Feature/memory redis (#152)

* [feature]Emotional memory cache

* [feature]Implicit memory cache

* [changes]Modify the expiration time of implicit memory to 24 hours.

* [feature]Emotional memory cache

* [feature]Implicit memory cache

* [changes]Modify the expiration time of implicit memory to 24 hours.

* [changes]Modify the code based on the AI review

* [feature]Emotional memory cache

* [feature]Implicit memory cache

* [changes]Modify the expiration time of implicit memory to 24 hours.

* [feature]Implicit memory cache

* [changes]Modify the code based on the AI review

* [changes]Modify the generated emotion cache to be "end_user_id"

* [feature]Emotional memory cache

* [feature]Implicit memory cache

* [changes]Modify the code based on the AI review

* [feature]Emotional memory cache

* [changes]Modify the code based on the AI review

* [changes]Modify the generated emotion cache to be "end_user_id"

api/app/controllers/emotion_controller.py

@@ -267,7 +267,7 @@ async def generate_emotion_suggestions(
     """生成个性化情绪建议（调用LLM并缓存）
 
     Args:
-        request: 包含 group_id、可选的 config_id 和 force_refresh
+        request: 包含 end_user_id
         db: 数据库会话
         current_user: 当前用户
 
@@ -275,47 +275,22 @@ async def generate_emotion_suggestions(
         新生成的个性化情绪建议响应
     """
     try:
-        # 验证 config_id（如果提供）
-        # 获取终端用户关联的配置
-        config_id = request.config_id
-        if config_id is None:
-            # 如果没有提供 config_id，尝试获取用户关联的配置
-            try:
-                from app.services.memory_agent_service import (
-                    get_end_user_connected_config,
-                )
-                connected_config = get_end_user_connected_config(request.group_id, db)
-                config_id = connected_config.get("memory_config_id")
-            except ValueError as e:
-                return fail(BizCode.INVALID_PARAMETER, "无法获取用户关联的配置", str(e))
-        else:
-            # 如果提供了 config_id，验证其有效性
-            from app.services.memory_config_service import MemoryConfigService
-            try:
-                config_service = MemoryConfigService(db)
-                config = config_service.get_config_by_id(config_id)
-                if not config:
-                    return fail(BizCode.INVALID_PARAMETER, "配置ID无效", f"配置 {config_id} 不存在")
-            except Exception as e:
-                return fail(BizCode.INVALID_PARAMETER, "配置ID验证失败", str(e))
-
         api_logger.info(
             f"用户 {current_user.username} 请求生成个性化情绪建议",
             extra={
-                "group_id": request.group_id,
-                "config_id": config_id
+                "end_user_id": request.end_user_id
             }
         )
 
         # 调用服务层生成建议
         data = await emotion_service.generate_emotion_suggestions(
-            end_user_id=request.group_id,
+            end_user_id=request.end_user_id,
             db=db
         )
 
         # 保存到缓存
         await emotion_service.save_suggestions_cache(
-            end_user_id=request.group_id,
+            end_user_id=request.end_user_id,
             suggestions_data=data,
             db=db,
             expires_hours=24
@@ -324,7 +299,7 @@ async def generate_emotion_suggestions(
         api_logger.info(
             "个性化建议生成成功",
             extra={
-                "group_id": request.group_id,
+                "end_user_id": request.end_user_id,
                 "suggestions_count": len(data.get("suggestions", []))
             }
         )
@@ -334,7 +309,7 @@ async def generate_emotion_suggestions(
     except Exception as e:
         api_logger.error(
             f"生成个性化建议失败: {str(e)}",
-            extra={"group_id": request.group_id},
+            extra={"end_user_id": request.end_user_id},
             exc_info=True
         )
         raise HTTPException(

api/app/schemas/emotion_schema.py

@@ -34,5 +34,4 @@ class EmotionSuggestionsRequest(BaseModel):
 
 class EmotionGenerateSuggestionsRequest(BaseModel):
     """生成个性化情绪建议请求"""
-    group_id: str = Field(..., description="组ID")
-    config_id: Optional[int] = Field(None, description="配置ID（用于指定LLM模型）")
+    end_user_id: str = Field(..., description="终端用户ID")