update:1.重构项目数据库初始化和迁移逻辑，使用Alembic数据库管理工具

backend/app/services/ai_service.py

@@ -4,7 +4,8 @@ from openai import AsyncOpenAI
 from anthropic import AsyncAnthropic
 from app.config import settings as app_settings
 from app.logger import get_logger
-from app.mcp.adapters import UniversalMCPAdapter, PromptInjectionAdapter
+from app.mcp.adapters import PromptInjectionAdapter
+from app.mcp.adapters.universal import universal_mcp_adapter
 import httpx
 import json
 import hashlib
@@ -145,11 +146,11 @@ class AIService:
         self.default_temperature = default_temperature or app_settings.default_temperature
         self.default_max_tokens = default_max_tokens or app_settings.default_max_tokens
         
-        # 初始化MCP适配器
+        # 使用全局MCP适配器单例
         self.enable_mcp_adapter = enable_mcp_adapter
         if enable_mcp_adapter:
-            self.mcp_adapter = UniversalMCPAdapter()
-            logger.info("✅ MCP通用适配器已启用")
+            self.mcp_adapter = universal_mcp_adapter
+            logger.info("✅ MCP通用适配器已启用（使用全局单例）")
         else:
             self.mcp_adapter = None
             logger.info("⚠️ MCP适配器已禁用")

backend/app/services/memory_service.py

@@ -18,21 +18,52 @@ from pathlib import Path
 if 'SENTENCE_TRANSFORMERS_HOME' not in os.environ:
     # 根据运行环境确定模型目录
     if getattr(sys, 'frozen', False):
-        # PyInstaller 打包后
-        base_dir = Path(sys.executable).parent
+        # PyInstaller 打包后 - 需要检查多个可能的位置
+        exe_dir = Path(sys.executable).parent
+        
+        # 检查顺序：
+        # 1. _MEIPASS/backend/embedding (临时解压目录)
+        # 2. exe同级/_internal/backend/embedding
+        # 3. exe同级/backend/embedding
+        possible_paths = []
+        
+        if hasattr(sys, '_MEIPASS'):
+            possible_paths.append(Path(sys._MEIPASS) / 'backend' / 'embedding')
+        
+        possible_paths.extend([
+            exe_dir / '_internal' / 'backend' / 'embedding',
+            exe_dir / 'backend' / 'embedding',
+            exe_dir / '_internal' / 'embedding',
+            exe_dir / 'embedding'
+        ])
+        
+        model_dir = None
+        for path in possible_paths:
+            if path.exists():
+                model_dir = path
+                logger.info(f"🔧 找到打包环境模型目录: {model_dir}")
+                break
+        
+        if model_dir:
+            os.environ['SENTENCE_TRANSFORMERS_HOME'] = str(model_dir)
+        else:
+            # 最后降级方案
+            fallback_dir = exe_dir / 'embedding'
+            os.environ['SENTENCE_TRANSFORMERS_HOME'] = str(fallback_dir)
+            logger.warning(f"⚠️ 未找到预打包模型，使用降级目录: {fallback_dir}")
+            logger.warning(f"   检查过的路径: {[str(p) for p in possible_paths]}")
     else:
         # 开发模式，从当前文件位置向上找到项目根目录
         base_dir = Path(__file__).parent.parent.parent
-    
-    model_dir = base_dir / 'backend' / 'embedding'
-    if model_dir.exists():
-        os.environ['SENTENCE_TRANSFORMERS_HOME'] = str(model_dir)
-        logger.info(f"🔧 设置模型目录: {model_dir}")
-    else:
-        # 降级到项目根目录的 embedding
-        fallback_dir = base_dir / 'embedding'
-        os.environ['SENTENCE_TRANSFORMERS_HOME'] = str(fallback_dir)
-        logger.info(f"🔧 使用降级模型目录: {fallback_dir}")
+        model_dir = base_dir / 'backend' / 'embedding'
+        if model_dir.exists():
+            os.environ['SENTENCE_TRANSFORMERS_HOME'] = str(model_dir)
+            logger.info(f"🔧 设置开发环境模型目录: {model_dir}")
+        else:
+            # 降级到项目根目录的 embedding
+            fallback_dir = base_dir / 'embedding'
+            os.environ['SENTENCE_TRANSFORMERS_HOME'] = str(fallback_dir)
+            logger.info(f"🔧 使用降级模型目录: {fallback_dir}")
 
 
 class MemoryService: