mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into PFC-test
Bakadax
commit
5e9177e775
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@ -1,6 +1,6 @@
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from fastapi import HTTPException
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from rich.traceback import install
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from src.config.config import BotConfig
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from src.config.config import Config
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from src.common.logger_manager import get_logger
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import os
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@ -14,8 +14,8 @@ async def reload_config():
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from src.config import config as config_module
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logger.debug("正在重载配置文件...")
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bot_config_path = os.path.join(BotConfig.get_config_dir(), "bot_config.toml")
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config_module.global_config = BotConfig.load_config(config_path=bot_config_path)
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bot_config_path = os.path.join(Config.get_config_dir(), "bot_config.toml")
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config_module.global_config = Config.load_config(config_path=bot_config_path)
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logger.debug("配置文件重载成功")
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return {"status": "reloaded"}
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except FileNotFoundError as e:
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@ -369,14 +369,15 @@ class EmojiManager:
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def __init__(self):
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self._initialized = None
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self._scan_task = None
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self.vlm = LLMRequest(model=global_config.vlm, temperature=0.3, max_tokens=1000, request_type="emoji")
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self.vlm = LLMRequest(model=global_config.model.vlm, temperature=0.3, max_tokens=1000, request_type="emoji")
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self.llm_emotion_judge = LLMRequest(
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model=global_config.llm_normal, max_tokens=600, request_type="emoji"
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model=global_config.model.normal, max_tokens=600, request_type="emoji"
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) # 更高的温度，更少的token（后续可以根据情绪来调整温度）
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self.emoji_num = 0
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self.emoji_num_max = global_config.max_emoji_num
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self.emoji_num_max_reach_deletion = global_config.max_reach_deletion
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self.emoji_num_max = global_config.emoji.max_reg_num
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self.emoji_num_max_reach_deletion = global_config.emoji.do_replace
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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
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logger.info("启动表情包管理器")
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@ -613,18 +614,18 @@ class EmojiManager:
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logger.warning(f"[警告] 表情包目录不存在: {EMOJI_DIR}")
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os.makedirs(EMOJI_DIR, exist_ok=True)
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logger.info(f"[创建] 已创建表情包目录: {EMOJI_DIR}")
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await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60)
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await asyncio.sleep(global_config.emoji.check_interval * 60)
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continue
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# 检查目录是否为空
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files = os.listdir(EMOJI_DIR)
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if not files:
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logger.warning(f"[警告] 表情包目录为空: {EMOJI_DIR}")
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await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60)
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await asyncio.sleep(global_config.emoji.check_interval * 60)
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continue
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# 检查是否需要处理表情包(数量超过最大值或不足)
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if (self.emoji_num > self.emoji_num_max and global_config.max_reach_deletion) or (
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if (self.emoji_num > self.emoji_num_max and global_config.emoji.do_replace) or (
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self.emoji_num < self.emoji_num_max
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):
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try:
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@ -651,7 +652,7 @@ class EmojiManager:
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except Exception as e:
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logger.error(f"[错误] 扫描表情包目录失败: {str(e)}")
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await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60)
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await asyncio.sleep(global_config.emoji.check_interval * 60)
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async def get_all_emoji_from_db(self):
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"""获取所有表情包并初始化为MaiEmoji类对象，更新 self.emoji_objects"""
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@ -788,7 +789,7 @@ class EmojiManager:
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# 构建提示词
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prompt = (
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f"{global_config.BOT_NICKNAME}的表情包存储已满({self.emoji_num}/{self.emoji_num_max})，"
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f"{global_config.bot.nickname}的表情包存储已满({self.emoji_num}/{self.emoji_num_max})，"
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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"
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f"新表情包信息：\n"
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f"描述: {new_emoji.description}\n\n"
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@ -871,10 +872,10 @@ class EmojiManager:
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description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
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# 审核表情包
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if global_config.EMOJI_CHECK:
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if global_config.emoji.content_filtration:
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prompt = f'''
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这是一个表情包，请对这个表情包进行审核，标准如下：
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1. 必须符合"{global_config.EMOJI_CHECK_PROMPT}"的要求
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1. 必须符合"{global_config.emoji.filtration_prompt}"的要求
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2. 不能是色情、暴力、等违法违规内容，必须符合公序良俗
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3. 不能是任何形式的截图，聊天记录或视频截图
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4. 不要出现5个以上文字
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@ -26,9 +26,10 @@ logger = get_logger("expressor")
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class DefaultExpressor:
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def __init__(self, chat_id: str):
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self.log_prefix = "expressor"
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# TODO: API-Adapter修改标记
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self.express_model = LLMRequest(
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model=global_config.llm_normal,
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temperature=global_config.llm_normal["temp"],
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model=global_config.model.normal,
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temperature=global_config.model.normal["temp"],
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max_tokens=256,
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request_type="response_heartflow",
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)
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@ -52,8 +53,8 @@ class DefaultExpressor:
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messageinfo = anchor_message.message_info
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thinking_time_point = parse_thinking_id_to_timestamp(thinking_id)
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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user_id=global_config.bot.qq_account,
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user_nickname=global_config.bot.nickname,
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platform=messageinfo.platform,
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)
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# logger.debug(f"创建思考消息：{anchor_message}")
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@ -153,7 +154,7 @@ class DefaultExpressor:
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try:
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# 1. 获取情绪影响因子并调整模型温度
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arousal_multiplier = mood_manager.get_arousal_multiplier()
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current_temp = float(global_config.llm_normal["temp"]) * arousal_multiplier
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current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier
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self.express_model.params["temperature"] = current_temp # 动态调整温度
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# 2. 获取信息捕捉器
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@ -195,6 +196,7 @@ class DefaultExpressor:
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try:
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with Timer("LLM生成", {}): # 内部计时器，可选保留
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# TODO: API-Adapter修改标记
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# logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n")
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content, reasoning_content, model_name = await self.express_model.generate_response(prompt)
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@ -342,8 +344,8 @@ class DefaultExpressor:
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thinking_start_time = await self.heart_fc_sender.get_thinking_start_time(self.chat_id, thinking_id)
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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user_id=global_config.bot.qq_account,
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user_nickname=global_config.bot.nickname,
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platform=self.chat_stream.platform,
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)
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@ -77,8 +77,9 @@ def init_prompt() -> None:
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class ExpressionLearner:
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def __init__(self) -> None:
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# TODO: API-Adapter修改标记
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self.express_learn_model: LLMRequest = LLMRequest(
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model=global_config.llm_normal,
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model=global_config.model.normal,
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temperature=0.1,
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max_tokens=256,
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request_type="response_heartflow",
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@ -289,7 +290,7 @@ class ExpressionLearner:
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# 构建prompt
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prompt = await global_prompt_manager.format_prompt(
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"personality_expression_prompt",
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personality=global_config.expression_style,
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personality=global_config.personality.expression_style,
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)
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# logger.info(f"个性表达方式提取prompt: {prompt}")
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@ -91,7 +91,6 @@ class HeartFChatting:
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self.action_manager = ActionManager()
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self.action_planner = ActionPlanner(log_prefix=self.log_prefix, action_manager=self.action_manager, stream_id=self.stream_id, chat_stream=self.chat_stream)
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# --- 处理器列表 ---
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self.processors: List[BaseProcessor] = []
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self._register_default_processors()
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@ -526,5 +525,3 @@ class HeartFChatting:
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if last_n is not None:
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history = history[-last_n:]
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return [cycle.to_dict() for cycle in history]
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@ -112,7 +112,7 @@ def _check_ban_words(text: str, chat, userinfo) -> bool:
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Returns:
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bool: 是否包含过滤词
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"""
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for word in global_config.ban_words:
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for word in global_config.chat.ban_words:
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if word in text:
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chat_name = chat.group_info.group_name if chat.group_info else "私聊"
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logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}")
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@ -132,7 +132,7 @@ def _check_ban_regex(text: str, chat, userinfo) -> bool:
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Returns:
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bool: 是否匹配过滤正则
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"""
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for pattern in global_config.ban_msgs_regex:
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for pattern in global_config.chat.ban_msgs_regex:
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if pattern.search(text):
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chat_name = chat.group_info.group_name if chat.group_info else "私聊"
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logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}")
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@ -143,7 +143,7 @@ async def _build_prompt_focus(
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message_list_before_now = get_raw_msg_before_timestamp_with_chat(
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chat_id=chat_stream.stream_id,
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timestamp=time.time(),
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limit=global_config.observation_context_size,
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limit=global_config.chat.observation_context_size,
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)
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chat_talking_prompt = await build_readable_messages(
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message_list_before_now,
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@ -216,7 +216,7 @@ async def _build_prompt_focus(
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chat_target=chat_target_1, # Used in group template
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# chat_talking_prompt=chat_talking_prompt,
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chat_info=chat_talking_prompt,
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bot_name=global_config.BOT_NICKNAME,
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bot_name=global_config.bot.nickname,
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# prompt_personality=prompt_personality,
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prompt_personality="",
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reason=reason,
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@ -232,7 +232,7 @@ async def _build_prompt_focus(
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info_from_tools=structured_info_prompt,
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sender_name=effective_sender_name, # Used in private template
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chat_talking_prompt=chat_talking_prompt,
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bot_name=global_config.BOT_NICKNAME,
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bot_name=global_config.bot.nickname,
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prompt_personality=prompt_personality,
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# chat_target and chat_target_2 are not used in private template
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current_mind_info=current_mind_info,
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@ -287,7 +287,7 @@ class PromptBuilder:
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who_chat_in_group = get_recent_group_speaker(
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chat_stream.stream_id,
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(chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None,
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limit=global_config.observation_context_size,
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limit=global_config.chat.observation_context_size,
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)
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elif chat_stream.user_info:
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who_chat_in_group.append(
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@ -335,7 +335,7 @@ class PromptBuilder:
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message_list_before_now = get_raw_msg_before_timestamp_with_chat(
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chat_id=chat_stream.stream_id,
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timestamp=time.time(),
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limit=global_config.observation_context_size,
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limit=global_config.chat.observation_context_size,
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)
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chat_talking_prompt = await build_readable_messages(
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message_list_before_now,
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@ -347,18 +347,15 @@ class PromptBuilder:
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# 关键词检测与反应
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keywords_reaction_prompt = ""
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for rule in global_config.keywords_reaction_rules:
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if rule.get("enable", False):
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if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
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logger.info(
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f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
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)
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keywords_reaction_prompt += rule.get("reaction", "") + "，"
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for rule in global_config.keyword_reaction.rules:
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if rule.enable:
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if any(keyword in message_txt for keyword in rule.keywords):
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logger.info(f"检测到以下关键词之一：{rule.keywords}，触发反应：{rule.reaction}")
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keywords_reaction_prompt += f"{rule.reaction}，"
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else:
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for pattern in rule.get("regex", []):
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result = pattern.search(message_txt)
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if result:
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reaction = rule.get("reaction", "")
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for pattern in rule.regex:
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if result := pattern.search(message_txt):
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reaction = rule.reaction
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for name, content in result.groupdict().items():
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reaction = reaction.replace(f"[{name}]", content)
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logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
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@ -410,8 +407,8 @@ class PromptBuilder:
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nickname_info=nickname_injection_str, # <--- 注入绰号信息
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chat_talking_prompt=chat_talking_prompt,
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message_txt=message_txt,
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bot_name=global_config.BOT_NICKNAME,
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bot_other_names="/".join(global_config.BOT_ALIAS_NAMES),
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bot_name=global_config.bot.nickname,
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bot_other_names="/".join(global_config.bot.alias_names),
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prompt_personality=prompt_personality,
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mood_prompt=mood_prompt,
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reply_style1=reply_style1_chosen,
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@ -432,8 +429,8 @@ class PromptBuilder:
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prompt_info=prompt_info,
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chat_talking_prompt=chat_talking_prompt,
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message_txt=message_txt,
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bot_name=global_config.BOT_NICKNAME,
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bot_other_names="/".join(global_config.BOT_ALIAS_NAMES),
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bot_name=global_config.bot.nickname,
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bot_other_names="/".join(global_config.bot.alias_names),
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prompt_personality=prompt_personality,
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mood_prompt=mood_prompt,
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reply_style1=reply_style1_chosen,
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|
|
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@ -26,8 +26,9 @@ class ChattingInfoProcessor(BaseProcessor):
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def __init__(self):
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"""初始化观察处理器"""
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super().__init__()
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# TODO: API-Adapter修改标记
|
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self.llm_summary = LLMRequest(
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model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
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model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
|
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)
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async def process_info(
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@ -108,12 +109,12 @@ class ChattingInfoProcessor(BaseProcessor):
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"created_at": datetime.now().timestamp(),
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}
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obs.mid_memorys.append(mid_memory)
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if len(obs.mid_memorys) > obs.max_mid_memory_len:
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obs.mid_memorys.pop(0) # 移除最旧的
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obs.mid_memories.append(mid_memory)
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if len(obs.mid_memories) > obs.max_mid_memory_len:
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obs.mid_memories.pop(0) # 移除最旧的
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mid_memory_str = "之前聊天的内容概述是：\n"
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for mid_memory_item in obs.mid_memorys: # 重命名循环变量以示区分
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for mid_memory_item in obs.mid_memories: # 重命名循环变量以示区分
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time_diff = int((datetime.now().timestamp() - mid_memory_item["created_at"]) / 60)
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mid_memory_str += (
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f"距离现在{time_diff}分钟前(聊天记录id:{mid_memory_item['id']})：{mid_memory_item['theme']}\n"
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|
|
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|
@ -81,8 +81,8 @@ class MindProcessor(BaseProcessor):
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self.subheartflow_id = subheartflow_id
|
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|
||||
self.llm_model = LLMRequest(
|
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model=global_config.llm_sub_heartflow,
|
||||
temperature=global_config.llm_sub_heartflow["temp"],
|
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model=global_config.model.sub_heartflow,
|
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temperature=global_config.model.sub_heartflow["temp"],
|
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max_tokens=800,
|
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request_type="sub_heart_flow",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -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",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -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 = []
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,5 @@
|
|||
from typing import Dict, List, Optional, Callable, Coroutine, Type, Any, Union
|
||||
import os
|
||||
import importlib
|
||||
from src.chat.focus_chat.planners.actions.base_action import BaseAction, _ACTION_REGISTRY, _DEFAULT_ACTIONS
|
||||
from typing import Dict, List, Optional, Callable, Coroutine, Type, Any
|
||||
from src.chat.focus_chat.planners.actions.base_action import BaseAction, _ACTION_REGISTRY
|
||||
from src.chat.heart_flow.observation.observation import Observation
|
||||
from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
|
|
@ -9,8 +7,6 @@ from src.chat.focus_chat.heartFC_Cycleinfo import CycleDetail
|
|||
from src.common.logger_manager import get_logger
|
||||
|
||||
# 导入动作类，确保装饰器被执行
|
||||
from src.chat.focus_chat.planners.actions.reply_action import ReplyAction
|
||||
from src.chat.focus_chat.planners.actions.no_reply_action import NoReplyAction
|
||||
|
||||
logger = get_logger("action_factory")
|
||||
|
||||
|
|
@ -31,20 +27,19 @@ class ActionManager:
|
|||
self._using_actions: Dict[str, ActionInfo] = {}
|
||||
# 临时备份原始使用中的动作
|
||||
self._original_actions_backup: Optional[Dict[str, ActionInfo]] = None
|
||||
|
||||
|
||||
# 默认动作集，仅作为快照，用于恢复默认
|
||||
self._default_actions: Dict[str, ActionInfo] = {}
|
||||
|
||||
|
||||
# 加载所有已注册动作
|
||||
self._load_registered_actions()
|
||||
|
||||
|
||||
# 初始化时将默认动作加载到使用中的动作
|
||||
self._using_actions = self._default_actions.copy()
|
||||
|
||||
|
||||
# logger.info(f"当前可用动作: {list(self._using_actions.keys())}")
|
||||
# for action_name, action_info in self._using_actions.items():
|
||||
# logger.info(f"动作名称: {action_name}, 动作信息: {action_info}")
|
||||
|
||||
# logger.info(f"动作名称: {action_name}, 动作信息: {action_info}")
|
||||
|
||||
def _load_registered_actions(self) -> None:
|
||||
"""
|
||||
|
|
@ -54,35 +49,35 @@ class ActionManager:
|
|||
# 从_ACTION_REGISTRY获取所有已注册动作
|
||||
for action_name, action_class in _ACTION_REGISTRY.items():
|
||||
# 获取动作相关信息
|
||||
action_description:str = getattr(action_class, "action_description", "")
|
||||
action_parameters:dict[str:str] = getattr(action_class, "action_parameters", {})
|
||||
action_require:list[str] = getattr(action_class, "action_require", [])
|
||||
is_default:bool = getattr(action_class, "default", False)
|
||||
|
||||
action_description: str = getattr(action_class, "action_description", "")
|
||||
action_parameters: dict[str:str] = getattr(action_class, "action_parameters", {})
|
||||
action_require: list[str] = getattr(action_class, "action_require", [])
|
||||
is_default: bool = getattr(action_class, "default", False)
|
||||
|
||||
if action_name and action_description:
|
||||
# 创建动作信息字典
|
||||
action_info = {
|
||||
"description": action_description,
|
||||
"parameters": action_parameters,
|
||||
"require": action_require
|
||||
"require": action_require,
|
||||
}
|
||||
|
||||
|
||||
# 注册2
|
||||
print("注册2")
|
||||
print(action_info)
|
||||
|
||||
|
||||
# 添加到所有已注册的动作
|
||||
self._registered_actions[action_name] = action_info
|
||||
|
||||
|
||||
# 添加到默认动作（如果是默认动作）
|
||||
if is_default:
|
||||
self._default_actions[action_name] = action_info
|
||||
|
||||
|
||||
logger.info(f"所有注册动作: {list(self._registered_actions.keys())}")
|
||||
logger.info(f"默认动作: {list(self._default_actions.keys())}")
|
||||
# for action_name, action_info in self._default_actions.items():
|
||||
# logger.info(f"动作名称: {action_name}, 动作信息: {action_info}")
|
||||
|
||||
# logger.info(f"动作名称: {action_name}, 动作信息: {action_info}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"加载已注册动作失败: {e}")
|
||||
|
||||
|
|
@ -129,7 +124,7 @@ class ActionManager:
|
|||
if action_name not in self._using_actions:
|
||||
logger.warning(f"当前不可用的动作类型: {action_name}")
|
||||
return None
|
||||
|
||||
|
||||
handler_class = _ACTION_REGISTRY.get(action_name)
|
||||
if not handler_class:
|
||||
logger.warning(f"未注册的动作类型: {action_name}")
|
||||
|
|
@ -153,7 +148,7 @@ class ActionManager:
|
|||
expressor=expressor,
|
||||
chat_stream=chat_stream,
|
||||
)
|
||||
|
||||
|
||||
return instance
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -167,7 +162,7 @@ class ActionManager:
|
|||
def get_default_actions(self) -> Dict[str, ActionInfo]:
|
||||
"""获取默认动作集"""
|
||||
return self._default_actions.copy()
|
||||
|
||||
|
||||
def get_using_actions(self) -> Dict[str, ActionInfo]:
|
||||
"""获取当前正在使用的动作集"""
|
||||
return self._using_actions.copy()
|
||||
|
|
@ -175,21 +170,21 @@ class ActionManager:
|
|||
def add_action_to_using(self, action_name: str) -> bool:
|
||||
"""
|
||||
添加已注册的动作到当前使用的动作集
|
||||
|
||||
|
||||
Args:
|
||||
action_name: 动作名称
|
||||
|
||||
|
||||
Returns:
|
||||
bool: 添加是否成功
|
||||
"""
|
||||
if action_name not in self._registered_actions:
|
||||
logger.warning(f"添加失败: 动作 {action_name} 未注册")
|
||||
return False
|
||||
|
||||
|
||||
if action_name in self._using_actions:
|
||||
logger.info(f"动作 {action_name} 已经在使用中")
|
||||
return True
|
||||
|
||||
|
||||
self._using_actions[action_name] = self._registered_actions[action_name]
|
||||
logger.info(f"添加动作 {action_name} 到使用集")
|
||||
return True
|
||||
|
|
@ -197,17 +192,17 @@ class ActionManager:
|
|||
def remove_action_from_using(self, action_name: str) -> bool:
|
||||
"""
|
||||
从当前使用的动作集中移除指定动作
|
||||
|
||||
|
||||
Args:
|
||||
action_name: 动作名称
|
||||
|
||||
|
||||
Returns:
|
||||
bool: 移除是否成功
|
||||
"""
|
||||
if action_name not in self._using_actions:
|
||||
logger.warning(f"移除失败: 动作 {action_name} 不在当前使用的动作集中")
|
||||
return False
|
||||
|
||||
|
||||
del self._using_actions[action_name]
|
||||
logger.info(f"已从使用集中移除动作 {action_name}")
|
||||
return True
|
||||
|
|
@ -215,30 +210,26 @@ class ActionManager:
|
|||
def add_action(self, action_name: str, description: str, parameters: Dict = None, require: List = None) -> bool:
|
||||
"""
|
||||
添加新的动作到注册集
|
||||
|
||||
|
||||
Args:
|
||||
action_name: 动作名称
|
||||
description: 动作描述
|
||||
parameters: 动作参数定义，默认为空字典
|
||||
require: 动作依赖项，默认为空列表
|
||||
|
||||
|
||||
Returns:
|
||||
bool: 添加是否成功
|
||||
"""
|
||||
if action_name in self._registered_actions:
|
||||
return False
|
||||
|
||||
|
||||
if parameters is None:
|
||||
parameters = {}
|
||||
if require is None:
|
||||
require = []
|
||||
|
||||
action_info = {
|
||||
"description": description,
|
||||
"parameters": parameters,
|
||||
"require": require
|
||||
}
|
||||
|
||||
|
||||
action_info = {"description": description, "parameters": parameters, "require": require}
|
||||
|
||||
self._registered_actions[action_name] = action_info
|
||||
return True
|
||||
|
||||
|
|
@ -264,7 +255,7 @@ class ActionManager:
|
|||
if self._original_actions_backup is not None:
|
||||
self._using_actions = self._original_actions_backup.copy()
|
||||
self._original_actions_backup = None
|
||||
|
||||
|
||||
def restore_default_actions(self) -> None:
|
||||
"""恢复默认动作集到使用集"""
|
||||
self._using_actions = self._default_actions.copy()
|
||||
|
|
@ -273,10 +264,10 @@ class ActionManager:
|
|||
def get_action(self, action_name: str) -> Optional[Type[BaseAction]]:
|
||||
"""
|
||||
获取指定动作的处理器类
|
||||
|
||||
|
||||
Args:
|
||||
action_name: 动作名称
|
||||
|
||||
|
||||
Returns:
|
||||
Optional[Type[BaseAction]]: 动作处理器类，如果不存在则返回None
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ _DEFAULT_ACTIONS: Dict[str, str] = {}
|
|||
def register_action(cls):
|
||||
"""
|
||||
动作注册装饰器
|
||||
|
||||
|
||||
用法:
|
||||
@register_action
|
||||
class MyAction(BaseAction):
|
||||
|
|
@ -24,22 +24,22 @@ def register_action(cls):
|
|||
if not hasattr(cls, "action_name") or not hasattr(cls, "action_description"):
|
||||
logger.error(f"动作类 {cls.__name__} 缺少必要的属性: action_name 或 action_description")
|
||||
return cls
|
||||
|
||||
action_name = getattr(cls, "action_name")
|
||||
action_description = getattr(cls, "action_description")
|
||||
|
||||
action_name = cls.action_name
|
||||
action_description = cls.action_description
|
||||
is_default = getattr(cls, "default", False)
|
||||
|
||||
|
||||
if not action_name or not action_description:
|
||||
logger.error(f"动作类 {cls.__name__} 的 action_name 或 action_description 为空")
|
||||
return cls
|
||||
|
||||
|
||||
# 将动作类注册到全局注册表
|
||||
_ACTION_REGISTRY[action_name] = cls
|
||||
|
||||
|
||||
# 如果是默认动作，添加到默认动作集
|
||||
if is_default:
|
||||
_DEFAULT_ACTIONS[action_name] = action_description
|
||||
|
||||
|
||||
logger.info(f"已注册动作: {action_name} -> {cls.__name__}，默认: {is_default}")
|
||||
return cls
|
||||
|
||||
|
|
@ -60,15 +60,14 @@ class BaseAction(ABC):
|
|||
cycle_timers: 计时器字典
|
||||
thinking_id: 思考ID
|
||||
"""
|
||||
#每个动作必须实现
|
||||
self.action_name:str = "base_action"
|
||||
self.action_description:str = "基础动作"
|
||||
self.action_parameters:dict = {}
|
||||
self.action_require:list[str] = []
|
||||
|
||||
self.default:bool = False
|
||||
|
||||
|
||||
# 每个动作必须实现
|
||||
self.action_name: str = "base_action"
|
||||
self.action_description: str = "基础动作"
|
||||
self.action_parameters: dict = {}
|
||||
self.action_require: list[str] = []
|
||||
|
||||
self.default: bool = False
|
||||
|
||||
self.action_data = action_data
|
||||
self.reasoning = reasoning
|
||||
self.cycle_timers = cycle_timers
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ class NoReplyAction(BaseAction):
|
|||
action_require = [
|
||||
"话题无关/无聊/不感兴趣/不懂",
|
||||
"最后一条消息是你自己发的且无人回应你",
|
||||
"你发送了太多消息，且无人回复"
|
||||
"你发送了太多消息，且无人回复",
|
||||
]
|
||||
default = True
|
||||
|
||||
|
|
@ -46,7 +46,7 @@ class NoReplyAction(BaseAction):
|
|||
total_no_reply_count: int = 0,
|
||||
total_waiting_time: float = 0.0,
|
||||
shutting_down: bool = False,
|
||||
**kwargs
|
||||
**kwargs,
|
||||
):
|
||||
"""初始化不回复动作处理器
|
||||
|
||||
|
|
|
|||
|
|
@ -2,9 +2,8 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
from src.common.logger_manager import get_logger
|
||||
from src.chat.utils.timer_calculator import Timer
|
||||
from src.chat.focus_chat.planners.actions.base_action import BaseAction, register_action
|
||||
from typing import Tuple, List, Optional
|
||||
from typing import Tuple, List
|
||||
from src.chat.heart_flow.observation.observation import Observation
|
||||
from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
|
|
@ -22,14 +21,14 @@ class ReplyAction(BaseAction):
|
|||
处理构建和发送消息回复的动作。
|
||||
"""
|
||||
|
||||
action_name:str = "reply"
|
||||
action_description:str = "表达想法，可以只包含文本、表情或两者都有"
|
||||
action_parameters:dict[str:str] = {
|
||||
action_name: str = "reply"
|
||||
action_description: str = "表达想法，可以只包含文本、表情或两者都有"
|
||||
action_parameters: dict[str:str] = {
|
||||
"text": "你想要表达的内容（可选）",
|
||||
"emojis": "描述当前使用表情包的场景（可选）",
|
||||
"target": "你想要回复的原始文本内容（非必须，仅文本，不包含发送者)（可选）",
|
||||
}
|
||||
action_require:list[str] = [
|
||||
action_require: list[str] = [
|
||||
"有实质性内容需要表达",
|
||||
"有人提到你，但你还没有回应他",
|
||||
"在合适的时候添加表情（不要总是添加）",
|
||||
|
|
@ -38,7 +37,7 @@ class ReplyAction(BaseAction):
|
|||
"一次只回复一个人，一次只回复一个话题,突出重点",
|
||||
"如果是自己发的消息想继续，需自然衔接",
|
||||
"避免重复或评价自己的发言,不要和自己聊天",
|
||||
"注意：回复尽量简短一些。可以参考贴吧，知乎和微博的回复风格，回复不要浮夸，不要用夸张修辞，平淡一些。"
|
||||
"注意：回复尽量简短一些。可以参考贴吧，知乎和微博的回复风格，回复不要浮夸，不要用夸张修辞，平淡一些。",
|
||||
]
|
||||
default = True
|
||||
|
||||
|
|
@ -54,7 +53,7 @@ class ReplyAction(BaseAction):
|
|||
chat_stream: ChatStream,
|
||||
current_cycle: CycleDetail,
|
||||
log_prefix: str,
|
||||
**kwargs
|
||||
**kwargs,
|
||||
):
|
||||
"""初始化回复动作处理器
|
||||
|
||||
|
|
@ -89,9 +88,9 @@ class ReplyAction(BaseAction):
|
|||
reasoning=self.reasoning,
|
||||
reply_data=self.action_data,
|
||||
cycle_timers=self.cycle_timers,
|
||||
thinking_id=self.thinking_id
|
||||
thinking_id=self.thinking_id,
|
||||
)
|
||||
|
||||
|
||||
async def _handle_reply(
|
||||
self, reasoning: str, reply_data: dict, cycle_timers: dict, thinking_id: str
|
||||
) -> tuple[bool, str]:
|
||||
|
|
|
|||
|
|
@ -6,7 +6,6 @@ from rich.traceback import install
|
|||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
from src.chat.models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from src.chat.focus_chat.heartflow_prompt_builder import prompt_builder
|
||||
from src.chat.focus_chat.info.info_base import InfoBase
|
||||
from src.chat.focus_chat.info.obs_info import ObsInfo
|
||||
from src.chat.focus_chat.info.cycle_info import CycleInfo
|
||||
|
|
@ -24,6 +23,7 @@ logger = get_logger("planner")
|
|||
|
||||
install(extra_lines=3)
|
||||
|
||||
|
||||
def init_prompt():
|
||||
Prompt(
|
||||
"""你的名字是{bot_name},{prompt_personality}，{chat_context_description}。需要基于以下信息决定如何参与对话：
|
||||
|
|
@ -50,8 +50,9 @@ def init_prompt():
|
|||
}}
|
||||
|
||||
请输出你的决策 JSON：""",
|
||||
"planner_prompt",)
|
||||
|
||||
"planner_prompt",
|
||||
)
|
||||
|
||||
Prompt(
|
||||
"""
|
||||
action_name: {action_name}
|
||||
|
|
@ -63,14 +64,14 @@ action_name: {action_name}
|
|||
""",
|
||||
"action_prompt",
|
||||
)
|
||||
|
||||
|
||||
|
||||
class ActionPlanner:
|
||||
def __init__(self, log_prefix: str, action_manager: ActionManager, stream_id: str, chat_stream: ChatStream):
|
||||
self.log_prefix = log_prefix
|
||||
# LLM规划器配置
|
||||
self.planner_llm = LLMRequest(
|
||||
model=global_config.llm_plan,
|
||||
model=global_config.model.plan,
|
||||
max_tokens=1000,
|
||||
request_type="action_planning", # 用于动作规划
|
||||
)
|
||||
|
|
@ -110,10 +111,10 @@ class ActionPlanner:
|
|||
cycle_info = info.get_observe_info()
|
||||
elif isinstance(info, StructuredInfo):
|
||||
logger.debug(f"{self.log_prefix} 结构化信息: {info}")
|
||||
structured_info = info.get_data()
|
||||
_structured_info = info.get_data()
|
||||
|
||||
current_available_actions = self.action_manager.get_using_actions()
|
||||
|
||||
|
||||
# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
|
||||
prompt = await self.build_planner_prompt(
|
||||
is_group_chat=is_group_chat, # <-- Pass HFC state
|
||||
|
|
@ -204,7 +205,6 @@ class ActionPlanner:
|
|||
# 返回结果字典
|
||||
return plan_result
|
||||
|
||||
|
||||
async def build_planner_prompt(
|
||||
self,
|
||||
is_group_chat: bool, # Now passed as argument
|
||||
|
|
@ -225,7 +225,6 @@ class ActionPlanner:
|
|||
)
|
||||
chat_context_description = f"你正在和 {chat_target_name} 私聊"
|
||||
|
||||
|
||||
chat_content_block = ""
|
||||
if observed_messages_str:
|
||||
chat_content_block = f"聊天记录：\n{observed_messages_str}"
|
||||
|
|
@ -241,7 +240,6 @@ class ActionPlanner:
|
|||
individuality = Individuality.get_instance()
|
||||
personality_block = individuality.get_prompt(x_person=2, level=2)
|
||||
|
||||
|
||||
action_options_block = ""
|
||||
for using_actions_name, using_actions_info in current_available_actions.items():
|
||||
# print(using_actions_name)
|
||||
|
|
@ -251,39 +249,38 @@ class ActionPlanner:
|
|||
# print(using_actions_info["description"])
|
||||
|
||||
using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt")
|
||||
|
||||
|
||||
param_text = ""
|
||||
for param_name, param_description in using_actions_info["parameters"].items():
|
||||
param_text += f"{param_name}: {param_description}\n"
|
||||
|
||||
|
||||
require_text = ""
|
||||
for require_item in using_actions_info["require"]:
|
||||
require_text += f"- {require_item}\n"
|
||||
|
||||
|
||||
using_action_prompt = using_action_prompt.format(
|
||||
action_name=using_actions_name,
|
||||
action_description=using_actions_info["description"],
|
||||
action_parameters=param_text,
|
||||
action_require=require_text,
|
||||
)
|
||||
|
||||
|
||||
action_options_block += using_action_prompt
|
||||
|
||||
# 需要获取用于上下文的历史消息
|
||||
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
|
||||
chat_id=self.stream_id,
|
||||
timestamp=time.time(), # 使用当前时间作为参考点
|
||||
limit=global_config.observation_context_size, # 使用与 prompt 构建一致的 limit
|
||||
limit=global_config.chat.observation_context_size, # 使用与 prompt 构建一致的 limit
|
||||
)
|
||||
# 调用工具函数获取格式化后的绰号字符串
|
||||
nickname_injection_str = await nickname_manager.get_nickname_prompt_injection(
|
||||
self.chat_stream, message_list_before_now
|
||||
)
|
||||
|
||||
|
||||
planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
|
||||
prompt = planner_prompt_template.format(
|
||||
bot_name=global_config.BOT_NICKNAME,
|
||||
bot_name=global_config.bot.nickname,
|
||||
prompt_personality=personality_block,
|
||||
chat_context_description=chat_context_description,
|
||||
chat_content_block=chat_content_block,
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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 --
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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")
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
)
|
||||
|
|
@ -64,21 +64,21 @@ 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 and global_config.enable_friend_whitelist:
|
||||
if groupinfo is None and global_config.experimental.enable_friend_whitelist:
|
||||
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
|
||||
elif not global_config.enable_friend_whitelist:
|
||||
elif not global_config.experimental.enable_friend_whitelist:
|
||||
logger.debug("私聊白名单模式未启用，跳过私聊权限检查。")
|
||||
|
||||
# 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
|
||||
|
||||
|
|
@ -94,7 +94,7 @@ class ChatBot:
|
|||
else:
|
||||
template_group_name = None
|
||||
|
||||
if not global_config.enable_Legacy_HFC:
|
||||
if not global_config.experimental.enable_Legacy_HFC:
|
||||
hfc_processor = self.heartflow_processor
|
||||
else:
|
||||
hfc_processor = self.legacy_hfc_processor
|
||||
|
|
@ -105,10 +105,10 @@ 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:
|
||||
if global_config.pfc.enable:
|
||||
logger.trace("进入PFC私聊处理流程")
|
||||
await self.pfc_processor.process_message(message_data)
|
||||
# 禁止PFC，进入普通的心流消息处理逻辑
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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}"
|
||||
)
|
||||
|
|
|
|||
|
|
@ -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")
|
||||
|
|
|
|||
|
|
@ -74,8 +74,8 @@ 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,
|
||||
)
|
||||
|
||||
|
|
@ -122,8 +122,8 @@ 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,
|
||||
|
|
@ -148,7 +148,7 @@ 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
|
||||
|
|
@ -161,8 +161,8 @@ 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,
|
||||
|
|
@ -187,7 +187,7 @@ 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:
|
||||
"""
|
||||
|
|
@ -432,7 +432,7 @@ 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'}]"
|
||||
|
|
@ -447,7 +447,7 @@ 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'}]"
|
||||
|
|
|
|||
|
|
@ -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」
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -60,8 +60,9 @@ 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",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -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")
|
||||
|
||||
|
|
@ -250,7 +250,7 @@ async def _build_readable_messages_internal(
|
|||
message_details_raw.sort(key=lambda x: x[0]) # 按时间戳(第一个元素)升序排序，越早的消息排在前面
|
||||
|
||||
# 应用截断逻辑 (如果 truncate 为 True)
|
||||
if not global_config.long_message_auto_truncate:
|
||||
if not global_config.memory.long_message_auto_truncate:
|
||||
truncate = False
|
||||
message_details: List[Tuple[float, str, str]] = []
|
||||
n_messages = len(message_details_raw)
|
||||
|
|
@ -429,7 +429,7 @@ 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:
|
||||
|
|
@ -503,7 +503,7 @@ 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)
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -173,8 +173,8 @@ 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
|
||||
|
|
@ -194,18 +194,18 @@ 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：",
|
||||
f"\[回复 [\s\S]*?\({str(global_config.bot.qq_account)}\)：[\s\S]*?\]，说：",
|
||||
message.processed_plain_text,
|
||||
):
|
||||
is_mentioned = True
|
||||
|
|
@ -219,7 +219,7 @@ 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
|
||||
|
|
@ -227,7 +227,8 @@ 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)
|
||||
|
|
@ -294,7 +295,7 @@ 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
|
||||
|
|
@ -476,7 +477,6 @@ def split_into_sentences_w_remove_punctuation(original_text: str) -> list[str]:
|
|||
processed_sentences_after_merge.append(s)
|
||||
|
||||
if perform_book_title_recovery_here and local_book_title_mapping:
|
||||
# 假设 processed_sentences_after_merge 是最终的句子列表
|
||||
processed_sentences_after_merge = recover_book_titles(processed_sentences_after_merge, local_book_title_mapping)
|
||||
return processed_sentences_after_merge
|
||||
|
||||
|
|
@ -510,7 +510,7 @@ 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:
|
||||
|
|
@ -528,8 +528,8 @@ 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:
|
||||
|
|
@ -537,20 +537,20 @@ 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:
|
||||
|
|
@ -560,14 +560,14 @@ 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:
|
||||
# sentences.append(content)
|
||||
|
||||
# 在所有句子处理完毕后，对包含占位符的列表进行恢复
|
||||
if global_config.enable_kaomoji_protection:
|
||||
if global_config.response_splitter.enable_kaomoji_protection:
|
||||
sentences = recover_kaomoji(sentences, kaomoji_mapping)
|
||||
|
||||
return sentences
|
||||
|
|
|
|||
|
|
@ -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.cache_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}"
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -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))})"
|
||||
|
|
@ -0,0 +1,540 @@
|
|||
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"""
|
||||
|
||||
enable_expression_learner: bool = True
|
||||
"""是否启用新发言习惯注入，关闭则启用旧方法"""
|
||||
|
||||
personality_sides: list[str] = field(default_factory=lambda: [])
|
||||
"""人格侧写"""
|
||||
|
||||
personality_detail_level: int = 0
|
||||
"""人设消息注入 prompt 详细等级 (0: 采用默认配置, 1: 核心/随机细节, 2: 核心+随机侧面/全部细节, 3: 全部)"""
|
||||
|
||||
@dataclass
|
||||
class IdentityConfig(ConfigBase):
|
||||
"""个体特征配置类"""
|
||||
|
||||
height: int = 170
|
||||
"""身高（单位：厘米）"""
|
||||
|
||||
weight: float = 50.0
|
||||
"""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"""
|
||||
|
||||
allow_remove_duplicates: bool = True
|
||||
"""是否开启心流去重（如果发现心流截断问题严重可尝试关闭）"""
|
||||
|
||||
|
||||
@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_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"""
|
||||
|
||||
long_message_auto_truncate: bool = True
|
||||
"""HFC 模式过长消息自动截断"""
|
||||
|
||||
|
||||
@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"""
|
||||
|
||||
enable_Legacy_HFC: bool = False
|
||||
"""是否启用 Legacy_HFC 处理器"""
|
||||
|
||||
enable_friend_whitelist: bool = True
|
||||
"""是否启用好友白名单"""
|
||||
|
||||
rename_person: bool = True
|
||||
"""是否启用改名工具"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class ScheduleConfig(ConfigBase):
|
||||
"""日程配置类"""
|
||||
|
||||
enable: bool = False
|
||||
"""是否启用日程生成"""
|
||||
|
||||
prompt_schedule_gen: str = "无日程"
|
||||
"""日程生成提示"""
|
||||
|
||||
schedule_doing_update_interval: int = 300
|
||||
"""日程表更新间隔 单位秒"""
|
||||
|
||||
schedule_temperature: float = 0.5
|
||||
"""日程表温度，建议0.5-1.0"""
|
||||
|
||||
time_zone: str = "Asia/Shanghai"
|
||||
"""时区"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class GroupNicknameConfig(ConfigBase):
|
||||
"""绰号处理系统配置类"""
|
||||
|
||||
enable_nickname_mapping: bool = False
|
||||
"""绰号映射功能总开关"""
|
||||
|
||||
max_nicknames_in_prompt: int = 10
|
||||
"""Prompt 中最多注入的绰号数量"""
|
||||
|
||||
nickname_probability_smoothing: int = 1
|
||||
"""绰号加权随机选择的平滑因子"""
|
||||
|
||||
nickname_queue_max_size: int = 100
|
||||
"""绰号处理队列最大容量"""
|
||||
|
||||
nickname_process_sleep_interval: float = 5.0
|
||||
"""绰号处理进程休眠间隔（秒）"""
|
||||
|
||||
nickname_analysis_history_limit: int = 30
|
||||
"""绰号处理可见最大上下文"""
|
||||
|
||||
nickname_analysis_probability: float = 0.1
|
||||
"""绰号随机概率命中"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class PFCConfig(ConfigBase):
|
||||
"""PFC配置类"""
|
||||
|
||||
enable: bool = False
|
||||
"""是否启用PFC"""
|
||||
|
||||
pfc_message_buffer_size: int = 2
|
||||
"""PFC 聊天消息缓冲数量"""
|
||||
|
||||
pfc_recent_history_display_count: int = 18
|
||||
"""PFC 对话最大可见上下文"""
|
||||
|
||||
enable_pfc_reply_checker: bool = True
|
||||
"""是否启用 PFC 的回复检查器"""
|
||||
|
||||
pfc_max_reply_attempts: int = 3
|
||||
"""发言最多尝试次数"""
|
||||
|
||||
pfc_max_chat_history_for_checker: int = 30
|
||||
"""checker聊天记录最大可见上文长度"""
|
||||
|
||||
pfc_emotion_update_intensity: float = 0.6
|
||||
"""情绪更新强度"""
|
||||
|
||||
pfc_emotion_history_count: int = 5
|
||||
"""情绪更新最大可见上下文长度"""
|
||||
|
||||
pfc_relationship_incremental_interval: int = 10
|
||||
"""关系值增值强度"""
|
||||
|
||||
pfc_relationship_incremental_msg_count: int = 10
|
||||
"""会话中，关系值判断最大可见上下文"""
|
||||
|
||||
pfc_relationship_incremental_default_change: float = 1.0
|
||||
"""会话中，关系值默认更新值"""
|
||||
|
||||
pfc_relationship_incremental_max_change: float = 5.0
|
||||
"""会话中，关系值最大可变值"""
|
||||
|
||||
pfc_relationship_final_msg_count: int = 30
|
||||
"""会话结束时，关系值判断最大可见上下文"""
|
||||
|
||||
pfc_relationship_final_default_change: float = 5.0
|
||||
"""会话结束时，关系值默认更新值"""
|
||||
|
||||
pfc_relationship_final_max_change: float = 50.0
|
||||
"""会话结束时，关系值最大可变值"""
|
||||
|
||||
pfc_historical_fallback_exclude_seconds: int = 45
|
||||
"""pfc 翻看聊天记录排除最近时长"""
|
||||
|
||||
enable_idle_chat: bool = True
|
||||
"""是否启用 pfc 主动发言"""
|
||||
|
||||
idle_check_interval: int = 10
|
||||
"""主动发言检查间隔（分钟）"""
|
||||
|
||||
min_cooldown: int = 7200
|
||||
"""主动发言最短冷却时间（秒）"""
|
||||
|
||||
max_cooldown: int = 18000
|
||||
"""主动发言最长冷却时间（秒）"""
|
||||
|
||||
|
||||
@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"""
|
||||
|
||||
nickname_mapping: dict[str, str] = field(default_factory=lambda: {})
|
||||
"""绰号映射LLM配置"""
|
||||
|
||||
scheduler_all: dict[str, str] = field(default_factory=lambda: {})
|
||||
"""全局日程LLM配置"""
|
||||
|
||||
scheduler_doing: dict[str, str] = field(default_factory=lambda: {})
|
||||
"""当前活动日程LLM配置"""
|
||||
|
||||
PFC_relationship_eval: dict[str, str] = field(default_factory=lambda: {})
|
||||
"""PFC关系评估LLM配置"""
|
||||
|
|
@ -222,8 +222,8 @@ class HeartFChatting:
|
|||
# --- 移除 gpt_instance, 直接初始化 LLM 模型 ---
|
||||
# self.gpt_instance = HeartFCGenerator() # <-- 移除
|
||||
self.model_normal = LLMRequest( # <-- 新增 LLM 初始化
|
||||
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",
|
||||
)
|
||||
|
|
@ -231,7 +231,7 @@ class HeartFChatting:
|
|||
|
||||
# LLM规划器配置
|
||||
self.planner_llm = LLMRequest(
|
||||
model=global_config.llm_plan,
|
||||
model=global_config.model.plan,
|
||||
max_tokens=1000,
|
||||
request_type="action_planning", # 用于动作规划
|
||||
)
|
||||
|
|
@ -876,7 +876,7 @@ class HeartFChatting:
|
|||
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
|
||||
chat_id=self.stream_id,
|
||||
timestamp=time.time(), # 使用当前时间作为参考点
|
||||
limit=global_config.observation_context_size, # 使用与 prompt 构建一致的 limit
|
||||
limit=global_config.chat.observation_context_size, # 使用与 prompt 构建一致的 limit
|
||||
)
|
||||
# 调用工具函数获取格式化后的绰号字符串
|
||||
nickname_injection_str = await nickname_manager.get_nickname_prompt_injection(
|
||||
|
|
@ -1196,8 +1196,8 @@ class HeartFChatting:
|
|||
first_bot_msg: Optional[MessageSending] = None
|
||||
reply_message_ids = [] # 记录实际发送的消息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=anchor_message.message_info.platform,
|
||||
)
|
||||
|
||||
|
|
@ -1263,8 +1263,8 @@ class HeartFChatting:
|
|||
thinking_time_point = round(time.time(), 2) # 用于唯一ID
|
||||
message_segment = Seg(type="emoji", data=emoji_cq)
|
||||
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=anchor_message.message_info.platform,
|
||||
)
|
||||
bot_message = MessageSending(
|
||||
|
|
@ -1318,7 +1318,7 @@ class HeartFChatting:
|
|||
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 = global_config.llm_normal["temp"] * arousal_multiplier
|
||||
current_temp = global_config.model.normal["temp"] * arousal_multiplier
|
||||
self.model_normal.temperature = current_temp # 动态调整温度
|
||||
|
||||
# 2. 获取信息捕捉器
|
||||
|
|
@ -1401,8 +1401,8 @@ class HeartFChatting:
|
|||
chat = anchor_message.chat_stream
|
||||
messageinfo = anchor_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,
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ class Heartflow:
|
|||
|
||||
# LLM模型配置
|
||||
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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -19,8 +19,8 @@ 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
|
||||
base_normal_chat_num = global_config.chat.base_normal_chat_num
|
||||
base_focused_chat_num = global_config.chat.base_focused_chat_num
|
||||
|
||||
|
||||
MAX_NORMAL_CHAT_NUM_PEEKING = int(base_normal_chat_num / 2)
|
||||
|
|
|
|||
|
|
@ -101,7 +101,7 @@ class Mind:
|
|||
else self.individuality.personality.personality_core
|
||||
)
|
||||
mood_info = mai_state_info.get_mood_prompt()
|
||||
bot_name = global_config.BOT_NICKNAME
|
||||
bot_name = global_config.bot.nickname
|
||||
|
||||
try:
|
||||
prompt = (await global_prompt_manager.get_prompt_async("mind_summary_prompt")).format(
|
||||
|
|
|
|||
|
|
@ -66,16 +66,16 @@ 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.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_memorys = []
|
||||
self.max_mid_memory_len = global_config.compress_length_limit
|
||||
self.max_mid_memory_len = global_config.focus_chat.compress_length_limit
|
||||
self.mid_memory_info = ""
|
||||
self.person_list = []
|
||||
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):
|
||||
|
|
|
|||
|
|
@ -190,8 +190,8 @@ class SubMind:
|
|||
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=1000,
|
||||
request_type="sub_heart_flow",
|
||||
)
|
||||
|
|
@ -594,7 +594,7 @@ class SubMind:
|
|||
message_list_for_nicknames = get_raw_msg_before_timestamp_with_chat(
|
||||
chat_id=self.subheartflow_id,
|
||||
timestamp=time.time(),
|
||||
limit=global_config.observation_context_size,
|
||||
limit=global_config.chat.observation_context_size,
|
||||
)
|
||||
nickname_injection_str = await nickname_manager.get_nickname_prompt_injection(
|
||||
chat_stream, message_list_for_nicknames
|
||||
|
|
@ -683,7 +683,7 @@ class SubMind:
|
|||
logger.warning(f"{self.log_prefix} LLM返回空结果，思考失败。")
|
||||
|
||||
# ---------- 7. 应用概率性去重和修饰 ----------
|
||||
if global_config.allow_remove_duplicates:
|
||||
if global_config.chat.allow_remove_duplicates:
|
||||
new_content = content # 保存 LLM 直接输出的结果
|
||||
try:
|
||||
similarity = calculate_similarity(previous_mind, new_content)
|
||||
|
|
|
|||
|
|
@ -77,7 +77,7 @@ 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
|
||||
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 +278,7 @@ 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 +766,7 @@ 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")
|
||||
|
|
|
|||
|
|
@ -5,10 +5,10 @@ from src.config.config import global_config
|
|||
from src.chat.message_receive.message import MessageRecv
|
||||
from src.chat.message_receive.storage import MessageStorage
|
||||
from src.chat.utils.utils import is_mentioned_bot_in_message
|
||||
from maim_message import Seg
|
||||
from maim_message import Seg, UserInfo
|
||||
from .heart_flow.heartflow import heartflow
|
||||
from src.common.logger_manager import get_logger
|
||||
from src.chat.message_receive.chat_stream import chat_manager
|
||||
from src.chat.message_receive.chat_stream import ChatStream, chat_manager
|
||||
from src.chat.message_receive.message_buffer import message_buffer
|
||||
from src.chat.utils.timer_calculator import Timer
|
||||
from src.chat.person_info.relationship_manager import relationship_manager
|
||||
|
|
@ -100,7 +100,7 @@ def _get_message_type(message: MessageRecv) -> str:
|
|||
return "seglist"
|
||||
|
||||
|
||||
def _check_ban_words(text: str, chat, userinfo) -> bool:
|
||||
def _check_ban_words(text: str, chat: ChatStream, userinfo: UserInfo) -> bool:
|
||||
"""检查消息是否包含过滤词
|
||||
|
||||
Args:
|
||||
|
|
@ -111,7 +111,7 @@ 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}")
|
||||
|
|
@ -120,7 +120,7 @@ def _check_ban_words(text: str, chat, userinfo) -> bool:
|
|||
return False
|
||||
|
||||
|
||||
def _check_ban_regex(text: str, chat, userinfo) -> bool:
|
||||
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
|
||||
|
||||
Args:
|
||||
|
|
@ -131,7 +131,7 @@ 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}")
|
||||
|
|
|
|||
|
|
@ -244,7 +244,7 @@ async def _build_prompt_focus(reason, current_mind_info, structured_info, chat_s
|
|||
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,
|
||||
|
|
@ -264,7 +264,7 @@ async def _build_prompt_focus(reason, current_mind_info, structured_info, chat_s
|
|||
prompt_ger += "**不用输出对方的网名或绰号**"
|
||||
if random.random() < 0.00:
|
||||
prompt_ger += "你喜欢用反问句"
|
||||
if is_group_chat and global_config.enable_expression_learner:
|
||||
if is_group_chat and global_config.personality.enable_expression_learner:
|
||||
# 从/data/expression/对应chat_id/expressions.json中读取表达方式
|
||||
(
|
||||
learnt_style_expressions,
|
||||
|
|
@ -350,7 +350,7 @@ async def _build_prompt_focus(reason, current_mind_info, structured_info, chat_s
|
|||
nickname_info=nickname_injection_str,
|
||||
chat_target=chat_target_1, # Used in group 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_2=chat_target_2, # Used in group template
|
||||
current_mind_info=current_mind_info,
|
||||
|
|
@ -370,7 +370,7 @@ async def _build_prompt_focus(reason, current_mind_info, structured_info, chat_s
|
|||
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,
|
||||
|
|
@ -426,7 +426,7 @@ 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(
|
||||
|
|
@ -473,7 +473,7 @@ 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,
|
||||
|
|
@ -485,7 +485,8 @@ class PromptBuilder:
|
|||
|
||||
# 关键词检测与反应
|
||||
keywords_reaction_prompt = ""
|
||||
for rule in global_config.keywords_reaction_rules:
|
||||
keywords_reaction_prompt = ""
|
||||
for rule in global_config.keyword_reaction.rules:
|
||||
if rule.get("enable", False):
|
||||
if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
|
||||
logger.info(
|
||||
|
|
@ -517,7 +518,7 @@ class PromptBuilder:
|
|||
end_time = time.time()
|
||||
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
|
||||
|
||||
if global_config.ENABLE_SCHEDULE_GEN:
|
||||
if global_config.schedule.enable:
|
||||
schedule_prompt = await global_prompt_manager.format_prompt(
|
||||
"schedule_prompt", schedule_info=bot_schedule.get_current_num_task(num=1, time_info=False)
|
||||
)
|
||||
|
|
@ -550,8 +551,8 @@ 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,
|
||||
|
|
@ -573,8 +574,8 @@ class PromptBuilder:
|
|||
schedule_prompt=schedule_prompt,
|
||||
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,
|
||||
|
|
@ -933,7 +934,7 @@ class PromptBuilder:
|
|||
planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
|
||||
|
||||
prompt = planner_prompt_template.format(
|
||||
bot_name=global_config.BOT_NICKNAME,
|
||||
bot_name=global_config.bot.nickname,
|
||||
nickname_info=nickname_info,
|
||||
prompt_personality=prompt_personality,
|
||||
chat_context_description=chat_context_description,
|
||||
|
|
|
|||
|
|
@ -75,8 +75,8 @@ 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,
|
||||
)
|
||||
|
||||
|
|
@ -123,8 +123,8 @@ 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,
|
||||
|
|
@ -149,7 +149,7 @@ 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
|
||||
|
|
@ -162,8 +162,8 @@ 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,
|
||||
|
|
@ -188,7 +188,7 @@ 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:
|
||||
"""
|
||||
|
|
@ -432,7 +432,7 @@ 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'}]"
|
||||
|
|
@ -447,7 +447,7 @@ 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'}]"
|
||||
|
|
|
|||
|
|
@ -16,20 +16,20 @@ logger = get_logger("llm")
|
|||
class NormalChatGenerator:
|
||||
def __init__(self):
|
||||
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 +37,7 @@ 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 +51,7 @@ 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 +113,7 @@ 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」
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ from src.common.logger import get_module_logger, SCHEDULE_STYLE_CONFIG, LogConfi
|
|||
from src.chat.models.utils_model import LLMRequest # noqa: E402
|
||||
from src.config.config import global_config # noqa: E402
|
||||
|
||||
TIME_ZONE = tz.gettz(global_config.TIME_ZONE) # 设置时区
|
||||
TIME_ZONE = tz.gettz(global_config.schedule.time_zone) # 设置时区
|
||||
|
||||
|
||||
schedule_config = LogConfig(
|
||||
|
|
@ -31,14 +31,14 @@ class ScheduleGenerator:
|
|||
# 使用离线LLM模型
|
||||
self.enable_output = None
|
||||
self.llm_scheduler_all = LLMRequest(
|
||||
model=global_config.llm_scheduler_all,
|
||||
temperature=global_config.llm_scheduler_all["temp"],
|
||||
model=global_config.model.scheduler_all,
|
||||
temperature=global_config.model.scheduler_all["temp"],
|
||||
max_tokens=7000,
|
||||
request_type="schedule",
|
||||
)
|
||||
self.llm_scheduler_doing = LLMRequest(
|
||||
model=global_config.llm_scheduler_doing,
|
||||
temperature=global_config.llm_scheduler_doing["temp"],
|
||||
model=global_config.model.scheduler_doing,
|
||||
temperature=global_config.model.scheduler_doing["temp"],
|
||||
max_tokens=2048,
|
||||
request_type="schedule",
|
||||
)
|
||||
|
|
@ -73,7 +73,7 @@ class ScheduleGenerator:
|
|||
async def mai_schedule_start(self):
|
||||
"""启动日程系统，每5分钟执行一次move_doing，并在日期变化时重新检查日程"""
|
||||
try:
|
||||
if global_config.ENABLE_SCHEDULE_GEN:
|
||||
if global_config.schedule.enable:
|
||||
logger.info(f"日程系统启动/刷新时间: {self.start_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
# 初始化日程
|
||||
await self.check_and_create_today_schedule()
|
||||
|
|
|
|||
|
|
@ -140,7 +140,7 @@ class IdleChat:
|
|||
self._lock: asyncio.Lock = asyncio.Lock()
|
||||
|
||||
# LLM请求对象，用于生成主动对话内容
|
||||
self.llm = LLMRequest(model=global_config.llm_normal, temperature=0.5, max_tokens=500, request_type="idle_chat")
|
||||
self.llm = LLMRequest(model=global_config.model.normal, temperature=0.5, max_tokens=500, request_type="idle_chat")
|
||||
|
||||
# 工作状态
|
||||
self.active_instances_count: int = 0
|
||||
|
|
@ -149,9 +149,9 @@ class IdleChat:
|
|||
self._task: Optional[asyncio.Task] = None
|
||||
|
||||
# 配置参数 - 从global_config加载
|
||||
self.min_cooldown = global_config.min_cooldown # 最短冷却时间（默认2小时）
|
||||
self.max_cooldown = global_config.max_cooldown # 最长冷却时间（默认5小时）
|
||||
self.check_interval = global_config.idle_check_interval * 60 # 检查间隔（默认10分钟，转换为秒）
|
||||
self.min_cooldown = global_config.pfc.min_cooldown # 最短冷却时间（默认2小时）
|
||||
self.max_cooldown = global_config.pfc.max_cooldown # 最长冷却时间（默认5小时）
|
||||
self.check_interval = global_config.pfc.idle_check_interval * 60 # 检查间隔（默认10分钟，转换为秒）
|
||||
self.active_hours_start = 7 # 活动开始时间
|
||||
self.active_hours_end = 23 # 活动结束时间
|
||||
|
||||
|
|
@ -162,7 +162,7 @@ class IdleChat:
|
|||
def start(self) -> None:
|
||||
"""启动主动聊天检测"""
|
||||
# 检查是否启用了主动聊天功能
|
||||
if not global_config.enable_idle_chat:
|
||||
if not global_config.pfc.enable_idle_chat:
|
||||
logger.info(f"[私聊][{self.private_name}]主动聊天功能已禁用（配置enable_idle_chat=False）")
|
||||
return
|
||||
|
||||
|
|
@ -352,7 +352,7 @@ class IdleChat:
|
|||
try:
|
||||
while self._running:
|
||||
# 检查是否启用了主动聊天功能
|
||||
if not global_config.enable_idle_chat:
|
||||
if not global_config.pfc.enable_idle_chat:
|
||||
# 如果禁用了功能，等待一段时间后再次检查配置
|
||||
await asyncio.sleep(60) # 每分钟检查一次配置变更
|
||||
continue
|
||||
|
|
@ -482,7 +482,7 @@ class IdleChat:
|
|||
relationship_description = full_relationship_text.split("：")[1].replace("。", "")
|
||||
|
||||
# 暂不使用
|
||||
# if global_config.ENABLE_SCHEDULE_GEN:
|
||||
# if global_config.schedule.enable:
|
||||
# schedule_prompt = await global_prompt_manager.format_prompt(
|
||||
# "schedule_prompt", schedule_info=bot_schedule.get_current_num_task(num=1, time_info=False)
|
||||
# )
|
||||
|
|
@ -491,7 +491,7 @@ class IdleChat:
|
|||
|
||||
# 构建提示词，暂存废弃部分这是你的日程{schedule_prompt}
|
||||
current_time = datetime.now().strftime("%H:%M")
|
||||
prompt = f"""你是{global_config.BOT_NICKNAME}。
|
||||
prompt = f"""你是{global_config.bot.nickname}。
|
||||
你正在与用户{self.private_name}进行QQ私聊，你们的关系是{relationship_description}
|
||||
现在时间{current_time}
|
||||
|
||||
|
|
|
|||
|
|
@ -127,7 +127,7 @@ class ActionHandler(ABC):
|
|||
"time": send_time,
|
||||
"user_info": {
|
||||
"user_id": self.conversation.bot_qq_str,
|
||||
"user_nickname": global_config.BOT_NICKNAME,
|
||||
"user_nickname": global_config.bot.nickname,
|
||||
"platform": self.conversation.chat_stream.platform
|
||||
if self.conversation.chat_stream
|
||||
else "unknown_platform",
|
||||
|
|
@ -138,7 +138,7 @@ class ActionHandler(ABC):
|
|||
observation_info.chat_history.append(bot_message_dict)
|
||||
observation_info.chat_history_count = len(observation_info.chat_history)
|
||||
self.logger.debug(
|
||||
f"[私聊][{self.conversation.private_name}] {global_config.BOT_NICKNAME}发送的消息 ('{message_content[:30]}...')已添加到 chat_history。当前历史数: {observation_info.chat_history_count}"
|
||||
f"[私聊][{self.conversation.private_name}] {global_config.bot.nickname}发送的消息 ('{message_content[:30]}...')已添加到 chat_history。当前历史数: {observation_info.chat_history_count}"
|
||||
)
|
||||
|
||||
# 限制历史记录长度
|
||||
|
|
@ -422,7 +422,7 @@ class BaseTextReplyHandler(ActionHandler):
|
|||
current_time_value_for_check = observation_info.current_time_str or "获取时间失败"
|
||||
|
||||
# 调用 ReplyChecker
|
||||
if global_config.enable_pfc_reply_checker:
|
||||
if global_config.pfc.enable_pfc_reply_checker:
|
||||
self.logger.debug(f"{log_prefix} 调用 ReplyChecker 检查 (配置已启用)...")
|
||||
is_suitable_check, reason_check, need_replan_check = await self.conversation.reply_checker.check(
|
||||
reply=current_content_for_check,
|
||||
|
|
|
|||
|
|
@ -159,9 +159,9 @@ class ActionPlanner:
|
|||
self.private_name = private_name
|
||||
# 初始化 LLM 请求对象
|
||||
try:
|
||||
llm_config = global_config.llm_PFC_action_planner
|
||||
llm_config = global_config.model.pfc_action_planner
|
||||
if not isinstance(llm_config, dict):
|
||||
raise TypeError(f"LLM config 'llm_PFC_action_planner' is not a dictionary: {llm_config}")
|
||||
raise TypeError(f"LLM config 'pfc_action_planner' is not a dictionary: {llm_config}")
|
||||
|
||||
self.llm = LLMRequest(
|
||||
model=llm_config,
|
||||
|
|
@ -178,7 +178,7 @@ class ActionPlanner:
|
|||
|
||||
# 获取个性化信息和机器人名称
|
||||
# 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
|
||||
# 获取 ChatObserver 实例 (单例模式)
|
||||
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
|
||||
|
||||
|
|
|
|||
|
|
@ -125,7 +125,7 @@ class ChatObserver:
|
|||
|
||||
# 获取消息的发送者
|
||||
user_info = message.get("user_info", {})
|
||||
if user_info and str(user_info.get("user_id")) != str(global_config.BOT_QQ):
|
||||
if user_info and str(user_info.get("user_id")) != str(global_config.bot.qq_account):
|
||||
# 用户发送了消息，通知IdleChat
|
||||
asyncio.create_task(IdleChat.register_user_response(self.private_name))
|
||||
logger.debug(f"[私聊][{self.private_name}] 检测到用户消息，已通知IdleChat更新用户响应状态")
|
||||
|
|
@ -352,7 +352,7 @@ 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"])
|
||||
|
|
|
|||
|
|
@ -74,7 +74,7 @@ class Conversation:
|
|||
|
||||
self._initialized: bool = False
|
||||
|
||||
self.bot_qq_str: Optional[str] = str(global_config.BOT_QQ) if global_config.BOT_QQ else None
|
||||
self.bot_qq_str: Optional[str] = str(global_config.bot.qq_account) if global_config.bot.qq_account else None
|
||||
if not self.bot_qq_str:
|
||||
logger.error(f"[私聊][{self.private_name}] 严重错误：未能从配置中获取 BOT_QQ ID！")
|
||||
|
||||
|
|
|
|||
|
|
@ -143,7 +143,7 @@ async def initialize_core_components(conversation_instance: "Conversation"):
|
|||
)
|
||||
|
||||
conversation_instance.relationship_updater = PfcRelationshipUpdater(
|
||||
private_name=conversation_instance.private_name, bot_name=global_config.BOT_NICKNAME
|
||||
private_name=conversation_instance.private_name, bot_name=global_config.bot.nickname
|
||||
)
|
||||
conversation_instance.relationship_translator = PfcRepationshipTranslator(
|
||||
private_name=conversation_instance.private_name
|
||||
|
|
@ -151,7 +151,7 @@ async def initialize_core_components(conversation_instance: "Conversation"):
|
|||
logger.debug(f"[私聊][{conversation_instance.private_name}] (Initializer) PfcRelationship 初始化完成。")
|
||||
|
||||
conversation_instance.emotion_updater = PfcEmotionUpdater(
|
||||
private_name=conversation_instance.private_name, bot_name=global_config.BOT_NICKNAME
|
||||
private_name=conversation_instance.private_name, bot_name=global_config.bot.nickname
|
||||
)
|
||||
logger.debug(f"[私聊][{conversation_instance.private_name}] (Initializer) PfcEmotion 初始化完成。")
|
||||
|
||||
|
|
|
|||
|
|
@ -176,7 +176,7 @@ async def run_conversation_loop(conversation_instance: "Conversation"):
|
|||
if action in ["wait", "listening"] and new_msg_count_action_planning > 0:
|
||||
should_interrupt_action_planning = True
|
||||
interrupt_reason_action_planning = f"规划 {action} 期间收到 {new_msg_count_action_planning} 条新消息"
|
||||
elif other_new_msg_count_action_planning > global_config.pfc_message_buffer_size:
|
||||
elif other_new_msg_count_action_planning > global_config.pfc.pfc_message_buffer_size:
|
||||
should_interrupt_action_planning = True
|
||||
interrupt_reason_action_planning = (
|
||||
f"规划 {action} 期间收到 {other_new_msg_count_action_planning} 条来自他人的新消息"
|
||||
|
|
@ -271,7 +271,7 @@ async def run_conversation_loop(conversation_instance: "Conversation"):
|
|||
f"[私聊][{conversation_instance.private_name}] (Loop) Found {len(other_new_messages_this_check)} 'other_new_messages_this_check'."
|
||||
)
|
||||
|
||||
if len(other_new_messages_this_check) > global_config.pfc_message_buffer_size:
|
||||
if len(other_new_messages_this_check) > global_config.pfc.pfc_message_buffer_size:
|
||||
logger.info(
|
||||
f"[私聊][{conversation_instance.private_name}] (Loop) LLM动作 '{action}' 执行期间收到 {len(other_new_messages_this_check)} 条来自他人的新消息，将取消LLM任务。"
|
||||
)
|
||||
|
|
|
|||
|
|
@ -39,8 +39,8 @@ class DirectMessageSender:
|
|||
try:
|
||||
# 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,
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -155,7 +155,7 @@ class ObservationInfo:
|
|||
try:
|
||||
from ...config.config import global_config
|
||||
|
||||
self.bot_id = str(global_config.BOT_QQ) if global_config.BOT_QQ else None
|
||||
self.bot_id = str(global_config.bot.qq_account) if global_config.bot.qq_account else None
|
||||
if not self.bot_id:
|
||||
logger.error(f"[私聊][{self.private_name}] 未能从配置中获取 BOT_QQ ID！")
|
||||
except ImportError:
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ class PfcEmotionUpdater:
|
|||
self.bot_name = bot_name
|
||||
self.mood_mng = mood_manager
|
||||
|
||||
# LLM 实例 (根据 global_config.llm_summary 配置)
|
||||
# LLM 实例 (根据 global_config.model.summary 配置)
|
||||
llm_config_summary = getattr(global_config, "llm_summary", None)
|
||||
if llm_config_summary and isinstance(llm_config_summary, dict):
|
||||
logger.debug(f"[私聊][{self.private_name}] 使用 llm_summary 配置初始化情绪判断LLM。")
|
||||
|
|
|
|||
|
|
@ -101,7 +101,7 @@ class PFCProcessor:
|
|||
chat_id = str(message.chat_stream.stream_id)
|
||||
private_name = str(message.message_info.user_info.user_nickname) # 假设 UserInfo 有 user_nickname
|
||||
|
||||
if global_config.enable_pfc_chatting:
|
||||
if global_config.pfc.enable:
|
||||
await self.pfc_manager.get_or_create_conversation(chat_id, private_name)
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -110,7 +110,7 @@ class PFCProcessor:
|
|||
@staticmethod
|
||||
def _check_ban_words(text: str, userinfo: UserInfo) -> bool: # 明确 userinfo 类型
|
||||
"""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"[私聊]{userinfo.user_nickname}:{text}") # 假设 UserInfo 有 user_nickname
|
||||
logger.info(f"[过滤词识别]消息中含有{word}，filtered")
|
||||
|
|
@ -120,7 +120,7 @@ class PFCProcessor:
|
|||
@staticmethod
|
||||
def _check_ban_regex(text: str, userinfo: UserInfo) -> bool: # 明确 userinfo 类型
|
||||
"""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): # 假设 ban_msgs_regex 中的元素是已编译的正则对象
|
||||
logger.info(f"[私聊]{userinfo.user_nickname}:{text}") # _nickname
|
||||
logger.info(f"[正则表达式过滤]消息匹配到{pattern.pattern}，filtered") # .pattern 获取原始表达式字符串
|
||||
|
|
|
|||
|
|
@ -712,7 +712,7 @@ async def build_chat_history_text(observation_info: ObservationInfo, private_nam
|
|||
if hasattr(observation_info, "chat_history_str") and observation_info.chat_history_str:
|
||||
chat_history_text = observation_info.chat_history_str
|
||||
elif hasattr(observation_info, "chat_history") and observation_info.chat_history:
|
||||
history_slice = observation_info.chat_history[-global_config.pfc_recent_history_display_count :]
|
||||
history_slice = observation_info.chat_history[-global_config.pfc.pfc_recent_history_display_count :]
|
||||
chat_history_text = await build_readable_messages(
|
||||
history_slice, replace_bot_name=True, merge_messages=False, timestamp_mode="relative", read_mark=0.0
|
||||
)
|
||||
|
|
@ -722,7 +722,7 @@ async def build_chat_history_text(observation_info: ObservationInfo, private_nam
|
|||
unread_count = getattr(observation_info, "new_messages_count", 0)
|
||||
unread_messages = getattr(observation_info, "unprocessed_messages", [])
|
||||
if unread_count > 0 and unread_messages:
|
||||
bot_qq_str = str(global_config.BOT_QQ) if global_config.BOT_QQ else None # 安全获取
|
||||
bot_qq_str = str(global_config.bot.qq_account) if global_config.bot.qq_account else None # 安全获取
|
||||
if bot_qq_str:
|
||||
other_unread_messages = [
|
||||
msg for msg in unread_messages if msg.get("user_info", {}).get("user_id") != bot_qq_str
|
||||
|
|
|
|||
|
|
@ -11,10 +11,10 @@ class ReplyChecker:
|
|||
"""回复检查器 - 新版：仅检查机器人自身发言的精确重复"""
|
||||
|
||||
def __init__(self, stream_id: str, private_name: str):
|
||||
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.bot_qq_str = str(global_config.BOT_QQ)
|
||||
self.bot_qq_str = str(global_config.bot.qq_account)
|
||||
|
||||
def _normalize_text(self, text: str) -> str:
|
||||
"""
|
||||
|
|
@ -51,7 +51,7 @@ class ReplyChecker:
|
|||
"""
|
||||
if not self.bot_qq_str:
|
||||
logger.error(
|
||||
f"[私聊][{self.private_name}] ReplyChecker: BOT_QQ 未配置，无法检查{global_config.BOT_NICKNAME}自身消息。"
|
||||
f"[私聊][{self.private_name}] ReplyChecker: BOT_QQ 未配置，无法检查{global_config.bot.nickname}自身消息。"
|
||||
)
|
||||
return True, "BOT_QQ未配置，跳过重复检查。", False # 无法检查则默认通过
|
||||
|
||||
|
|
@ -79,7 +79,7 @@ class ReplyChecker:
|
|||
normalized_historical_text = self._normalize_text(historical_message_text)
|
||||
|
||||
logger.debug(
|
||||
f"[私聊][{self.private_name}] ReplyChecker: 历史记录 (反向索引 {i}) ({global_config.BOT_NICKNAME}): "
|
||||
f"[私聊][{self.private_name}] ReplyChecker: 历史记录 (反向索引 {i}) ({global_config.bot.nickname}): "
|
||||
f"原始='{historical_message_text[:50]}...', 规范化后='{normalized_historical_text[:50]}...'"
|
||||
)
|
||||
if (
|
||||
|
|
@ -87,7 +87,7 @@ class ReplyChecker:
|
|||
): # 确保规范化后不为空串才比较
|
||||
logger.warning(f"[私聊][{self.private_name}] ReplyChecker: !!! 成功拦截一次复读 !!!")
|
||||
logger.warning(
|
||||
f"[私聊][{self.private_name}] ReplyChecker 检测到{global_config.BOT_NICKNAME}自身重复消息 (规范化后内容相同): '{normalized_reply[:50]}...'"
|
||||
f"[私聊][{self.private_name}] ReplyChecker 检测到{global_config.bot.nickname}自身重复消息 (规范化后内容相同): '{normalized_reply[:50]}...'"
|
||||
)
|
||||
match_found = True
|
||||
# 返回: 不合适, 原因, 不需要重规划 (让上层逻辑决定是否重试生成)
|
||||
|
|
|
|||
|
|
@ -160,13 +160,13 @@ class ReplyGenerator:
|
|||
|
||||
def __init__(self, stream_id: str, private_name: str):
|
||||
self.llm = LLMRequest(
|
||||
model=global_config.llm_PFC_chat,
|
||||
temperature=global_config.llm_PFC_chat["temp"],
|
||||
model=global_config.model.pfc_chat,
|
||||
temperature=global_config.model.pfc_chat["temp"],
|
||||
max_tokens=300, # 对于JSON输出，这个可能需要适当调整，但一般回复短，JSON结构也简单
|
||||
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)
|
||||
|
|
@ -239,7 +239,7 @@ class ReplyGenerator:
|
|||
|
||||
# 我们先做一个合理的假设： “最近聊天记录” 字符串 chat_history_text 是基于
|
||||
# observation_info.chat_history 的一个有限的尾部片段生成的。
|
||||
# 假设这个片段的长度由 global_config.pfc_recent_history_display_count 控制，默认为20条。
|
||||
# 假设这个片段的长度由 global_config.pfc.pfc_recent_history_display_count 控制，默认为20条。
|
||||
recent_history_display_count = getattr(global_config, "pfc_recent_history_display_count", 20)
|
||||
|
||||
if observation_info and observation_info.chat_history and len(observation_info.chat_history) > 0:
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ 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
|
||||
|
||||
|
|
|
|||
|
|
@ -206,11 +206,11 @@ class Individuality:
|
|||
if not self.personality or not self.identity:
|
||||
return "个体特征尚未完全初始化。"
|
||||
|
||||
if global_config.personality_detail_level == 1:
|
||||
if global_config.personality.personality_detail_level == 1:
|
||||
level = 1
|
||||
elif global_config.personality_detail_level == 2:
|
||||
elif global_config.personality.personality_detail_level == 2:
|
||||
level = 2
|
||||
elif global_config.personality_detail_level == 3:
|
||||
elif global_config.personality.personality_detail_level == 3:
|
||||
level = 3
|
||||
else: # level = 0
|
||||
pass
|
||||
|
|
|
|||
42
src/main.py
42
src/main.py
|
|
@ -42,7 +42,7 @@ 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())
|
||||
|
|
@ -83,10 +83,10 @@ class MainSystem:
|
|||
|
||||
# 初始化日程
|
||||
bot_schedule.initialize(
|
||||
name=global_config.BOT_NICKNAME,
|
||||
personality=global_config.personality_core,
|
||||
behavior=global_config.PROMPT_SCHEDULE_GEN,
|
||||
interval=global_config.SCHEDULE_DOING_UPDATE_INTERVAL,
|
||||
name=global_config.bot.nickname,
|
||||
personality=global_config.personality.personality_core,
|
||||
behavior=global_config.schedule.prompt_schedule_gen,
|
||||
interval=global_config.schedule.schedule_doing_update_interval,
|
||||
)
|
||||
asyncio.create_task(bot_schedule.mai_schedule_start())
|
||||
|
||||
|
|
@ -95,7 +95,7 @@ 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
|
||||
|
|
@ -103,15 +103,15 @@ 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")
|
||||
|
||||
|
|
@ -124,7 +124,7 @@ class MainSystem:
|
|||
logger.success("全局消息管理器启动成功")
|
||||
|
||||
# 启动心流系统主循环
|
||||
if not global_config.enable_Legacy_HFC:
|
||||
if not global_config.experimental.enable_Legacy_HFC:
|
||||
asyncio.create_task(heartflow.heartflow_start_working())
|
||||
logger.success("心流系统启动成功")
|
||||
else:
|
||||
|
|
@ -156,7 +156,7 @@ 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()
|
||||
|
||||
|
|
@ -164,16 +164,18 @@ 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")
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
|
|
|||
|
|
@ -154,7 +154,7 @@ class NicknameManager:
|
|||
if not self.is_enabled:
|
||||
logger.info("绰号处理功能已禁用，处理器未启动。")
|
||||
return
|
||||
if global_config.max_nicknames_in_prompt == 0: # 考虑有神秘的用户输入为0的可能性
|
||||
if global_config.group_nickname.max_nicknames_in_prompt == 0: # 考虑有神秘的用户输入为0的可能性
|
||||
logger.error("[错误] 绰号注入数量不合适，绰号处理功能已禁用！")
|
||||
return
|
||||
|
||||
|
|
@ -210,7 +210,7 @@ class NicknameManager:
|
|||
if not self.is_enabled:
|
||||
return
|
||||
|
||||
if random.random() < global_config.nickname_analysis_probability:
|
||||
if random.random() < global_config.group_nickname.nickname_analysis_probability:
|
||||
logger.debug("跳过绰号分析：随机概率未命中。")
|
||||
return
|
||||
|
||||
|
|
@ -266,8 +266,8 @@ class NicknameManager:
|
|||
None,
|
||||
)
|
||||
user_name_map[user_id] = (
|
||||
latest_nickname or f"{global_config.BOT_NICKNAME}"
|
||||
if user_id == global_config.BOT_QQ
|
||||
latest_nickname or f"{global_config.bot.nickname}"
|
||||
if user_id == global_config.bot.qq_account
|
||||
else "未知"
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -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)}")
|
||||
|
|
|
|||
|
|
@ -65,7 +65,7 @@ def register_tool(tool_class: Type[BaseTool]):
|
|||
if not tool_name:
|
||||
raise ValueError(f"工具类 {tool_class.__name__} 没有定义 name 属性")
|
||||
|
||||
if not global_config.rename_person and tool_name == "rename_person":
|
||||
if not global_config.experimental.rename_person and tool_name == "rename_person":
|
||||
logger.info("改名功能已关闭，改名工具未注册")
|
||||
return
|
||||
|
||||
|
|
|
|||
|
|
@ -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"
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -1,18 +1,10 @@
|
|||
[inner]
|
||||
version = "1.7.1"
|
||||
version = "2.0.1"
|
||||
|
||||
#----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 = "1.7.1"
|
|||
#----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,
|
||||
|
|
@ -54,10 +46,13 @@ 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
|
||||
|
||||
[schedule]
|
||||
enable_schedule_gen = true # 是否启用日程表
|
||||
|
|
@ -95,11 +90,10 @@ 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
|
||||
|
|
@ -110,8 +104,8 @@ 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
|
||||
|
|
@ -120,14 +114,14 @@ 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
|
||||
|
||||
[group_nickname]
|
||||
enable_nickname_mapping = false # 绰号映射功能总开关（默认关闭，建议关闭）
|
||||
|
|
@ -139,10 +133,10 @@ nickname_analysis_history_limit = 30 # 绰号处理可见最大上下文
|
|||
nickname_analysis_probability = 0.1 # 绰号随机概率命中，该值越大，绰号分析越频繁
|
||||
|
||||
[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
|
||||
|
|
@ -154,9 +148,7 @@ 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" ]
|
||||
|
||||
long_message_auto_truncate = true # HFC 模式过长消息自动截断，防止他人 prompt 恶意注入，减少token消耗，但可能损失图片/长文信息，按需选择状态（默认开启）
|
||||
|
||||
|
|
@ -165,40 +157,38 @@ 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<n>\\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
|
||||
|
|
@ -249,14 +239,17 @@ max_cooldown = 18000 # 最长冷却时间，5小时 (18000秒)
|
|||
# stream = <true|false> : 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）
|
||||
|
|
@ -264,13 +257,13 @@ 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
|
||||
|
|
@ -282,27 +275,27 @@ 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
|
||||
|
|
@ -320,7 +313,7 @@ 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
|
||||
|
|
@ -328,7 +321,7 @@ 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
|
||||
|
|
@ -375,7 +368,7 @@ 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
|
||||
|
|
|
|||
|
|
@ -0,0 +1,7 @@
|
|||
from src.config.config import global_config
|
||||
|
||||
|
||||
class TestConfig:
|
||||
def test_load(self):
|
||||
config = global_config
|
||||
print(config)
|
||||
Loading…
Reference in New Issue