Feature/default ontology (#424)

* [add]Create a workspace and initialize the default ontology engineering scenario

* [add]The language parameters for creating the workspace determine the default language for switching in the ontology project.

* [changes]Standardized return format

* [add]The default ontology is associated with the default configuration.

* [add]Create a workspace and initialize the default ontology engineering scenario

* [add]The language parameters for creating the workspace determine the default language for switching in the ontology project.

* [changes]Standardized return format

* [add]The default ontology is associated with the default configuration.

api/app/config/__init__.py

@@ -0,0 +1 @@
+"""Configuration module for application settings."""

api/app/config/default_ontology_config.py

@@ -0,0 +1,239 @@
+"""默认本体场景配置
+
+本模块定义系统预设的本体场景和实体类型配置。
+这些配置用于在工作空间创建时自动初始化默认场景。
+支持中英文双语配置，根据用户语言偏好创建对应语言的场景。
+"""
+
+# 在线教育场景配置
+ONLINE_EDUCATION_SCENE = {
+    "name_chinese": "在线教育",
+    "name_english": "Online Education",
+    "description_chinese": "适用于在线教育平台的本体建模，包含学生、教师、课程等核心实体类型",
+    "description_english": "Ontology modeling for online education platforms, including core entity types such as students, teachers, and courses",
+    "types": [
+        {
+            "name_chinese": "学生",
+            "name_english": "Student",
+            "description_chinese": "在教育系统中接受教育的个体，包含姓名、学号、年级、班级等属性",
+            "description_english": "Individuals receiving education in the education system, including attributes such as name, student ID, grade, and class"
+        },
+        {
+            "name_chinese": "教师",
+            "name_english": "Teacher",
+            "description_chinese": "在教育系统中提供教学服务的个体，包含姓名、工号、任教学科、职称等属性",
+            "description_english": "Individuals providing teaching services in the education system, including attributes such as name, employee ID, teaching subject, and title"
+        },
+        {
+            "name_chinese": "课程",
+            "name_english": "Course",
+            "description_chinese": "教育系统中的教学内容单元，包含课程名称、课程代码、学分、学时等属性",
+            "description_english": "Teaching content units in the education system, including attributes such as course name, course code, credits, and class hours"
+        },
+        {
+            "name_chinese": "作业",
+            "name_english": "Assignment",
+            "description_chinese": "课程中布置的学习任务，包含作业标题、截止日期、所属课程、提交状态等属性",
+            "description_english": "Learning tasks assigned in courses, including attributes such as assignment title, deadline, course, and submission status"
+        },
+        {
+            "name_chinese": "成绩",
+            "name_english": "Grade",
+            "description_chinese": "学生学习成果的评价结果，包含分数、评级、考试类型、所属课程等属性",
+            "description_english": "Evaluation results of student learning outcomes, including attributes such as score, rating, exam type, and course"
+        },
+        {
+            "name_chinese": "考试",
+            "name_english": "Exam",
+            "description_chinese": "评估学生学习成果的测试活动，包含考试名称、时间、地点、科目等属性",
+            "description_english": "Test activities to assess student learning outcomes, including attributes such as exam name, time, location, and subject"
+        },
+        {
+            "name_chinese": "教室",
+            "name_english": "Classroom",
+            "description_chinese": "进行教学活动的物理或虚拟空间，包含教室编号、容量、设备等属性",
+            "description_english": "Physical or virtual spaces for teaching activities, including attributes such as classroom number, capacity, and equipment"
+        },
+        {
+            "name_chinese": "学科",
+            "name_english": "Subject",
+            "description_chinese": "知识的分类领域，包含学科名称、代码、所属院系等属性",
+            "description_english": "Classification domains of knowledge, including attributes such as subject name, code, and department"
+        },
+        {
+            "name_chinese": "教材",
+            "name_english": "Textbook",
+            "description_chinese": "教学使用的书籍或资料，包含书名、作者、出版社、ISBN等属性",
+            "description_english": "Books or materials used for teaching, including attributes such as title, author, publisher, and ISBN"
+        },
+        {
+            "name_chinese": "班级",
+            "name_english": "Class",
+            "description_chinese": "学生的组织单位，包含班级名称、年级、人数、班主任等属性",
+            "description_english": "Organizational units of students, including attributes such as class name, grade, number of students, and class teacher"
+        },
+        {
+            "name_chinese": "学期",
+            "name_english": "Semester",
+            "description_chinese": "教学时间的划分单位，包含学期名称、开始时间、结束时间等属性",
+            "description_english": "Time division units for teaching, including attributes such as semester name, start time, and end time"
+        },
+        {
+            "name_chinese": "课时",
+            "name_english": "Class Hour",
+            "description_chinese": "课程的时间单位，包含上课时间、地点、教师、课程等属性",
+            "description_english": "Time units of courses, including attributes such as class time, location, teacher, and course"
+        },
+        {
+            "name_chinese": "教学计划",
+            "name_english": "Teaching Plan",
+            "description_chinese": "课程的教学安排，包含教学目标、内容安排、进度计划等属性",
+            "description_english": "Teaching arrangements for courses, including attributes such as teaching objectives, content arrangement, and progress plan"
+        }
+    ]
+}
+
+# 情感陪伴场景配置
+EMOTIONAL_COMPANION_SCENE = {
+    "name_chinese": "情感陪伴",
+    "name_english": "Emotional Companion",
+    "description_chinese": "适用于情感陪伴应用的本体建模，包含用户、情绪、活动等核心实体类型",
+    "description_english": "Ontology modeling for emotional companion applications, including core entity types such as users, emotions, and activities",
+    "types": [
+        {
+            "name_chinese": "用户",
+            "name_english": "User",
+            "description_chinese": "使用情感陪伴服务的个体，包含姓名、昵称、性格特征、偏好等属性",
+            "description_english": "Individuals using emotional companion services, including attributes such as name, nickname, personality traits, and preferences"
+        },
+        {
+            "name_chinese": "情绪",
+            "name_english": "Emotion",
+            "description_chinese": "用户的情感状态，包含情绪类型、强度、触发原因、持续时间等属性",
+            "description_english": "Emotional states of users, including attributes such as emotion type, intensity, trigger cause, and duration"
+        },
+        {
+            "name_chinese": "活动",
+            "name_english": "Activity",
+            "description_chinese": "用户参与的各类活动，包含活动名称、类型、参与者、时间地点等属性",
+            "description_english": "Various activities users participate in, including attributes such as activity name, type, participants, time, and location"
+        },
+        {
+            "name_chinese": "对话",
+            "name_english": "Conversation",
+            "description_chinese": "用户之间的交流记录，包含对话主题、参与者、时间、关键内容等属性",
+            "description_english": "Communication records between users, including attributes such as conversation topic, participants, time, and key content"
+        },
+        {
+            "name_chinese": "兴趣爱好",
+            "name_english": "Hobby",
+            "description_chinese": "用户的兴趣和爱好，包含爱好名称、类别、熟练程度、相关活动等属性",
+            "description_english": "User interests and hobbies, including attributes such as hobby name, category, proficiency level, and related activities"
+        },
+        {
+            "name_chinese": "日常事件",
+            "name_english": "Daily Event",
+            "description_chinese": "用户日常生活中的事件，包含事件描述、时间、地点、相关人物等属性",
+            "description_english": "Events in users' daily lives, including attributes such as event description, time, location, and related people"
+        },
+        {
+            "name_chinese": "关系",
+            "name_english": "Relationship",
+            "description_chinese": "用户之间的社会关系，包含关系类型、亲密度、建立时间等属性",
+            "description_english": "Social relationships between users, including attributes such as relationship type, intimacy, and establishment time"
+        },
+        {
+            "name_chinese": "回忆",
+            "name_english": "Memory",
+            "description_chinese": "用户的重要记忆片段，包含回忆内容、时间、地点、相关人物等属性",
+            "description_english": "Important memory fragments of users, including attributes such as memory content, time, location, and related people"
+        },
+        {
+            "name_chinese": "地点",
+            "name_english": "Location",
+            "description_chinese": "用户活动的地理位置，包含地点名称、地址、类型、相关事件等属性",
+            "description_english": "Geographic locations of user activities, including attributes such as location name, address, type, and related events"
+        },
+        {
+            "name_chinese": "时间节点",
+            "name_english": "Time Point",
+            "description_chinese": "重要的时间标记，包含日期、事件、意义等属性",
+            "description_english": "Important time markers, including attributes such as date, event, and significance"
+        },
+        {
+            "name_chinese": "目标",
+            "name_english": "Goal",
+            "description_chinese": "用户设定的目标，包含目标描述、截止时间、完成状态、相关活动等属性",
+            "description_english": "Goals set by users, including attributes such as goal description, deadline, completion status, and related activities"
+        },
+        {
+            "name_chinese": "成就",
+            "name_english": "Achievement",
+            "description_chinese": "用户获得的成就，包含成就名称、获得时间、描述、相关目标等属性",
+            "description_english": "Achievements obtained by users, including attributes such as achievement name, acquisition time, description, and related goals"
+        }
+    ]
+}
+
+# 导出默认场景列表
+DEFAULT_SCENES = [ONLINE_EDUCATION_SCENE, EMOTIONAL_COMPANION_SCENE]
+
+
+def get_scene_name(scene_config: dict, language: str = "zh") -> str:
+    """获取场景名称（根据语言）
+    
+    Args:
+        scene_config: 场景配置字典
+        language: 语言类型 ("zh" 或 "en")
+        
+    Returns:
+        对应语言的场景名称
+    """
+    if language == "en":
+        return scene_config.get("name_english", scene_config.get("name_chinese"))
+    return scene_config.get("name_chinese")
+
+
+def get_scene_description(scene_config: dict, language: str = "zh") -> str:
+    """获取场景描述（根据语言）
+    
+    Args:
+        scene_config: 场景配置字典
+        language: 语言类型 ("zh" 或 "en")
+        
+    Returns:
+        对应语言的场景描述
+    """
+    if language == "en":
+        return scene_config.get("description_english", scene_config.get("description_chinese"))
+    return scene_config.get("description_chinese")
+
+
+def get_type_name(type_config: dict, language: str = "zh") -> str:
+    """获取类型名称（根据语言）
+    
+    Args:
+        type_config: 类型配置字典
+        language: 语言类型 ("zh" 或 "en")
+        
+    Returns:
+        对应语言的类型名称
+    """
+    if language == "en":
+        return type_config.get("name_english", type_config.get("name_chinese"))
+    return type_config.get("name_chinese")
+
+
+def get_type_description(type_config: dict, language: str = "zh") -> str:
+    """获取类型描述（根据语言）
+    
+    Args:
+        type_config: 类型配置字典
+        language: 语言类型 ("zh" 或 "en")
+        
+    Returns:
+        对应语言的类型描述
+    """
+    if language == "en":
+        return type_config.get("description_english", type_config.get("description_chinese"))
+    return type_config.get("description_chinese")

api/app/config/default_ontology_initializer.py

@@ -0,0 +1,249 @@
+# -*- coding: utf-8 -*-
+"""默认本体场景初始化器
+
+本模块提供默认本体场景和类型的自动初始化功能。
+在工作空间创建时，自动添加预设的本体场景和实体类型。
+
+Classes:
+    DefaultOntologyInitializer: 默认本体场景初始化器
+"""
+
+import logging
+from typing import List, Optional, Tuple
+from uuid import UUID
+
+from sqlalchemy.orm import Session
+
+from app.config.default_ontology_config import (
+    DEFAULT_SCENES,
+    get_scene_name,
+    get_scene_description,
+    get_type_name,
+    get_type_description,
+)
+from app.core.logging_config import get_business_logger
+from app.repositories.ontology_scene_repository import OntologySceneRepository
+from app.repositories.ontology_class_repository import OntologyClassRepository
+
+
+class DefaultOntologyInitializer:
+    """默认本体场景初始化器
+    
+    负责在工作空间创建时自动初始化默认的本体场景和类型。
+    遵循最小侵入原则，确保初始化失败不阻止工作空间创建。
+    
+    Attributes:
+        db: 数据库会话
+        scene_repo: 场景Repository
+        class_repo: 类型Repository
+        logger: 业务日志记录器
+    """
+    
+    def __init__(self, db: Session):
+        """初始化
+        
+        Args:
+            db: 数据库会话
+        """
+        self.db = db
+        self.scene_repo = OntologySceneRepository(db)
+        self.class_repo = OntologyClassRepository(db)
+        self.logger = get_business_logger()
+    
+    def initialize_default_scenes(
+        self,
+        workspace_id: UUID,
+        language: str = "zh"
+    ) -> Tuple[bool, str]:
+        """为工作空间初始化默认场景
+        
+        创建两个默认场景（在线教育、情感陪伴）及其对应的实体类型。
+        如果创建失败，记录错误日志但不抛出异常。
+        
+        Args:
+            workspace_id: 工作空间ID
+            language: 语言类型 ("zh" 或 "en")，默认为 "zh"
+            
+        Returns:
+            Tuple[bool, str]: (是否成功, 错误信息)
+        """
+        try:
+            self.logger.info(
+                f"开始初始化默认本体场景 - workspace_id={workspace_id}, language={language}"
+            )
+            
+            scenes_created = 0
+            total_types_created = 0
+            
+            # 遍历默认场景配置
+            for scene_config in DEFAULT_SCENES:
+                scene_name = get_scene_name(scene_config, language)
+                
+                # 创建场景及其类型
+                scene_id = self._create_scene_with_types(workspace_id, scene_config, language)
+                
+                if scene_id:
+                    scenes_created += 1
+                    # 统计类型数量
+                    types_count = len(scene_config.get("types", []))
+                    total_types_created += types_count
+                    
+                    self.logger.info(
+                        f"场景创建成功 - scene_name={scene_name}, "
+                        f"scene_id={scene_id}, types_count={types_count}, language={language}"
+                    )
+                else:
+                    self.logger.warning(
+                        f"场景创建失败 - scene_name={scene_name}, "
+                        f"workspace_id={workspace_id}, language={language}"
+                    )
+            
+            # 记录总体结果
+            self.logger.info(
+                f"默认场景初始化完成 - workspace_id={workspace_id}, "
+                f"language={language}, scenes_created={scenes_created}, "
+                f"total_types_created={total_types_created}"
+            )
+            
+            # 如果至少创建了一个场景，视为成功
+            if scenes_created > 0:
+                return True, ""
+            else:
+                error_msg = "所有默认场景创建失败"
+                self.logger.error(
+                    f"默认场景初始化失败 - workspace_id={workspace_id}, "
+                    f"language={language}, error={error_msg}"
+                )
+                return False, error_msg
+                
+        except Exception as e:
+            error_msg = f"默认场景初始化异常: {str(e)}"
+            self.logger.error(
+                f"默认场景初始化异常 - workspace_id={workspace_id}, "
+                f"language={language}, error={str(e)}",
+                exc_info=True
+            )
+            return False, error_msg
+    
+    def _create_scene_with_types(
+        self,
+        workspace_id: UUID,
+        scene_config: dict,
+        language: str = "zh"
+    ) -> Optional[UUID]:
+        """创建场景及其类型
+        
+        Args:
+            workspace_id: 工作空间ID
+            scene_config: 场景配置字典
+            language: 语言类型 ("zh" 或 "en")
+            
+        Returns:
+            Optional[UUID]: 创建的场景ID，失败返回None
+        """
+        try:
+            scene_name = get_scene_name(scene_config, language)
+            scene_description = get_scene_description(scene_config, language)
+            
+            # 检查是否已存在同名场景（支持向后兼容）
+            existing_scene = self.scene_repo.get_by_name(scene_name, workspace_id)
+            if existing_scene:
+                self.logger.info(
+                    f"场景已存在，跳过创建 - scene_name={scene_name}, "
+                    f"workspace_id={workspace_id}, scene_id={existing_scene.scene_id}, "
+                    f"language={language}"
+                )
+                return None
+            
+            # 创建场景记录，设置 is_system_default=true
+            scene_data = {
+                "scene_name": scene_name,
+                "scene_description": scene_description
+            }
+            
+            scene = self.scene_repo.create(scene_data, workspace_id)
+            
+            # 设置系统默认标识
+            scene.is_system_default = True
+            self.db.flush()
+            
+            self.logger.info(
+                f"场景创建成功 - scene_name={scene_name}, "
+                f"scene_id={scene.scene_id}, is_system_default=True, language={language}"
+            )
+            
+            # 批量创建类型
+            types_config = scene_config.get("types", [])
+            types_created = self._batch_create_types(scene.scene_id, types_config, language)
+            
+            self.logger.info(
+                f"场景类型创建完成 - scene_id={scene.scene_id}, "
+                f"types_created={types_created}/{len(types_config)}, language={language}"
+            )
+            
+            return scene.scene_id
+            
+        except Exception as e:
+            scene_name = get_scene_name(scene_config, language)
+            self.logger.error(
+                f"场景创建失败 - scene_name={scene_name}, "
+                f"workspace_id={workspace_id}, language={language}, error={str(e)}",
+                exc_info=True
+            )
+            return None
+    
+    def _batch_create_types(
+        self,
+        scene_id: UUID,
+        types_config: List[dict],
+        language: str = "zh"
+    ) -> int:
+        """批量创建实体类型
+        
+        Args:
+            scene_id: 场景ID
+            types_config: 类型配置列表
+            language: 语言类型 ("zh" 或 "en")
+            
+        Returns:
+            int: 成功创建的类型数量
+        """
+        created_count = 0
+        
+        for type_config in types_config:
+            try:
+                type_name = get_type_name(type_config, language)
+                type_description = get_type_description(type_config, language)
+                
+                # 创建类型数据
+                class_data = {
+                    "class_name": type_name,
+                    "class_description": type_description
+                }
+                
+                # 创建类型
+                ontology_class = self.class_repo.create(class_data, scene_id)
+                
+                # 设置系统默认标识
+                ontology_class.is_system_default = True
+                self.db.flush()
+                
+                created_count += 1
+                
+                self.logger.debug(
+                    f"类型创建成功 - class_name={type_name}, "
+                    f"class_id={ontology_class.class_id}, "
+                    f"scene_id={scene_id}, is_system_default=True, language={language}"
+                )
+                
+            except Exception as e:
+                type_name = get_type_name(type_config, language)
+                self.logger.warning(
+                    f"单个类型创建失败，继续创建其他类型 - "
+                    f"class_name={type_name}, scene_id={scene_id}, "
+                    f"language={language}, error={str(e)}"
+                )
+                # 继续创建其他类型
+                continue
+        
+        return created_count

api/app/controllers/memory_agent_controller.py

@@ -633,11 +633,11 @@ async def get_knowledge_type_stats_api(
     current_user: User = Depends(get_current_user)
 ):
     """
-    统计当前空间下各知识库类型的数量，包含 General | Web | Third-party | Folder | memory。
+    统计当前空间下各知识库类型的数量，包含 General | Web | Third-party | Folder | Memory。
     会对缺失类型补 0，返回字典形式。
     可选按状态过滤。
     - 知识库类型根据当前用户的 current_workspace_id 过滤
-    - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (end_user_id) 过滤
+    - Memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (end_user_id) 过滤
     - 如果用户没有当前工作空间或未提供 end_user_id，对应的统计返回 0
     """
     api_logger.info(f"Knowledge type stats requested for workspace_id: {current_user.current_workspace_id}, end_user_id: {end_user_id}")

api/app/controllers/ontology_controller.py

@@ -31,7 +31,7 @@ from sqlalchemy.orm import Session
 from app.core.config import settings
 from app.core.error_codes import BizCode
 from app.core.language_utils import get_language_from_header
-from app.core.logging_config import get_api_logger
+from app.core.logging_config import get_api_logger, get_business_logger
 from app.core.response_utils import fail, success
 from app.db import get_db
 from app.dependencies import get_current_user
@@ -61,6 +61,7 @@ from app.repositories.ontology_scene_repository import OntologySceneRepository
 
 
 api_logger = get_api_logger()
+business_logger = get_business_logger()
 logger = logging.getLogger(__name__)
 
 router = APIRouter(
@@ -399,6 +400,20 @@ async def update_scene(
             api_logger.warning(f"User {current_user.id} has no current workspace")
             return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
         
+        # 检查是否为系统默认场景
+        scene_repo = OntologySceneRepository(db)
+        scene = scene_repo.get_by_id(scene_uuid)
+        if scene and scene.is_system_default:
+            business_logger.warning(
+                f"尝试修改系统默认场景: user_id={current_user.id}, "
+                f"scene_id={scene_id}, scene_name={scene.scene_name}"
+            )
+            return fail(
+                BizCode.BAD_REQUEST,
+                "系统默认场景不可修改",
+                "该场景为系统预设场景，不允许修改"
+            )
+        
         # 创建OntologyService实例
         from app.core.memory.llm_tools.openai_client import OpenAIClient
         from app.core.models.base import RedBearModelConfig
@@ -491,6 +506,20 @@ async def delete_scene(
             api_logger.warning(f"User {current_user.id} has no current workspace")
             return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
         
+        # 检查是否为系统默认场景
+        scene_repo = OntologySceneRepository(db)
+        scene = scene_repo.get_by_id(scene_uuid)
+        if scene and scene.is_system_default:
+            business_logger.warning(
+                f"尝试删除系统默认场景: user_id={current_user.id}, "
+                f"scene_id={scene_id}, scene_name={scene.scene_name}"
+            )
+            return fail(
+                BizCode.BAD_REQUEST,
+                "系统默认场景不可删除",
+                "该场景为系统预设场景，不允许删除"
+            )
+        
         # 创建OntologyService实例
         from app.core.memory.llm_tools.openai_client import OpenAIClient
         from app.core.models.base import RedBearModelConfig

api/app/controllers/ontology_secondary_routes.py

@@ -11,7 +11,7 @@ from fastapi import Depends
 from sqlalchemy.orm import Session
 
 from app.core.error_codes import BizCode
-from app.core.logging_config import get_api_logger
+from app.core.logging_config import get_api_logger, get_business_logger
 from app.core.response_utils import fail, success
 from app.db import get_db
 from app.dependencies import get_current_user
@@ -30,9 +30,11 @@ from app.schemas.response_schema import ApiResponse
 from app.services.ontology_service import OntologyService
 from app.core.memory.llm_tools.openai_client import OpenAIClient
 from app.core.models.base import RedBearModelConfig
+from app.repositories.ontology_class_repository import OntologyClassRepository
 
 
 api_logger = get_api_logger()
+business_logger = get_business_logger()
 
 
 def _get_dummy_ontology_service(db: Session) -> OntologyService:
@@ -366,6 +368,20 @@ async def update_class_handler(
             api_logger.warning(f"User {current_user.id} has no current workspace")
             return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
         
+        # 检查是否为系统默认类型
+        class_repo = OntologyClassRepository(db)
+        ontology_class = class_repo.get_by_id(class_uuid)
+        if ontology_class and ontology_class.is_system_default:
+            business_logger.warning(
+                f"尝试修改系统默认类型: user_id={current_user.id}, "
+                f"class_id={class_id}, class_name={ontology_class.class_name}"
+            )
+            return fail(
+                BizCode.BAD_REQUEST,
+                "系统默认类型不可修改",
+                "该类型为系统预设类型，不允许修改"
+            )
+        
         # 创建Service
         service = _get_dummy_ontology_service(db)
         
@@ -429,6 +445,20 @@ async def delete_class_handler(
             api_logger.warning(f"User {current_user.id} has no current workspace")
             return fail(BizCode.BAD_REQUEST, "请求参数无效", "当前用户没有工作空间")
         
+        # 检查是否为系统默认类型
+        class_repo = OntologyClassRepository(db)
+        ontology_class = class_repo.get_by_id(class_uuid)
+        if ontology_class and ontology_class.is_system_default:
+            business_logger.warning(
+                f"尝试删除系统默认类型: user_id={current_user.id}, "
+                f"class_id={class_id}, class_name={ontology_class.class_name}"
+            )
+            return fail(
+                BizCode.BAD_REQUEST,
+                "系统默认类型不可删除",
+                "该类型为系统预设类型，不允许删除"
+            )
+        
         # 创建Service
         service = _get_dummy_ontology_service(db)
         

api/app/controllers/workspace_controller.py

@@ -1,7 +1,7 @@
 import uuid
 from typing import List, Optional
 
-from fastapi import APIRouter, Depends, HTTPException, Query, status
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, status
 from sqlalchemy.orm import Session
 
 from app.core.logging_config import get_api_logger
@@ -95,16 +95,29 @@ def get_workspaces(
 @router.post("", response_model=ApiResponse)
 def create_workspace(
     workspace: WorkspaceCreate,
+    language_type: str = Header(default="zh", alias="X-Language-Type"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_superuser),
 ):
     """创建新的工作空间"""
-    api_logger.info(f"用户 {current_user.username} 请求创建工作空间: {workspace.name}")
+    from app.core.language_utils import get_language_from_header
+    
+    # 验证并获取语言参数
+    language = get_language_from_header(language_type)
+    
+    api_logger.info(
+        f"用户 {current_user.username} 请求创建工作空间: {workspace.name}, "
+        f"language={language}"
+    )
 
     result = workspace_service.create_workspace(
-        db=db, workspace=workspace, user=current_user)
+        db=db, workspace=workspace, user=current_user, language=language
+    )
 
-    api_logger.info(f"工作空间创建成功 - 名称: {workspace.name}, ID: {result.id}, 创建者: {current_user.username}")
+    api_logger.info(
+        f"工作空间创建成功 - 名称: {workspace.name}, ID: {result.id}, "
+        f"创建者: {current_user.username}, language={language}"
+    )
     result_schema = WorkspaceResponse.model_validate(result)
     return success(data=result_schema, msg="工作空间创建成功")
 

api/app/models/ontology_class.py

@@ -9,7 +9,7 @@ Classes:
 
 import datetime
 import uuid
-from sqlalchemy import Column, String, DateTime, Text, ForeignKey
+from sqlalchemy import Column, String, DateTime, Text, ForeignKey, Boolean
 from sqlalchemy.dialects.postgresql import UUID
 from sqlalchemy.orm import relationship
 from app.db import Base
@@ -25,6 +25,9 @@ class OntologyClass(Base):
     # 类型信息
     class_name = Column(String(200), nullable=False, comment="类型名称")
     class_description = Column(Text, nullable=True, comment="类型描述")
+    
+    # 系统默认标识
+    is_system_default = Column(Boolean, default=False, nullable=False, comment="是否为系统默认类型")
 
     # 外键：关联到本体场景
     scene_id = Column(UUID(as_uuid=True), ForeignKey("ontology_scene.scene_id", ondelete="CASCADE"), nullable=False, index=True, comment="所属场景ID")

api/app/models/ontology_scene.py

@@ -9,7 +9,7 @@ Classes:
 
 import datetime
 import uuid
-from sqlalchemy import Column, String, DateTime, Integer, Text, ForeignKey, UniqueConstraint
+from sqlalchemy import Column, String, DateTime, Integer, Text, ForeignKey, UniqueConstraint, Boolean
 from sqlalchemy.dialects.postgresql import UUID
 from sqlalchemy.orm import relationship
 from app.db import Base
@@ -28,6 +28,9 @@ class OntologyScene(Base):
     # 场景信息
     scene_name = Column(String(200), nullable=False, comment="场景名称")
     scene_description = Column(Text, nullable=True, comment="场景描述")
+    
+    # 系统默认标识
+    is_system_default = Column(Boolean, default=False, nullable=False, index=True, comment="是否为系统默认场景")
 
     # 外键：关联到工作空间
     workspace_id = Column(UUID(as_uuid=True), ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False, index=True, comment="所属工作空间ID")

api/app/services/memory_agent_service.py

@@ -816,11 +816,11 @@ class MemoryAgentService:
         """
         统计知识库类型分布，包含：
         1. PostgreSQL 中的知识库类型：General, Web, Third-party, Folder（根据 workspace_id 过滤）
-        2. Neo4j 中的 memory 类型（仅统计 Chunk 数量，根据 end_user_id/end_user_id 过滤）
+        2. Neo4j 中的 Memory 类型（仅统计 Chunk 数量，根据 end_user_id/end_user_id 过滤）
         3. total: 所有类型的总和
 
         参数：
-        - end_user_id: 用户组ID（可选，未提供时 memory 统计为 0）
+        - end_user_id: 用户组ID（可选，未提供时 Memory 统计为 0）
         - only_active: 是否仅统计有效记录
         - current_workspace_id: 当前工作空间ID（可选，未提供时知识库统计为 0）
         - db: 数据库会话
@@ -831,7 +831,7 @@ class MemoryAgentService:
             "Web": count,
             "Third-party": count,
             "Folder": count,
-            "memory": chunk_count,
+            "Memory": chunk_count,
             "total": sum_of_all
         }
         """
@@ -912,17 +912,17 @@ class MemoryAgentService:
                     total_chunks += chunk_count
                     logger.debug(f"EndUser {end_user_id_str} Chunk数量: {chunk_count}")
 
-                result["memory"] = total_chunks
+                result["Memory"] = total_chunks
                 logger.info(f"Neo4j memory统计成功: 总Chunk数={total_chunks}, 宿主数={len(end_users)}")
             else:
                 # 没有 workspace_id 时，返回 0
-                result["memory"] = 0
+                result["Memory"] = 0
                 logger.info("未提供 workspace_id，memory 统计为 0")
 
         except Exception as e:
             logger.error(f"Neo4j memory统计失败: {e}", exc_info=True)
             # 如果 Neo4j 查询失败，memory 设为 0
-            result["memory"] = 0
+            result["Memory"] = 0
 
         # 3. 计算知识库类型总和（不包括 memory）
         result["total"] = (

api/app/services/workspace_service.py

@@ -30,6 +30,7 @@ from app.schemas.workspace_schema import (
     WorkspaceModelsUpdate,
     WorkspaceUpdate,
 )
+from app.config.default_ontology_initializer import DefaultOntologyInitializer
 
 # 获取业务逻辑专用日志器
 business_logger = get_business_logger()
@@ -129,7 +130,7 @@ def _create_workspace_only(
         raise
 
 def create_workspace(
-        db: Session, workspace: WorkspaceCreate, user: User
+        db: Session, workspace: WorkspaceCreate, user: User, language: str = "zh"
 ) -> Workspace:
     business_logger.info(
         f"创建工作空间: {workspace.name}, 创建者: {user.username}, "
@@ -145,10 +146,68 @@ def create_workspace(
             db=db, workspace=workspace, tenant_id=user.tenant_id
         )
         business_logger.info(f"工作空间创建成功: {db_workspace.name} (ID: {db_workspace.id}), 创建者: {user.username}")
-        db.commit()
+        db.flush()  # 使用 flush 而不是 commit，获取 ID 但不提交事务
         db.refresh(db_workspace)
 
+        # Initialize default ontology scenes for the workspace (先创建本体场景)
+        default_scene_id = None
+        try:
+            initializer = DefaultOntologyInitializer(db)
+            success, error_msg = initializer.initialize_default_scenes(
+                db_workspace.id, language=language
+            )
+            
+            if success:
+                business_logger.info(
+                    f"为工作空间 {db_workspace.id} 创建默认本体场景成功 (language={language})"
+                )
+                
+                # 获取默认场景ID，优先使用"在线教育"场景，如果不存在则使用"情感陪伴"场景
+                from app.repositories.ontology_scene_repository import OntologySceneRepository
+                from app.config.default_ontology_config import (
+                    ONLINE_EDUCATION_SCENE, 
+                    EMOTIONAL_COMPANION_SCENE,
+                    get_scene_name
+                )
+                
+                scene_repo = OntologySceneRepository(db)
+                
+                # 优先尝试获取教育场景
+                education_scene_name = get_scene_name(ONLINE_EDUCATION_SCENE, language)
+                education_scene = scene_repo.get_by_name(education_scene_name, db_workspace.id)
+                
+                if education_scene:
+                    default_scene_id = education_scene.scene_id
+                    business_logger.info(
+                        f"获取到教育场景ID用于默认记忆配置: {default_scene_id} (scene_name={education_scene_name})"
+                    )
+                else:
+                    # 如果教育场景不存在，尝试获取情感陪伴场景
+                    companion_scene_name = get_scene_name(EMOTIONAL_COMPANION_SCENE, language)
+                    companion_scene = scene_repo.get_by_name(companion_scene_name, db_workspace.id)
+                    
+                    if companion_scene:
+                        default_scene_id = companion_scene.scene_id
+                        business_logger.info(
+                            f"教育场景不存在，使用情感陪伴场景ID用于默认记忆配置: {default_scene_id} (scene_name={companion_scene_name})"
+                        )
+                    else:
+                        business_logger.warning(
+                            f"未找到任何默认场景 (education={education_scene_name}, companion={companion_scene_name})"
+                        )
+            else:
+                business_logger.warning(
+                    f"为工作空间 {db_workspace.id} 创建默认本体场景失败: {error_msg} (language={language})"
+                )
+        except Exception as ontology_error:
+            business_logger.error(
+                f"为工作空间 {db_workspace.id} 创建默认本体场景异常: {str(ontology_error)} (language={language})"
+            )
+            # Don't fail workspace creation if default ontology initialization fails
+            # The workspace can still function without default ontology scenes
+
         # Create default memory config for the workspace (only for neo4j storage types)
+        # 将默认场景ID（教育场景或情感陪伴场景）关联到记忆配置
         if workspace.storage_type == 'neo4j':
             try:
                 _create_default_memory_config(
@@ -158,9 +217,10 @@ def create_workspace(
                     llm_id=llm,
                     embedding_id=embedding,
                     rerank_id=rerank,
+                    scene_id=default_scene_id,  # 传入默认场景ID（优先教育场景，其次情感陪伴场景）
                 )
                 business_logger.info(
-                    f"为工作空间 {db_workspace.id} 创建默认记忆配置成功"
+                    f"为工作空间 {db_workspace.id} 创建默认记忆配置成功 (scene_id={default_scene_id})"
                 )
             except Exception as mc_error:
                 business_logger.error(
@@ -209,7 +269,6 @@ def create_workspace(
                     db=db,
                     knowledge=knowledge_data
                 )
-                db.commit()
                 business_logger.info(
                     f"为工作空间 {db_workspace.id} 自动创建知识库成功: "
                     f"{db_knowledge.name} (ID: {db_knowledge.id})"
@@ -224,6 +283,12 @@ def create_workspace(
                     BizCode.INTERNAL_ERROR
                 )
 
+        # 统一提交所有更改
+        db.commit()
+        business_logger.info(
+            f"工作空间 {db_workspace.id} 及相关资源创建完成并已提交"
+        )
+        
         return db_workspace
 
     except Exception as e:
@@ -919,6 +984,43 @@ def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None:
             f"Workspace {workspace.id} missing default memory config, creating one"
         )
         
+        # 尝试获取默认场景ID，优先教育场景，其次情感陪伴场景
+        default_scene_id = None
+        try:
+            from app.repositories.ontology_scene_repository import OntologySceneRepository
+            from app.config.default_ontology_config import (
+                ONLINE_EDUCATION_SCENE,
+                EMOTIONAL_COMPANION_SCENE,
+                get_scene_name
+            )
+            
+            scene_repo = OntologySceneRepository(db)
+            # 尝试中文和英文场景名称
+            for language in ["zh", "en"]:
+                # 优先尝试教育场景
+                education_scene_name = get_scene_name(ONLINE_EDUCATION_SCENE, language)
+                education_scene = scene_repo.get_by_name(education_scene_name, workspace.id)
+                if education_scene:
+                    default_scene_id = education_scene.scene_id
+                    business_logger.info(
+                        f"找到教育场景用于默认记忆配置: scene_id={default_scene_id}, scene_name={education_scene_name}"
+                    )
+                    break
+                
+                # 如果教育场景不存在，尝试情感陪伴场景
+                companion_scene_name = get_scene_name(EMOTIONAL_COMPANION_SCENE, language)
+                companion_scene = scene_repo.get_by_name(companion_scene_name, workspace.id)
+                if companion_scene:
+                    default_scene_id = companion_scene.scene_id
+                    business_logger.info(
+                        f"教育场景不存在，找到情感陪伴场景用于默认记忆配置: scene_id={default_scene_id}, scene_name={companion_scene_name}"
+                    )
+                    break
+        except Exception as scene_error:
+            business_logger.warning(
+                f"获取默认场景失败，将创建不关联场景的记忆配置: {str(scene_error)}"
+            )
+        
         try:
             _create_default_memory_config(
                 db=db,
@@ -927,6 +1029,7 @@ def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None:
                 llm_id=uuid.UUID(workspace.llm) if workspace.llm else None,
                 embedding_id=uuid.UUID(workspace.embedding) if workspace.embedding else None,
                 rerank_id=uuid.UUID(workspace.rerank) if workspace.rerank else None,
+                scene_id=default_scene_id,  # 传入默认场景ID（优先教育场景，其次情感陪伴场景）
             )
         except Exception as e:
             business_logger.error(
@@ -1008,6 +1111,7 @@ def _create_default_memory_config(
     llm_id: Optional[uuid.UUID] = None,
     embedding_id: Optional[uuid.UUID] = None,
     rerank_id: Optional[uuid.UUID] = None,
+    scene_id: Optional[uuid.UUID] = None,
 ) -> None:
     """Create a default memory config for a newly created workspace.
     
@@ -1018,6 +1122,7 @@ def _create_default_memory_config(
         llm_id: Optional LLM model ID
         embedding_id: Optional embedding model ID
         rerank_id: Optional rerank model ID
+        scene_id: Optional ontology scene ID (默认关联教育场景)
     """
     from app.models.memory_config_model import MemoryConfig
     
@@ -1031,12 +1136,13 @@ def _create_default_memory_config(
         llm_id=str(llm_id) if llm_id else None,
         embedding_id=str(embedding_id) if embedding_id else None,
         rerank_id=str(rerank_id) if rerank_id else None,
+        scene_id=scene_id,  # 关联本体场景ID
         state=True,  # Active by default
         is_default=True,  # Mark as workspace default
     )
     
     db.add(default_config)
-    db.commit()
+    db.flush()  # 使用 flush 而不是 commit，让调用者统一提交
     
     business_logger.info(
         "Created default memory config for workspace",
@@ -1044,5 +1150,6 @@ def _create_default_memory_config(
             "workspace_id": str(workspace_id),
             "config_id": str(config_id),
             "config_name": default_config.config_name,
+            "scene_id": str(scene_id) if scene_id else None,
         }
     )