diff --git a/src/config/official_configs.py b/src/config/official_configs.py index 7c8786bea..652440e69 100644 --- a/src/config/official_configs.py +++ b/src/config/official_configs.py @@ -17,7 +17,7 @@ from src.config.config_base import ConfigBase @dataclass class BotConfig(ConfigBase): """QQ机器人配置类""" - + platform: str """平台""" @@ -43,7 +43,7 @@ class PersonalityConfig(ConfigBase): identity: str = "" """身份特征""" - + reply_style: str = "" """表达风格""" @@ -71,7 +71,6 @@ class ChatConfig(ConfigBase): max_context_size: int = 18 """上下文长度""" - replyer_random_probability: float = 0.5 """ @@ -129,7 +128,7 @@ class ChatConfig(ConfigBase): """ if not self.talk_frequency_adjust: return self.talk_frequency - + # 优先检查聊天流特定的配置 if chat_stream_id: stream_frequency = self._get_stream_specific_frequency(chat_stream_id) @@ -138,11 +137,7 @@ class ChatConfig(ConfigBase): # 检查全局时段配置（第一个元素为空字符串的配置） global_frequency = self._get_global_frequency() - if global_frequency is not None: - return global_frequency - - # 如果都没有匹配，返回默认值 - return self.talk_frequency + return self.talk_frequency if global_frequency is None else global_frequency def _get_time_based_frequency(self, time_freq_list: list[str]) -> Optional[float]: """ @@ -294,6 +289,7 @@ class NormalChatConfig(ConfigBase): willing_mode: str = "classical" """意愿模式""" + @dataclass class ExpressionConfig(ConfigBase): """表达配置类""" @@ -326,10 +322,10 @@ class ExpressionConfig(ConfigBase): def _parse_stream_config_to_chat_id(self, stream_config_str: str) -> Optional[str]: """ 解析流配置字符串并生成对应的 chat_id - + Args: stream_config_str: 格式为 "platform:id:type" 的字符串 - + Returns: str: 生成的 chat_id，如果解析失败则返回 None """ @@ -337,116 +333,116 @@ class ExpressionConfig(ConfigBase): parts = stream_config_str.split(":") if len(parts) != 3: return None - + platform = parts[0] - id_str = parts[1] + id_str = parts[1] stream_type = parts[2] - + # 判断是否为群聊 is_group = stream_type == "group" - + # 使用与 ChatStream.get_stream_id 相同的逻辑生成 chat_id import hashlib - + if is_group: components = [platform, str(id_str)] else: components = [platform, str(id_str), "private"] key = "_".join(components) return hashlib.md5(key.encode()).hexdigest() - + except (ValueError, IndexError): return None def get_expression_config_for_chat(self, chat_stream_id: Optional[str] = None) -> tuple[bool, bool, int]: """ 根据聊天流ID获取表达配置 - + Args: chat_stream_id: 聊天流ID，格式为哈希值 - + Returns: tuple: (是否使用表达, 是否学习表达, 学习间隔) """ if not self.expression_learning: # 如果没有配置，使用默认值：启用表达，启用学习，300秒间隔 return True, True, 300 - + # 优先检查聊天流特定的配置 if chat_stream_id: - specific_config = self._get_stream_specific_config(chat_stream_id) - if specific_config is not None: - return specific_config - + specific_expression_config = self._get_stream_specific_config(chat_stream_id) + if specific_expression_config is not None: + return specific_expression_config + # 检查全局配置（第一个元素为空字符串的配置） - global_config = self._get_global_config() - if global_config is not None: - return global_config - + global_expression_config = self._get_global_config() + if global_expression_config is not None: + return global_expression_config + # 如果都没有匹配，返回默认值 return True, True, 300 def _get_stream_specific_config(self, chat_stream_id: str) -> Optional[tuple[bool, bool, int]]: """ 获取特定聊天流的表达配置 - + Args: chat_stream_id: 聊天流ID（哈希值） - + Returns: tuple: (是否使用表达, 是否学习表达, 学习间隔)，如果没有配置则返回 None """ for config_item in self.expression_learning: if not config_item or len(config_item) < 4: continue - + stream_config_str = config_item[0] # 例如 "qq:1026294844:group" - + # 如果是空字符串，跳过（这是全局配置） if stream_config_str == "": continue - + # 解析配置字符串并生成对应的 chat_id config_chat_id = self._parse_stream_config_to_chat_id(stream_config_str) if config_chat_id is None: continue - + # 比较生成的 chat_id if config_chat_id != chat_stream_id: continue - + # 解析配置 try: - use_expression = config_item[1].lower() == "enable" - enable_learning = config_item[2].lower() == "enable" - learning_intensity = float(config_item[3]) - return use_expression, enable_learning, learning_intensity + use_expression: bool = config_item[1].lower() == "enable" + enable_learning: bool = config_item[2].lower() == "enable" + learning_intensity: float = float(config_item[3]) + return use_expression, enable_learning, learning_intensity # type: ignore except (ValueError, IndexError): continue - + return None def _get_global_config(self) -> Optional[tuple[bool, bool, int]]: """ 获取全局表达配置 - + Returns: tuple: (是否使用表达, 是否学习表达, 学习间隔)，如果没有配置则返回 None """ for config_item in self.expression_learning: if not config_item or len(config_item) < 4: continue - + # 检查是否为全局配置（第一个元素为空字符串） if config_item[0] == "": try: - use_expression = config_item[1].lower() == "enable" - enable_learning = config_item[2].lower() == "enable" + use_expression: bool = config_item[1].lower() == "enable" + enable_learning: bool = config_item[2].lower() == "enable" learning_intensity = float(config_item[3]) - return use_expression, enable_learning, learning_intensity + return use_expression, enable_learning, learning_intensity # type: ignore except (ValueError, IndexError): continue - + return None @@ -456,7 +452,8 @@ class ToolConfig(ConfigBase): enable_tool: bool = False """是否在聊天中启用工具""" - + + @dataclass class VoiceConfig(ConfigBase): """语音识别配置类""" @@ -542,7 +539,7 @@ class MemoryConfig(ConfigBase): 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""" - + enable_instant_memory: bool = True """是否启用即时记忆""" @@ -553,7 +550,7 @@ class MoodConfig(ConfigBase): enable_mood: bool = False """是否启用情绪系统""" - + mood_update_threshold: float = 1.0 """情绪更新阈值,越高，更新越慢""" @@ -604,6 +601,7 @@ class KeywordReactionConfig(ConfigBase): if not isinstance(rule, KeywordRuleConfig): raise ValueError(f"规则必须是KeywordRuleConfig类型，而不是{type(rule).__name__}") + @dataclass class CustomPromptConfig(ConfigBase): """自定义提示词配置类""" @@ -752,4 +750,3 @@ class LPMMKnowledgeConfig(ConfigBase): embedding_dimension: int = 1024 """嵌入向量维度，应该与模型的输出维度一致""" -