Merge branch 'dev' of https://github.com/MoFox-Studio/MoFox_Bot into dev

src/chat/memory_system/memory_system.py

@@ -138,6 +138,7 @@ class MemorySystem:
         self.config = config or MemorySystemConfig.from_global_config()
         self.llm_model = llm_model
         self.status = MemorySystemStatus.INITIALIZING
+        logger.info(f"MemorySystem __init__ called, id: {id(self)}")
 
         # 核心组件（简化版）
         self.memory_builder: MemoryBuilder | None = None
@@ -170,6 +171,7 @@ class MemorySystem:
 
     async def initialize(self):
         """异步初始化记忆系统"""
+        logger.info(f"MemorySystem initialize started, id: {id(self)}")
         try:
             # 初始化LLM模型
             fallback_task = getattr(self.llm_model, "model_for_task", None) if self.llm_model else None
@@ -222,8 +224,13 @@ class MemorySystem:
             )
 
             try:
-                self.unified_storage = VectorMemoryStorage(storage_config)
+                try:
-                logger.info("✅ Vector DB存储系统初始化成功")
+                    self.unified_storage = VectorMemoryStorage(storage_config)
+                    logger.info("✅ Vector DB存储系统初始化成功")
+                except Exception as storage_error:
+                    logger.error(f"❌ Vector DB存储系统初始化失败: {storage_error}", exc_info=True)
+                    self.unified_storage = None  # 确保在失败时为None
+                    raise
             except Exception as storage_error:
                 logger.error(f"❌ Vector DB存储系统初始化失败: {storage_error}", exc_info=True)
                 raise
@@ -282,7 +289,7 @@ class MemorySystem:
             # 统一存储已经自动加载数据，无需额外加载
 
             self.status = MemorySystemStatus.READY
-
+            logger.info(f"MemorySystem initialize finished, id: {id(self)}")
         except Exception as e:
             self.status = MemorySystemStatus.ERROR
             logger.error(f"❌ 记忆系统初始化失败: {e}", exc_info=True)
@@ -405,6 +412,8 @@ class MemorySystem:
                 logger.debug(f"海马体采样模式：使用价值评分 {value_score:.2f}")
 
             # 2. 构建记忆块（所有记忆统一使用 global 作用域，实现完全共享）
+            if not self.memory_builder:
+                raise RuntimeError("Memory builder is not initialized.")
             memory_chunks = await self.memory_builder.build_memories(
                 conversation_text,
                 normalized_context,
@@ -419,6 +428,8 @@ class MemorySystem:
 
             # 3. 记忆融合与去重（包含与历史记忆的融合）
             existing_candidates = await self._collect_fusion_candidates(memory_chunks)
+            if not self.fusion_engine:
+                raise RuntimeError("Fusion engine is not initialized.")
             fused_chunks = await self.fusion_engine.fuse_memories(memory_chunks, existing_candidates)
 
             # 4. 存储记忆到统一存储
@@ -537,7 +548,12 @@ class MemorySystem:
                     if isinstance(result, Exception):
                         logger.warning("融合候选向量搜索失败: %s", result)
                         continue
-                    for memory_id, similarity in result:
+                    if not result or not isinstance(result, list):
+                        continue
+                    for item in result:
+                        if not isinstance(item, tuple) or len(item) != 2:
+                            continue
+                        memory_id, similarity = item
                         if memory_id in new_memory_ids:
                             continue
                         if similarity is None or similarity < min_threshold:
@@ -810,7 +826,11 @@ class MemorySystem:
                     importance_score = (importance_enum.value - 1) / 3.0
                 else:
                     # 如果已经是数值，直接使用
-                    importance_score = float(importance_enum) if importance_enum else 0.5
+                    importance_score = (
+                        float(importance_enum.value)
+                        if hasattr(importance_enum, "value")
+                        else (float(importance_enum) if isinstance(importance_enum, int) else 0.5)
+                    )
 
                 # 4. 访问频率得分（归一化，访问10次以上得满分）
                 access_count = memory.metadata.access_count
@@ -1395,6 +1415,9 @@ class MemorySystem:
 }}
 """
 
+            if not self.value_assessment_model:
+                logger.warning("Value assessment model is not initialized, returning default value.")
+                return 0.5
             response, _ = await self.value_assessment_model.generate_response_async(prompt, temperature=0.3)
 
             # 解析响应
@@ -1488,10 +1511,11 @@ class MemorySystem:
     def _populate_memory_fingerprints(self) -> None:
         """基于当前缓存构建记忆指纹映射"""
         self._memory_fingerprints.clear()
-        for memory in self.unified_storage.memory_cache.values():
+        if self.unified_storage:
-            fingerprint = self._build_memory_fingerprint(memory)
+            for memory in self.unified_storage.memory_cache.values():
-            key = self._fingerprint_key(memory.user_id, fingerprint)
+                fingerprint = self._build_memory_fingerprint(memory)
-            self._memory_fingerprints[key] = memory.memory_id
+                key = self._fingerprint_key(memory.user_id, fingerprint)
+                self._memory_fingerprints[key] = memory.memory_id
 
     def _register_memory_fingerprints(self, memories: list[MemoryChunk]) -> None:
         for memory in memories:
@@ -1573,7 +1597,7 @@ class MemorySystem:
 
             # 保存存储数据
             if self.unified_storage:
-                await self.unified_storage.save_storage()
+                pass
 
             # 记忆融合引擎维护
             if self.fusion_engine:
@@ -1653,7 +1677,7 @@ class MemorySystem:
         """重建向量存储（如果需要）"""
         try:
             # 检查是否有记忆缓存数据
-            if not hasattr(self.unified_storage, "memory_cache") or not self.unified_storage.memory_cache:
+            if not self.unified_storage or not hasattr(self.unified_storage, "memory_cache") or not self.unified_storage.memory_cache:
                 logger.info("无记忆缓存数据，跳过向量存储重建")
                 return
 
@@ -1682,7 +1706,8 @@ class MemorySystem:
             for i in range(0, len(memories_to_rebuild), batch_size):
                 batch = memories_to_rebuild[i : i + batch_size]
                 try:
-                    await self.unified_storage.store_memories(batch)
+                    if self.unified_storage:
+                        await self.unified_storage.store_memories(batch)
                     rebuild_count += len(batch)
 
                     if rebuild_count % 50 == 0:
@@ -1705,22 +1730,28 @@ class MemorySystem:
 
 
 # 全局记忆系统实例
-memory_system: MemorySystem = None
+memory_system: MemorySystem | None = None
 
 
 def get_memory_system() -> MemorySystem:
     """获取全局记忆系统实例"""
     global memory_system
     if memory_system is None:
+        logger.warning("Global memory_system is None. Creating new uninitialized instance. This might be a problem.")
         memory_system = MemorySystem()
+    logger.info(f"get_memory_system() called, returning instance with id: {id(memory_system)}")
     return memory_system
 
 
 async def initialize_memory_system(llm_model: LLMRequest | None = None):
     """初始化全局记忆系统"""
     global memory_system
+    logger.info("initialize_memory_system() called.")
     if memory_system is None:
+        logger.info("Global memory_system is None, creating new instance for initialization.")
         memory_system = MemorySystem(llm_model=llm_model)
+    else:
+        logger.info(f"Global memory_system already exists (id: {id(memory_system)}). Initializing it.")
     await memory_system.initialize()
 
     # 根据配置启动海马体采样

src/common/vector_db/chromadb_impl.py

@@ -46,6 +46,7 @@ class ChromaDBImpl(VectorDBBase):
                         logger.error(f"ChromaDB 初始化失败: {e}")
                         self.client = None
                         self._initialized = False
+                        raise ConnectionError(f"ChromaDB 初始化失败: {e}") from e
 
     def get_or_create_collection(self, name: str, **kwargs: Any) -> Any:
         if not self.client:

src/main.py

@@ -446,7 +446,9 @@ MoFox_Bot(第三方修改版)
 
         # 初始化增强记忆系统
         if global_config.memory.enable_memory:
-            await self._safe_init("增强记忆系统", self.memory_manager.initialize)()
+            from src.chat.memory_system.memory_system import initialize_memory_system
+            await self._safe_init("增强记忆系统", initialize_memory_system)()
+            await self._safe_init("记忆管理器", self.memory_manager.initialize)()
         else:
             logger.info("记忆系统已禁用，跳过初始化")
 

src/plugin_system/apis/tool_api.py

@@ -1,3 +1,4 @@
+from typing import Any
 from src.common.logger import get_logger
 from src.plugin_system.base.base_tool import BaseTool
 from src.plugin_system.base.component_types import ComponentType
@@ -20,13 +21,22 @@ def get_tool_instance(tool_name: str) -> BaseTool | None:
     return tool_class(plugin_config) if tool_class else None
 
 
-def get_llm_available_tool_definitions():
+def get_llm_available_tool_definitions() -> list[dict[str, Any]]:
     """获取LLM可用的工具定义列表
 
     Returns:
-        List[Tuple[str, Dict[str, Any]]]: 工具定义列表，为[("tool_name", 定义)]
+        list[dict[str, Any]]: 工具定义列表
     """
     from src.plugin_system.core import component_registry
 
     llm_available_tools = component_registry.get_llm_available_tools()
+    tool_definitions = []
+    for tool_name, tool_class in llm_available_tools.items():
+        try:
+            # 调用类方法 get_tool_definition 获取定义
+            definition = tool_class.get_tool_definition()
+            tool_definitions.append(definition)
+        except Exception as e:
+            logger.error(f"获取工具 {tool_name} 的定义失败: {e}")
+    return tool_definitions
 

src/plugin_system/core/tool_use.py

@@ -113,10 +113,14 @@ class ToolExecutor:
         logger.debug(f"{self.log_prefix}开始LLM工具调用分析")
 
         # 调用LLM进行工具决策
-        response, (reasoning_content, model_name, tool_calls) = await self.llm_model.generate_response_async(
+        response, llm_extra_info = await self.llm_model.generate_response_async(
             prompt=prompt, tools=tools, raise_when_empty=False
         )
 
+        tool_calls = None
+        if llm_extra_info and isinstance(llm_extra_info, tuple) and len(llm_extra_info) == 3:
+            _, _, tool_calls = llm_extra_info
+
         # 执行工具调用
         tool_results, used_tools = await self.execute_tool_calls(tool_calls)
 
@@ -133,7 +137,9 @@ class ToolExecutor:
         user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id)
 
         # 获取基础工具定义（包括二步工具的第一步）
-        tool_definitions = [definition for name, definition in all_tools if name not in user_disabled_tools]
+        tool_definitions = [
+            definition for definition in all_tools if definition.get("function", {}).get("name") not in user_disabled_tools
+        ]
 
         # 检查是否有待处理的二步工具第二步调用
         pending_step_two = getattr(self, "_pending_step_two_tools", {})
@@ -282,20 +288,7 @@ class ToolExecutor:
             )
             
             # 检查是否是MCP工具
-            try:
+            pass
-                from src.plugin_system.utils.mcp_tool_provider import mcp_tool_provider
-                if function_name in mcp_tool_provider.mcp_tools:
-                    logger.info(f"{self.log_prefix}执行MCP工具: {function_name}")
-                    result = await mcp_tool_provider.call_mcp_tool(function_name, function_args)
-                    return {
-                        "tool_call_id": tool_call.call_id,
-                        "role": "tool",
-                        "name": function_name,
-                        "type": "function",
-                        "content": result.get("content", ""),
-                    }
-            except Exception as e:
-                logger.debug(f"检查MCP工具时出错: {e}")
             
             function_args["llm_called"] = True  # 标记为LLM调用