompt=chat_talking_prompt, - bot_name=global_config.BOT_NICKNAME, + bot_name=global_config.bot.nickname, prompt_personality=prompt_personality, # chat_target and chat_target_2 are not used in private template current_mind_info=current_mind_info, @@ -280,7 +279,7 @@ class PromptBuilder: who_chat_in_group = get_recent_group_speaker( chat_stream.stream_id, (chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None, - limit=global_config.observation_context_size, + limit=global_config.chat.observation_context_size, ) elif chat_stream.user_info: who_chat_in_group.append( @@ -328,7 +327,7 @@ class PromptBuilder: message_list_before_now = get_raw_msg_before_timestamp_with_chat( chat_id=chat_stream.stream_id, timestamp=time.time(), - limit=global_config.observation_context_size, + limit=global_config.chat.observation_context_size, ) chat_talking_prompt = await build_readable_messages( message_list_before_now, @@ -340,18 +339,15 @@ class PromptBuilder: # 关键词检测与反应 keywords_reaction_prompt = "" - for rule in global_config.keywords_reaction_rules: - if rule.get("enable", False): - if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])): - logger.info( - f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}" - ) - keywords_reaction_prompt += rule.get("reaction", "") + "，" + for rule in global_config.keyword_reaction.rules: + if rule.enable: + if any(keyword in message_txt for keyword in rule.keywords): + logger.info(f"检测到以下关键词之一：{rule.keywords}，触发反应：{rule.reaction}") + keywords_reaction_prompt += f"{rule.reaction}，" else: - for pattern in rule.get("regex", []): - result = pattern.search(message_txt) - if result: - reaction = rule.get("reaction", "") + for pattern in rule.regex: + if result := pattern.search(message_txt): + reaction = rule.reaction for name, content in result.groupdict().items(): reaction = reaction.replace(f"[{name}]", content) logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}") @@ -397,8 +393,8 @@ class PromptBuilder: chat_target_2=chat_target_2, chat_talking_prompt=chat_talking_prompt, message_txt=message_txt, - bot_name=global_config.BOT_NICKNAME, - bot_other_names="/".join(global_config.BOT_ALIAS_NAMES), + bot_name=global_config.bot.nickname, + bot_other_names="/".join(global_config.bot.alias_names), prompt_personality=prompt_personality, mood_prompt=mood_prompt, reply_style1=reply_style1_chosen, @@ -419,8 +415,8 @@ class PromptBuilder: prompt_info=prompt_info, chat_talking_prompt=chat_talking_prompt, message_txt=message_txt, - bot_name=global_config.BOT_NICKNAME, - bot_other_names="/".join(global_config.BOT_ALIAS_NAMES), + bot_name=global_config.bot.nickname, + bot_other_names="/".join(global_config.bot.alias_names), prompt_personality=prompt_personality, mood_prompt=mood_prompt, reply_style1=reply_style1_chosen, diff --git a/src/chat/focus_chat/info_processors/chattinginfo_processor.py b/src/chat/focus_chat/info_processors/chattinginfo_processor.py index 12bc8560a..bb70c043a 100644 --- a/src/chat/focus_chat/info_processors/chattinginfo_processor.py +++ b/src/chat/focus_chat/info_processors/chattinginfo_processor.py @@ -26,8 +26,9 @@ class ChattingInfoProcessor(BaseProcessor): def __init__(self): """初始化观察处理器""" super().__init__() + # TODO: API-Adapter修改标记 self.llm_summary = LLMRequest( - model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation" + model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation" ) async def process_info( @@ -108,12 +109,12 @@ class ChattingInfoProcessor(BaseProcessor): "created_at": datetime.now().timestamp(), } - obs.mid_memorys.append(mid_memory) - if len(obs.mid_memorys) > obs.max_mid_memory_len: - obs.mid_memorys.pop(0) # 移除最旧的 + obs.mid_memories.append(mid_memory) + if len(obs.mid_memories) > obs.max_mid_memory_len: + obs.mid_memories.pop(0) # 移除最旧的 mid_memory_str = "之前聊天的内容概述是：\n" - for mid_memory_item in obs.mid_memorys: # 重命名循环变量以示区分 + for mid_memory_item in obs.mid_memories: # 重命名循环变量以示区分 time_diff = int((datetime.now().timestamp() - mid_memory_item["created_at"]) / 60) mid_memory_str += ( f"距离现在{time_diff}分钟前(聊天记录id:{mid_memory_item['id']})：{mid_memory_item['theme']}\n" diff --git a/src/chat/focus_chat/info_processors/mind_processor.py b/src/chat/focus_chat/info_processors/mind_processor.py index 1a104e123..221935e3d 100644 --- a/src/chat/focus_chat/info_processors/mind_processor.py +++ b/src/chat/focus_chat/info_processors/mind_processor.py @@ -81,8 +81,8 @@ class MindProcessor(BaseProcessor): self.subheartflow_id = subheartflow_id self.llm_model = LLMRequest( - model=global_config.llm_sub_heartflow, - temperature=global_config.llm_sub_heartflow["temp"], + model=global_config.model.sub_heartflow, + temperature=global_config.model.sub_heartflow["temp"], max_tokens=800, request_type="sub_heart_flow", ) diff --git a/src/chat/focus_chat/info_processors/tool_processor.py b/src/chat/focus_chat/info_processors/tool_processor.py index 8840c1ae4..57bac5f79 100644 --- a/src/chat/focus_chat/info_processors/tool_processor.py +++ b/src/chat/focus_chat/info_processors/tool_processor.py @@ -52,7 +52,7 @@ class ToolProcessor(BaseProcessor): self.subheartflow_id = subheartflow_id self.log_prefix = f"[{subheartflow_id}:ToolExecutor] " self.llm_model = LLMRequest( - model=global_config.llm_tool_use, + model=global_config.model.tool_use, max_tokens=500, request_type="tool_execution", ) diff --git a/src/chat/focus_chat/memory_activator.py b/src/chat/focus_chat/memory_activator.py index 2d7fea034..4faf43747 100644 --- a/src/chat/focus_chat/memory_activator.py +++ b/src/chat/focus_chat/memory_activator.py @@ -34,8 +34,9 @@ def init_prompt(): class MemoryActivator: def __init__(self): + # TODO: API-Adapter修改标记 self.summary_model = LLMRequest( - model=global_config.llm_summary, temperature=0.7, max_tokens=50, request_type="chat_observation" + model=global_config.model.summary, temperature=0.7, max_tokens=50, request_type="chat_observation" ) self.running_memory = [] diff --git a/src/chat/heart_flow/heartflow.py b/src/chat/heart_flow/heartflow.py index ad876bcf0..748c8331e 100644 --- a/src/chat/heart_flow/heartflow.py +++ b/src/chat/heart_flow/heartflow.py @@ -35,8 +35,9 @@ class Heartflow: self.subheartflow_manager: SubHeartflowManager = SubHeartflowManager(self.current_state) # LLM模型配置 + # TODO: API-Adapter修改标记 self.llm_model = LLMRequest( - model=global_config.llm_heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow" + model=global_config.model.heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow" ) # 外部依赖模块 diff --git a/src/chat/heart_flow/interest_chatting.py b/src/chat/heart_flow/interest_chatting.py index 45f7fe952..bce372b5c 100644 --- a/src/chat/heart_flow/interest_chatting.py +++ b/src/chat/heart_flow/interest_chatting.py @@ -20,9 +20,9 @@ MAX_REPLY_PROBABILITY = 1 class InterestChatting: def __init__( self, - decay_rate=global_config.default_decay_rate_per_second, + decay_rate=global_config.focus_chat.default_decay_rate_per_second, max_interest=MAX_INTEREST, - trigger_threshold=global_config.reply_trigger_threshold, + trigger_threshold=global_config.focus_chat.reply_trigger_threshold, max_probability=MAX_REPLY_PROBABILITY, ): # 基础属性初始化 diff --git a/src/chat/heart_flow/mai_state_manager.py b/src/chat/heart_flow/mai_state_manager.py index 7dea910e9..017656ad2 100644 --- a/src/chat/heart_flow/mai_state_manager.py +++ b/src/chat/heart_flow/mai_state_manager.py @@ -18,19 +18,14 @@ enable_unlimited_hfc_chat = True # 调试用：无限专注聊天 prevent_offline_state = True # 目前默认不启用OFFLINE状态 -# 不同状态下普通聊天的最大消息数 -base_normal_chat_num = global_config.base_normal_chat_num -base_focused_chat_num = global_config.base_focused_chat_num - - -MAX_NORMAL_CHAT_NUM_PEEKING = int(base_normal_chat_num / 2) -MAX_NORMAL_CHAT_NUM_NORMAL = base_normal_chat_num -MAX_NORMAL_CHAT_NUM_FOCUSED = base_normal_chat_num + 1 +MAX_NORMAL_CHAT_NUM_PEEKING = int(global_config.chat.base_normal_chat_num / 2) +MAX_NORMAL_CHAT_NUM_NORMAL = global_config.chat.base_normal_chat_num +MAX_NORMAL_CHAT_NUM_FOCUSED = global_config.chat.base_normal_chat_num + 1 # 不同状态下专注聊天的最大消息数 -MAX_FOCUSED_CHAT_NUM_PEEKING = int(base_focused_chat_num / 2) -MAX_FOCUSED_CHAT_NUM_NORMAL = base_focused_chat_num -MAX_FOCUSED_CHAT_NUM_FOCUSED = base_focused_chat_num + 2 +MAX_FOCUSED_CHAT_NUM_PEEKING = int(global_config.chat.base_focused_chat_num / 2) +MAX_FOCUSED_CHAT_NUM_NORMAL = global_config.chat.base_focused_chat_num +MAX_FOCUSED_CHAT_NUM_FOCUSED = global_config.chat.base_focused_chat_num + 2 # -- 状态定义 -- diff --git a/src/chat/heart_flow/observation/chatting_observation.py b/src/chat/heart_flow/observation/chatting_observation.py index a51eba5e2..c30bc8e43 100644 --- a/src/chat/heart_flow/observation/chatting_observation.py +++ b/src/chat/heart_flow/observation/chatting_observation.py @@ -53,19 +53,20 @@ class ChattingObservation(Observation): self.talking_message = [] self.talking_message_str = "" self.talking_message_str_truncate = "" - self.name = global_config.BOT_NICKNAME - self.nick_name = global_config.BOT_ALIAS_NAMES - self.max_now_obs_len = global_config.observation_context_size - self.overlap_len = global_config.compressed_length - self.mid_memorys = [] - self.max_mid_memory_len = global_config.compress_length_limit + self.name = global_config.bot.nickname + self.nick_name = global_config.bot.alias_names + self.max_now_obs_len = global_config.chat.observation_context_size + self.overlap_len = global_config.focus_chat.compressed_length + self.mid_memories = [] + self.max_mid_memory_len = global_config.focus_chat.compress_length_limit self.mid_memory_info = "" self.person_list = [] self.oldest_messages = [] self.oldest_messages_str = "" self.compressor_prompt = "" + # TODO: API-Adapter修改标记 self.llm_summary = LLMRequest( - model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation" + model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation" ) async def initialize(self): @@ -83,7 +84,7 @@ class ChattingObservation(Observation): for id in ids: print(f"id：{id}") try: - for mid_memory in self.mid_memorys: + for mid_memory in self.mid_memories: if mid_memory["id"] == id: mid_memory_by_id = mid_memory msg_str = "" @@ -101,7 +102,7 @@ class ChattingObservation(Observation): else: mid_memory_str = "之前的聊天内容：\n" - for mid_memory in self.mid_memorys: + for mid_memory in self.mid_memories: mid_memory_str += f"{mid_memory['theme']}\n" return mid_memory_str + "现在群里正在聊：\n" + self.talking_message_str diff --git a/src/chat/heart_flow/subheartflow_manager.py b/src/chat/heart_flow/subheartflow_manager.py index a4bff8338..bf4ddf7e1 100644 --- a/src/chat/heart_flow/subheartflow_manager.py +++ b/src/chat/heart_flow/subheartflow_manager.py @@ -76,8 +76,9 @@ class SubHeartflowManager: # 为 LLM 状态评估创建一个 LLMRequest 实例 # 使用与 Heartflow 相同的模型和参数 + # TODO: API-Adapter修改标记 self.llm_state_evaluator = LLMRequest( - model=global_config.llm_heartflow, # 与 Heartflow 一致 + model=global_config.model.heartflow, # 与 Heartflow 一致 temperature=0.6, # 与 Heartflow 一致 max_tokens=1000, # 与 Heartflow 一致 (虽然可能不需要这么多) request_type="subheartflow_state_eval", # 保留特定的请求类型 @@ -278,7 +279,7 @@ class SubHeartflowManager: focused_limit = current_state.get_focused_chat_max_num() # --- 新增：检查是否允许进入 FOCUS 模式 --- # - if not global_config.allow_focus_mode: + if not global_config.chat.allow_focus_mode: if int(time.time()) % 60 == 0: # 每60秒输出一次日志避免刷屏 logger.trace("未开启 FOCUSED 状态 (allow_focus_mode=False)") return # 如果不允许，直接返回 @@ -766,7 +767,7 @@ class SubHeartflowManager: focused_limit = current_mai_state.get_focused_chat_max_num() # --- 检查是否允许 FOCUS 模式 --- # - if not global_config.allow_focus_mode: + if not global_config.chat.allow_focus_mode: # Log less frequently to avoid spam # if int(time.time()) % 60 == 0: # logger.debug(f"{log_prefix_task} 配置不允许进入 FOCUSED 状态") diff --git a/src/chat/memory_system/Hippocampus.py b/src/chat/memory_system/Hippocampus.py index 70eb679c9..d8c7c50e6 100644 --- a/src/chat/memory_system/Hippocampus.py +++ b/src/chat/memory_system/Hippocampus.py @@ -19,9 +19,10 @@ from ..utils.chat_message_builder import ( build_readable_messages, ) # 导入 build_readable_messages from ..utils.utils import translate_timestamp_to_human_readable -from .memory_config import MemoryConfig from rich.traceback import install +from ...config.config import global_config + install(extra_lines=3) @@ -195,18 +196,16 @@ class Hippocampus: self.llm_summary = None self.entorhinal_cortex = None self.parahippocampal_gyrus = None - self.config = None - def initialize(self, global_config): - # 使用导入的 MemoryConfig dataclass 和其 from_global_config 方法 - self.config = MemoryConfig.from_global_config(global_config) + def initialize(self): # 初始化子组件 self.entorhinal_cortex = EntorhinalCortex(self) self.parahippocampal_gyrus = ParahippocampalGyrus(self) # 从数据库加载记忆图 self.entorhinal_cortex.sync_memory_from_db() - self.llm_topic_judge = LLMRequest(self.config.llm_topic_judge, request_type="memory") - self.llm_summary = LLMRequest(self.config.llm_summary, request_type="memory") + # TODO: API-Adapter修改标记 + self.llm_topic_judge = LLMRequest(global_config.model.topic_judge, request_type="memory") + self.llm_summary = LLMRequest(global_config.model.summary, request_type="memory") def get_all_node_names(self) -> list: """获取记忆图中所有节点的名字列表""" @@ -792,7 +791,6 @@ class EntorhinalCortex: def __init__(self, hippocampus: Hippocampus): self.hippocampus = hippocampus self.memory_graph = hippocampus.memory_graph - self.config = hippocampus.config def get_memory_sample(self): """从数据库获取记忆样本""" @@ -801,13 +799,13 @@ class EntorhinalCortex: # 创建双峰分布的记忆调度器 sample_scheduler = MemoryBuildScheduler( - n_hours1=self.config.memory_build_distribution[0], - std_hours1=self.config.memory_build_distribution[1], - weight1=self.config.memory_build_distribution[2], - n_hours2=self.config.memory_build_distribution[3], - std_hours2=self.config.memory_build_distribution[4], - weight2=self.config.memory_build_distribution[5], - total_samples=self.config.build_memory_sample_num, + n_hours1=global_config.memory.memory_build_distribution[0], + std_hours1=global_config.memory.memory_build_distribution[1], + weight1=global_config.memory.memory_build_distribution[2], + n_hours2=global_config.memory.memory_build_distribution[3], + std_hours2=global_config.memory.memory_build_distribution[4], + weight2=global_config.memory.memory_build_distribution[5], + total_samples=global_config.memory.memory_build_sample_num, ) timestamps = sample_scheduler.get_timestamp_array() @@ -818,7 +816,7 @@ class EntorhinalCortex: for timestamp in timestamps: # 调用修改后的 random_get_msg_snippet messages = self.random_get_msg_snippet( - timestamp, self.config.build_memory_sample_length, max_memorized_time_per_msg + timestamp, global_config.memory.memory_build_sample_length, max_memorized_time_per_msg ) if messages: time_diff = (datetime.datetime.now().timestamp() - timestamp) / 3600 @@ -1099,7 +1097,6 @@ class ParahippocampalGyrus: def __init__(self, hippocampus: Hippocampus): self.hippocampus = hippocampus self.memory_graph = hippocampus.memory_graph - self.config = hippocampus.config async def memory_compress(self, messages: list, compress_rate=0.1): """压缩和总结消息内容，生成记忆主题和摘要。 @@ -1159,7 +1156,7 @@ class ParahippocampalGyrus: # 3. 过滤掉包含禁用关键词的topic filtered_topics = [ - topic for topic in topics if not any(keyword in topic for keyword in self.config.memory_ban_words) + topic for topic in topics if not any(keyword in topic for keyword in global_config.memory.memory_ban_words) ] logger.debug(f"过滤后话题: {filtered_topics}") @@ -1222,7 +1219,7 @@ class ParahippocampalGyrus: bar = "█" * filled_length + "-" * (bar_length - filled_length) logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})") - compress_rate = self.config.memory_compress_rate + compress_rate = global_config.memory.memory_compress_rate try: compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate) except Exception as e: @@ -1322,7 +1319,7 @@ class ParahippocampalGyrus: edge_data = self.memory_graph.G[source][target] last_modified = edge_data.get("last_modified") - if current_time - last_modified > 3600 * self.config.memory_forget_time: + if current_time - last_modified > 3600 * global_config.memory.memory_forget_time: current_strength = edge_data.get("strength", 1) new_strength = current_strength - 1 @@ -1430,8 +1427,8 @@ class ParahippocampalGyrus: async def operation_consolidate_memory(self): """整合记忆：合并节点内相似的记忆项""" start_time = time.time() - percentage = self.config.consolidate_memory_percentage - similarity_threshold = self.config.consolidation_similarity_threshold + percentage = global_config.memory.consolidate_memory_percentage + similarity_threshold = global_config.memory.consolidation_similarity_threshold logger.info(f"[整合] 开始检查记忆节点... 检查比例: {percentage:.2%}, 合并阈值: {similarity_threshold}") # 获取所有至少有2条记忆项的节点 @@ -1544,7 +1541,6 @@ class ParahippocampalGyrus: class HippocampusManager: _instance = None _hippocampus = None - _global_config = None _initialized = False @classmethod @@ -1559,19 +1555,15 @@ class HippocampusManager: raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法") return cls._hippocampus - def initialize(self, global_config): + def initialize(self): """初始化海马体实例""" if self._initialized: return self._hippocampus - self._global_config = global_config self._hippocampus = Hippocampus() - self._hippocampus.initialize(global_config) + self._hippocampus.initialize() self._initialized = True - # 输出记忆系统参数信息 - config = self._hippocampus.config - # 输出记忆图统计信息 memory_graph = self._hippocampus.memory_graph.G node_count = len(memory_graph.nodes()) @@ -1579,9 +1571,9 @@ class HippocampusManager: logger.success(f"""-------------------------------- 记忆系统参数配置: - 构建间隔: {global_config.build_memory_interval}秒|样本数: {config.build_memory_sample_num},长度: {config.build_memory_sample_length}|压缩率: {config.memory_compress_rate} - 记忆构建分布: {config.memory_build_distribution} - 遗忘间隔: {global_config.forget_memory_interval}秒|遗忘比例: {global_config.memory_forget_percentage}|遗忘: {config.memory_forget_time}小时之后 + 构建间隔: {global_config.memory.memory_build_interval}秒|样本数: {global_config.memory.memory_build_sample_num},长度: {global_config.memory.memory_build_sample_length}|压缩率: {global_config.memory.memory_compress_rate} + 记忆构建分布: {global_config.memory.memory_build_distribution} + 遗忘间隔: {global_config.memory.forget_memory_interval}秒|遗忘比例: {global_config.memory.memory_forget_percentage}|遗忘: {global_config.memory.memory_forget_time}小时之后 记忆图统计信息: 节点数量: {node_count}, 连接数量: {edge_count} --------------------------------""") # noqa: E501 diff --git a/src/chat/memory_system/debug_memory.py b/src/chat/memory_system/debug_memory.py index baf745409..b09e703a1 100644 --- a/src/chat/memory_system/debug_memory.py +++ b/src/chat/memory_system/debug_memory.py @@ -7,7 +7,6 @@ import os # 添加项目根目录到系统路径 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))) from src.chat.memory_system.Hippocampus import HippocampusManager -from src.config.config import global_config from rich.traceback import install install(extra_lines=3) @@ -19,7 +18,7 @@ async def test_memory_system(): # 初始化记忆系统 print("开始初始化记忆系统...") hippocampus_manager = HippocampusManager.get_instance() - hippocampus_manager.initialize(global_config=global_config) + hippocampus_manager.initialize() print("记忆系统初始化完成") # 测试记忆构建 diff --git a/src/chat/memory_system/memory_config.py b/src/chat/memory_system/memory_config.py deleted file mode 100644 index b82e54ec1..000000000 --- a/src/chat/memory_system/memory_config.py +++ /dev/null @@ -1,48 +0,0 @@ -from dataclasses import dataclass -from typing import List - - -@dataclass -class MemoryConfig: - """记忆系统配置类""" - - # 记忆构建相关配置 - memory_build_distribution: List[float] # 记忆构建的时间分布参数 - build_memory_sample_num: int # 每次构建记忆的样本数量 - build_memory_sample_length: int # 每个样本的消息长度 - memory_compress_rate: float # 记忆压缩率 - - # 记忆遗忘相关配置 - memory_forget_time: int # 记忆遗忘时间（小时） - - # 记忆过滤相关配置 - memory_ban_words: List[str] # 记忆过滤词列表 - - # 新增：记忆整合相关配置 - consolidation_similarity_threshold: float # 相似度阈值 - consolidate_memory_percentage: float # 检查节点比例 - consolidate_memory_interval: int # 记忆整合间隔 - - llm_topic_judge: str # 话题判断模型 - llm_summary: str # 话题总结模型 - - @classmethod - def from_global_config(cls, global_config): - """从全局配置创建记忆系统配置""" - # 使用 getattr 提供默认值，防止全局配置缺少这些项 - return cls( - memory_build_distribution=getattr( - global_config, "memory_build_distribution", (24, 12, 0.5, 168, 72, 0.5) - ), # 添加默认值 - build_memory_sample_num=getattr(global_config, "build_memory_sample_num", 5), - build_memory_sample_length=getattr(global_config, "build_memory_sample_length", 30), - memory_compress_rate=getattr(global_config, "memory_compress_rate", 0.1), - memory_forget_time=getattr(global_config, "memory_forget_time", 24 * 7), - memory_ban_words=getattr(global_config, "memory_ban_words", []), - # 新增加载整合配置，并提供默认值 - consolidation_similarity_threshold=getattr(global_config, "consolidation_similarity_threshold", 0.7), - consolidate_memory_percentage=getattr(global_config, "consolidate_memory_percentage", 0.01), - consolidate_memory_interval=getattr(global_config, "consolidate_memory_interval", 1000), - llm_topic_judge=getattr(global_config, "llm_topic_judge", "default_judge_model"), # 添加默认模型名 - llm_summary=getattr(global_config, "llm_summary", "default_summary_model"), # 添加默认模型名 - ) diff --git a/src/chat/message_receive/bot.py b/src/chat/message_receive/bot.py index 3c9e4420c..0e35f6f6e 100644 --- a/src/chat/message_receive/bot.py +++ b/src/chat/message_receive/bot.py @@ -41,7 +41,7 @@ class ChatBot: chat_id = str(message.chat_stream.stream_id) private_name = str(message.message_info.user_info.user_nickname) - if global_config.enable_pfc_chatting: + if global_config.experimental.enable_pfc_chatting: await self.pfc_manager.get_or_create_conversation(chat_id, private_name) except Exception as e: @@ -78,19 +78,19 @@ class ChatBot: userinfo = message.message_info.user_info # 用户黑名单拦截 - if userinfo.user_id in global_config.ban_user_id: + if userinfo.user_id in global_config.chat_target.ban_user_id: logger.debug(f"用户{userinfo.user_id}被禁止回复") return if groupinfo is None: logger.trace("检测到私聊消息，检查") # 好友黑名单拦截 - if userinfo.user_id not in global_config.talk_allowed_private: + if userinfo.user_id not in global_config.experimental.talk_allowed_private: logger.debug(f"用户{userinfo.user_id}没有私聊权限") return # 群聊黑名单拦截 - if groupinfo is not None and groupinfo.group_id not in global_config.talk_allowed_groups: + if groupinfo is not None and groupinfo.group_id not in global_config.chat_target.talk_allowed_groups: logger.trace(f"群{groupinfo.group_id}被禁止回复") return @@ -112,7 +112,7 @@ class ChatBot: if groupinfo is None: logger.trace("检测到私聊消息") # 是否在配置信息中开启私聊模式 - if global_config.enable_friend_chat: + if global_config.experimental.enable_friend_chat: logger.trace("私聊模式已启用") # 是否进入PFC if global_config.enable_pfc_chatting: diff --git a/src/chat/message_receive/message_buffer.py b/src/chat/message_receive/message_buffer.py index f3cf63d0a..2df256ce5 100644 --- a/src/chat/message_receive/message_buffer.py +++ b/src/chat/message_receive/message_buffer.py @@ -38,7 +38,7 @@ class MessageBuffer: async def start_caching_messages(self, message: MessageRecv): """添加消息，启动缓冲""" - if not global_config.message_buffer: + if not global_config.chat.message_buffer: person_id = person_info_manager.get_person_id( message.message_info.user_info.platform, message.message_info.user_info.user_id ) @@ -107,7 +107,7 @@ class MessageBuffer: async def query_buffer_result(self, message: MessageRecv) -> bool: """查询缓冲结果，并清理""" - if not global_config.message_buffer: + if not global_config.chat.message_buffer: return True person_id_ = self.get_person_id_( message.message_info.platform, message.message_info.user_info.user_id, message.message_info.group_info diff --git a/src/chat/message_receive/message_sender.py b/src/chat/message_receive/message_sender.py index 5db34fdea..cf5877989 100644 --- a/src/chat/message_receive/message_sender.py +++ b/src/chat/message_receive/message_sender.py @@ -279,7 +279,7 @@ class MessageManager: ) # 检查是否超时 - if thinking_time > global_config.thinking_timeout: + if thinking_time > global_config.normal_chat.thinking_timeout: logger.warning( f"[{chat_id}] 消息思考超时 ({thinking_time:.1f}秒)，移除消息 {message_earliest.message_info.message_id}" ) diff --git a/src/chat/models/utils_model.py b/src/chat/models/utils_model.py index e662a8e33..a161ae4d9 100644 --- a/src/chat/models/utils_model.py +++ b/src/chat/models/utils_model.py @@ -111,8 +111,8 @@ class LLMRequest: def __init__(self, model: dict, **kwargs): # 将大写的配置键转换为小写并从config中获取实际值 try: - self.api_key = os.environ[model["key"]] - self.base_url = os.environ[model["base_url"]] + self.api_key = os.environ[f"{model['provider']}_KEY"] + self.base_url = os.environ[f"{model['provider']}_BASE_URL"] except AttributeError as e: logger.error(f"原始 model dict 信息：{model}") logger.error(f"配置错误：找不到对应的配置项 - {str(e)}") @@ -500,11 +500,11 @@ class LLMRequest: logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}") # 对全局配置进行更新 - if global_config.llm_normal.get("name") == old_model_name: - global_config.llm_normal["name"] = self.model_name + if global_config.model.normal.get("name") == old_model_name: + global_config.model.normal["name"] = self.model_name logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}") - if global_config.llm_reasoning.get("name") == old_model_name: - global_config.llm_reasoning["name"] = self.model_name + if global_config.model.reasoning.get("name") == old_model_name: + global_config.model.reasoning["name"] = self.model_name logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}") if payload and "model" in payload: @@ -636,7 +636,7 @@ class LLMRequest: **params_copy, } if "max_tokens" not in payload and "max_completion_tokens" not in payload: - payload["max_tokens"] = global_config.model_max_output_length + payload["max_tokens"] = global_config.model.model_max_output_length # 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查 if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION and "max_tokens" in payload: payload["max_completion_tokens"] = payload.pop("max_tokens") diff --git a/src/chat/normal_chat/normal_chat.py b/src/chat/normal_chat/normal_chat.py index 9dc2454ff..96cc2b8cb 100644 --- a/src/chat/normal_chat/normal_chat.py +++ b/src/chat/normal_chat/normal_chat.py @@ -73,8 +73,8 @@ class NormalChat: messageinfo = message.message_info bot_user_info = UserInfo( - user_id=global_config.BOT_QQ, - user_nickname=global_config.BOT_NICKNAME, + user_id=global_config.bot.qq_account, + user_nickname=global_config.bot.nickname, platform=messageinfo.platform, ) @@ -121,8 +121,8 @@ class NormalChat: message_id=thinking_id, chat_stream=self.chat_stream, # 使用 self.chat_stream bot_user_info=UserInfo( - user_id=global_config.BOT_QQ, - user_nickname=global_config.BOT_NICKNAME, + user_id=global_config.bot.qq_account, + user_nickname=global_config.bot.nickname, platform=message.message_info.platform, ), sender_info=message.message_info.user_info, @@ -147,7 +147,7 @@ class NormalChat: # 改为实例方法 async def _handle_emoji(self, message: MessageRecv, response: str): """处理表情包""" - if random() < global_config.emoji_chance: + if random() < global_config.normal_chat.emoji_chance: emoji_raw = await emoji_manager.get_emoji_for_text(response) if emoji_raw: emoji_path, description = emoji_raw @@ -160,8 +160,8 @@ class NormalChat: message_id="mt" + str(thinking_time_point), chat_stream=self.chat_stream, # 使用 self.chat_stream bot_user_info=UserInfo( - user_id=global_config.BOT_QQ, - user_nickname=global_config.BOT_NICKNAME, + user_id=global_config.bot.qq_account, + user_nickname=global_config.bot.nickname, platform=message.message_info.platform, ), sender_info=message.message_info.user_info, @@ -186,7 +186,7 @@ class NormalChat: label=emotion, stance=stance, # 使用 self.chat_stream ) - self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor) + self.mood_manager.update_mood_from_emotion(emotion, global_config.mood.mood_intensity_factor) async def _reply_interested_message(self) -> None: """ @@ -430,7 +430,7 @@ class NormalChat: def _check_ban_words(text: str, chat: ChatStream, userinfo: UserInfo) -> bool: """检查消息中是否包含过滤词""" stream_name = chat_manager.get_stream_name(chat.stream_id) or chat.stream_id - for word in global_config.ban_words: + for word in global_config.chat.ban_words: if word in text: logger.info( f"[{stream_name}][{chat.group_info.group_name if chat.group_info else '私聊'}]" @@ -445,7 +445,7 @@ class NormalChat: def _check_ban_regex(text: str, chat: ChatStream, userinfo: UserInfo) -> bool: """检查消息是否匹配过滤正则表达式""" stream_name = chat_manager.get_stream_name(chat.stream_id) or chat.stream_id - for pattern in global_config.ban_msgs_regex: + for pattern in global_config.chat.ban_msgs_regex: if pattern.search(text): logger.info( f"[{stream_name}][{chat.group_info.group_name if chat.group_info else '私聊'}]" diff --git a/src/chat/normal_chat/normal_chat_generator.py b/src/chat/normal_chat/normal_chat_generator.py index aec65ed1d..631f7baa5 100644 --- a/src/chat/normal_chat/normal_chat_generator.py +++ b/src/chat/normal_chat/normal_chat_generator.py @@ -15,21 +15,22 @@ logger = get_logger("llm") class NormalChatGenerator: def __init__(self): + # TODO: API-Adapter修改标记 self.model_reasoning = LLMRequest( - model=global_config.llm_reasoning, + model=global_config.model.reasoning, temperature=0.7, max_tokens=3000, request_type="response_reasoning", ) self.model_normal = LLMRequest( - model=global_config.llm_normal, - temperature=global_config.llm_normal["temp"], + model=global_config.model.normal, + temperature=global_config.model.normal["temp"], max_tokens=256, request_type="response_reasoning", ) self.model_sum = LLMRequest( - model=global_config.llm_summary, temperature=0.7, max_tokens=3000, request_type="relation" + model=global_config.model.summary, temperature=0.7, max_tokens=3000, request_type="relation" ) self.current_model_type = "r1" # 默认使用 R1 self.current_model_name = "unknown model" @@ -37,7 +38,7 @@ class NormalChatGenerator: async def generate_response(self, message: MessageThinking, thinking_id: str) -> Optional[Union[str, List[str]]]: """根据当前模型类型选择对应的生成函数""" # 从global_config中获取模型概率值并选择模型 - if random.random() < global_config.model_reasoning_probability: + if random.random() < global_config.normal_chat.reasoning_model_probability: self.current_model_type = "深深地" current_model = self.model_reasoning else: @@ -51,7 +52,7 @@ class NormalChatGenerator: model_response = await self._generate_response_with_model(message, current_model, thinking_id) if model_response: - logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}") + logger.info(f"{global_config.bot.nickname}的回复是：{model_response}") model_response = await self._process_response(model_response) return model_response @@ -113,7 +114,7 @@ class NormalChatGenerator: - "中立"：不表达明确立场或无关回应 2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签 3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒" - 4. 考虑回复者的人格设定为{global_config.personality_core} + 4. 考虑回复者的人格设定为{global_config.personality.personality_core} 对话示例： 被回复：「A就是笨」 diff --git a/src/chat/normal_chat/willing/mode_classical.py b/src/chat/normal_chat/willing/mode_classical.py index e96aa77a7..a9f04273a 100644 --- a/src/chat/normal_chat/willing/mode_classical.py +++ b/src/chat/normal_chat/willing/mode_classical.py @@ -1,18 +1,20 @@ import asyncio + +from src.config.config import global_config from .willing_manager import BaseWillingManager class ClassicalWillingManager(BaseWillingManager): def __init__(self): super().__init__() - self._decay_task: asyncio.Task = None + self._decay_task: asyncio.Task | None = None async def _decay_reply_willing(self): """定期衰减回复意愿""" while True: await asyncio.sleep(1) for chat_id in self.chat_reply_willing: - self.chat_reply_willing[chat_id] = max(0, self.chat_reply_willing[chat_id] * 0.9) + self.chat_reply_willing[chat_id] = max(0.0, self.chat_reply_willing[chat_id] * 0.9) async def async_task_starter(self): if self._decay_task is None: @@ -23,35 +25,33 @@ class ClassicalWillingManager(BaseWillingManager): chat_id = willing_info.chat_id current_willing = self.chat_reply_willing.get(chat_id, 0) - interested_rate = willing_info.interested_rate * self.global_config.response_interested_rate_amplifier + interested_rate = willing_info.interested_rate * global_config.normal_chat.response_interested_rate_amplifier if interested_rate > 0.4: current_willing += interested_rate - 0.3 - if willing_info.is_mentioned_bot and current_willing < 1.0: - current_willing += 1 - elif willing_info.is_mentioned_bot: - current_willing += 0.05 + if willing_info.is_mentioned_bot: + current_willing += 1 if current_willing < 1.0 else 0.05 is_emoji_not_reply = False if willing_info.is_emoji: - if self.global_config.emoji_response_penalty != 0: - current_willing *= self.global_config.emoji_response_penalty + if global_config.normal_chat.emoji_response_penalty != 0: + current_willing *= global_config.normal_chat.emoji_response_penalty else: is_emoji_not_reply = True self.chat_reply_willing[chat_id] = min(current_willing, 3.0) reply_probability = min( - max((current_willing - 0.5), 0.01) * self.global_config.response_willing_amplifier * 2, 1 + max((current_willing - 0.5), 0.01) * global_config.normal_chat.response_willing_amplifier * 2, 1 ) # 检查群组权限（如果是群聊） if ( willing_info.group_info - and willing_info.group_info.group_id in self.global_config.talk_frequency_down_groups + and willing_info.group_info.group_id in global_config.chat_target.talk_frequency_down_groups ): - reply_probability = reply_probability / self.global_config.down_frequency_rate + reply_probability = reply_probability / global_config.normal_chat.down_frequency_rate if is_emoji_not_reply: reply_probability = 0 @@ -61,7 +61,7 @@ class ClassicalWillingManager(BaseWillingManager): async def before_generate_reply_handle(self, message_id): chat_id = self.ongoing_messages[message_id].chat_id current_willing = self.chat_reply_willing.get(chat_id, 0) - self.chat_reply_willing[chat_id] = max(0, current_willing - 1.8) + self.chat_reply_willing[chat_id] = max(0.0, current_willing - 1.8) async def after_generate_reply_handle(self, message_id): if message_id not in self.ongoing_messages: @@ -70,7 +70,7 @@ class ClassicalWillingManager(BaseWillingManager): chat_id = self.ongoing_messages[message_id].chat_id current_willing = self.chat_reply_willing.get(chat_id, 0) if current_willing < 1: - self.chat_reply_willing[chat_id] = min(1, current_willing + 0.4) + self.chat_reply_willing[chat_id] = min(1.0, current_willing + 0.4) async def bombing_buffer_message_handle(self, message_id): return await super().bombing_buffer_message_handle(message_id) diff --git a/src/chat/normal_chat/willing/mode_mxp.py b/src/chat/normal_chat/willing/mode_mxp.py index 78120ac53..1e7d5856d 100644 --- a/src/chat/normal_chat/willing/mode_mxp.py +++ b/src/chat/normal_chat/willing/mode_mxp.py @@ -19,6 +19,7 @@ Mxp 模式：梦溪畔独家赞助 下下策是询问一个菜鸟（@梦溪畔） """ +from src.config.config import global_config from .willing_manager import BaseWillingManager from typing import Dict import asyncio @@ -50,8 +51,6 @@ class MxpWillingManager(BaseWillingManager): self.mention_willing_gain = 0.6 # 提及意愿增益 self.interest_willing_gain = 0.3 # 兴趣意愿增益 - self.emoji_response_penalty = self.global_config.emoji_response_penalty # 表情包回复惩罚 - self.down_frequency_rate = self.global_config.down_frequency_rate # 降低回复频率的群组惩罚系数 self.single_chat_gain = 0.12 # 单聊增益 self.fatigue_messages_triggered_num = self.expected_replies_per_min # 疲劳消息触发数量(int) @@ -179,10 +178,10 @@ class MxpWillingManager(BaseWillingManager): probability = self._willing_to_probability(current_willing) if w_info.is_emoji: - probability *= self.emoji_response_penalty + probability *= global_config.normal_chat.emoji_response_penalty - if w_info.group_info and w_info.group_info.group_id in self.global_config.talk_frequency_down_groups: - probability /= self.down_frequency_rate + if w_info.group_info and w_info.group_info.group_id in global_config.chat_target.talk_frequency_down_groups: + probability /= global_config.normal_chat.down_frequency_rate self.temporary_willing = current_willing diff --git a/src/chat/normal_chat/willing/willing_manager.py b/src/chat/normal_chat/willing/willing_manager.py index 37e623d11..bbc5dcc0a 100644 --- a/src/chat/normal_chat/willing/willing_manager.py +++ b/src/chat/normal_chat/willing/willing_manager.py @@ -1,6 +1,6 @@ from src.common.logger import LogConfig, WILLING_STYLE_CONFIG, LoguruLogger, get_module_logger from dataclasses import dataclass -from src.config.config import global_config, BotConfig +from src.config.config import global_config from src.chat.message_receive.chat_stream import ChatStream, GroupInfo from src.chat.message_receive.message import MessageRecv from src.chat.person_info.person_info import person_info_manager, PersonInfoManager @@ -93,7 +93,6 @@ class BaseWillingManager(ABC): self.chat_reply_willing: Dict[str, float] = {} # 存储每个聊天流的回复意愿(chat_id) self.ongoing_messages: Dict[str, WillingInfo] = {} # 当前正在进行的消息(message_id) self.lock = asyncio.Lock() - self.global_config: BotConfig = global_config self.logger: LoguruLogger = logger def setup(self, message: MessageRecv, chat: ChatStream, is_mentioned_bot: bool, interested_rate: float): @@ -173,7 +172,7 @@ def init_willing_manager() -> BaseWillingManager: Returns: 对应mode的WillingManager实例 """ - mode = global_config.willing_mode.lower() + mode = global_config.normal_chat.willing_mode.lower() return BaseWillingManager.create(mode) diff --git a/src/chat/person_info/person_info.py b/src/chat/person_info/person_info.py index 605b86b23..aadbb1d2e 100644 --- a/src/chat/person_info/person_info.py +++ b/src/chat/person_info/person_info.py @@ -59,8 +59,9 @@ person_info_default = { class PersonInfoManager: def __init__(self): self.person_name_list = {} + # TODO: API-Adapter修改标记 self.qv_name_llm = LLMRequest( - model=global_config.llm_normal, + model=global_config.model.normal, max_tokens=256, request_type="qv_name", ) diff --git a/src/chat/utils/chat_message_builder.py b/src/chat/utils/chat_message_builder.py index 15b1e4fc6..de018bdb8 100644 --- a/src/chat/utils/chat_message_builder.py +++ b/src/chat/utils/chat_message_builder.py @@ -190,8 +190,8 @@ async def _build_readable_messages_internal( person_id = person_info_manager.get_person_id(platform, user_id) # 根据 replace_bot_name 参数决定是否替换机器人名称 - if replace_bot_name and user_id == global_config.BOT_QQ: - person_name = f"{global_config.BOT_NICKNAME}(你)" + if replace_bot_name and user_id == global_config.bot.qq_account: + person_name = f"{global_config.bot.nickname}(你)" else: person_name = await person_info_manager.get_value(person_id, "person_name") @@ -427,7 +427,7 @@ async def build_anonymous_messages(messages: List[Dict[str, Any]]) -> str: output_lines = [] def get_anon_name(platform, user_id): - if user_id == global_config.BOT_QQ: + if user_id == global_config.bot.qq_account: return "SELF" person_id = person_info_manager.get_person_id(platform, user_id) if person_id not in person_map: @@ -501,7 +501,7 @@ async def get_person_id_list(messages: List[Dict[str, Any]]) -> List[str]: user_id = user_info.get("user_id") # 检查必要信息是否存在 且 不是机器人自己 - if not all([platform, user_id]) or user_id == global_config.BOT_QQ: + if not all([platform, user_id]) or user_id == global_config.bot.qq_account: continue person_id = person_info_manager.get_person_id(platform, user_id) diff --git a/src/chat/utils/info_catcher.py b/src/chat/utils/info_catcher.py index 174bb5b49..a5b04d704 100644 --- a/src/chat/utils/info_catcher.py +++ b/src/chat/utils/info_catcher.py @@ -9,7 +9,6 @@ from typing import List class InfoCatcher: def __init__(self): self.chat_history = [] # 聊天历史，长度为三倍使用的上下文喵~ - self.context_length = global_config.observation_context_size self.chat_history_in_thinking = [] # 思考期间的聊天内容喵~ self.chat_history_after_response = [] # 回复后的聊天内容，长度为一倍上下文喵~ @@ -143,7 +142,7 @@ class InfoCatcher: messages_before = ( db.messages.find({"chat_id": chat_id, "message_id": {"$lt": message_id}}) .sort("time", -1) - .limit(self.context_length * 3) + .limit(global_config.chat.observation_context_size * 3) ) # 获取更多历史信息 return list(messages_before) diff --git a/src/chat/utils/utils.py b/src/chat/utils/utils.py index 8fe8334b8..58eb49de8 100644 --- a/src/chat/utils/utils.py +++ b/src/chat/utils/utils.py @@ -43,8 +43,8 @@ def db_message_to_str(message_dict: dict) -> str: def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]: """检查消息是否提到了机器人""" - keywords = [global_config.BOT_NICKNAME] - nicknames = global_config.BOT_ALIAS_NAMES + keywords = [global_config.bot.nickname] + nicknames = global_config.bot.alias_names reply_probability = 0.0 is_at = False is_mentioned = False @@ -64,18 +64,18 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]: ) # 判断是否被@ - if re.search(f"@[\s\S]*?（id:{global_config.BOT_QQ}）", message.processed_plain_text): + if re.search(f"@[\s\S]*?（id:{global_config.bot.qq_account}）", message.processed_plain_text): is_at = True is_mentioned = True - if is_at and global_config.at_bot_inevitable_reply: + if is_at and global_config.normal_chat.at_bot_inevitable_reply: reply_probability = 1.0 logger.info("被@，回复概率设置为100%") else: if not is_mentioned: # 判断是否被回复 if re.match( - f"\[回复 [\s\S]*?\({str(global_config.BOT_QQ)}\)：[\s\S]*?]，说：", message.processed_plain_text + f"\[回复 [\s\S]*?\({str(global_config.bot.qq_account)}\)：[\s\S]*?]，说：", message.processed_plain_text ): is_mentioned = True else: @@ -88,7 +88,7 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]: for nickname in nicknames: if nickname in message_content: is_mentioned = True - if is_mentioned and global_config.mentioned_bot_inevitable_reply: + if is_mentioned and global_config.normal_chat.mentioned_bot_inevitable_reply: reply_probability = 1.0 logger.info("被提及，回复概率设置为100%") return is_mentioned, reply_probability @@ -96,7 +96,8 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]: async def get_embedding(text, request_type="embedding"): """获取文本的embedding向量""" - llm = LLMRequest(model=global_config.embedding, request_type=request_type) + # TODO: API-Adapter修改标记 + llm = LLMRequest(model=global_config.model.embedding, request_type=request_type) # return llm.get_embedding_sync(text) try: embedding = await llm.get_embedding(text) @@ -163,7 +164,7 @@ def get_recent_group_speaker(chat_stream_id: int, sender, limit: int = 12) -> li user_info = UserInfo.from_dict(msg_db_data["user_info"]) if ( (user_info.platform, user_info.user_id) != sender - and user_info.user_id != global_config.BOT_QQ + and user_info.user_id != global_config.bot.qq_account and (user_info.platform, user_info.user_id, user_info.user_nickname) not in who_chat_in_group and len(who_chat_in_group) < 5 ): # 排除重复，排除消息发送者，排除bot，限制加载的关系数目 @@ -321,7 +322,7 @@ def random_remove_punctuation(text: str) -> str: def process_llm_response(text: str) -> list[str]: # 先保护颜文字 - if global_config.enable_kaomoji_protection: + if global_config.response_splitter.enable_kaomoji_protection: protected_text, kaomoji_mapping = protect_kaomoji(text) logger.trace(f"保护颜文字后的文本: {protected_text}") else: @@ -340,8 +341,8 @@ def process_llm_response(text: str) -> list[str]: logger.debug(f"{text}去除括号处理后的文本: {cleaned_text}") # 对清理后的文本进行进一步处理 - max_length = global_config.response_max_length * 2 - max_sentence_num = global_config.response_max_sentence_num + max_length = global_config.response_splitter.max_length * 2 + max_sentence_num = global_config.response_splitter.max_sentence_num # 如果基本上是中文，则进行长度过滤 if get_western_ratio(cleaned_text) < 0.1: if len(cleaned_text) > max_length: @@ -349,20 +350,20 @@ def process_llm_response(text: str) -> list[str]: return ["懒得说"] typo_generator = ChineseTypoGenerator( - error_rate=global_config.chinese_typo_error_rate, - min_freq=global_config.chinese_typo_min_freq, - tone_error_rate=global_config.chinese_typo_tone_error_rate, - word_replace_rate=global_config.chinese_typo_word_replace_rate, + error_rate=global_config.chinese_typo.error_rate, + min_freq=global_config.chinese_typo.min_freq, + tone_error_rate=global_config.chinese_typo.tone_error_rate, + word_replace_rate=global_config.chinese_typo.word_replace_rate, ) - if global_config.enable_response_splitter: + if global_config.response_splitter.enable: split_sentences = split_into_sentences_w_remove_punctuation(cleaned_text) else: split_sentences = [cleaned_text] sentences = [] for sentence in split_sentences: - if global_config.chinese_typo_enable: + if global_config.chinese_typo.enable: typoed_text, typo_corrections = typo_generator.create_typo_sentence(sentence) sentences.append(typoed_text) if typo_corrections: @@ -372,7 +373,7 @@ def process_llm_response(text: str) -> list[str]: if len(sentences) > max_sentence_num: logger.warning(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复") - return [f"{global_config.BOT_NICKNAME}不知道哦"] + return [f"{global_config.bot.nickname}不知道哦"] # if extracted_contents: # for content in extracted_contents: diff --git a/src/chat/utils/utils_image.py b/src/chat/utils/utils_image.py index 455038246..6958bc26b 100644 --- a/src/chat/utils/utils_image.py +++ b/src/chat/utils/utils_image.py @@ -36,7 +36,7 @@ class ImageManager: self._ensure_description_collection() self._ensure_image_dir() self._initialized = True - self._llm = LLMRequest(model=global_config.vlm, temperature=0.4, max_tokens=300, request_type="image") + self._llm = LLMRequest(model=global_config.model.vlm, temperature=0.4, max_tokens=300, request_type="image") def _ensure_image_dir(self): """确保图像存储目录存在""" @@ -134,7 +134,7 @@ class ImageManager: return f"[表情包，含义看起来是：{cached_description}]" # 根据配置决定是否保存图片 - if global_config.save_emoji: + if global_config.emoji.save_emoji: # 生成文件名和路径 timestamp = int(time.time()) filename = f"{timestamp}_{image_hash[:8]}.{image_format}" @@ -200,7 +200,7 @@ class ImageManager: return "[图片]" # 根据配置决定是否保存图片 - if global_config.save_pic: + if global_config.emoji.save_pic: # 生成文件名和路径 timestamp = int(time.time()) filename = f"{timestamp}_{image_hash[:8]}.{image_format}" diff --git a/src/common/remote.py b/src/common/remote.py index 1d26df01b..b1108be9c 100644 --- a/src/common/remote.py +++ b/src/common/remote.py @@ -35,7 +35,7 @@ class TelemetryHeartBeatTask(AsyncTask): info_dict = { "os_type": "Unknown", "py_version": platform.python_version(), - "mmc_version": global_config.MAI_VERSION, + "mmc_version": global_config.MMC_VERSION, } match platform.system(): @@ -133,10 +133,9 @@ class TelemetryHeartBeatTask(AsyncTask): async def run(self): # 发送心跳 - if global_config.remote_enable: - if self.client_uuid is None: - if not await self._req_uuid(): - logger.error("获取UUID失败，跳过此次心跳") - return + if global_config.telemetry.enable: + if self.client_uuid is None and not await self._req_uuid(): + logger.error("获取UUID失败，跳过此次心跳") + return await self._send_heartbeat() diff --git a/src/config/config.py b/src/config/config.py index b186f3b83..e6b7c5326 100644 --- a/src/config/config.py +++ b/src/config/config.py @@ -1,64 +1,68 @@ import os -import re -from dataclasses import dataclass, field -from typing import Dict, List, Optional +from dataclasses import field, dataclass -import tomli import tomlkit import shutil from datetime import datetime -from pathlib import Path -from packaging import version -from packaging.version import Version, InvalidVersion -from packaging.specifiers import SpecifierSet, InvalidSpecifier + +from tomlkit import TOMLDocument +from tomlkit.items import Table from src.common.logger_manager import get_logger from rich.traceback import install +from src.config.config_base import ConfigBase +from src.config.official_configs import ( + BotConfig, + ChatTargetConfig, + PersonalityConfig, + IdentityConfig, + PlatformsConfig, + ChatConfig, + NormalChatConfig, + FocusChatConfig, + EmojiConfig, + MemoryConfig, + MoodConfig, + KeywordReactionConfig, + ChineseTypoConfig, + ResponseSplitterConfig, + TelemetryConfig, + ExperimentalConfig, + ModelConfig, +) + install(extra_lines=3) # 配置主程序日志格式 logger = get_logger("config") -# 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码 -is_test = True -mai_version_main = "0.6.4" -mai_version_fix = "snapshot-1" +CONFIG_DIR = "config" +TEMPLATE_DIR = "template" -if mai_version_fix: - if is_test: - mai_version = f"test-{mai_version_main}-{mai_version_fix}" - else: - mai_version = f"{mai_version_main}-{mai_version_fix}" -else: - if is_test: - mai_version = f"test-{mai_version_main}" - else: - mai_version = mai_version_main +# 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码 +# 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/ +MMC_VERSION = "0.7.0-snapshot.1" def update_config(): # 获取根目录路径 - root_dir = Path(__file__).parent.parent.parent - template_dir = root_dir / "template" - config_dir = root_dir / "config" - old_config_dir = config_dir / "old" + old_config_dir = f"{CONFIG_DIR}/old" # 定义文件路径 - template_path = template_dir / "bot_config_template.toml" - old_config_path = config_dir / "bot_config.toml" - new_config_path = config_dir / "bot_config.toml" + template_path = f"{TEMPLATE_DIR}/bot_config_template.toml" + old_config_path = f"{CONFIG_DIR}/bot_config.toml" + new_config_path = f"{CONFIG_DIR}/bot_config.toml" # 检查配置文件是否存在 - if not old_config_path.exists(): + if not os.path.exists(old_config_path): logger.info("配置文件不存在，从模板创建新配置") - # 创建文件夹 - old_config_dir.mkdir(parents=True, exist_ok=True) - shutil.copy2(template_path, old_config_path) + os.makedirs(CONFIG_DIR, exist_ok=True) # 创建文件夹 + shutil.copy2(template_path, old_config_path) # 复制模板文件 logger.info(f"已创建新配置文件，请填写后重新运行: {old_config_path}") # 如果是新创建的配置文件,直接返回 - return quit() + quit() # 读取旧配置文件和模板文件 with open(old_config_path, "r", encoding="utf-8") as f: @@ -75,13 +79,15 @@ def update_config(): return else: logger.info(f"检测到版本号不同: 旧版本 v{old_version} -> 新版本 v{new_version}") + else: + logger.info("已有配置文件未检测到版本号，可能是旧版本。将进行更新") # 创建old目录（如果不存在） - old_config_dir.mkdir(exist_ok=True) + os.makedirs(old_config_dir, exist_ok=True) # 生成带时间戳的新文件名 timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - old_backup_path = old_config_dir / f"bot_config_{timestamp}.toml" + old_backup_path = f"{old_config_dir}/bot_config_{timestamp}.toml" # 移动旧配置文件到old目录 shutil.move(old_config_path, old_backup_path) @@ -91,24 +97,23 @@ def update_config(): shutil.copy2(template_path, new_config_path) logger.info(f"已创建新配置文件: {new_config_path}") - # 递归更新配置 - def update_dict(target, source): + def update_dict(target: TOMLDocument | dict, source: TOMLDocument | dict): + """ + 将source字典的值更新到target字典中（如果target中存在相同的键） + """ for key, value in source.items(): # 跳过version字段的更新 if key == "version": continue if key in target: - if isinstance(value, dict) and isinstance(target[key], (dict, tomlkit.items.Table)): + if isinstance(value, dict) and isinstance(target[key], (dict, Table)): update_dict(target[key], value) else: try: # 对数组类型进行特殊处理 if isinstance(value, list): # 如果是空数组，确保它保持为空数组 - if not value: - target[key] = tomlkit.array() - else: - target[key] = tomlkit.array(value) + target[key] = tomlkit.array(str(value)) if value else tomlkit.array() else: # 其他类型使用item方法创建新值 target[key] = tomlkit.item(value) @@ -123,619 +128,57 @@ def update_config(): # 保存更新后的配置（保留注释和格式） with open(new_config_path, "w", encoding="utf-8") as f: f.write(tomlkit.dumps(new_config)) - logger.info("配置文件更新完成") + logger.info("配置文件更新完成，建议检查新配置文件中的内容，以免丢失重要信息") + quit() @dataclass -class BotConfig: - """机器人配置类""" - - INNER_VERSION: Version = None - MAI_VERSION: str = mai_version # 硬编码的版本信息 - - # bot - BOT_QQ: Optional[str] = "114514" - BOT_NICKNAME: Optional[str] = None - BOT_ALIAS_NAMES: List[str] = field(default_factory=list) # 别名，可以通过这个叫它 - - # group - talk_allowed_groups = set() - talk_frequency_down_groups = set() - ban_user_id = set() - - # personality - personality_core = "用一句话或几句话描述人格的核心特点" # 建议20字以内，谁再写3000字小作文敲谁脑袋 - personality_sides: List[str] = field( - default_factory=lambda: [ - "用一句话或几句话描述人格的一些侧面", - "用一句话或几句话描述人格的一些侧面", - "用一句话或几句话描述人格的一些侧面", - ] - ) - expression_style = "描述麦麦说话的表达风格，表达习惯" - # identity - identity_detail: List[str] = field( - default_factory=lambda: [ - "身份特点", - "身份特点", - ] - ) - height: int = 170 # 身高 单位厘米 - weight: int = 50 # 体重 单位千克 - age: int = 20 # 年龄 单位岁 - gender: str = "男" # 性别 - appearance: str = "用几句话描述外貌特征" # 外貌特征 - - # chat - allow_focus_mode: bool = True # 是否允许专注聊天状态 - - base_normal_chat_num: int = 3 # 最多允许多少个群进行普通聊天 - base_focused_chat_num: int = 2 # 最多允许多少个群进行专注聊天 - - observation_context_size: int = 12 # 心流观察到的最长上下文大小，超过这个值的上下文会被压缩 - - message_buffer: bool = True # 消息缓冲器 - - ban_words = set() - ban_msgs_regex = set() - - # focus_chat - reply_trigger_threshold: float = 3.0 # 心流聊天触发阈值，越低越容易触发 - default_decay_rate_per_second: float = 0.98 # 默认衰减率，越大衰减越慢 - consecutive_no_reply_threshold = 3 - - compressed_length: int = 5 # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5 - compress_length_limit: int = 5 # 最多压缩份数，超过该数值的压缩上下文会被删除 - - # normal_chat - model_reasoning_probability: float = 0.7 # 麦麦回答时选择推理模型(主要)模型概率 - model_normal_probability: float = 0.3 # 麦麦回答时选择一般模型(次要)模型概率 - - emoji_chance: float = 0.2 # 发送表情包的基础概率 - thinking_timeout: int = 120 # 思考时间 - - willing_mode: str = "classical" # 意愿模式 - response_willing_amplifier: float = 1.0 # 回复意愿放大系数 - response_interested_rate_amplifier: float = 1.0 # 回复兴趣度放大系数 - down_frequency_rate: float = 3 # 降低回复频率的群组回复意愿降低系数 - emoji_response_penalty: float = 0.0 # 表情包回复惩罚 - mentioned_bot_inevitable_reply: bool = False # 提及 bot 必然回复 - at_bot_inevitable_reply: bool = False # @bot 必然回复 - - # emoji - max_emoji_num: int = 200 # 表情包最大数量 - max_reach_deletion: bool = True # 开启则在达到最大数量时删除表情包，关闭则不会继续收集表情包 - EMOJI_CHECK_INTERVAL: int = 120 # 表情包检查间隔（分钟） - - save_pic: bool = False # 是否保存图片 - save_emoji: bool = False # 是否保存表情包 - steal_emoji: bool = True # 是否偷取表情包，让麦麦可以发送她保存的这些表情包 - - EMOJI_CHECK: bool = False # 是否开启过滤 - EMOJI_CHECK_PROMPT: str = "符合公序良俗" # 表情包过滤要求 - - # memory - build_memory_interval: int = 600 # 记忆构建间隔（秒） - memory_build_distribution: list = field( - default_factory=lambda: [4, 2, 0.6, 24, 8, 0.4] - ) # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重 - build_memory_sample_num: int = 10 # 记忆构建采样数量 - build_memory_sample_length: int = 20 # 记忆构建采样长度 - memory_compress_rate: float = 0.1 # 记忆压缩率 - - forget_memory_interval: int = 600 # 记忆遗忘间隔（秒） - memory_forget_time: int = 24 # 记忆遗忘时间（小时） - memory_forget_percentage: float = 0.01 # 记忆遗忘比例 - - consolidate_memory_interval: int = 1000 # 记忆整合间隔（秒） - consolidation_similarity_threshold: float = 0.7 # 相似度阈值 - consolidate_memory_percentage: float = 0.01 # 检查节点比例 - - memory_ban_words: list = field( - default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"] - ) # 添加新的配置项默认值 - - # mood - mood_update_interval: float = 1.0 # 情绪更新间隔 单位秒 - mood_decay_rate: float = 0.95 # 情绪衰减率 - mood_intensity_factor: float = 0.7 # 情绪强度因子 - - # keywords - keywords_reaction_rules = [] # 关键词回复规则 - - # chinese_typo - chinese_typo_enable = True # 是否启用中文错别字生成器 - chinese_typo_error_rate = 0.03 # 单字替换概率 - chinese_typo_min_freq = 7 # 最小字频阈值 - chinese_typo_tone_error_rate = 0.2 # 声调错误概率 - chinese_typo_word_replace_rate = 0.02 # 整词替换概率 - - # response_splitter - enable_kaomoji_protection = False # 是否启用颜文字保护 - enable_response_splitter = True # 是否启用回复分割器 - response_max_length = 100 # 回复允许的最大长度 - response_max_sentence_num = 3 # 回复允许的最大句子数 - - model_max_output_length: int = 800 # 最大回复长度 - - # remote - remote_enable: bool = True # 是否启用远程控制 - - # experimental - enable_friend_chat: bool = False # 是否启用好友聊天 - # enable_think_flow: bool = False # 是否启用思考流程 - talk_allowed_private = set() - enable_pfc_chatting: bool = False # 是否启用PFC聊天 - - # 模型配置 - llm_reasoning: dict[str, str] = field(default_factory=lambda: {}) - # llm_reasoning_minor: dict[str, str] = field(default_factory=lambda: {}) - llm_normal: Dict[str, str] = field(default_factory=lambda: {}) - llm_topic_judge: Dict[str, str] = field(default_factory=lambda: {}) - llm_summary: Dict[str, str] = field(default_factory=lambda: {}) - embedding: Dict[str, str] = field(default_factory=lambda: {}) - vlm: Dict[str, str] = field(default_factory=lambda: {}) - moderation: Dict[str, str] = field(default_factory=lambda: {}) - - llm_observation: Dict[str, str] = field(default_factory=lambda: {}) - llm_sub_heartflow: Dict[str, str] = field(default_factory=lambda: {}) - llm_heartflow: Dict[str, str] = field(default_factory=lambda: {}) - llm_tool_use: Dict[str, str] = field(default_factory=lambda: {}) - llm_plan: Dict[str, str] = field(default_factory=lambda: {}) - - api_urls: Dict[str, str] = field(default_factory=lambda: {}) - - @staticmethod - def get_config_dir() -> str: - """获取配置文件目录""" - current_dir = os.path.dirname(os.path.abspath(__file__)) - root_dir = os.path.abspath(os.path.join(current_dir, "..", "..")) - config_dir = os.path.join(root_dir, "config") - if not os.path.exists(config_dir): - os.makedirs(config_dir) - return config_dir - - @classmethod - def convert_to_specifierset(cls, value: str) -> SpecifierSet: - """将 字符串 版本表达式转换成 SpecifierSet - Args: - value[str]: 版本表达式(字符串) - Returns: - SpecifierSet - """ - - try: - converted = SpecifierSet(value) - except InvalidSpecifier: - logger.error(f"{value} 分类使用了错误的版本约束表达式\n", "请阅读 https://semver.org/lang/zh-CN/ 修改代码") - exit(1) - - return converted - - @classmethod - def get_config_version(cls, toml: dict) -> Version: - """提取配置文件的 SpecifierSet 版本数据 - Args: - toml[dict]: 输入的配置文件字典 - Returns: - Version - """ - - if "inner" in toml: - try: - config_version: str = toml["inner"]["version"] - except KeyError as e: - logger.error("配置文件中 inner 段 不存在, 这是错误的配置文件") - raise KeyError(f"配置文件中 inner 段 不存在 {e}, 这是错误的配置文件") from e - else: - toml["inner"] = {"version": "0.0.0"} - config_version = toml["inner"]["version"] - - try: - ver = version.parse(config_version) - except InvalidVersion as e: - logger.error( - "配置文件中 inner段 的 version 键是错误的版本描述\n" - "请阅读 https://semver.org/lang/zh-CN/ 修改配置，并参考本项目指定的模板进行修改\n" - "本项目在不同的版本下有不同的模板，请注意识别" - ) - raise InvalidVersion("配置文件中 inner段 的 version 键是错误的版本描述\n") from e - - return ver - - @classmethod - def load_config(cls, config_path: str = None) -> "BotConfig": - """从TOML配置文件加载配置""" - config = cls() - - def personality(parent: dict): - personality_config = parent["personality"] - if config.INNER_VERSION in SpecifierSet(">=1.2.4"): - config.personality_core = personality_config.get("personality_core", config.personality_core) - config.personality_sides = personality_config.get("personality_sides", config.personality_sides) - if config.INNER_VERSION in SpecifierSet(">=1.7.0"): - config.expression_style = personality_config.get("expression_style", config.expression_style) - - def identity(parent: dict): - identity_config = parent["identity"] - if config.INNER_VERSION in SpecifierSet(">=1.2.4"): - config.identity_detail = identity_config.get("identity_detail", config.identity_detail) - config.height = identity_config.get("height", config.height) - config.weight = identity_config.get("weight", config.weight) - config.age = identity_config.get("age", config.age) - config.gender = identity_config.get("gender", config.gender) - config.appearance = identity_config.get("appearance", config.appearance) - - def emoji(parent: dict): - emoji_config = parent["emoji"] - config.EMOJI_CHECK_INTERVAL = emoji_config.get("check_interval", config.EMOJI_CHECK_INTERVAL) - config.EMOJI_CHECK_PROMPT = emoji_config.get("check_prompt", config.EMOJI_CHECK_PROMPT) - config.EMOJI_CHECK = emoji_config.get("enable_check", config.EMOJI_CHECK) - if config.INNER_VERSION in SpecifierSet(">=1.1.1"): - config.max_emoji_num = emoji_config.get("max_emoji_num", config.max_emoji_num) - config.max_reach_deletion = emoji_config.get("max_reach_deletion", config.max_reach_deletion) - if config.INNER_VERSION in SpecifierSet(">=1.4.2"): - config.save_pic = emoji_config.get("save_pic", config.save_pic) - config.save_emoji = emoji_config.get("save_emoji", config.save_emoji) - config.steal_emoji = emoji_config.get("steal_emoji", config.steal_emoji) - - def bot(parent: dict): - # 机器人基础配置 - bot_config = parent["bot"] - bot_qq = bot_config.get("qq") - config.BOT_QQ = str(bot_qq) - config.BOT_NICKNAME = bot_config.get("nickname", config.BOT_NICKNAME) - config.BOT_ALIAS_NAMES = bot_config.get("alias_names", config.BOT_ALIAS_NAMES) - - def chat(parent: dict): - chat_config = parent["chat"] - config.allow_focus_mode = chat_config.get("allow_focus_mode", config.allow_focus_mode) - config.base_normal_chat_num = chat_config.get("base_normal_chat_num", config.base_normal_chat_num) - config.base_focused_chat_num = chat_config.get("base_focused_chat_num", config.base_focused_chat_num) - config.observation_context_size = chat_config.get( - "observation_context_size", config.observation_context_size - ) - config.message_buffer = chat_config.get("message_buffer", config.message_buffer) - config.ban_words = chat_config.get("ban_words", config.ban_words) - for r in chat_config.get("ban_msgs_regex", config.ban_msgs_regex): - config.ban_msgs_regex.add(re.compile(r)) - - def normal_chat(parent: dict): - normal_chat_config = parent["normal_chat"] - config.model_reasoning_probability = normal_chat_config.get( - "model_reasoning_probability", config.model_reasoning_probability - ) - config.model_normal_probability = normal_chat_config.get( - "model_normal_probability", config.model_normal_probability - ) - config.emoji_chance = normal_chat_config.get("emoji_chance", config.emoji_chance) - config.thinking_timeout = normal_chat_config.get("thinking_timeout", config.thinking_timeout) - - config.willing_mode = normal_chat_config.get("willing_mode", config.willing_mode) - config.response_willing_amplifier = normal_chat_config.get( - "response_willing_amplifier", config.response_willing_amplifier - ) - config.response_interested_rate_amplifier = normal_chat_config.get( - "response_interested_rate_amplifier", config.response_interested_rate_amplifier - ) - config.down_frequency_rate = normal_chat_config.get("down_frequency_rate", config.down_frequency_rate) - config.emoji_response_penalty = normal_chat_config.get( - "emoji_response_penalty", config.emoji_response_penalty - ) - - config.mentioned_bot_inevitable_reply = normal_chat_config.get( - "mentioned_bot_inevitable_reply", config.mentioned_bot_inevitable_reply - ) - config.at_bot_inevitable_reply = normal_chat_config.get( - "at_bot_inevitable_reply", config.at_bot_inevitable_reply - ) - - def focus_chat(parent: dict): - focus_chat_config = parent["focus_chat"] - config.compressed_length = focus_chat_config.get("compressed_length", config.compressed_length) - config.compress_length_limit = focus_chat_config.get("compress_length_limit", config.compress_length_limit) - config.reply_trigger_threshold = focus_chat_config.get( - "reply_trigger_threshold", config.reply_trigger_threshold - ) - config.default_decay_rate_per_second = focus_chat_config.get( - "default_decay_rate_per_second", config.default_decay_rate_per_second - ) - config.consecutive_no_reply_threshold = focus_chat_config.get( - "consecutive_no_reply_threshold", config.consecutive_no_reply_threshold - ) - - def model(parent: dict): - # 加载模型配置 - model_config: dict = parent["model"] - - config_list = [ - "llm_reasoning", - # "llm_reasoning_minor", - "llm_normal", - "llm_topic_judge", - "llm_summary", - "vlm", - "embedding", - "llm_tool_use", - "llm_observation", - "llm_sub_heartflow", - "llm_plan", - "llm_heartflow", - "llm_PFC_action_planner", - "llm_PFC_chat", - "llm_PFC_reply_checker", - ] - - for item in config_list: - if item in model_config: - cfg_item: dict = model_config[item] - - # base_url 的例子： SILICONFLOW_BASE_URL - # key 的例子： SILICONFLOW_KEY - cfg_target = { - "name": "", - "base_url": "", - "key": "", - "stream": False, - "pri_in": 0, - "pri_out": 0, - "temp": 0.7, - } - - if config.INNER_VERSION in SpecifierSet("<=0.0.0"): - cfg_target = cfg_item - - elif config.INNER_VERSION in SpecifierSet(">=0.0.1"): - stable_item = ["name", "pri_in", "pri_out"] - - stream_item = ["stream"] - if config.INNER_VERSION in SpecifierSet(">=1.0.1"): - stable_item.append("stream") - - pricing_item = ["pri_in", "pri_out"] - - # 从配置中原始拷贝稳定字段 - for i in stable_item: - # 如果 字段 属于计费项 且获取不到，那默认值是 0 - if i in pricing_item and i not in cfg_item: - cfg_target[i] = 0 - - if i in stream_item and i not in cfg_item: - cfg_target[i] = False - - else: - # 没有特殊情况则原样复制 - try: - cfg_target[i] = cfg_item[i] - except KeyError as e: - logger.error(f"{item} 中的必要字段不存在，请检查") - raise KeyError(f"{item} 中的必要字段 {e} 不存在，请检查") from e - - # 如果配置中有temp参数，就使用配置中的值 - if "temp" in cfg_item: - cfg_target["temp"] = cfg_item["temp"] - else: - # 如果没有temp参数，就删除默认值 - cfg_target.pop("temp", None) - - provider = cfg_item.get("provider") - if provider is None: - logger.error(f"provider 字段在模型配置 {item} 中不存在，请检查") - raise KeyError(f"provider 字段在模型配置 {item} 中不存在，请检查") - - cfg_target["base_url"] = f"{provider}_BASE_URL" - cfg_target["key"] = f"{provider}_KEY" - - # 如果 列表中的项目在 model_config 中，利用反射来设置对应项目 - setattr(config, item, cfg_target) - else: - logger.error(f"模型 {item} 在config中不存在，请检查，或尝试更新配置文件") - raise KeyError(f"模型 {item} 在config中不存在，请检查，或尝试更新配置文件") - - def memory(parent: dict): - memory_config = parent["memory"] - config.build_memory_interval = memory_config.get("build_memory_interval", config.build_memory_interval) - config.forget_memory_interval = memory_config.get("forget_memory_interval", config.forget_memory_interval) - config.memory_ban_words = set(memory_config.get("memory_ban_words", [])) - config.memory_forget_time = memory_config.get("memory_forget_time", config.memory_forget_time) - config.memory_forget_percentage = memory_config.get( - "memory_forget_percentage", config.memory_forget_percentage - ) - config.memory_compress_rate = memory_config.get("memory_compress_rate", config.memory_compress_rate) - if config.INNER_VERSION in SpecifierSet(">=0.0.11"): - config.memory_build_distribution = memory_config.get( - "memory_build_distribution", config.memory_build_distribution - ) - config.build_memory_sample_num = memory_config.get( - "build_memory_sample_num", config.build_memory_sample_num - ) - config.build_memory_sample_length = memory_config.get( - "build_memory_sample_length", config.build_memory_sample_length - ) - if config.INNER_VERSION in SpecifierSet(">=1.5.1"): - config.consolidate_memory_interval = memory_config.get( - "consolidate_memory_interval", config.consolidate_memory_interval - ) - config.consolidation_similarity_threshold = memory_config.get( - "consolidation_similarity_threshold", config.consolidation_similarity_threshold - ) - config.consolidate_memory_percentage = memory_config.get( - "consolidate_memory_percentage", config.consolidate_memory_percentage - ) - - def remote(parent: dict): - remote_config = parent["remote"] - config.remote_enable = remote_config.get("enable", config.remote_enable) - - def mood(parent: dict): - mood_config = parent["mood"] - config.mood_update_interval = mood_config.get("mood_update_interval", config.mood_update_interval) - config.mood_decay_rate = mood_config.get("mood_decay_rate", config.mood_decay_rate) - config.mood_intensity_factor = mood_config.get("mood_intensity_factor", config.mood_intensity_factor) - - def keywords_reaction(parent: dict): - keywords_reaction_config = parent["keywords_reaction"] - if keywords_reaction_config.get("enable", False): - config.keywords_reaction_rules = keywords_reaction_config.get("rules", config.keywords_reaction_rules) - for rule in config.keywords_reaction_rules: - if rule.get("enable", False) and "regex" in rule: - rule["regex"] = [re.compile(r) for r in rule.get("regex", [])] - - def chinese_typo(parent: dict): - chinese_typo_config = parent["chinese_typo"] - config.chinese_typo_enable = chinese_typo_config.get("enable", config.chinese_typo_enable) - config.chinese_typo_error_rate = chinese_typo_config.get("error_rate", config.chinese_typo_error_rate) - config.chinese_typo_min_freq = chinese_typo_config.get("min_freq", config.chinese_typo_min_freq) - config.chinese_typo_tone_error_rate = chinese_typo_config.get( - "tone_error_rate", config.chinese_typo_tone_error_rate - ) - config.chinese_typo_word_replace_rate = chinese_typo_config.get( - "word_replace_rate", config.chinese_typo_word_replace_rate - ) - - def response_splitter(parent: dict): - response_splitter_config = parent["response_splitter"] - config.enable_response_splitter = response_splitter_config.get( - "enable_response_splitter", config.enable_response_splitter - ) - config.response_max_length = response_splitter_config.get("response_max_length", config.response_max_length) - config.response_max_sentence_num = response_splitter_config.get( - "response_max_sentence_num", config.response_max_sentence_num - ) - if config.INNER_VERSION in SpecifierSet(">=1.4.2"): - config.enable_kaomoji_protection = response_splitter_config.get( - "enable_kaomoji_protection", config.enable_kaomoji_protection - ) - if config.INNER_VERSION in SpecifierSet(">=1.6.0"): - config.model_max_output_length = response_splitter_config.get( - "model_max_output_length", config.model_max_output_length - ) - - def groups(parent: dict): - groups_config = parent["groups"] - # config.talk_allowed_groups = set(groups_config.get("talk_allowed", [])) - config.talk_allowed_groups = set(str(group) for group in groups_config.get("talk_allowed", [])) - # config.talk_frequency_down_groups = set(groups_config.get("talk_frequency_down", [])) - config.talk_frequency_down_groups = set( - str(group) for group in groups_config.get("talk_frequency_down", []) - ) - # config.ban_user_id = set(groups_config.get("ban_user_id", [])) - config.ban_user_id = set(str(user) for user in groups_config.get("ban_user_id", [])) - - def experimental(parent: dict): - experimental_config = parent["experimental"] - config.enable_friend_chat = experimental_config.get("enable_friend_chat", config.enable_friend_chat) - # config.enable_think_flow = experimental_config.get("enable_think_flow", config.enable_think_flow) - config.talk_allowed_private = set(str(user) for user in experimental_config.get("talk_allowed_private", [])) - if config.INNER_VERSION in SpecifierSet(">=1.1.0"): - config.enable_pfc_chatting = experimental_config.get("pfc_chatting", config.enable_pfc_chatting) - - # 版本表达式：>=1.0.0,<2.0.0 - # 允许字段：func: method, support: str, notice: str, necessary: bool - # 如果使用 notice 字段，在该组配置加载时，会展示该字段对用户的警示 - # 例如："notice": "personality 将在 1.3.2 后被移除"，那么在有效版本中的用户就会虽然可以 - # 正常执行程序，但是会看到这条自定义提示 - - # 版本格式：主版本号.次版本号.修订号，版本号递增规则如下： - # 主版本号：当你做了不兼容的 API 修改， - # 次版本号：当你做了向下兼容的功能性新增， - # 修订号：当你做了向下兼容的问题修正。 - # 先行版本号及版本编译信息可以加到"主版本号.次版本号.修订号"的后面，作为延伸。 - - # 如果你做了break的修改，就应该改动主版本号 - # 如果做了一个兼容修改，就不应该要求这个选项是必须的！ - include_configs = { - "bot": {"func": bot, "support": ">=0.0.0"}, - "groups": {"func": groups, "support": ">=0.0.0"}, - "personality": {"func": personality, "support": ">=0.0.0"}, - "identity": {"func": identity, "support": ">=1.2.4"}, - "emoji": {"func": emoji, "support": ">=0.0.0"}, - "model": {"func": model, "support": ">=0.0.0"}, - "memory": {"func": memory, "support": ">=0.0.0", "necessary": False}, - "mood": {"func": mood, "support": ">=0.0.0"}, - "remote": {"func": remote, "support": ">=0.0.10", "necessary": False}, - "keywords_reaction": {"func": keywords_reaction, "support": ">=0.0.2", "necessary": False}, - "chinese_typo": {"func": chinese_typo, "support": ">=0.0.3", "necessary": False}, - "response_splitter": {"func": response_splitter, "support": ">=0.0.11", "necessary": False}, - "experimental": {"func": experimental, "support": ">=0.0.11", "necessary": False}, - "chat": {"func": chat, "support": ">=1.6.0", "necessary": False}, - "normal_chat": {"func": normal_chat, "support": ">=1.6.0", "necessary": False}, - "focus_chat": {"func": focus_chat, "support": ">=1.6.0", "necessary": False}, - } - - # 原地修改，将 字符串版本表达式 转换成 版本对象 - for key in include_configs: - item_support = include_configs[key]["support"] - include_configs[key]["support"] = cls.convert_to_specifierset(item_support) - - if os.path.exists(config_path): - with open(config_path, "rb") as f: - try: - toml_dict = tomli.load(f) - except tomli.TOMLDecodeError as e: - logger.critical(f"配置文件bot_config.toml填写有误，请检查第{e.lineno}行第{e.colno}处：{e.msg}") - exit(1) - - # 获取配置文件版本 - config.INNER_VERSION = cls.get_config_version(toml_dict) - - # 如果在配置中找到了需要的项，调用对应项的闭包函数处理 - for key in include_configs: - if key in toml_dict: - group_specifierset: SpecifierSet = include_configs[key]["support"] - - # 检查配置文件版本是否在支持范围内 - if config.INNER_VERSION in group_specifierset: - # 如果版本在支持范围内，检查是否存在通知 - if "notice" in include_configs[key]: - logger.warning(include_configs[key]["notice"]) - - include_configs[key]["func"](toml_dict) - - else: - # 如果版本不在支持范围内，崩溃并提示用户 - logger.error( - f"配置文件中的 '{key}' 字段的版本 ({config.INNER_VERSION}) 不在支持范围内。\n" - f"当前程序仅支持以下版本范围: {group_specifierset}" - ) - raise InvalidVersion(f"当前程序仅支持以下版本范围: {group_specifierset}") - - # 如果 necessary 项目存在，而且显式声明是 False，进入特殊处理 - elif "necessary" in include_configs[key] and include_configs[key].get("necessary") is False: - # 通过 pass 处理的项虽然直接忽略也是可以的，但是为了不增加理解困难，依然需要在这里显式处理 - if key == "keywords_reaction": - pass - - else: - # 如果用户根本没有需要的配置项，提示缺少配置 - logger.error(f"配置文件中缺少必需的字段: '{key}'") - raise KeyError(f"配置文件中缺少必需的字段: '{key}'") - - # identity_detail字段非空检查 - if not config.identity_detail: - logger.error("配置文件错误：[identity] 部分的 identity_detail 不能为空字符串") - raise ValueError("配置文件错误：[identity] 部分的 identity_detail 不能为空字符串") - - logger.success(f"成功加载配置文件: {config_path}") - - return config +class Config(ConfigBase): + """总配置类""" + + MMC_VERSION: str = field(default=MMC_VERSION, repr=False, init=False) # 硬编码的版本信息 + + bot: BotConfig + chat_target: ChatTargetConfig + personality: PersonalityConfig + identity: IdentityConfig + platforms: PlatformsConfig + chat: ChatConfig + normal_chat: NormalChatConfig + focus_chat: FocusChatConfig + emoji: EmojiConfig + memory: MemoryConfig + mood: MoodConfig + keyword_reaction: KeywordReactionConfig + chinese_typo: ChineseTypoConfig + response_splitter: ResponseSplitterConfig + telemetry: TelemetryConfig + experimental: ExperimentalConfig + model: ModelConfig + + +def load_config(config_path: str) -> Config: + """ + 加载配置文件 + :param config_path: 配置文件路径 + :return: Config对象 + """ + # 读取配置文件 + with open(config_path, "r", encoding="utf-8") as f: + config_data = tomlkit.load(f) + + # 创建Config对象 + try: + return Config.from_dict(config_data) + except Exception as e: + logger.critical("配置文件解析失败") + raise e # 获取配置文件路径 -logger.info(f"MaiCore当前版本: {mai_version}") +logger.info(f"MaiCore当前版本: {MMC_VERSION}") update_config() -bot_config_floder_path = BotConfig.get_config_dir() -logger.info(f"正在品鉴配置文件目录: {bot_config_floder_path}") - -bot_config_path = os.path.join(bot_config_floder_path, "bot_config.toml") - -if os.path.exists(bot_config_path): - # 如果开发环境配置文件不存在，则使用默认配置文件 - logger.info(f"异常的新鲜，异常的美味: {bot_config_path}") -else: - # 配置文件不存在 - logger.error("配置文件不存在，请检查路径: {bot_config_path}") - raise FileNotFoundError(f"配置文件不存在: {bot_config_path}") - -global_config = BotConfig.load_config(config_path=bot_config_path) +logger.info("正在品鉴配置文件...") +global_config = load_config(config_path=f"{CONFIG_DIR}/bot_config.toml") +logger.info("非常的新鲜，非常的美味！") diff --git a/src/config/config_base.py b/src/config/config_base.py new file mode 100644 index 000000000..92f6cf9d4 --- /dev/null +++ b/src/config/config_base.py @@ -0,0 +1,116 @@ +from dataclasses import dataclass, fields, MISSING +from typing import TypeVar, Type, Any, get_origin, get_args + +T = TypeVar("T", bound="ConfigBase") + +TOML_DICT_TYPE = { + int, + float, + str, + bool, + list, + dict, +} + + +@dataclass +class ConfigBase: + """配置类的基类""" + + @classmethod + def from_dict(cls: Type[T], data: dict[str, Any]) -> T: + """从字典加载配置字段""" + if not isinstance(data, dict): + raise TypeError(f"Expected a dictionary, got {type(data).__name__}") + + init_args: dict[str, Any] = {} + + for f in fields(cls): + field_name = f.name + + if field_name.startswith("_"): + # 跳过以 _ 开头的字段 + continue + + if field_name not in data: + if f.default is not MISSING or f.default_factory is not MISSING: + # 跳过未提供且有默认值/默认构造方法的字段 + continue + else: + raise ValueError(f"Missing required field: '{field_name}'") + + value = data[field_name] + field_type = f.type + + try: + init_args[field_name] = cls._convert_field(value, field_type) + except TypeError as e: + raise TypeError(f"Field '{field_name}' has a type error: {e}") from e + except Exception as e: + raise RuntimeError(f"Failed to convert field '{field_name}' to target type: {e}") from e + + return cls(**init_args) + + @classmethod + def _convert_field(cls, value: Any, field_type: Type[Any]) -> Any: + """ + 转换字段值为指定类型 + + 1. 对于嵌套的 dataclass，递归调用相应的 from_dict 方法 + 2. 对于泛型集合类型（list, set, tuple），递归转换每个元素 + 3. 对于基础类型（int, str, float, bool），直接转换 + 4. 对于其他类型，尝试直接转换，如果失败则抛出异常 + """ + + # 如果是嵌套的 dataclass，递归调用 from_dict 方法 + if isinstance(field_type, type) and issubclass(field_type, ConfigBase): + if not isinstance(value, dict): + raise TypeError(f"Expected a dictionary for {field_type.__name__}, got {type(value).__name__}") + return field_type.from_dict(value) + + # 处理泛型集合类型（list, set, tuple） + field_origin_type = get_origin(field_type) + field_type_args = get_args(field_type) + + if field_origin_type in {list, set, tuple}: + # 检查提供的value是否为list + if not isinstance(value, list): + raise TypeError(f"Expected an list for {field_type.__name__}, got {type(value).__name__}") + + if field_origin_type is list: + return [cls._convert_field(item, field_type_args[0]) for item in value] + elif field_origin_type is set: + return {cls._convert_field(item, field_type_args[0]) for item in value} + elif field_origin_type is tuple: + # 检查提供的value长度是否与类型参数一致 + if len(value) != len(field_type_args): + raise TypeError( + f"Expected {len(field_type_args)} items for {field_type.__name__}, got {len(value)}" + ) + return tuple(cls._convert_field(item, arg) for item, arg in zip(value, field_type_args)) + + if field_origin_type is dict: + # 检查提供的value是否为dict + if not isinstance(value, dict): + raise TypeError(f"Expected a dictionary for {field_type.__name__}, got {type(value).__name__}") + + # 检查字典的键值类型 + if len(field_type_args) != 2: + raise TypeError(f"Expected a dictionary with two type arguments for {field_type.__name__}") + key_type, value_type = field_type_args + + return {cls._convert_field(k, key_type): cls._convert_field(v, value_type) for k, v in value.items()} + + # 处理基础类型，例如 int, str 等 + if field_type is Any or isinstance(value, field_type): + return value + + # 其他类型，尝试直接转换 + try: + return field_type(value) + except (ValueError, TypeError) as e: + raise TypeError(f"Cannot convert {type(value).__name__} to {field_type.__name__}") from e + + def __str__(self): + """返回配置类的字符串表示""" + return f"{self.__class__.__name__}({', '.join(f'{f.name}={getattr(self, f.name)}' for f in fields(self))})" diff --git a/src/config/official_configs.py b/src/config/official_configs.py new file mode 100644 index 000000000..d92d925d6 --- /dev/null +++ b/src/config/official_configs.py @@ -0,0 +1,399 @@ +from dataclasses import dataclass, field +from typing import Any + +from src.config.config_base import ConfigBase + +""" +须知： +1. 本文件中记录了所有的配置项 +2. 所有新增的class都需要继承自ConfigBase +3. 所有新增的class都应在config.py中的Config类中添加字段 +4. 对于新增的字段，若为可选项，则应在其后添加field()并设置default_factory或default +""" + + +@dataclass +class BotConfig(ConfigBase): + """QQ机器人配置类""" + + qq_account: str + """QQ账号""" + + nickname: str + """昵称""" + + alias_names: list[str] = field(default_factory=lambda: []) + """别名列表""" + + +@dataclass +class ChatTargetConfig(ConfigBase): + """ + 聊天目标配置类 + 此类中有聊天的群组和用户配置 + """ + + talk_allowed_groups: set[str] = field(default_factory=lambda: set()) + """允许聊天的群组列表""" + + talk_frequency_down_groups: set[str] = field(default_factory=lambda: set()) + """降低聊天频率的群组列表""" + + ban_user_id: set[str] = field(default_factory=lambda: set()) + """禁止聊天的用户列表""" + + +@dataclass +class PersonalityConfig(ConfigBase): + """人格配置类""" + + personality_core: str + """核心人格""" + + expression_style: str + """表达风格""" + + personality_sides: list[str] = field(default_factory=lambda: []) + """人格侧写""" + + +@dataclass +class IdentityConfig(ConfigBase): + """个体特征配置类""" + + height: int = 170 + """身高（单位：厘米）""" + + weight: float = 50 + """体重（单位：千克）""" + + age: int = 18 + """年龄（单位：岁）""" + + gender: str = "女" + """性别（男/女）""" + + appearance: str = "可爱" + """外貌描述""" + + identity_detail: list[str] = field(default_factory=lambda: []) + """身份特征""" + + +@dataclass +class PlatformsConfig(ConfigBase): + """平台配置类""" + + qq: str + """QQ适配器连接URL配置""" + + +@dataclass +class ChatConfig(ConfigBase): + """聊天配置类""" + + allow_focus_mode: bool = True + """是否允许专注聊天状态""" + + base_normal_chat_num: int = 3 + """最多允许多少个群进行普通聊天""" + + base_focused_chat_num: int = 2 + """最多允许多少个群进行专注聊天""" + + observation_context_size: int = 12 + """可观察到的最长上下文大小，超过这个值的上下文会被压缩""" + + message_buffer: bool = True + """消息缓冲器""" + + ban_words: set[str] = field(default_factory=lambda: set()) + """过滤词列表""" + + ban_msgs_regex: set[str] = field(default_factory=lambda: set()) + """过滤正则表达式列表""" + + +@dataclass +class NormalChatConfig(ConfigBase): + """普通聊天配置类""" + + reasoning_model_probability: float = 0.3 + """ + 发言时选择推理模型的概率（0-1之间） + 选择普通模型的概率为 1 - reasoning_normal_model_probability + """ + + emoji_chance: float = 0.2 + """发送表情包的基础概率""" + + thinking_timeout: int = 120 + """最长思考时间""" + + willing_mode: str = "classical" + """意愿模式""" + + response_willing_amplifier: float = 1.0 + """回复意愿放大系数""" + + response_interested_rate_amplifier: float = 1.0 + """回复兴趣度放大系数""" + + down_frequency_rate: float = 3.0 + """降低回复频率的群组回复意愿降低系数""" + + emoji_response_penalty: float = 0.0 + """表情包回复惩罚系数""" + + mentioned_bot_inevitable_reply: bool = False + """提及 bot 必然回复""" + + at_bot_inevitable_reply: bool = False + """@bot 必然回复""" + + +@dataclass +class FocusChatConfig(ConfigBase): + """专注聊天配置类""" + + reply_trigger_threshold: float = 3.0 + """心流聊天触发阈值，越低越容易触发""" + + default_decay_rate_per_second: float = 0.98 + """默认衰减率，越大衰减越快""" + + consecutive_no_reply_threshold: int = 3 + """连续不回复的次数阈值""" + + compressed_length: int = 5 + """心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5""" + + compress_length_limit: int = 5 + """最多压缩份数，超过该数值的压缩上下文会被删除""" + + +@dataclass +class EmojiConfig(ConfigBase): + """表情包配置类""" + + max_reg_num: int = 200 + """表情包最大注册数量""" + + do_replace: bool = True + """达到最大注册数量时替换旧表情包""" + + check_interval: int = 120 + """表情包检查间隔（分钟）""" + + save_pic: bool = False + """是否保存图片""" + + cache_emoji: bool = True + """是否缓存表情包""" + + steal_emoji: bool = True + """是否偷取表情包，让麦麦可以发送她保存的这些表情包""" + + content_filtration: bool = False + """是否开启表情包过滤""" + + filtration_prompt: str = "符合公序良俗" + """表情包过滤要求""" + + +@dataclass +class MemoryConfig(ConfigBase): + """记忆配置类""" + + memory_build_interval: int = 600 + """记忆构建间隔（秒）""" + + memory_build_distribution: tuple[ + float, + float, + float, + float, + float, + float, + ] = field(default_factory=lambda: (6.0, 3.0, 0.6, 32.0, 12.0, 0.4)) + """记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重""" + + memory_build_sample_num: int = 8 + """记忆构建采样数量""" + + memory_build_sample_length: int = 40 + """记忆构建采样长度""" + + memory_compress_rate: float = 0.1 + """记忆压缩率""" + + forget_memory_interval: int = 1000 + """记忆遗忘间隔（秒）""" + + memory_forget_time: int = 24 + """记忆遗忘时间（小时）""" + + memory_forget_percentage: float = 0.01 + """记忆遗忘比例""" + + consolidate_memory_interval: int = 1000 + """记忆整合间隔（秒）""" + + consolidation_similarity_threshold: float = 0.7 + """整合相似度阈值""" + + consolidate_memory_percentage: float = 0.01 + """整合检查节点比例""" + + memory_ban_words: list[str] = field(default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"]) + """不允许记忆的词列表""" + + +@dataclass +class MoodConfig(ConfigBase): + """情绪配置类""" + + mood_update_interval: int = 1 + """情绪更新间隔（秒）""" + + mood_decay_rate: float = 0.95 + """情绪衰减率""" + + mood_intensity_factor: float = 0.7 + """情绪强度因子""" + + +@dataclass +class KeywordRuleConfig(ConfigBase): + """关键词规则配置类""" + + enable: bool = True + """是否启用关键词规则""" + + keywords: list[str] = field(default_factory=lambda: []) + """关键词列表""" + + regex: list[str] = field(default_factory=lambda: []) + """正则表达式列表""" + + reaction: str = "" + """关键词触发的反应""" + + +@dataclass +class KeywordReactionConfig(ConfigBase): + """关键词配置类""" + + enable: bool = True + """是否启用关键词反应""" + + rules: list[KeywordRuleConfig] = field(default_factory=lambda: []) + """关键词反应规则列表""" + + +@dataclass +class ChineseTypoConfig(ConfigBase): + """中文错别字配置类""" + + enable: bool = True + """是否启用中文错别字生成器""" + + error_rate: float = 0.01 + """单字替换概率""" + + min_freq: int = 9 + """最小字频阈值""" + + tone_error_rate: float = 0.1 + """声调错误概率""" + + word_replace_rate: float = 0.006 + """整词替换概率""" + + +@dataclass +class ResponseSplitterConfig(ConfigBase): + """回复分割器配置类""" + + enable: bool = True + """是否启用回复分割器""" + + max_length: int = 256 + """回复允许的最大长度""" + + max_sentence_num: int = 3 + """回复允许的最大句子数""" + + enable_kaomoji_protection: bool = False + """是否启用颜文字保护""" + + +@dataclass +class TelemetryConfig(ConfigBase): + """遥测配置类""" + + enable: bool = True + """是否启用遥测""" + + +@dataclass +class ExperimentalConfig(ConfigBase): + """实验功能配置类""" + + enable_friend_chat: bool = False + """是否启用好友聊天""" + + talk_allowed_private: set[str] = field(default_factory=lambda: set()) + """允许聊天的私聊列表""" + + pfc_chatting: bool = False + """是否启用PFC""" + + +@dataclass +class ModelConfig(ConfigBase): + """模型配置类""" + + model_max_output_length: int = 800 # 最大回复长度 + + reasoning: dict[str, Any] = field(default_factory=lambda: {}) + """推理模型配置""" + + normal: dict[str, Any] = field(default_factory=lambda: {}) + """普通模型配置""" + + topic_judge: dict[str, Any] = field(default_factory=lambda: {}) + """主题判断模型配置""" + + summary: dict[str, Any] = field(default_factory=lambda: {}) + """摘要模型配置""" + + vlm: dict[str, Any] = field(default_factory=lambda: {}) + """视觉语言模型配置""" + + heartflow: dict[str, Any] = field(default_factory=lambda: {}) + """心流模型配置""" + + observation: dict[str, Any] = field(default_factory=lambda: {}) + """观察模型配置""" + + sub_heartflow: dict[str, Any] = field(default_factory=lambda: {}) + """子心流模型配置""" + + plan: dict[str, Any] = field(default_factory=lambda: {}) + """计划模型配置""" + + embedding: dict[str, Any] = field(default_factory=lambda: {}) + """嵌入模型配置""" + + pfc_action_planner: dict[str, Any] = field(default_factory=lambda: {}) + """PFC动作规划模型配置""" + + pfc_chat: dict[str, Any] = field(default_factory=lambda: {}) + """PFC聊天模型配置""" + + pfc_reply_checker: dict[str, Any] = field(default_factory=lambda: {}) + """PFC回复检查模型配置""" + + tool_use: dict[str, Any] = field(default_factory=lambda: {}) + """工具使用模型配置""" diff --git a/src/experimental/PFC/action_planner.py b/src/experimental/PFC/action_planner.py index b4182c9aa..c0bff5887 100644 --- a/src/experimental/PFC/action_planner.py +++ b/src/experimental/PFC/action_planner.py @@ -114,7 +114,7 @@ class ActionPlanner: request_type="action_planning", ) self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3) - self.name = global_config.BOT_NICKNAME + self.name = global_config.bot.nickname self.private_name = private_name self.chat_observer = ChatObserver.get_instance(stream_id, private_name) # self.action_planner_info = ActionPlannerInfo() # 移除未使用的变量 @@ -140,7 +140,7 @@ class ActionPlanner: # (这部分逻辑不变) time_since_last_bot_message_info = "" try: - bot_id = str(global_config.BOT_QQ) + bot_id = str(global_config.bot.qq_account) if hasattr(observation_info, "chat_history") and observation_info.chat_history: for i in range(len(observation_info.chat_history) - 1, -1, -1): msg = observation_info.chat_history[i] diff --git a/src/experimental/PFC/chat_observer.py b/src/experimental/PFC/chat_observer.py index 704eeb330..6135bd0f7 100644 --- a/src/experimental/PFC/chat_observer.py +++ b/src/experimental/PFC/chat_observer.py @@ -323,7 +323,7 @@ class ChatObserver: for msg in messages: try: user_info = UserInfo.from_dict(msg.get("user_info", {})) - if user_info.user_id == global_config.BOT_QQ: + if user_info.user_id == global_config.bot.qq_account: self.update_bot_speak_time(msg["time"]) else: self.update_user_speak_time(msg["time"]) diff --git a/src/experimental/PFC/message_sender.py b/src/experimental/PFC/message_sender.py index 181bf171b..4b193a41d 100644 --- a/src/experimental/PFC/message_sender.py +++ b/src/experimental/PFC/message_sender.py @@ -42,8 +42,8 @@ class DirectMessageSender: # 获取麦麦的信息 bot_user_info = UserInfo( - user_id=global_config.BOT_QQ, - user_nickname=global_config.BOT_NICKNAME, + user_id=global_config.bot.qq_account, + user_nickname=global_config.bot.nickname, platform=chat_stream.platform, ) diff --git a/src/experimental/PFC/pfc.py b/src/experimental/PFC/pfc.py index 84fb9f8dc..686d4af49 100644 --- a/src/experimental/PFC/pfc.py +++ b/src/experimental/PFC/pfc.py @@ -42,13 +42,14 @@ class GoalAnalyzer: """对话目标分析器""" def __init__(self, stream_id: str, private_name: str): + # TODO: API-Adapter修改标记 self.llm = LLMRequest( - model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal" + model=global_config.model.normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal" ) self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3) - self.name = global_config.BOT_NICKNAME - self.nick_name = global_config.BOT_ALIAS_NAMES + self.name = global_config.bot.nickname + self.nick_name = global_config.bot.alias_names self.private_name = private_name self.chat_observer = ChatObserver.get_instance(stream_id, private_name) diff --git a/src/experimental/PFC/pfc_KnowledgeFetcher.py b/src/experimental/PFC/pfc_KnowledgeFetcher.py index 8ebc307e2..4c1d8c759 100644 --- a/src/experimental/PFC/pfc_KnowledgeFetcher.py +++ b/src/experimental/PFC/pfc_KnowledgeFetcher.py @@ -14,9 +14,10 @@ class KnowledgeFetcher: """知识调取器""" def __init__(self, private_name: str): + # TODO: API-Adapter修改标记 self.llm = LLMRequest( - model=global_config.llm_normal, - temperature=global_config.llm_normal["temp"], + model=global_config.model.normal, + temperature=global_config.model.normal["temp"], max_tokens=1000, request_type="knowledge_fetch", ) diff --git a/src/experimental/PFC/reply_checker.py b/src/experimental/PFC/reply_checker.py index a76e8a0da..5bca9d601 100644 --- a/src/experimental/PFC/reply_checker.py +++ b/src/experimental/PFC/reply_checker.py @@ -16,7 +16,7 @@ class ReplyChecker: self.llm = LLMRequest( model=global_config.llm_PFC_reply_checker, temperature=0.50, max_tokens=1000, request_type="reply_check" ) - self.name = global_config.BOT_NICKNAME + self.name = global_config.bot.nickname self.private_name = private_name self.chat_observer = ChatObserver.get_instance(stream_id, private_name) self.max_retries = 3 # 最大重试次数 @@ -43,7 +43,7 @@ class ReplyChecker: bot_messages = [] for msg in reversed(chat_history): user_info = UserInfo.from_dict(msg.get("user_info", {})) - if str(user_info.user_id) == str(global_config.BOT_QQ): # 确保比较的是字符串 + if str(user_info.user_id) == str(global_config.bot.qq_account): # 确保比较的是字符串 bot_messages.append(msg.get("processed_plain_text", "")) if len(bot_messages) >= 2: # 只和最近的两条比较 break diff --git a/src/experimental/PFC/reply_generator.py b/src/experimental/PFC/reply_generator.py index 6dcda69af..bac8a769f 100644 --- a/src/experimental/PFC/reply_generator.py +++ b/src/experimental/PFC/reply_generator.py @@ -93,7 +93,7 @@ class ReplyGenerator: request_type="reply_generation", ) self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3) - self.name = global_config.BOT_NICKNAME + self.name = global_config.bot.nickname self.private_name = private_name self.chat_observer = ChatObserver.get_instance(stream_id, private_name) self.reply_checker = ReplyChecker(stream_id, private_name) diff --git a/src/experimental/PFC/waiter.py b/src/experimental/PFC/waiter.py index af5cf7ad0..452446589 100644 --- a/src/experimental/PFC/waiter.py +++ b/src/experimental/PFC/waiter.py @@ -19,7 +19,7 @@ class Waiter: def __init__(self, stream_id: str, private_name: str): self.chat_observer = ChatObserver.get_instance(stream_id, private_name) - self.name = global_config.BOT_NICKNAME + self.name = global_config.bot.nickname self.private_name = private_name # self.wait_accumulated_time = 0 # 不再需要累加计时 diff --git a/src/experimental/only_message_process.py b/src/experimental/only_message_process.py index 3d1432703..62f73c700 100644 --- a/src/experimental/only_message_process.py +++ b/src/experimental/only_message_process.py @@ -16,7 +16,7 @@ class MessageProcessor: @staticmethod def _check_ban_words(text: str, chat, userinfo) -> bool: """检查消息中是否包含过滤词""" - for word in global_config.ban_words: + for word in global_config.chat.ban_words: if word in text: logger.info( f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}" @@ -28,7 +28,7 @@ class MessageProcessor: @staticmethod def _check_ban_regex(text: str, chat, userinfo) -> bool: """检查消息是否匹配过滤正则表达式""" - for pattern in global_config.ban_msgs_regex: + for pattern in global_config.chat.ban_msgs_regex: if pattern.search(text): logger.info( f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}" diff --git a/src/main.py b/src/main.py index 34b7eda3d..4f8af28ef 100644 --- a/src/main.py +++ b/src/main.py @@ -40,7 +40,7 @@ class MainSystem: async def initialize(self): """初始化系统组件""" - logger.debug(f"正在唤醒{global_config.BOT_NICKNAME}......") + logger.debug(f"正在唤醒{global_config.bot.nickname}......") # 其他初始化任务 await asyncio.gather(self._init_components()) @@ -84,7 +84,7 @@ class MainSystem: asyncio.create_task(chat_manager._auto_save_task()) # 使用HippocampusManager初始化海马体 - self.hippocampus_manager.initialize(global_config=global_config) + self.hippocampus_manager.initialize() # await asyncio.sleep(0.5) #防止logger输出飞了 # 将bot.py中的chat_bot.message_process消息处理函数注册到api.py的消息处理基类中 @@ -92,15 +92,15 @@ class MainSystem: # 初始化个体特征 self.individuality.initialize( - bot_nickname=global_config.BOT_NICKNAME, - personality_core=global_config.personality_core, - personality_sides=global_config.personality_sides, - identity_detail=global_config.identity_detail, - height=global_config.height, - weight=global_config.weight, - age=global_config.age, - gender=global_config.gender, - appearance=global_config.appearance, + bot_nickname=global_config.bot.nickname, + personality_core=global_config.personality.personality_core, + personality_sides=global_config.personality.personality_sides, + identity_detail=global_config.identity.identity_detail, + height=global_config.identity.height, + weight=global_config.identity.weight, + age=global_config.identity.age, + gender=global_config.identity.gender, + appearance=global_config.identity.appearance, ) logger.success("个体特征初始化成功") @@ -141,7 +141,7 @@ class MainSystem: async def build_memory_task(): """记忆构建任务""" while True: - await asyncio.sleep(global_config.build_memory_interval) + await asyncio.sleep(global_config.memory.memory_build_interval) logger.info("正在进行记忆构建") await HippocampusManager.get_instance().build_memory() @@ -149,16 +149,18 @@ class MainSystem: async def forget_memory_task(): """记忆遗忘任务""" while True: - await asyncio.sleep(global_config.forget_memory_interval) + await asyncio.sleep(global_config.memory.forget_memory_interval) print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...") - await HippocampusManager.get_instance().forget_memory(percentage=global_config.memory_forget_percentage) + await HippocampusManager.get_instance().forget_memory( + percentage=global_config.memory.memory_forget_percentage + ) print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成") @staticmethod async def consolidate_memory_task(): """记忆整合任务""" while True: - await asyncio.sleep(global_config.consolidate_memory_interval) + await asyncio.sleep(global_config.memory.consolidate_memory_interval) print("\033[1;32m[记忆整合]\033[0m 开始整合记忆...") await HippocampusManager.get_instance().consolidate_memory() print("\033[1;32m[记忆整合]\033[0m 记忆整合完成") diff --git a/src/manager/mood_manager.py b/src/manager/mood_manager.py index 42677d4e1..c83fbeb7c 100644 --- a/src/manager/mood_manager.py +++ b/src/manager/mood_manager.py @@ -34,14 +34,14 @@ class MoodUpdateTask(AsyncTask): def __init__(self): super().__init__( task_name="Mood Update Task", - wait_before_start=global_config.mood_update_interval, - run_interval=global_config.mood_update_interval, + wait_before_start=global_config.mood.mood_update_interval, + run_interval=global_config.mood.mood_update_interval, ) # 从配置文件获取衰减率 - self.decay_rate_valence: float = 1 - global_config.mood_decay_rate + self.decay_rate_valence: float = 1 - global_config.mood.mood_decay_rate """愉悦度衰减率""" - self.decay_rate_arousal: float = 1 - global_config.mood_decay_rate + self.decay_rate_arousal: float = 1 - global_config.mood.mood_decay_rate """唤醒度衰减率""" self.last_update = time.time() diff --git a/src/tools/not_used/change_mood.py b/src/tools/not_used/change_mood.py index c34bebb93..69fc3bb78 100644 --- a/src/tools/not_used/change_mood.py +++ b/src/tools/not_used/change_mood.py @@ -44,7 +44,7 @@ class ChangeMoodTool(BaseTool): _ori_response = ",".join(response_set) # _stance, emotion = await gpt._get_emotion_tags(ori_response, message_processed_plain_text) emotion = "平静" - mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor) + mood_manager.update_mood_from_emotion(emotion, global_config.mood.mood_intensity_factor) return {"name": "change_mood", "content": f"你的心情刚刚变化了，现在的心情是: {emotion}"} except Exception as e: logger.error(f"心情改变工具执行失败: {str(e)}") diff --git a/src/tools/tool_use.py b/src/tools/tool_use.py index c55170b88..ff36085d6 100644 --- a/src/tools/tool_use.py +++ b/src/tools/tool_use.py @@ -15,7 +15,7 @@ logger = get_logger("tool_use") class ToolUser: def __init__(self): self.llm_model_tool = LLMRequest( - model=global_config.llm_tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use" + model=global_config.model.tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use" ) @staticmethod @@ -37,7 +37,7 @@ class ToolUser: # print(f"intol111111111111111111111111111111111222222222222mid_memory_info：{mid_memory_info}") # 这些信息应该从调用者传入，而不是从self获取 - bot_name = global_config.BOT_NICKNAME + bot_name = global_config.bot.nickname prompt = "" prompt += mid_memory_info prompt += "你正在思考如何回复群里的消息。\n" diff --git a/template/bot_config_meta.toml b/template/bot_config_meta.toml deleted file mode 100644 index c3541baad..000000000 --- a/template/bot_config_meta.toml +++ /dev/null @@ -1,104 +0,0 @@ -[inner.version] -describe = "版本号" -important = true -can_edit = false - -[bot.qq] -describe = "机器人的QQ号" -important = true -can_edit = true - -[bot.nickname] -describe = "机器人的昵称" -important = true -can_edit = true - -[bot.alias_names] -describe = "机器人的别名列表，该选项还在调试中，暂时未生效" -important = false -can_edit = true - -[groups.talk_allowed] -describe = "可以回复消息的群号码列表" -important = true -can_edit = true - -[groups.talk_frequency_down] -describe = "降低回复频率的群号码列表" -important = false -can_edit = true - -[groups.ban_user_id] -describe = "禁止回复和读取消息的QQ号列表" -important = false -can_edit = true - -[personality.personality_core] -describe = "用一句话或几句话描述人格的核心特点，建议20字以内" -important = true -can_edit = true - -[personality.personality_sides] -describe = "用一句话或几句话描述人格的一些细节，条数任意，不能为0，该选项还在调试中" -important = false -can_edit = true - -[identity.identity_detail] -describe = "身份特点列表，条数任意，不能为0，该选项还在调试中" -important = false -can_edit = true - -[identity.age] -describe = "年龄，单位岁" -important = false -can_edit = true - -[identity.gender] -describe = "性别" -important = false -can_edit = true - -[identity.appearance] -describe = "外貌特征描述，该选项还在调试中，暂时未生效" -important = false -can_edit = true - -[platforms.nonebot-qq] -describe = "nonebot-qq适配器提供的链接" -important = true -can_edit = true - -[chat.allow_focus_mode] -describe = "是否允许专注聊天状态" -important = false -can_edit = true - -[chat.base_normal_chat_num] -describe = "最多允许多少个群进行普通聊天" -important = false -can_edit = true - -[chat.base_focused_chat_num] -describe = "最多允许多少个群进行专注聊天" -important = false -can_edit = true - -[chat.observation_context_size] -describe = "观察到的最长上下文大小，建议15，太短太长都会导致脑袋尖尖" -important = false -can_edit = true - -[chat.message_buffer] -describe = "启用消息缓冲器，启用此项以解决消息的拆分问题，但会使麦麦的回复延迟" -important = false -can_edit = true - -[chat.ban_words] -describe = "需要过滤的消息列表" -important = false -can_edit = true - -[chat.ban_msgs_regex] -describe = "需要过滤的消息（原始消息）匹配的正则表达式，匹配到的消息将被过滤（支持CQ码）" -important = false -can_edit = true \ No newline at end of file diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml index 931afe2ed..64e51da77 100644 --- a/template/bot_config_template.toml +++ b/template/bot_config_template.toml @@ -1,18 +1,10 @@ [inner] -version = "1.7.0" +version = "2.0.0" #----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读---- #如果你想要修改配置文件，请在修改后将version的值进行变更 -#如果新增项目，请在BotConfig类下新增相应的变量 -#1.如果你修改的是[]层级项目，例如你新增了 [memory],那么请在config.py的 load_config函数中的include_configs字典中新增"内容":{ -#"func":memory, -#"support":">=0.0.0", #新的版本号 -#"necessary":False #是否必须 -#} -#2.如果你修改的是[]下的项目，例如你新增了[memory]下的 memory_ban_words ,那么请在config.py的 load_config函数中的 memory函数下新增版本判断: - # if config.INNER_VERSION in SpecifierSet(">=0.0.2"): - # config.memory_ban_words = set(memory_config.get("memory_ban_words", [])) - +#如果新增项目，请阅读src/config/official_configs.py中的说明 +# # 版本格式：主版本号.次版本号.修订号，版本号递增规则如下： # 主版本号：当你做了不兼容的 API 修改， # 次版本号：当你做了向下兼容的功能性新增， @@ -21,11 +13,11 @@ version = "1.7.0" #----以上是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读---- [bot] -qq = 1145141919810 +qq_account = 1145141919810 nickname = "麦麦" alias_names = ["麦叠", "牢麦"] #该选项还在调试中，暂时未生效 -[groups] +[chat_target] talk_allowed = [ 123, 123, @@ -53,10 +45,13 @@ identity_detail = [ "身份特点", "身份特点", ]# 条数任意，不能为0, 该选项还在调试中 + #外貌特征 -age = 20 # 年龄 单位岁 -gender = "男" # 性别 -appearance = "用几句话描述外貌特征" # 外貌特征 该选项还在调试中，暂时未生效 +age = 18 # 年龄 单位岁 +gender = "女" # 性别 +height = "170" # 身高（单位cm） +weight = "50" # 体重（单位kg） +appearance = "用一句或几句话描述外貌特征" # 外貌特征 该选项还在调试中，暂时未生效 [platforms] # 必填项目，填写每个平台适配器提供的链接 qq="http://127.0.0.1:18002/api/message" @@ -85,11 +80,10 @@ ban_msgs_regex = [ [normal_chat] #普通聊天 #一般回复参数 -model_reasoning_probability = 0.7 # 麦麦回答时选择推理模型 模型的概率 -model_normal_probability = 0.3 # 麦麦回答时选择一般模型 模型的概率 +reasoning_model_probability = 0.3 # 麦麦回答时选择推理模型的概率（与之相对的，普通模型的概率为1 - reasoning_model_probability） emoji_chance = 0.2 # 麦麦一般回复时使用表情包的概率，设置为1让麦麦自己决定发不发 -thinking_timeout = 100 # 麦麦最长思考时间，超过这个时间的思考会放弃（往往是api反应太慢） +thinking_timeout = 120 # 麦麦最长思考时间，超过这个时间的思考会放弃（往往是api反应太慢） willing_mode = "classical" # 回复意愿模式 —— 经典模式：classical，mxp模式：mxp，自定义模式：custom（需要你自己实现） response_willing_amplifier = 1 # 麦麦回复意愿放大系数，一般为1 @@ -100,8 +94,8 @@ mentioned_bot_inevitable_reply = false # 提及 bot 必然回复 at_bot_inevitable_reply = false # @bot 必然回复 [focus_chat] #专注聊天 -reply_trigger_threshold = 3.6 # 专注聊天触发阈值，越低越容易进入专注聊天 -default_decay_rate_per_second = 0.95 # 默认衰减率，越大衰减越快，越高越难进入专注聊天 +reply_trigger_threshold = 3.0 # 专注聊天触发阈值，越低越容易进入专注聊天 +default_decay_rate_per_second = 0.98 # 默认衰减率，越大衰减越快，越高越难进入专注聊天 consecutive_no_reply_threshold = 3 # 连续不回复的阈值，越低越容易结束专注聊天 # 以下选项暂时无效 @@ -110,20 +104,20 @@ compress_length_limit = 5 #最多压缩份数，超过该数值的压缩上下 [emoji] -max_emoji_num = 40 # 表情包最大数量 -max_reach_deletion = true # 开启则在达到最大数量时删除表情包，关闭则达到最大数量时不删除，只是不会继续收集表情包 -check_interval = 10 # 检查表情包（注册，破损，删除）的时间间隔(分钟) +max_reg_num = 40 # 表情包最大注册数量 +do_replace = true # 开启则在达到最大数量时删除（替换）表情包，关闭则达到最大数量时不会继续收集表情包 +check_interval = 120 # 检查表情包（注册，破损，删除）的时间间隔(分钟) save_pic = false # 是否保存图片 -save_emoji = false # 是否保存表情包 +cache_emoji = true # 是否缓存表情包 steal_emoji = true # 是否偷取表情包，让麦麦可以发送她保存的这些表情包 -enable_check = false # 是否启用表情包过滤，只有符合该要求的表情包才会被保存 -check_prompt = "符合公序良俗" # 表情包过滤要求，只有符合该要求的表情包才会被保存 +content_filtration = false # 是否启用表情包过滤，只有符合该要求的表情包才会被保存 +filtration_prompt = "符合公序良俗" # 表情包过滤要求，只有符合该要求的表情包才会被保存 [memory] -build_memory_interval = 2000 # 记忆构建间隔 单位秒 间隔越低，麦麦学习越多，但是冗余信息也会增多 -build_memory_distribution = [6.0,3.0,0.6,32.0,12.0,0.4] # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重 -build_memory_sample_num = 8 # 采样数量，数值越高记忆采样次数越多 -build_memory_sample_length = 40 # 采样长度，数值越高一段记忆内容越丰富 +memory_build_interval = 2000 # 记忆构建间隔 单位秒 间隔越低，麦麦学习越多，但是冗余信息也会增多 +memory_build_distribution = [6.0, 3.0, 0.6, 32.0, 12.0, 0.4] # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重 +memory_build_sample_num = 8 # 采样数量，数值越高记忆采样次数越多 +memory_build_sample_length = 40 # 采样长度，数值越高一段记忆内容越丰富 memory_compress_rate = 0.1 # 记忆压缩率 控制记忆精简程度 建议保持默认,调高可以获得更多信息，但是冗余信息也会增多 forget_memory_interval = 1000 # 记忆遗忘间隔 单位秒 间隔越低，麦麦遗忘越频繁，记忆更精简，但更难学习 @@ -135,49 +129,45 @@ consolidation_similarity_threshold = 0.7 # 相似度阈值 consolidation_check_percentage = 0.01 # 检查节点比例 #不希望记忆的词，已经记忆的不会受到影响 -memory_ban_words = [ - # "403","张三" -] +memory_ban_words = [ "表情包", "图片", "回复", "聊天记录" ] [mood] mood_update_interval = 1.0 # 情绪更新间隔 单位秒 mood_decay_rate = 0.95 # 情绪衰减率 mood_intensity_factor = 1.0 # 情绪强度因子 -[keywords_reaction] # 针对某个关键词作出反应 +[keyword_reaction] # 针对某个关键词作出反应 enable = true # 关键词反应功能的总开关 -[[keywords_reaction.rules]] # 如果想要新增多个关键词，直接复制本条，修改keywords和reaction即可 +[[keyword_reaction.rules]] # 如果想要新增多个关键词，直接复制本条，修改keywords和reaction即可 enable = true # 是否启用此条（为了人类在未来AI战争能更好地识别AI（bushi），默认开启） keywords = ["人机", "bot", "机器", "入机", "robot", "机器人","ai","AI"] # 会触发反应的关键词 reaction = "有人说你是人机或者机器人，否定这一事实，攻击或反问他。注意，只有认为你是机器人才回复，否则不要否认" # 触发之后添加的提示词 -[[keywords_reaction.rules]] # 就像这样复制 +[[keyword_reaction.rules]] # 就像这样复制 enable = false # 仅作示例，不会触发 keywords = ["测试关键词回复","test",""] reaction = "回答“测试成功”" # 修复错误的引号 -[[keywords_reaction.rules]] # 使用正则表达式匹配句式 +[[keyword_reaction.rules]] # 使用正则表达式匹配句式 enable = false # 仅作示例，不会触发 regex = ["^(?P\\S{1,20})是这样的$"] # 将匹配到的词汇命名为n，反应中对应的[n]会被替换为匹配到的内容，若不了解正则表达式请勿编写 reaction = "请按照以下模板造句：[n]是这样的，xx只要xx就可以，可是[n]要考虑的事情就很多了，比如什么时候xx，什么时候xx，什么时候xx。（请自由发挥替换xx部分，只需保持句式结构，同时表达一种将[n]过度重视的反讽意味）" [chinese_typo] enable = true # 是否启用中文错别字生成器 -error_rate=0.001 # 单字替换概率 +error_rate=0.01 # 单字替换概率 min_freq=9 # 最小字频阈值 tone_error_rate=0.1 # 声调错误概率 word_replace_rate=0.006 # 整词替换概率 [response_splitter] -enable_response_splitter = true # 是否启用回复分割器 -response_max_length = 256 # 回复允许的最大长度 -response_max_sentence_num = 4 # 回复允许的最大句子数 +enable = true # 是否启用回复分割器 +max_length = 256 # 回复允许的最大长度 +max_sentence_num = 4 # 回复允许的最大句子数 enable_kaomoji_protection = false # 是否启用颜文字保护 -model_max_output_length = 256 # 模型单次返回的最大token数 - -[remote] #发送统计信息，主要是看全球有多少只麦麦 +[telemetry] #发送统计信息，主要是看全球有多少只麦麦 enable = true [experimental] #实验性功能 @@ -194,14 +184,17 @@ pfc_chatting = false # 是否启用PFC聊天，该功能仅作用于私聊，与 # stream = : 用于指定模型是否是使用流式输出 # 如果不指定，则该项是 False +[model] +model_max_output_length = 800 # 模型单次返回的最大token数 + #这个模型必须是推理模型 -[model.llm_reasoning] # 一般聊天模式的推理回复模型 +[model.reasoning] # 一般聊天模式的推理回复模型 name = "Pro/deepseek-ai/DeepSeek-R1" provider = "SILICONFLOW" pri_in = 1.0 #模型的输入价格（非必填，可以记录消耗） pri_out = 4.0 #模型的输出价格（非必填，可以记录消耗） -[model.llm_normal] #V3 回复模型 专注和一般聊天模式共用的回复模型 +[model.normal] #V3 回复模型 专注和一般聊天模式共用的回复模型 name = "Pro/deepseek-ai/DeepSeek-V3" provider = "SILICONFLOW" pri_in = 2 #模型的输入价格（非必填，可以记录消耗） @@ -209,13 +202,13 @@ pri_out = 8 #模型的输出价格（非必填，可以记录消耗） #默认temp 0.2 如果你使用的是老V3或者其他模型，请自己修改temp参数 temp = 0.2 #模型的温度，新V3建议0.1-0.3 -[model.llm_topic_judge] #主题判断模型：建议使用qwen2.5 7b +[model.topic_judge] #主题判断模型：建议使用qwen2.5 7b name = "Pro/Qwen/Qwen2.5-7B-Instruct" provider = "SILICONFLOW" pri_in = 0.35 pri_out = 0.35 -[model.llm_summary] #概括模型，建议使用qwen2.5 32b 及以上 +[model.summary] #概括模型，建议使用qwen2.5 32b 及以上 name = "Qwen/Qwen2.5-32B-Instruct" provider = "SILICONFLOW" pri_in = 1.26 @@ -227,27 +220,27 @@ provider = "SILICONFLOW" pri_in = 0.35 pri_out = 0.35 -[model.llm_heartflow] # 用于控制麦麦是否参与聊天的模型 +[model.heartflow] # 用于控制麦麦是否参与聊天的模型 name = "Qwen/Qwen2.5-32B-Instruct" provider = "SILICONFLOW" pri_in = 1.26 pri_out = 1.26 -[model.llm_observation] #观察模型，压缩聊天内容，建议用免费的 +[model.observation] #观察模型，压缩聊天内容，建议用免费的 # name = "Pro/Qwen/Qwen2.5-7B-Instruct" name = "Qwen/Qwen2.5-7B-Instruct" provider = "SILICONFLOW" pri_in = 0 pri_out = 0 -[model.llm_sub_heartflow] #心流：认真水群时,生成麦麦的内心想法，必须使用具有工具调用能力的模型 +[model.sub_heartflow] #心流：认真水群时,生成麦麦的内心想法，必须使用具有工具调用能力的模型 name = "Pro/deepseek-ai/DeepSeek-V3" provider = "SILICONFLOW" pri_in = 2 pri_out = 8 temp = 0.3 #模型的温度，新V3建议0.1-0.3 -[model.llm_plan] #决策：认真水群时,负责决定麦麦该做什么 +[model.plan] #决策：认真水群时,负责决定麦麦该做什么 name = "Pro/deepseek-ai/DeepSeek-V3" provider = "SILICONFLOW" pri_in = 2 @@ -265,7 +258,7 @@ pri_out = 0 #私聊PFC：需要开启PFC功能，默认三个模型均为硅基流动v3，如果需要支持多人同时私聊或频繁调用，建议把其中的一个或两个换成官方v3或其它模型，以免撞到429 #PFC决策模型 -[model.llm_PFC_action_planner] +[model.pfc_action_planner] name = "Pro/deepseek-ai/DeepSeek-V3" provider = "SILICONFLOW" temp = 0.3 @@ -273,7 +266,7 @@ pri_in = 2 pri_out = 8 #PFC聊天模型 -[model.llm_PFC_chat] +[model.pfc_chat] name = "Pro/deepseek-ai/DeepSeek-V3" provider = "SILICONFLOW" temp = 0.3 @@ -281,7 +274,7 @@ pri_in = 2 pri_out = 8 #PFC检查模型 -[model.llm_PFC_reply_checker] +[model.pfc_reply_checker] name = "Pro/deepseek-ai/DeepSeek-V3" provider = "SILICONFLOW" pri_in = 2 @@ -294,7 +287,7 @@ pri_out = 8 #以下模型暂时没有使用！！ #以下模型暂时没有使用！！ -[model.llm_tool_use] #工具调用模型，需要使用支持工具调用的模型，建议使用qwen2.5 32b +[model.tool_use] #工具调用模型，需要使用支持工具调用的模型，建议使用qwen2.5 32b name = "Qwen/Qwen2.5-32B-Instruct" provider = "SILICONFLOW" pri_in = 1.26 diff --git a/tests/test_config.py b/tests/test_config.py new file mode 100644 index 000000000..1a1239601 --- /dev/null +++ b/tests/test_config.py @@ -0,0 +1,7 @@ +from src.config.config import global_config + + +class TestConfig: + def test_load(self): + config = global_config + print(config)