feat: 重构记忆系统配置，移除三层记忆相关配置，优化全局记忆管理逻辑，支持批量生成文本向量

src/chat/message_manager/context_manager.py

@@ -118,7 +118,7 @@ class SingleStreamContextManager:
 
                 # 三层记忆系统集成：将消息添加到感知记忆层
                 try:
-                    if global_config.three_tier_memory and global_config.three_tier_memory.enable:
+                    if global_config.memory and global_config.memory.enable:
                         unified_manager = _get_unified_memory_manager()
                         if unified_manager:
                             # 构建消息字典

src/chat/replyer/default_generator.py

@@ -575,7 +575,7 @@ class DefaultReplyer:
             str: 记忆信息字符串
         """
         # 检查是否启用三层记忆系统
-        if not (global_config.three_tier_memory and global_config.three_tier_memory.enable):
+        if not (global_config.memory and global_config.memory.enable):
             return ""
 
         try:

src/config/config.py

@@ -39,7 +39,6 @@ from src.config.official_configs import (
     ReactionConfig,
     ResponsePostProcessConfig,
     ResponseSplitterConfig,
-    ThreeTierMemoryConfig,
     ToolConfig,
     VideoAnalysisConfig,
     VoiceConfig,
@@ -382,7 +381,6 @@ class Config(ValidatedConfigBase):
     emoji: EmojiConfig = Field(..., description="表情配置")
     expression: ExpressionConfig = Field(..., description="表达配置")
     memory: MemoryConfig | None = Field(default=None, description="记忆配置")
-    three_tier_memory: ThreeTierMemoryConfig | None = Field(default=None, description="三层记忆系统配置")
     mood: MoodConfig = Field(..., description="情绪配置")
     reaction: ReactionConfig = Field(default_factory=ReactionConfig, description="反应规则配置")
     chinese_typo: ChineseTypoConfig = Field(..., description="中文错别字配置")

src/config/official_configs.py

@@ -434,8 +434,6 @@ class MemoryConfig(ValidatedConfigBase):
     search_top_k: int = Field(default=10, description="默认检索返回数量")
     search_min_importance: float = Field(default=0.3, description="最小重要性阈值")
     search_similarity_threshold: float = Field(default=0.5, description="向量相似度阈值")
-    search_max_expand_depth: int = Field(default=2, description="检索时图扩展深度（0-3）")
-    search_expand_semantic_threshold: float = Field(default=0.3, description="图扩展时语义相似度阈值（建议0.3-0.5，过低可能引入无关记忆，过高无法扩展）")
     enable_query_optimization: bool = Field(default=True, description="启用查询优化")
 
     # 路径扩展配置 (新算法)
@@ -453,30 +451,6 @@ class MemoryConfig(ValidatedConfigBase):
     enable_memory_deduplication: bool = Field(default=True, description="启用检索结果去重（合并相似记忆）")
     memory_deduplication_threshold: float = Field(default=0.85, description="记忆相似度阈值（0.85表示85%相似即合并）")
 
-    # 检索权重配置 (记忆图系统)
-    search_vector_weight: float = Field(default=0.4, description="向量相似度权重")
-    search_graph_distance_weight: float = Field(default=0.2, description="图距离权重")
-    search_importance_weight: float = Field(default=0.2, description="重要性权重")
-    search_recency_weight: float = Field(default=0.2, description="时效性权重")
-
-    # 记忆整合配置
-    consolidation_enabled: bool = Field(default=False, description="是否启用记忆整合")
-    consolidation_interval_hours: float = Field(default=2.0, description="整合任务执行间隔（小时）")
-    consolidation_deduplication_threshold: float = Field(default=0.93, description="相似记忆去重阈值")
-    consolidation_time_window_hours: float = Field(default=2.0, description="整合时间窗口（小时）- 统一用于去重和关联")
-    consolidation_max_batch_size: int = Field(default=30, description="单次最多处理的记忆数量")
-
-    # 记忆关联配置（整合功能的子模块）
-    consolidation_linking_enabled: bool = Field(default=True, description="是否启用记忆关联建立")
-    consolidation_linking_max_candidates: int = Field(default=10, description="每个记忆最多关联的候选数")
-    consolidation_linking_max_memories: int = Field(default=20, description="单次最多处理的记忆总数")
-    consolidation_linking_min_importance: float = Field(default=0.5, description="最低重要性阈值")
-    consolidation_linking_pre_filter_threshold: float = Field(default=0.7, description="向量相似度预筛选阈值")
-    consolidation_linking_max_pairs_for_llm: int = Field(default=5, description="最多发送给LLM分析的候选对数")
-    consolidation_linking_min_confidence: float = Field(default=0.7, description="LLM分析最低置信度阈值")
-    consolidation_linking_llm_temperature: float = Field(default=0.2, description="LLM分析温度参数")
-    consolidation_linking_llm_max_tokens: int = Field(default=1500, description="LLM分析最大输出长度")
-
     # 遗忘配置 (记忆图系统)
     forgetting_enabled: bool = Field(default=True, description="是否启用自动遗忘")
     forgetting_activation_threshold: float = Field(default=0.1, description="激活度阈值")
@@ -500,26 +474,13 @@ class MemoryConfig(ValidatedConfigBase):
     node_merger_context_match_required: bool = Field(default=True, description="节点合并是否要求上下文匹配")
     node_merger_merge_batch_size: int = Field(default=50, description="节点合并批量处理大小")
 
-
+    # ==================== 三层记忆系统配置 (Three-Tier Memory System) ====================
-class MoodConfig(ValidatedConfigBase):
-    """情绪配置类"""
-
-    enable_mood: bool = Field(default=False, description="启用情绪")
-    mood_update_threshold: float = Field(default=1.0, description="情绪更新阈值")
-
-
-class ThreeTierMemoryConfig(ValidatedConfigBase):
-    """三层记忆系统配置类"""
-
-    enable: bool = Field(default=False, description="启用三层记忆系统（实验性功能）")
-    data_dir: str = Field(default="data/memory_graph/three_tier", description="数据存储目录")
-
     # 感知记忆层配置
     perceptual_max_blocks: int = Field(default=50, description="记忆堆最大容量（全局）")
     perceptual_block_size: int = Field(default=5, description="每个记忆块包含的消息数量")
     perceptual_similarity_threshold: float = Field(default=0.55, description="相似度阈值（0-1）")
     perceptual_topk: int = Field(default=3, description="TopK召回数量")
-    activation_threshold: int = Field(default=3, description="激活阈值（召回次数→短期）")
+    perceptual_activation_threshold: int = Field(default=3, description="激活阈值（召回次数→短期）")
 
     # 短期记忆层配置
     short_term_max_memories: int = Field(default=30, description="短期记忆最大数量")
@@ -532,10 +493,12 @@ class ThreeTierMemoryConfig(ValidatedConfigBase):
     long_term_decay_factor: float = Field(default=0.95, description="衰减因子")
     long_term_auto_transfer_interval: int = Field(default=60, description="自动转移间隔（秒）")
 
-    # Judge模型配置
+
-    judge_model_name: str = Field(default="utils_small", description="用于决策的LLM模型")
+class MoodConfig(ValidatedConfigBase):
-    judge_temperature: float = Field(default=0.1, description="Judge模型的温度参数")
+    """情绪配置类"""
-    enable_judge_retrieval: bool = Field(default=True, description="启用智能检索判断")
+
+    enable_mood: bool = Field(default=False, description="启用情绪")
+    mood_update_threshold: float = Field(default=1.0, description="情绪更新阈值")
 
 
 class ReactionRuleConfig(ValidatedConfigBase):

src/main.py

@@ -479,7 +479,7 @@ MoFox_Bot(第三方修改版)
 
         # 初始化三层记忆系统（如果启用）
         try:
-            if global_config.three_tier_memory and global_config.three_tier_memory.enable:
+            if global_config.memory and global_config.memory.enable:
                 from src.memory_graph.manager_singleton import initialize_unified_memory_manager
                 logger.info("三层记忆系统已启用，正在初始化...")
                 await initialize_unified_memory_manager()

src/memory_graph/manager.py

@@ -138,20 +138,24 @@ class MemoryManager:
             )
 
             # 检查配置值
-            expand_depth = self.config.search_max_expand_depth
+            # 兼容性处理：如果配置项不存在，使用默认值或映射到新配置项
-            expand_semantic_threshold = self.config.search_expand_semantic_threshold
+            expand_depth = getattr(self.config, "path_expansion_max_hops", 2)
-            search_top_k = self.config.search_top_k
+            expand_semantic_threshold = getattr(self.config, "search_similarity_threshold", 0.5)
+            search_top_k = getattr(self.config, "search_top_k", 10)
+            
             # 读取权重配置
-            search_vector_weight = self.config.search_vector_weight
+            search_vector_weight = getattr(self.config, "vector_weight", 0.65)
-            search_importance_weight = self.config.search_importance_weight
+            # context_weight 近似映射为 importance_weight
-            search_recency_weight = self.config.search_recency_weight
+            search_importance_weight = getattr(self.config, "context_weight", 0.25)
+            search_recency_weight = getattr(self.config, "recency_weight", 0.10)
+            
             # 读取阈值过滤配置
-            search_min_importance = self.config.search_min_importance
+            search_min_importance = getattr(self.config, "search_min_importance", 0.3)
-            search_similarity_threshold = self.config.search_similarity_threshold
+            search_similarity_threshold = getattr(self.config, "search_similarity_threshold", 0.5)
 
             logger.info(
-                f"📊 配置检查: search_max_expand_depth={expand_depth}, "
+                f"📊 配置检查: expand_depth={expand_depth}, "
-                f"search_expand_semantic_threshold={expand_semantic_threshold}, "
+                f"expand_semantic_threshold={expand_semantic_threshold}, "
                 f"search_top_k={search_top_k}"
             )
             logger.info(
@@ -422,7 +426,7 @@ class MemoryManager:
                 "query": query,
                 "top_k": top_k,
                 "use_multi_query": use_multi_query,
-                "expand_depth": expand_depth or global_config.memory.search_max_expand_depth,  # 传递图扩展深度
+                "expand_depth": expand_depth or getattr(global_config.memory, "path_expansion_max_hops", 2),  # 传递图扩展深度
                 "context": context,
                 "prefer_node_types": prefer_node_types or [],  # 🆕 传递偏好节点类型
             }

src/memory_graph/manager_singleton.py

@@ -139,29 +139,29 @@ async def initialize_unified_memory_manager():
         from src.memory_graph.unified_manager import UnifiedMemoryManager
 
         # 检查是否启用三层记忆系统
-        if not hasattr(global_config, "three_tier_memory") or not getattr(
+        if not hasattr(global_config, "memory") or not getattr(
-            global_config.three_tier_memory, "enable", False
+            global_config.memory, "enable", False
         ):
             logger.warning("三层记忆系统未启用，跳过初始化")
             return None
 
-        config = global_config.three_tier_memory
+        config = global_config.memory
 
         # 创建管理器实例
         _unified_memory_manager = UnifiedMemoryManager(
-            data_dir=Path(getattr(config, "data_dir", "data/memory_graph/three_tier")),
+            data_dir=Path(getattr(config, "data_dir", "data/memory_graph")),
             # 感知记忆配置
             perceptual_max_blocks=getattr(config, "perceptual_max_blocks", 50),
             perceptual_block_size=getattr(config, "perceptual_block_size", 5),
             perceptual_activation_threshold=getattr(config, "perceptual_activation_threshold", 3),
-            perceptual_recall_top_k=getattr(config, "perceptual_recall_top_k", 5),
+            perceptual_recall_top_k=getattr(config, "perceptual_topk", 5),
-            perceptual_recall_threshold=getattr(config, "perceptual_recall_threshold", 0.55),
+            perceptual_recall_threshold=getattr(config, "perceptual_similarity_threshold", 0.55),
             # 短期记忆配置
             short_term_max_memories=getattr(config, "short_term_max_memories", 30),
             short_term_transfer_threshold=getattr(config, "short_term_transfer_threshold", 0.6),
             # 长期记忆配置
             long_term_batch_size=getattr(config, "long_term_batch_size", 10),
-            long_term_search_top_k=getattr(config, "long_term_search_top_k", 5),
+            long_term_search_top_k=getattr(config, "search_top_k", 5),
             long_term_decay_factor=getattr(config, "long_term_decay_factor", 0.95),
             long_term_auto_transfer_interval=getattr(config, "long_term_auto_transfer_interval", 600),
             # 智能检索配置

src/memory_graph/perceptual_manager.py

@@ -279,6 +279,28 @@ class PerceptualMemoryManager:
             logger.error(f"生成向量失败: {e}", exc_info=True)
             return None
 
+    async def _generate_embeddings_batch(self, texts: list[str]) -> list[np.ndarray | None]:
+        """
+        批量生成文本向量
+
+        Args:
+            texts: 文本列表
+
+        Returns:
+            向量列表，与输入一一对应
+        """
+        try:
+            if not self.embedding_generator:
+                logger.error("嵌入生成器未初始化")
+                return [None] * len(texts)
+
+            embeddings = await self.embedding_generator.generate_batch(texts)
+            return embeddings
+
+        except Exception as e:
+            logger.error(f"批量生成向量失败: {e}", exc_info=True)
+            return [None] * len(texts)
+
     async def recall_blocks(
         self,
         query_text: str,
@@ -528,11 +550,29 @@ class PerceptualMemoryManager:
 
         logger.info("重新生成记忆块向量...")
 
-        for block in self.perceptual_memory.blocks:
+        blocks_to_process = []
-            if block.embedding is None and block.combined_text:
+        texts_to_process = []
-                block.embedding = await self._generate_embedding(block.combined_text)
 
-        logger.info(f"✅ 向量重新生成完成（{len(self.perceptual_memory.blocks)} 个块）")
+        for block in self.perceptual_memory.blocks:
+            if block.embedding is None and block.combined_text and block.combined_text.strip():
+                blocks_to_process.append(block)
+                texts_to_process.append(block.combined_text)
+
+        if not blocks_to_process:
+            logger.info("没有需要重新生成向量的块")
+            return
+
+        logger.info(f"开始批量生成 {len(blocks_to_process)} 个块的向量...")
+
+        embeddings = await self._generate_embeddings_batch(texts_to_process)
+
+        success_count = 0
+        for block, embedding in zip(blocks_to_process, embeddings):
+            if embedding is not None:
+                block.embedding = embedding
+                success_count += 1
+
+        logger.info(f"✅ 向量重新生成完成（成功: {success_count}/{len(blocks_to_process)}）")
 
     async def shutdown(self) -> None:
         """关闭管理器"""

src/memory_graph/short_term_manager.py

@@ -492,6 +492,28 @@ class ShortTermMemoryManager:
             logger.error(f"生成向量失败: {e}", exc_info=True)
             return None
 
+    async def _generate_embeddings_batch(self, texts: list[str]) -> list[np.ndarray | None]:
+        """
+        批量生成文本向量
+
+        Args:
+            texts: 文本列表
+
+        Returns:
+            向量列表，与输入一一对应
+        """
+        try:
+            if not self.embedding_generator:
+                logger.error("嵌入生成器未初始化")
+                return [None] * len(texts)
+
+            embeddings = await self.embedding_generator.generate_batch(texts)
+            return embeddings
+
+        except Exception as e:
+            logger.error(f"批量生成向量失败: {e}", exc_info=True)
+            return [None] * len(texts)
+
     def _parse_json_response(self, response: str) -> dict[str, Any] | None:
         """解析 LLM 的 JSON 响应"""
         try:
@@ -684,11 +706,29 @@ class ShortTermMemoryManager:
         """重新生成记忆的向量"""
         logger.info("重新生成短期记忆向量...")
 
-        for memory in self.memories:
+        memories_to_process = []
-            if memory.embedding is None and memory.content:
+        texts_to_process = []
-                memory.embedding = await self._generate_embedding(memory.content)
 
-        logger.info(f"✅ 向量重新生成完成（{len(self.memories)} 条记忆）")
+        for memory in self.memories:
+            if memory.embedding is None and memory.content and memory.content.strip():
+                memories_to_process.append(memory)
+                texts_to_process.append(memory.content)
+
+        if not memories_to_process:
+            logger.info("没有需要重新生成向量的短期记忆")
+            return
+
+        logger.info(f"开始批量生成 {len(memories_to_process)} 条短期记忆的向量...")
+
+        embeddings = await self._generate_embeddings_batch(texts_to_process)
+
+        success_count = 0
+        for memory, embedding in zip(memories_to_process, embeddings):
+            if embedding is not None:
+                memory.embedding = embedding
+                success_count += 1
+
+        logger.info(f"✅ 向量重新生成完成（成功: {success_count}/{len(memories_to_process)}）")
 
     async def shutdown(self) -> None:
         """关闭管理器"""

template/bot_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "7.8.1"
+version = "7.8.2"
 
 #----以下是给开发人员阅读的，如果你只是部署了MoFox-Bot，不需要阅读----
 #如果你想要修改配置文件，请递增version的值
@@ -251,49 +251,10 @@ vector_db_path = "data/memory_graph/chroma_db" # 向量数据库路径 (使用
 search_top_k = 10 # 默认检索返回数量
 search_min_importance = 0.3 # 最小重要性阈值 (0.0-1.0)
 search_similarity_threshold = 0.6 # 向量相似度阈值
-search_expand_semantic_threshold = 0.3 # 图扩展时语义相似度阈值（建议0.3-0.5，过低可能引入无关记忆，过高无法扩展）
 
 # 智能查询优化
 enable_query_optimization = true # 启用查询优化（使用小模型分析对话历史，生成综合性搜索查询）
 
-# === 记忆整合配置 ===
-# 记忆整合包含两个功能：1)去重（合并相似记忆）2)关联（建立记忆关系）
-# 注意：整合任务会遍历所有记忆进行相似度计算，可能占用较多资源
-# 建议：1) 降低执行频率；2) 提高相似度阈值减少误判；3) 限制批量大小
-consolidation_enabled = true # 是否启用记忆整合
-consolidation_interval_hours = 1.0 # 整合任务执行间隔
-consolidation_deduplication_threshold = 0.9 # 相似记忆去重阈值
-consolidation_time_window_hours = 2.0 # 整合时间窗口（小时）- 统一用于去重和关联
-consolidation_max_batch_size = 100 # 单次最多处理的记忆数量
-
-# 记忆关联配置（整合功能的子模块）
-consolidation_linking_enabled = true # 是否启用记忆关联建立
-consolidation_linking_max_candidates = 10 # 每个记忆最多关联的候选数
-consolidation_linking_max_memories = 20 # 单次最多处理的记忆总数
-consolidation_linking_min_importance = 0.5 # 最低重要性阈值（低于此值的记忆不参与关联）
-consolidation_linking_pre_filter_threshold = 0.7 # 向量相似度预筛选阈值
-consolidation_linking_max_pairs_for_llm = 5 # 最多发送给LLM分析的候选对数
-consolidation_linking_min_confidence = 0.7 # LLM分析最低置信度阈值
-consolidation_linking_llm_temperature = 0.2 # LLM分析温度参数
-consolidation_linking_llm_max_tokens = 1500 # LLM分析最大输出长度
-
-# === 记忆遗忘配置 ===
-forgetting_enabled = true # 是否启用自动遗忘
-forgetting_activation_threshold = 0.1 # 激活度阈值（低于此值的记忆会被遗忘）
-forgetting_min_importance = 0.8 # 最小保护重要性（高于此值的记忆不会被遗忘）
-
-# === 记忆激活配置 ===
-activation_decay_rate = 0.9 # 激活度衰减率（每天衰减10%）
-activation_propagation_strength = 0.5 # 激活传播强度（传播到相关记忆的激活度比例）
-activation_propagation_depth = 1 # 激活传播深度（最多传播几层，建议1-2）
-
-# === 记忆检索配置 ===
-search_max_expand_depth = 2 # 检索时图扩展深度（0=仅直接匹配，1=扩展1跳，2=扩展2跳，推荐1-2）
-search_vector_weight = 0.4 # 向量相似度权重
-search_graph_distance_weight = 0.2 # 图距离权重
-search_importance_weight = 0.2 # 重要性权重
-search_recency_weight = 0.2 # 时效性权重
-
 # === 路径评分扩展算法配置（实验性功能）===
 # 这是一种全新的图检索算法，通过路径传播和分数聚合来发现相关记忆
 # 优势：更精确的图结构利用、路径合并机制、动态剪枝优化
@@ -308,6 +269,48 @@ path_expansion_path_score_weight = 0.50 # 路径分数在最终评分中的权
 path_expansion_importance_weight = 0.30 # 重要性在最终评分中的权重
 path_expansion_recency_weight = 0.20 # 时效性在最终评分中的权重
 
+# 🆕 路径扩展 - 记忆去重配置
+enable_memory_deduplication = true # 启用检索结果去重（合并相似记忆）
+memory_deduplication_threshold = 0.85 # 记忆相似度阈值（0.85表示85%相似即合并）
+
+# === 记忆遗忘配置 ===
+forgetting_enabled = true # 是否启用自动遗忘
+forgetting_activation_threshold = 0.1 # 激活度阈值（低于此值的记忆会被遗忘）
+forgetting_min_importance = 0.8 # 最小保护重要性（高于此值的记忆不会被遗忘）
+
+# === 记忆激活配置 ===
+activation_decay_rate = 0.9 # 激活度衰减率（每天衰减10%）
+activation_propagation_strength = 0.5 # 激活传播强度（传播到相关记忆的激活度比例）
+activation_propagation_depth = 1 # 激活传播深度（最多传播几层，建议1-2）
+
+# === 记忆激活配置（强制执行）===
+auto_activate_base_strength = 0.1 # 记忆被检索时自动激活的基础强度
+auto_activate_max_count = 10 # 单次搜索最多自动激活的记忆数量
+
+# === 三层记忆系统配置 ===
+# 感知记忆层配置
+perceptual_max_blocks = 50 # 记忆堆最大容量（全局）
+perceptual_block_size = 5 # 每个记忆块包含的消息数量
+perceptual_similarity_threshold = 0.55 # 相似度阈值（0-1）
+perceptual_topk = 3 # TopK召回数量
+perceptual_activation_threshold = 3 # 激活阈值（召回次数→短期）
+
+# 短期记忆层配置
+short_term_max_memories = 30 # 短期记忆最大数量
+short_term_transfer_threshold = 0.6 # 转移到长期记忆的重要性阈值
+short_term_search_top_k = 5 # 搜索时返回的最大数量
+short_term_decay_factor = 0.98 # 衰减因子
+
+# 长期记忆层配置
+long_term_batch_size = 10 # 批量转移大小
+long_term_decay_factor = 0.95 # 衰减因子
+long_term_auto_transfer_interval = 180 # 自动转移间隔（秒）
+
+# 节点去重合并配置
+node_merger_similarity_threshold = 0.85 # 节点去重相似度阈值
+node_merger_context_match_required = true # 节点合并是否要求上下文匹配
+node_merger_merge_batch_size = 50 # 节点合并批量处理大小
+
 # === 性能配置 ===
 max_memory_nodes_per_memory = 10 # 每条记忆最多包含的节点数
 max_related_memories = 5 # 激活传播时最多影响的相关记忆数
@@ -317,29 +320,6 @@ max_related_memories = 5 # 激活传播时最多影响的相关记忆数
 # 1. 感知记忆层 (Perceptual Memory) - 消息块的短期缓存，自动收集
 # 2. 短期记忆层 (Short-term Memory) - 结构化的活跃记忆，模型格式化
 # 3. 长期记忆层 (Long-term Memory) - 持久化的图结构记忆，批量转移
-[three_tier_memory]
-enable = false # 是否启用三层记忆系统（已经是核心系统，建议启用）
-data_dir = "data/memory_graph/three_tier" # 数据存储目录
-
-# --- 感知记忆层配置 ---
-perceptual_max_blocks = 50 # 记忆堆最大容量（全局）
-perceptual_block_size = 5 # 每个记忆块包含的消息数量
-perceptual_similarity_threshold = 0.55 # 相似度阈值（0-1）
-perceptual_topk = 5 # TopK召回数量
-perceptual_activation_threshold = 3 # 激活阈值（召回次数达到此值→转为短期记忆）
-
-# --- 短期记忆层配置 ---
-short_term_max_memories = 30 # 短期记忆最大数量（达到上限触发批量转移）
-short_term_transfer_threshold = 0.6 # 转移到长期记忆的重要性阈值（0.0-1.0）
-short_term_search_top_k = 5 # 搜索时返回的最大数量
-
-# --- 长期记忆层配置 ---
-long_term_batch_size = 10 # 批量转移大小（每次转移多少条短期记忆）
-long_term_search_top_k = 5 # 长期记忆二次检索返回数量
-
-# --- 记忆检索配置 ---
-enable_judge_retrieval = true # 启用智能检索判断（裁判模型评估是否需要二次检索）
-judge_confidence_threshold = 0.7 # 裁判模型置信度阈值
 
 [voice]
 enable_asr = true # 是否启用语音识别，启用后MoFox-Bot可以识别语音消息，启用该功能需要配置语音识别模型[model.voice]