Merge pull request #141 from SuanmoSuanyangTechnology/fix/web_zy

Fix/web zy

.gitignore

@@ -21,7 +21,6 @@ examples/
 
 # Temporary outputs
 .DS_Store
-.hypothesis/
 time.log
 celerybeat-schedule.db
 search_results.json
@@ -36,5 +35,3 @@ nltk_data/
 tika-server*.jar*
 cl100k_base.tiktoken
 libssl*.deb
-
-sandbox/lib/seccomp_redbear/target

README.md

@@ -334,13 +334,7 @@ step6: Log In to the Frontend Interface.
 ## License
 This project is licensed under the Apache License 2.0. For details, see the LICENSE file.
 
-## Community & Support
-
-Join our community to ask questions, share your work, and connect with fellow developers.
-
-- **GitHub Issues**: Report bugs, request features, or track known issues via [GitHub Issues](https://github.com/SuanmoSuanyangTechnology/MemoryBear/issues).
-- **GitHub Pull Requests**: Contribute code improvements or fixes through [Pull Requests](https://github.com/SuanmoSuanyangTechnology/MemoryBear/pulls).
-- **GitHub Discussions**: Ask questions, share ideas, and engage with the community in [GitHub Discussions](https://github.com/SuanmoSuanyangTechnology/MemoryBear/discussions).
-- **WeChat**: Scan the QR code below to join our WeChat community group.
-- ![wecom-temp-114020-47fe87a75da439f09f5dc93a01593046](https://github.com/user-attachments/assets/8c81885c-4134-40d5-96e2-7f78cc082dc6)
-- **Contact**: If you are interested in contributing or collaborating, feel free to reach out at tianyou_hubm@redbearai.com
+## Acknowledgements & Community
+- Feedback & Issues: Please submit an Issue in the repository for bug reports or discussions.
+- Contributions Welcome: When submitting a Pull Request, please create a feature branch and follow conventional commit message guidelines.
+- Contact: If you are interested in contributing or collaborating, feel free to reach out at tianyou_hubm@redbearai.com

api/app/cache/__init__.py

@@ -1,11 +0,0 @@
-"""
-Cache 缓存模块
-
-提供各种缓存功能的统一入口
-"""
-from .memory import EmotionMemoryCache, ImplicitMemoryCache
-
-__all__ = [
-    "EmotionMemoryCache",
-    "ImplicitMemoryCache",
-]

api/app/cache/memory/__init__.py

@@ -1,12 +0,0 @@
-"""
-Memory 缓存模块
-
-提供记忆系统相关的缓存功能
-"""
-from .emotion_memory import EmotionMemoryCache
-from .implicit_memory import ImplicitMemoryCache
-
-__all__ = [
-    "EmotionMemoryCache",
-    "ImplicitMemoryCache",
-]

api/app/cache/memory/emotion_memory.py

@@ -1,134 +0,0 @@
-"""
-Emotion Suggestions Cache
-
-情绪个性化建议缓存模块
-用于缓存用户的情绪个性化建议数据
-"""
-import json
-import logging
-from typing import Optional, Dict, Any
-from datetime import datetime
-
-from app.aioRedis import aio_redis
-
-logger = logging.getLogger(__name__)
-
-
-class EmotionMemoryCache:
-    """情绪建议缓存类"""
-    
-    # Key 前缀
-    PREFIX = "cache:memory:emotion_memory"
-    
-    @classmethod
-    def _get_key(cls, *parts: str) -> str:
-        """生成 Redis key
-        
-        Args:
-            *parts: key 的各个部分
-            
-        Returns:
-            完整的 Redis key
-        """
-        return ":".join([cls.PREFIX] + list(parts))
-    
-    @classmethod
-    async def set_emotion_suggestions(
-        cls,
-        user_id: str,
-        suggestions_data: Dict[str, Any],
-        expire: int = 86400
-    ) -> bool:
-        """设置用户情绪建议缓存
-        
-        Args:
-            user_id: 用户ID（end_user_id）
-            suggestions_data: 建议数据字典，包含：
-                - health_summary: 健康状态摘要
-                - suggestions: 建议列表
-                - generated_at: 生成时间（可选）
-            expire: 过期时间（秒），默认24小时（86400秒）
-            
-        Returns:
-            是否设置成功
-        """
-        try:
-            key = cls._get_key("suggestions", user_id)
-            
-            # 添加生成时间戳
-            if "generated_at" not in suggestions_data:
-                suggestions_data["generated_at"] = datetime.now().isoformat()
-            
-            # 添加缓存标记
-            suggestions_data["cached"] = True
-            
-            value = json.dumps(suggestions_data, ensure_ascii=False)
-            await aio_redis.set(key, value, ex=expire)
-            logger.info(f"设置情绪建议缓存成功: {key}, 过期时间: {expire}秒")
-            return True
-        except Exception as e:
-            logger.error(f"设置情绪建议缓存失败: {e}", exc_info=True)
-            return False
-    
-    @classmethod
-    async def get_emotion_suggestions(cls, user_id: str) -> Optional[Dict[str, Any]]:
-        """获取用户情绪建议缓存
-        
-        Args:
-            user_id: 用户ID（end_user_id）
-            
-        Returns:
-            建议数据字典，如果不存在或已过期返回 None
-        """
-        try:
-            key = cls._get_key("suggestions", user_id)
-            value = await aio_redis.get(key)
-            
-            if value:
-                data = json.loads(value)
-                logger.info(f"成功获取情绪建议缓存: {key}")
-                return data
-            
-            logger.info(f"情绪建议缓存不存在或已过期: {key}")
-            return None
-        except Exception as e:
-            logger.error(f"获取情绪建议缓存失败: {e}", exc_info=True)
-            return None
-    
-    @classmethod
-    async def delete_emotion_suggestions(cls, user_id: str) -> bool:
-        """删除用户情绪建议缓存
-        
-        Args:
-            user_id: 用户ID（end_user_id）
-            
-        Returns:
-            是否删除成功
-        """
-        try:
-            key = cls._get_key("suggestions", user_id)
-            result = await aio_redis.delete(key)
-            logger.info(f"删除情绪建议缓存: {key}, 结果: {result}")
-            return result > 0
-        except Exception as e:
-            logger.error(f"删除情绪建议缓存失败: {e}", exc_info=True)
-            return False
-    
-    @classmethod
-    async def get_suggestions_ttl(cls, user_id: str) -> int:
-        """获取情绪建议缓存的剩余过期时间
-        
-        Args:
-            user_id: 用户ID（end_user_id）
-            
-        Returns:
-            剩余秒数，-1表示永不过期，-2表示key不存在
-        """
-        try:
-            key = cls._get_key("suggestions", user_id)
-            ttl = await aio_redis.ttl(key)
-            logger.debug(f"情绪建议缓存TTL: {key} = {ttl}秒")
-            return ttl
-        except Exception as e:
-            logger.error(f"获取情绪建议缓存TTL失败: {e}")
-            return -2

api/app/cache/memory/implicit_memory.py

@@ -1,136 +0,0 @@
-"""
-Implicit Memory Profile Cache
-
-隐式记忆用户画像缓存模块
-用于缓存用户的完整画像数据（偏好标签、四维画像、兴趣领域、行为习惯）
-"""
-import json
-import logging
-from typing import Optional, Dict, Any
-from datetime import datetime
-
-from app.aioRedis import aio_redis
-
-logger = logging.getLogger(__name__)
-
-
-class ImplicitMemoryCache:
-    """隐式记忆用户画像缓存类"""
-    
-    # Key 前缀
-    PREFIX = "cache:memory:implicit_memory"
-    
-    @classmethod
-    def _get_key(cls, *parts: str) -> str:
-        """生成 Redis key
-        
-        Args:
-            *parts: key 的各个部分
-            
-        Returns:
-            完整的 Redis key
-        """
-        return ":".join([cls.PREFIX] + list(parts))
-    
-    @classmethod
-    async def set_user_profile(
-        cls,
-        user_id: str,
-        profile_data: Dict[str, Any],
-        expire: int = 86400
-    ) -> bool:
-        """设置用户完整画像缓存
-        
-        Args:
-            user_id: 用户ID（end_user_id）
-            profile_data: 画像数据字典，包含：
-                - preferences: 偏好标签列表
-                - portrait: 四维画像对象
-                - interest_areas: 兴趣领域分布对象
-                - habits: 行为习惯列表
-                - generated_at: 生成时间（可选）
-            expire: 过期时间（秒），默认24小时（86400秒）
-            
-        Returns:
-            是否设置成功
-        """
-        try:
-            key = cls._get_key("profile", user_id)
-            
-            # 添加生成时间戳
-            if "generated_at" not in profile_data:
-                profile_data["generated_at"] = datetime.now().isoformat()
-            
-            # 添加缓存标记
-            profile_data["cached"] = True
-            
-            value = json.dumps(profile_data, ensure_ascii=False)
-            await aio_redis.set(key, value, ex=expire)
-            logger.info(f"设置用户画像缓存成功: {key}, 过期时间: {expire}秒")
-            return True
-        except Exception as e:
-            logger.error(f"设置用户画像缓存失败: {e}", exc_info=True)
-            return False
-    
-    @classmethod
-    async def get_user_profile(cls, user_id: str) -> Optional[Dict[str, Any]]:
-        """获取用户完整画像缓存
-        
-        Args:
-            user_id: 用户ID（end_user_id）
-            
-        Returns:
-            画像数据字典，如果不存在或已过期返回 None
-        """
-        try:
-            key = cls._get_key("profile", user_id)
-            value = await aio_redis.get(key)
-            
-            if value:
-                data = json.loads(value)
-                logger.info(f"成功获取用户画像缓存: {key}")
-                return data
-            
-            logger.info(f"用户画像缓存不存在或已过期: {key}")
-            return None
-        except Exception as e:
-            logger.error(f"获取用户画像缓存失败: {e}", exc_info=True)
-            return None
-    
-    @classmethod
-    async def delete_user_profile(cls, user_id: str) -> bool:
-        """删除用户完整画像缓存
-        
-        Args:
-            user_id: 用户ID（end_user_id）
-            
-        Returns:
-            是否删除成功
-        """
-        try:
-            key = cls._get_key("profile", user_id)
-            result = await aio_redis.delete(key)
-            logger.info(f"删除用户画像缓存: {key}, 结果: {result}")
-            return result > 0
-        except Exception as e:
-            logger.error(f"删除用户画像缓存失败: {e}", exc_info=True)
-            return False
-    
-    @classmethod
-    async def get_profile_ttl(cls, user_id: str) -> int:
-        """获取用户画像缓存的剩余过期时间
-        
-        Args:
-            user_id: 用户ID（end_user_id）
-            
-        Returns:
-            剩余秒数，-1表示永不过期，-2表示key不存在
-        """
-        try:
-            key = cls._get_key("profile", user_id)
-            ttl = await aio_redis.ttl(key)
-            logger.debug(f"用户画像缓存TTL: {key} = {ttl}秒")
-            return ttl
-        except Exception as e:
-            logger.error(f"获取用户画像缓存TTL失败: {e}")
-            return -2

api/app/celery_app.py

@@ -1,15 +1,9 @@
 import os
-import platform
 from datetime import timedelta
 from urllib.parse import quote
 
-from celery import Celery
-
 from app.core.config import settings
-
-# macOS fork() safety - must be set before any Celery initialization
-if platform.system() == 'Darwin':
-    os.environ.setdefault('OBJC_DISABLE_INITIALIZE_FORK_SAFETY', 'YES')
+from celery import Celery
 
 # 创建 Celery 应用实例
 # broker: 任务队列（使用 Redis DB 0）
@@ -20,12 +14,27 @@ celery_app = Celery(
     backend=f"redis://:{quote(settings.REDIS_PASSWORD)}@{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.CELERY_BACKEND}",
 )
 
-# Default queue for unrouted tasks
-celery_app.conf.task_default_queue = 'memory_tasks'
+# 配置使用本地队列，避免与远程 worker 冲突
+celery_app.conf.task_default_queue = 'localhost_test_wyl'
+celery_app.conf.task_default_exchange = 'localhost_test_wyl'
+celery_app.conf.task_default_routing_key = 'localhost_test_wyl'
 
 # macOS 兼容性配置
-if platform.system() == 'Darwin':
+import platform
+
+if platform.system() == 'Darwin':  # macOS
+    # 设置环境变量解决 fork 问题
     os.environ.setdefault('OBJC_DISABLE_INITIALIZE_FORK_SAFETY', 'YES')
+    
+    # 使用 solo 池避免多进程问题
+    celery_app.conf.worker_pool = 'solo'
+    
+    # 设置唯一的节点名称
+    import socket
+    import time
+    hostname = socket.gethostname()
+    timestamp = int(time.time())
+    celery_app.conf.worker_name = f"celery@{hostname}-{timestamp}"
 
 # Celery 配置
 celery_app.conf.update(
@@ -43,61 +52,49 @@ celery_app.conf.update(
     task_ignore_result=False,
     
     # 超时设置
-    task_time_limit=1800,  # 30分钟硬超时
-    task_soft_time_limit=1500,  # 25分钟软超时
+    task_time_limit=30 * 60,  # 30 分钟硬超时
+    task_soft_time_limit=25 * 60,  # 25 分钟软超时
     
-    # Worker 设置 (per-worker settings are in docker-compose command line)
-    worker_prefetch_multiplier=1,  # Don't hoard tasks, fairer distribution
+    # Worker 设置 - 针对 macOS 优化
+    worker_prefetch_multiplier=1,  # 减少预取任务数，避免内存堆积
+    worker_max_tasks_per_child=10,  # 大幅减少每个 worker 执行的任务数，频繁重启防止内存泄漏
+    worker_max_memory_per_child=200000,  # 200MB 内存限制，超过后重启 worker
     
     # 结果过期时间
-    result_expires=3600,  # 结果保存1小时
+    result_expires=3600,  # 结果保存 1 小时
     
     # 任务确认设置
-    task_acks_late=True,
-    task_reject_on_worker_lost=True,
-    worker_disable_rate_limits=True,
+    task_acks_late=True,  # 任务完成后才确认，避免任务丢失
+    worker_disable_rate_limits=True,  # 禁用速率限制
     
-    # FLower setting 
-    worker_send_task_events=True,
-    task_send_sent_event=True,
-    
-    # task routing
-    task_routes={
-        # Memory tasks → memory_tasks queue (threads worker)
-        'app.core.memory.agent.read_message_priority': {'queue': 'memory_tasks'},
-        'app.core.memory.agent.read_message': {'queue': 'memory_tasks'},
-        'app.core.memory.agent.write_message': {'queue': 'memory_tasks'},
-        
-        # Long-term storage tasks → memory_tasks queue (batched write strategies)
-        'app.core.memory.agent.long_term_storage.window': {'queue': 'memory_tasks'},
-        'app.core.memory.agent.long_term_storage.time': {'queue': 'memory_tasks'},
-        'app.core.memory.agent.long_term_storage.aggregate': {'queue': 'memory_tasks'},
-        
-        # Document tasks → document_tasks queue (prefork worker)
-        'app.core.rag.tasks.parse_document': {'queue': 'document_tasks'},
-        'app.core.rag.tasks.build_graphrag_for_kb': {'queue': 'document_tasks'},
-        'app.core.rag.tasks.sync_knowledge_for_kb': {'queue': 'document_tasks'},
-        
-        # Beat/periodic tasks → periodic_tasks queue (dedicated periodic worker)
-        'app.tasks.workspace_reflection_task': {'queue': 'periodic_tasks'},
-        'app.tasks.regenerate_memory_cache': {'queue': 'periodic_tasks'},
-        'app.tasks.run_forgetting_cycle_task': {'queue': 'periodic_tasks'},
-        'app.controllers.memory_storage_controller.search_all': {'queue': 'periodic_tasks'},
-    },
+    # 任务路由（可选，用于不同队列）
+    # task_routes={
+    #     'app.core.rag.tasks.parse_document': {'queue': 'document_processing'},
+    #     'app.core.memory.agent.read_message': {'queue': 'memory_processing'},
+    #     'app.core.memory.agent.write_message': {'queue': 'memory_processing'},
+    #     'tasks.process_item': {'queue': 'default'},
+    # },
 )
 
 # 自动发现任务模块
 celery_app.autodiscover_tasks(['app'])
 
 # Celery Beat schedule for periodic tasks
+reflection_schedule = timedelta(seconds=settings.REFLECTION_INTERVAL_SECONDS)
+health_schedule = timedelta(seconds=settings.HEALTH_CHECK_SECONDS)
 memory_increment_schedule = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS)
 memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS)
-# 这个30秒的设计不合理
 workspace_reflection_schedule = timedelta(seconds=30)  # 每30秒运行一次settings.REFLECTION_INTERVAL_TIME
 forgetting_cycle_schedule = timedelta(hours=24)  # 每24小时运行一次遗忘周期
 
-#构建定时任务配置
+# 构建定时任务配置
 beat_schedule_config = {
+
+    # "check-read-service": {
+    #     "task": "app.core.memory.agent.health.check_read_service",
+    #     "schedule": health_schedule,
+    #     "args": (),
+    # },
     "run-workspace-reflection": {
         "task": "app.tasks.workspace_reflection_task",
         "schedule": workspace_reflection_schedule,
@@ -117,7 +114,7 @@ beat_schedule_config = {
     },
 }
 
-#如果配置了默认工作空间ID，则添加记忆总量统计任务
+# 如果配置了默认工作空间ID，则添加记忆总量统计任务
 if settings.DEFAULT_WORKSPACE_ID:
     beat_schedule_config["write-total-memory"] = {
         "task": "app.controllers.memory_storage_controller.search_all",

api/app/celery_worker.py

@@ -3,12 +3,6 @@ Celery Worker 入口点
 用于启动 Celery Worker: celery -A app.celery_worker worker --loglevel=info
 """
 from app.celery_app import celery_app
-from app.core.logging_config import LoggingConfig, get_logger
-
-# Initialize logging system for Celery worker
-LoggingConfig.setup_logging()
-logger = get_logger(__name__)
-logger.info("Celery worker logging initialized")
 
 # 导入任务模块以注册任务
 import app.tasks

api/app/controllers/__init__.py

@@ -14,23 +14,18 @@ from . import (
     emotion_config_controller,
     emotion_controller,
     file_controller,
-    file_storage_controller,
     home_page_controller,
     implicit_memory_controller,
     knowledge_controller,
     knowledgeshare_controller,
-    mcp_market_controller,
-    mcp_market_config_controller,
     memory_agent_controller,
     memory_dashboard_controller,
     memory_episodic_controller,
     memory_explicit_controller,
     memory_forget_controller,
-    memory_perceptual_controller,
     memory_reflection_controller,
     memory_short_term_controller,
     memory_storage_controller,
-    memory_working_controller,
     model_controller,
     multi_agent_controller,
     prompt_optimizer_controller,
@@ -43,9 +38,12 @@ from . import (
     upload_controller,
     user_controller,
     user_memory_controllers,
+    workflow_controller,
     workspace_controller,
-    ontology_controller,
-    skill_controller
+    memory_forget_controller,
+    home_page_controller,
+    memory_perceptual_controller,
+    memory_working_controller,
 )
 
 # 创建管理端 API 路由器
@@ -62,8 +60,6 @@ manager_router.include_router(model_controller.router)
 manager_router.include_router(file_controller.router)
 manager_router.include_router(document_controller.router)
 manager_router.include_router(knowledge_controller.router)
-manager_router.include_router(mcp_market_controller.router)
-manager_router.include_router(mcp_market_config_controller.router)
 manager_router.include_router(chunk_controller.router)
 manager_router.include_router(test_controller.router)
 manager_router.include_router(knowledgeshare_controller.router)
@@ -80,6 +76,7 @@ manager_router.include_router(release_share_controller.router)
 manager_router.include_router(public_share_controller.router)  # 公开路由（无需认证）
 manager_router.include_router(memory_dashboard_controller.router)
 manager_router.include_router(multi_agent_controller.router)
+manager_router.include_router(workflow_controller.router)
 manager_router.include_router(emotion_controller.router)
 manager_router.include_router(emotion_config_controller.router)
 manager_router.include_router(prompt_optimizer_controller.router)
@@ -91,8 +88,5 @@ manager_router.include_router(home_page_controller.router)
 manager_router.include_router(implicit_memory_controller.router)
 manager_router.include_router(memory_perceptual_controller.router)
 manager_router.include_router(memory_working_controller.router)
-manager_router.include_router(file_storage_controller.router)
-manager_router.include_router(ontology_controller.router)
-manager_router.include_router(skill_controller.router)
 
 __all__ = ["manager_router"]

api/app/controllers/app_controller.py

@@ -7,7 +7,7 @@ from sqlalchemy.orm import Session
 
 from app.core.error_codes import BizCode
 from app.core.logging_config import get_business_logger
-from app.core.response_utils import success, fail
+from app.core.response_utils import success
 from app.db import get_db
 from app.dependencies import get_current_user, cur_workspace_access_guard
 from app.models import User
@@ -22,7 +22,6 @@ from app.services import app_service, workspace_service
 from app.services.agent_config_helper import enrich_agent_config
 from app.services.app_service import AppService
 from app.services.workflow_service import WorkflowService, get_workflow_service
-from app.services.app_statistics_service import AppStatisticsService
 
 router = APIRouter(prefix="/apps", tags=["Apps"])
 logger = get_business_logger()
@@ -455,8 +454,7 @@ async def draft_run(
                         user_id=payload.user_id or str(current_user.id),
                         variables=payload.variables,
                         storage_type=storage_type,
-                        user_rag_memory_id=user_rag_memory_id,
-                        files=payload.files  # 传递多模态文件
+                        user_rag_memory_id=user_rag_memory_id
                 ):
                     yield event
 
@@ -477,8 +475,7 @@ async def draft_run(
                 "app_id": str(app_id),
                 "message_length": len(payload.message),
                 "has_conversation_id": bool(payload.conversation_id),
-                "has_variables": bool(payload.variables),
-                "has_files": bool(payload.files)
+                "has_variables": bool(payload.variables)
             }
         )
 
@@ -493,8 +490,7 @@ async def draft_run(
             user_id=payload.user_id or str(current_user.id),
             variables=payload.variables,
             storage_type=storage_type,
-            user_rag_memory_id=user_rag_memory_id,
-            files=payload.files  # 传递多模态文件
+            user_rag_memory_id=user_rag_memory_id
         )
 
         logger.debug(
@@ -665,11 +661,6 @@ async def draft_run(
             data=result,
             msg="工作流任务执行成功"
         )
-    else:
-        return fail(
-            msg="未知应用类型",
-            code=422
-        )
 
 
 @router.post("/{app_id}/draft/run/compare", summary="多模型对比试运行")
@@ -802,8 +793,7 @@ async def draft_run_compare(
                     web_search=True,
                     memory=True,
                     parallel=payload.parallel,
-                    timeout=payload.timeout or 60,
-                    files=payload.files
+                    timeout=payload.timeout or 60
             ):
                 yield event
 
@@ -877,75 +867,3 @@ async def update_workflow_config(
     workspace_id = current_user.current_workspace_id
     cfg = app_service.update_workflow_config(db, app_id=app_id, data=payload, workspace_id=workspace_id)
     return success(data=WorkflowConfigSchema.model_validate(cfg))
-
-
-@router.get("/{app_id}/statistics", summary="应用统计数据")
-@cur_workspace_access_guard()
-def get_app_statistics(
-        app_id: uuid.UUID,
-        start_date: int,
-        end_date: int,
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """获取应用统计数据
-    
-    Args:
-        app_id: 应用ID
-        start_date: 开始时间戳（毫秒）
-        end_date: 结束时间戳（毫秒）
-    
-    Returns:
-        - daily_conversations: 每日会话数统计
-        - total_conversations: 总会话数
-        - daily_new_users: 每日新增用户数
-        - total_new_users: 总新增用户数
-        - daily_api_calls: 每日API调用次数
-        - total_api_calls: 总API调用次数
-        - daily_tokens: 每日token消耗
-        - total_tokens: 总token消耗
-    """
-    workspace_id = current_user.current_workspace_id
-    stats_service = AppStatisticsService(db)
-
-    result = stats_service.get_app_statistics(
-        app_id=app_id,
-        workspace_id=workspace_id,
-        start_date=start_date,
-        end_date=end_date
-    )
-
-    return success(data=result)
-
-
-@router.get("/workspace/api-statistics", summary="工作空间API调用统计")
-@cur_workspace_access_guard()
-def get_workspace_api_statistics(
-        start_date: int,
-        end_date: int,
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """获取工作空间API调用统计
-    
-    Args:
-        start_date: 开始时间戳（毫秒）
-        end_date: 结束时间戳（毫秒）
-    
-    Returns:
-        每日统计数据列表，每项包含：
-        - date: 日期
-        - total_calls: 当日总调用次数
-        - app_calls: 当日应用调用次数
-        - service_calls: 当日服务调用次数
-    """
-    workspace_id = current_user.current_workspace_id
-    stats_service = AppStatisticsService(db)
-
-    result = stats_service.get_workspace_api_statistics(
-        workspace_id=workspace_id,
-        start_date=start_date,
-        end_date=end_date
-    )
-
-    return success(data=result)

api/app/controllers/auth_controller.py

@@ -61,7 +61,6 @@ async def login_for_access_token(
                 user = auth_service.register_user_with_invite(
                 db=db,
                 email=form_data.email,
-                username=form_data.username,
                 password=form_data.password,
                 invite_token=form_data.invite,
                 workspace_id=invite_info.workspace_id

api/app/controllers/emotion_config_controller.py

@@ -7,13 +7,11 @@ Routes:
     GET /memory/config/emotion - 获取情绪引擎配置
     POST /memory/config/emotion - 更新情绪引擎配置
 """
-import uuid
 
 from fastapi import APIRouter, Depends, Query, HTTPException, status
 from pydantic import BaseModel, Field
-from typing import Optional, Union
+from typing import Optional
 from sqlalchemy.orm import Session
-from uuid import UUID
 
 from app.core.response_utils import success
 from app.dependencies import get_current_user
@@ -22,7 +20,6 @@ from app.schemas.response_schema import ApiResponse
 from app.services.emotion_config_service import EmotionConfigService
 from app.core.logging_config import get_api_logger
 from app.db import get_db
-from app.utils.config_utils import resolve_config_id
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -35,11 +32,11 @@ router = APIRouter(
 
 class EmotionConfigQuery(BaseModel):
     """情绪配置查询请求模型"""
-    config_id: UUID = Field(..., description="配置ID")
+    config_id: int = Field(..., description="配置ID")
 
 class EmotionConfigUpdate(BaseModel):
     """情绪配置更新请求模型"""
-    config_id: Union[uuid.UUID, int, str]= Field(..., description="配置ID")
+    config_id: int = Field(..., description="配置ID")
     emotion_enabled: bool = Field(..., description="是否启用情绪提取")
     emotion_model_id: Optional[str] = Field(None, description="情绪分析专用模型ID")
     emotion_extract_keywords: bool = Field(..., description="是否提取情绪关键词")
@@ -48,7 +45,7 @@ class EmotionConfigUpdate(BaseModel):
 
 @router.get("/read_config", response_model=ApiResponse)
 def get_emotion_config(
-    config_id: UUID|int = Query(..., description="配置ID"),
+    config_id: int = Query(..., description="配置ID"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
@@ -81,7 +78,7 @@ def get_emotion_config(
             f"用户 {current_user.username} 请求获取情绪配置",
             extra={"config_id": config_id}
         )
-        config_id=resolve_config_id(config_id, db)
+        
         # 初始化服务
         config_service = EmotionConfigService(db)
         
@@ -160,7 +157,6 @@ def update_emotion_config(
             }
         }
     """
-    config.config_id=resolve_config_id(config.config_id, db)
     try:
         api_logger.info(
             f"用户 {current_user.username} 请求更新情绪配置",

api/app/controllers/emotion_controller.py

@@ -11,7 +11,6 @@ Routes:
 """
 
 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.response_utils import fail, success
 from app.dependencies import get_current_user, get_db
@@ -25,7 +24,7 @@ from app.schemas.emotion_schema import (
 )
 from app.schemas.response_schema import ApiResponse
 from app.services.emotion_analytics_service import EmotionAnalyticsService
-from fastapi import APIRouter, Depends, HTTPException, status,Header
+from fastapi import APIRouter, Depends, HTTPException, status
 from sqlalchemy.orm import Session
 
 # 获取API专用日志器
@@ -46,51 +45,45 @@ emotion_service = EmotionAnalyticsService()
 @router.post("/tags", response_model=ApiResponse)
 async def get_emotion_tags(
     request: EmotionTagsRequest,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
 ):
 
     try:
-        # 使用集中化的语言校验
-        language = get_language_from_header(language_type)
-        
         api_logger.info(
             f"用户 {current_user.username} 请求获取情绪标签统计",
             extra={
-                "end_user_id": request.end_user_id,
+                "group_id": request.group_id,
                 "emotion_type": request.emotion_type,
                 "start_date": request.start_date,
                 "end_date": request.end_date,
-                "limit": request.limit,
-                "language_type": language
+                "limit": request.limit
             }
         )
-
+        
         # 调用服务层
         data = await emotion_service.get_emotion_tags(
-            end_user_id=request.end_user_id,
+            end_user_id=request.group_id,
             emotion_type=request.emotion_type,
             start_date=request.start_date,
             end_date=request.end_date,
-            limit=request.limit,
-            language=language
+            limit=request.limit
         )
-
+        
         api_logger.info(
             "情绪标签统计获取成功",
             extra={
-                "end_user_id": request.end_user_id,
+                "group_id": request.group_id,
                 "total_count": data.get("total_count", 0),
                 "tags_count": len(data.get("tags", []))
             }
         )
-
+        
         return success(data=data, msg="情绪标签获取成功")
-
+        
     except Exception as e:
         api_logger.error(
             f"获取情绪标签统计失败: {str(e)}",
-            extra={"end_user_id": request.end_user_id},
+            extra={"group_id": request.group_id},
             exc_info=True
         )
         raise HTTPException(
@@ -103,44 +96,40 @@ async def get_emotion_tags(
 @router.post("/wordcloud", response_model=ApiResponse)
 async def get_emotion_wordcloud(
     request: EmotionWordcloudRequest,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
 ):
 
     try:
-        # 使用集中化的语言校验
-        language = get_language_from_header(language_type)
-        
         api_logger.info(
             f"用户 {current_user.username} 请求获取情绪词云数据",
             extra={
-                "end_user_id": request.end_user_id,
+                "group_id": request.group_id,
                 "emotion_type": request.emotion_type,
                 "limit": request.limit
             }
         )
-
+        
         # 调用服务层
         data = await emotion_service.get_emotion_wordcloud(
-            end_user_id=request.end_user_id,
+            end_user_id=request.group_id,
             emotion_type=request.emotion_type,
             limit=request.limit
         )
-
+        
         api_logger.info(
             "情绪词云数据获取成功",
             extra={
-                "end_user_id": request.end_user_id,
+                "group_id": request.group_id,
                 "total_keywords": data.get("total_keywords", 0)
             }
         )
-
+        
         return success(data=data, msg="情绪词云获取成功")
-
+        
     except Exception as e:
         api_logger.error(
             f"获取情绪词云数据失败: {str(e)}",
-            extra={"end_user_id": request.end_user_id},
+            extra={"group_id": request.group_id},
             exc_info=True
         )
         raise HTTPException(
@@ -153,52 +142,48 @@ async def get_emotion_wordcloud(
 @router.post("/health", response_model=ApiResponse)
 async def get_emotion_health(
     request: EmotionHealthRequest,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
 ):
 
     try:
-        # 使用集中化的语言校验
-        language = get_language_from_header(language_type)
-        
         # 验证时间范围参数
         if request.time_range not in ["7d", "30d", "90d"]:
             raise HTTPException(
                 status_code=status.HTTP_400_BAD_REQUEST,
                 detail="时间范围参数无效，必须是 7d、30d 或 90d"
             )
-
+        
         api_logger.info(
             f"用户 {current_user.username} 请求获取情绪健康指数",
             extra={
-                "end_user_id": request.end_user_id,
+                "group_id": request.group_id,
                 "time_range": request.time_range
             }
         )
-
+        
         # 调用服务层
         data = await emotion_service.calculate_emotion_health_index(
-            end_user_id=request.end_user_id,
+            end_user_id=request.group_id,
             time_range=request.time_range
         )
-
+        
         api_logger.info(
             "情绪健康指数获取成功",
             extra={
-                "end_user_id": request.end_user_id,
-                "health_score": data.get("health_score") or 0,
+                "group_id": request.group_id,
+                "health_score": data.get("health_score", 0),
                 "level": data.get("level", "未知")
             }
         )
-
+        
         return success(data=data, msg="情绪健康指数获取成功")
-
+        
     except HTTPException:
         raise
     except Exception as e:
         api_logger.error(
             f"获取情绪健康指数失败: {str(e)}",
-            extra={"end_user_id": request.end_user_id},
+            extra={"group_id": request.group_id},
             exc_info=True
         )
         raise HTTPException(
@@ -211,94 +196,60 @@ async def get_emotion_health(
 @router.post("/suggestions", response_model=ApiResponse)
 async def get_emotion_suggestions(
     request: EmotionSuggestionsRequest,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
     """获取个性化情绪建议（从缓存读取）
-
+    
     Args:
-        request: 包含 end_user_id 和可选的 config_id
+        request: 包含 group_id 和可选的 config_id
         db: 数据库会话
         current_user: 当前用户
-
+        
     Returns:
         缓存的个性化情绪建议响应
     """
     try:
-        # 使用集中化的语言校验
-        language = get_language_from_header(language_type)
-        
         api_logger.info(
             f"用户 {current_user.username} 请求获取个性化情绪建议（缓存）",
             extra={
-                "end_user_id": request.end_user_id,
+                "group_id": request.group_id,
                 "config_id": request.config_id
             }
         )
-
+        
         # 从缓存获取建议
         data = await emotion_service.get_cached_suggestions(
-            end_user_id=request.end_user_id,
+            end_user_id=request.group_id,
             db=db
         )
-
+        
         if data is None:
-            # 缓存不存在或已过期，自动触发生成
+            # 缓存不存在或已过期
             api_logger.info(
-                f"用户 {request.end_user_id} 的建议缓存不存在或已过期，自动生成新建议",
-                extra={"end_user_id": request.end_user_id}
+                f"用户 {request.group_id} 的建议缓存不存在或已过期",
+                extra={"group_id": request.group_id}
             )
-            try:
-                data = await emotion_service.generate_emotion_suggestions(
-                    end_user_id=request.end_user_id,
-                    db=db,
-                    language=language
-                )
-                # 保存到缓存
-                await emotion_service.save_suggestions_cache(
-                    end_user_id=request.end_user_id,
-                    suggestions_data=data,
-                    db=db,
-                    expires_hours=24
-                )
-            except (ValueError, KeyError) as gen_e:
-                # 预期内的业务异常：配置缺失、数据格式问题等
-                api_logger.warning(
-                    f"自动生成建议失败（业务异常）: {str(gen_e)}",
-                    extra={"end_user_id": request.end_user_id}
-                )
-                return fail(
-                    BizCode.NOT_FOUND,
-                    f"自动生成建议失败: {str(gen_e)}",
-                    ""
-                )
-            except Exception as gen_e:
-                # 非预期异常：记录完整 traceback 便于排查
-                api_logger.error(
-                    f"自动生成建议时发生未预期异常: {str(gen_e)}",
-                    extra={"end_user_id": request.end_user_id},
-                    exc_info=True
-                )
-                raise HTTPException(
-                    status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                    detail=f"生成建议时发生内部错误: {str(gen_e)}"
-                )
-
+            return fail(
+                BizCode.RESOURCE_NOT_FOUND,
+                "建议缓存不存在或已过期，请调用 /generate_suggestions 接口生成新建议",
+                None
+            )
+        
         api_logger.info(
             "个性化建议获取成功（缓存）",
             extra={
-                "end_user_id": request.end_user_id,
+                "group_id": request.group_id,
                 "suggestions_count": len(data.get("suggestions", []))
             }
         )
-
+        
         return success(data=data, msg="个性化建议获取成功（缓存）")
-
+        
     except Exception as e:
         api_logger.error(
             f"获取个性化建议失败: {str(e)}",
-            extra={"end_user_id": request.end_user_id},
+            extra={"group_id": request.group_id},
             exc_info=True
         )
         raise HTTPException(
@@ -310,60 +261,80 @@ async def get_emotion_suggestions(
 @router.post("/generate_suggestions", response_model=ApiResponse)
 async def generate_emotion_suggestions(
     request: EmotionGenerateSuggestionsRequest,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
     """生成个性化情绪建议（调用LLM并缓存）
-
+    
     Args:
-        request: 包含 end_user_id
+        request: 包含 group_id、可选的 config_id 和 force_refresh
         db: 数据库会话
         current_user: 当前用户
-
+        
     Returns:
         新生成的个性化情绪建议响应
     """
     try:
-        # 使用集中化的语言校验
-        language = get_language_from_header(language_type)
+        # 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={
-                "end_user_id": request.end_user_id
+                "group_id": request.group_id,
+                "config_id": config_id
             }
         )
-
+        
         # 调用服务层生成建议
         data = await emotion_service.generate_emotion_suggestions(
-            end_user_id=request.end_user_id,
-            db=db,
-            language=language
+            end_user_id=request.group_id,
+            db=db
         )
-
+        
         # 保存到缓存
         await emotion_service.save_suggestions_cache(
-            end_user_id=request.end_user_id,
+            end_user_id=request.group_id,
             suggestions_data=data,
             db=db,
             expires_hours=24
         )
-
+        
         api_logger.info(
             "个性化建议生成成功",
             extra={
-                "end_user_id": request.end_user_id,
+                "group_id": request.group_id,
                 "suggestions_count": len(data.get("suggestions", []))
             }
         )
-
+        
         return success(data=data, msg="个性化建议生成成功")
-
+        
     except Exception as e:
         api_logger.error(
             f"生成个性化建议失败: {str(e)}",
-            extra={"end_user_id": request.end_user_id},
+            extra={"group_id": request.group_id},
             exc_info=True
         )
         raise HTTPException(

api/app/controllers/file_storage_controller.py

@@ -1,634 +0,0 @@
-"""
-File storage controller module.
-
-This module provides API endpoints for file storage operations using the
-configurable storage backend. It is a new controller that does not modify
-the existing file_controller.py.
-
-Routes:
-    POST /storage/files - Upload a file
-    GET /storage/files/{file_id} - Download a file
-    DELETE /storage/files/{file_id} - Delete a file
-"""
-
-import os
-import uuid
-from typing import Any
-
-from fastapi import APIRouter, Depends, File, HTTPException, UploadFile, status
-from fastapi.responses import FileResponse, RedirectResponse
-from sqlalchemy.orm import Session
-
-from app.core.config import settings
-from app.core.logging_config import get_api_logger
-from app.core.response_utils import success
-from app.core.storage import LocalStorage
-from app.core.storage.url_signer import generate_signed_url, verify_signed_url
-from app.core.storage_exceptions import (
-    StorageDeleteError,
-    StorageUploadError,
-)
-from app.db import get_db
-from app.dependencies import get_current_user, get_share_user_id, ShareTokenData
-from app.models.file_metadata_model import FileMetadata
-from app.models.user_model import User
-from app.schemas.response_schema import ApiResponse
-from app.services.file_storage_service import (
-    FileStorageService,
-    generate_file_key,
-    get_file_storage_service,
-)
-
-api_logger = get_api_logger()
-
-router = APIRouter(
-    prefix="/storage",
-    tags=["storage"]
-)
-
-
-@router.post("/files", response_model=ApiResponse)
-async def upload_file(
-    file: UploadFile = File(...),
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-    storage_service: FileStorageService = Depends(get_file_storage_service),
-):
-    """
-    Upload a file to the configured storage backend.
-    """
-    tenant_id = current_user.tenant_id
-    workspace_id = current_user.current_workspace_id
-
-    api_logger.info(
-        f"Storage upload request: tenant_id={tenant_id}, workspace_id={workspace_id}, "
-        f"filename={file.filename}, username={current_user.username}"
-    )
-
-    # Read file contents
-    contents = await file.read()
-    file_size = len(contents)
-
-    # Validate file size
-    if file_size == 0:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="The file is empty."
-        )
-
-    if file_size > settings.MAX_FILE_SIZE:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"The file size exceeds the {settings.MAX_FILE_SIZE} byte limit"
-        )
-
-    # Extract file extension
-    _, file_extension = os.path.splitext(file.filename)
-    file_ext = file_extension.lower()
-
-    # Generate file_id and file_key
-    file_id = uuid.uuid4()
-    file_key = generate_file_key(
-        tenant_id=tenant_id,
-        workspace_id=workspace_id,
-        file_id=file_id,
-        file_ext=file_ext,
-    )
-
-    # Create file metadata record with pending status
-    file_metadata = FileMetadata(
-        id=file_id,
-        tenant_id=tenant_id,
-        workspace_id=workspace_id,
-        file_key=file_key,
-        file_name=file.filename,
-        file_ext=file_ext,
-        file_size=file_size,
-        content_type=file.content_type,
-        status="pending",
-    )
-    db.add(file_metadata)
-    db.commit()
-    db.refresh(file_metadata)
-
-    # Upload file to storage backend
-    try:
-        await storage_service.upload_file(
-            tenant_id=tenant_id,
-            workspace_id=workspace_id,
-            file_id=file_id,
-            file_ext=file_ext,
-            content=contents,
-            content_type=file.content_type,
-        )
-        # Update status to completed
-        file_metadata.status = "completed"
-        db.commit()
-        api_logger.info(f"File uploaded to storage: file_key={file_key}")
-    except StorageUploadError as e:
-        # Update status to failed
-        file_metadata.status = "failed"
-        db.commit()
-        api_logger.error(f"Storage upload failed: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"File storage failed: {str(e)}"
-        )
-
-    api_logger.info(f"File upload successful: {file.filename} (file_id: {file_id})")
-
-    return success(
-        data={"file_id": str(file_id), "file_key": file_key},
-        msg="File upload successful"
-    )
-
-
-@router.post("/share/files", response_model=ApiResponse)
-async def upload_file_with_share_token(
-    file: UploadFile = File(...),
-    db: Session = Depends(get_db),
-    share_data: ShareTokenData = Depends(get_share_user_id),
-    storage_service: FileStorageService = Depends(get_file_storage_service),
-):
-    """
-    Upload a file to the configured storage backend using share_token authentication.
-    """
-    from app.services.release_share_service import ReleaseShareService
-    from app.models.app_model import App
-    from app.models.workspace_model import Workspace
-    
-    # Get share and release info from share_token
-    service = ReleaseShareService(db)
-    share_info = service.get_shared_release_info(share_token=share_data.share_token)
-    
-    # Get share object to access app_id
-    share = service.repo.get_by_share_token(share_data.share_token)
-    if not share:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Shared app not found"
-        )
-    
-    # Get app to access workspace_id
-    app = db.query(App).filter(
-        App.id == share.app_id,
-        App.is_active.is_(True)
-    ).first()
-    
-    if not app:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="App not found"
-        )
-    
-    # Get workspace to access tenant_id
-    workspace = db.query(Workspace).filter(
-        Workspace.id == app.workspace_id
-    ).first()
-    
-    if not workspace:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Workspace not found"
-        )
-    
-    tenant_id = workspace.tenant_id
-    workspace_id = app.workspace_id
-
-    api_logger.info(
-        f"Storage upload request (share): tenant_id={tenant_id}, workspace_id={workspace_id}, "
-        f"filename={file.filename}, share_token={share_data.share_token}"
-    )
-
-    # Read file contents
-    contents = await file.read()
-    file_size = len(contents)
-
-    # Validate file size
-    if file_size == 0:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="The file is empty."
-        )
-
-    if file_size > settings.MAX_FILE_SIZE:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"The file size exceeds the {settings.MAX_FILE_SIZE} byte limit"
-        )
-
-    # Extract file extension
-    _, file_extension = os.path.splitext(file.filename)
-    file_ext = file_extension.lower()
-
-    # Generate file_id and file_key
-    file_id = uuid.uuid4()
-    file_key = generate_file_key(
-        tenant_id=tenant_id,
-        workspace_id=workspace_id,
-        file_id=file_id,
-        file_ext=file_ext,
-    )
-
-    # Create file metadata record with pending status
-    file_metadata = FileMetadata(
-        id=file_id,
-        tenant_id=tenant_id,
-        workspace_id=workspace_id,
-        file_key=file_key,
-        file_name=file.filename,
-        file_ext=file_ext,
-        file_size=file_size,
-        content_type=file.content_type,
-        status="pending",
-    )
-    db.add(file_metadata)
-    db.commit()
-    db.refresh(file_metadata)
-
-    # Upload file to storage backend
-    try:
-        await storage_service.upload_file(
-            tenant_id=tenant_id,
-            workspace_id=workspace_id,
-            file_id=file_id,
-            file_ext=file_ext,
-            content=contents,
-            content_type=file.content_type,
-        )
-        # Update status to completed
-        file_metadata.status = "completed"
-        db.commit()
-        api_logger.info(f"File uploaded to storage (share): file_key={file_key}")
-    except StorageUploadError as e:
-        # Update status to failed
-        file_metadata.status = "failed"
-        db.commit()
-        api_logger.error(f"Storage upload failed (share): {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"File storage failed: {str(e)}"
-        )
-
-    api_logger.info(f"File upload successful (share): {file.filename} (file_id: {file_id})")
-
-    return success(
-        data={"file_id": str(file_id), "file_key": file_key},
-        msg="File upload successful"
-    )
-
-
-@router.get("/files/{file_id}", response_model=Any)
-async def download_file(
-    file_id: uuid.UUID,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-    storage_service: FileStorageService = Depends(get_file_storage_service),
-) -> Any:
-    """
-    Download a file from the configured storage backend.
-    """
-    api_logger.info(f"Storage download request: file_id={file_id}")
-
-    # Query file metadata from database
-    file_metadata = db.query(FileMetadata).filter(FileMetadata.id == file_id).first()
-    if not file_metadata:
-        api_logger.warning(f"File not found in database: file_id={file_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The file does not exist"
-        )
-
-    if file_metadata.status != "completed":
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"File upload not completed, status: {file_metadata.status}"
-        )
-
-    file_key = file_metadata.file_key
-    storage = storage_service.storage
-
-    if isinstance(storage, LocalStorage):
-        full_path = storage._get_full_path(file_key)
-
-        if not full_path.exists():
-            api_logger.warning(f"File not found on disk: file_key={file_key}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="File not found (possibly deleted)"
-            )
-
-        api_logger.info(f"Serving local file: file_key={file_key}")
-        return FileResponse(
-            path=str(full_path),
-            filename=file_metadata.file_name,
-            media_type=file_metadata.content_type or "application/octet-stream"
-        )
-    else:
-        try:
-            presigned_url = await storage_service.get_file_url(file_key, expires=3600)
-            api_logger.info(f"Redirecting to presigned URL: file_key={file_key}")
-            return RedirectResponse(url=presigned_url, status_code=status.HTTP_302_FOUND)
-        except FileNotFoundError:
-            api_logger.warning(f"File not found in remote storage: file_key={file_key}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="File not found in storage"
-            )
-        except Exception as e:
-            api_logger.error(f"Failed to get presigned URL: {e}")
-            raise HTTPException(
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                detail=f"Failed to retrieve file: {str(e)}"
-            )
-
-
-@router.delete("/files/{file_id}", response_model=ApiResponse)
-async def delete_file(
-    file_id: uuid.UUID,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-    storage_service: FileStorageService = Depends(get_file_storage_service),
-):
-    """
-    Delete a file from the configured storage backend.
-    """
-    api_logger.info(
-        f"Storage delete request: file_id={file_id}, username={current_user.username}"
-    )
-
-    # Query file metadata from database
-    file_metadata = db.query(FileMetadata).filter(FileMetadata.id == file_id).first()
-    if not file_metadata:
-        api_logger.warning(f"File not found in database: file_id={file_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The file does not exist"
-        )
-
-    file_key = file_metadata.file_key
-
-    # Delete file from storage
-    try:
-        deleted = await storage_service.delete_file(file_key)
-        if deleted:
-            api_logger.info(f"File deleted from storage: file_key={file_key}")
-        else:
-            api_logger.info(f"File did not exist in storage: file_key={file_key}")
-    except StorageDeleteError as e:
-        api_logger.error(f"Storage delete failed: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Failed to delete file from storage: {str(e)}"
-        )
-
-    # Delete database record
-    try:
-        db.delete(file_metadata)
-        db.commit()
-        api_logger.info(f"File record deleted from database: file_id={file_id}")
-    except Exception as e:
-        api_logger.error(f"Database delete failed: {e}")
-        db.rollback()
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Failed to delete file record: {str(e)}"
-        )
-
-    return success(msg="File deleted successfully")
-
-
-@router.get("/files/{file_id}/url", response_model=ApiResponse)
-async def get_file_url(
-    file_id: uuid.UUID,
-    expires: int = None,
-    permanent: bool = False,
-    db: Session = Depends(get_db),
-    storage_service: FileStorageService = Depends(get_file_storage_service),
-):
-    """
-    Get an access URL for a file (no authentication required).
-
-    Args:
-        file_id: The UUID of the file.
-        expires: URL validity period in seconds (default from FILE_URL_EXPIRES env).
-        permanent: If True, return a permanent URL without expiration.
-        db: Database session.
-        storage_service: The file storage service.
-
-    Returns:
-        ApiResponse with the access URL.
-    """
-    if expires is None:
-        expires = settings.FILE_URL_EXPIRES
-
-    api_logger.info(f"Get file URL request: file_id={file_id}, expires={expires}, permanent={permanent}")
-
-    # Query file metadata from database
-    file_metadata = db.query(FileMetadata).filter(FileMetadata.id == file_id).first()
-    if not file_metadata:
-        api_logger.warning(f"File not found in database: file_id={file_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The file does not exist"
-        )
-
-    if file_metadata.status != "completed":
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"File upload not completed, status: {file_metadata.status}"
-        )
-
-    file_key = file_metadata.file_key
-    storage = storage_service.storage
-
-    try:
-        if permanent:
-            # Generate permanent URL (no expiration check)
-            server_url = settings.FILE_LOCAL_SERVER_URL
-            url = f"{server_url}/storage/permanent/{file_id}"
-            return success(
-                data={
-                    "url": url,
-                    "expires_in": None,
-                    "permanent": True,
-                    "file_name": file_metadata.file_name,
-                },
-                msg="Permanent file URL generated successfully"
-            )
-
-        if isinstance(storage, LocalStorage):
-            # For local storage, generate signed URL with expiration
-            url = generate_signed_url(str(file_id), expires)
-        else:
-            # For remote storage (OSS/S3), get presigned URL
-            url = await storage_service.get_file_url(file_key, expires=expires)
-
-        api_logger.info(f"Generated file URL: file_id={file_id}")
-        return success(
-            data={
-                "url": url,
-                "expires_in": expires,
-                "permanent": False,
-                "file_name": file_metadata.file_name,
-            },
-            msg="File URL generated successfully"
-        )
-    except Exception as e:
-        api_logger.error(f"Failed to generate file URL: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Failed to generate file URL: {str(e)}"
-        )
-
-
-@router.get("/public/{file_id}", response_model=Any)
-async def public_download_file(
-    file_id: uuid.UUID,
-    expires: int = 0,
-    signature: str = "",
-    db: Session = Depends(get_db),
-    storage_service: FileStorageService = Depends(get_file_storage_service),
-) -> Any:
-    """
-    Public file download endpoint with signature verification.
-
-    This endpoint allows downloading files without authentication,
-    but requires a valid signature and non-expired timestamp.
-
-    Args:
-        file_id: The UUID of the file.
-        expires: Expiration timestamp.
-        signature: HMAC signature for verification.
-        db: Database session.
-        storage_service: The file storage service.
-
-    Returns:
-        FileResponse for the requested file.
-    """
-    api_logger.info(f"Public download request: file_id={file_id}")
-
-    # Verify signature
-    is_valid, error_msg = verify_signed_url(str(file_id), expires, signature)
-    if not is_valid:
-        api_logger.warning(f"Invalid signed URL: file_id={file_id}, error={error_msg}")
-        raise HTTPException(
-            status_code=status.HTTP_403_FORBIDDEN,
-            detail=error_msg
-        )
-
-    # Query file metadata from database
-    file_metadata = db.query(FileMetadata).filter(FileMetadata.id == file_id).first()
-    if not file_metadata:
-        api_logger.warning(f"File not found in database: file_id={file_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The file does not exist"
-        )
-
-    if file_metadata.status != "completed":
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"File upload not completed, status: {file_metadata.status}"
-        )
-
-    file_key = file_metadata.file_key
-    storage = storage_service.storage
-
-    if isinstance(storage, LocalStorage):
-        full_path = storage._get_full_path(file_key)
-
-        if not full_path.exists():
-            api_logger.warning(f"File not found on disk: file_key={file_key}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="File not found"
-            )
-
-        api_logger.info(f"Serving public file: file_key={file_key}")
-        return FileResponse(
-            path=str(full_path),
-            filename=file_metadata.file_name,
-            media_type=file_metadata.content_type or "application/octet-stream"
-        )
-    else:
-        # For remote storage, redirect to presigned URL
-        try:
-            presigned_url = await storage_service.get_file_url(file_key, expires=3600)
-            return RedirectResponse(url=presigned_url, status_code=status.HTTP_302_FOUND)
-        except Exception as e:
-            api_logger.error(f"Failed to get presigned URL: {e}")
-            raise HTTPException(
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                detail=f"Failed to retrieve file: {str(e)}"
-            )
-
-
-@router.get("/permanent/{file_id}", response_model=Any)
-async def permanent_download_file(
-    file_id: uuid.UUID,
-    db: Session = Depends(get_db),
-    storage_service: FileStorageService = Depends(get_file_storage_service),
-) -> Any:
-    """
-    Permanent file download endpoint (no expiration, no signature required).
-
-    This endpoint allows downloading files without authentication or expiration.
-    Use with caution as URLs are permanently accessible.
-
-    Args:
-        file_id: The UUID of the file.
-        db: Database session.
-        storage_service: The file storage service.
-
-    Returns:
-        FileResponse for the requested file.
-    """
-    api_logger.info(f"Permanent download request: file_id={file_id}")
-
-    # Query file metadata from database
-    file_metadata = db.query(FileMetadata).filter(FileMetadata.id == file_id).first()
-    if not file_metadata:
-        api_logger.warning(f"File not found in database: file_id={file_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The file does not exist"
-        )
-
-    if file_metadata.status != "completed":
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"File upload not completed, status: {file_metadata.status}"
-        )
-
-    file_key = file_metadata.file_key
-    storage = storage_service.storage
-
-    if isinstance(storage, LocalStorage):
-        full_path = storage._get_full_path(file_key)
-
-        if not full_path.exists():
-            api_logger.warning(f"File not found on disk: file_key={file_key}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="File not found"
-            )
-
-        api_logger.info(f"Serving permanent file: file_key={file_key}")
-        return FileResponse(
-            path=str(full_path),
-            filename=file_metadata.file_name,
-            media_type=file_metadata.content_type or "application/octet-stream"
-        )
-    else:
-        # For remote storage, redirect to presigned URL with long expiration
-        try:
-            # Use a very long expiration (7 days max for most cloud providers)
-            presigned_url = await storage_service.get_file_url(file_key, expires=604800)
-            return RedirectResponse(url=presigned_url, status_code=status.HTTP_302_FOUND)
-        except Exception as e:
-            api_logger.error(f"Failed to get presigned URL: {e}")
-            raise HTTPException(
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                detail=f"Failed to retrieve file: {str(e)}"
-            )

api/app/controllers/implicit_memory_controller.py

@@ -122,10 +122,10 @@ def validate_confidence_threshold(threshold: float) -> None:
         raise ValueError("confidence_threshold must be between 0.0 and 1.0")
 
 
-@router.get("/preferences/{end_user_id}", response_model=ApiResponse)
+@router.get("/preferences/{user_id}", response_model=ApiResponse)
 @cur_workspace_access_guard()
 async def get_preference_tags(
-    end_user_id: str,
+    user_id: str,
     confidence_threshold: float = Query(0.5, ge=0.0, le=1.0, description="Minimum confidence threshold"),
     tag_category: Optional[str] = Query(None, description="Filter by tag category"),
     start_date: Optional[datetime] = Query(None, description="Filter start date"),
@@ -137,7 +137,7 @@ async def get_preference_tags(
     Get user preference tags from cache.
     
     Args:
-        end_user_id: Target end user ID
+        user_id: Target user ID
         confidence_threshold: Minimum confidence score (0.0-1.0)
         tag_category: Optional category filter
         start_date: Optional start date filter
@@ -146,24 +146,24 @@ async def get_preference_tags(
     Returns:
         List of preference tags from cache
     """
-    api_logger.info(f"Preference tags requested for user: {end_user_id} (from cache)")
+    api_logger.info(f"Preference tags requested for user: {user_id} (from cache)")
     
     try:
         # Validate inputs
-        validate_user_id(end_user_id)
+        validate_user_id(user_id)
         
         # Create service with user-specific config
-        service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
+        service = ImplicitMemoryService(db=db, end_user_id=user_id)
         
         # Get cached profile
-        cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
+        cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db)
         
         if cached_profile is None:
-            api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
+            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
             return fail(
-                BizCode.NOT_FOUND,
-                "画像缓存不存在或已过期，请右上角刷新生成新画像",
-                ""
+                BizCode.RESOURCE_NOT_FOUND,
+                "画像缓存不存在或已过期，请调用 /generate_profile 接口生成新画像",
+                None
             )
         
         # Extract preferences from cache
@@ -192,17 +192,17 @@ async def get_preference_tags(
             
             filtered_preferences.append(pref)
         
-        api_logger.info(f"Retrieved {len(filtered_preferences)} preference tags for user: {end_user_id} (from cache)")
+        api_logger.info(f"Retrieved {len(filtered_preferences)} preference tags for user: {user_id} (from cache)")
         return success(data=filtered_preferences, msg="偏好标签获取成功（缓存）")
         
     except Exception as e:
-        return handle_implicit_memory_error(e, "偏好标签获取", end_user_id)
+        return handle_implicit_memory_error(e, "偏好标签获取", user_id)
 
 
-@router.get("/portrait/{end_user_id}", response_model=ApiResponse)
+@router.get("/portrait/{user_id}", response_model=ApiResponse)
 @cur_workspace_access_guard()
 async def get_dimension_portrait(
-    end_user_id: str,
+    user_id: str,
     include_history: bool = Query(False, description="Include historical trends"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user)
@@ -211,46 +211,46 @@ async def get_dimension_portrait(
     Get user's four-dimension personality portrait from cache.
     
     Args:
-        end_user_id: Target end user ID
+        user_id: Target user ID
         include_history: Whether to include historical trend data (ignored for cached data)
         
     Returns:
         Four-dimension personality portrait from cache
     """
-    api_logger.info(f"Dimension portrait requested for user: {end_user_id} (from cache)")
+    api_logger.info(f"Dimension portrait requested for user: {user_id} (from cache)")
     
     try:
         # Validate inputs
-        validate_user_id(end_user_id)
+        validate_user_id(user_id)
         
         # Create service with user-specific config
-        service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
+        service = ImplicitMemoryService(db=db, end_user_id=user_id)
         
         # Get cached profile
-        cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
+        cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db)
         
         if cached_profile is None:
-            api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
+            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
             return fail(
-                BizCode.NOT_FOUND,
-                "画像缓存不存在或已过期，请右上角刷新生成新画像",
-                ""
+                BizCode.RESOURCE_NOT_FOUND,
+                "画像缓存不存在或已过期，请调用 /generate_profile 接口生成新画像",
+                None
             )
         
         # Extract portrait from cache
         portrait = cached_profile.get("portrait", {})
         
-        api_logger.info(f"Dimension portrait retrieved for user: {end_user_id} (from cache)")
+        api_logger.info(f"Dimension portrait retrieved for user: {user_id} (from cache)")
         return success(data=portrait, msg="四维画像获取成功（缓存）")
         
     except Exception as e:
-        return handle_implicit_memory_error(e, "四维画像获取", end_user_id)
+        return handle_implicit_memory_error(e, "四维画像获取", user_id)
 
 
-@router.get("/interest-areas/{end_user_id}", response_model=ApiResponse)
+@router.get("/interest-areas/{user_id}", response_model=ApiResponse)
 @cur_workspace_access_guard()
 async def get_interest_area_distribution(
-    end_user_id: str,
+    user_id: str,
     include_trends: bool = Query(False, description="Include trend analysis"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user)
@@ -259,46 +259,46 @@ async def get_interest_area_distribution(
     Get user's interest area distribution from cache.
     
     Args:
-        end_user_id: Target end user ID
+        user_id: Target user ID
         include_trends: Whether to include trend analysis data (ignored for cached data)
         
     Returns:
         Interest area distribution from cache
     """
-    api_logger.info(f"Interest area distribution requested for user: {end_user_id} (from cache)")
+    api_logger.info(f"Interest area distribution requested for user: {user_id} (from cache)")
     
     try:
         # Validate inputs
-        validate_user_id(end_user_id)
+        validate_user_id(user_id)
         
         # Create service with user-specific config
-        service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
+        service = ImplicitMemoryService(db=db, end_user_id=user_id)
         
         # Get cached profile
-        cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
+        cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db)
         
         if cached_profile is None:
-            api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
+            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
             return fail(
-                BizCode.NOT_FOUND,
-                "画像缓存不存在或已过期，请右上角刷新生成新画像",
-                ""
+                BizCode.RESOURCE_NOT_FOUND,
+                "画像缓存不存在或已过期，请调用 /generate_profile 接口生成新画像",
+                None
             )
         
         # Extract interest areas from cache
         interest_areas = cached_profile.get("interest_areas", {})
         
-        api_logger.info(f"Interest area distribution retrieved for user: {end_user_id} (from cache)")
+        api_logger.info(f"Interest area distribution retrieved for user: {user_id} (from cache)")
         return success(data=interest_areas, msg="兴趣领域分布获取成功（缓存）")
         
     except Exception as e:
-        return handle_implicit_memory_error(e, "兴趣领域分布获取", end_user_id)
+        return handle_implicit_memory_error(e, "兴趣领域分布获取", user_id)
 
 
-@router.get("/habits/{end_user_id}", response_model=ApiResponse)
+@router.get("/habits/{user_id}", response_model=ApiResponse)
 @cur_workspace_access_guard()
 async def get_behavior_habits(
-    end_user_id: str,
+    user_id: str,
     confidence_level: Optional[str] = Query(None, regex="^(high|medium|low)$", description="Filter by confidence level"),
     frequency_pattern: Optional[str] = Query(None, regex="^(daily|weekly|monthly|seasonal|occasional|event_triggered)$", description="Filter by frequency pattern"),
     time_period: Optional[str] = Query(None, regex="^(current|past)$", description="Filter by time period"),
@@ -309,7 +309,7 @@ async def get_behavior_habits(
     Get user's behavioral habits from cache.
     
     Args:
-        end_user_id: Target end user ID
+        user_id: Target user ID
         confidence_level: Filter by confidence level (high, medium, low)
         frequency_pattern: Filter by frequency pattern (daily, weekly, monthly, seasonal, occasional, event_triggered)
         time_period: Filter by time period (current, past)
@@ -317,24 +317,24 @@ async def get_behavior_habits(
     Returns:
         List of behavioral habits from cache
     """
-    api_logger.info(f"Behavior habits requested for user: {end_user_id} (from cache)")
+    api_logger.info(f"Behavior habits requested for user: {user_id} (from cache)")
     
     try:
         # Validate inputs
-        validate_user_id(end_user_id)
+        validate_user_id(user_id)
         
         # Create service with user-specific config
-        service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
+        service = ImplicitMemoryService(db=db, end_user_id=user_id)
         
         # Get cached profile
-        cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
+        cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db)
         
         if cached_profile is None:
-            api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
+            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
             return fail(
-                BizCode.NOT_FOUND,
-                "画像缓存不存在或已过期，请右上角刷新生成新画像",
-                ""
+                BizCode.RESOURCE_NOT_FOUND,
+                "画像缓存不存在或已过期，请调用 /generate_profile 接口生成新画像",
+                None
             )
         
         # Extract habits from cache
@@ -368,11 +368,11 @@ async def get_behavior_habits(
             
             filtered_habits.append(habit)
         
-        api_logger.info(f"Retrieved {len(filtered_habits)} behavior habits for user: {end_user_id} (from cache)")
+        api_logger.info(f"Retrieved {len(filtered_habits)} behavior habits for user: {user_id} (from cache)")
         return success(data=filtered_habits, msg="行为习惯获取成功（缓存）")
         
     except Exception as e:
-        return handle_implicit_memory_error(e, "行为习惯获取", end_user_id)
+        return handle_implicit_memory_error(e, "行为习惯获取", user_id)
 
 
 

api/app/controllers/knowledge_controller.py

@@ -9,16 +9,13 @@ from sqlalchemy import or_
 from sqlalchemy.orm import Session
 
 from app.celery_app import celery_app
-from app.core.error_codes import BizCode
 from app.core.logging_config import get_api_logger
 from app.core.rag.common import settings
-from app.core.rag.integrations.feishu.client import FeishuAPIClient
-from app.core.rag.integrations.yuque.client import YuqueAPIClient
 from app.core.rag.llm.chat_model import Base
 from app.core.rag.nlp import rag_tokenizer, search
 from app.core.rag.prompts.generator import graph_entity_types
 from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
-from app.core.response_utils import success, fail
+from app.core.response_utils import success
 from app.db import get_db
 from app.dependencies import get_current_user
 from app.models import knowledge_model
@@ -487,99 +484,3 @@ async def rebuild_knowledge_graph(
     except Exception as e:
         api_logger.error(f"Failed to rebuild knowledge graph: knowledge_id={knowledge_id} - {str(e)}")
         raise
-
-
-@router.get("/check/yuque/auth", response_model=ApiResponse)
-async def check_yuque_auth(
-        yuque_user_id: str,
-        yuque_token: str,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    check yuque auth info
-    """
-    api_logger.info(f"check yuque auth info, username: {current_user.username}")
-
-    try:
-        api_client = YuqueAPIClient(
-            user_id=yuque_user_id,
-            token=yuque_token
-        )
-        async with api_client as client:
-            repos = await client.get_user_repos()
-            if repos:
-                return success(msg="Successfully auth yuque info")
-        return fail(BizCode.UNAUTHORIZED, msg="auth yuque info failed", error="user_id or token is incorrect")
-    except HTTPException:
-        raise
-    except Exception as e:
-        api_logger.error(f"auth yuque info failed: {str(e)}")
-        raise
-
-
-@router.get("/check/feishu/auth", response_model=ApiResponse)
-async def check_feishu_auth(
-        feishu_app_id: str,
-        feishu_app_secret: str,
-        feishu_folder_token: str,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    check feishu auth info
-    """
-    api_logger.info(f"check feishu auth info, username: {current_user.username}")
-
-    try:
-        api_client = FeishuAPIClient(
-            app_id=feishu_app_id,
-            app_secret=feishu_app_secret
-        )
-        async with api_client as client:
-            files = await client.list_all_folder_files(feishu_folder_token, recursive=True)
-            if files:
-                return success(msg="Successfully auth feishu info")
-        return fail(BizCode.UNAUTHORIZED, msg="auth feishu info failed", error="app_id or app_secret or feishu_folder_token is incorrect")
-    except HTTPException:
-        raise
-    except Exception as e:
-        api_logger.error(f"auth feishu info failed: {str(e)}")
-        raise
-
-
-@router.post("/{knowledge_id}/sync", response_model=ApiResponse)
-async def sync_knowledge(
-        knowledge_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    sync knowledge base information based on knowledge_id
-    """
-    api_logger.info(f"Obtain details of the knowledge base: knowledge_id={knowledge_id}, username: {current_user.username}")
-
-    try:
-        # 1. Query knowledge base information from the database
-        api_logger.debug(f"Query knowledge base: {knowledge_id}")
-        db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=knowledge_id, current_user=current_user)
-        if not db_knowledge:
-            api_logger.warning(f"The knowledge base does not exist or access is denied: knowledge_id={knowledge_id}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="The knowledge base does not exist or access is denied"
-            )
-
-        # 2. sync knowledge
-        # from app.tasks import sync_knowledge_for_kb
-        # sync_knowledge_for_kb(kb_id)
-        task = celery_app.send_task("app.core.rag.tasks.sync_knowledge_for_kb", args=[knowledge_id])
-        result = {
-             "task_id": task.id
-        }
-        return success(data=result, msg="Task accepted. sync knowledge is being processed in the background.")
-    except HTTPException:
-        raise
-    except Exception as e:
-        api_logger.error(f"Failed to sync knowledge: knowledge_id={knowledge_id} - {str(e)}")
-        raise

api/app/controllers/mcp_market_config_controller.py

@@ -1,336 +0,0 @@
-import datetime
-import json
-from typing import Optional
-import uuid
-
-from fastapi import APIRouter, Depends, HTTPException, status, Query
-from fastapi.encoders import jsonable_encoder
-import requests
-from sqlalchemy import or_
-from sqlalchemy.orm import Session
-from modelscope.hub.errors import raise_for_http_status
-from modelscope.hub.mcp_api import MCPApi
-
-from app.core.logging_config import get_api_logger
-from app.core.response_utils import success, fail
-from app.db import get_db
-from app.dependencies import get_current_user
-from app.models import mcp_market_config_model
-from app.models.user_model import User
-from app.schemas import mcp_market_config_schema
-from app.schemas.response_schema import ApiResponse
-from app.services import mcp_market_config_service
-
-# Obtain a dedicated API logger
-api_logger = get_api_logger()
-
-router = APIRouter(
-    prefix="/mcp_market_configs",
-    tags=["mcp_market_configs"],
-    dependencies=[Depends(get_current_user)]  # Apply auth to all routes in this controller
-)
-
-
-@router.get("/mcp_servers", response_model=ApiResponse)
-async def get_mcp_servers(
-        mcp_market_config_id: uuid.UUID,
-        page: int = Query(1, gt=0),  # Default: 1, which must be greater than 0
-        pagesize: int = Query(20, gt=0, le=100),  # Default: 20 items per page, maximum: 100 items
-        keywords: Optional[str] = Query(None, description="Search keywords (Optional search query string,e.g. Chinese service name, English service name, author/owner username)"),
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    Query the mcp servers list in pages
-    - Support keyword search for name,author,owner
-    - Return paging metadata + mcp server list
-    """
-    api_logger.info(
-        f"Query mcp server list: tenant_id={current_user.tenant_id}, page={page}, pagesize={pagesize}, keywords={keywords}, username: {current_user.username}")
-
-    # 1. parameter validation
-    if page < 1 or pagesize < 1:
-        api_logger.warning(f"Error in paging parameters: page={page}, pagesize={pagesize}")
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="The paging parameter must be greater than 0"
-        )
-
-    # 2. Query mcp market config information from the database
-    api_logger.debug(f"Query mcp market config: {mcp_market_config_id}")
-    db_mcp_market_config = mcp_market_config_service.get_mcp_market_config_by_id(db,
-                                                                                 mcp_market_config_id=mcp_market_config_id,
-                                                                                 current_user=current_user)
-    if not db_mcp_market_config:
-        api_logger.warning(
-            f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The mcp market config does not exist or access is denied"
-        )
-
-    # 3. Execute paged query
-    api = MCPApi()
-    token = db_mcp_market_config.token
-    api.login(token)
-
-    body = {
-        'filter': {},
-        'page_number': page,
-        'page_size': pagesize,
-        'search': keywords
-    }
-
-    try:
-        cookies = api.get_cookies(token)
-        r = api.session.put(
-            url=api.mcp_base_url,
-            headers=api.builder_headers(api.headers),
-            json=body,
-            cookies=cookies)
-        raise_for_http_status(r)
-    except requests.exceptions.RequestException as e:
-        api_logger.error(f"mFailed to get MCP servers: {str(e)}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Failed to get MCP servers: {str(e)}"
-        )
-
-    data = api._handle_response(r)
-    total = data.get('total_count', 0)
-    mcp_server_list = data.get('mcp_server_list', [])
-    # items = [{
-    #     'name': item.get('name', ''),
-    #     'id': item.get('id', ''),
-    #     'description': item.get('description', '')
-    # } for item in mcp_server_list]
-
-    # 4. Return structured response
-    result = {
-        "items": mcp_server_list,
-        "page": {
-            "page": page,
-            "pagesize": pagesize,
-            "total": total,
-            "has_next": True if page * pagesize < total else False
-        }
-    }
-    return success(data=result, msg="Query of mcp servers list successful")
-
-
-@router.get("/mcp_server", response_model=ApiResponse)
-async def get_mcp_server(
-        mcp_market_config_id: uuid.UUID,
-        server_id: str,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    Get detailed information for a specific MCP Server
-    """
-    api_logger.info(
-        f"Query mcp server: tenant_id={current_user.tenant_id}, mcp_market_config_id={mcp_market_config_id}, server_id={server_id}, username: {current_user.username}")
-
-    # 1. Query mcp market config information from the database
-    api_logger.debug(f"Query mcp market config: {mcp_market_config_id}")
-    db_mcp_market_config = mcp_market_config_service.get_mcp_market_config_by_id(db,
-                                                                                 mcp_market_config_id=mcp_market_config_id,
-                                                                                 current_user=current_user)
-    if not db_mcp_market_config:
-        api_logger.warning(
-            f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The mcp market config does not exist or access is denied"
-        )
-
-    # 2. Get detailed information for a specific MCP Server
-    api = MCPApi()
-    token = db_mcp_market_config.token
-    api.login(token)
-
-    result = api.get_mcp_server(server_id=server_id)
-    return success(data=result, msg="Query of mcp servers list successful")
-
-
-@router.post("/mcp_market_config", response_model=ApiResponse)
-async def create_mcp_market_config(
-        create_data: mcp_market_config_schema.McpMarketConfigCreate,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    create mcp market config
-    """
-    api_logger.info(
-        f"Request to create a mcp market config: mcp_market_id={create_data.mcp_market_id}, tenant_id={current_user.tenant_id}, username: {current_user.username}")
-
-    try:
-        api_logger.debug(f"Start creating the mcp market config: {create_data.mcp_market_id}")
-        # 1. Check if the mcp market name already exists
-        db_mcp_market_config_exist = mcp_market_config_service.get_mcp_market_config_by_mcp_market_id(db, mcp_market_id=create_data.mcp_market_id, current_user=current_user)
-        if db_mcp_market_config_exist:
-            api_logger.warning(f"The mcp market id already exists: {create_data.mcp_market_id}")
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"The mcp market id already exists: {create_data.mcp_market_id}"
-            )
-        db_mcp_market_config = mcp_market_config_service.create_mcp_market_config(db=db, mcp_market_config=create_data, current_user=current_user)
-        api_logger.info(
-            f"The mcp market config has been successfully created: (ID: {db_mcp_market_config.id})")
-        return success(data=jsonable_encoder(mcp_market_config_schema.McpMarketConfig.model_validate(db_mcp_market_config)),
-                       msg="The mcp market config has been successfully created")
-    except Exception as e:
-        api_logger.error(f"The creation of the mcp market config failed: {create_data.mcp_market_id} - {str(e)}")
-        raise
-
-
-@router.get("/{mcp_market_config_id}", response_model=ApiResponse)
-async def get_mcp_market_config(
-        mcp_market_config_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    Retrieve mcp market config information based on mcp_market_config_id
-    """
-    api_logger.info(
-        f"Obtain details of the mcp market config: mcp_market_config_id={mcp_market_config_id}, username: {current_user.username}")
-
-    try:
-        # 1. Query mcp market config information from the database
-        api_logger.debug(f"Query mcp market config: {mcp_market_config_id}")
-        db_mcp_market_config = mcp_market_config_service.get_mcp_market_config_by_id(db, mcp_market_config_id=mcp_market_config_id, current_user=current_user)
-        if not db_mcp_market_config:
-            api_logger.warning(f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="The mcp market config does not exist or access is denied"
-            )
-
-        api_logger.info(f"mcp market config query successful: (ID: {db_mcp_market_config.id})")
-        return success(data=jsonable_encoder(mcp_market_config_schema.McpMarketConfig.model_validate(db_mcp_market_config)),
-                       msg="Successfully obtained mcp market config information")
-    except HTTPException:
-        raise
-    except Exception as e:
-        api_logger.error(f"mcp market config query failed: mcp_market_config_id={mcp_market_config_id} - {str(e)}")
-        raise
-
-
-@router.get("/mcp_market_id/{mcp_market_id}", response_model=ApiResponse)
-async def get_mcp_market_config_by_mcp_market_id(
-        mcp_market_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    Retrieve mcp market config information based on mcp_market_id
-    """
-    api_logger.info(
-        f"Request to create a mcp market config: mcp_market_id={mcp_market_id}, tenant_id={current_user.tenant_id}, username: {current_user.username}")
-
-    try:
-        # 1. Query mcp market config information from the database
-        api_logger.debug(f"Query mcp market config: mcp_market_id={mcp_market_id}")
-        db_mcp_market_config = mcp_market_config_service.get_mcp_market_config_by_mcp_market_id(db, mcp_market_id=mcp_market_id, current_user=current_user)
-        if not db_mcp_market_config:
-            api_logger.warning(f"The mcp market config does not exist or access is denied: mcp_market_id={mcp_market_id}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="The mcp market config does not exist or access is denied"
-            )
-
-        api_logger.info(f"mcp market config query successful: (ID: {db_mcp_market_config.id})")
-        return success(data=jsonable_encoder(mcp_market_config_schema.McpMarketConfig.model_validate(db_mcp_market_config)),
-                       msg="Successfully obtained mcp market config information")
-    except HTTPException:
-        raise
-    except Exception as e:
-        api_logger.error(f"mcp market config query failed: mcp_market_id={mcp_market_id} - {str(e)}")
-        raise
-
-
-@router.put("/{mcp_market_config_id}", response_model=ApiResponse)
-async def update_mcp_market_config(
-        mcp_market_config_id: uuid.UUID,
-        update_data: mcp_market_config_schema.McpMarketConfigUpdate,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    # 1. Check if the mcp market config exists
-    api_logger.debug(f"Query the mcp market config to be updated: {mcp_market_config_id}")
-    db_mcp_market_config = mcp_market_config_service.get_mcp_market_config_by_id(db, mcp_market_config_id=mcp_market_config_id, current_user=current_user)
-
-    if not db_mcp_market_config:
-        api_logger.warning(
-            f"The mcp market config does not exist or you do not have permission to access it: mcp_market_config_id={mcp_market_config_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The mcp market config does not exist or you do not have permission to access it"
-        )
-
-    # 2. Update fields (only update non-null fields)
-    api_logger.debug(f"Start updating the mcp market config fields: {mcp_market_config_id}")
-    update_dict = update_data.dict(exclude_unset=True)
-    updated_fields = []
-    for field, value in update_dict.items():
-        if hasattr(db_mcp_market_config, field):
-            old_value = getattr(db_mcp_market_config, field)
-            if old_value != value:
-                # update value
-                setattr(db_mcp_market_config, field, value)
-                updated_fields.append(f"{field}: {old_value} -> {value}")
-
-    if updated_fields:
-        api_logger.debug(f"updated fields: {', '.join(updated_fields)}")
-
-    # 3. Save to database
-    try:
-        db.commit()
-        db.refresh(db_mcp_market_config)
-        api_logger.info(f"The mcp market config has been successfully updated: (ID: {db_mcp_market_config.id})")
-    except Exception as e:
-        db.rollback()
-        api_logger.error(f"The mcp market config update failed: mcp_market_config_id={mcp_market_config_id} - {str(e)}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"The mcp market config update failed: {str(e)}"
-        )
-
-    # 4. Return the updated mcp market config
-    return success(data=jsonable_encoder(mcp_market_config_schema.McpMarketConfig.model_validate(db_mcp_market_config)),
-                   msg="The mcp market config information updated successfully")
-
-
-@router.delete("/{mcp_market_config_id}", response_model=ApiResponse)
-async def delete_mcp_market_config(
-        mcp_market_config_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    delete mcp market config
-    """
-    api_logger.info(f"Request to delete mcp market config: mcp_market_config_id={mcp_market_config_id}, username: {current_user.username}")
-
-    try:
-        # 1. Check whether the mcp market config exists
-        api_logger.debug(f"Check whether the mcp market config exists: {mcp_market_config_id}")
-        db_mcp_market_config = mcp_market_config_service.get_mcp_market_config_by_id(db, mcp_market_config_id=mcp_market_config_id, current_user=current_user)
-
-        if not db_mcp_market_config:
-            api_logger.warning(
-                f"The mcp market config does not exist or you do not have permission to access it: mcp_market_config_id={mcp_market_config_id}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="The mcp market config does not exist or you do not have permission to access it"
-            )
-
-        # 2. Deleting mcp market config
-        mcp_market_config_service.delete_mcp_market_config_by_id(db, mcp_market_config_id=mcp_market_config_id, current_user=current_user)
-        api_logger.info(f"The mcp market config has been successfully deleted: (ID: {mcp_market_config_id})")
-        return success(msg="The mcp market config has been successfully deleted")
-    except Exception as e:
-        api_logger.error(f"Failed to delete from the mcp market config: mcp_market_config_id={mcp_market_config_id} - {str(e)}")
-        raise

api/app/controllers/mcp_market_controller.py

@@ -1,262 +0,0 @@
-import datetime
-import json
-from typing import Optional
-import uuid
-
-from fastapi import APIRouter, Depends, HTTPException, status, Query
-from fastapi.encoders import jsonable_encoder
-from sqlalchemy import or_
-from sqlalchemy.orm import Session
-
-from app.core.logging_config import get_api_logger
-from app.core.response_utils import success, fail
-from app.db import get_db
-from app.dependencies import get_current_user
-from app.models import mcp_market_model
-from app.models.user_model import User
-from app.schemas import mcp_market_schema
-from app.schemas.response_schema import ApiResponse
-from app.services import mcp_market_service
-
-# Obtain a dedicated API logger
-api_logger = get_api_logger()
-
-router = APIRouter(
-    prefix="/mcp_markets",
-    tags=["mcp_markets"],
-    dependencies=[Depends(get_current_user)]  # Apply auth to all routes in this controller
-)
-
-
-@router.get("/mcp_markets", response_model=ApiResponse)
-async def get_mcp_markets(
-        page: int = Query(1, gt=0),  # Default: 1, which must be greater than 0
-        pagesize: int = Query(20, gt=0, le=100),  # Default: 20 items per page, maximum: 100 items
-        orderby: Optional[str] = Query(None, description="Sort fields, such as: category, created_at"),
-        desc: Optional[bool] = Query(False, description="Is it descending order"),
-        keywords: Optional[str] = Query(None, description="Search keywords (mcp_market base name)"),
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    Query the mcp markets list in pages
-    - Support keyword search for name,description
-    - Support dynamic sorting
-    - Return paging metadata + mcp_market list
-    """
-    api_logger.info(
-        f"Query mcp market list: tenant_id={current_user.tenant_id}, page={page}, pagesize={pagesize}, keywords={keywords}, username: {current_user.username}")
-
-    # 1. parameter validation
-    if page < 1 or pagesize < 1:
-        api_logger.warning(f"Error in paging parameters: page={page}, pagesize={pagesize}")
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="The paging parameter must be greater than 0"
-        )
-
-    # 2. Construct query conditions
-    filters = []
-
-    # Keyword search (fuzzy matching of mcp market name,description)
-    if keywords:
-        api_logger.debug(f"Add keyword search criteria: {keywords}")
-        filters.append(
-            or_(
-                mcp_market_model.McpMarket.name.ilike(f"%{keywords}%"),
-                mcp_market_model.McpMarket.description.ilike(f"%{keywords}%")
-            )
-        )
-    # 3. Execute paged query
-    try:
-        api_logger.debug("Start executing mcp market paging query")
-        total, items = mcp_market_service.get_mcp_markets_paginated(
-            db=db,
-            filters=filters,
-            page=page,
-            pagesize=pagesize,
-            orderby=orderby,
-            desc=desc,
-            current_user=current_user
-        )
-        api_logger.info(f"mcp market query successful: total={total}, returned={len(items)} records")
-    except Exception as e:
-        api_logger.error(f"mcp market query failed: {str(e)}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Query failed: {str(e)}"
-        )
-
-    # 4. Return structured response
-    result = {
-        "items": items,
-        "page": {
-            "page": page,
-            "pagesize": pagesize,
-            "total": total,
-            "has_next": True if page * pagesize < total else False
-        }
-    }
-    return success(data=jsonable_encoder(result), msg="Query of mcp market list successful")
-
-
-@router.post("/mcp_market", response_model=ApiResponse)
-async def create_mcp_market(
-        create_data: mcp_market_schema.McpMarketCreate,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    create mcp market
-    """
-    api_logger.info(
-        f"Request to create a mcp market: name={create_data.name}, tenant_id={current_user.tenant_id}, username: {current_user.username}")
-
-    try:
-        api_logger.debug(f"Start creating the mcp market: {create_data.name}")
-        # 1. Check if the mcp market name already exists
-        db_mcp_market_exist = mcp_market_service.get_mcp_market_by_name(db, name=create_data.name, current_user=current_user)
-        if db_mcp_market_exist:
-            api_logger.warning(f"The mcp market name already exists: {create_data.name}")
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"The mcp market name already exists: {create_data.name}"
-            )
-        db_mcp_market = mcp_market_service.create_mcp_market(db=db, mcp_market=create_data, current_user=current_user)
-        api_logger.info(
-            f"The mcp market has been successfully created: {db_mcp_market.name} (ID: {db_mcp_market.id})")
-        return success(data=jsonable_encoder(mcp_market_schema.McpMarket.model_validate(db_mcp_market)),
-                       msg="The mcp market has been successfully created")
-    except Exception as e:
-        api_logger.error(f"The creation of the mcp market failed: {create_data.name} - {str(e)}")
-        raise
-
-
-@router.get("/{mcp_market_id}", response_model=ApiResponse)
-async def get_mcp_market(
-        mcp_market_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    Retrieve mcp market information based on mcp_market_id
-    """
-    api_logger.info(
-        f"Obtain details of the mcp market: mcp_market_id={mcp_market_id}, username: {current_user.username}")
-
-    try:
-        # 1. Query mcp market information from the database
-        api_logger.debug(f"Query mcp market: {mcp_market_id}")
-        db_mcp_market = mcp_market_service.get_mcp_market_by_id(db, mcp_market_id=mcp_market_id, current_user=current_user)
-        if not db_mcp_market:
-            api_logger.warning(f"The mcp market does not exist or access is denied: mcp_market_id={mcp_market_id}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="The mcp market does not exist or access is denied"
-            )
-
-        api_logger.info(f"mcp market query successful: {db_mcp_market.name} (ID: {db_mcp_market.id})")
-        return success(data=jsonable_encoder(mcp_market_schema.McpMarket.model_validate(db_mcp_market)),
-                       msg="Successfully obtained mcp market information")
-    except HTTPException:
-        raise
-    except Exception as e:
-        api_logger.error(f"mcp market query failed: mcp_market_id={mcp_market_id} - {str(e)}")
-        raise
-
-
-@router.put("/{mcp_market_id}", response_model=ApiResponse)
-async def update_mcp_market(
-        mcp_market_id: uuid.UUID,
-        update_data: mcp_market_schema.McpMarketUpdate,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    # 1. Check if the mcp market exists
-    api_logger.debug(f"Query the mcp market to be updated: {mcp_market_id}")
-    db_mcp_market = mcp_market_service.get_mcp_market_by_id(db, mcp_market_id=mcp_market_id, current_user=current_user)
-
-    if not db_mcp_market:
-        api_logger.warning(
-            f"The mcp market does not exist or you do not have permission to access it: mcp_market_id={mcp_market_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The mcp market does not exist or you do not have permission to access it"
-        )
-
-    # 2. not updating the name (name already exists)
-    update_dict = update_data.dict(exclude_unset=True)
-    if "name" in update_dict:
-        name = update_dict["name"]
-        if name != db_mcp_market.name:
-            # Check if the mcp market name already exists
-            db_mcp_market_exist = mcp_market_service.get_mcp_market_by_name(db, name=name, current_user=current_user)
-            if db_mcp_market_exist:
-                api_logger.warning(f"The mcp market name already exists: {name}")
-                raise HTTPException(
-                    status_code=status.HTTP_400_BAD_REQUEST,
-                    detail=f"The mcp market name already exists: {name}"
-                )
-    # 3. Update fields (only update non-null fields)
-    api_logger.debug(f"Start updating the mcp market fields: {mcp_market_id}")
-    updated_fields = []
-    for field, value in update_dict.items():
-        if hasattr(db_mcp_market, field):
-            old_value = getattr(db_mcp_market, field)
-            if old_value != value:
-                # update value
-                setattr(db_mcp_market, field, value)
-                updated_fields.append(f"{field}: {old_value} -> {value}")
-
-    if updated_fields:
-        api_logger.debug(f"updated fields: {', '.join(updated_fields)}")
-
-    # 4. Save to database
-    try:
-        db.commit()
-        db.refresh(db_mcp_market)
-        api_logger.info(f"The mcp market has been successfully updated: {db_mcp_market.name} (ID: {db_mcp_market.id})")
-    except Exception as e:
-        db.rollback()
-        api_logger.error(f"The mcp market update failed: mcp_market_id={mcp_market_id} - {str(e)}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"The mcp market update failed: {str(e)}"
-        )
-
-    # 5. Return the updated mcp market
-    return success(data=jsonable_encoder(mcp_market_schema.McpMarket.model_validate(db_mcp_market)),
-                   msg="The mcp market information updated successfully")
-
-
-@router.delete("/{mcp_market_id}", response_model=ApiResponse)
-async def delete_mcp_market(
-        mcp_market_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    delete mcp market
-    """
-    api_logger.info(f"Request to delete mcp market: mcp_market_id={mcp_market_id}, username: {current_user.username}")
-
-    try:
-        # 1. Check whether the mcp market exists
-        api_logger.debug(f"Check whether the mcp market exists: {mcp_market_id}")
-        db_mcp_market = mcp_market_service.get_mcp_market_by_id(db, mcp_market_id=mcp_market_id, current_user=current_user)
-
-        if not db_mcp_market:
-            api_logger.warning(
-                f"The mcp market does not exist or you do not have permission to access it: mcp_market_id={mcp_market_id}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="The mcp market does not exist or you do not have permission to access it"
-            )
-
-        # 2. Deleting mcp market
-        mcp_market_service.delete_mcp_market_by_id(db, mcp_market_id=mcp_market_id, current_user=current_user)
-        api_logger.info(f"The mcp market has been successfully deleted: (ID: {mcp_market_id})")
-        return success(msg="The mcp market has been successfully deleted")
-    except Exception as e:
-        api_logger.error(f"Failed to delete from the mcp market: mcp_market_id={mcp_market_id} - {str(e)}")
-        raise

api/app/controllers/memory_agent_controller.py

@@ -2,7 +2,6 @@ from typing import List, Optional
 
 from app.celery_app import celery_app
 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.rag.llm.cv_model import QWenCV
 from app.core.response_utils import fail, success
@@ -10,16 +9,14 @@ from app.db import get_db
 from app.dependencies import cur_workspace_access_guard, get_current_user
 from app.models import ModelApiKey
 from app.models.user_model import User
-from app.core.memory.agent.utils.session_tools import SessionService
-from app.core.memory.agent.utils.redis_tool import store
-from app.repositories import knowledge_repository, WorkspaceRepository
+from app.repositories import knowledge_repository
 from app.schemas.memory_agent_schema import UserInput, Write_UserInput
 from app.schemas.response_schema import ApiResponse
 from app.services import task_service, workspace_service
 from app.services.memory_agent_service import MemoryAgentService
 from app.services.model_service import ModelConfigService
 from dotenv import load_dotenv
-from fastapi import APIRouter, Depends, File, Form, Query, UploadFile,Header
+from fastapi import APIRouter, Depends, File, Form, Query, UploadFile
 from sqlalchemy.orm import Session
 from starlette.responses import StreamingResponse
 
@@ -119,7 +116,6 @@ async def download_log(
 @cur_workspace_access_guard()
 async def write_server(
     user_input: Write_UserInput,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user)
 ):
@@ -127,18 +123,14 @@ async def write_server(
     Write service endpoint - processes write operations synchronously
     
     Args:
-        user_input: Write request containing message and end_user_id
-        language_type: 语言类型 ("zh" 中文, "en" 英文)，通过 X-Language-Type Header 传递
+        user_input: Write request containing message and group_id
     
     Returns:
         Response with write operation status
     """
-    # 使用集中化的语言校验
-    language = get_language_from_header(language_type)
-    
     config_id = user_input.config_id
     workspace_id = current_user.current_workspace_id
-    api_logger.info(f"Write service: workspace_id={workspace_id}, config_id={config_id}, language_type={language}")
+    api_logger.info(f"Write service: workspace_id={workspace_id}, config_id={config_id}")
     
     # 获取 storage_type，如果为 None 则使用默认值
     storage_type = workspace_service.get_workspace_storage_type(
@@ -166,19 +158,16 @@ async def write_server(
             api_logger.warning("workspace_id 为空，无法使用 rag 存储，将使用 neo4j 存储")
             storage_type = 'neo4j'
     
-    api_logger.info(f"Write service requested for group {user_input.end_user_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}")
+    api_logger.info(f"Write service requested for group {user_input.group_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}")
     try:
-        messages_list = memory_agent_service.get_messages_list(user_input)
         result = await memory_agent_service.write_memory(
-            user_input.end_user_id,
-            messages_list,
+            user_input.group_id, 
+            user_input.message, 
             config_id,
             db,
             storage_type, 
-            user_rag_memory_id,
-            language
+            user_rag_memory_id
         )
-
         return success(data=result, msg="写入成功")
     except BaseException as e:
         # Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup
@@ -195,7 +184,6 @@ async def write_server(
 @cur_workspace_access_guard()
 async def write_server_async(
     user_input: Write_UserInput,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user)
 ):
@@ -203,19 +191,15 @@ async def write_server_async(
     Async write service endpoint - enqueues write processing to Celery
     
     Args:
-        user_input: Write request containing message and end_user_id
-        language_type: 语言类型 ("zh" 中文, "en" 英文)，通过 X-Language-Type Header 传递
+        user_input: Write request containing message and group_id
     
     Returns:
         Task ID for tracking async operation
         Use GET /memory/write_result/{task_id} to check task status and get result
     """
-    # 使用集中化的语言校验
-    language = get_language_from_header(language_type)
-    
     config_id = user_input.config_id
     workspace_id = current_user.current_workspace_id
-    api_logger.info(f"Async write service: workspace_id={workspace_id}, config_id={config_id}, language_type={language}")
+    api_logger.info(f"Async write service: workspace_id={workspace_id}, config_id={config_id}")
 
     # 获取 storage_type，如果为 None 则使用默认值
     storage_type = workspace_service.get_workspace_storage_type(
@@ -235,12 +219,9 @@ async def write_server_async(
         if knowledge: user_rag_memory_id = str(knowledge.id)
     api_logger.info(f"Async write: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
     try:
-        # 获取标准化的消息列表
-        messages_list = memory_agent_service.get_messages_list(user_input)
-
         task = celery_app.send_task(
             "app.core.memory.agent.write_message",
-            args=[user_input.end_user_id, messages_list, config_id, storage_type, user_rag_memory_id, language]
+            args=[user_input.group_id, user_input.message, config_id, storage_type, user_rag_memory_id]
         )
         api_logger.info(f"Write task queued: {task.id}")
         
@@ -266,14 +247,16 @@ async def read_server(
     - "2": Direct answer based on context
     
     Args:
-        user_input: Read request with message, history, search_switch, and end_user_id
+        user_input: Read request with message, history, search_switch, and group_id
     
     Returns:
         Response with query answer
     """
     config_id = user_input.config_id
     workspace_id = current_user.current_workspace_id
+    api_logger.info(f"Read service: workspace_id={workspace_id}, config_id={config_id}")
 
+    # 获取 storage_type，如果为 None 则使用默认值
     storage_type = workspace_service.get_workspace_storage_type(
         db=db,
         workspace_id=workspace_id,
@@ -288,13 +271,12 @@ async def read_server(
             name="USER_RAG_MERORY",
             workspace_id=workspace_id
         )
-        if knowledge:
-            user_rag_memory_id = str(knowledge.id)
+        if knowledge: user_rag_memory_id = str(knowledge.id)
     
-    api_logger.info(f"Read service: group={user_input.end_user_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}")
+    api_logger.info(f"Read service: group={user_input.group_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}")
     try:
         result = await memory_agent_service.read_memory(
-            user_input.end_user_id,
+            user_input.group_id,
             user_input.message,
             user_input.history,
             user_input.search_switch,
@@ -303,22 +285,6 @@ async def read_server(
             storage_type,
             user_rag_memory_id
         )
-        if str(user_input.search_switch) == "2":
-            retrieve_info = result['answer']
-            history = await SessionService(store).get_history(user_input.end_user_id, user_input.end_user_id, user_input.end_user_id)
-            query = user_input.message
-
-            # 调用 memory_agent_service 的方法生成最终答案
-            result['answer'] = await memory_agent_service.generate_summary_from_retrieve(
-                end_user_id=user_input.end_user_id,
-                retrieve_info=retrieve_info,
-                history=history,
-                query=query,
-                config_id=config_id,
-                db=db
-            )
-            if "信息不足，无法回答" in result['answer']:
-                result['answer']=retrieve_info
         return success(data=result, msg="回复对话消息成功")
     except BaseException as e:
         # Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup
@@ -416,7 +382,7 @@ async def read_server_async(
     try:
         task = celery_app.send_task(
             "app.core.memory.agent.read_message",
-            args=[user_input.end_user_id, user_input.message, user_input.history, user_input.search_switch,
+            args=[user_input.group_id, user_input.message, user_input.history, user_input.search_switch,
                   config_id, storage_type, user_rag_memory_id]
         )
         api_logger.info(f"Read task queued: {task.id}")
@@ -460,7 +426,7 @@ async def get_read_task_result(
                 return success(
                     data={
                         "result": task_result.get("result"),
-                        "end_user_id": task_result.get("end_user_id"),
+                        "group_id": task_result.get("group_id"),
                         "elapsed_time": task_result.get("elapsed_time"),
                         "task_id": task_id
                     },
@@ -537,7 +503,7 @@ async def get_write_task_result(
                 return success(
                     data={
                         "result": task_result.get("result"),
-                        "end_user_id": task_result.get("end_user_id"),
+                        "group_id": task_result.get("group_id"),
                         "elapsed_time": task_result.get("elapsed_time"),
                         "task_id": task_id
                     },
@@ -591,30 +557,15 @@ async def status_type(
     Determine the type of user message (read or write)
     
     Args:
-        user_input: Request containing user message and end_user_id
+        user_input: Request containing user message and group_id
     
     Returns:
         Type classification result
     """
-    api_logger.info(f"Status type check requested for group {user_input.end_user_id}")
+    api_logger.info(f"Status type check requested for group {user_input.group_id}")
     try:
-        # 获取标准化的消息列表
-        messages_list = memory_agent_service.get_messages_list(user_input)
-
-        # 将消息列表转换为字符串用于分类
-        # 只取最后一条用户消息进行分类
-        last_user_message = ""
-        for msg in reversed(messages_list):
-            if msg.get('role') == 'user':
-                last_user_message = msg.get('content', '')
-                break
-
-        if not last_user_message:
-            # 如果没有用户消息，使用所有消息的内容
-            last_user_message = " ".join([msg.get('content', '') for msg in messages_list])
-
         result = await memory_agent_service.classify_message_type(
-            last_user_message,
+            user_input.message,
             user_input.config_id,
             db
         )
@@ -637,7 +588,7 @@ async def get_knowledge_type_stats_api(
     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 (group_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}")
@@ -666,14 +617,11 @@ async def get_knowledge_type_stats_api(
 async def get_hot_memory_tags_by_user_api(
     end_user_id: Optional[str] = Query(None, description="用户ID（可选）"),
     limit: int = Query(20, description="返回标签数量限制"),
-    current_user: User = Depends(get_current_user),
-    db: Session=Depends(get_db),
+    current_user: User = Depends(get_current_user)
 ):
     """
     获取指定用户的热门记忆标签
     
-    注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译
-    
     返回格式：
     [
         {"name": "标签名", "frequency": 频次},

api/app/controllers/memory_dashboard_controller.py

@@ -5,6 +5,7 @@ from app.core.response_utils import success
 from app.db import get_db
 from app.dependencies import get_current_user
 from app.models.user_model import User
+from app.schemas.memory_agent_schema import End_User_Information
 from app.schemas.response_schema import ApiResponse
 
 from app.services import memory_dashboard_service, memory_storage_service, workspace_service
@@ -39,7 +40,54 @@ def get_workspace_total_end_users(
     api_logger.info(f"成功获取最新用户总数: total_num={total_end_users.get('total_num', 0)}")
     return success(data=total_end_users, msg="用户数量获取成功")
 
+@router.post("/update/end_users", response_model=ApiResponse)
+async def update_workspace_end_users(
+    user_input: End_User_Information,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+):
+    """
+    更新工作空间的宿主信息
+    """
+    username = user_input.end_user_name  # 要更新的用户名
+    end_user_input_id = user_input.id  # 宿主ID
+    workspace_id = current_user.current_workspace_id
+    
+    api_logger.info(f"用户 {current_user.username} 请求更新工作空间 {workspace_id} 的宿主信息")
+    api_logger.info(f"更新参数: username={username}, end_user_id={end_user_input_id}")
 
+    try:
+        # 导入更新函数
+        from app.repositories.end_user_repository import update_end_user_other_name
+        import uuid
+        
+        # 转换 end_user_id 为 UUID 类型
+        end_user_uuid = uuid.UUID(end_user_input_id)
+        
+        # 直接更新数据库中的 other_name 字段
+        updated_count = update_end_user_other_name(
+            db=db,
+            end_user_id=end_user_uuid,
+            other_name=username
+        )
+        
+        api_logger.info(f"成功更新宿主 {end_user_input_id} 的 other_name 为: {username}")
+
+        return success(
+            data={
+                "updated_count": updated_count,
+                "end_user_id": end_user_input_id,
+                "updated_other_name": username
+            },
+            msg=f"成功更新 {updated_count} 个宿主的信息"
+        )
+        
+    except Exception as e:
+        api_logger.error(f"更新宿主信息失败: {str(e)}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"更新宿主信息失败: {str(e)}"
+        )
 
 
 
@@ -49,134 +97,63 @@ async def get_workspace_end_users(
     current_user: User = Depends(get_current_user),
 ):
     """
-    获取工作空间的宿主列表（高性能优化版本 v2）
+    获取工作空间的宿主列表
     
-    优化策略：
-    1. 批量查询 end_users（一次查询而非循环）
-    2. 并发查询所有用户的记忆数量（Neo4j）
-    3. RAG 模式使用批量查询（一次 SQL）
-    4. 只返回必要字段减少数据传输
-    5. 添加短期缓存减少重复查询
-    6. 并发执行配置查询和记忆数量查询
-    
-    返回格式：
-    {
-        "end_user": {"id": "uuid", "other_name": "名称"},
-        "memory_num": {"total": 数量},
-        "memory_config": {"memory_config_id": "id", "memory_config_name": "名称"}
-    }
+    返回格式与原 memory_list 接口中的 end_users 字段相同，
+    并包含每个用户的记忆配置信息（memory_config_id 和 memory_config_name）
     """
-    import asyncio
-    import json
-    from app.aioRedis import aio_redis_get, aio_redis_set
-    
     workspace_id = current_user.current_workspace_id
-    
-    # 尝试从缓存获取（30秒缓存）
-    cache_key = f"end_users:workspace:{workspace_id}"
-    try:
-        cached_data = await aio_redis_get(cache_key)
-        if cached_data:
-            api_logger.info(f"从缓存获取宿主列表: workspace_id={workspace_id}")
-            return success(data=json.loads(cached_data), msg="宿主列表获取成功")
-    except Exception as e:
-        api_logger.warning(f"Redis 缓存读取失败: {str(e)}")
-    
     # 获取当前空间类型
     current_workspace_type = memory_dashboard_service.get_current_workspace_type(db, workspace_id, current_user)
     api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {workspace_id} 的宿主列表")
-    
-    # 获取 end_users（已优化为批量查询）
     end_users = memory_dashboard_service.get_workspace_end_users(
         db=db,
         workspace_id=workspace_id,
         current_user=current_user
     )
-    if not end_users:
-        api_logger.info("工作空间下没有宿主")
-        # 缓存空结果，避免重复查询
-        try:
-            await aio_redis_set(cache_key, json.dumps([]), expire=30)
-        except Exception as e:
-            api_logger.warning(f"Redis 缓存写入失败: {str(e)}")
-        return success(data=[], msg="宿主列表获取成功")
     
+    # 批量获取所有用户的记忆配置信息（优化：一次查询而非 N 次）
     end_user_ids = [str(user.id) for user in end_users]
-    
-    # 并发执行两个独立的查询任务
-    async def get_memory_configs():
-        """获取记忆配置（在线程池中执行同步查询）"""
+    memory_configs_map = {}
+    if end_user_ids:
         try:
-            return await asyncio.to_thread(
-                get_end_users_connected_configs_batch,
-                end_user_ids, db
-            )
+            memory_configs_map = get_end_users_connected_configs_batch(end_user_ids, db)
         except Exception as e:
             api_logger.error(f"批量获取记忆配置失败: {str(e)}")
-            return {}
+            # 失败时使用空字典，不影响其他数据返回
     
-    async def get_memory_nums():
-        """获取记忆数量"""
-        if current_workspace_type == "rag":
-            # RAG 模式：批量查询
-            try:
-                chunk_map = await asyncio.to_thread(
-                    memory_dashboard_service.get_users_total_chunk_batch,
-                    end_user_ids, db, current_user
-                )
-                return {uid: {"total": count} for uid, count in chunk_map.items()}
-            except Exception as e:
-                api_logger.error(f"批量获取 RAG chunk 数量失败: {str(e)}")
-                return {uid: {"total": 0} for uid in end_user_ids}
-        
-        elif current_workspace_type == "neo4j":
-            # Neo4j 模式：并发查询（带并发限制）
-            # 使用信号量限制并发数，避免大量用户时压垮 Neo4j
-            MAX_CONCURRENT_QUERIES = 10
-            semaphore = asyncio.Semaphore(MAX_CONCURRENT_QUERIES)
-            
-            async def get_neo4j_memory_num(end_user_id: str):
-                async with semaphore:
-                    try:
-                        return await memory_storage_service.search_all(end_user_id)
-                    except Exception as e:
-                        api_logger.error(f"获取用户 {end_user_id} Neo4j 记忆数量失败: {str(e)}")
-                        return {"total": 0}
-            
-            memory_nums_list = await asyncio.gather(*[get_neo4j_memory_num(uid) for uid in end_user_ids])
-            return {end_user_ids[i]: memory_nums_list[i] for i in range(len(end_user_ids))}
-        
-        return {uid: {"total": 0} for uid in end_user_ids}
-    
-    # 并发执行配置查询和记忆数量查询
-    memory_configs_map, memory_nums_map = await asyncio.gather(
-        get_memory_configs(),
-        get_memory_nums()
-    )
-    
-    # 构建结果（优化：使用列表推导式）
     result = []
     for end_user in end_users:
-        user_id = str(end_user.id)
-        config_info = memory_configs_map.get(user_id, {})
-        result.append({
-            'end_user': {
-                'id': user_id,
-                'other_name': end_user.other_name
-            },
-            'memory_num': memory_nums_map.get(user_id, {"total": 0}),
-            'memory_config': {
-                "memory_config_id": config_info.get("memory_config_id"),
-                "memory_config_name": config_info.get("memory_config_name")
+        memory_num = {}
+        if current_workspace_type == "neo4j":
+            # EndUser 是 Pydantic 模型，直接访问属性而不是使用 .get()
+            memory_num = await memory_storage_service.search_all(str(end_user.id))
+        elif current_workspace_type == "rag":
+            memory_num = {
+                "total":memory_dashboard_service.get_current_user_total_chunk(str(end_user.id), db, current_user)
             }
+        
+        # 从批量查询结果中获取配置信息
+        user_id = str(end_user.id)
+        memory_config_info = memory_configs_map.get(user_id, {
+            "memory_config_id": None,
+            "memory_config_name": None
         })
-    
-    # 写入缓存（30秒过期）
-    try:
-        await aio_redis_set(cache_key, json.dumps(result), expire=30)
-    except Exception as e:
-        api_logger.warning(f"Redis 缓存写入失败: {str(e)}")
-    
+        
+        # 只保留需要的字段，移除 error 字段（如果有）
+        memory_config = {
+            "memory_config_id": memory_config_info.get("memory_config_id"),
+            "memory_config_name": memory_config_info.get("memory_config_name")
+        }
+        
+        result.append(
+            {
+                'end_user': end_user,
+                'memory_num': memory_num,
+                'memory_config': memory_config
+            }
+        )
+        
     api_logger.info(f"成功获取 {len(end_users)} 个宿主记录")
     return success(data=result, msg="宿主列表获取成功")
 

api/app/controllers/memory_episodic_controller.py

@@ -3,10 +3,9 @@
 包含情景记忆总览和详情查询接口
 """
 
-from fastapi import APIRouter, Depends, Header
+from fastapi import APIRouter, Depends
 
 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.response_utils import fail, success
 from app.dependencies import get_current_user
@@ -15,7 +14,6 @@ from app.schemas.response_schema import ApiResponse
 from app.schemas.memory_episodic_schema import (
     EpisodicMemoryOverviewRequest,
     EpisodicMemoryDetailsRequest,
-    translate_episodic_type,
 )
 from app.services.memory_episodic_service import memory_episodic_service
 
@@ -86,7 +84,6 @@ async def get_episodic_memory_overview_api(
 @router.post("/details", response_model=ApiResponse)
 async def get_episodic_memory_details_api(
     request: EpisodicMemoryDetailsRequest,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
 ) -> dict:
     """
@@ -114,11 +111,6 @@ async def get_episodic_memory_details_api(
             summary_id=request.summary_id
         )
         
-        # 根据语言参数翻译 episodic_type
-        language = get_language_from_header(language_type)
-        if "episodic_type" in result:
-            result["episodic_type"] = translate_episodic_type(result["episodic_type"], language)
-        
         api_logger.info(
             f"成功获取情景记忆详情: end_user_id={request.end_user_id}, summary_id={request.summary_id}"
         )

api/app/controllers/memory_forget_controller.py

@@ -11,7 +11,6 @@
 """
 
 from typing import Optional
-from uuid import UUID
 
 from fastapi import APIRouter, Depends
 from sqlalchemy.orm import Session
@@ -34,7 +33,7 @@ from app.schemas.memory_storage_schema import (
 )
 from app.schemas.response_schema import ApiResponse
 from app.services.memory_forget_service import MemoryForgetService
-from app.utils.config_utils import resolve_config_id
+
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -84,8 +83,7 @@ async def trigger_forgetting_cycle(
         
         connected_config = get_end_user_connected_config(end_user_id, db)
         config_id = connected_config.get("memory_config_id")
-        config_id = resolve_config_id((config_id), db)
-
+        
         if config_id is None:
             api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置")
             return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None")
@@ -108,7 +106,7 @@ async def trigger_forgetting_cycle(
         # 调用服务层执行遗忘周期
         report = await forget_service.trigger_forgetting_cycle(
             db=db,
-            end_user_id=end_user_id,  # 服务层方法的参数名是 end_user_id
+            group_id=end_user_id,  # 服务层方法的参数名是 group_id
             max_merge_batch_size=payload.max_merge_batch_size,
             min_days_since_access=payload.min_days_since_access,
             config_id=config_id
@@ -130,7 +128,7 @@ async def trigger_forgetting_cycle(
 
 @router.get("/read_config", response_model=ApiResponse)
 async def read_forgetting_config(
-    config_id: UUID|int,
+    config_id: int,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db)
 ):
@@ -159,7 +157,6 @@ async def read_forgetting_config(
     )
     
     try:
-        config_id=resolve_config_id(config_id, db)
         # 调用服务层读取配置
         config = forget_service.read_forgetting_config(db=db, config_id=config_id)
         
@@ -197,8 +194,6 @@ async def update_forgetting_config(
         ApiResponse: 包含更新结果的响应
     """
     workspace_id = current_user.current_workspace_id
-    payload.config_id=resolve_config_id((payload.config_id), db)
-
     
     # 检查用户是否已选择工作空间
     if workspace_id is None:
@@ -241,7 +236,7 @@ async def update_forgetting_config(
 
 @router.get("/stats", response_model=ApiResponse)
 async def get_forgetting_stats(
-    end_user_id: Optional[str] = None,
+    group_id: Optional[str] = None,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db)
 ):
@@ -251,7 +246,7 @@ async def get_forgetting_stats(
     返回知识层节点统计、激活值分布等信息。
     
     Args:
-        end_user_id: 组ID（即 end_user_id，可选）
+        group_id: 组ID（即 end_user_id，可选）
         current_user: 当前用户
         db: 数据库会话
     
@@ -259,25 +254,26 @@ async def get_forgetting_stats(
         ApiResponse: 包含统计信息的响应
     """
     workspace_id = current_user.current_workspace_id
+    
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘引擎统计但未选择工作空间")
         return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
-    # 如果提供了 end_user_id，通过它获取 config_id
+    
+    # 如果提供了 group_id，通过它获取 config_id
     config_id = None
-    if end_user_id:
+    if group_id:
         try:
             from app.services.memory_agent_service import get_end_user_connected_config
             
-            connected_config = get_end_user_connected_config(end_user_id, db)
+            connected_config = get_end_user_connected_config(group_id, db)
             config_id = connected_config.get("memory_config_id")
-            config_id = resolve_config_id(config_id, db)
             
             if config_id is None:
-                api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置")
-                return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None")
+                api_logger.warning(f"终端用户 {group_id} 未关联记忆配置")
+                return fail(BizCode.INVALID_PARAMETER, f"终端用户 {group_id} 未关联记忆配置", "memory_config_id is None")
             
-            api_logger.debug(f"通过 end_user_id={end_user_id} 获取到 config_id={config_id}")
+            api_logger.debug(f"通过 group_id={group_id} 获取到 config_id={config_id}")
         except ValueError as e:
             api_logger.warning(f"获取终端用户配置失败: {str(e)}")
             return fail(BizCode.INVALID_PARAMETER, str(e), "ValueError")
@@ -287,14 +283,14 @@ async def get_forgetting_stats(
     
     api_logger.info(
         f"用户 {current_user.username} 在工作空间 {workspace_id} 请求获取遗忘引擎统计: "
-        f"end_user_id={end_user_id}, config_id={config_id}"
+        f"group_id={group_id}, config_id={config_id}"
     )
     
     try:
         # 调用服务层获取统计信息
         stats = await forget_service.get_forgetting_stats(
             db=db,
-            end_user_id=end_user_id,
+            group_id=group_id,
             config_id=config_id
         )
         
@@ -328,7 +324,7 @@ async def get_forgetting_curve(
         ApiResponse: 包含遗忘曲线数据的响应
     """
     workspace_id = current_user.current_workspace_id
-    request.config_id = resolve_config_id((request.config_id), db)
+    
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘曲线但未选择工作空间")

api/app/controllers/memory_perceptual_controller.py

@@ -27,27 +27,27 @@ router = APIRouter(
 )
 
 
-@router.get("/{end_user_id}/count", response_model=ApiResponse)
+@router.get("/{group_id}/count", response_model=ApiResponse)
 def get_memory_count(
-        end_user_id: uuid.UUID,
+        group_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     """Retrieve perceptual memory statistics for a user group.
 
     Args:
-        end_user_id: ID of the user group (usually end_user_id in this context)
+        group_id: ID of the user group (usually end_user_id in this context)
         current_user: Current authenticated user
         db: Database session
 
     Returns:
         ApiResponse: Response containing memory count statistics
     """
-    api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, end_user_id={end_user_id}")
+    api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, group_id={group_id}")
 
     try:
         service = MemoryPerceptualService(db)
-        count_stats = service.get_memory_count(end_user_id)
+        count_stats = service.get_memory_count(group_id)
 
         api_logger.info(f"Memory statistics fetched successfully: total={count_stats.get('total', 0)}")
 
@@ -57,37 +57,37 @@ def get_memory_count(
         )
 
     except Exception as e:
-        api_logger.error(f"Failed to fetch memory statistics: end_user_id={end_user_id}, error={str(e)}")
+        api_logger.error(f"Failed to fetch memory statistics: group_id={group_id}, error={str(e)}")
         return fail(
             code=BizCode.INTERNAL_ERROR,
             msg="Failed to fetch memory statistics",
         )
 
 
-@router.get("/{end_user_id}/last_visual", response_model=ApiResponse)
+@router.get("/{group_id}/last_visual", response_model=ApiResponse)
 def get_last_visual_memory(
-        end_user_id: uuid.UUID,
+        group_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     """Retrieve the most recent VISION-type memory for a user.
 
     Args:
-        end_user_id: ID of the user group
+        group_id: ID of the user group
         current_user: Current authenticated user
         db: Database session
 
     Returns:
         ApiResponse: Metadata of the latest visual memory
     """
-    api_logger.info(f"Fetching latest visual memory: user={current_user.username}, end_user_id={end_user_id}")
+    api_logger.info(f"Fetching latest visual memory: user={current_user.username}, group_id={group_id}")
 
     try:
         service = MemoryPerceptualService(db)
-        visual_memory = service.get_latest_visual_memory(end_user_id)
+        visual_memory = service.get_latest_visual_memory(group_id)
 
         if visual_memory is None:
-            api_logger.info(f"No visual memory found: end_user_id={end_user_id}")
+            api_logger.info(f"No visual memory found: group_id={group_id}")
             return success(
                 data=None,
                 msg="No visual memory available"
@@ -101,37 +101,37 @@ def get_last_visual_memory(
         )
 
     except Exception as e:
-        api_logger.error(f"Failed to fetch latest visual memory: end_user_id={end_user_id}, error={str(e)}")
+        api_logger.error(f"Failed to fetch latest visual memory: group_id={group_id}, error={str(e)}")
         return fail(
             code=BizCode.INTERNAL_ERROR,
             msg="Failed to fetch latest visual memory",
         )
 
 
-@router.get("/{end_user_id}/last_listen", response_model=ApiResponse)
+@router.get("/{group_id}/last_listen", response_model=ApiResponse)
 def get_last_memory_listen(
-        end_user_id: uuid.UUID,
+        group_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     """Retrieve the most recent AUDIO-type memory for a user.
 
     Args:
-        end_user_id: ID of the user group
+        group_id: ID of the user group
         current_user: Current authenticated user
         db: Database session
 
     Returns:
         ApiResponse: Metadata of the latest audio memory
     """
-    api_logger.info(f"Fetching latest audio memory: user={current_user.username}, end_user_id={end_user_id}")
+    api_logger.info(f"Fetching latest audio memory: user={current_user.username}, group_id={group_id}")
 
     try:
         service = MemoryPerceptualService(db)
-        audio_memory = service.get_latest_audio_memory(end_user_id)
+        audio_memory = service.get_latest_audio_memory(group_id)
 
         if audio_memory is None:
-            api_logger.info(f"No audio memory found: end_user_id={end_user_id}")
+            api_logger.info(f"No audio memory found: group_id={group_id}")
             return success(
                 data=None,
                 msg="No audio memory available"
@@ -145,38 +145,38 @@ def get_last_memory_listen(
         )
 
     except Exception as e:
-        api_logger.error(f"Failed to fetch latest audio memory: end_user_id={end_user_id}, error={str(e)}")
+        api_logger.error(f"Failed to fetch latest audio memory: group_id={group_id}, error={str(e)}")
         return fail(
             code=BizCode.INTERNAL_ERROR,
             msg="Failed to fetch latest audio memory",
         )
 
 
-@router.get("/{end_user_id}/last_text", response_model=ApiResponse)
+@router.get("/{group_id}/last_text", response_model=ApiResponse)
 def get_last_text_memory(
-        end_user_id: uuid.UUID,
+        group_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     """Retrieve the most recent TEXT-type memory for a user.
 
     Args:
-        end_user_id: ID of the user group
+        group_id: ID of the user group
         current_user: Current authenticated user
         db: Database session
 
     Returns:
         ApiResponse: Metadata of the latest text memory
     """
-    api_logger.info(f"Fetching latest text memory: user={current_user.username}, end_user_id={end_user_id}")
+    api_logger.info(f"Fetching latest text memory: user={current_user.username}, group_id={group_id}")
 
     try:
         # 调用服务层获取最近的文本记忆
         service = MemoryPerceptualService(db)
-        text_memory = service.get_latest_text_memory(end_user_id)
+        text_memory = service.get_latest_text_memory(group_id)
 
         if text_memory is None:
-            api_logger.info(f"No text memory found: end_user_id={end_user_id}")
+            api_logger.info(f"No text memory found: group_id={group_id}")
             return success(
                 data=None,
                 msg="No text memory available"
@@ -190,16 +190,16 @@ def get_last_text_memory(
         )
 
     except Exception as e:
-        api_logger.error(f"Failed to fetch latest text memory: end_user_id={end_user_id}, error={str(e)}")
+        api_logger.error(f"Failed to fetch latest text memory: group_id={group_id}, error={str(e)}")
         return fail(
             code=BizCode.INTERNAL_ERROR,
             msg="Failed to fetch latest text memory",
         )
 
 
-@router.get("/{end_user_id}/timeline", response_model=ApiResponse)
+@router.get("/{group_id}/timeline", response_model=ApiResponse)
 def get_memory_time_line(
-        end_user_id: uuid.UUID,
+        group_id: uuid.UUID,
         perceptual_type: Optional[PerceptualType] = Query(None, description="感知类型过滤"),
         page: int = Query(1, ge=1, description="页码"),
         page_size: int = Query(10, ge=1, le=100, description="每页大小"),
@@ -209,7 +209,7 @@ def get_memory_time_line(
     """Retrieve a timeline of perceptual memories for a user group.
 
     Args:
-        end_user_id: ID of the user group
+        group_id: ID of the user group
         perceptual_type: Optional filter for perceptual type
         page: Page number for pagination
         page_size: Number of items per page
@@ -221,7 +221,7 @@ def get_memory_time_line(
     """
     api_logger.info(
         f"Fetching perceptual memory timeline: user={current_user.username}, "
-        f"end_user_id={end_user_id}, type={perceptual_type}, page={page}"
+        f"group_id={group_id}, type={perceptual_type}, page={page}"
     )
 
     try:
@@ -232,7 +232,7 @@ def get_memory_time_line(
         )
 
         service = MemoryPerceptualService(db)
-        timeline_data = service.get_time_line(end_user_id, query)
+        timeline_data = service.get_time_line(group_id, query)
 
         api_logger.info(
             f"Perceptual memory timeline retrieved successfully: total={timeline_data.total}, "
@@ -246,7 +246,7 @@ def get_memory_time_line(
 
     except Exception as e:
         api_logger.error(
-            f"Failed to fetch perceptual memory timeline: end_user_id={end_user_id}, "
+            f"Failed to fetch perceptual memory timeline: group_id={group_id}, "
             f"error={str(e)}"
         )
         return fail(

api/app/controllers/memory_reflection_controller.py

@@ -1,19 +1,16 @@
 import asyncio
 import time
-import uuid
-from uuid import UUID
 
-from app.core.language_utils import get_language_from_header
 from app.core.logging_config import get_api_logger
 from app.core.memory.storage_services.reflection_engine.self_reflexion import (
     ReflectionConfig,
-    ReflectionEngine, ReflectionRange, ReflectionBaseline,
+    ReflectionEngine,
 )
 from app.core.response_utils import success
 from app.db import get_db
 from app.dependencies import get_current_user
 from app.models.user_model import User
-from app.repositories.memory_config_repository import MemoryConfigRepository
+from app.repositories.data_config_repository import DataConfigRepository
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_reflection_schemas import Memory_Reflection
 from app.services.memory_reflection_service import (
@@ -22,12 +19,10 @@ from app.services.memory_reflection_service import (
 )
 from app.services.model_service import ModelConfigService
 from dotenv import load_dotenv
-from fastapi import APIRouter, Depends, HTTPException, status,Header
+from fastapi import APIRouter, Depends, HTTPException, status
 from sqlalchemy import text
 from sqlalchemy.orm import Session
 
-from app.utils.config_utils import resolve_config_id
-
 load_dotenv()
 api_logger = get_api_logger()
 
@@ -44,40 +39,64 @@ async def save_reflection_config(
     db: Session = Depends(get_db),
 ) -> dict:
     """Save reflection configuration to data_comfig table"""
+    
+
+    
     try:
         config_id = request.config_id
-        config_id = resolve_config_id(config_id, db)
         if not config_id:
             raise HTTPException(
                 status_code=status.HTTP_400_BAD_REQUEST,
                 detail="缺少必需参数: config_id"
             )
+
         api_logger.info(f"用户 {current_user.username} 保存反思配置，config_id: {config_id}")
 
-        memory_config = MemoryConfigRepository.update_reflection_config(
-            db,
-            config_id=config_id,
-            enable_self_reflexion=request.reflection_enabled,
-            iteration_period=request.reflection_period_in_hours,
-            reflexion_range=request.reflexion_range,
-            baseline=request.baseline,
-            reflection_model_id=request.reflection_model_id,
-            memory_verify=request.memory_verify,
-            quality_assessment=request.quality_assessment
-        )
+        update_params = {
+            "enable_self_reflexion": request.reflection_enabled,
+            "iteration_period": request.reflection_period_in_hours,
+            "reflexion_range": request.reflexion_range,
+            "baseline": request.baseline,
+            "reflection_model_id": request.reflection_model_id,
+            "memory_verify": request.memory_verify,
+            "quality_assessment": request.quality_assessment,
+        }
 
+
+
+        query, params = DataConfigRepository.build_update_reflection(config_id, **update_params)
+
+        result = db.execute(text(query), params)
+        if result.rowcount == 0:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail=f"未找到config_id为 {config_id} 的配置"
+            )
+        
         db.commit()
-        db.refresh(memory_config)
+        
+        # 查询更新后的配置
+        select_query, select_params = DataConfigRepository.build_select_reflection(config_id)
+        result = db.execute(text(select_query), select_params).fetchone()
+        
+        if not result:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail=f"更新后未找到config_id为 {config_id} 的配置"
+            )
+        
+        api_logger.info(f"成功保存反思配置到数据库，config_id: {config_id}")
 
         reflection_result={
-                "config_id": memory_config.config_id,
-                "enable_self_reflexion": memory_config.enable_self_reflexion,
-                "iteration_period": memory_config.iteration_period,
-                "reflexion_range": memory_config.reflexion_range,
-                "baseline": memory_config.baseline,
-                "reflection_model_id": memory_config.reflection_model_id,
-                "memory_verify": memory_config.memory_verify,
-                "quality_assessment": memory_config.quality_assessment}
+                "config_id": result.config_id,
+                "enable_self_reflexion": result.enable_self_reflexion,
+                "iteration_period": result.iteration_period,
+                "reflexion_range": result.reflexion_range,
+                "baseline": result.baseline,
+                "reflection_model_id": result.reflection_model_id,
+                "memory_verify": result.memory_verify,
+                "quality_assessment": result.quality_assessment,
+                "user_id": result.user_id}
 
         return success(data=reflection_result, msg="反思配置成功")
         
@@ -97,76 +116,48 @@ async def save_reflection_config(
         )
 
 
-@router.get("/reflection")
+@router.post("/reflection")
 async def start_workspace_reflection(
+    config_id: int,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
-    """启动工作空间中所有匹配应用的反思功能"""
+    """Activate the reflection function for all matching applications in the workspace"""
     workspace_id = current_user.current_workspace_id
+    reflection_service = MemoryReflectionService(db)
 
     try:
         api_logger.info(f"用户 {current_user.username} 启动workspace反思，workspace_id: {workspace_id}")
 
-        # 使用独立的数据库会话来获取工作空间应用详情，避免事务失败
-        from app.db import get_db_context
-        with get_db_context() as query_db:
-            service = WorkspaceAppService(query_db)
-            result = service.get_workspace_apps_detailed(workspace_id)
+        service = WorkspaceAppService(db)
+        result = service.get_workspace_apps_detailed(workspace_id)
         
         reflection_results = []
         
         for data in result['apps_detailed_info']:
-            # 跳过没有配置的应用
-            if not data['memory_configs']:
-                api_logger.debug(f"应用 {data['id']} 没有memory_configs，跳过")
+            if data['data_configs'] == []: 
                 continue
-
+                
             releases = data['releases']
-            memory_configs = data['memory_configs']
+            data_configs = data['data_configs']
             end_users = data['end_users']
-
-            # 为每个配置和用户组合执行反思
-            for config in memory_configs:
-                config_id_str = str(config['config_id'])
-
-                # 找到匹配此配置的所有release
-                matching_releases = [r for r in releases if str(r['config']) == config_id_str]
-
-                if not matching_releases:
-                    api_logger.debug(f"配置 {config_id_str} 没有匹配的release")
-                    continue
-
-                # 为每个用户执行反思 - 使用独立的数据库会话
-                for user in end_users:
-                    api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config_id_str}")
-
-                    # 为每个用户创建独立的数据库会话，避免事务失败影响其他用户
-                    with get_db_context() as user_db:
-                        try:
-                            reflection_service = MemoryReflectionService(user_db)
-                            reflection_result = await reflection_service.start_text_reflection(
-                                config_data=config,
-                                end_user_id=user['id']
-                            )
-
-                            reflection_results.append({
-                                "app_id": data['id'],
-                                "config_id": config_id_str,
-                                "end_user_id": user['id'],
-                                "reflection_result": reflection_result
-                            })
-                        except Exception as e:
-                            api_logger.error(f"用户 {user['id']} 反思失败: {str(e)}")
-                            reflection_results.append({
-                                "app_id": data['id'],
-                                "config_id": config_id_str,
-                                "end_user_id": user['id'],
-                                "reflection_result": {
-                                    "status": "错误",
-                                    "message": f"反思失败: {str(e)}"
-                                }
-                            })
+            
+            for base, config, user in zip(releases, data_configs, end_users):
+                if int(base['config']) == int(config['config_id']) and base['app_id'] == user['app_id']:
+                    # 调用反思服务
+                    api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config['config_id']}")
+                    
+                    reflection_result = await reflection_service.start_reflection_from_data(
+                        config_data=config,
+                        end_user_id=user['id']
+                    )
+                    
+                    reflection_results.append({
+                        "app_id": base['app_id'],
+                        "config_id": config['config_id'],
+                        "end_user_id": user['id'],
+                        "reflection_result": reflection_result
+                    })
 
         return success(data=reflection_results, msg="反思配置成功")
 
@@ -180,27 +171,35 @@ async def start_workspace_reflection(
 
 @router.get("/reflection/configs")
 async def start_reflection_configs(
-        config_id: uuid.UUID|int,
+        config_id: int,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db),
 ) -> dict:
-    """通过config_id查询memory_config表中的反思配置信息"""
-    config_id = resolve_config_id(config_id, db)
+    """通过config_id查询data_config表中的反思配置信息"""
     try:
-        config_id=resolve_config_id(config_id,db)
         api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
-        result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id)
-        memory_config_id = resolve_config_id(result.config_id, db)
+        
+        # 使用DataConfigRepository查询反思配置
+        select_query, select_params = DataConfigRepository.build_select_reflection(config_id)
+        result = db.execute(text(select_query), select_params).fetchone()
+        
+        if not result:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail=f"未找到config_id为 {config_id} 的配置"
+            )
+        
         # 构建返回数据
         reflection_config = {
-            "config_id": memory_config_id,
+            "config_id": result.config_id,
             "reflection_enabled": result.enable_self_reflexion,
             "reflection_period_in_hours": result.iteration_period,
             "reflexion_range": result.reflexion_range,
             "baseline": result.baseline,
             "reflection_model_id": result.reflection_model_id,
             "memory_verify": result.memory_verify,
-            "quality_assessment": result.quality_assessment
+            "quality_assessment": result.quality_assessment,
+            "user_id": result.user_id
         }
         api_logger.info(f"成功查询反思配置，config_id: {config_id}")
         return success(data=reflection_config, msg="反思配置查询成功")
@@ -218,19 +217,19 @@ async def start_reflection_configs(
 
 @router.get("/reflection/run")
 async def reflection_run(
-    config_id: UUID|int,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
+    config_id: int,
+    language_type: str = "zh",
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
     """Activate the reflection function for all matching applications in the workspace"""
-    # 使用集中化的语言校验
-    language = get_language_from_header(language_type)
 
     api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
-    config_id = resolve_config_id(config_id, db)
-    # 使用MemoryConfigRepository查询反思配置
-    result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id)
+
+    # 使用DataConfigRepository查询反思配置
+    select_query, select_params = DataConfigRepository.build_select_reflection(config_id)
+    result = db.execute(text(select_query), select_params).fetchone()
+
     if not result:
         raise HTTPException(
             status_code=status.HTTP_404_NOT_FOUND,
@@ -243,7 +242,7 @@ async def reflection_run(
     model_id = result.reflection_model_id
     if model_id:
         try:
-            ModelConfigService.get_model_by_id(db=db, model_id=uuid.UUID(model_id))
+            ModelConfigService.get_model_by_id(db=db, model_id=model_id)
             api_logger.info(f"模型ID验证成功: {model_id}")
         except Exception as e:
             api_logger.warning(f"模型ID '{model_id}' 不存在，将使用默认模型: {str(e)}")
@@ -253,8 +252,8 @@ async def reflection_run(
     config = ReflectionConfig(
         enabled=result.enable_self_reflexion,
         iteration_period=result.iteration_period,
-        reflexion_range=ReflectionRange(result.reflexion_range),
-        baseline=ReflectionBaseline(result.baseline),
+        reflexion_range=result.reflexion_range,
+        baseline=result.baseline,
         output_example='',
         memory_verify=result.memory_verify,
         quality_assessment=result.quality_assessment,

api/app/controllers/memory_short_term_controller.py

@@ -1,5 +1,4 @@
-from fastapi import APIRouter, Depends, HTTPException, status,Header
-from app.core.language_utils import get_language_from_header
+from fastapi import APIRouter, Depends, HTTPException, status
 from app.core.logging_config import get_api_logger
 from app.core.response_utils import success
 from app.db import get_db
@@ -21,13 +20,9 @@ router = APIRouter(
 @router.get("/short_term")
 async def short_term_configs(
         end_user_id: str,
-        language_type:str = Header(default=None, alias="X-Language-Type"),
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db),
 ):
-    # 使用集中化的语言校验
-    language = get_language_from_header(language_type)
-    
     # 获取短期记忆数据
     short_term=ShortService(end_user_id)
     short_result=short_term.get_short_databasets()

api/app/controllers/memory_storage_controller.py

@@ -1,13 +1,10 @@
+import os
+import uuid
 from typing import Optional
-from uuid import UUID
-
-from fastapi import APIRouter, Depends, Query
-from fastapi.responses import StreamingResponse
-from sqlalchemy.orm import Session
 
 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.memory.utils.self_reflexion_utils import self_reflexion
 from app.core.response_utils import fail, success
 from app.db import get_db
 from app.dependencies import get_current_user
@@ -15,6 +12,7 @@ from app.models.user_model import User
 from app.schemas.memory_storage_schema import (
     ConfigKey,
     ConfigParamsCreate,
+    ConfigParamsDelete,
     ConfigPilotRun,
     ConfigUpdate,
     ConfigUpdateExtracted,
@@ -32,14 +30,13 @@ from app.services.memory_storage_service import (
     search_dialogue,
     search_edges,
     search_entity,
+    search_entity_graph,
     search_statement,
 )
-from fastapi import APIRouter, Depends, Header
+from fastapi import APIRouter, Depends
 from fastapi.responses import StreamingResponse
 from sqlalchemy.orm import Session
 
-from app.utils.config_utils import resolve_config_id
-
 # Get API logger
 api_logger = get_api_logger()
 
@@ -75,9 +72,68 @@ async def get_storage_info(
         return fail(BizCode.INTERNAL_ERROR, "存储信息获取失败", str(e))
 
 
+# --- DB connection dependency ---
+_CONN: Optional[object] = None
 
 
+"""PostgreSQL 连接生成与管理（使用 psycopg2）。"""
+# 这个可以转移，可能是已经有的
+# PostgreSQL 数据库连接
+def _make_pgsql_conn() -> Optional[object]:  # 创建 PostgreSQL 数据库连接
+    host = os.getenv("DB_HOST")
+    user = os.getenv("DB_USER")
+    password = os.getenv("DB_PASSWORD")
+    database = os.getenv("DB_NAME")
+    port_str = os.getenv("DB_PORT")
+    try:
+        import psycopg2  # type: ignore
+        port = int(port_str) if port_str else 5432
+        conn = psycopg2.connect(
+            host=host or "localhost",
+            port=port,
+            user=user,
+            password=password,
+            dbname=database,
+        )
+        # 设置自动提交，避免显式事务管理
+        conn.autocommit = True
+        # 设置会话时区为中国标准时间（Asia/Shanghai），便于直接以本地时区展示
+        try:
+            cur = conn.cursor()
+            cur.execute("SET TIME ZONE 'Asia/Shanghai'")
+            cur.close()
+        except Exception:
+            # 时区设置失败不影响连接，仅记录但不抛出
+            pass
+        return conn
+    except Exception as e:
+        try:
+            print(f"[PostgreSQL] 连接失败: {e}")
+        except Exception:
+            pass
+        return None
 
+def get_db_conn() -> Optional[object]:  # 获取 PostgreSQL 数据库连接
+    global _CONN
+    if _CONN is None:
+        _CONN = _make_pgsql_conn()
+    return _CONN
+
+
+def reset_db_conn() -> bool:  # 重置 PostgreSQL 数据库连接
+    """Close and recreate the global DB connection."""
+    global _CONN
+    try:
+        if _CONN:
+            try:
+                _CONN.close()
+            except Exception:
+                pass
+        _CONN = _make_pgsql_conn()
+        return _CONN is not None
+    except Exception:
+        _CONN = None
+        return False
 
 
 @router.post("/create_config", response_model=ApiResponse)   # 创建配置文件，其他参数默认
@@ -85,8 +141,9 @@ def create_config(
     payload: ConfigParamsCreate,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
-) -> dict:
+    ) -> dict:
     workspace_id = current_user.current_workspace_id
+    
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试创建配置但未选择工作空间")
@@ -106,96 +163,39 @@ def create_config(
 
 @router.delete("/delete_config", response_model=ApiResponse)  # 删除数据库中的内容（按配置名称）
 def delete_config(
-    config_id: UUID|int,
-    force: bool = Query(False, description="是否强制删除（即使有终端用户正在使用）"),
+    config_id: str,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
-) -> dict:
-    """删除记忆配置（带终端用户保护）
-    
-    - 检查是否为默认配置，默认配置不允许删除
-    - 检查是否有终端用户连接到该配置
-    - 如果有连接且 force=False，返回警告
-    - 如果 force=True，清除终端用户引用后删除配置
-    
-    Query Parameters:
-        force: 设置为 true 可强制删除（即使有终端用户正在使用）
-    """
+    ) -> dict:
     workspace_id = current_user.current_workspace_id
-    config_id=resolve_config_id(config_id, db)
+    
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试删除配置但未选择工作空间")
         return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
     
-    api_logger.info(
-        f"用户 {current_user.username} 在工作空间 {workspace_id} 请求删除配置: "
-        f"config_id={config_id}, force={force}"
-    )
-    
+    api_logger.info(f"用户 {current_user.username} 在工作空间 {workspace_id} 请求删除配置: {config_id}")
     try:
-        # 使用带保护的删除服务
-        from app.services.memory_config_service import MemoryConfigService
-        
-        config_service = MemoryConfigService(db)
-        result = config_service.delete_config(config_id=config_id, force=force)
-        
-        if result["status"] == "error":
-            api_logger.warning(
-                f"记忆配置删除被拒绝: config_id={config_id}, reason={result['message']}"
-            )
-            return fail(
-                code=BizCode.FORBIDDEN,
-                msg=result["message"],
-                data={"config_id": str(config_id), "is_default": result.get("is_default", False)}
-            )
-        
-        if result["status"] == "warning":
-            api_logger.warning(
-                f"记忆配置正在使用，无法删除: config_id={config_id}, "
-                f"connected_count={result['connected_count']}"
-            )
-            return fail(
-                code=BizCode.RESOURCE_IN_USE,
-                msg=result["message"],
-                data={
-                    "connected_count": result["connected_count"],
-                    "force_required": result["force_required"]
-                }
-            )
-        
-        api_logger.info(
-            f"记忆配置删除成功: config_id={config_id}, "
-            f"affected_users={result['affected_users']}"
-        )
-        return success(
-            msg=result["message"],
-            data={"affected_users": result["affected_users"]}
-        )
-        
+        svc = DataConfigService(db)
+        result = svc.delete(ConfigParamsDelete(config_id=config_id))
+        return success(data=result, msg="删除成功")
     except Exception as e:
-        api_logger.error(f"Delete config failed: {str(e)}", exc_info=True)
+        api_logger.error(f"Delete config failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "删除配置失败", str(e))
 
-
 @router.post("/update_config", response_model=ApiResponse)  # 更新配置文件中name和desc
 def update_config(
     payload: ConfigUpdate,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
-) -> dict:
+    ) -> dict:
     workspace_id = current_user.current_workspace_id
-    payload.config_id = resolve_config_id(payload.config_id, db)
+    
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试更新配置但未选择工作空间")
         return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
     
-    # 校验至少有一个字段需要更新
-    if payload.config_name is None and payload.config_desc is None and payload.scene_id is None:
-        api_logger.warning(f"用户 {current_user.username} 尝试更新配置但未提供任何更新字段")
-        return fail(BizCode.INVALID_PARAMETER, "请至少提供一个需要更新的字段", "config_name, config_desc, scene_id 均为空")
-    
     api_logger.info(f"用户 {current_user.username} 在工作空间 {workspace_id} 请求更新配置: {payload.config_id}")
     try:
         svc = DataConfigService(db)
@@ -211,9 +211,9 @@ def update_config_extracted(
     payload: ConfigUpdateExtracted,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
-) -> dict:
+    ) -> dict:
     workspace_id = current_user.current_workspace_id
-    payload.config_id = resolve_config_id(payload.config_id, db)
+    
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试更新提取配置但未选择工作空间")
@@ -235,12 +235,12 @@ def update_config_extracted(
 
 @router.get("/read_config_extracted", response_model=ApiResponse) # 通过查询参数读取某条配置（固定路径） 没有意义的话就删除
 def read_config_extracted(
-    config_id: UUID | int,
+    config_id: str,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
-) -> dict:
+    ) -> dict:
     workspace_id = current_user.current_workspace_id
-    config_id = resolve_config_id(config_id, db)
+    
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试读取提取配置但未选择工作空间")
@@ -259,7 +259,7 @@ def read_config_extracted(
 def read_all_config(
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
-) -> dict:
+    ) -> dict:
     workspace_id = current_user.current_workspace_id
     
     # 检查用户是否已选择工作空间
@@ -281,22 +281,16 @@ def read_all_config(
 @router.post("/pilot_run", response_model=None)
 async def pilot_run(
     payload: ConfigPilotRun,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> StreamingResponse:
-    # 使用集中化的语言校验
-    language = get_language_from_header(language_type)
-    
     api_logger.info(
         f"Pilot run requested: config_id={payload.config_id}, "
-        f"dialogue_text_length={len(payload.dialogue_text)}, "
-        f"custom_text_length={len(payload.custom_text) if payload.custom_text else 0}"
+        f"dialogue_text_length={len(payload.dialogue_text)}"
     )
-    payload.config_id = resolve_config_id(payload.config_id, db)
     svc = DataConfigService(db)
     return StreamingResponse(
-        svc.pilot_run_stream(payload, language=language),
+        svc.pilot_run_stream(payload),
         media_type="text/event-stream",
         headers={
             "Cache-Control": "no-cache",
@@ -305,8 +299,9 @@ async def pilot_run(
         },
     )
 
-
-# ==================== Search & Analytics ====================
+"""
+以下为搜索与分析接口，直接挂载到同一 router，统一响应为 ApiResponse。
+"""
 
 @router.get("/search/kb_type_distribution", response_model=ApiResponse)
 async def get_kb_type_distribution(
@@ -419,7 +414,21 @@ async def search_entity_edges(
         api_logger.error(f"Search edges failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "边查询失败", str(e))
 
-
+@router.get("/search/entity_graph", response_model=ApiResponse)
+async def search_for_entity_graph(
+    end_user_id: Optional[str] = None,
+    current_user: User = Depends(get_current_user),
+    ) -> dict:
+    """
+    搜索所有实体之间的关系网络
+    """
+    api_logger.info(f"Search entity graph requested for end_user_id: {end_user_id}")
+    try:
+        result = await search_entity_graph(end_user_id)
+        return success(data=result, msg="查询成功")
+    except Exception as e:
+        api_logger.error(f"Search entity graph failed: {str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "实体图查询失败", str(e))
 
 
 @router.get("/analytics/hot_memory_tags", response_model=ApiResponse)
@@ -428,96 +437,15 @@ async def get_hot_memory_tags_api(
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
     ) -> dict:
-    """
-    获取热门记忆标签（带Redis缓存）
-    
-    缓存策略：
-    - 缓存键：workspace_id + limit
-    - 过期时间：5分钟（300秒）
-    - 缓存命中：~50ms
-    - 缓存未命中：~600-800ms（取决于LLM速度）
-    """
-    workspace_id = current_user.current_workspace_id
-    
-    # 构建缓存键
-    cache_key = f"hot_memory_tags:{workspace_id}:{limit}"
-    
-    api_logger.info(f"Hot memory tags requested for workspace: {workspace_id}, limit: {limit}")
-    
+    api_logger.info(f"Hot memory tags requested for current_user: {current_user.id}")
     try:
-        # 尝试从Redis缓存获取
-        import json
-
-        from app.aioRedis import aio_redis_get, aio_redis_set
-        
-        cached_result = await aio_redis_get(cache_key)
-        if cached_result:
-            api_logger.info(f"Cache hit for key: {cache_key}")
-            try:
-                data = json.loads(cached_result)
-                return success(data=data, msg="查询成功（缓存）")
-            except json.JSONDecodeError:
-                api_logger.warning(f"Failed to parse cached data, will refresh")
-        
-        # 缓存未命中，执行查询
-        api_logger.info(f"Cache miss for key: {cache_key}, executing query")
         result = await analytics_hot_memory_tags(db, current_user, limit)
-        
-        # 写入缓存（过期时间：5分钟）
-        # 注意：result是列表，需要转换为JSON字符串
-        try:
-            cache_data = json.dumps(result, ensure_ascii=False)
-            await aio_redis_set(cache_key, cache_data, expire=300)
-            api_logger.info(f"Cached result for key: {cache_key}")
-        except Exception as cache_error:
-            # 缓存写入失败不影响主流程
-            api_logger.warning(f"Failed to cache result: {str(cache_error)}")
-        
         return success(data=result, msg="查询成功")
-        
     except Exception as e:
         api_logger.error(f"Hot memory tags failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "热门标签查询失败", str(e))
 
 
-@router.delete("/analytics/hot_memory_tags/cache", response_model=ApiResponse)
-async def clear_hot_memory_tags_cache(
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
-    """
-    清除热门标签缓存
-    
-    用于：
-    - 手动刷新数据
-    - 调试和测试
-    - 数据更新后立即生效
-    """
-    workspace_id = current_user.current_workspace_id
-    
-    api_logger.info(f"Clear hot memory tags cache requested for workspace: {workspace_id}")
-    
-    try:
-        from app.aioRedis import aio_redis_delete
-        
-        # 清除所有limit的缓存（常见的limit值）
-        cleared_count = 0
-        for limit in [5, 10, 15, 20, 30, 50]:
-            cache_key = f"hot_memory_tags:{workspace_id}:{limit}"
-            result = await aio_redis_delete(cache_key)
-            if result:
-                cleared_count += 1
-                api_logger.info(f"Cleared cache for key: {cache_key}")
-        
-        return success(
-            data={"cleared_count": cleared_count}, 
-            msg=f"成功清除 {cleared_count} 个缓存"
-        )
-        
-    except Exception as e:
-        api_logger.error(f"Clear cache failed: {str(e)}")
-        return fail(BizCode.INTERNAL_ERROR, "清除缓存失败", str(e))
-
-
 @router.get("/analytics/recent_activity_stats", response_model=ApiResponse)
 async def get_recent_activity_stats_api(
     current_user: User = Depends(get_current_user),
@@ -530,3 +458,18 @@ async def get_recent_activity_stats_api(
         api_logger.error(f"Recent activity stats failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "最近活动统计失败", str(e))
 
+
+
+
+@router.get("/self_reflexion")
+async def self_reflexion_endpoint(host_id: uuid.UUID) -> str:
+    """
+    自我反思接口，自动对检索出的信息进行自我反思并返回自我反思结果。
+
+    Args:
+        None
+    Returns:
+        自我反思结果。
+    """
+    return await self_reflexion(host_id)
+

api/app/controllers/memory_working_controller.py

@@ -20,18 +20,18 @@ router = APIRouter(
 )
 
 
-@router.get("/{end_user_id}/count", response_model=ApiResponse)
+@router.get("/{group_id}/count", response_model=ApiResponse)
 def get_memory_count(
-        end_user_id: uuid.UUID,
+        group_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     pass
 
 
-@router.get("/{end_user_id}/conversations", response_model=ApiResponse)
+@router.get("/{group_id}/conversations", response_model=ApiResponse)
 def get_conversations(
-        end_user_id: uuid.UUID,
+        group_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
@@ -39,7 +39,7 @@ def get_conversations(
     Retrieve all conversations for the current user in a specific group.
 
     Args:
-        end_user_id (UUID): The group identifier.
+        group_id (UUID): The group identifier.
         current_user (User, optional): The authenticated user.
         db (Session, optional): SQLAlchemy session.
 
@@ -53,7 +53,7 @@ def get_conversations(
     """
     conversation_service = ConversationService(db)
     conversations = conversation_service.get_user_conversations(
-        end_user_id
+        group_id
     )
     return success(data=[
         {
@@ -63,7 +63,7 @@ def get_conversations(
     ], msg="get conversations success")
 
 
-@router.get("/{end_user_id}/messages", response_model=ApiResponse)
+@router.get("/{group_id}/messages", response_model=ApiResponse)
 def get_messages(
         conversation_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
@@ -100,7 +100,7 @@ def get_messages(
     return success(data=messages, msg="get conversation history success")
 
 
-@router.get("/{end_user_id}/detail", response_model=ApiResponse)
+@router.get("/{group_id}/detail", response_model=ApiResponse)
 async def get_conversation_detail(
         conversation_id: uuid.UUID,
         current_user: User = Depends(get_current_user),

api/app/controllers/model_controller.py

@@ -3,17 +3,15 @@ from sqlalchemy.orm import Session
 from typing import Optional
 import uuid
 
-from app.core.error_codes import BizCode
-from app.core.exceptions import BusinessException
+
 from app.db import get_db
 from app.dependencies import get_current_user
-from app.models.models_model import ModelProvider, ModelType, LoadBalanceStrategy
+from app.models.models_model import ModelProvider, ModelType
 from app.models.user_model import User
-from app.repositories.model_repository import ModelConfigRepository
 from app.schemas import model_schema
 from app.core.response_utils import success
 from app.schemas.response_schema import ApiResponse, PageData
-from app.services.model_service import ModelConfigService, ModelApiKeyService, ModelBaseService
+from app.services.model_service import ModelConfigService, ModelApiKeyService
 from app.core.logging_config import get_api_logger
 
 # 获取API专用日志器
@@ -26,54 +24,44 @@ router = APIRouter(
 
 @router.get("/type", response_model=ApiResponse)
 def get_model_types():
+    
     return success(msg="获取模型类型成功", data=list(ModelType))
 
 
 @router.get("/provider", response_model=ApiResponse)
 def get_model_providers():
-    providers = [p for p in ModelProvider if p != ModelProvider.COMPOSITE]
-    return success(msg="获取模型提供商成功", data=providers)
-
-@router.get("/strategy", response_model=ApiResponse)
-def get_model_strategies():
-    return success(msg="获取模型策略成功", data=list(LoadBalanceStrategy))
+    return success(msg="获取模型提供商成功", data=list(ModelProvider))
 
 
 @router.get("", response_model=ApiResponse)
 def get_model_list(
-        type: Optional[list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"),
-        provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于API Key)"),
-        is_active: Optional[bool] = Query(None, description="激活状态筛选"),
-        is_public: Optional[bool] = Query(None, description="公开状态筛选"),
-        search: Optional[str] = Query(None, description="搜索关键词"),
-        page: int = Query(1, ge=1, description="页码"),
-        pagesize: int = Query(10, ge=1, le=100, description="每页数量"),
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
+    type: Optional[str] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"),
+    provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于API Key)"),
+    is_active: Optional[bool] = Query(None, description="激活状态筛选"),
+    is_public: Optional[bool] = Query(None, description="公开状态筛选"),
+    search: Optional[str] = Query(None, description="搜索关键词"),
+    page: int = Query(1, ge=1, description="页码"),
+    pagesize: int = Query(10, ge=1, le=100, description="每页数量"),
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
 ):
     """
     获取模型配置列表
-
+    
     支持多个 type 参数：
     - 单个：?type=LLM
     - 多个（逗号分隔）：?type=LLM,EMBEDDING
     - 多个（重复参数）：?type=LLM&type=EMBEDDING
     """
-    api_logger.info(
-        f"获取模型配置列表请求: type={type}, provider={provider}, page={page}, pagesize={pagesize}, tenant_id={current_user.tenant_id}")
-
+    api_logger.info(f"获取模型配置列表请求: type={type}, provider={provider}, page={page}, pagesize={pagesize}, tenant_id={current_user.tenant_id}")
+    
     try:
         # 解析 type 参数（支持逗号分隔）
-        type_list = []
-        if type is not None:
-            flat_type = []
-            for item in type:
-                split_items = [t.strip() for t in item.split(',') if t.strip()]
-                flat_type.extend(split_items)
-
-            unique_flat_type = list(dict.fromkeys(flat_type))
-            type_list = [ModelType(t.lower()) for t in unique_flat_type]
-
+        type_list = None
+        if type:
+            type_values = [t.strip() for t in type.split(',')]
+            type_list = [model_schema.ModelType(t.lower()) for t in type_values if t]
+        
         api_logger.error(f"获取模型type_list: {type_list}")
         query = model_schema.ModelConfigQuery(
             type=type_list,
@@ -84,7 +72,7 @@ def get_model_list(
             page=page,
             pagesize=pagesize
         )
-
+        
         api_logger.debug(f"开始获取模型配置列表: {query.dict()}")
         result_orm = ModelConfigService.get_model_list(db=db, query=query, tenant_id=current_user.tenant_id)
         result = PageData.model_validate(result_orm)
@@ -95,146 +83,6 @@ def get_model_list(
         raise
 
 
-@router.get("/new", response_model=ApiResponse)
-def get_model_list_new(
-    type: Optional[list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"),
-    provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于ModelConfig)"),
-    is_active: Optional[bool] = Query(None, description="激活状态筛选"),
-    is_public: Optional[bool] = Query(None, description="公开状态筛选"),
-    search: Optional[str] = Query(None, description="搜索关键词"),
-    is_composite: Optional[bool] = Query(None, description="组合模型筛选"),
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """
-    获取模型配置列表
-    
-    支持多个 type 参数：
-    - 单个：?type=LLM
-    - 多个（逗号分隔）：?type=LLM,EMBEDDING
-    - 多个（重复参数）：?type=LLM&type=EMBEDDING
-    """
-    api_logger.info(f"获取模型配置列表请求: type={type}, provider={provider}, tenant_id={current_user.tenant_id}")
-    
-    try:
-        # 解析 type 参数（支持逗号分隔）
-        type_list = []
-        if type is not None:
-            flat_type = []
-            for item in type:
-                split_items = [t.strip() for t in item.split(',') if t.strip()]
-                flat_type.extend(split_items)
-
-            unique_flat_type = list(dict.fromkeys(flat_type))
-            type_list = [ModelType(t.lower()) for t in unique_flat_type]
-        
-        api_logger.info(f"获取模型type_list: {type_list}")
-        query = model_schema.ModelConfigQueryNew(
-            type=type_list,
-            provider=provider,
-            is_active=is_active,
-            is_public=is_public,
-            is_composite=is_composite,
-            search=search
-        )
-        
-        api_logger.debug(f"开始获取模型配置列表: {query.model_dump()}")
-        result = ModelConfigService.get_model_list_new(db=db, query=query, tenant_id=current_user.tenant_id)
-        api_logger.info(f"模型配置列表获取成功: 分组数={len(result)}, 总模型数={sum(len(item['models']) for item in result)}")
-        return success(data=result, msg="模型配置列表获取成功")
-    except Exception as e:
-        api_logger.error(f"获取模型配置列表失败: {str(e)}")
-        raise
-
-
-@router.get("/model_plaza", response_model=ApiResponse)
-def get_model_plaza_list(
-    type: Optional[ModelType] = Query(None, description="模型类型"),
-    provider: Optional[ModelProvider] = Query(None, description="供应商"),
-    is_official: Optional[bool] = Query(None, description="是否官方模型"),
-    is_deprecated: Optional[bool] = Query(None, description="是否弃用"),
-    search: Optional[str] = Query(None, description="搜索关键词"),
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """模型广场查询接口（按供应商分组）"""
-    
-    query = model_schema.ModelBaseQuery(
-        type=type,
-        provider=provider,
-        is_official=is_official,
-        is_deprecated=is_deprecated,
-        search=search
-    )
-    result = ModelBaseService.get_model_base_list(db=db, query=query, tenant_id=current_user.tenant_id)
-    return success(data=result, msg="模型广场列表获取成功")
-
-
-@router.get("/model_plaza/{model_base_id}", response_model=ApiResponse)
-def get_model_base_by_id(
-    model_base_id: uuid.UUID,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """获取基础模型详情"""
-    
-    result = ModelBaseService.get_model_base_by_id(db=db, model_base_id=model_base_id)
-    return success(data=model_schema.ModelBase.model_validate(result), msg="基础模型获取成功")
-
-
-@router.post("/model_plaza", response_model=ApiResponse)
-def create_model_base(
-    data: model_schema.ModelBaseCreate,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """创建基础模型"""
-    
-    result = ModelBaseService.create_model_base(db=db, data=data)
-    return success(data=model_schema.ModelBase.model_validate(result), msg="基础模型创建成功")
-
-
-@router.put("/model_plaza/{model_base_id}", response_model=ApiResponse)
-def update_model_base(
-    model_base_id: uuid.UUID,
-    data: model_schema.ModelBaseUpdate,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """更新基础模型"""
-    
-    # 不允许更改type类型
-    if data.type is not None or data.provider is not None:
-        raise BusinessException("不允许更改模型类型和供应商", BizCode.INVALID_PARAMETER)
-    
-    result = ModelBaseService.update_model_base(db=db, model_base_id=model_base_id, data=data)
-    return success(data=model_schema.ModelBase.model_validate(result), msg="基础模型更新成功")
-
-
-@router.delete("/model_plaza/{model_base_id}", response_model=ApiResponse)
-def delete_model_base(
-    model_base_id: uuid.UUID,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """删除基础模型"""
-    
-    ModelBaseService.delete_model_base(db=db, model_base_id=model_base_id)
-    return success(msg="基础模型删除成功")
-
-
-@router.post("/model_plaza/{model_base_id}/add", response_model=ApiResponse)
-def add_model_from_plaza(
-    model_base_id: uuid.UUID,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """从模型广场添加模型到模型列表"""
-    
-    result = ModelBaseService.add_model_from_plaza(db=db, model_base_id=model_base_id, tenant_id=current_user.tenant_id)
-    return success(data=model_schema.ModelConfig.model_validate(result), msg="模型添加成功")
-
-
 @router.get("/{model_id}", response_model=ApiResponse)
 def get_model_by_id(
     model_id: uuid.UUID,
@@ -290,73 +138,6 @@ async def create_model(
         raise
 
 
-@router.post("/composite", response_model=ApiResponse)
-async def create_composite_model(
-    model_data: model_schema.CompositeModelCreate,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """
-    创建组合模型
-    
-    - 绑定一个或多个现有的 API Key
-    - 所有 API Key 必须来自非组合模型
-    - 所有 API Key 关联的模型类型必须与组合模型类型一致
-    """
-    api_logger.info(f"创建组合模型请求: {model_data.name}, 用户: {current_user.username}, tenant_id={current_user.tenant_id}")
-    
-    try:
-        result_orm = await ModelConfigService.create_composite_model(db=db, model_data=model_data, tenant_id=current_user.tenant_id)
-        api_logger.info(f"组合模型创建成功: {result_orm.name} (ID: {result_orm.id})")
-        
-        result = model_schema.ModelConfig.model_validate(result_orm)
-        return success(data=result, msg="组合模型创建成功")
-    except Exception as e:
-        api_logger.error(f"创建组合模型失败: {model_data.name} - {str(e)}")
-        raise
-
-
-@router.put("/composite/{model_id}", response_model=ApiResponse)
-async def update_composite_model(
-    model_id: uuid.UUID,
-    model_data: model_schema.CompositeModelCreate,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """更新组合模型"""
-    api_logger.info(f"更新组合模型请求: model_id={model_id}, 用户: {current_user.username}")
-    
-    try:
-        if model_data.type is not None:
-            raise BusinessException("不允许更改模型类型", BizCode.INVALID_PARAMETER)
-        result_orm = await ModelConfigService.update_composite_model(db=db, model_id=model_id, model_data=model_data, tenant_id=current_user.tenant_id)
-        api_logger.info(f"组合模型更新成功: {result_orm.name} (ID: {model_id})")
-        
-        result = model_schema.ModelConfig.model_validate(result_orm)
-        return success(data=result, msg="组合模型更新成功")
-    except Exception as e:
-        api_logger.error(f"更新组合模型失败: model_id={model_id} - {str(e)}")
-        raise
-
-
-@router.delete("/composite/{model_id}", response_model=ApiResponse)
-def delete_composite_model(
-    model_id: uuid.UUID,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """删除组合模型"""
-    api_logger.info(f"删除组合模型请求: model_id={model_id}, 用户: {current_user.username}")
-    
-    try:
-        ModelConfigService.delete_model(db=db, model_id=model_id, tenant_id=current_user.tenant_id)
-        api_logger.info(f"组合模型删除成功: model_id={model_id}")
-        return success(msg="组合模型删除成功")
-    except Exception as e:
-        api_logger.error(f"删除组合模型失败: model_id={model_id} - {str(e)}")
-        raise
-
-
 @router.put("/{model_id}", response_model=ApiResponse)
 def update_model(
     model_id: uuid.UUID,
@@ -368,9 +149,6 @@ def update_model(
     更新模型配置
     """
     api_logger.info(f"更新模型配置请求: model_id={model_id}, 用户: {current_user.username}, tenant_id={current_user.tenant_id}")
-
-    if model_data.type is not None or model_data.provider is not None:
-        raise BusinessException("不允许更改模型类型和供应商", BizCode.INVALID_PARAMETER)
     
     try:
         api_logger.debug(f"开始更新模型配置: model_id={model_id}")
@@ -436,53 +214,6 @@ def get_model_api_keys(
         raise
 
 
-@router.post("/provider/apikeys", response_model=ApiResponse)
-async def create_model_api_key_by_provider(
-        api_key_data: model_schema.ModelApiKeyCreateByProvider,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    根据供应商为所有匹配的模型创建API Key
-    """
-    api_logger.info(f"创建API Key请求: provider={api_key_data.provider}, 用户: {current_user.username}")
-
-    try:
-        # 根据tenant_id和provider筛选model_config_id列表
-        model_config_ids = api_key_data.model_config_ids
-        if not model_config_ids:
-            model_config_ids = ModelConfigRepository.get_model_config_ids_by_provider(
-                db=db,
-                tenant_id=current_user.tenant_id,
-                provider=api_key_data.provider
-            )
-        
-        if not model_config_ids:
-            raise BusinessException(f"未找到供应商 {api_key_data.provider} 的模型配置", BizCode.MODEL_NOT_FOUND)
-        
-        # 构造schema并调用service
-        create_data = model_schema.ModelApiKeyCreateByProvider(
-            provider=api_key_data.provider,
-            api_key=api_key_data.api_key,
-            api_base=api_key_data.api_base,
-            description=api_key_data.description,
-            config=api_key_data.config,
-            is_active=api_key_data.is_active,
-            priority=api_key_data.priority,
-            model_config_ids=model_config_ids
-        )
-        created_keys, failed_models = await ModelApiKeyService.create_api_key_by_provider(db=db, data=create_data)
-        
-        api_logger.info(f"API Key创建成功: 关联{len(created_keys)}个模型")
-        # result_list = [model_schema.ModelApiKey.model_validate(key) for key in created_keys]
-        result = "API Key已存在" if len(created_keys) == 0 and len(failed_models) == 0 else \
-            f"成功为 {len(created_keys)} 个模型创建API Key, 失败模型列表{failed_models}"
-        return success(data=result, msg=f"成功为 {len(created_keys)} 个模型创建API Key")
-    except Exception as e:
-        api_logger.error(f"创建API Key失败: {str(e)}")
-        raise
-
-
 @router.post("/{model_id}/apikeys", response_model=ApiResponse, status_code=status.HTTP_201_CREATED)
 async def create_model_api_key(
     model_id: uuid.UUID,
@@ -497,12 +228,11 @@ async def create_model_api_key(
     
     try:
         # 设置模型配置ID
-        api_key_data.model_config_ids = [model_id]
+        api_key_data.model_config_id = model_id
         
         api_logger.debug(f"开始创建模型API Key: {api_key_data.model_name}")
-        result_orm = await ModelApiKeyService.create_api_key(db=db, api_key_data=api_key_data)
-        api_logger.info(f"模型API Key创建成功: {result_orm.model_name} (ID: {result_orm.id})")
-        result = model_schema.ModelApiKey.model_validate(result_orm)
+        result = await ModelApiKeyService.create_api_key(db=db, api_key_data=api_key_data)
+        api_logger.info(f"模型API Key创建成功: {result.model_name} (ID: {result.id})")
         return success(data=result, msg="模型API Key创建成功")
     except Exception as e:
         api_logger.error(f"创建模型API Key失败: {api_key_data.model_name} - {str(e)}")
@@ -604,3 +334,5 @@ async def validate_model_config(
     return success(data=model_schema.ModelValidateResponse(**result), msg="验证完成")
 
 
+
+

api/app/controllers/ontology_secondary_routes.py

@@ -1,611 +0,0 @@
-# -*- coding: utf-8 -*-
-"""本体场景和类型路由（续）
-
-由于主Controller文件较大，将剩余路由放在此文件中。
-"""
-
-from uuid import UUID
-from typing import Optional
-
-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.response_utils import fail, success
-from app.db import get_db
-from app.dependencies import get_current_user
-from app.models.user_model import User
-from app.schemas.ontology_schemas import (
-    SceneResponse,
-    SceneListResponse,
-    PaginationInfo,
-    ClassCreateRequest,
-    ClassUpdateRequest,
-    ClassResponse,
-    ClassListResponse,
-    ClassBatchCreateResponse,
-)
-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
-
-
-api_logger = get_api_logger()
-
-
-def _get_dummy_ontology_service(db: Session) -> OntologyService:
-    """获取OntologyService实例（不需要LLM）
-    
-    场景和类型管理不需要LLM，创建一个dummy配置。
-    """
-    dummy_config = RedBearModelConfig(
-        model_name="dummy",
-        provider="openai",
-        api_key="dummy",
-        base_url="https://api.openai.com/v1"
-    )
-    llm_client = OpenAIClient(model_config=dummy_config)
-    return OntologyService(llm_client=llm_client, db=db)
-
-
-# 这些函数将被导入到主Controller中
-
-async def scenes_handler(
-    workspace_id: Optional[str] = None,
-    scene_name: Optional[str] = None,
-    page: Optional[int] = None,
-    page_size: Optional[int] = None,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """获取场景列表（支持模糊搜索和全量查询，全量查询支持分页）
-    
-    当提供 scene_name 参数时，进行模糊搜索（不分页）；
-    当不提供 scene_name 参数时，返回所有场景（支持分页）。
-    
-    Args:
-        workspace_id: 工作空间ID（可选，默认当前用户工作空间）
-        scene_name: 场景名称关键词（可选，支持模糊匹配）
-        page: 页码（可选，从1开始，仅在全量查询时有效）
-        page_size: 每页数量（可选，仅在全量查询时有效）
-        db: 数据库会话
-        current_user: 当前用户
-    """
-    operation = "search" if scene_name else "list"
-    api_logger.info(
-        f"Scene {operation} requested by user {current_user.id}, "
-        f"workspace_id={workspace_id}, keyword={scene_name}, page={page}, page_size={page_size}"
-    )
-    
-    try:
-        # 确定工作空间ID
-        if workspace_id:
-            try:
-                ws_uuid = UUID(workspace_id)
-            except ValueError:
-                api_logger.warning(f"Invalid workspace_id format: {workspace_id}")
-                return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的工作空间ID格式")
-        else:
-            ws_uuid = current_user.current_workspace_id
-            if not ws_uuid:
-                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")
-        
-        # 创建Service
-        service = _get_dummy_ontology_service(db)
-        
-        # 根据是否提供 scene_name 决定查询方式
-        if scene_name and scene_name.strip():
-            # 验证分页参数（模糊搜索也支持分页）
-            if page is not None and page < 1:
-                api_logger.warning(f"Invalid page number: {page}")
-                return fail(BizCode.BAD_REQUEST, "请求参数无效", "页码必须大于0")
-            
-            if page_size is not None and page_size < 1:
-                api_logger.warning(f"Invalid page_size: {page_size}")
-                return fail(BizCode.BAD_REQUEST, "请求参数无效", "每页数量必须大于0")
-            
-            # 如果只提供了page或page_size中的一个，返回错误
-            if (page is not None and page_size is None) or (page is None and page_size is not None):
-                api_logger.warning(f"Incomplete pagination params: page={page}, page_size={page_size}")
-                return fail(BizCode.BAD_REQUEST, "请求参数无效", "分页参数page和pagesize必须同时提供")
-            
-            # 模糊搜索场景（支持分页）
-            scenes = service.search_scenes_by_name(scene_name.strip(), ws_uuid)
-            total = len(scenes)
-            
-            # 如果提供了分页参数，进行分页处理
-            if page is not None and page_size is not None:
-                start_idx = (page - 1) * page_size
-                end_idx = start_idx + page_size
-                scenes = scenes[start_idx:end_idx]
-            
-            # 构建响应
-            items = []
-            for scene in scenes:
-                # 获取前3个class_name作为entity_type
-                entity_type = [cls.class_name for cls in scene.classes[:3]] if scene.classes else None
-                # 动态计算 type_num
-                type_num = len(scene.classes) if scene.classes else 0
-                
-                items.append(SceneResponse(
-                    scene_id=scene.scene_id,
-                    scene_name=scene.scene_name,
-                    scene_description=scene.scene_description,
-                    type_num=type_num,
-                    entity_type=entity_type,
-                    workspace_id=scene.workspace_id,
-                    created_at=scene.created_at,
-                    updated_at=scene.updated_at,
-                    classes_count=type_num
-                ))
-            
-            # 构建响应（包含分页信息）
-            if page is not None and page_size is not None:
-                # 计算是否有下一页
-                hasnext = (page * page_size) < total
-                
-                pagination_info = PaginationInfo(
-                    page=page,
-                    pagesize=page_size,
-                    total=total,
-                    hasnext=hasnext
-                )
-                response = SceneListResponse(items=items, page=pagination_info)
-            else:
-                response = SceneListResponse(items=items)
-            
-            api_logger.info(
-                f"Scene search completed: found {len(items)} scenes matching '{scene_name}' "
-                f"in workspace {ws_uuid}, total={total}"
-            )
-        else:
-            # 获取所有场景（支持分页）
-            # 验证分页参数
-            if page is not None and page < 1:
-                api_logger.warning(f"Invalid page number: {page}")
-                return fail(BizCode.BAD_REQUEST, "请求参数无效", "页码必须大于0")
-            
-            if page_size is not None and page_size < 1:
-                api_logger.warning(f"Invalid page_size: {page_size}")
-                return fail(BizCode.BAD_REQUEST, "请求参数无效", "每页数量必须大于0")
-            
-            # 如果只提供了page或page_size中的一个，返回错误
-            if (page is not None and page_size is None) or (page is None and page_size is not None):
-                api_logger.warning(f"Incomplete pagination params: page={page}, page_size={page_size}")
-                return fail(BizCode.BAD_REQUEST, "请求参数无效", "分页参数page和pagesize必须同时提供")
-            
-            scenes, total = service.list_scenes(ws_uuid, page, page_size)
-            
-            # 构建响应
-            items = []
-            for scene in scenes:
-                # 获取前3个class_name作为entity_type
-                entity_type = [cls.class_name for cls in scene.classes[:3]] if scene.classes else None
-                # 动态计算 type_num
-                type_num = len(scene.classes) if scene.classes else 0
-                
-                items.append(SceneResponse(
-                    scene_id=scene.scene_id,
-                    scene_name=scene.scene_name,
-                    scene_description=scene.scene_description,
-                    type_num=type_num,
-                    entity_type=entity_type,
-                    workspace_id=scene.workspace_id,
-                    created_at=scene.created_at,
-                    updated_at=scene.updated_at,
-                    classes_count=type_num
-                ))
-            
-            # 构建响应（包含分页信息）
-            if page is not None and page_size is not None:
-                # 计算是否有下一页
-                hasnext = (page * page_size) < total
-                
-                pagination_info = PaginationInfo(
-                    page=page,
-                    pagesize=page_size,
-                    total=total,
-                    hasnext=hasnext
-                )
-                response = SceneListResponse(items=items, page=pagination_info)
-            else:
-                response = SceneListResponse(items=items)
-            
-            api_logger.info(f"Scene list retrieved successfully, count={len(items)}, total={total}")
-        
-        return success(data=response.model_dump(mode='json'), msg="查询成功")
-        
-    except ValueError as e:
-        api_logger.warning(f"Validation error in scene {operation}: {str(e)}")
-        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
-        
-    except RuntimeError as e:
-        api_logger.error(f"Runtime error in scene {operation}: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))
-        
-    except Exception as e:
-        api_logger.error(f"Unexpected error in scene {operation}: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))
-
-
-# ==================== 本体类型管理接口 ====================
-
-async def create_class_handler(
-    request: ClassCreateRequest,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """创建本体类型（统一使用列表形式，支持单个或批量）"""
-    
-    # 根据列表长度判断是单个还是批量
-    count = len(request.classes)
-    mode = "single" if count == 1 else "batch"
-    
-    api_logger.info(
-        f"Class creation ({mode}) requested by user {current_user.id}, "
-        f"scene_id={request.scene_id}, count={count}"
-    )
-    
-    try:
-        # 获取当前工作空间ID
-        workspace_id = current_user.current_workspace_id
-        if not workspace_id:
-            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")
-        
-        # 创建Service
-        service = _get_dummy_ontology_service(db)
-        
-        # 准备类型数据
-        classes_data = [
-            {
-                "class_name": item.class_name,
-                "class_description": item.class_description
-            }
-            for item in request.classes
-        ]
-        
-        if count == 1:
-            # 单个创建
-            class_data = classes_data[0]
-            ontology_class = service.create_class(
-                scene_id=request.scene_id,
-                class_name=class_data["class_name"],
-                class_description=class_data["class_description"],
-                workspace_id=workspace_id
-            )
-            
-            # 构建单个响应
-            response = ClassResponse(
-                class_id=ontology_class.class_id,
-                class_name=ontology_class.class_name,
-                class_description=ontology_class.class_description,
-                scene_id=ontology_class.scene_id,
-                created_at=ontology_class.created_at,
-                updated_at=ontology_class.updated_at
-            )
-            
-            api_logger.info(f"Class created successfully: {ontology_class.class_id}")
-            
-            return success(data=response.model_dump(mode='json'), msg="类型创建成功")
-            
-        else:
-            # 批量创建
-            created_classes, errors = service.create_classes_batch(
-                scene_id=request.scene_id,
-                classes=classes_data,
-                workspace_id=workspace_id
-            )
-            
-            # 构建批量响应
-            items = []
-            for ontology_class in created_classes:
-                items.append(ClassResponse(
-                    class_id=ontology_class.class_id,
-                    class_name=ontology_class.class_name,
-                    class_description=ontology_class.class_description,
-                    scene_id=ontology_class.scene_id,
-                    created_at=ontology_class.created_at,
-                    updated_at=ontology_class.updated_at
-                ))
-            
-            response = ClassBatchCreateResponse(
-                total=len(classes_data),
-                success_count=len(created_classes),
-                failed_count=len(errors),
-                items=items,
-                errors=errors if errors else None
-            )
-            
-            api_logger.info(
-                f"Batch class creation completed: "
-                f"success={len(created_classes)}, failed={len(errors)}"
-            )
-            
-            return success(data=response.model_dump(mode='json'), msg="批量创建完成")
-        
-    except ValueError as e:
-        api_logger.warning(f"Validation error in class creation: {str(e)}")
-        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
-        
-    except RuntimeError as e:
-        api_logger.error(f"Runtime error in class creation: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "类型创建失败", str(e))
-        
-    except Exception as e:
-        api_logger.error(f"Unexpected error in class creation: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "类型创建失败", str(e))
-
-
-async def update_class_handler(
-    class_id: str,
-    request: ClassUpdateRequest,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """更新本体类型"""
-    api_logger.info(
-        f"Class update requested by user {current_user.id}, "
-        f"class_id={class_id}"
-    )
-    
-    try:
-        # 验证UUID格式
-        try:
-            class_uuid = UUID(class_id)
-        except ValueError:
-            api_logger.warning(f"Invalid class_id format: {class_id}")
-            return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的类型ID格式")
-        
-        # 获取当前工作空间ID
-        workspace_id = current_user.current_workspace_id
-        if not workspace_id:
-            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")
-        
-        # 创建Service
-        service = _get_dummy_ontology_service(db)
-        
-        # 更新类型
-        ontology_class = service.update_class(
-            class_id=class_uuid,
-            class_name=request.class_name,
-            class_description=request.class_description,
-            workspace_id=workspace_id
-        )
-        
-        # 构建响应
-        response = ClassResponse(
-            class_id=ontology_class.class_id,
-            class_name=ontology_class.class_name,
-            class_description=ontology_class.class_description,
-            scene_id=ontology_class.scene_id,
-            created_at=ontology_class.created_at,
-            updated_at=ontology_class.updated_at
-        )
-        
-        api_logger.info(f"Class updated successfully: {class_id}")
-        
-        return success(data=response.model_dump(mode='json'), msg="类型更新成功")
-        
-    except ValueError as e:
-        api_logger.warning(f"Validation error in class update: {str(e)}")
-        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
-        
-    except RuntimeError as e:
-        api_logger.error(f"Runtime error in class update: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "类型更新失败", str(e))
-        
-    except Exception as e:
-        api_logger.error(f"Unexpected error in class update: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "类型更新失败", str(e))
-
-
-async def delete_class_handler(
-    class_id: str,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """删除本体类型"""
-    api_logger.info(
-        f"Class deletion requested by user {current_user.id}, "
-        f"class_id={class_id}"
-    )
-    
-    try:
-        # 验证UUID格式
-        try:
-            class_uuid = UUID(class_id)
-        except ValueError:
-            api_logger.warning(f"Invalid class_id format: {class_id}")
-            return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的类型ID格式")
-        
-        # 获取当前工作空间ID
-        workspace_id = current_user.current_workspace_id
-        if not workspace_id:
-            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")
-        
-        # 创建Service
-        service = _get_dummy_ontology_service(db)
-        
-        # 删除类型
-        success_flag = service.delete_class(
-            class_id=class_uuid,
-            workspace_id=workspace_id
-        )
-        
-        api_logger.info(f"Class deleted successfully: {class_id}")
-        
-        return success(data={"deleted": success_flag}, msg="类型删除成功")
-        
-    except ValueError as e:
-        api_logger.warning(f"Validation error in class deletion: {str(e)}")
-        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
-        
-    except RuntimeError as e:
-        api_logger.error(f"Runtime error in class deletion: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "类型删除失败", str(e))
-        
-    except Exception as e:
-        api_logger.error(f"Unexpected error in class deletion: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "类型删除失败", str(e))
-
-
-async def get_class_handler(
-    class_id: str,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """获取单个本体类型"""
-    api_logger.info(
-        f"Get class requested by user {current_user.id}, "
-        f"class_id={class_id}"
-    )
-    
-    try:
-        # 验证UUID格式
-        try:
-            class_uuid = UUID(class_id)
-        except ValueError:
-            api_logger.warning(f"Invalid class_id format: {class_id}")
-            return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的类型ID格式")
-        
-        # 获取当前工作空间ID
-        workspace_id = current_user.current_workspace_id
-        if not workspace_id:
-            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")
-        
-        # 创建Service
-        service = _get_dummy_ontology_service(db)
-        
-        # 获取类型（会抛出ValueError如果不存在）
-        ontology_class = service.get_class_by_id(class_uuid, workspace_id)
-        
-        # 构建响应
-        response = ClassResponse(
-            class_id=ontology_class.class_id,
-            class_name=ontology_class.class_name,
-            class_description=ontology_class.class_description,
-            scene_id=ontology_class.scene_id,
-            created_at=ontology_class.created_at,
-            updated_at=ontology_class.updated_at
-        )
-        
-        api_logger.info(f"Class retrieved successfully: {class_id}")
-        
-        return success(data=response.model_dump(mode='json'), msg="查询成功")
-        
-    except ValueError as e:
-        # 类型不存在或无权限访问
-        api_logger.warning(f"Validation error in get class: {str(e)}")
-        return fail(BizCode.NOT_FOUND, "请求参数无效", str(e))
-        
-    except RuntimeError as e:
-        api_logger.error(f"Runtime error in get class: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))
-        
-    except Exception as e:
-        api_logger.error(f"Unexpected error in get class: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))
-
-
-async def classes_handler(
-    scene_id: str,
-    class_name: Optional[str] = None,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """获取类型列表（支持模糊搜索和全量查询）
-    
-    当提供 class_name 参数时，进行模糊搜索；
-    当不提供 class_name 参数时，返回场景下的所有类型。
-    
-    Args:
-        scene_id: 场景ID（必填）
-        class_name: 类型名称关键词（可选，支持模糊匹配）
-        db: 数据库会话
-        current_user: 当前用户
-    """
-    operation = "search" if class_name else "list"
-    api_logger.info(
-        f"Class {operation} requested by user {current_user.id}, "
-        f"keyword={class_name}, scene_id={scene_id}"
-    )
-    
-    try:
-        # 验证UUID格式
-        try:
-            scene_uuid = UUID(scene_id)
-        except ValueError:
-            api_logger.warning(f"Invalid scene_id format: {scene_id}")
-            return fail(BizCode.BAD_REQUEST, "请求参数无效", "无效的场景ID格式")
-        
-        # 获取当前工作空间ID
-        workspace_id = current_user.current_workspace_id
-        if not workspace_id:
-            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")
-        
-        # 创建Service
-        service = _get_dummy_ontology_service(db)
-        
-        # 获取场景信息
-        scene = service.get_scene_by_id(scene_uuid, workspace_id)
-        if not scene:
-            api_logger.warning(f"Scene not found: {scene_id}")
-            return fail(BizCode.NOT_FOUND, "场景不存在", f"未找到ID为 {scene_id} 的场景")
-        
-        # 根据是否提供 class_name 决定查询方式
-        if class_name and class_name.strip():
-            # 模糊搜索类型
-            classes = service.search_classes_by_name(class_name.strip(), scene_uuid, workspace_id)
-        else:
-            # 获取所有类型
-            classes = service.list_classes_by_scene(scene_uuid, workspace_id)
-        
-        # 构建响应
-        items = []
-        for ontology_class in classes:
-            items.append(ClassResponse(
-                class_id=ontology_class.class_id,
-                class_name=ontology_class.class_name,
-                class_description=ontology_class.class_description,
-                scene_id=ontology_class.scene_id,
-                created_at=ontology_class.created_at,
-                updated_at=ontology_class.updated_at
-            ))
-        
-        response = ClassListResponse(
-            total=len(items),
-            scene_id=scene_uuid,
-            scene_name=scene.scene_name,
-            scene_description=scene.scene_description,
-            items=items
-        )
-        
-        if class_name:
-            api_logger.info(
-                f"Class search completed: found {len(items)} classes matching '{class_name}' "
-                f"in scene {scene_id}"
-            )
-        else:
-            api_logger.info(f"Class list retrieved successfully, count={len(items)}")
-        
-        return success(data=response.model_dump(mode='json'), msg="查询成功")
-        
-    except ValueError as e:
-        api_logger.warning(f"Validation error in class {operation}: {str(e)}")
-        return fail(BizCode.BAD_REQUEST, "请求参数无效", str(e))
-        
-    except RuntimeError as e:
-        api_logger.error(f"Runtime error in class {operation}: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))
-        
-    except Exception as e:
-        api_logger.error(f"Unexpected error in class {operation}: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "查询失败", str(e))

api/app/controllers/prompt_optimizer_controller.py

@@ -1,5 +1,5 @@
-import json
 import uuid
+import json
 
 from fastapi import APIRouter, Depends, Path
 from sqlalchemy.orm import Session
@@ -8,13 +8,9 @@ from starlette.responses import StreamingResponse
 from app.core.logging_config import get_api_logger
 from app.core.response_utils import success
 from app.dependencies import get_current_user, get_db
-from app.schemas.prompt_optimizer_schema import (
-    PromptOptMessage,
-    CreateSessionResponse,
-    SessionHistoryResponse,
-    SessionMessage,
-    PromptSaveRequest
-)
+from app.models.prompt_optimizer_model import RoleType
+from app.schemas.prompt_optimizer_schema import PromptOptMessage, PromptOptModelSet, CreateSessionResponse, \
+    OptimizePromptResponse, SessionHistoryResponse, SessionMessage
 from app.schemas.response_schema import ApiResponse
 from app.services.prompt_optimizer_service import PromptOptimizerService
 
@@ -120,8 +116,7 @@ async def get_prompt_opt(
                     session_id=session_id,
                     user_id=current_user.id,
                     current_prompt=data.current_prompt,
-                    user_require=data.message,
-                    skill=data.skill
+                    user_require=data.message
             ):
                 # chunk 是 prompt 的增量内容
                 yield f"event:message\ndata: {json.dumps(chunk)}\n\n"
@@ -140,109 +135,3 @@ async def get_prompt_opt(
             "X-Accel-Buffering": "no"
         }
     )
-
-
-@router.post(
-    "/releases",
-    summary="Get prompt optimization",
-    response_model=ApiResponse
-)
-def save_prompt(
-        data: PromptSaveRequest,
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """
-       Save a prompt release for the current tenant.
-
-       Args:
-           data (PromptSaveRequest): Request body containing session_id, title, and prompt.
-           db (Session): SQLAlchemy database session, injected via dependency.
-           current_user: Currently authenticated user object, injected via dependency.
-
-       Returns:
-           ApiResponse: Standard API response containing the saved prompt release info:
-               - id: UUID of the prompt release
-               - session_id: associated session
-               - title: prompt title
-               - prompt: prompt content
-               - created_at: timestamp of creation
-
-       Raises:
-           Any database or service exceptions are propagated to the global exception handler.
-       """
-    service = PromptOptimizerService(db)
-    prompt_info = service.save_prompt(
-        tenant_id=current_user.tenant_id,
-        session_id=data.session_id,
-        title=data.title,
-        prompt=data.prompt
-    )
-    return success(data=prompt_info)
-
-
-@router.delete(
-    "/releases/{prompt_id}",
-    summary="Delete prompt (soft delete)",
-    response_model=ApiResponse
-)
-def delete_prompt(
-        prompt_id: uuid.UUID = Path(..., description="Prompt ID"),
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """
-    Soft delete a prompt release.
-
-    Args:
-        prompt_id
-        db (Session): Database session
-        current_user: Current logged-in user
-
-    Returns:
-        ApiResponse: Success message confirming deletion
-    """
-    service = PromptOptimizerService(db)
-    service.delete_prompt(
-        tenant_id=current_user.tenant_id,
-        prompt_id=prompt_id
-    )
-    return success(msg="Prompt deleted successfully")
-
-
-@router.get(
-    "/releases/list",
-    summary="Get paginated list of released prompts with optional filter",
-    response_model=ApiResponse
-)
-def get_release_list(
-        page: int = 1,
-        page_size: int = 20,
-        keyword: str | None = None,
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """
-    Retrieve paginated list of released prompts for the current tenant.
-    Optionally filter by keyword in title.
-
-    Args:
-        page (int): Page number (starting from 1)
-        page_size (int): Number of items per page (max 100)
-        keyword (str | None): Optional keyword to filter prompt titles
-        db (Session): Database session
-        current_user: Current logged-in user
-
-    Returns:
-        ApiResponse: Contains paginated list of prompt releases with metadata
-    """
-    service = PromptOptimizerService(db)
-    result = service.get_release_list(
-        tenant_id=current_user.tenant_id,
-        page=max(1, page),
-        page_size=min(max(1, page_size), 100),
-        filter_keyword=keyword
-    )
-    return success(data=result)
-
-

api/app/controllers/public_share_controller.py

@@ -8,10 +8,9 @@ from sqlalchemy.orm import Session
 
 from app.core.logging_config import get_business_logger
 from app.core.response_utils import success
-from app.db import get_db, get_db_read
+from app.db import get_db
 from app.dependencies import get_share_user_id, ShareTokenData
 from app.repositories import knowledge_repository
-from app.repositories.workflow_repository import WorkflowConfigRepository
 from app.schemas import release_share_schema, conversation_schema
 from app.schemas.response_schema import PageData, PageMeta
 from app.services import workspace_service
@@ -20,8 +19,7 @@ from app.services.conversation_service import ConversationService
 from app.services.release_share_service import ReleaseShareService
 from app.services.shared_chat_service import SharedChatService
 from app.services.app_chat_service import AppChatService, get_app_chat_service
-from app.utils.app_config_utils import dict_to_multi_agent_config, workflow_config_4_app_release, \
-    agent_config_4_app_release, multi_agent_config_4_app_release
+from app.utils.app_config_utils import dict_to_multi_agent_config, workflow_config_4_app_release, agent_config_4_app_release, multi_agent_config_4_app_release
 
 router = APIRouter(prefix="/public/share", tags=["Public Share"])
 logger = get_business_logger()
@@ -67,10 +65,10 @@ def get_or_generate_user_id(payload_user_id: str, request: Request) -> str:
     summary="获取访问 token"
 )
 def get_access_token(
-        share_token: str,
-        payload: release_share_schema.TokenRequest,
-        request: Request,
-        db: Session = Depends(get_db),
+    share_token: str,
+    payload: release_share_schema.TokenRequest,
+    request: Request,
+    db: Session = Depends(get_db),
 ):
     """获取访问 token
 
@@ -115,9 +113,9 @@ def get_access_token(
     response_model=None
 )
 def get_shared_release(
-        password: str = Query(None, description="访问密码（如果需要）"),
-        share_data: ShareTokenData = Depends(get_share_user_id),
-        db: Session = Depends(get_db),
+    password: str = Query(None, description="访问密码（如果需要）"),
+    share_data: ShareTokenData = Depends(get_share_user_id),
+    db: Session = Depends(get_db),
 ):
     """获取公开分享的发布版本信息
 
@@ -139,9 +137,9 @@ def get_shared_release(
     summary="验证访问密码"
 )
 def verify_password(
-        payload: release_share_schema.PasswordVerifyRequest,
-        share_data: ShareTokenData = Depends(get_share_user_id),
-        db: Session = Depends(get_db),
+    payload: release_share_schema.PasswordVerifyRequest,
+    share_data: ShareTokenData = Depends(get_share_user_id),
+    db: Session = Depends(get_db),
 ):
     """验证分享的访问密码
 
@@ -161,11 +159,11 @@ def verify_password(
     summary="获取嵌入代码"
 )
 def get_embed_code(
-        width: str = Query("100%", description="iframe 宽度"),
-        height: str = Query("600px", description="iframe 高度"),
-        request: Request = None,
-        share_data: ShareTokenData = Depends(get_share_user_id),
-        db: Session = Depends(get_db),
+    width: str = Query("100%", description="iframe 宽度"),
+    height: str = Query("600px", description="iframe 高度"),
+    request: Request = None,
+    share_data: ShareTokenData = Depends(get_share_user_id),
+    db: Session = Depends(get_db),
 ):
     """获取嵌入代码
 
@@ -185,6 +183,7 @@ def get_embed_code(
     return success(data=embed_code)
 
 
+
 # ---------- 会话管理接口 ----------
 
 @router.get(
@@ -192,11 +191,11 @@ def get_embed_code(
     summary="获取会话列表"
 )
 def list_conversations(
-        password: str = Query(None, description="访问密码"),
-        page: int = Query(1, ge=1),
-        pagesize: int = Query(20, ge=1, le=100),
-        share_data: ShareTokenData = Depends(get_share_user_id),
-        db: Session = Depends(get_db),
+    password: str = Query(None, description="访问密码"),
+    page: int = Query(1, ge=1),
+    pagesize: int = Query(20, ge=1, le=100),
+    share_data: ShareTokenData = Depends(get_share_user_id),
+    db: Session = Depends(get_db),
 ):
     """获取分享应用的会话列表
 
@@ -210,9 +209,9 @@ def list_conversations(
     from app.repositories.end_user_repository import EndUserRepository
     end_user_repo = EndUserRepository(db)
     new_end_user = end_user_repo.get_or_create_end_user(
-        app_id=share.app_id,
-        other_id=other_id
-    )
+            app_id=share.app_id,
+            other_id=other_id
+        )
     logger.debug(new_end_user.id)
     service = SharedChatService(db)
     conversations, total = service.list_conversations(
@@ -234,10 +233,10 @@ def list_conversations(
     summary="获取会话详情（含消息）"
 )
 def get_conversation(
-        conversation_id: uuid.UUID,
-        password: str = Query(None, description="访问密码"),
-        share_data: ShareTokenData = Depends(get_share_user_id),
-        db: Session = Depends(get_db),
+    conversation_id: uuid.UUID,
+    password: str = Query(None, description="访问密码"),
+    share_data: ShareTokenData = Depends(get_share_user_id),
+    db: Session = Depends(get_db),
 ):
     """获取会话详情和消息历史"""
     chat_service = SharedChatService(db)
@@ -267,10 +266,10 @@ def get_conversation(
     summary="发送消息（支持流式和非流式）"
 )
 async def chat(
-        payload: conversation_schema.ChatRequest,
-        share_data: ShareTokenData = Depends(get_share_user_id),
-        db: Session = Depends(get_db),
-        app_chat_service: Annotated[AppChatService, Depends(get_app_chat_service)] = None,
+    payload: conversation_schema.ChatRequest,
+    share_data: ShareTokenData = Depends(get_share_user_id),
+    db: Session = Depends(get_db),
+    app_chat_service: Annotated[AppChatService, Depends(get_app_chat_service)] = None,
 ):
     """发送消息并获取回复
 
@@ -314,15 +313,12 @@ async def chat(
         )
         end_user_id = str(new_end_user.id)
 
-        appid = share.app_id
+        appid=share.app_id
         """获取存储类型和工作空间的ID"""
 
-        # 直接通过 SQLAlchemy 查询 app（仅查询未删除的应用）
+        # 直接通过 SQLAlchemy 查询 app
         from app.models.app_model import App
-        app = db.query(App).filter(
-            App.id == appid,
-            App.is_active.is_(True)
-        ).first()
+        app = db.query(App).filter(App.id == appid).first()
         if not app:
             raise BusinessException("应用不存在", BizCode.APP_NOT_FOUND)
 
@@ -429,17 +425,16 @@ async def chat(
             # )
             async def event_generator():
                 async for event in app_chat_service.agnet_chat_stream(
-                        message=payload.message,
-                        conversation_id=conversation.id,  # 使用已创建的会话 ID
-                        user_id=str(new_end_user.id),  # 转换为字符串
-                        variables=payload.variables,
-                        web_search=payload.web_search,
-                        config=agent_config,
-                        memory=payload.memory,
-                        storage_type=storage_type,
-                        user_rag_memory_id=user_rag_memory_id,
-                        workspace_id=workspace_id,
-                        files=payload.files  # 传递多模态文件
+                    message=payload.message,
+                    conversation_id=conversation.id,  # 使用已创建的会话 ID
+                    user_id= str(new_end_user.id),  # 转换为字符串
+                    variables=payload.variables,
+                    web_search=payload.web_search,
+                    config=agent_config,
+                    memory=payload.memory,
+                    storage_type=storage_type,
+                    user_rag_memory_id=user_rag_memory_id,
+                    workspace_id=workspace_id
                 ):
                     yield event
 
@@ -476,8 +471,7 @@ async def chat(
             memory=payload.memory,
             storage_type=storage_type,
             user_rag_memory_id=user_rag_memory_id,
-            workspace_id=workspace_id,
-            files=payload.files  # 传递多模态文件
+            workspace_id=workspace_id
         )
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
     elif app_type == AppType.MULTI_AGENT:
@@ -487,15 +481,15 @@ async def chat(
             async def event_generator():
                 async for event in app_chat_service.multi_agent_chat_stream(
 
-                        message=payload.message,
-                        conversation_id=conversation.id,  # 使用已创建的会话 ID
-                        user_id=str(new_end_user.id),  # 转换为字符串
-                        variables=payload.variables,
-                        config=config,
-                        web_search=payload.web_search,
-                        memory=payload.memory,
-                        storage_type=storage_type,
-                        user_rag_memory_id=user_rag_memory_id
+                    message=payload.message,
+                    conversation_id=conversation.id,  # 使用已创建的会话 ID
+                    user_id=str(new_end_user.id),  # 转换为字符串
+                    variables=payload.variables,
+                    config=config,
+                    web_search=payload.web_search,
+                    memory=payload.memory,
+                    storage_type=storage_type,
+                    user_rag_memory_id=user_rag_memory_id
                 ):
                     yield event
 
@@ -567,29 +561,24 @@ async def chat(
 
         # return success(data=conversation_schema.ChatResponse(**result))
     elif app_type == AppType.WORKFLOW:
+
         config = workflow_config_4_app_release(release)
-        if not config.id:
-            with get_db_read() as db:
-                source_config = WorkflowConfigRepository(db).get_by_app_id(release.app_id)
-                config.id = source_config.id
-        config.id = uuid.UUID(config.id)
         if payload.stream:
             async def event_generator():
+
                 async for event in app_chat_service.workflow_chat_stream(
-                        message=payload.message,
-                        conversation_id=conversation.id,  # 使用已创建的会话 ID
-                        user_id=end_user_id,  # 转换为字符串
-                        variables=payload.variables,
-                        files=payload.files,
-                        config=config,
-                        web_search=payload.web_search,
-                        memory=payload.memory,
-                        storage_type=storage_type,
-                        user_rag_memory_id=user_rag_memory_id,
-                        app_id=release.app_id,
-                        workspace_id=workspace_id,
-                        release_id=release.id,
-                        public=True
+
+                    message=payload.message,
+                    conversation_id=conversation.id,  # 使用已创建的会话 ID
+                    user_id=end_user_id,  # 转换为字符串
+                    variables=payload.variables,
+                    config=config,
+                    web_search=payload.web_search,
+                    memory=payload.memory,
+                    storage_type=storage_type,
+                    user_rag_memory_id=user_rag_memory_id,
+                    app_id=release.app_id,
+                    workspace_id=workspace_id
                 ):
                     event_type = event.get("event", "message")
                     event_data = event.get("data", {})
@@ -621,8 +610,7 @@ async def chat(
             storage_type=storage_type,
             user_rag_memory_id=user_rag_memory_id,
             app_id=release.app_id,
-            workspace_id=workspace_id,
-            release_id=release.id
+            workspace_id=workspace_id
         )
         logger.debug(
             "工作流试运行返回结果",

api/app/controllers/service/app_api_controller.py

@@ -12,6 +12,7 @@ from app.core.exceptions import BusinessException
 from app.core.logging_config import get_business_logger
 from app.core.response_utils import success
 from app.db import get_db
+from app.dependencies import get_app_or_workspace
 from app.models.app_model import App
 from app.models.app_model import AppType
 from app.repositories import knowledge_repository
@@ -20,10 +21,9 @@ from app.schemas import AppChatRequest, conversation_schema
 from app.schemas.api_key_schema import ApiKeyAuth
 from app.services import workspace_service
 from app.services.app_chat_service import AppChatService, get_app_chat_service
-from app.services.app_service import get_app_service, AppService
 from app.services.conversation_service import ConversationService, get_conversation_service
-from app.utils.app_config_utils import workflow_config_4_app_release, \
-    agent_config_4_app_release, multi_agent_config_4_app_release
+from app.utils.app_config_utils import dict_to_multi_agent_config, workflow_config_4_app_release, agent_config_4_app_release, multi_agent_config_4_app_release
+from app.services.app_service import get_app_service, AppService
 
 router = APIRouter(prefix="/app", tags=["V1 - App API"])
 logger = get_business_logger()
@@ -34,7 +34,6 @@ async def list_apps():
     """列出可访问的应用（占位）"""
     return success(data=[], msg="App API - Coming Soon")
 
-
 # /v1/app/chat
 
 # @router.post("/chat")
@@ -74,17 +73,16 @@ def _checkAppConfig(app: App):
     else:
         raise BusinessException("不支持的应用类型", BizCode.AGENT_CONFIG_MISSING)
 
-
 @router.post("/chat")
 @require_api_key(scopes=["app"])
 async def chat(
-        request: Request,
-        api_key_auth: ApiKeyAuth = None,
-        db: Session = Depends(get_db),
-        conversation_service: Annotated[ConversationService, Depends(get_conversation_service)] = None,
-        app_chat_service: Annotated[AppChatService, Depends(get_app_chat_service)] = None,
-        app_service: Annotated[AppService, Depends(get_app_service)] = None,
-        message: str = Body(..., description="聊天消息内容"),
+    request:Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    conversation_service: Annotated[ConversationService, Depends(get_conversation_service)] = None,
+    app_chat_service: Annotated[AppChatService, Depends(get_app_chat_service)] = None,
+    app_service: Annotated[AppService, Depends(get_app_service)] = None,
+    message: str = Body(..., description="聊天消息内容"),
 ):
     body = await request.json()
     payload = AppChatRequest(**body)
@@ -100,8 +98,8 @@ async def chat(
         original_user_id=other_id  # Save original user_id to other_id
     )
     end_user_id = str(new_end_user.id)
-    web_search = True
-    memory = True
+    web_search=True
+    memory=True
     # 提前验证和准备（在流式响应开始前完成）
     storage_type = workspace_service.get_workspace_storage_type_without_auth(
         db=db,
@@ -148,17 +146,16 @@ async def chat(
         if payload.stream:
             async def event_generator():
                 async for event in app_chat_service.agnet_chat_stream(
-                        message=payload.message,
-                        conversation_id=conversation.id,  # 使用已创建的会话 ID
-                        user_id=end_user_id,  # 转换为字符串
-                        variables=payload.variables,
-                        web_search=web_search,
-                        config=agent_config,
-                        memory=memory,
-                        storage_type=storage_type,
-                        user_rag_memory_id=user_rag_memory_id,
-                        workspace_id=workspace_id,
-                        files=payload.files  # 传递多模态文件
+                    message=payload.message,
+                    conversation_id=conversation.id,  # 使用已创建的会话 ID
+                    user_id= end_user_id,  # 转换为字符串
+                    variables=payload.variables,
+                    web_search=web_search,
+                    config=agent_config,
+                    memory=memory,
+                    storage_type=storage_type,
+                    user_rag_memory_id=user_rag_memory_id,
+                    workspace_id=workspace_id
                 ):
                     yield event
 
@@ -178,13 +175,12 @@ async def chat(
             conversation_id=conversation.id,  # 使用已创建的会话 ID
             user_id=end_user_id,  # 转换为字符串
             variables=payload.variables,
-            config=agent_config,
+            config= agent_config,
             web_search=web_search,
             memory=memory,
             storage_type=storage_type,
             user_rag_memory_id=user_rag_memory_id,
-            workspace_id=workspace_id,
-            files=payload.files  # 传递多模态文件
+            workspace_id=workspace_id
         )
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
     elif app_type == AppType.MULTI_AGENT:
@@ -194,15 +190,15 @@ async def chat(
             async def event_generator():
                 async for event in app_chat_service.multi_agent_chat_stream(
 
-                        message=payload.message,
-                        conversation_id=conversation.id,  # 使用已创建的会话 ID
-                        user_id=end_user_id,  # 转换为字符串
-                        variables=payload.variables,
-                        config=config,
-                        web_search=web_search,
-                        memory=memory,
-                        storage_type=storage_type,
-                        user_rag_memory_id=user_rag_memory_id
+                    message=payload.message,
+                    conversation_id=conversation.id,  # 使用已创建的会话 ID
+                    user_id=end_user_id,  # 转换为字符串
+                    variables=payload.variables,
+                    config=config,
+                    web_search=web_search,
+                    memory=memory,
+                    storage_type=storage_type,
+                    user_rag_memory_id=user_rag_memory_id
                 ):
                     yield event
 
@@ -236,19 +232,18 @@ async def chat(
         if payload.stream:
             async def event_generator():
                 async for event in app_chat_service.workflow_chat_stream(
-                        message=payload.message,
-                        conversation_id=conversation.id,  # 使用已创建的会话 ID
-                        user_id=end_user_id,  # 转换为字符串
-                        variables=payload.variables,
-                        files=payload.files,
-                        config=config,
-                        web_search=web_search,
-                        memory=memory,
-                        storage_type=storage_type,
-                        user_rag_memory_id=user_rag_memory_id,
-                        app_id=app.id,
-                        workspace_id=workspace_id,
-                        release_id=app.current_release.id,
+
+                    message=payload.message,
+                    conversation_id=conversation.id,  # 使用已创建的会话 ID
+                    user_id=new_end_user.id,  # 转换为字符串
+                    variables=payload.variables,
+                    config=config,
+                    web_search=payload.web_search,
+                    memory=payload.memory,
+                    storage_type=storage_type,
+                    user_rag_memory_id=user_rag_memory_id,
+                    app_id=app.app_id,
+                    workspace_id=workspace_id
                 ):
                     event_type = event.get("event", "message")
                     event_data = event.get("data", {})
@@ -272,16 +267,15 @@ async def chat(
 
             message=payload.message,
             conversation_id=conversation.id,  # 使用已创建的会话 ID
-            user_id=end_user_id,  # 转换为字符串
+            user_id=new_end_user.id,  # 转换为字符串
             variables=payload.variables,
             config=config,
-            web_search=web_search,
-            memory=memory,
+            web_search=payload.web_search,
+            memory=payload.memory,
             storage_type=storage_type,
             user_rag_memory_id=user_rag_memory_id,
-            app_id=app.id,
-            workspace_id=workspace_id,
-            release_id=app.current_release.id
+            app_id=app.app_id,
+            workspace_id=workspace_id
         )
         logger.debug(
             "工作流试运行返回结果",
@@ -298,3 +292,4 @@ async def chat(
         from app.core.exceptions import BusinessException
         from app.core.error_codes import BizCode
         raise BusinessException(f"不支持的应用类型: {app_type}", BizCode.APP_TYPE_NOT_SUPPORTED)
+

api/app/controllers/service/memory_api_controller.py

@@ -39,7 +39,7 @@ async def write_memory_api_service(
     
     Stores memory content for the specified end user using the Memory API Service.
     """
-    logger.info(f"Memory write request - end_user_id: {payload.end_user_id}, tenant_id: {api_key_auth.tenant_id}")
+    logger.info(f"Memory write request - end_user_id: {payload.end_user_id}")
     
     memory_api_service = MemoryAPIService(db)
     

api/app/controllers/service/rag_api_knowledge_controller.py

@@ -246,73 +246,3 @@ async def rebuild_knowledge_graph(
                                                               db=db,
                                                               current_user=current_user)
 
-
-@router.get("/check/yuque/auth", response_model=ApiResponse)
-@require_api_key(scopes=["rag"])
-async def check_yuque_auth(
-    yuque_user_id: str,
-    yuque_token: str,
-    request: Request,
-    api_key_auth: ApiKeyAuth = None,
-    db: Session = Depends(get_db),
-):
-    """
-    check yuque auth info
-    """
-    api_key = api_key_service.ApiKeyService.get_api_key(db, api_key_auth.api_key_id, api_key_auth.workspace_id)
-    current_user = api_key.creator
-    current_user.current_workspace_id = api_key_auth.workspace_id
-
-    api_logger.info(f"check yuque auth info, username: {current_user.username}")
-
-    return await knowledge_controller.check_yuque_auth(yuque_user_id=yuque_user_id,
-                                                       yuque_token=yuque_token,
-                                                       db=db,
-                                                       current_user=current_user)
-
-
-@router.get("/check/feishu/auth", response_model=ApiResponse)
-@require_api_key(scopes=["rag"])
-async def check_feishu_auth(
-    feishu_app_id: str,
-    feishu_app_secret: str,
-    feishu_folder_token: str,
-    request: Request,
-    api_key_auth: ApiKeyAuth = None,
-    db: Session = Depends(get_db),
-):
-    """
-    check feishu auth info
-    """
-    api_key = api_key_service.ApiKeyService.get_api_key(db, api_key_auth.api_key_id, api_key_auth.workspace_id)
-    current_user = api_key.creator
-    current_user.current_workspace_id = api_key_auth.workspace_id
-
-    api_logger.info(f"check feishu auth info, username: {current_user.username}")
-
-    return await knowledge_controller.check_feishu_auth(feishu_app_id=feishu_app_id,
-                                                        feishu_app_secret=feishu_app_secret,
-                                                        feishu_folder_token=feishu_folder_token,
-                                                        db=db,
-                                                        current_user=current_user)
-
-
-@router.post("/{knowledge_id}/sync", response_model=ApiResponse)
-@require_api_key(scopes=["rag"])
-async def sync_knowledge(
-    knowledge_id: uuid.UUID,
-    request: Request,
-    api_key_auth: ApiKeyAuth = None,
-    db: Session = Depends(get_db),
-):
-    """
-    sync knowledge base information based on knowledge_id
-    """
-    api_key = api_key_service.ApiKeyService.get_api_key(db, api_key_auth.api_key_id, api_key_auth.workspace_id)
-    current_user = api_key.creator
-    current_user.current_workspace_id = api_key_auth.workspace_id
-
-    return await knowledge_controller.sync_knowledge(knowledge_id=knowledge_id,
-                                                     db=db,
-                                                     current_user=current_user)
-

api/app/controllers/skill_controller.py

@@ -1,85 +0,0 @@
-"""Skill Controller - 技能市场管理"""
-from fastapi import APIRouter, Depends, Query
-from sqlalchemy.orm import Session
-from typing import Optional
-import uuid
-
-from app.db import get_db
-from app.dependencies import get_current_user
-from app.models import User
-from app.schemas import skill_schema
-from app.schemas.response_schema import PageData, PageMeta
-from app.services.skill_service import SkillService
-from app.core.response_utils import success
-
-router = APIRouter(prefix="/skills", tags=["Skills"])
-
-
-@router.post("", summary="创建技能")
-def create_skill(
-    data: skill_schema.SkillCreate,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """创建技能 - 可以关联现有工具（内置、MCP、自定义）"""
-    tenant_id = current_user.tenant_id
-    skill = SkillService.create_skill(db, data, tenant_id)
-    return success(data=skill_schema.Skill.model_validate(skill), msg="技能创建成功")
-
-
-@router.get("", summary="技能列表")
-def list_skills(
-    search: Optional[str] = Query(None, description="搜索关键词"),
-    is_active: Optional[bool] = Query(None, description="是否激活"),
-    is_public: Optional[bool] = Query(None, description="是否公开"),
-    page: int = Query(1, ge=1, description="页码"),
-    pagesize: int = Query(10, ge=1, le=100, description="每页数量"),
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """技能市场列表 - 包含本工作空间和公开的技能"""
-    tenant_id = current_user.tenant_id
-    skills, total = SkillService.list_skills(
-        db, tenant_id, search, is_active, is_public, page, pagesize
-    )
-    
-    items = [skill_schema.Skill.model_validate(s) for s in skills]
-    meta = PageMeta(page=page, pagesize=pagesize, total=total, hasnext=(page * pagesize) < total)
-    return success(data=PageData(page=meta, items=items), msg="技能市场列表获取成功")
-
-
-@router.get("/{skill_id}", summary="获取技能详情")
-def get_skill(
-    skill_id: uuid.UUID,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """获取技能详情"""
-    tenant_id = current_user.tenant_id
-    skill = SkillService.get_skill(db, skill_id, tenant_id)
-    return success(data=skill_schema.Skill.model_validate(skill), msg="获取技能详情成功")
-
-
-@router.put("/{skill_id}", summary="更新技能")
-def update_skill(
-    skill_id: uuid.UUID,
-    data: skill_schema.SkillUpdate,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """更新技能"""
-    tenant_id = current_user.tenant_id
-    skill = SkillService.update_skill(db, skill_id, data, tenant_id)
-    return success(data=skill_schema.Skill.model_validate(skill), msg="技能更新成功")
-
-
-@router.delete("/{skill_id}", summary="删除技能")
-def delete_skill(
-    skill_id: uuid.UUID,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-):
-    """删除技能"""
-    tenant_id = current_user.tenant_id
-    SkillService.delete_skill(db, skill_id, tenant_id)
-    return success(msg="技能删除成功")

api/app/controllers/user_controller.py

@@ -2,23 +2,15 @@ from fastapi import APIRouter, Depends
 from sqlalchemy.orm import Session
 import uuid
 
-from app.core.error_codes import BizCode
-from app.core.exceptions import BusinessException
 from app.db import get_db
 from app.dependencies import get_current_user, get_current_superuser
 from app.models.user_model import User
 from app.schemas import user_schema
-from app.schemas.user_schema import (
-    ChangePasswordRequest,
-    AdminChangePasswordRequest,
-    SendEmailCodeRequest,
-    VerifyEmailCodeRequest,
-    VerifyPasswordRequest)
+from app.schemas.user_schema import ChangePasswordRequest, AdminChangePasswordRequest
 from app.schemas.response_schema import ApiResponse
 from app.services import user_service
 from app.core.logging_config import get_api_logger
 from app.core.response_utils import success
-from app.core.security import verify_password
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -100,7 +92,7 @@ def get_current_user_info(
             result_schema.current_workspace_name = current_workspace.name
         
         for ws in result.workspaces:
-            if ws.workspace_id == current_user.current_workspace_id and ws.is_active:
+            if ws.workspace_id == current_user.current_workspace_id:
                 result_schema.role = ws.role
                 break
     
@@ -128,7 +120,6 @@ def get_tenant_superusers(
     return success(data=superusers_schema, msg="租户超管列表获取成功")
 
 
-
 @router.get("/{user_id}", response_model=ApiResponse)
 def get_user_info_by_id(
     user_id: uuid.UUID,
@@ -189,54 +180,4 @@ async def admin_change_password(
         return success(msg="密码修改成功")
     else:
         api_logger.info(f"管理员密码重置成功: 用户 {request.user_id}, 随机密码已生成")
-        return success(data=generated_password, msg="密码重置成功")
-
-
-@router.post("/verify_pwd", response_model=ApiResponse)
-def verify_pwd(
-    request: VerifyPasswordRequest,
-    current_user: User = Depends(get_current_user),
-):
-    """验证当前用户密码"""
-    api_logger.info(f"用户验证密码请求: {current_user.username}")
-    
-    is_valid = verify_password(request.password, current_user.hashed_password)
-    api_logger.info(f"用户密码验证结果: {current_user.username}, valid={is_valid}")
-    if not is_valid:
-        raise BusinessException("密码验证失败", code=BizCode.VALIDATION_FAILED)
-    return success(data={"valid": is_valid}, msg="验证完成")
-
-
-@router.post("/send-email-code", response_model=ApiResponse)
-async def send_email_code(
-    request: SendEmailCodeRequest,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-):
-    """发送邮箱验证码"""
-    api_logger.info(f"用户请求发送邮箱验证码: {current_user.username}, email={request.email}")
-    
-    await user_service.send_email_code_method(db=db, email=request.email, user_id=current_user.id)
-    
-    api_logger.info(f"邮箱验证码已发送: {current_user.username}")
-    return success(msg="验证码已发送到您的邮箱，请查收")
-
-
-@router.put("/change-email", response_model=ApiResponse)
-async def change_email(
-    request: VerifyEmailCodeRequest,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-):
-    """验证验证码并修改邮箱"""
-    api_logger.info(f"用户修改邮箱: {current_user.username}, new_email={request.new_email}")
-
-    await user_service.verify_and_change_email(
-        db=db,
-        user_id=current_user.id,
-        new_email=request.new_email,
-        code=request.code
-    )
-    
-    api_logger.info(f"用户邮箱修改成功: {current_user.username}")
-    return success(msg="邮箱修改成功")
+        return success(data=generated_password, msg="密码重置成功")

api/app/controllers/user_memory_controllers.py

@@ -5,10 +5,9 @@
 from typing import Optional
 import datetime
 from sqlalchemy.orm import Session
-from fastapi import APIRouter, Depends,Header
+from fastapi import APIRouter, Depends
 
 from app.db import get_db
-from app.core.language_utils import get_language_from_header
 from app.core.logging_config import get_api_logger
 from app.core.response_utils import success, fail
 from app.core.error_codes import BizCode
@@ -21,7 +20,7 @@ from app.services.user_memory_service import (
 from app.services.memory_entity_relationship_service import MemoryEntityService,MemoryEmotion,MemoryInteraction
 from app.schemas.response_schema import ApiResponse
 from app.schemas.memory_storage_schema import GenerateCacheRequest
-from app.repositories.workspace_repository import WorkspaceRepository
+
 from app.schemas.end_user_schema import (
     EndUserProfileResponse,
     EndUserProfileUpdate,
@@ -73,7 +72,6 @@ async def get_memory_insight_report_api(
 @router.get("/analytics/user_summary", response_model=ApiResponse)
 async def get_user_summary_api(
     end_user_id: str,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
@@ -82,26 +80,11 @@ async def get_user_summary_api(
 
     此接口仅查询数据库中已缓存的用户摘要数据，不执行生成操作。
     如需生成新的用户摘要，请使用专门的生成接口。
-    
-    语言控制：
-    - 使用 X-Language-Type Header 指定语言
-    - 如果未传 Header，默认使用中文 (zh)
     """
-    # 使用集中化的语言校验
-    language = get_language_from_header(language_type)
-    
-    workspace_id = current_user.current_workspace_id
-    workspace_repo = WorkspaceRepository(db)
-    workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
-
-    if workspace_models:
-        model_id = workspace_models.get("llm", None)
-    else:
-        model_id = None
     api_logger.info(f"用户摘要查询请求: end_user_id={end_user_id}, user={current_user.username}")
     try:
         # 调用服务层获取缓存数据
-        result = await user_memory_service.get_cached_user_summary(db, end_user_id,model_id,language)
+        result = await user_memory_service.get_cached_user_summary(db, end_user_id)
 
         if result["is_cached"]:
             api_logger.info(f"成功返回缓存的用户摘要: end_user_id={end_user_id}")
@@ -117,7 +100,6 @@ async def get_user_summary_api(
 @router.post("/analytics/generate_cache", response_model=ApiResponse)
 async def generate_cache_api(
     request: GenerateCacheRequest,
-    language_type: str = Header(default=None, alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
@@ -126,14 +108,7 @@ async def generate_cache_api(
 
     - 如果提供 end_user_id，只为该用户生成
     - 如果不提供，为当前工作空间的所有用户生成
-    
-    语言控制：
-    - 使用 X-Language-Type Header 指定语言 ("zh" 中文, "en" 英文)
-    - 如果未传 Header，默认使用中文 (zh)
     """
-    # 使用集中化的语言校验
-    language = get_language_from_header(language_type)
-    
     workspace_id = current_user.current_workspace_id
 
     # 检查用户是否已选择工作空间
@@ -141,27 +116,27 @@ async def generate_cache_api(
         api_logger.warning(f"用户 {current_user.username} 尝试生成缓存但未选择工作空间")
         return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
 
-    end_user_id = request.end_user_id
+    group_id = request.end_user_id
 
     api_logger.info(
         f"缓存生成请求: user={current_user.username}, workspace={workspace_id}, "
-        f"end_user_id={end_user_id if end_user_id else '全部用户'}, language={language}"
+        f"end_user_id={group_id if group_id else '全部用户'}"
     )
 
     try:
-        if end_user_id:
+        if group_id:
             # 为单个用户生成
-            api_logger.info(f"开始为单个用户生成缓存: end_user_id={end_user_id}")
+            api_logger.info(f"开始为单个用户生成缓存: end_user_id={group_id}")
 
             # 生成记忆洞察
-            insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id, language=language)
+            insight_result = await user_memory_service.generate_and_cache_insight(db, group_id, workspace_id)
 
             # 生成用户摘要
-            summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id, language=language)
+            summary_result = await user_memory_service.generate_and_cache_summary(db, group_id, workspace_id)
 
             # 构建响应
             result = {
-                "end_user_id": end_user_id,
+                "end_user_id": group_id,
                 "insight_success": insight_result["success"],
                 "summary_success": summary_result["success"],
                 "errors": []
@@ -181,9 +156,9 @@ async def generate_cache_api(
 
             # 记录结果
             if result["insight_success"] and result["summary_success"]:
-                api_logger.info(f"成功为用户 {end_user_id} 生成缓存")
+                api_logger.info(f"成功为用户 {group_id} 生成缓存")
             else:
-                api_logger.warning(f"用户 {end_user_id} 的缓存生成部分失败: {result['errors']}")
+                api_logger.warning(f"用户 {group_id} 的缓存生成部分失败: {result['errors']}")
 
             return success(data=result, msg="生成完成")
 
@@ -191,7 +166,7 @@ async def generate_cache_api(
             # 为整个工作空间生成
             api_logger.info(f"开始为工作空间 {workspace_id} 批量生成缓存")
 
-            result = await user_memory_service.generate_cache_for_workspace(db, workspace_id, language=language)
+            result = await user_memory_service.generate_cache_for_workspace(db, workspace_id)
 
             # 记录统计信息
             api_logger.info(
@@ -278,6 +253,7 @@ async def get_graph_data_api(
             depth=depth,
             center_node_id=center_node_id
         )
+
         # 检查是否有错误消息
         if "message" in result and result["statistics"]["total_nodes"] == 0:
             api_logger.warning(f"图数据查询返回空结果: {result.get('message')}")
@@ -302,13 +278,7 @@ async def get_end_user_profile(
     db: Session = Depends(get_db),
 ) -> dict:
     workspace_id = current_user.current_workspace_id
-    workspace_repo = WorkspaceRepository(db)
-    workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
 
-    if workspace_models:
-        model_id = workspace_models.get("llm", None)
-    else:
-        model_id = None
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试查询用户信息但未选择工作空间")
@@ -326,6 +296,7 @@ async def get_end_user_profile(
         if not end_user:
             api_logger.warning(f"终端用户不存在: end_user_id={end_user_id}")
             return fail(BizCode.INVALID_PARAMETER, "终端用户不存在", f"end_user_id={end_user_id}")
+
         # 构建响应数据
         profile_data = EndUserProfileResponse(
             id=end_user.id,
@@ -357,11 +328,12 @@ async def update_end_user_profile(
 
     该接口可以更新用户的姓名、职位、部门、联系方式、电话和入职日期等信息。
     所有字段都是可选的，只更新提供的字段。
+
     """
     workspace_id = current_user.current_workspace_id
     end_user_id = profile_update.end_user_id
 
-    # 验证工作空间
+    # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试更新用户信息但未选择工作空间")
         return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
@@ -371,44 +343,65 @@ async def update_end_user_profile(
         f"workspace={workspace_id}"
     )
 
-    # 调用 Service 层处理业务逻辑
-    result = user_memory_service.update_end_user_profile(db, end_user_id, profile_update)
+    try:
+        # 查询终端用户
+        end_user = db.query(EndUser).filter(EndUser.id == end_user_id).first()
 
-    if result["success"]:
-        api_logger.info(f"成功更新用户信息: end_user_id={end_user_id}")
-        return success(data=result["data"], msg="更新成功")
-    else:
-        error_msg = result["error"]
-        api_logger.error(f"用户信息更新失败: end_user_id={end_user_id}, error={error_msg}")
-        
-        # 根据错误类型映射到合适的业务错误码
-        if error_msg == "终端用户不存在":
-            return fail(BizCode.USER_NOT_FOUND, "终端用户不存在", error_msg)
-        elif error_msg == "无效的用户ID格式":
-            return fail(BizCode.INVALID_USER_ID, "无效的用户ID格式", error_msg)
-        else:
-            # 只有未预期的错误才使用 INTERNAL_ERROR
-            return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", error_msg)
+        if not end_user:
+            api_logger.warning(f"终端用户不存在: end_user_id={end_user_id}")
+            return fail(BizCode.INVALID_PARAMETER, "终端用户不存在", f"end_user_id={end_user_id}")
+
+        # 更新字段（只更新提供的字段，排除 end_user_id）
+        # 允许 None 值来重置字段（如 hire_date）
+        update_data = profile_update.model_dump(exclude_unset=True, exclude={'end_user_id'})
+
+        # 特殊处理 hire_date：如果提供了时间戳，转换为 DateTime
+        if 'hire_date' in update_data:
+            hire_date_timestamp = update_data['hire_date']
+            if hire_date_timestamp is not None:
+                update_data['hire_date'] = timestamp_to_datetime(hire_date_timestamp)
+            # 如果是 None，保持 None（允许清空）
+
+        for field, value in update_data.items():
+            setattr(end_user, field, value)
+
+        # 更新 updated_at 时间戳
+        end_user.updated_at = datetime.datetime.now()
+
+        # 更新 updatetime_profile 为当前时间
+        end_user.updatetime_profile = datetime.datetime.now()
+
+        # 提交更改
+        db.commit()
+        db.refresh(end_user)
+
+        # 构建响应数据
+        profile_data = EndUserProfileResponse(
+            id=end_user.id,
+            other_name=end_user.other_name,
+            position=end_user.position,
+            department=end_user.department,
+            contact=end_user.contact,
+            phone=end_user.phone,
+            hire_date=end_user.hire_date,
+            updatetime_profile=end_user.updatetime_profile
+        )
+
+        api_logger.info(f"成功更新用户信息: end_user_id={end_user_id}, updated_fields={list(update_data.keys())}")
+        return success(data=UserMemoryService.convert_profile_to_dict_with_timestamp(profile_data), msg="更新成功")
+
+    except Exception as e:
+        db.rollback()
+        api_logger.error(f"用户信息更新失败: end_user_id={end_user_id}, error={str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", str(e))
 
 @router.get("/memory_space/timeline_memories", response_model=ApiResponse)
-async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str = Header(default=None, alias="X-Language-Type"),
+async def memory_space_timeline_of_shared_memories(id: str, label: str,
                                       current_user: User = Depends(get_current_user),
                                       db: Session = Depends(get_db),
                                       ):
-    # 使用集中化的语言校验
-    language = get_language_from_header(language_type)
-    
-    workspace_id=current_user.current_workspace_id
-    workspace_repo = WorkspaceRepository(db)
-    workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
-
-    if workspace_models:
-        model_id = workspace_models.get("llm", None)
-    else:
-        model_id = None
     MemoryEntity = MemoryEntityService(id, label)
-    timeline_memories_result = await MemoryEntity.get_timeline_memories_server(model_id, language)
-
+    timeline_memories_result = await MemoryEntity.get_timeline_memories_server()
     return success(data=timeline_memories_result, msg="共同记忆时间线")
 @router.get("/memory_space/relationship_evolution", response_model=ApiResponse)
 async def memory_space_relationship_evolution(id: str, label: str,

api/app/controllers/workflow_controller.py

@@ -0,0 +1,610 @@
+"""
+工作流 API 控制器
+"""
+
+import logging
+import uuid
+from typing import Annotated
+
+from fastapi import APIRouter, Depends, Path, Query
+from sqlalchemy.orm import Session
+
+from app.db import get_db
+from app.dependencies import get_current_user, cur_workspace_access_guard
+
+from app.models.user_model import User
+from app.models.app_model import App
+from app.services.workflow_service import WorkflowService, get_workflow_service
+from app.schemas.workflow_schema import (
+    WorkflowConfigCreate,
+    WorkflowConfigUpdate,
+    WorkflowConfig,
+    WorkflowValidationResponse,
+    WorkflowExecution,
+    WorkflowNodeExecution,
+    WorkflowExecutionRequest,
+    WorkflowExecutionResponse
+)
+from app.core.response_utils import success, fail
+from app.core.exceptions import BusinessException
+from app.core.error_codes import BizCode
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/apps", tags=["workflow"])
+
+
+# ==================== 工作流配置管理 ====================
+
+@router.post("/{app_id}/workflow")
+@cur_workspace_access_guard()
+async def create_workflow_config(
+        app_id: Annotated[uuid.UUID, Path(description="应用 ID")],
+        config: WorkflowConfigCreate,
+        db: Annotated[Session, Depends(get_db)],
+        current_user: Annotated[User, Depends(get_current_user)],
+        service: Annotated[WorkflowService, Depends(get_workflow_service)]
+):
+    """创建工作流配置
+
+    创建或更新应用的工作流配置。配置会进行基础验证，但允许保存不完整的配置（草稿）。
+    """
+    try:
+        # 验证应用是否存在且属于当前工作空间
+        app = db.query(App).filter(
+            App.id == app_id,
+            App.workspace_id == current_user.current_workspace_id,
+            App.is_active == True
+        ).first()
+
+        if not app:
+            return fail(
+                code=BizCode.NOT_FOUND,
+                msg="应用不存在或无权访问"
+            )
+
+        # 验证应用类型
+        if app.type != "workflow":
+            return fail(
+                code=BizCode.INVALID_PARAMETER,
+                msg=f"应用类型必须为 workflow，当前为 {app.type}"
+            )
+
+        # 创建工作流配置
+        workflow_config = service.create_workflow_config(
+            app_id=app_id,
+            nodes=[node.model_dump() for node in config.nodes],
+            edges=[edge.model_dump() for edge in config.edges],
+            variables=[var.model_dump() for var in config.variables],
+            execution_config=config.execution_config.model_dump(),
+            triggers=[trigger.model_dump() for trigger in config.triggers],
+            validate=True  # 进行基础验证
+        )
+
+        return success(
+            data=WorkflowConfig.model_validate(workflow_config),
+            msg="工作流配置创建成功"
+        )
+
+    except BusinessException as e:
+        logger.warning(f"创建工作流配置失败: {e.message}")
+        return fail(code=e.error_code, msg=e.message)
+    except Exception as e:
+        logger.error(f"创建工作流配置异常: {e}", exc_info=True)
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg=f"创建工作流配置失败: {str(e)}"
+        )
+
+
+#
+# @router.get("/{app_id}/workflow")
+# async def get_workflow_config(
+#     app_id: Annotated[uuid.UUID, Path(description="应用 ID")],
+#     db: Annotated[Session, Depends(get_db)],
+#     current_user: Annotated[User, Depends(get_current_user)]
+#
+# ):
+#     """获取工作流配置
+#
+#     获取应用的工作流配置详情。
+#     """
+#     try:
+#         # 验证应用是否存在且属于当前工作空间
+#         app = db.query(App).filter(
+#             App.id == app_id,
+#             App.workspace_id == current_user.current_workspace_id,
+#             App.is_active == True
+#         ).first()
+#
+#         if not app:
+#             return fail(
+#                 code=BizCode.NOT_FOUND,
+#                 msg="应用不存在或无权访问"
+#             )
+#
+#         # 获取工作流配置
+#         service = WorkflowService(db)
+#         workflow_config = service.get_workflow_config(app_id)
+#
+#         if not workflow_config:
+#             return fail(
+#                 code=BizCode.NOT_FOUND,
+#                 msg="工作流配置不存在"
+#             )
+#
+#         return success(
+#             data=WorkflowConfig.model_validate(workflow_config)
+#         )
+#
+#     except Exception as e:
+#         logger.error(f"获取工作流配置异常: {e}", exc_info=True)
+#         return fail(
+#             code=BizCode.INTERNAL_ERROR,
+#             msg=f"获取工作流配置失败: {str(e)}"
+#         )
+
+
+# @router.put("/{app_id}/workflow")
+# async def update_workflow_config(
+#     app_id: Annotated[uuid.UUID, Path(description="应用 ID")],
+#     config: WorkflowConfigUpdate,
+#     db: Annotated[Session, Depends(get_db)],
+#     current_user: Annotated[User, Depends(get_current_user)],
+#     service: Annotated[WorkflowService, Depends(get_workflow_service)]
+# ):
+#     """更新工作流配置
+
+#     更新应用的工作流配置。可以部分更新，未提供的字段保持不变。
+#     """
+#     try:
+#         # 验证应用是否存在且属于当前工作空间
+#         app = db.query(App).filter(
+#             App.id == app_id,
+#             App.workspace_id == current_user.current_workspace_id,
+#             App.is_active == True
+#         ).first()
+
+#         if not app:
+#             return fail(
+#                 code=BizCode.NOT_FOUND,
+#                 msg="应用不存在或无权访问"
+#             )
+
+#         # 更新工作流配置
+#         workflow_config = service.update_workflow_config(
+#             app_id=app_id,
+#             nodes=[node.model_dump() for node in config.nodes] if config.nodes else None,
+#             edges=[edge.model_dump() for edge in config.edges] if config.edges else None,
+#             variables=[var.model_dump() for var in config.variables] if config.variables else None,
+#             execution_config=config.execution_config.model_dump() if config.execution_config else None,
+#             triggers=[trigger.model_dump() for trigger in config.triggers] if config.triggers else None,
+#             validate=True
+#         )
+
+#         return success(
+#             data=WorkflowConfig.model_validate(workflow_config),
+#             msg="工作流配置更新成功"
+#         )
+
+#     except BusinessException as e:
+#         logger.warning(f"更新工作流配置失败: {e.message}")
+#         return fail(code=e.error_code, msg=e.message)
+#     except Exception as e:
+#         logger.error(f"更新工作流配置异常: {e}", exc_info=True)
+#         return fail(
+#             code=BizCode.INTERNAL_ERROR,
+#             msg=f"更新工作流配置失败: {str(e)}"
+#         )
+
+
+@router.delete("/{app_id}/workflow")
+async def delete_workflow_config(
+        app_id: Annotated[uuid.UUID, Path(description="应用 ID")],
+        db: Annotated[Session, Depends(get_db)],
+        current_user: Annotated[User, Depends(get_current_user)],
+        service: Annotated[WorkflowService, Depends(get_workflow_service)]
+):
+    """删除工作流配置
+
+    删除应用的工作流配置。
+    """
+    try:
+        # 验证应用是否存在且属于当前工作空间
+        app = db.query(App).filter(
+            App.id == app_id,
+            App.workspace_id == current_user.current_workspace_id,
+            App.is_active == True
+        ).first()
+
+        if not app:
+            return fail(
+                code=BizCode.NOT_FOUND,
+                msg="应用不存在或无权访问"
+            )
+
+        # 删除工作流配置
+        deleted = service.delete_workflow_config(app_id)
+
+        if not deleted:
+            return fail(
+                code=BizCode.NOT_FOUND,
+                msg="工作流配置不存在"
+            )
+
+        return success(msg="工作流配置删除成功")
+
+    except Exception as e:
+        logger.error(f"删除工作流配置异常: {e}", exc_info=True)
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg=f"删除工作流配置失败: {str(e)}"
+        )
+
+
+@router.post("/{app_id}/workflow/validate")
+async def validate_workflow_config(
+        app_id: Annotated[uuid.UUID, Path(description="应用 ID")],
+        db: Annotated[Session, Depends(get_db)],
+        current_user: Annotated[User, Depends(get_current_user)],
+        service: Annotated[WorkflowService, Depends(get_workflow_service)],
+        for_publish: Annotated[bool, Query(description="是否为发布验证")] = False
+):
+    """验证工作流配置
+
+    验证工作流配置是否有效。可以选择是否进行发布级别的严格验证。
+    """
+    try:
+        # 验证应用是否存在且属于当前工作空间
+        app = db.query(App).filter(
+            App.id == app_id,
+            App.workspace_id == current_user.current_workspace_id,
+            App.is_active == True
+        ).first()
+
+        if not app:
+            return fail(
+                code=BizCode.NOT_FOUND,
+                msg="应用不存在或无权访问"
+            )
+
+        # 验证工作流配置
+
+        if for_publish:
+            is_valid, errors = service.validate_workflow_config_for_publish(app_id)
+        else:
+            workflow_config = service.get_workflow_config(app_id)
+            if not workflow_config:
+                return fail(
+                    code=BizCode.NOT_FOUND,
+                    msg="工作流配置不存在"
+                )
+
+            from app.core.workflow.validator import validate_workflow_config as validate_config
+            config_dict = {
+                "nodes": workflow_config.nodes,
+                "edges": workflow_config.edges,
+                "variables": workflow_config.variables,
+                "execution_config": workflow_config.execution_config,
+                "triggers": workflow_config.triggers
+            }
+            is_valid, errors = validate_config(config_dict, for_publish=False)
+
+        return success(
+            data=WorkflowValidationResponse(
+                is_valid=is_valid,
+                errors=errors,
+                warnings=[]
+            )
+        )
+
+    except BusinessException as e:
+        logger.warning(f"验证工作流配置失败: {e.message}")
+        return fail(code=e.error_code, msg=e.message)
+    except Exception as e:
+        logger.error(f"验证工作流配置异常: {e}", exc_info=True)
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg=f"验证工作流配置失败: {str(e)}"
+        )
+
+
+# ==================== 工作流执行管理 ====================
+
+@router.get("/{app_id}/workflow/executions")
+async def get_workflow_executions(
+        app_id: Annotated[uuid.UUID, Path(description="应用 ID")],
+        db: Annotated[Session, Depends(get_db)],
+        current_user: Annotated[User, Depends(get_current_user)],
+        service: Annotated[WorkflowService, Depends(get_workflow_service)],
+        limit: Annotated[int, Query(ge=1, le=100)] = 50,
+        offset: Annotated[int, Query(ge=0)] = 0
+):
+    """获取工作流执行记录列表
+
+    获取应用的工作流执行历史记录。
+    """
+    try:
+        # 验证应用是否存在且属于当前工作空间
+        app = db.query(App).filter(
+            App.id == app_id,
+            App.workspace_id == current_user.current_workspace_id,
+            App.is_active == True
+        ).first()
+
+        if not app:
+            return fail(
+                code=BizCode.NOT_FOUND,
+                msg="应用不存在或无权访问"
+            )
+
+        # 获取执行记录
+        executions = service.get_executions_by_app(app_id, limit, offset)
+
+        # 获取统计信息
+        statistics = service.get_execution_statistics(app_id)
+
+        return success(
+            data={
+                "executions": [WorkflowExecution.model_validate(e) for e in executions],
+                "statistics": statistics,
+                "pagination": {
+                    "limit": limit,
+                    "offset": offset,
+                    "total": statistics["total"]
+                }
+            }
+        )
+
+    except Exception as e:
+        logger.error(f"获取工作流执行记录异常: {e}", exc_info=True)
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg=f"获取工作流执行记录失败: {str(e)}"
+        )
+
+
+@router.get("/workflow/executions/{execution_id}")
+async def get_workflow_execution(
+        execution_id: Annotated[str, Path(description="执行 ID")],
+        db: Annotated[Session, Depends(get_db)],
+        current_user: Annotated[User, Depends(get_current_user)],
+        service: Annotated[WorkflowService, Depends(get_workflow_service)]
+):
+    """获取工作流执行详情
+
+    获取单个工作流执行的详细信息，包括所有节点的执行记录。
+    """
+    try:
+        # 获取执行记录
+        execution = service.get_execution(execution_id)
+
+        if not execution:
+            return fail(
+                code=BizCode.NOT_FOUND,
+                msg="执行记录不存在"
+            )
+
+        # 验证应用是否属于当前工作空间
+        app = db.query(App).filter(
+            App.id == execution.app_id,
+            App.workspace_id == current_user.current_workspace_id,
+            App.is_active == True
+        ).first()
+
+        if not app:
+            return fail(
+                code=BizCode.NOT_FOUND,
+                msg="无权访问该执行记录"
+            )
+
+        # 获取节点执行记录
+        node_executions = service.node_execution_repo.get_by_execution_id(execution.id)
+
+        return success(
+            data={
+                "execution": WorkflowExecution.model_validate(execution),
+                "node_executions": [
+                    WorkflowNodeExecution.model_validate(ne) for ne in node_executions
+                ]
+            }
+        )
+
+    except Exception as e:
+        logger.error(f"获取工作流执行详情异常: {e}", exc_info=True)
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg=f"获取工作流执行详情失败: {str(e)}"
+        )
+
+
+# ==================== 工作流执行 ====================
+@router.post("/{app_id}/workflow/run")
+async def run_workflow(
+        app_id: Annotated[uuid.UUID, Path(description="应用 ID")],
+        request: WorkflowExecutionRequest,
+        db: Annotated[Session, Depends(get_db)],
+        current_user: Annotated[User, Depends(get_current_user)],
+        service: Annotated[WorkflowService, Depends(get_workflow_service)]
+):
+    """执行工作流
+
+    执行工作流并返回结果。支持流式和非流式两种模式。
+
+    **非流式模式**：等待工作流执行完成后返回完整结果。
+
+    **流式模式**：实时返回执行过程中的事件（节点开始、节点完成、工作流完成等）。
+    """
+    try:
+        # 验证应用是否存在且属于当前工作空间
+        app = db.query(App).filter(
+            App.id == app_id,
+            App.workspace_id == current_user.current_workspace_id,
+            App.is_active == True
+        ).first()
+
+        if not app:
+            return fail(
+                code=BizCode.NOT_FOUND,
+                msg="应用不存在或无权访问"
+            )
+
+        # 验证应用类型
+        if app.type != "workflow":
+            return fail(
+                code=BizCode.INVALID_PARAMETER,
+                msg=f"应用类型必须为 workflow，当前为 {app.type}"
+            )
+
+        # 准备输入数据
+        input_data = {
+            "message": request.message or "",
+            "variables": request.variables
+        }
+
+        # 执行工作流
+
+        if request.stream:
+            # 流式执行
+            from fastapi.responses import StreamingResponse
+            import json
+
+            async def event_generator():
+                """生成 SSE 事件
+                
+                SSE 格式：
+                event: <event_type>
+                data: <json_data>
+                
+                支持的事件类型：
+                - workflow_start: 工作流开始
+                - workflow_end: 工作流结束
+                - node_start: 节点开始执行
+                - node_end: 节点执行完成
+                - node_chunk: 中间节点的流式输出
+                - message: 最终消息的流式输出（End 节点及其相邻节点）
+                """
+                try:
+                    async for event in await service.run_workflow(
+                            app_id=app_id,
+                            input_data=input_data,
+                            triggered_by=current_user.id,
+                            conversation_id=uuid.UUID(request.conversation_id) if request.conversation_id else None,
+                            stream=True
+                    ):
+                        # 提取事件类型和数据
+                        event_type = event.get("event", "message")
+                        event_data = event.get("data", {})
+
+                        # 转换为标准 SSE 格式（字符串）
+                        # event: <type>
+                        # data: <json>
+                        sse_message = f"event: {event_type}\ndata: {json.dumps(event_data)}\n\n"
+                        yield sse_message
+
+                except Exception as e:
+                    logger.error(f"流式执行异常: {e}", exc_info=True)
+                    # 发送错误事件
+                    sse_error = f"event: error\ndata: {json.dumps({'error': str(e)})}\n\n"
+                    yield sse_error
+
+            return StreamingResponse(
+                event_generator(),
+                media_type="text/event-stream",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "keep-alive",
+                    "X-Accel-Buffering": "no"  # 禁用 nginx 缓冲
+                }
+            )
+        else:
+            # 非流式执行
+            result = await service.run_workflow(
+                app_id=app_id,
+                input_data=input_data,
+                triggered_by=current_user.id,
+                conversation_id=uuid.UUID(request.conversation_id) if request.conversation_id else None,
+                stream=False
+            )
+
+            return success(
+                data=WorkflowExecutionResponse(
+                    execution_id=result["execution_id"],
+                    status=result["status"],
+                    output=result.get("output"),
+                    output_data=result.get("output_data"),
+                    error_message=result.get("error_message"),
+                    elapsed_time=result.get("elapsed_time"),
+                    token_usage=result.get("token_usage")
+                ),
+                msg="工作流执行完成"
+            )
+
+    except BusinessException as e:
+        logger.warning(f"执行工作流失败: {e.message}")
+        return fail(code=e.error_code, msg=e.message)
+    except Exception as e:
+        logger.error(f"执行工作流异常: {e}", exc_info=True)
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg=f"执行工作流失败: {str(e)}"
+        )
+
+
+@router.post("/workflow/executions/{execution_id}/cancel")
+async def cancel_workflow_execution(
+        execution_id: Annotated[str, Path(description="执行 ID")],
+        db: Annotated[Session, Depends(get_db)],
+        current_user: Annotated[User, Depends(get_current_user)],
+        service: Annotated[WorkflowService, Depends(get_workflow_service)]
+):
+    """取消工作流执行
+
+    取消正在运行的工作流执行。
+
+    **注意**：当前版本仅更新状态为 cancelled，实际的执行取消功能待实现。
+    """
+    try:
+        # 获取执行记录
+        execution = service.get_execution(execution_id)
+
+        if not execution:
+            return fail(
+                code=BizCode.NOT_FOUND,
+                msg="执行记录不存在"
+            )
+
+        # 验证应用是否属于当前工作空间
+        app = db.query(App).filter(
+            App.id == execution.app_id,
+            App.workspace_id == current_user.current_workspace_id,
+            App.is_active == True
+        ).first()
+
+        if not app:
+            return fail(
+                code=BizCode.NOT_FOUND,
+                msg="无权访问该执行记录"
+            )
+
+        # 检查执行状态
+        if execution.status not in ["pending", "running"]:
+            return fail(
+                code=BizCode.INVALID_PARAMETER,
+                msg=f"无法取消状态为 {execution.status} 的执行"
+            )
+
+        # 更新状态为 cancelled
+        service.update_execution_status(execution_id, "cancelled")
+
+        return success(msg="工作流执行已取消")
+
+    except BusinessException as e:
+        logger.warning(f"取消工作流执行失败: {e.message}")
+        return fail(code=e.code, msg=e.message)
+    except Exception as e:
+        logger.error(f"取消工作流执行异常: {e}", exc_info=True)
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg=f"取消工作流执行失败: {str(e)}"
+        )

api/app/core/__init__.py

@@ -1,4 +0,0 @@
-# -*- coding: UTF-8 -*-
-# Author: Eternity
-# @Email: 1533512157@qq.com
-# @Time : 2026/2/9 16:24

api/app/core/agent/agent_middleware.py

@@ -1,162 +0,0 @@
-"""Agent Middleware - 动态技能过滤"""
-import uuid
-from typing import List, Dict, Any, Optional
-from langchain_core.runnables import RunnablePassthrough
-
-from app.services.skill_service import SkillService
-from app.repositories.skill_repository import SkillRepository
-
-
-class AgentMiddleware:
-    """Agent 中间件 - 用于动态过滤和加载技能"""
-    
-    def __init__(self, skills: Optional[dict] = None):
-        """
-        初始化中间件
-        
-        Args:
-            skills: 技能配置字典 {"enabled": bool, "all_skills": bool, "skill_ids": [...]}
-        """
-        self.skills = skills or {}
-        self.enabled = self.skills.get('enabled', False)
-        self.all_skills = self.skills.get('all_skills', False)
-        self.skill_ids = self.skills.get('skill_ids', [])
-
-    @staticmethod
-    def filter_tools(
-        tools: List, 
-        message: str = "", 
-        skill_configs: Dict[str, Any] = None,
-        tool_to_skill_map: Dict[str, str] = None
-    ) -> tuple[List, List[str]]:
-        """
-        根据消息内容和技能配置动态过滤工具
-        
-        Args:
-            tools: 所有可用工具列表
-            message: 用户消息（可用于智能过滤）
-            skill_configs: 技能配置字典 {skill_id: {"keywords": [...], "enabled": True, "prompt": "..."}}
-            tool_to_skill_map: 工具到技能的映射 {tool_name: skill_id}
-        
-        Returns:
-            (过滤后的工具列表, 激活的技能ID列表)
-        """
-        if not tools:
-            return [], []
-        
-        # 如果没有技能配置，返回所有工具
-        if not skill_configs:
-            return tools, []
-        
-        # 基于关键词匹配激活技能
-        activated_skill_ids = []
-        message_lower = message.lower()
-        
-        for skill_id, config in skill_configs.items():
-            if not config.get('enabled', True):
-                continue
-            
-            keywords = config.get('keywords', [])
-            # 如果没有关键词限制，或消息包含关键词，则激活该技能
-            if not keywords or any(kw.lower() in message_lower for kw in keywords):
-                activated_skill_ids.append(skill_id)
-        
-        # 如果没有工具映射关系，返回所有工具
-        if not tool_to_skill_map:
-            return tools, activated_skill_ids
-        
-        # 根据激活的技能过滤工具
-        filtered_tools = []
-        for tool in tools:
-            tool_name = getattr(tool, 'name', str(id(tool)))
-            # 如果工具不属于任何skill（base_tools），或者工具所属的skill被激活，则保留
-            if tool_name not in tool_to_skill_map or tool_to_skill_map[tool_name] in activated_skill_ids:
-                filtered_tools.append(tool)
-        
-        return filtered_tools, activated_skill_ids
-    
-    def load_skill_tools(self, db, tenant_id: uuid.UUID, base_tools: List = None) -> tuple[List, Dict[str, Any], Dict[str, str]]:
-        """
-        加载技能关联的工具
-        
-        Args:
-            db: 数据库会话
-            tenant_id: 租户id
-            base_tools: 基础工具列表
-        
-        Returns:
-            (工具列表, 技能配置字典, 工具到技能的映射 {tool_name: skill_id})
-        """
-
-        tools_dict = {}
-        tool_to_skill_map = {}  # 工具名称到技能ID的映射
-        
-        if base_tools:
-            for tool in base_tools:
-                tool_name = getattr(tool, 'name', str(id(tool)))
-                tools_dict[tool_name] = tool
-                # base_tools 不属于任何 skill，不加入映射
-
-        skill_configs = {}
-        skill_ids_to_load = []
-        
-        # 如果启用技能且 all_skills 为 True，加载租户下所有激活的技能
-        if self.enabled and self.all_skills:
-            skills, _ = SkillRepository.list_skills(db, tenant_id, is_active=True, page=1, pagesize=1000)
-            skill_ids_to_load = [str(skill.id) for skill in skills]
-        elif self.enabled and self.skill_ids:
-            skill_ids_to_load = self.skill_ids
-        
-        if skill_ids_to_load:
-            for skill_id in skill_ids_to_load:
-                try:
-                    skill = SkillRepository.get_by_id(db, uuid.UUID(skill_id), tenant_id)
-                    if skill and skill.is_active:
-                        # 保存技能配置（包含prompt）
-                        config = skill.config or {}
-                        config['prompt'] = skill.prompt
-                        config['name'] = skill.name
-                        skill_configs[skill_id] = config
-                except Exception:
-                    continue
-            
-            # 加载技能工具并获取映射关系
-            skill_tools, skill_tool_map = SkillService.load_skill_tools(db, skill_ids_to_load, tenant_id)
-            
-            # 只添加不冲突的 skill_tools
-            for tool in skill_tools:
-                tool_name = getattr(tool, 'name', str(id(tool)))
-                if tool_name not in tools_dict:
-                    tools_dict[tool_name] = tool
-                    # 复制映射关系
-                    if tool_name in skill_tool_map:
-                        tool_to_skill_map[tool_name] = skill_tool_map[tool_name]
-        
-        return list(tools_dict.values()), skill_configs, tool_to_skill_map
-
-    @staticmethod
-    def get_active_prompts(activated_skill_ids: List[str], skill_configs: Dict[str, Any]) -> str:
-        """
-        根据激活的技能ID获取对应的提示词
-        
-        Args:
-            activated_skill_ids: 被激活的技能ID列表
-            skill_configs: 技能配置字典
-        
-        Returns:
-            合并后的提示词
-        """
-        prompts = []
-        for skill_id in activated_skill_ids:
-            config = skill_configs.get(skill_id, {})
-            prompt = config.get('prompt')
-            name = config.get('name', 'Skill')
-            if prompt:
-                prompts.append(f"# {name}\n{prompt}")
-        
-        return "\n\n".join(prompts) if prompts else ""
-
-    @staticmethod
-    def create_runnable():
-        """创建可运行的中间件"""
-        return RunnablePassthrough()

api/app/core/agent/langchain_agent.py

@@ -7,21 +7,27 @@ LangChain Agent 封装
 - 支持流式输出
 - 使用 RedBearLLM 支持多提供商
 """
-
+import os
 import time
 from typing import Any, AsyncGenerator, Dict, List, Optional, Sequence
 
-from app.core.memory.agent.langgraph_graph.write_graph import  write_long_term
+
 from app.db import get_db
 from app.core.logging_config import get_business_logger
+from app.core.memory.agent.utils.redis_tool import store
 from app.core.models import RedBearLLM, RedBearModelConfig
 from app.models.models_model import ModelType
+from app.repositories.memory_short_repository import LongTermMemoryRepository
 from app.services.memory_agent_service import (
     get_end_user_connected_config,
 )
+from app.services.memory_konwledges_server import write_rag
+from app.services.task_service import get_task_memory_write_result
+from app.tasks import write_message_task
 from langchain.agents import create_agent
 from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
 from langchain_core.tools import BaseTool
+
 logger = get_business_logger()
 
 
@@ -37,9 +43,7 @@ class LangChainAgent:
         max_tokens: int = 2000,
         system_prompt: Optional[str] = None,
         tools: Optional[Sequence[BaseTool]] = None,
-        streaming: bool = False,
-        max_iterations: Optional[int] = None,  # 最大迭代次数（None 表示自动计算）
-        max_tool_consecutive_calls: int = 3  # 单个工具最大连续调用次数
+        streaming: bool = False
     ):
         """初始化 LangChain Agent
 
@@ -52,36 +56,13 @@ class LangChainAgent:
             max_tokens: 最大 token 数
             system_prompt: 系统提示词
             tools: 工具列表（可选，框架自动走 ReAct 循环）
-            streaming: 是否启用流式输出
-            max_iterations: 最大迭代次数（None 表示自动计算：基础 5 次 + 每个工具 2 次）
-            max_tool_consecutive_calls: 单个工具最大连续调用次数（默认 3 次）
+            streaming: 是否启用流式输出（默认 True）
         """
         self.model_name = model_name
         self.provider = provider
+        self.system_prompt = system_prompt or "你是一个专业的AI助手"
         self.tools = tools or []
         self.streaming = streaming
-        self.max_tool_consecutive_calls = max_tool_consecutive_calls
-        
-        # 工具调用计数器：记录每个工具的连续调用次数
-        self.tool_call_counter: Dict[str, int] = {}
-        self.last_tool_called: Optional[str] = None
-        
-        # 根据工具数量动态调整最大迭代次数
-        # 基础值 + 每个工具额外的调用机会
-        if max_iterations is None:
-            # 自动计算：基础 5 次 + 每个工具 2 次额外机会
-            self.max_iterations = 5 + len(self.tools) * 2
-        else:
-            self.max_iterations = max_iterations
-        
-        self.system_prompt = system_prompt or "你是一个专业的AI助手"
-        
-        logger.debug(
-            f"Agent 迭代次数配置: max_iterations={self.max_iterations}, "
-            f"tool_count={len(self.tools)}, "
-            f"max_tool_consecutive_calls={self.max_tool_consecutive_calls}, "
-            f"auto_calculated={max_iterations is None}"
-        )
 
         # 创建 RedBearLLM（支持多提供商）
         model_config = RedBearModelConfig(
@@ -105,14 +86,11 @@ class LangChainAgent:
         if streaming and hasattr(self._underlying_llm, 'streaming'):
             self._underlying_llm.streaming = True
 
-        # 包装工具以跟踪连续调用次数
-        wrapped_tools = self._wrap_tools_with_tracking(self.tools) if self.tools else None
-
         # 使用 create_agent 创建 agent graph（LangChain 1.x 标准方式）
         # 无论是否有工具，都使用 agent 统一处理
         self.agent = create_agent(
             model=self.llm,
-            tools=wrapped_tools,
+            tools=self.tools if self.tools else None,
             system_prompt=self.system_prompt
         )
 
@@ -124,91 +102,17 @@ class LangChainAgent:
                 "has_api_base": bool(api_base),
                 "temperature": temperature,
                 "streaming": streaming,
-                "max_iterations": self.max_iterations,
-                "max_tool_consecutive_calls": self.max_tool_consecutive_calls,
                 "tool_count": len(self.tools),
                 "tool_names": [tool.name for tool in self.tools] if self.tools else [],
-                # "tool_count": len(self.tools)
+                "tool_count": len(self.tools)
             }
         )
 
-    def _wrap_tools_with_tracking(self, tools: Sequence[BaseTool]) -> List[BaseTool]:
-        """包装工具以跟踪连续调用次数
-        
-        Args:
-            tools: 原始工具列表
-            
-        Returns:
-            List[BaseTool]: 包装后的工具列表
-        """
-        from langchain_core.tools import StructuredTool
-        from functools import wraps
-        
-        wrapped_tools = []
-        
-        for original_tool in tools:
-            tool_name = original_tool.name
-            original_func = original_tool.func if hasattr(original_tool, 'func') else None
-            
-            if not original_func:
-                # 如果无法获取原始函数，直接使用原工具
-                wrapped_tools.append(original_tool)
-                continue
-            
-            # 创建包装函数
-            def make_wrapped_func(tool_name, original_func):
-                """创建包装函数的工厂函数，避免闭包问题"""
-                @wraps(original_func)
-                def wrapped_func(*args, **kwargs):
-                    """包装后的工具函数，跟踪连续调用次数"""
-                    # 检查是否是连续调用同一个工具
-                    if self.last_tool_called == tool_name:
-                        self.tool_call_counter[tool_name] = self.tool_call_counter.get(tool_name, 0) + 1
-                    else:
-                        # 切换到新工具，重置计数器
-                        self.tool_call_counter[tool_name] = 1
-                        self.last_tool_called = tool_name
-                    
-                    current_count = self.tool_call_counter[tool_name]
-                    
-                    logger.debug(
-                        f"工具调用: {tool_name}, 连续调用次数: {current_count}/{self.max_tool_consecutive_calls}"
-                    )
-                    
-                    # 检查是否超过最大连续调用次数
-                    if current_count > self.max_tool_consecutive_calls:
-                        logger.warning(
-                            f"工具 '{tool_name}' 连续调用次数已达上限 ({self.max_tool_consecutive_calls})，"
-                            f"返回提示信息"
-                        )
-                        return (
-                            f"工具 '{tool_name}' 已连续调用 {self.max_tool_consecutive_calls} 次，"
-                            f"未找到有效结果。请尝试其他方法或直接回答用户的问题。"
-                        )
-                    
-                    # 调用原始工具函数
-                    return original_func(*args, **kwargs)
-                
-                return wrapped_func
-            
-            # 使用 StructuredTool 创建新工具
-            wrapped_tool = StructuredTool(
-                name=original_tool.name,
-                description=original_tool.description,
-                func=make_wrapped_func(tool_name, original_func),
-                args_schema=original_tool.args_schema if hasattr(original_tool, 'args_schema') else None
-            )
-            
-            wrapped_tools.append(wrapped_tool)
-        
-        return wrapped_tools
-
     def _prepare_messages(
         self,
         message: str,
         history: Optional[List[Dict[str, str]]] = None,
-        context: Optional[str] = None,
-        files: Optional[List[Dict[str, Any]]] = None
+        context: Optional[str] = None
     ) -> List[BaseMessage]:
         """准备消息列表
 
@@ -216,7 +120,6 @@ class LangChainAgent:
             message: 用户消息
             history: 历史消息列表
             context: 上下文信息
-            files: 多模态文件内容列表（已处理）
 
         Returns:
             List[BaseMessage]: 消息列表
@@ -239,46 +142,47 @@ class LangChainAgent:
         if context:
             user_content = f"参考信息：\n{context}\n\n用户问题：\n{user_content}"
 
-        # 构建用户消息（支持多模态）
-        if files and len(files) > 0:
-            content_parts = self._build_multimodal_content(user_content, files)
-            messages.append(HumanMessage(content=content_parts))
-        else:
-            # 纯文本消息
-            messages.append(HumanMessage(content=user_content))
+        messages.append(HumanMessage(content=user_content))
 
         return messages
-    
-    def _build_multimodal_content(self, text: str, files: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-        """
-        构建多模态消息内容
-        
-        Args:
-            text: 文本内容
-            files: 文件列表（已由 MultimodalService 处理为对应 provider 的格式）
-            
-        Returns:
-            List[Dict]: 消息内容列表
-        """
-        # 根据 provider 使用不同的文本格式
-        if self.provider.lower() in ["bedrock", "anthropic"]:
-            # Anthropic/Bedrock: {"type": "text", "text": "..."}
-            content_parts = [{"type": "text", "text": text}]
+    async def term_memory_save(self,messages,end_user_end,aimessages):
+        '''短长期存储redis，为不影响正常使用6句一段话，存储用户名加一个前缀，当数据存够6条返回给neo4j'''
+        end_user_end=f"Term_{end_user_end}"
+        print(messages)
+        print(aimessages)
+        session_id = store.save_session(
+                        userid=end_user_end,
+                        messages=messages,
+                        apply_id=end_user_end,
+                        group_id=end_user_end,
+                        aimessages=aimessages
+                    )
+        store.delete_duplicate_sessions()
+        # logger.info(f'Redis_Agent:{end_user_end};{session_id}')
+        return session_id
+    async def term_memory_redis_read(self,end_user_end):
+        end_user_end = f"Term_{end_user_end}"
+        history = store.find_user_apply_group(end_user_end, end_user_end, end_user_end)
+        # logger.info(f'Redis_Agent:{end_user_end};{history}')
+        messagss_list=[]
+        retrieved_content=[]
+        for messages in history:
+            query = messages.get("Query")
+            aimessages = messages.get("Answer")
+            messagss_list.append(f'用户:{query}。AI回复:{aimessages}')
+            retrieved_content.append({query: aimessages})
+        return messagss_list,retrieved_content
+
+
+    async def write(self,storage_type,end_user_id,message,user_rag_memory_id,actual_end_user_id,content,actual_config_id):
+        if storage_type == "rag":
+            await write_rag(end_user_id, message, user_rag_memory_id)
+            logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}')
         else:
-            # 通义千问等: {"text": "..."}
-            content_parts = [{"text": text}]
-        
-        # 添加文件内容
-        # MultimodalService 已经根据 provider 返回了正确格式，直接使用
-        content_parts.extend(files)
-        
-        logger.debug(
-            f"构建多模态消息: provider={self.provider}, "
-            f"parts={len(content_parts)}, "
-            f"files={len(files)}"
-        )
-        
-        return content_parts
+            write_id = write_message_task.delay(actual_end_user_id, content, actual_config_id, storage_type,
+                                                user_rag_memory_id)
+            write_status = get_task_memory_write_result(str(write_id))
+            logger.info(f'Agent:{actual_end_user_id};{write_status}')
 
     async def chat(
             self,
@@ -289,8 +193,7 @@ class LangChainAgent:
             config_id: Optional[str] = None,  # 添加这个参数
             storage_type: Optional[str] = None,
             user_rag_memory_id: Optional[str] = None,
-            memory_flag: Optional[bool] = True,
-            files: Optional[List[Dict[str, Any]]] = None  # 新增：多模态文件
+            memory_flag: Optional[bool] = True
     ) -> Dict[str, Any]:
         """执行对话
 
@@ -324,9 +227,32 @@ class LangChainAgent:
         actual_end_user_id = end_user_id if end_user_id is not None else "unknown"
         logger.info(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}')
         print(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}')
+
+        history_term_memory_result = await self.term_memory_redis_read(end_user_id)
+        history_term_memory = history_term_memory_result[0]
+        db_for_memory = next(get_db())
+        if memory_flag:
+            if len(history_term_memory)>=4 and storage_type != "rag":
+                history_term_memory = ';'.join(history_term_memory)
+                retrieved_content = history_term_memory_result[1]
+                print(retrieved_content)
+                # 为长期记忆操作获取新的数据库连接
+                try:
+                    repo = LongTermMemoryRepository(db_for_memory)
+                    repo.upsert(end_user_id, retrieved_content)
+                    logger.info(
+                        f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
+                except Exception as e:
+                    logger.error(f"Failed to write to LongTermMemory: {e}")
+                    raise
+                finally:
+                    db_for_memory.close()
+
+                await self.write(storage_type,end_user_id,history_term_memory,user_rag_memory_id,actual_end_user_id,history_term_memory,actual_config_id)
+            await self.write(storage_type,end_user_id,message,user_rag_memory_id,actual_end_user_id,message,actual_config_id)
         try:
-            # 准备消息列表（支持多模态）
-            messages = self._prepare_messages(message, history, context, files)
+            # 准备消息列表
+            messages = self._prepare_messages(message, history, context)
 
             logger.debug(
                 "准备调用 LangChain Agent",
@@ -334,85 +260,25 @@ class LangChainAgent:
                     "has_context": bool(context),
                     "has_history": bool(history),
                     "has_tools": bool(self.tools),
-                    "has_files": bool(files),
-                    "message_count": len(messages),
-                    "max_iterations": self.max_iterations
+                    "message_count": len(messages)
                 }
             )
 
             # 统一使用 agent.invoke 调用
-            # 通过 recursion_limit 限制最大迭代次数，防止工具调用死循环
-            try:
-                result = await self.agent.ainvoke(
-                    {"messages": messages},
-                    config={"recursion_limit": self.max_iterations}
-                )
-            except RecursionError as e:
-                logger.warning(
-                    f"Agent 达到最大迭代次数限制 ({self.max_iterations})，可能存在工具调用循环",
-                    extra={"error": str(e)}
-                )
-                # 返回一个友好的错误提示
-                return {
-                    "content": f"抱歉，我在处理您的请求时遇到了问题。已达到最大处理步骤限制（{self.max_iterations}次）。请尝试简化您的问题或稍后再试。",
-                    "model": self.model_name,
-                    "elapsed_time": time.time() - start_time,
-                    "usage": {
-                        "prompt_tokens": 0,
-                        "completion_tokens": 0,
-                        "total_tokens": 0
-                    }
-                }
+            result = await self.agent.ainvoke({"messages": messages})
 
             # 获取最后的 AI 消息
             output_messages = result.get("messages", [])
             content = ""
-            
-            logger.debug(f"输出消息数量: {len(output_messages)}")
-            total_tokens = 0
             for msg in reversed(output_messages):
                 if isinstance(msg, AIMessage):
-                    logger.debug(f"找到 AI 消息，content 类型: {type(msg.content)}")
-                    logger.debug(f"AI 消息内容: {msg.content}")
-                    
-                    # 处理多模态响应：content 可能是字符串或列表
-                    if isinstance(msg.content, str):
-                        content = msg.content
-                        logger.debug(f"提取字符串内容，长度: {len(content)}")
-                    elif isinstance(msg.content, list):
-                        # 多模态响应：提取文本部分
-                        logger.debug(f"多模态响应，列表长度: {len(msg.content)}")
-                        text_parts = []
-                        for item in msg.content:
-                            logger.debug(f"处理项: {item}")
-                            if isinstance(item, dict):
-                                # 通义千问格式: {"text": "..."}
-                                if "text" in item:
-                                    text = item.get("text", "")
-                                    text_parts.append(text)
-                                    logger.debug(f"提取文本: {text[:100]}...")
-                                # OpenAI 格式: {"type": "text", "text": "..."}
-                                elif item.get("type") == "text":
-                                    text = item.get("text", "")
-                                    text_parts.append(text)
-                                    logger.debug(f"提取文本: {text[:100]}...")
-                            elif isinstance(item, str):
-                                text_parts.append(item)
-                                logger.debug(f"提取字符串: {item[:100]}...")
-                        content = "".join(text_parts)
-                        logger.debug(f"合并后内容长度: {len(content)}")
-                    else:
-                        content = str(msg.content)
-                        logger.debug(f"转换为字符串: {content[:100]}...")
-                    response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
-                    total_tokens = response_meta.get("token_usage", {}).get("total_tokens", 0) if response_meta else 0
+                    content = msg.content
                     break
-            
-            logger.info(f"最终提取的内容长度: {len(content)}")
 
             elapsed_time = time.time() - start_time
             if memory_flag:
-                await write_long_term(storage_type, end_user_id, message_chat, content, user_rag_memory_id, actual_config_id)
+                await self.write(storage_type,end_user_id,content,user_rag_memory_id,actual_end_user_id,content,actual_config_id)
+                await self.term_memory_save(message_chat,end_user_id,content)
             response = {
                 "content": content,
                 "model": self.model_name,
@@ -420,7 +286,7 @@ class LangChainAgent:
                 "usage": {
                     "prompt_tokens": 0,
                     "completion_tokens": 0,
-                    "total_tokens": total_tokens
+                    "total_tokens": 0
                 }
             }
 
@@ -447,8 +313,7 @@ class LangChainAgent:
         config_id: Optional[str] = None,
         storage_type:Optional[str] = None,
         user_rag_memory_id:Optional[str] = None,
-        memory_flag: Optional[bool] = True,
-        files: Optional[List[Dict[str, Any]]] = None  # 新增：多模态文件
+        memory_flag: Optional[bool] = True
     ) -> AsyncGenerator[str, None]:
         """执行流式对话
 
@@ -482,14 +347,32 @@ class LangChainAgent:
             except Exception as e:
                 logger.warning(f"Failed to get db session: {e}")
 
+        history_term_memory_result = await self.term_memory_redis_read(end_user_id)
+        history_term_memory = history_term_memory_result[0]
+        if memory_flag:
+            if len(history_term_memory) >= 4 and storage_type != "rag":
+                history_term_memory = ';'.join(history_term_memory)
+                retrieved_content = history_term_memory_result[1]
+                db_for_memory = next(get_db())
+                try:
+                    repo = LongTermMemoryRepository(db_for_memory)
+                    repo.upsert(end_user_id, retrieved_content)
+                    logger.info(
+                        f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
+                    await self.write(storage_type, end_user_id, history_term_memory, user_rag_memory_id, end_user_id,
+                                     history_term_memory, actual_config_id)
+                except Exception as e:
+                    logger.error(f"Failed to write to long term memory: {e}")
+                finally:
+                    db_for_memory.close()
 
-            # 注意：不在这里写入用户消息，等 AI 回复后一起写入
+            await self.write(storage_type, end_user_id, message, user_rag_memory_id, end_user_id, message, actual_config_id)
         try:
-            # 准备消息列表（支持多模态）
-            messages = self._prepare_messages(message, history, context, files)
+            # 准备消息列表
+            messages = self._prepare_messages(message, history, context)
 
             logger.debug(
-                f"准备流式调用，has_tools={bool(self.tools)}, has_files={bool(files)}, message_count={len(messages)}"
+                f"准备流式调用，has_tools={bool(self.tools)}, message_count={len(messages)}"
             )
 
             chunk_count = 0
@@ -497,12 +380,11 @@ class LangChainAgent:
 
             # 统一使用 agent 的 astream_events 实现流式输出
             logger.debug("使用 Agent astream_events 实现流式输出")
-            full_content = ''
+            full_content=''
             try:
                 async for event in self.agent.astream_events(
                     {"messages": messages},
-                    version="v2",
-                    config={"recursion_limit": self.max_iterations}
+                    version="v2"
                 ):
                     chunk_count += 1
                     kind = event.get("event")
@@ -511,70 +393,20 @@ class LangChainAgent:
                     if kind == "on_chat_model_stream":
                         # LLM 流式输出
                         chunk = event.get("data", {}).get("chunk")
-                        if chunk and hasattr(chunk, "content"):
-                            # 处理多模态响应：content 可能是字符串或列表
-                            chunk_content = chunk.content
-                            if isinstance(chunk_content, str) and chunk_content:
-                                full_content += chunk_content
-                                yield chunk_content
-                                yielded_content = True
-                            elif isinstance(chunk_content, list):
-                                # 多模态响应：提取文本部分
-                                for item in chunk_content:
-                                    if isinstance(item, dict):
-                                        # 通义千问格式: {"text": "..."}
-                                        if "text" in item:
-                                            text = item.get("text", "")
-                                            if text:
-                                                full_content += text
-                                                yield text
-                                                yielded_content = True
-                                        # OpenAI 格式: {"type": "text", "text": "..."}
-                                        elif item.get("type") == "text":
-                                            text = item.get("text", "")
-                                            if text:
-                                                full_content += text
-                                                yield text
-                                                yielded_content = True
-                                    elif isinstance(item, str):
-                                        full_content += item
-                                        yield item
-                                        yielded_content = True
+                        full_content+=chunk.content
+                        if chunk and hasattr(chunk, "content") and chunk.content:
+                            yield chunk.content
+                            yielded_content = True
                     
                     elif kind == "on_llm_stream":
                         # 另一种 LLM 流式事件
                         chunk = event.get("data", {}).get("chunk")
                         if chunk:
-                            if hasattr(chunk, "content"):
-                                chunk_content = chunk.content
-                                if isinstance(chunk_content, str) and chunk_content:
-                                    full_content += chunk_content
-                                    yield chunk_content
-                                    yielded_content = True
-                                elif isinstance(chunk_content, list):
-                                    # 多模态响应：提取文本部分
-                                    for item in chunk_content:
-                                        if isinstance(item, dict):
-                                            # 通义千问格式: {"text": "..."}
-                                            if "text" in item:
-                                                text = item.get("text", "")
-                                                if text:
-                                                    full_content += text
-                                                    yield text
-                                                    yielded_content = True
-                                            # OpenAI 格式: {"type": "text", "text": "..."}
-                                            elif item.get("type") == "text":
-                                                text = item.get("text", "")
-                                                if text:
-                                                    full_content += text
-                                                    yield text
-                                                    yielded_content = True
-                                        elif isinstance(item, str):
-                                            full_content += item
-                                            yield item
-                                            yielded_content = True
+                            if hasattr(chunk, "content") and chunk.content:
+                                full_content+=chunk.content
+                                yield chunk.content
+                                yielded_content = True
                             elif isinstance(chunk, str):
-                                full_content += chunk
                                 yield chunk
                                 yielded_content = True
                     
@@ -585,17 +417,10 @@ class LangChainAgent:
                         logger.debug(f"工具调用结束: {event.get('name')}")
                 
                 logger.debug(f"Agent 流式完成，共 {chunk_count} 个事件")
-                # 统计token消耗
-                output_messages = event.get("data", {}).get("output", {}).get("messages", [])
-                for msg in reversed(output_messages):
-                    if isinstance(msg, AIMessage):
-                        response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
-                        total_tokens = response_meta.get("token_usage", {}).get("total_tokens",
-                                                                                0) if response_meta else 0
-                        yield total_tokens
-                        break
                 if memory_flag:
-                    await write_long_term(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id, actual_config_id)
+                    await self.write(storage_type, end_user_id,full_content, user_rag_memory_id, end_user_id,full_content, actual_config_id)
+                    await self.term_memory_save(message_chat, end_user_id, full_content)
+                
             except Exception as e:
                 logger.error(f"Agent astream_events 失败: {str(e)}", exc_info=True)
                 raise

api/app/core/config.py

@@ -9,25 +9,6 @@ load_dotenv()
 
 
 class Settings:
-    # ========================================================================
-    # Deployment Mode Configuration
-    # ========================================================================
-    # community: 社区版（开源，功能受限）
-    # cloud: SaaS 云服务版（全功能，按量计费）
-    # enterprise: 企业私有化版（License 控制）
-    DEPLOYMENT_MODE: str = os.getenv("DEPLOYMENT_MODE", "community")
-    
-    # License 配置（企业版）
-    LICENSE_FILE: str = os.getenv("LICENSE_FILE", "/etc/app/license.json")
-    LICENSE_SERVER_URL: str = os.getenv("LICENSE_SERVER_URL", "https://license.yourcompany.com")
-    
-    # 计费服务配置（SaaS 版）
-    BILLING_SERVICE_URL: str = os.getenv("BILLING_SERVICE_URL", "")
-    
-    # 基础 URL（用于 SSO 回调等）
-    BASE_URL: str = os.getenv("BASE_URL", "http://localhost:8000")
-    FRONTEND_URL: str = os.getenv("FRONTEND_URL", "http://localhost:3000")
-    
     ENABLE_SINGLE_WORKSPACE: bool = os.getenv("ENABLE_SINGLE_WORKSPACE", "true").lower() == "true"
     # API Keys Configuration
     OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "")
@@ -57,7 +38,6 @@ class Settings:
     REDIS_PORT: int = int(os.getenv("REDIS_PORT", "6379"))
     REDIS_DB: int = int(os.getenv("REDIS_DB", "1"))
     REDIS_PASSWORD: str = os.getenv("REDIS_PASSWORD", "")
-    
 
     # ElasticSearch configuration
     ELASTICSEARCH_HOST: str = os.getenv("ELASTICSEARCH_HOST", "https://127.0.0.1")
@@ -91,30 +71,10 @@ class Settings:
 
     # Single Sign-On configuration
     ENABLE_SINGLE_SESSION: bool = os.getenv("ENABLE_SINGLE_SESSION", "false").lower() == "true"
-    
-    # SSO 免登配置
-    SSO_TOKEN_EXPIRE_SECONDS: int = int(os.getenv("SSO_TOKEN_EXPIRE_SECONDS", "300"))
-    SSO_TRUSTED_SOURCES_CONFIG: str = os.getenv("SSO_TRUSTED_SOURCES_CONFIG", "{}")
 
     # File Upload
     MAX_FILE_SIZE: int = int(os.getenv("MAX_FILE_SIZE", "52428800"))
     FILE_PATH: str = os.getenv("FILE_PATH", "/files")
-    FILE_URL_EXPIRES: int = int(os.getenv("FILE_URL_EXPIRES", "3600"))
-
-    # Storage Configuration
-    STORAGE_TYPE: str = os.getenv("STORAGE_TYPE", "local")
-
-    # Aliyun OSS Configuration
-    OSS_ENDPOINT: str = os.getenv("OSS_ENDPOINT", "")
-    OSS_ACCESS_KEY_ID: str = os.getenv("OSS_ACCESS_KEY_ID", "")
-    OSS_ACCESS_KEY_SECRET: str = os.getenv("OSS_ACCESS_KEY_SECRET", "")
-    OSS_BUCKET_NAME: str = os.getenv("OSS_BUCKET_NAME", "")
-
-    # AWS S3 Configuration
-    S3_REGION: str = os.getenv("S3_REGION", "")
-    S3_ACCESS_KEY_ID: str = os.getenv("S3_ACCESS_KEY_ID", "")
-    S3_SECRET_ACCESS_KEY: str = os.getenv("S3_SECRET_ACCESS_KEY", "")
-    S3_BUCKET_NAME: str = os.getenv("S3_BUCKET_NAME", "")
 
     # VOLC ASR settings
     VOLC_APP_KEY: str = os.getenv("VOLC_APP_KEY", "")
@@ -130,7 +90,6 @@ class Settings:
 
     # Server Configuration
     SERVER_IP: str = os.getenv("SERVER_IP", "127.0.0.1")
-    FILE_LOCAL_SERVER_URL : str = os.getenv("FILE_LOCAL_SERVER_URL", "http://localhost:8000/api")
 
     # ========================================================================
     # Internal Configuration (not in .env, used by application code)
@@ -157,11 +116,6 @@ class Settings:
         if origin.strip()
     ]
 
-    # Language Configuration
-    # Supported values: "zh" (Chinese), "en" (English)
-    # This controls the language used for memory summary titles and other generated content
-    DEFAULT_LANGUAGE: str = os.getenv("DEFAULT_LANGUAGE", "zh")
-
     # Logging settings
     LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
     LOG_FORMAT: str = os.getenv("LOG_FORMAT", "%(asctime)s - %(name)s - %(levelname)s - %(message)s")
@@ -192,13 +146,6 @@ class Settings:
     # Celery configuration (internal)
     CELERY_BROKER: int = int(os.getenv("CELERY_BROKER", "1"))
     CELERY_BACKEND: int = int(os.getenv("CELERY_BACKEND", "2"))
-
-    # SMTP Email Configuration
-    SMTP_SERVER: str = os.getenv("SMTP_SERVER", "smtp.gmail.com")
-    SMTP_PORT: int = int(os.getenv("SMTP_PORT", "587"))
-    SMTP_USER: str = os.getenv("SMTP_USER", "")
-    SMTP_PASSWORD: str = os.getenv("SMTP_PASSWORD", "")
-
     REFLECTION_INTERVAL_SECONDS: float = float(os.getenv("REFLECTION_INTERVAL_SECONDS", "300"))
     HEALTH_CHECK_SECONDS: float = float(os.getenv("HEALTH_CHECK_SECONDS", "600"))
     MEMORY_INCREMENT_INTERVAL_HOURS: float = float(os.getenv("MEMORY_INCREMENT_INTERVAL_HOURS", "24"))
@@ -219,36 +166,11 @@ class Settings:
     ENABLE_TOOL_MANAGEMENT: bool = os.getenv("ENABLE_TOOL_MANAGEMENT", "true").lower() == "true"
 
     # official environment system version
-    SYSTEM_VERSION: str = os.getenv("SYSTEM_VERSION", "v0.2.1")
-
-    # model square loading
-    LOAD_MODEL: bool = os.getenv("LOAD_MODEL", "false").lower() == "true"
+    SYSTEM_VERSION: str = os.getenv("SYSTEM_VERSION", "v0.2.0")
 
     # workflow config
     WORKFLOW_NODE_TIMEOUT: int = int(os.getenv("WORKFLOW_NODE_TIMEOUT", 600))
 
-    # ========================================================================
-    # General Ontology Type Configuration
-    # ========================================================================
-    # 通用本体文件路径列表（逗号分隔）
-    GENERAL_ONTOLOGY_FILES: str = os.getenv("GENERAL_ONTOLOGY_FILES", "General_purpose_entity.ttl")
-    
-    # 是否启用通用本体类型功能
-    ENABLE_GENERAL_ONTOLOGY_TYPES: bool = os.getenv("ENABLE_GENERAL_ONTOLOGY_TYPES", "true").lower() == "true"
-    
-    # Prompt 中最大类型数量
-    MAX_ONTOLOGY_TYPES_IN_PROMPT: int = int(os.getenv("MAX_ONTOLOGY_TYPES_IN_PROMPT", "50"))
-    
-    # 核心通用类型列表（逗号分隔）
-    CORE_GENERAL_TYPES: str = os.getenv(
-        "CORE_GENERAL_TYPES",
-        "Person,Organization,Company,GovernmentAgency,Place,Location,City,Country,Building,"
-        "Event,SportsEvent,SocialEvent,Work,Book,Film,Software,Concept,TopicalConcept,AcademicSubject"
-    )
-    
-    # 实验模式开关（允许通过 API 动态切换本体配置）
-    ONTOLOGY_EXPERIMENT_MODE: bool = os.getenv("ONTOLOGY_EXPERIMENT_MODE", "true").lower() == "true"
-
     def get_memory_output_path(self, filename: str = "") -> str:
         """
         Get the full path for memory module output files.

api/app/core/error_codes.py

@@ -46,7 +46,6 @@ class BizCode(IntEnum):
     RESOURCE_ALREADY_EXISTS = 5002
     VERSION_ALREADY_EXISTS = 5003
     STATE_CONFLICT = 5004
-    RESOURCE_IN_USE = 5005
 
     # 应用发布（6xxx）
     PUBLISH_FAILED = 6001
@@ -126,7 +125,6 @@ HTTP_MAPPING = {
     BizCode.RESOURCE_ALREADY_EXISTS: 409,
     BizCode.VERSION_ALREADY_EXISTS: 409,
     BizCode.STATE_CONFLICT: 409,
-    BizCode.RESOURCE_IN_USE: 409,
     BizCode.PUBLISH_FAILED: 500,
     BizCode.NO_DRAFT_TO_PUBLISH: 400,
     BizCode.ROLLBACK_TARGET_NOT_FOUND: 400,

api/app/core/language_utils.py

@@ -1,82 +0,0 @@
-# -*- coding: utf-8 -*-
-"""语言处理工具模块
-
-本模块提供集中化的语言校验和处理功能，确保整个应用中语言参数的一致性。
-
-Functions:
-    validate_language: 校验语言参数，确保其为有效值
-    get_language_from_header: 从请求头获取并校验语言参数
-"""
-
-from typing import Optional
-
-from app.core.logging_config import get_logger
-
-logger = get_logger(__name__)
-
-# 支持的语言列表
-SUPPORTED_LANGUAGES = {"zh", "en"}
-
-# 默认回退语言
-DEFAULT_LANGUAGE = "zh"
-
-
-def validate_language(language: Optional[str]) -> str:
-    """
-    校验语言参数，确保其为有效值。
-    
-    Args:
-        language: 待校验的语言代码，可以是 None、"zh"、"en" 或其他值
-        
-    Returns:
-        有效的语言代码（"zh" 或 "en"）
-        
-    Examples:
-        >>> validate_language("zh")
-        'zh'
-        >>> validate_language("en")
-        'en'
-        >>> validate_language("EN")  # 大小写不敏感
-        'en'
-        >>> validate_language(None)  # None 回退到默认值
-        'zh'
-        >>> validate_language("fr")  # 不支持的语言回退到默认值
-        'zh'
-    """
-    if language is None:
-        return DEFAULT_LANGUAGE
-    
-    # 标准化：转小写并去除空白
-    lang = str(language).lower().strip()
-    
-    if lang in SUPPORTED_LANGUAGES:
-        return lang
-    
-    logger.warning(
-        f"无效的语言参数 '{language}'，已回退到默认值 '{DEFAULT_LANGUAGE}'。"
-        f"支持的语言: {SUPPORTED_LANGUAGES}"
-    )
-    return DEFAULT_LANGUAGE
-
-
-def get_language_from_header(language_type: Optional[str]) -> str:
-    """
-    从请求头获取并校验语言参数。
-    
-    这是一个便捷函数，用于在 controller 层统一处理 X-Language-Type Header。
-    
-    Args:
-        language_type: 从 X-Language-Type Header 获取的语言值
-        
-    Returns:
-        有效的语言代码（"zh" 或 "en"）
-        
-    Examples:
-        >>> get_language_from_header(None)  # Header 未传递
-        'zh'
-        >>> get_language_from_header("en")
-        'en'
-        >>> get_language_from_header("invalid")  # 无效值回退
-        'zh'
-    """
-    return validate_language(language_type)

api/app/core/logging_config.py

@@ -38,56 +38,6 @@ class SensitiveDataLoggingFilter(logging.Filter):
         return True
 
 
-class Neo4jSuccessNotificationFilter(logging.Filter):
-    """Neo4j 日志过滤器：过滤成功/信息性状态的通知，保留真正的警告和错误
-    
-    Neo4j 驱动会以 WARNING 级别记录所有数据库通知，包括成功的操作。
-    这个过滤器会过滤掉以下 GQL 状态码的通知，只保留真正的警告和错误：
-      - 00000: 成功完成 (successful completion)
-      - 00N00: 无数据 (no data)
-      - 00NA0: 无数据，信息性通知 (no data, informational notification)
-    
-    使用正则表达式进行更严格的匹配，避免误过滤无关的警告。
-    """
-    
-    import re
-    
-    # 编译正则表达式以提高性能
-    # 匹配所有"成功/信息性"的 GQL 状态码：
-    # 00000 = 成功完成, 00N00 = 无数据, 00NA0 = 无数据信息性通知
-    GQL_STATUS_PATTERN = re.compile(r"gql_status=['\"](00000|00N00|00NA0)['\"]")
-    
-    # 匹配 status_description 中的成功完成或信息性通知消息
-    SUCCESS_DESC_PATTERN = re.compile(r"status_description=['\"]note:\s*(successful\s+completion|no\s+data)['\"]", re.IGNORECASE)
-    
-    def filter(self, record: logging.LogRecord) -> bool:
-        """
-        过滤 Neo4j 成功通知
-        
-        Args:
-            record: 日志记录
-            
-        Returns:
-            True表示允许记录，False表示拒绝（过滤掉）
-        """
-        # 只处理 INFO 和 WARNING 级别的日志
-        # Neo4j 驱动对 severity='INFORMATION' 的通知使用 INFO 级别，
-        # 对 severity='WARNING' 的通知使用 WARNING 级别
-        if record.levelno not in (logging.INFO, logging.WARNING):
-            return True
-        
-        # 检查是否是 Neo4j 的成功通知
-        message = str(record.msg)
-        
-        # 使用正则表达式进行更严格的匹配
-        # 这样可以避免误过滤包含这些子字符串但不是 Neo4j 通知的日志
-        if self.GQL_STATUS_PATTERN.search(message) or self.SUCCESS_DESC_PATTERN.search(message):
-            return False  # 过滤掉这条日志
-        
-        # 保留其他所有日志（包括真正的警告和错误）
-        return True
-
-
 class LoggingConfig:
     """全局日志配置类"""
     
@@ -115,22 +65,6 @@ class LoggingConfig:
         # 清除现有处理器
         root_logger.handlers.clear()
         
-        # Neo4j 通知过滤器 - 挂在 handler 上确保所有传播上来的日志都能被过滤
-        neo4j_filter = Neo4jSuccessNotificationFilter()
-        
-        # 抑制 Neo4j 通知日志
-        # Neo4j 驱动内部会给 neo4j.notifications logger 配置自己的 handler，
-        # 导致日志绕过根 logger 的 filter 直接输出。
-        # 多管齐下确保过滤生效：
-        # 1. 设置 neo4j.notifications 级别为 WARNING（过滤 INFO 级别的 00NA0 通知）
-        # 2. 在所有 neo4j logger 上添加 filter（过滤 WARNING 级别的成功通知）
-        # 3. 在根 handler 上也添加 filter（兜底）
-        neo4j_notifications_logger = logging.getLogger("neo4j.notifications")
-        neo4j_notifications_logger.setLevel(logging.WARNING)
-        for neo4j_logger_name in ["neo4j", "neo4j.io", "neo4j.pool", "neo4j.notifications"]:
-            neo4j_logger = logging.getLogger(neo4j_logger_name)
-            neo4j_logger.addFilter(neo4j_filter)
-        
         # 创建格式化器
         formatter = logging.Formatter(
             fmt=settings.LOG_FORMAT,
@@ -146,7 +80,6 @@ class LoggingConfig:
             console_handler.setFormatter(formatter)
             console_handler.setLevel(getattr(logging, settings.LOG_LEVEL.upper()))
             console_handler.addFilter(sensitive_filter)
-            console_handler.addFilter(neo4j_filter)
             root_logger.addHandler(console_handler)
         
         # 文件处理器（带轮转）
@@ -160,7 +93,6 @@ class LoggingConfig:
             file_handler.setFormatter(formatter)
             file_handler.setLevel(getattr(logging, settings.LOG_LEVEL.upper()))
             file_handler.addFilter(sensitive_filter)
-            file_handler.addFilter(neo4j_filter)
             root_logger.addHandler(file_handler)
         
         cls._initialized = True

api/app/core/memory/agent/langgraph_graph/__init__.py

@@ -0,0 +1,16 @@
+"""
+LangGraph Graph package for memory agent.
+
+This package provides the LangGraph workflow orchestrator with modular
+node implementations, routing logic, and state management.
+
+Package structure:
+- read_graph: Main graph factory for read operations
+- write_graph: Main graph factory for write operations
+- nodes: LangGraph node implementations
+- routing: State routing logic
+- state: State management utilities
+"""
+from app.core.memory.agent.langgraph_graph.read_graph import make_read_graph
+
+__all__ = ['make_read_graph']

api/app/core/memory/agent/langgraph_graph/nodes/__init__.py

@@ -4,7 +4,7 @@ LangGraph node implementations.
 This module contains custom node implementations for the LangGraph workflow.
 """
 
-# from app.core.memory.agent.langgraph_graph.nodes.tool_node import ToolExecutionNode
-# from app.core.memory.agent.langgraph_graph.nodes.input_node import create_input_message
-#
-# __all__ = ["ToolExecutionNode", "create_input_message"]
+from app.core.memory.agent.langgraph_graph.nodes.tool_node import ToolExecutionNode
+from app.core.memory.agent.langgraph_graph.nodes.input_node import create_input_message
+
+__all__ = ["ToolExecutionNode", "create_input_message"]

api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py

@@ -1,16 +0,0 @@
-from app.core.memory.agent.utils.llm_tools import ReadState, WriteState
-
-
-def content_input_node(state: ReadState) -> ReadState:
-    """开始节点 - 提取内容并保持状态信息"""
-
-    content = state['messages'][0].content if state.get('messages') else ''
-    # 返回内容并保持所有状态信息
-    return {"data": content}
-
-def content_input_write(state: WriteState) -> WriteState:
-    """开始节点 - 提取内容并保持状态信息"""
-
-    content = state['messages'][0].content if state.get('messages') else ''
-    # 返回内容并保持所有状态信息
-    return {"data": content}

api/app/core/memory/agent/langgraph_graph/nodes/input_node.py

@@ -0,0 +1,150 @@
+"""
+Input node for LangGraph workflow entry point.
+
+This module provides the create_input_message function which processes initial
+user input with multimodal support and creates the first tool call message.
+"""
+
+import logging
+import re
+import uuid
+from datetime import datetime
+from typing import Any, Dict
+
+from app.core.memory.agent.utils.multimodal import MultimodalProcessor
+from app.schemas.memory_config_schema import MemoryConfig
+from langchain_core.messages import AIMessage
+
+logger = logging.getLogger(__name__)
+
+
+async def create_input_message(
+    state: Dict[str, Any],
+    tool_name: str,
+    session_id: str,
+    search_switch: str,
+    apply_id: str,
+    group_id: str,
+    multimodal_processor: MultimodalProcessor,
+    memory_config: MemoryConfig,
+) -> Dict[str, Any]:
+    """
+    Create initial tool call message from user input.
+    
+    This function:
+    1. Extracts the last message content from state
+    2. Processes multimodal inputs (images/audio) using the multimodal processor
+    3. Generates a unique message ID
+    4. Extracts namespace from session_id
+    5. Handles verified_data extraction for backward compatibility
+    6. Returns AIMessage with complete tool_calls structure
+    
+    Args:
+        state: LangGraph state dictionary containing messages
+        tool_name: Name of the tool to invoke (typically "Split_The_Problem")
+        session_id: Session identifier (format: "call_id_{namespace}")
+        search_switch: Search routing parameter
+        apply_id: Application identifier
+        group_id: Group identifier
+        multimodal_processor: Processor for handling image/audio inputs
+        memory_config: MemoryConfig object containing all configuration
+        
+    Returns:
+        State update with AIMessage containing tool_call
+        
+    Examples:
+        >>> state = {"messages": [HumanMessage(content="What is AI?")]}
+        >>> result = await create_input_message(
+        ...     state, "Split_The_Problem", "call_id_user123", "0", "app1", "group1", processor, config
+        ... )
+        >>> result["messages"][0].tool_calls[0]["name"]
+        'Split_The_Problem'
+    """
+    messages = state.get("messages", [])
+    
+    # Extract last message content
+    if messages:
+        last_message = messages[-1].content if hasattr(messages[-1], 'content') else str(messages[-1])
+    else:
+        logger.warning("[create_input_message] No messages in state, using empty string")
+        last_message = ""
+    
+    logger.debug(f"[create_input_message] Original input: {last_message[:100]}...")
+    
+    # Process multimodal input (images/audio)
+    try:
+        processed_content = await multimodal_processor.process_input(last_message)
+        if processed_content != last_message:
+            logger.info(
+                f"[create_input_message] Multimodal processing converted input "
+                f"from {len(last_message)} to {len(processed_content)} chars"
+            )
+        last_message = processed_content
+    except Exception as e:
+        logger.error(
+            f"[create_input_message] Multimodal processing failed: {e}",
+            exc_info=True
+        )
+        # Continue with original content
+    
+    # Generate unique message ID
+    uuid_str = uuid.uuid4()
+    time_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Extract namespace from session_id
+    # Expected format: "call_id_{namespace}" or similar
+    try:
+        namespace = str(session_id).split('_id_')[1]
+    except (IndexError, AttributeError):
+        logger.warning(
+            f"[create_input_message] Could not extract namespace from session_id: {session_id}"
+        )
+        namespace = "unknown"
+    
+    # Handle verified_data extraction (backward compatibility)
+    # This regex-based extraction is kept for compatibility with existing data formats
+    if 'verified_data' in str(last_message):
+        try:
+            messages_last = str(last_message).replace('\\n', '').replace('\\', '')
+            query_match = re.findall(r'"query": "(.*?)",', messages_last)
+            if query_match:
+                last_message = query_match[0]
+                logger.debug(
+                    f"[create_input_message] Extracted query from verified_data: {last_message}"
+                )
+        except Exception as e:
+            logger.warning(
+                f"[create_input_message] Failed to extract query from verified_data: {e}"
+            )
+    
+    # Construct tool call message
+    tool_call_id = f"{session_id}_{uuid_str}"
+    
+    logger.info(
+        f"[create_input_message] Creating tool call for '{tool_name}' "
+        f"with ID: {tool_call_id}"
+    )
+    
+    # Build tool arguments
+    tool_args = {
+        "sentence": last_message,
+        "sessionid": session_id,
+        "messages_id": str(uuid_str),
+        "search_switch": search_switch,
+        "apply_id": apply_id,
+        "group_id": group_id,
+        "memory_config": memory_config,
+    }
+    
+    return {
+        "messages": [
+            AIMessage(
+                content="",
+                tool_calls=[{
+                    "name": tool_name,
+                    "args": tool_args,
+                    "id": tool_call_id
+                }]
+            )
+        ]
+    }

api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py

@@ -1,249 +0,0 @@
-import os
-import json
-import time
-from app.core.logging_config import get_agent_logger
-from app.db import get_db
-
-from app.core.memory.agent.models.problem_models import ProblemExtensionResponse
-from app.core.memory.agent.utils.llm_tools import (
-    PROJECT_ROOT_,
-    ReadState,
-)
-from app.core.memory.agent.utils.redis_tool import store
-from app.core.memory.agent.utils.session_tools import SessionService
-from app.core.memory.agent.utils.template_tools import TemplateService
-from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
-
-template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
-db_session = next(get_db())
-logger = get_agent_logger(__name__)
-
-
-class ProblemNodeService(LLMServiceMixin):
-    """问题处理节点服务类"""
-
-    def __init__(self):
-        super().__init__()
-        self.template_service = TemplateService(template_root)
-
-
-# 创建全局服务实例
-problem_service = ProblemNodeService()
-
-
-async def Split_The_Problem(state: ReadState) -> ReadState:
-    """问题分解节点"""
-    # 从状态中获取数据
-    content = state.get('data', '')
-    end_user_id = state.get('end_user_id', '')
-    memory_config = state.get('memory_config', None)
-
-    history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id)
-
-    # 生成 JSON schema 以指导 LLM 输出正确格式
-    json_schema = ProblemExtensionResponse.model_json_schema()
-
-    system_prompt = await problem_service.template_service.render_template(
-        template_name='problem_breakdown_prompt.jinja2',
-        operation_name='split_the_problem',
-        history=history,
-        sentence=content,
-        json_schema=json_schema
-    )
-
-    try:
-        # 使用优化的LLM服务
-        structured = await problem_service.call_llm_structured(
-            state=state,
-            db_session=db_session,
-            system_prompt=system_prompt,
-            response_model=ProblemExtensionResponse,
-            fallback_value=[]
-        )
-
-        # 添加更详细的日志记录
-        logger.info(f"Split_The_Problem: 开始处理问题分解，内容长度: {len(content)}")
-
-        # 验证结构化响应
-        if not structured or not hasattr(structured, 'root'):
-            logger.warning("Split_The_Problem: 结构化响应为空或格式不正确")
-            split_result = json.dumps([], ensure_ascii=False)
-        elif not structured.root:
-            logger.warning("Split_The_Problem: 结构化响应的root为空")
-            split_result = json.dumps([], ensure_ascii=False)
-        else:
-            split_result = json.dumps(
-                [item.model_dump() for item in structured.root],
-                ensure_ascii=False
-            )
-
-        split_result_dict = []
-        for index, item in enumerate(json.loads(split_result)):
-            split_data = {
-                "id": f"Q{index + 1}",
-                "question": item['extended_question'],
-                "type": item['type'],
-                "reason": item['reason']
-            }
-            split_result_dict.append(split_data)
-
-        logger.info(f"Split_The_Problem: 成功生成 {len(structured.root) if structured.root else 0} 个分解项")
-
-        result = {
-            "context": split_result,
-            "original": content,
-            "_intermediate": {
-                "type": "problem_split",
-                "title": "问题拆分",
-                "data": split_result_dict,
-                "original_query": content
-            }
-        }
-
-    except Exception as e:
-        logger.error(
-            f"Split_The_Problem failed: {e}",
-            exc_info=True
-        )
-
-        # 提供更详细的错误信息
-        error_details = {
-            "error_type": type(e).__name__,
-            "error_message": str(e),
-            "content_length": len(content),
-            "llm_model_id": memory_config.llm_model_id if memory_config else None
-        }
-
-        logger.error(f"Split_The_Problem error details: {error_details}")
-
-        # 创建默认的空结果
-        result = {
-            "context": json.dumps([], ensure_ascii=False),
-            "original": content,
-            "error": str(e),
-            "_intermediate": {
-                "type": "problem_split",
-                "title": "问题拆分",
-                "data": [],
-                "original_query": content,
-                "error": error_details
-            }
-        }
-
-    # 返回更新后的状态，包含spit_context字段
-    return {"spit_data": result}
-
-
-async def Problem_Extension(state: ReadState) -> ReadState:
-    """问题扩展节点"""
-    # 获取原始数据和分解结果
-    start = time.time()
-    content = state.get('data', '')
-    data = state.get('spit_data', '')['context']
-    end_user_id = state.get('end_user_id', '')
-    storage_type = state.get('storage_type', '')
-    user_rag_memory_id = state.get('user_rag_memory_id', '')
-    memory_config = state.get('memory_config', None)
-
-    databasets = {}
-    try:
-        data = json.loads(data)
-        for i in data:
-            databasets[i['extended_question']] = i['type']
-    except (json.JSONDecodeError, KeyError, TypeError) as e:
-        logger.error(f"Problem_Extension: 数据解析失败: {e}")
-        # 使用空字典作为fallback
-        databasets = {}
-        data = []
-
-    history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id)
-
-    # 生成 JSON schema 以指导 LLM 输出正确格式
-    json_schema = ProblemExtensionResponse.model_json_schema()
-
-    system_prompt = await problem_service.template_service.render_template(
-        template_name='Problem_Extension_prompt.jinja2',
-        operation_name='problem_extension',
-        history=history,
-        questions=databasets,
-        json_schema=json_schema
-    )
-
-    try:
-        # 使用优化的LLM服务
-        response_content = await problem_service.call_llm_structured(
-            state=state,
-            db_session=db_session,
-            system_prompt=system_prompt,
-            response_model=ProblemExtensionResponse,
-            fallback_value=[]
-        )
-
-        logger.info(f"Problem_Extension: 开始处理问题扩展，问题数量: {len(databasets)}")
-
-        # 验证结构化响应
-        if not response_content or not hasattr(response_content, 'root'):
-            logger.warning("Problem_Extension: 结构化响应为空或格式不正确")
-            aggregated_dict = {}
-        elif not response_content.root:
-            logger.warning("Problem_Extension: 结构化响应的root为空")
-            aggregated_dict = {}
-        else:
-            # Aggregate results by original question
-            aggregated_dict = {}
-            for item in response_content.root:
-                try:
-                    key = getattr(item, "original_question", None) or (
-                        item.get("original_question") if isinstance(item, dict) else None
-                    )
-                    value = getattr(item, "extended_question", None) or (
-                        item.get("extended_question") if isinstance(item, dict) else None
-                    )
-                    if not key or not value:
-                        logger.warning(f"Problem_Extension: 跳过无效项: key={key}, value={value}")
-                        continue
-                    aggregated_dict.setdefault(key, []).append(value)
-                except Exception as item_error:
-                    logger.warning(f"Problem_Extension: 处理项目时出错: {item_error}")
-                    continue
-
-        logger.info(f"Problem_Extension: 成功生成 {len(aggregated_dict)} 个扩展问题组")
-
-    except Exception as e:
-        logger.error(
-            f"LLM call failed for Problem_Extension: {e}",
-            exc_info=True
-        )
-
-        # 提供更详细的错误信息
-        error_details = {
-            "error_type": type(e).__name__,
-            "error_message": str(e),
-            "questions_count": len(databasets),
-            "llm_model_id": memory_config.llm_model_id if memory_config else None
-        }
-
-        logger.error(f"Problem_Extension error details: {error_details}")
-        aggregated_dict = {}
-
-    logger.info("Problem extension")
-    logger.info(f"Problem extension result: {aggregated_dict}")
-
-    # Emit intermediate output for frontend
-    print(time.time() - start)
-    result = {
-        "context": aggregated_dict,
-        "original": data,
-        "storage_type": storage_type,
-        "user_rag_memory_id": user_rag_memory_id,
-        "_intermediate": {
-            "type": "problem_extension",
-            "title": "问题扩展",
-            "data": aggregated_dict,
-            "original_query": content,
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id
-        }
-    }
-
-    return {"problem_extension": result}

api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py

@@ -1,417 +0,0 @@
-# ===== 标准库 =====
-import asyncio
-import json
-import os
-
-# ===== 第三方库 =====
-from langchain.agents import create_agent
-from langchain_openai import ChatOpenAI
-from app.core.logging_config import get_agent_logger
-from app.db import get_db, get_db_context
-
-from app.schemas import model_schema
-from app.services.memory_config_service import MemoryConfigService
-from app.services.model_service import ModelConfigService
-
-from app.core.memory.agent.services.search_service import SearchService
-from app.core.memory.agent.utils.llm_tools import (
-    COUNTState,
-    ReadState,
-    deduplicate_entries,
-    merge_to_key_value_pairs,
-)
-from app.core.memory.agent.langgraph_graph.tools.tool import (
-    create_hybrid_retrieval_tool_sync,
-    create_time_retrieval_tool,
-    extract_tool_message_content,
-)
-
-from app.core.rag.nlp.search import knowledge_retrieval
-
-logger = get_agent_logger(__name__)
-db = next(get_db())
-
-
-
-async def rag_config(state):
-    user_rag_memory_id = state.get('user_rag_memory_id', '')
-    kb_config = {
-        "knowledge_bases": [
-            {
-                "kb_id": user_rag_memory_id,
-                "similarity_threshold": 0.7,
-                "vector_similarity_weight": 0.5,
-                "top_k": 10,
-                "retrieve_type": "participle"
-            }
-        ],
-        "merge_strategy": "weight",
-        "reranker_id": os.getenv('reranker_id'),
-        "reranker_top_k": 10
-    }
-    return kb_config
-async def rag_knowledge(state,question):
-    kb_config = await rag_config(state)
-    end_user_id = state.get('end_user_id', '')
-    user_rag_memory_id=state.get("user_rag_memory_id",'')
-    retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(end_user_id)])
-    try:
-        retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
-        clean_content = '\n\n'.join(retrieval_knowledge)
-        cleaned_query = question
-        raw_results = clean_content
-        logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
-    except  Exception :
-        retrieval_knowledge=[]
-        clean_content = ''
-        raw_results = ''
-        cleaned_query = question
-        logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
-    return retrieval_knowledge,clean_content,cleaned_query,raw_results
-
-
-async def llm_infomation(state: ReadState) -> ReadState:
-    memory_config = state.get('memory_config', None)
-    model_id = memory_config.llm_model_id
-    tenant_id = memory_config.tenant_id
-
-    # 使用现有的 memory_config 而不是重新查询数据库
-    # 或者使用线程安全的数据库访问
-    with get_db_context() as db:
-        result_orm = ModelConfigService.get_model_by_id(db=db, model_id=model_id, tenant_id=tenant_id)
-        result_pydantic = model_schema.ModelConfig.model_validate(result_orm)
-    return result_pydantic
-
-
-async def clean_databases(data) -> str:
-    """
-    简化的数据库搜索结果清理函数
-    
-    Args:
-        data: 搜索结果数据
-        
-    Returns:
-        清理后的内容字符串
-    """
-    try:
-        # 解析JSON字符串
-        if isinstance(data, str):
-            try:
-                data = json.loads(data)
-            except json.JSONDecodeError:
-                return data
-
-        if not isinstance(data, dict):
-            return str(data)
-
-        # 获取结果数据
-        # with open("搜索结果.json","w",encoding='utf-8') as f:
-        #     f.write(json.dumps(data, indent=4, ensure_ascii=False))
-        results = data.get('results', data)
-        if not isinstance(results, dict):
-            return str(results)
-
-        # 收集所有内容
-        content_list = []
-        
-        # 处理重排序结果
-        reranked = results.get('reranked_results', {})
-        if reranked:
-            for category in ['summaries', 'statements', 'chunks', 'entities']:
-                items = reranked.get(category, [])
-                if isinstance(items, list):
-                    content_list.extend(items)
-        # 处理时间搜索结果
-        time_search = results.get('time_search', {})
-        if time_search:
-            if isinstance(time_search, dict):
-                statements = time_search.get('statements', time_search.get('time_search', []))
-                if isinstance(statements, list):
-                    content_list.extend(statements)
-            elif isinstance(time_search, list):
-                content_list.extend(time_search)
-
-        # 提取文本内容
-        text_parts = []
-        for item in content_list:
-            if isinstance(item, dict):
-                text = item.get('statement') or item.get('content', '')
-                if text:
-                    text_parts.append(text)
-            elif isinstance(item, str):
-                text_parts.append(item)
-
-
-        return '\n'.join(text_parts).strip()
-
-    except Exception as e:
-        logger.error(f"clean_databases failed: {e}", exc_info=True)
-        return str(data)
-
-
-async def retrieve_nodes(state: ReadState) -> ReadState:
-
-    '''
-
-    模型信息
-    '''
-
-    problem_extension=state.get('problem_extension', '')['context']
-    storage_type=state.get('storage_type', '')
-    user_rag_memory_id=state.get('user_rag_memory_id', '')
-    end_user_id=state.get('end_user_id', '')
-    memory_config = state.get('memory_config', None)
-    original=state.get('data', '')
-    problem_list=[]
-    for key,values in problem_extension.items():
-        for data in values:
-            problem_list.append(data)
-    logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
-    # 创建异步任务处理单个问题
-    async def process_question_nodes(idx, question):
-        try:
-            # Prepare search parameters based on storage type
-            search_params = {
-                "end_user_id": end_user_id,
-                "question": question,
-                "return_raw_results": True
-            }
-            if storage_type == "rag" and user_rag_memory_id:
-                retrieval_knowledge, clean_content, cleaned_query, raw_results = await rag_knowledge(state, question)
-            else:
-                clean_content, cleaned_query, raw_results = await SearchService().execute_hybrid_search(
-                    **search_params, memory_config=memory_config
-                )
-
-            return {
-                "Query_small": cleaned_query,
-                "Result_small": clean_content,
-                "_intermediate": {
-                    "type": "search_result",
-                    "query": cleaned_query,
-                    "raw_results": raw_results,
-                    "index": idx + 1,
-                    "total": len(problem_list)
-                }
-            }
-
-        except Exception as e:
-            logger.error(
-                f"Retrieve: hybrid_search failed for question '{question}': {e}",
-                exc_info=True
-            )
-            # Return empty result for this question
-            return {
-                "Query_small": question,
-                "Result_small": "",
-                "_intermediate": {
-                    "type": "search_result",
-                    "query": question,
-                    "raw_results": [],
-                    "index": idx + 1,
-                    "total": len(problem_list)
-                }
-            }
-
-    # 并发处理所有问题
-    tasks = [process_question_nodes(idx, question) for idx, question in enumerate(problem_list)]
-    databases_anser = await asyncio.gather(*tasks)
-    databases_data = {
-        "Query": original,
-        "Expansion_issue": databases_anser
-    }
-
-    # Collect intermediate outputs before deduplication
-    intermediate_outputs = []
-    for item in databases_anser:
-        if '_intermediate' in item:
-            intermediate_outputs.append(item['_intermediate'])
-
-    # Deduplicate and merge results
-    deduplicated_data = deduplicate_entries(databases_data['Expansion_issue'])
-    deduplicated_data_merged = merge_to_key_value_pairs(
-        deduplicated_data,
-        'Query_small',
-        'Result_small'
-    )
-
-    # Restructure for Verify/Retrieve_Summary compatibility
-    keys, val = [], []
-    for item in deduplicated_data_merged:
-        for items_key, items_value in item.items():
-            keys.append(items_key)
-            val.append(items_value)
-
-    send_verify = []
-    for i, j in zip(keys, val, strict=False):
-        if j!=['']:
-            send_verify.append({
-                "Query_small": i,
-                "Answer_Small": j
-            })
-
-    dup_databases = {
-        "Query": original,
-        "Expansion_issue": send_verify,
-        "_intermediate_outputs": intermediate_outputs  # Preserve intermediate outputs
-    }
-
-    logger.info(f"Collected {len(intermediate_outputs)} intermediate outputs from search results")
-    return {'retrieve':dup_databases}
-
-
-
-
-async def retrieve(state: ReadState) -> ReadState:
-    # 从state中获取end_user_id
-    import time
-    start=time.time()
-    problem_extension = state.get('problem_extension', '')['context']
-    storage_type = state.get('storage_type', '')
-    user_rag_memory_id = state.get('user_rag_memory_id', '')
-    end_user_id = state.get('end_user_id', '')
-    memory_config = state.get('memory_config', None)
-    original = state.get('data', '')
-    problem_list = []
-    for key, values in problem_extension.items():
-        for data in values:
-            problem_list.append(data)
-    logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
-    databases_anser = []
-
-    async def get_llm_info():
-        with get_db_context() as db:  # 使用同步数据库上下文管理器
-            config_service = MemoryConfigService(db)
-            return await llm_infomation(state)
-    llm_config = await get_llm_info()
-    api_key_obj = llm_config.api_keys[0]
-    api_key = api_key_obj.api_key
-    api_base = api_key_obj.api_base
-    model_name = api_key_obj.model_name
-    llm = ChatOpenAI(
-        model=model_name,
-        api_key=api_key,
-        base_url=api_base,
-        temperature=0.2,
-    )
-
-    time_retrieval_tool = create_time_retrieval_tool(end_user_id)
-    search_params = { "end_user_id": end_user_id, "return_raw_results": True }
-    hybrid_retrieval=create_hybrid_retrieval_tool_sync(memory_config, **search_params)
-    agent = create_agent(
-        llm,
-        tools=[time_retrieval_tool,hybrid_retrieval],
-        system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的end_user_id是: {end_user_id}"
-    )
-
-    # 创建异步任务处理单个问题
-    import asyncio
-
-    # 在模块级别定义信号量，限制最大并发数
-    SEMAPHORE = asyncio.Semaphore(5)  # 限制最多5个并发数据库操作
-
-    async def process_question(idx, question):
-        async with SEMAPHORE:  # 限制并发
-            try:
-                if storage_type == "rag" and user_rag_memory_id:
-                    retrieval_knowledge, clean_content, cleaned_query, raw_results = await rag_knowledge(state, question)
-                else:
-                    cleaned_query = question
-                    # 使用 asyncio 在线程池中运行同步的 agent.invoke
-                    import asyncio
-                    response = await asyncio.get_event_loop().run_in_executor(
-                        None,
-                        lambda: agent.invoke({"messages": question})
-                    )
-                    tool_results = extract_tool_message_content(response)
-                    if tool_results == None:
-                        raw_results = []
-                        clean_content = ''
-                    else:
-                        raw_results = tool_results['content']
-                        clean_content = await clean_databases(raw_results)
-
-                        try:
-                            raw_results = raw_results['results']
-                        except Exception:
-                            raw_results = []
-
-                return {
-                    "Query_small": cleaned_query,
-                    "Result_small": clean_content,
-                    "_intermediate": {
-                        "type": "search_result",
-                        "query": cleaned_query,
-                        "raw_results": raw_results,
-                        "index": idx + 1,
-                        "total": len(problem_list)
-                    }
-                }
-
-            except Exception as e:
-                logger.error(
-                    f"Retrieve: hybrid_search failed for question '{question}': {e}",
-                    exc_info=True
-                )
-                # Return empty result for this question
-                return {
-                    "Query_small": question,
-                    "Result_small": "",
-                    "_intermediate": {
-                        "type": "search_result",
-                        "query": question,
-                        "raw_results": [],
-                        "index": idx + 1,
-                        "total": len(problem_list)
-                    }
-                }
-
-    # 并发处理所有问题
-    import asyncio
-    tasks = [process_question(idx, question) for idx, question in enumerate(problem_list)]
-    databases_anser = await asyncio.gather(*tasks)
-    databases_data = {
-        "Query": original,
-        "Expansion_issue": databases_anser
-    }
-
-    # Collect intermediate outputs before deduplication
-    intermediate_outputs = []
-    for item in databases_anser:
-        if '_intermediate' in item:
-            intermediate_outputs.append(item['_intermediate'])
-
-    # Deduplicate and merge results
-    deduplicated_data = deduplicate_entries(databases_data['Expansion_issue'])
-    deduplicated_data_merged = merge_to_key_value_pairs(
-        deduplicated_data,
-        'Query_small',
-        'Result_small'
-    )
-
-    # Restructure for Verify/Retrieve_Summary compatibility
-    keys, val = [], []
-    for item in deduplicated_data_merged:
-        for items_key, items_value in item.items():
-            keys.append(items_key)
-            val.append(items_value)
-
-    send_verify = []
-    for i, j in zip(keys, val, strict=False):
-        if j != ['']:
-            send_verify.append({
-                "Query_small": i,
-                "Answer_Small": j
-            })
-
-    dup_databases = {
-        "Query": original,
-        "Expansion_issue": send_verify,
-        "_intermediate_outputs": intermediate_outputs  # Preserve intermediate outputs
-    }
-    # with open('retrieve_text.json', 'w') as f:
-    #     json.dump(dup_databases, f, indent=4)
-    logger.info(f"Collected {len(intermediate_outputs)} intermediate outputs from search results")
-    return {'retrieve': dup_databases}
-
-

api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py

@@ -1,320 +0,0 @@
-
-
-import os
-import time
-
-from app.core.logging_config import get_agent_logger, log_time
-from app.core.memory.agent.models.summary_models import (
-    RetrieveSummaryResponse,
-    SummaryResponse,
-)
-from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
-from app.core.memory.agent.services.search_service import SearchService
-from app.core.memory.agent.utils.llm_tools import (
-    PROJECT_ROOT_,
-    ReadState,
-)
-from app.core.memory.agent.utils.redis_tool import store
-from app.core.memory.agent.utils.session_tools import SessionService
-from app.core.memory.agent.utils.template_tools import TemplateService
-from app.db import get_db
-
-template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
-logger = get_agent_logger(__name__)
-db_session = next(get_db())
-
-class SummaryNodeService(LLMServiceMixin):
-    """总结节点服务类"""
-    
-    def __init__(self):
-        super().__init__()
-        self.template_service = TemplateService(template_root)
-
-# 创建全局服务实例
-summary_service = SummaryNodeService()
-
-async def summary_history(state: ReadState) -> ReadState:
-    end_user_id = state.get("end_user_id", '')
-    history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id)
-    return history
-
-async def summary_llm(state: ReadState, history, retrieve_info, template_name, operation_name, response_model,search_mode) -> str:
-    """
-    增强的summary_llm函数，包含更好的错误处理和数据验证
-    """
-    data = state.get("data", '')
-    
-    # 构建系统提示词
-    if str(search_mode) == "0":
-        system_prompt = await summary_service.template_service.render_template(
-            template_name=template_name,
-            operation_name=operation_name,
-            data=retrieve_info,
-            query=data
-        )
-    else:
-        system_prompt = await summary_service.template_service.render_template(
-            template_name=template_name,
-            operation_name=operation_name,
-            query=data,
-            history=history,
-            retrieve_info=retrieve_info
-        )
-    try:
-        # 使用优化的LLM服务进行结构化输出
-        structured = await summary_service.call_llm_structured(
-            state=state,
-            db_session=db_session,
-            system_prompt=system_prompt,
-            response_model=response_model,
-            fallback_value=None
-        )
-        # 验证结构化响应
-        if structured is None:
-            logger.warning(f"LLM返回None，使用默认回答")
-            return "信息不足，无法回答"
-        
-        # 根据操作类型提取答案
-        if operation_name == "summary":
-            aimessages = getattr(structured, 'query_answer', None) or "信息不足，无法回答"
-        else:
-            # 处理RetrieveSummaryResponse
-            if hasattr(structured, 'data') and structured.data:
-                aimessages = getattr(structured.data, 'query_answer', None) or "信息不足，无法回答"
-            else:
-                logger.warning(f"结构化响应缺少data字段")
-                aimessages = "信息不足，无法回答"
-        
-        # 验证答案不为空
-        if not aimessages or aimessages.strip() == "":
-            aimessages = "信息不足，无法回答"
-            
-        return aimessages
-        
-    except Exception as e:
-        logger.error(f"结构化输出失败: {e}", exc_info=True)
-        
-        # 尝试非结构化输出作为fallback
-        try:
-            logger.info("尝试非结构化输出作为fallback")
-            response = await summary_service.call_llm_simple(
-                state=state,
-                db_session=db_session,
-                system_prompt=system_prompt,
-                fallback_message="信息不足，无法回答"
-            )
-            
-            if response and response.strip():
-                # 简单清理响应
-                cleaned_response = response.strip()
-                # 移除可能的JSON标记
-                if cleaned_response.startswith('```'):
-                    lines = cleaned_response.split('\n')
-                    cleaned_response = '\n'.join(lines[1:-1])
-                
-                return cleaned_response
-            else:
-                return "信息不足，无法回答"
-                
-        except Exception as fallback_error:
-            logger.error(f"Fallback也失败: {fallback_error}")
-            return "信息不足，无法回答"
-
-async def summary_redis_save(state: ReadState,aimessages) -> ReadState:
-    data = state.get("data", '')
-    end_user_id = state.get("end_user_id", '')
-    await SessionService(store).save_session(
-        user_id=end_user_id,
-        query=data,
-        apply_id=end_user_id,
-        end_user_id=end_user_id,
-        ai_response=aimessages
-    )
-    await SessionService(store).cleanup_duplicates()
-    logger.info(f"sessionid: {aimessages} 写入成功")
-async def summary_prompt(state: ReadState,aimessages,raw_results) -> ReadState:
-    storage_type=state.get("storage_type",'')
-    user_rag_memory_id=state.get("user_rag_memory_id",'')
-    data=state.get("data", '')
-    input_summary = {
-        "status": "success",
-        "summary_result": aimessages,
-        "storage_type": storage_type,
-        "user_rag_memory_id": user_rag_memory_id,
-        "_intermediate": {
-            "type": "input_summary",
-            "title": "快速答案",
-            "summary": aimessages,
-            "query": data,
-            "raw_results": raw_results,
-            "search_mode": "quick_search",
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id
-        }
-    }
-    retrieve={
-        "status": "success",
-        "summary_result": aimessages,
-        "storage_type": storage_type,
-        "user_rag_memory_id": user_rag_memory_id,
-        "_intermediate": {
-            "type": "retrieval_summary",
-            "title":"快速检索",
-            "summary": aimessages,
-            "query": data,
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id
-        }
-    }
-
-    return input_summary,retrieve
-
-async def Input_Summary(state: ReadState) -> ReadState:
-    start=time.time()
-    storage_type=state.get("storage_type",'')
-    memory_config = state.get('memory_config', None)
-    user_rag_memory_id=state.get("user_rag_memory_id",'')
-    data=state.get("data", '')
-    end_user_id=state.get("end_user_id", '')
-    logger.info(f"Input_Summary: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
-    history = await summary_history( state)
-    search_params = {
-        "end_user_id": end_user_id,
-        "question": data,
-        "return_raw_results": True,
-        "include": ["summaries"]  # Only search summary nodes for faster performance
-    }
-
-    try:
-        retrieve_info, question, raw_results = await SearchService().execute_hybrid_search(**search_params, memory_config=memory_config)
-    except Exception as e:
-        logger.error( f"Input_Summary: hybrid_search failed, using empty results: {e}",  exc_info=True )
-        retrieve_info, question, raw_results = "", data, []
-
-
-    try:
-        # aimessages=await summary_llm(state,history,retrieve_info,'Retrieve_Summary_prompt.jinja2',
-        #                              'input_summary',RetrieveSummaryResponse)
-        # logger.info(f"快速答案总结==>>:{storage_type}--{user_rag_memory_id}--{aimessages}")
-        summary_result = await summary_prompt(state, retrieve_info, retrieve_info)
-        summary = summary_result[0]
-    except Exception as e:
-        logger.error( f"Input_Summary failed: {e}", exc_info=True )
-        summary= {
-            "status": "fail",
-            "summary_result": "信息不足，无法回答",
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id,
-            "error": str(e)
-        }
-    end = time.time()
-    try:
-        duration = end - start
-    except Exception:
-        duration = 0.0
-    log_time('检索', duration)
-    return {"summary":summary}
-
-async def Retrieve_Summary(state: ReadState)-> ReadState:
-    retrieve=state.get("retrieve", '')
-    history = await summary_history( state)
-    import json
-    with open("检索.json","w",encoding='utf-8') as f:
-        f.write(json.dumps(retrieve, indent=4, ensure_ascii=False))
-    retrieve=retrieve.get("Expansion_issue", [])
-    start=time.time()
-    retrieve_info_str=[]
-    for data in retrieve:
-        if data=='':
-            retrieve_info_str=''
-        else:
-            for key, value in data.items():
-                if key=='Answer_Small':
-                    for i in value:
-                        retrieve_info_str.append(i)
-    retrieve_info_str=list(set(retrieve_info_str))
-    retrieve_info_str='\n'.join(retrieve_info_str)
-
-    aimessages=await  summary_llm(state,history,retrieve_info_str,
-                                  'direct_summary_prompt.jinja2','retrieve_summary',RetrieveSummaryResponse,"1")
-    if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "":
-        await summary_redis_save(state, aimessages)
-    if aimessages == '':
-        aimessages = '信息不足，无法回答'
-    logger.info(f"Summary after retrieval: {aimessages}")
-    end = time.time()
-    try:
-        duration = end - start
-    except Exception:
-        duration = 0.0
-    log_time('Retrieval summary', duration)
-    
-    # 修复协程调用 - 先await，然后访问返回值
-    summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
-    summary = summary_result[1]
-    return {"summary":summary}
-
-
-async def Summary(state: ReadState)-> ReadState:
-    start=time.time()
-    query = state.get("data", '')
-    verify=state.get("verify", '')
-    verify_expansion_issue=verify.get("verified_data", '')
-    retrieve_info_str=''
-    for data in verify_expansion_issue:
-        for key, value in data.items():
-            if key=='answer_small':
-                for i in value:
-                    retrieve_info_str+=i+'\n'
-    history=await summary_history(state)
-
-    data = {
-        "query": query,
-        "history": history,
-        "retrieve_info": retrieve_info_str
-    }
-    aimessages=await  summary_llm(state,history,data,
-                                  'summary_prompt.jinja2','summary',SummaryResponse,0)
-
-    if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "":
-        await summary_redis_save(state, aimessages)
-    if aimessages == '':
-        aimessages = '信息不足，无法回答'
-    try:
-        duration = time.time() - start
-    except Exception:
-        duration = 0.0
-    log_time('Retrieval summary', duration)
-
-    # 修复协程调用 - 先await，然后访问返回值
-    summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
-    summary = summary_result[1]
-    return {"summary":summary}
-
-async def Summary_fails(state: ReadState)-> ReadState:
-    storage_type=state.get("storage_type", '')
-    user_rag_memory_id=state.get("user_rag_memory_id", '')
-    history = await summary_history(state)
-    query = state.get("data", '')
-    verify = state.get("verify", '')
-    verify_expansion_issue = verify.get("verified_data", '')
-    retrieve_info_str = ''
-    for data in verify_expansion_issue:
-        for key, value in data.items():
-            if key == 'answer_small':
-                for i in value:
-                    retrieve_info_str += i + '\n'
-    data = {
-        "query": query,
-        "history": history,
-        "retrieve_info": retrieve_info_str
-    }
-    aimessages = await  summary_llm(state, history, data,
-                                 'fail_summary_prompt.jinja2', 'summary', SummaryResponse, 0)
-    result= {
-        "status": "success",
-        "summary_result": aimessages,
-        "storage_type": storage_type,
-        "user_rag_memory_id": user_rag_memory_id
-    }
-    return {"summary":result}

api/app/core/memory/agent/langgraph_graph/nodes/tool_node.py

@@ -0,0 +1,234 @@
+"""
+Tool execution node for LangGraph workflow.
+
+This module provides the ToolExecutionNode class which wraps tool execution
+with parameter transformation logic using the ParameterBuilder service.
+"""
+
+import logging
+import time
+from typing import Any, Callable, Dict
+
+from app.core.memory.agent.langgraph_graph.state.extractors import (
+    extract_content_payload,
+    extract_tool_call_id,
+)
+from app.core.memory.agent.mcp_server.services.parameter_builder import ParameterBuilder
+from app.schemas.memory_config_schema import MemoryConfig
+from langchain_core.messages import AIMessage
+from langgraph.prebuilt import ToolNode
+
+logger = logging.getLogger(__name__)
+
+
+class ToolExecutionNode:
+    """
+    Custom LangGraph node that wraps tool execution with parameter transformation.
+    
+    This node extracts content from previous tool results, transforms parameters
+    based on tool type using ParameterBuilder, and invokes the tool with the
+    correct argument structure.
+    
+    Attributes:
+        tool_node: LangGraph ToolNode wrapping the actual tool
+        id: Node identifier for message IDs
+        tool_name: Name of the tool being executed
+        namespace: Namespace for session management
+        search_switch: Search routing parameter
+        apply_id: Application identifier
+        group_id: Group identifier
+        parameter_builder: Service for building tool-specific arguments
+        memory_config: MemoryConfig object containing all configuration
+    """
+
+    def __init__(
+        self,
+        tool: Callable,
+        node_id: str,
+        namespace: str,
+        search_switch: str,
+        apply_id: str,
+        group_id: str,
+        parameter_builder: ParameterBuilder,
+        storage_type: str,
+        user_rag_memory_id: str,
+        memory_config: MemoryConfig,
+    ):
+        """
+        Initialize the tool execution node.
+        
+        Args:
+            tool: The tool function to execute
+            node_id: Identifier for this node (used in message IDs)
+            namespace: Namespace for session management
+            search_switch: Search routing parameter
+            apply_id: Application identifier
+            group_id: Group identifier
+            parameter_builder: Service for building tool-specific arguments
+            storage_type: Storage type for the workspace
+            user_rag_memory_id: User RAG memory identifier
+            memory_config: MemoryConfig object containing all configuration
+        """
+        self.tool_node = ToolNode([tool])
+        self.id = node_id
+        self.tool_name = tool.name if hasattr(tool, 'name') else str(tool)
+        self.namespace = namespace
+        self.search_switch = search_switch
+        self.apply_id = apply_id
+        self.group_id = group_id
+        self.parameter_builder = parameter_builder
+        self.storage_type = storage_type
+        self.user_rag_memory_id = user_rag_memory_id
+        self.memory_config = memory_config
+
+        logger.info(
+            f"[ToolExecutionNode] Initialized node '{self.id}' for tool '{self.tool_name}'"
+        )
+    
+    async def __call__(self, state: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Execute the tool with transformed parameters.
+        
+        This method:
+        1. Extracts the last message from state
+        2. Extracts tool call ID using state extractors
+        3. Extracts content payload using state extractors
+        4. Builds tool arguments using parameter builder
+        5. Constructs AIMessage with tool_calls
+        6. Invokes the tool and returns the result
+        
+        Args:
+            state: LangGraph state dictionary
+            
+        Returns:
+            Updated state with tool result in messages
+        """
+        messages = state.get("messages", [])
+        logger.debug( self.tool_name)
+        
+        if not messages:
+            logger.warning(f"[ToolExecutionNode] {self.id} - No messages in state")
+            return {"messages": [AIMessage(content="Error: No messages in state")]}
+        
+        last_message = messages[-1]
+        logger.debug(
+            f"[ToolExecutionNode] {self.id} - Processing message at {time.time()}"
+        )
+        
+        try:
+            # Extract tool call ID using state extractors
+            tool_call_id = extract_tool_call_id(last_message)
+            logger.debug(f"[ToolExecutionNode] {self.id} - Extracted tool_call_id: {tool_call_id}")
+            
+        except ValueError as e:
+            logger.error(
+                f"[ToolExecutionNode] {self.id} - Failed to extract tool call ID: {e}"
+            )
+            return {"messages": [AIMessage(content=f"Error: {str(e)}")]}
+        
+        try:
+            # Extract content payload using state extractors
+            content = extract_content_payload(last_message)
+            logger.debug(
+                f"[ToolExecutionNode] {self.id} - Extracted content type: {type(content)}, content_keys: {list(content.keys()) if isinstance(content, dict) else 'N/A'}"
+            )
+            # Log raw message content for debugging
+            if hasattr(last_message, 'content'):
+                raw = last_message.content
+                logger.debug(f"[ToolExecutionNode] {self.id} - Raw message content (first 500 chars): {str(raw)[:500]}")
+            
+        except Exception as e:
+            logger.error(
+                f"[ToolExecutionNode] {self.id} - Failed to extract content: {e}",
+                exc_info=True
+            )
+            content = {}
+        
+        try:
+            # Build tool arguments using parameter builder
+            tool_args = self.parameter_builder.build_tool_args(
+                tool_name=self.tool_name,
+                content=content,
+                tool_call_id=tool_call_id,
+                search_switch=self.search_switch,
+                apply_id=self.apply_id,
+                group_id=self.group_id,
+                memory_config=self.memory_config,
+                storage_type=self.storage_type,
+                user_rag_memory_id=self.user_rag_memory_id,
+            )
+            logger.debug(
+                f"[ToolExecutionNode] {self.id} - Built tool args with keys: {list(tool_args.keys())}"
+            )
+            
+        except Exception as e:
+            logger.error(
+                f"[ToolExecutionNode] {self.id} - Failed to build tool args: {e}",
+                exc_info=True
+            )
+            return {"messages": [AIMessage(content=f"Error building arguments: {str(e)}")]}
+        
+        # Construct tool input message
+        tool_input = {
+            "messages": [
+                AIMessage(
+                    content="",
+                    tool_calls=[{
+                        "name": self.tool_name,
+                        "args": tool_args,
+                        "id": f"{self.id}_{tool_call_id}",
+                    }]
+                )
+            ]
+        }
+        
+        try:
+            # Invoke the tool
+            result = await self.tool_node.ainvoke(tool_input)
+            
+            logger.debug(
+                f"[ToolExecutionNode] {self.id} - Tool execution completed"
+            )
+            
+            # Check for error in tool response
+            error_entry = None
+            if result and "messages" in result:
+                for msg in result["messages"]:
+                    if hasattr(msg, 'content'):
+                        try:
+                            import json
+                            content = msg.content
+                            if isinstance(content, str):
+                                parsed = json.loads(content)
+                                if isinstance(parsed, dict) and "error" in parsed:
+                                    error_msg = parsed["error"]
+                                    logger.warning(
+                                        f"[ToolExecutionNode] {self.id} - Tool returned error: {error_msg}"
+                                    )
+                                    error_entry = {"tool": self.tool_name, "error": error_msg, "node_id": self.id}
+                        except (json.JSONDecodeError, TypeError):
+                            pass
+            
+            # Return result with error tracking if error was found
+            if error_entry:
+                result["errors"] = [error_entry]
+            
+            return result
+            
+        except Exception as e:
+            logger.error(
+                f"[ToolExecutionNode] {self.id} - Tool execution failed: {e}",
+                exc_info=True
+            )
+            # Track error in state and return error message
+            from langchain_core.messages import ToolMessage
+            error_entry = {"tool": self.tool_name, "error": str(e), "node_id": self.id}
+            return {
+                "messages": [
+                    ToolMessage(
+                        content=f"Error executing tool: {str(e)}",
+                        tool_call_id=f"{self.id}_{tool_call_id}"
+                    )
+                ],
+                "errors": [error_entry]
+            }

api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py

@@ -1,155 +0,0 @@
-import os
-from app.core.logging_config import get_agent_logger
-from app.db import get_db
-
-from app.core.memory.agent.models.verification_models import VerificationResult
-from app.core.memory.agent.utils.llm_tools import (
-    PROJECT_ROOT_,
-    ReadState,
-)
-from app.core.memory.agent.utils.redis_tool import store
-from app.core.memory.agent.utils.session_tools import SessionService
-from app.core.memory.agent.utils.template_tools import TemplateService
-from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
-
-template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
-db_session = next(get_db())
-logger = get_agent_logger(__name__)
-
-class VerificationNodeService(LLMServiceMixin):
-    """验证节点服务类"""
-    
-    def __init__(self):
-        super().__init__()
-        self.template_service = TemplateService(template_root)
-
-# 创建全局服务实例
-verification_service = VerificationNodeService()
-
-async def Verify_prompt(state: ReadState, messages_deal: VerificationResult):
-    """处理验证结果并生成输出格式"""
-    storage_type = state.get('storage_type', '')
-    user_rag_memory_id = state.get('user_rag_memory_id', '')
-    data = state.get('data', '')
-    
-    # 将 VerificationItem 对象转换为字典列表
-    verified_data = []
-    if messages_deal.expansion_issue:
-        for item in messages_deal.expansion_issue:
-            if hasattr(item, 'model_dump'):
-                verified_data.append(item.model_dump())
-            elif isinstance(item, dict):
-                verified_data.append(item)
-    
-    Verify_result = {
-        "status": messages_deal.split_result,
-        "verified_data": verified_data,
-        "storage_type": storage_type,
-        "user_rag_memory_id": user_rag_memory_id,
-        "_intermediate": {
-            "type": "verification",
-            "title": "Data Verification",
-            "result": messages_deal.split_result,
-            "reason": messages_deal.reason or "验证完成",
-            "query": messages_deal.query,
-            "verified_count": len(verified_data),
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id
-        }
-    }
-    return Verify_result
-async def Verify(state: ReadState):
-    logger.info("=== Verify 节点开始执行 ===")
-    try:
-        content = state.get('data', '')
-        end_user_id = state.get('end_user_id', '')
-        memory_config = state.get('memory_config', None)
-        
-        logger.info(f"Verify: content={content[:50] if content else 'empty'}..., end_user_id={end_user_id}")
-
-        history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id)
-        logger.info(f"Verify: 获取历史记录完成，history length={len(history)}")
-
-        retrieve = state.get("retrieve", {})
-        logger.info(f"Verify: retrieve data type={type(retrieve)}, keys={retrieve.keys() if isinstance(retrieve, dict) else 'N/A'}")
-        
-        retrieve_expansion = retrieve.get("Expansion_issue", []) if isinstance(retrieve, dict) else []
-        logger.info(f"Verify: Expansion_issue length={len(retrieve_expansion)}")
-        
-        messages = {
-            "Query": content,
-            "Expansion_issue": retrieve_expansion
-        }
-
-        logger.info("Verify: 开始渲染模板")
-        
-        # 生成 JSON schema 以指导 LLM 输出正确格式
-        json_schema = VerificationResult.model_json_schema()
-        
-        system_prompt = await verification_service.template_service.render_template(
-            template_name='split_verify_prompt.jinja2',
-            operation_name='split_verify_prompt',
-            history=history,
-            sentence=messages,
-            json_schema=json_schema
-        )
-        logger.info(f"Verify: 模板渲染完成，prompt length={len(system_prompt)}")
-        
-        # 使用优化的LLM服务，添加超时保护
-        logger.info("Verify: 开始调用 LLM")
-        try:
-            # 添加 asyncio.wait_for 超时包裹，防止无限等待
-            # 超时时间设置为 150 秒（比 LLM 配置的 120 秒稍长）
-            import asyncio
-            structured = await asyncio.wait_for(
-                verification_service.call_llm_structured(
-                    state=state,
-                    db_session=db_session,
-                    system_prompt=system_prompt,
-                    response_model=VerificationResult,
-                    fallback_value={
-                        "query": content,
-                        "history": history if isinstance(history, list) else [],
-                        "expansion_issue": [],
-                        "split_result": "failed",
-                        "reason": "验证失败或超时"
-                    }
-                ),
-                timeout=150.0  # 150秒超时
-            )
-            logger.info(f"Verify: LLM 调用完成，result={structured}")
-        except asyncio.TimeoutError:
-            logger.error("Verify: LLM 调用超时（150秒），使用 fallback 值")
-            structured = VerificationResult(
-                query=content,
-                history=history if isinstance(history, list) else [],
-                expansion_issue=[],
-                split_result="failed",
-                reason="LLM调用超时"
-            )
-        
-        result = await Verify_prompt(state, structured)
-        logger.info("=== Verify 节点执行完成 ===")
-        return {"verify": result}
-        
-    except Exception as e:
-        logger.error(f"Verify 节点执行失败: {e}", exc_info=True)
-        # 返回失败的验证结果
-        return {
-            "verify": {
-                "status": "failed",
-                "verified_data": [],
-                "storage_type": state.get('storage_type', ''),
-                "user_rag_memory_id": state.get('user_rag_memory_id', ''),
-                "_intermediate": {
-                    "type": "verification",
-                    "title": "Data Verification",
-                    "result": "failed",
-                    "reason": f"验证过程出错: {str(e)}",
-                    "query": state.get('data', ''),
-                    "verified_count": 0,
-                    "storage_type": state.get('storage_type', ''),
-                    "user_rag_memory_id": state.get('user_rag_memory_id', '')
-                }
-            }
-        }

api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py

@@ -1,57 +0,0 @@
-from app.core.memory.agent.utils.llm_tools import WriteState
-from app.core.memory.agent.utils.write_tools import write
-from app.core.logging_config import get_agent_logger
-
-logger = get_agent_logger(__name__)
-
-
-async def write_node(state: WriteState) -> WriteState:
-    """
-        Write data to the database/file system.
-
-        Args:
-            state: WriteState containing messages, end_user_id, memory_config, and language
-
-        Returns:
-            dict: Contains 'write_result' with status and data fields
-        """
-    messages = state.get('messages', [])
-    end_user_id = state.get('end_user_id', '')
-    memory_config = state.get('memory_config', '')
-    language = state.get('language', 'zh')  # 默认中文
-
-    # Convert LangChain messages to structured format expected by write()
-    structured_messages = []
-    for msg in messages:
-        if hasattr(msg, 'type') and hasattr(msg, 'content'):
-            # Map LangChain message types to role names
-            role = 'user' if msg.type == 'human' else 'assistant' if msg.type == 'ai' else msg.type
-            structured_messages.append({
-                "role": role,
-                "content": msg.content  # content is now guaranteed to be a string
-            })
-
-    try:
-        result = await write(
-            messages=structured_messages,
-            end_user_id=end_user_id,
-            memory_config=memory_config,
-            language=language,
-        )
-        logger.info(f"Write completed successfully! Config: {memory_config.config_name}")
-
-        write_result = {
-            "status": "success",
-            "data": structured_messages,
-            "config_id": memory_config.config_id,
-            "config_name": memory_config.config_name,
-        }
-        return {"write_result": write_result}
-
-    except Exception as e:
-        logger.error(f"Data_write failed: {e}", exc_info=True)
-        write_result = {
-            "status": "error",
-            "message": str(e),
-        }
-        return {"write_result": write_result}

api/app/core/memory/agent/langgraph_graph/read_graph.py

@@ -1,177 +1,469 @@
-#!/usr/bin/env python3
+import json
+import os
+import re
+import time
+import warnings
 from contextlib import asynccontextmanager
+from typing import Literal
 
-from langchain_core.messages import HumanMessage
-from langgraph.constants import START, END
+from app.core.logging_config import get_agent_logger
+from app.core.memory.agent.langgraph_graph.nodes import (
+    ToolExecutionNode,
+    create_input_message,
+)
+from app.core.memory.agent.mcp_server.services.parameter_builder import ParameterBuilder
+from app.core.memory.agent.utils.llm_tools import COUNTState, ReadState
+from app.core.memory.agent.utils.multimodal import MultimodalProcessor
+from app.schemas.memory_config_schema import MemoryConfig
+from dotenv import load_dotenv
+from langchain_core.messages import AIMessage
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.constants import END, START
 from langgraph.graph import StateGraph
+from langgraph.prebuilt import ToolNode
+
+logger = get_agent_logger(__name__)
+
+warnings.filterwarnings("ignore", category=RuntimeWarning)
+load_dotenv()
+redishost=os.getenv("REDISHOST")
+redisport=os.getenv('REDISPORT')
+redisdb=os.getenv('REDISDB')
+redispassword=os.getenv('REDISPASSWORD')
+counter = COUNTState(limit=3)
+
+# Update loop count in workflow
+async def update_loop_count(state):
+    """Update loop counter"""
+    current_count = state.get("loop_count", 0)
+    return {"loop_count": current_count + 1}
 
 
-from app.db import get_db
-from app.services.memory_config_service import MemoryConfigService
+def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]:
+    messages = state["messages"]
 
-from app.core.memory.agent.utils.llm_tools import ReadState
-from app.core.memory.agent.langgraph_graph.nodes.data_nodes import content_input_node
-from app.core.memory.agent.langgraph_graph.nodes.problem_nodes import (
-    Split_The_Problem,
-    Problem_Extension,
-)
-from app.core.memory.agent.langgraph_graph.nodes.retrieve_nodes import (
-    retrieve,
-)
-from app.core.memory.agent.langgraph_graph.nodes.summary_nodes import (
-    Input_Summary,
-    Retrieve_Summary,
-    Summary_fails,
-    Summary,
-)
-from app.core.memory.agent.langgraph_graph.nodes.verification_nodes import Verify
-from app.core.memory.agent.langgraph_graph.routing.routers import (
-    Split_continue,
-    Retrieve_continue,
-    Verify_continue,
-)
+    # Add boundary check
+    if not messages:
+        return END
+    counter.add(1)  # Increment by 1
 
+    loop_count = counter.get_total()
+    logger.debug(f"[should_continue] Current loop count: {loop_count}")
+
+    last_message = messages[-1]
+    last_message_str = str(last_message).replace('\\', '')
+    status_tools = re.findall(r'"split_result": "(.*?)"', last_message_str)
+    logger.debug(f"Status tools: {status_tools}")
+
+    if "success" in status_tools:
+        counter.reset()
+        return "Summary"
+    elif "failed" in status_tools:
+        if loop_count < 2:  # Maximum loop count is 3
+            return "content_input"
+        else:
+            counter.reset()
+            return "Summary_fails"
+    else:
+        # Add default return value to avoid returning None
+        counter.reset()
+        return "Summary"  # Default based on business requirements
+
+
+def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]:
+    """
+    Determine routing based on search_switch value.
+
+    Args:
+        state: State dictionary containing search_switch
+
+    Returns:
+        Next node to execute
+    """
+    # Direct dictionary access instead of regex parsing
+    search_switch = state.get("search_switch")
+
+    # Handle case where search_switch might be in messages
+    if search_switch is None and "messages" in state:
+        messages = state.get("messages", [])
+        if messages:
+            last_message = messages[-1]
+            # Try to extract from tool_calls args
+            if hasattr(last_message, "tool_calls") and last_message.tool_calls:
+                for tool_call in last_message.tool_calls:
+                    if isinstance(tool_call, dict) and "args" in tool_call:
+                        search_switch = tool_call["args"].get("search_switch")
+                        break
+
+    # Convert to string for comparison if needed
+    if search_switch is not None:
+        search_switch = str(search_switch)
+        if search_switch == '0':
+            return 'Verify'
+        elif search_switch == '1':
+            return 'Retrieve_Summary'
+
+    # Add default return value to avoid returning None
+    return 'Retrieve_Summary'  # Default based on business logic
+
+
+def Split_continue(state) -> Literal["Split_The_Problem", "Input_Summary"]:
+    """
+    Determine routing based on search_switch value.
+
+    Args:
+        state: State dictionary containing search_switch
+
+    Returns:
+        Next node to execute
+    """
+    logger.debug(f"Split_continue state: {state}")
+
+    # Direct dictionary access instead of regex parsing
+    search_switch = state.get("search_switch")
+
+    # Handle case where search_switch might be in messages
+    if search_switch is None and "messages" in state:
+        messages = state.get("messages", [])
+        if messages:
+            last_message = messages[-1]
+            # Try to extract from tool_calls args
+            if hasattr(last_message, "tool_calls") and last_message.tool_calls:
+                for tool_call in last_message.tool_calls:
+                    if isinstance(tool_call, dict) and "args" in tool_call:
+                        search_switch = tool_call["args"].get("search_switch")
+                        break
+
+    # Convert to string for comparison if needed
+    if search_switch is not None:
+        search_switch = str(search_switch)
+        if search_switch == '2':
+            return 'Input_Summary'
+    return 'Split_The_Problem'  # Default case
+
+
+class ProblemExtensionNode:
+    def __init__(self, tool, id, namespace, search_switch, apply_id, group_id, storage_type="", user_rag_memory_id=""):
+        self.tool_node = ToolNode([tool])
+        self.id = id
+        self.tool_name = tool.name if hasattr(tool, 'name') else str(tool)
+        self.namespace = namespace
+        self.search_switch = search_switch
+        self.apply_id = apply_id
+        self.group_id = group_id
+        self.storage_type = storage_type
+        self.user_rag_memory_id = user_rag_memory_id
+
+    async def __call__(self, state):
+        messages = state["messages"]
+        last_message = messages[-1] if messages else ""
+        logger.debug(f"ProblemExtensionNode {self.id} - Current time: {time.time()} - Message: {last_message}")
+        if self.tool_name == 'Input_Summary':
+            tool_call = re.findall("'id': '(.*?)'", str(last_message))[0]
+        else:
+            tool_call = str(re.findall(r"tool_call_id=.*?'(.*?)'", str(last_message))[0]).replace('\\', '').split('_id')[1]
+        
+        # Try to extract actual content payload from previous tool result
+        raw_msg = last_message.content if hasattr(last_message, 'content') else str(last_message)
+        extracted_payload = None
+        # Capture ToolMessage content field (supports single/double quotes), avoid greedy matching
+        m = re.search(r"content=(?:\"|\')(.*?)(?:\"|\'),\s*name=", raw_msg, flags=re.S)
+        if m:
+            extracted_payload = m.group(1)
+        else:
+            # Fallback: use raw string directly
+            extracted_payload = raw_msg
+
+        # Try to parse content as JSON first
+        try:
+            content = json.loads(extracted_payload)
+        except Exception:
+            # Try to extract JSON fragment from text and parse
+            parsed = None
+            candidates = re.findall(r"[\[{].*[\]}]", extracted_payload, flags=re.S)
+            for cand in candidates:
+                try:
+                    parsed = json.loads(cand)
+                    break
+                except Exception:
+                    continue
+            # If still fails, use raw string as content
+            content = parsed if parsed is not None else extracted_payload
+
+        # Build correct parameters based on tool name
+        tool_args = {}
+
+        if self.tool_name == "Verify":
+            # Verify tool requires context and usermessages parameters
+            if isinstance(content, dict):
+                tool_args["context"] = content
+            else:
+                tool_args["context"] = {"content": content}
+            tool_args["usermessages"] = str(tool_call)
+            tool_args["apply_id"] = str(self.apply_id)
+            tool_args["group_id"] = str(self.group_id)
+        elif self.tool_name == "Retrieve":
+            # Retrieve tool requires context and usermessages parameters
+            if isinstance(content, dict):
+                tool_args["context"] = content
+            else:
+                tool_args["context"] = {"content": content}
+            tool_args["usermessages"] = str(tool_call)
+            tool_args["search_switch"] = str(self.search_switch)
+            tool_args["apply_id"] = str(self.apply_id)
+            tool_args["group_id"] = str(self.group_id)
+        elif self.tool_name == "Summary":
+            # Summary tool requires string type context parameter
+            if isinstance(content, dict):
+                # Convert dict to JSON string
+                tool_args["context"] = json.dumps(content, ensure_ascii=False)
+            else:
+                tool_args["context"] = str(content)
+            tool_args["usermessages"] = str(tool_call)
+            tool_args["apply_id"] = str(self.apply_id)
+            tool_args["group_id"] = str(self.group_id)
+        elif self.tool_name == "Summary_fails":
+            # Summary_fails tool requires string type context parameter
+            if isinstance(content, dict):
+                # Convert dict to JSON string
+                tool_args["context"] = json.dumps(content, ensure_ascii=False)
+            else:
+                tool_args["context"] = str(content)
+            tool_args["usermessages"] = str(tool_call)
+            tool_args["apply_id"] = str(self.apply_id)
+            tool_args["group_id"] = str(self.group_id)
+        elif self.tool_name == 'Input_Summary':
+            tool_args["context"] = str(last_message)
+            tool_args["usermessages"] = str(tool_call)
+            tool_args["search_switch"] = str(self.search_switch)
+            tool_args["apply_id"] = str(self.apply_id)
+            tool_args["group_id"] = str(self.group_id)
+            tool_args["storage_type"] = getattr(self, 'storage_type', "")
+            tool_args["user_rag_memory_id"] = getattr(self, 'user_rag_memory_id', "")
+        elif self.tool_name == 'Retrieve_Summary':
+            # Retrieve_Summary expects dict directly, not JSON string
+            # content might be a JSON string, try to parse it
+            if isinstance(content, str):
+                try:
+                    parsed_content = json.loads(content)
+                    # Check if it has a "context" key
+                    if isinstance(parsed_content, dict) and "context" in parsed_content:
+                        tool_args["context"] = parsed_content["context"]
+                    else:
+                        tool_args["context"] = parsed_content
+                except json.JSONDecodeError:
+                    # If parsing fails, wrap the string
+                    tool_args["context"] = {"content": content}
+            elif isinstance(content, dict):
+                # Check if content has a "context" key that needs unwrapping
+                if "context" in content:
+                    tool_args["context"] = content["context"]
+                else:
+                    tool_args["context"] = content
+            else:
+                tool_args["context"] = {"content": str(content)}
+
+            tool_args["usermessages"] = str(tool_call)
+            tool_args["apply_id"] = str(self.apply_id)
+            tool_args["group_id"] = str(self.group_id)
+        else:
+            # Other tools use context parameter
+            if isinstance(content, dict):
+                tool_args["context"] = content
+            else:
+                tool_args["context"] = {"content": content}
+            tool_args["usermessages"] = str(tool_call)
+            tool_args["apply_id"] = str(self.apply_id)
+            tool_args["group_id"] = str(self.group_id)
+
+
+        tool_input = {
+            "messages": [
+                AIMessage(
+                    content="",
+                    tool_calls=[{
+                        "name": self.tool_name,
+                        "args": tool_args,
+                        "id": self.id + f"{tool_call}",
+                    }]
+                )
+            ]
+        }
+        result = await self.tool_node.ainvoke(tool_input)
+        result_text = str(result)
+
+        return {"messages": [AIMessage(content=result_text)]}
 
 
 @asynccontextmanager
-async def make_read_graph():
-    """创建并返回 LangGraph 工作流"""
-    try:
-        # Build workflow graph
-        workflow = StateGraph(ReadState)
-        workflow.add_node("content_input", content_input_node)
-        workflow.add_node("Split_The_Problem", Split_The_Problem)
-        workflow.add_node("Problem_Extension", Problem_Extension)
-        workflow.add_node("Input_Summary", Input_Summary)
-        # workflow.add_node("Retrieve", retrieve_nodes)
-        workflow.add_node("Retrieve", retrieve)
-        workflow.add_node("Verify", Verify)
-        workflow.add_node("Retrieve_Summary", Retrieve_Summary)
-        workflow.add_node("Summary", Summary)
-        workflow.add_node("Summary_fails", Summary_fails)
-        
-        # 添加边
-        workflow.add_edge(START, "content_input")
-        workflow.add_conditional_edges("content_input", Split_continue)
-        workflow.add_edge("Input_Summary", END)
-        workflow.add_edge("Split_The_Problem", "Problem_Extension")
-        workflow.add_edge("Problem_Extension", "Retrieve")
-        workflow.add_conditional_edges("Retrieve", Retrieve_continue)
-        workflow.add_edge("Retrieve_Summary", END)
-        workflow.add_conditional_edges("Verify", Verify_continue)
-        workflow.add_edge("Summary_fails", END)
-        workflow.add_edge("Summary", END)
-
-
-        '''-----'''
-        # workflow.add_edge("Retrieve", END)
-        
-        # 编译工作流
-        graph = workflow.compile()
-        yield graph
-        
-    except Exception as e:
-        print(f"创建工作流失败: {e}")
-        raise
-    finally:
-        print("工作流创建完成")
-
-async def main():
-    """主函数 - 运行工作流"""
-    message = "昨天有什么好看的电影"
-    end_user_id = '88a459f5_text09'  # 组ID
-    storage_type = 'neo4j'  # 存储类型
-    search_switch = '1'  # 搜索开关
-    user_rag_memory_id = 'wwwwwwww'  # 用户RAG记忆ID
-
-    # 获取数据库会话
-    db_session = next(get_db())
-    config_service = MemoryConfigService(db_session)
-    memory_config = config_service.load_memory_config(
-        config_id=17,  # 改为整数
-        service_name="MemoryAgentService"
+async def make_read_graph(namespace, tools, search_switch, apply_id, group_id, memory_config: MemoryConfig, storage_type=None, user_rag_memory_id=None):
+    """
+    Create a read graph workflow for memory operations.
+    
+    Args:
+        namespace: Namespace identifier
+        tools: MCP tools loaded from session
+        search_switch: Search mode switch ("0", "1", or "2")
+        apply_id: Application identifier
+        group_id: Group identifier
+        memory_config: MemoryConfig object containing all configuration
+        storage_type: Storage type (optional)
+        user_rag_memory_id: User RAG memory ID (optional)
+    """
+    memory = InMemorySaver()
+    tool = [i.name for i in tools]
+    logger.info(f"Initializing read graph with tools: {tool}")
+    logger.info(f"Using memory_config: {memory_config.config_name} (id={memory_config.config_id})")
+    
+    # Extract tool functions
+    Split_The_Problem_ = next((t for t in tools if t.name == "Split_The_Problem"), None)
+    Problem_Extension_ = next((t for t in tools if t.name == "Problem_Extension"), None)
+    Retrieve_ = next((t for t in tools if t.name == "Retrieve"), None)
+    Verify_ = next((t for t in tools if t.name == "Verify"), None)
+    Summary_ = next((t for t in tools if t.name == "Summary"), None)
+    Summary_fails_ = next((t for t in tools if t.name == "Summary_fails"), None)
+    Retrieve_Summary_ = next((t for t in tools if t.name == "Retrieve_Summary"), None)
+    Input_Summary_ = next((t for t in tools if t.name == "Input_Summary"), None)
+    
+    # Instantiate services
+    parameter_builder = ParameterBuilder()
+    multimodal_processor = MultimodalProcessor()
+    
+    # Create nodes using new modular components
+    Split_The_Problem_node = ToolNode([Split_The_Problem_])
+    
+    Problem_Extension_node = ToolExecutionNode(
+        tool=Problem_Extension_,
+        node_id="Problem_Extension_id",
+        namespace=namespace,
+        search_switch=search_switch,
+        apply_id=apply_id,
+        group_id=group_id,
+        parameter_builder=parameter_builder,
+        storage_type=storage_type,
+        user_rag_memory_id=user_rag_memory_id,
+        memory_config=memory_config,
     )
-    import time
-    start=time.time()
-    try:
-        async with make_read_graph() as graph:
-            config = {"configurable": {"thread_id": end_user_id}}
-            # 初始状态 - 包含所有必要字段
-            initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"end_user_id":end_user_id
-                             ,"storage_type":storage_type,"user_rag_memory_id":user_rag_memory_id,"memory_config":memory_config}
-            # 获取节点更新信息
-            _intermediate_outputs = []
-            summary = ''
-            
-            async for update_event in graph.astream(
-                    initial_state,
-                    stream_mode="updates",
-                    config=config
-            ):
-                for node_name, node_data in update_event.items():
-                    print(f"处理节点: {node_name}")
-                    
-                    # 处理不同Summary节点的返回结构
-                    if 'Summary' in node_name:
-                        if 'InputSummary' in node_data and 'summary_result' in node_data['InputSummary']:
-                            summary = node_data['InputSummary']['summary_result']
-                        elif 'RetrieveSummary' in node_data and 'summary_result' in node_data['RetrieveSummary']:
-                            summary = node_data['RetrieveSummary']['summary_result']
-                        elif 'summary' in node_data and 'summary_result' in node_data['summary']:
-                            summary = node_data['summary']['summary_result']
-                        elif 'SummaryFails' in node_data and 'summary_result' in node_data['SummaryFails']:
-                            summary = node_data['SummaryFails']['summary_result']
 
-                    spit_data = node_data.get('spit_data', {}).get('_intermediate', None)
-                    if spit_data and spit_data != [] and spit_data != {}:
-                        _intermediate_outputs.append(spit_data)
-                    
-                    # Problem_Extension 节点
-                    problem_extension = node_data.get('problem_extension', {}).get('_intermediate', None)
-                    if problem_extension and problem_extension != [] and problem_extension != {}:
-                        _intermediate_outputs.append(problem_extension)
-                    
-                    # Retrieve 节点
-                    retrieve_node = node_data.get('retrieve', {}).get('_intermediate_outputs', None)
-                    if retrieve_node and retrieve_node != [] and retrieve_node != {}:
-                        _intermediate_outputs.extend(retrieve_node)
-                    
-                    # Verify 节点
-                    verify_n = node_data.get('verify', {}).get('_intermediate', None)
-                    if verify_n and verify_n != [] and verify_n != {}:
-                        _intermediate_outputs.append(verify_n)
+    Retrieve_node = ToolExecutionNode(
+        tool=Retrieve_,
+        node_id="Retrieve_id",
+        namespace=namespace,
+        search_switch=search_switch,
+        apply_id=apply_id,
+        group_id=group_id,
+        parameter_builder=parameter_builder,
+        storage_type=storage_type,
+        user_rag_memory_id=user_rag_memory_id,
+        memory_config=memory_config,
+    )
 
-                    
-                    # Summary 节点
-                    summary_n = node_data.get('summary', {}).get('_intermediate', None)
-                    if summary_n and summary_n != [] and summary_n != {}:
-                        _intermediate_outputs.append(summary_n)
+    Verify_node = ToolExecutionNode(
+        tool=Verify_,
+        node_id="Verify_id",
+        namespace=namespace,
+        search_switch=search_switch,
+        apply_id=apply_id,
+        group_id=group_id,
+        parameter_builder=parameter_builder,
+        storage_type=storage_type,
+        user_rag_memory_id=user_rag_memory_id,
+        memory_config=memory_config,
+    )
+    
+    Summary_node = ToolExecutionNode(
+        tool=Summary_,
+        node_id="Summary_id",
+        namespace=namespace,
+        search_switch=search_switch,
+        apply_id=apply_id,
+        group_id=group_id,
+        parameter_builder=parameter_builder,
+        storage_type=storage_type,
+        user_rag_memory_id=user_rag_memory_id,
+        memory_config=memory_config,
+    )
 
-            # # 过滤掉空值
-            # _intermediate_outputs = [item for item in _intermediate_outputs if item and item != [] and item != {}]
-            #
-            # # 优化搜索结果
-            # print("=== 开始优化搜索结果 ===")
-            # optimized_outputs = merge_multiple_search_results(_intermediate_outputs)
-            # result=reorder_output_results(optimized_outputs)
-            # # 保存优化后的结果到文件
-            # with open('_intermediate_outputs_optimized.json', 'w', encoding='utf-8') as f:
-            #     import json
-            #     f.write(json.dumps(result, indent=4, ensure_ascii=False))
-            #
-            print(f"=== 最终摘要 ===")
-            print(summary)
-                
-    except Exception as e:
-        import traceback
-        traceback.print_exc()
+    Summary_fails_node = ToolExecutionNode(
+        tool=Summary_fails_,
+        node_id="Summary_fails_id",
+        namespace=namespace,
+        search_switch=search_switch,
+        apply_id=apply_id,
+        group_id=group_id,
+        parameter_builder=parameter_builder,
+        storage_type=storage_type,
+        user_rag_memory_id=user_rag_memory_id,
+        memory_config=memory_config,
+    )
 
-    end=time.time()
-    print(100*'y')
-    print(f"总耗时: {end-start}s")
-    print(100*'y')
+    Retrieve_Summary_node = ToolExecutionNode(
+        tool=Retrieve_Summary_,
+        node_id="Retrieve_Summary_id",
+        namespace=namespace,
+        search_switch=search_switch,
+        apply_id=apply_id,
+        group_id=group_id,
+        parameter_builder=parameter_builder,
+        storage_type=storage_type,
+        user_rag_memory_id=user_rag_memory_id,
+        memory_config=memory_config,
+    )
 
+    Input_Summary_node = ToolExecutionNode(
+        tool=Input_Summary_,
+        node_id="Input_Summary_id",
+        namespace=namespace,
+        search_switch=search_switch,
+        apply_id=apply_id,
+        group_id=group_id,
+        parameter_builder=parameter_builder,
+        storage_type=storage_type,
+        user_rag_memory_id=user_rag_memory_id,
+        memory_config=memory_config,
+    )
 
-if __name__ == "__main__":
-    import asyncio
-    asyncio.run(main())
+    async def content_input_node(state):
+        state_search_switch = state.get("search_switch", search_switch)
+
+        tool_name = "Input_Summary" if state_search_switch == '2' else "Split_The_Problem"
+        session_prefix = "input_summary_call_id" if state_search_switch == '2' else "split_call_id"
+
+        return await create_input_message(
+            state=state,
+            tool_name=tool_name,
+            session_id=f"{session_prefix}_{namespace}",
+            search_switch=search_switch,
+            apply_id=apply_id,
+            group_id=group_id,
+            multimodal_processor=multimodal_processor,
+            memory_config=memory_config,
+        )
+
+    
+    # Build workflow graph
+    workflow = StateGraph(ReadState)
+    workflow.add_node("content_input", content_input_node)
+    workflow.add_node("Split_The_Problem", Split_The_Problem_node)
+    workflow.add_node("Problem_Extension", Problem_Extension_node)
+    workflow.add_node("Retrieve", Retrieve_node)
+    workflow.add_node("Verify", Verify_node)
+    workflow.add_node("Summary", Summary_node)
+    workflow.add_node("Summary_fails", Summary_fails_node)
+    workflow.add_node("Retrieve_Summary", Retrieve_Summary_node)
+    workflow.add_node("Input_Summary", Input_Summary_node)
+
+    # Add edges using imported routers
+    workflow.add_edge(START, "content_input")
+    workflow.add_conditional_edges("content_input", Split_continue)
+    workflow.add_edge("Input_Summary", END)
+    workflow.add_edge("Split_The_Problem", "Problem_Extension")
+    workflow.add_edge("Problem_Extension", "Retrieve")
+    workflow.add_conditional_edges("Retrieve", Retrieve_continue)
+    workflow.add_edge("Retrieve_Summary", END)
+    workflow.add_conditional_edges("Verify", Verify_continue)
+    workflow.add_edge("Summary_fails", END)
+    workflow.add_edge("Summary", END)
+
+    graph = workflow.compile(checkpointer=memory)
+    yield graph

api/app/core/memory/agent/langgraph_graph/routing/__init__.py

@@ -0,0 +1,13 @@
+"""LangGraph routing logic."""
+
+from app.core.memory.agent.langgraph_graph.routing.routers import (
+    Verify_continue,
+    Retrieve_continue,
+    Split_continue,
+)
+
+__all__ = [
+    "Verify_continue",
+    "Retrieve_continue",
+    "Split_continue",
+]

api/app/core/memory/agent/langgraph_graph/routing/routers.py

@@ -1,61 +1,123 @@
+"""
+Routing functions for LangGraph conditional edges.
 
+This module provides routing functions that determine the next node to execute
+based on state values. All functions return Literal types for type safety.
+"""
+
+import logging
+import re
 from typing import Literal
 
-from app.core.logging_config import get_agent_logger
+from app.core.memory.agent.langgraph_graph.state.extractors import extract_search_switch
 from app.core.memory.agent.utils.llm_tools import ReadState, COUNTState
 
+logger = logging.getLogger(__name__)
 
-logger = get_agent_logger(__name__)
+# Global counter for Verify routing
 counter = COUNTState(limit=3)
-def Split_continue(state:ReadState) -> Literal["Split_The_Problem", "Input_Summary"]:
-    """
-    Determine routing based on search_switch value.
 
-    Args:
-        state: State dictionary containing search_switch
 
-    Returns:
-        Next node to execute
-    """
-    logger.debug(f"Split_continue state: {state}")
-    search_switch = state.get('search_switch', '')
-    if search_switch is not None:
-        search_switch = str(search_switch)
-        if search_switch == '2':
-            return 'Input_Summary'
-    return 'Split_The_Problem'  # 默认情况
-
-def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]:
-    """
-    Determine routing based on search_switch value.
-
-    Args:
-        state: State dictionary containing search_switch
-
-    Returns:
-        Next node to execute
-    """
-    search_switch = state.get('search_switch', '')
-    if search_switch is not None:
-        search_switch = str(search_switch)
-        if search_switch == '0':
-            return 'Verify'
-        elif search_switch == '1':
-            return 'Retrieve_Summary'
-    return 'Retrieve_Summary'  # Default based on business logic
 def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]:
-    status=state.get('verify', '')['status']
-    # loop_count = counter.get_total()
-    if "success" in status:
-        # counter.reset()
+    """
+    Determine routing after Verify node based on verification result.
+    
+    This function checks the verification result in the last message and routes to:
+    - Summary: if verification succeeded
+    - content_input: if verification failed and retry limit not reached
+    - Summary_fails: if verification failed and retry limit reached
+    
+    Args:
+        state: LangGraph state containing messages
+        
+    Returns:
+        Next node name as Literal type
+    """
+    messages = state.get("messages", [])
+    
+    # Boundary check
+    if not messages:
+        logger.warning("[Verify_continue] No messages in state, defaulting to Summary")
+        counter.reset()
         return "Summary"
-    elif "failed" in status:
-        # if loop_count < 2:  # Maximum loop count is 3
-        #     return "content_input"
-        # else:
-            # counter.reset()
-        return "Summary_fails"
+    
+    # Increment counter
+    counter.add(1)
+    loop_count = counter.get_total()
+    logger.debug(f"[Verify_continue] Current loop count: {loop_count}")
+    
+    # Extract verification result from last message
+    last_message = messages[-1]
+    last_message_str = str(last_message).replace('\\', '')
+    status_tools = re.findall(r'"split_result": "(.*?)"', last_message_str)
+    logger.debug(f"[Verify_continue] Status tools: {status_tools}")
+    
+    # Route based on verification result
+    if "success" in status_tools:
+        counter.reset()
+        return "Summary"
+    elif "failed" in status_tools:
+        if loop_count < 2:  # Max retry count is 2
+            return "content_input"
+        else:
+            counter.reset()
+            return "Summary_fails"
     else:
-        # Add default return value to avoid returning None
-        # counter.reset()
-        return "Summary"  # Default based on business requirements
+        # Default to Summary if status is unclear
+        counter.reset()
+        return "Summary"
+
+
+def Retrieve_continue(state: dict) -> Literal["Verify", "Retrieve_Summary"]:
+    """
+    Determine routing after Retrieve node based on search_switch value.
+    
+    This function routes based on the search_switch parameter:
+    - search_switch == '0': Route to Verify (verification needed)
+    - search_switch == '1': Route to Retrieve_Summary (direct summary)
+    
+    Args:
+        state: LangGraph state dictionary
+        
+    Returns:
+        Next node name as Literal type
+    """
+    search_switch = extract_search_switch(state)
+    
+    logger.debug(f"[Retrieve_continue] search_switch: {search_switch}")
+    
+    if search_switch == '0':
+        return 'Verify'
+    elif search_switch == '1':
+        return 'Retrieve_Summary'
+    
+    # Default to Retrieve_Summary
+    logger.debug("[Retrieve_continue] No valid search_switch, defaulting to Retrieve_Summary")
+    return 'Retrieve_Summary'
+
+
+def Split_continue(state: dict) -> Literal["Split_The_Problem", "Input_Summary"]:
+    """
+    Determine routing after content_input node based on search_switch value.
+    
+    This function routes based on the search_switch parameter:
+    - search_switch == '2': Route to Input_Summary (direct input summary)
+    - Otherwise: Route to Split_The_Problem (problem decomposition)
+    
+    Args:
+        state: LangGraph state dictionary
+        
+    Returns:
+        Next node name as Literal type
+    """
+    logger.debug(f"[Split_continue] state keys: {state.keys()}")
+    
+    search_switch = extract_search_switch(state)
+    
+    logger.debug(f"[Split_continue] search_switch: {search_switch}")
+    
+    if search_switch == '2':
+        return 'Input_Summary'
+    
+    # Default to Split_The_Problem
+    return 'Split_The_Problem'

api/app/core/memory/agent/langgraph_graph/routing/write_router.py

@@ -1,238 +0,0 @@
-import json
-import os
-
-from app.core.logging_config import get_agent_logger
-from app.core.memory.agent.langgraph_graph.tools.write_tool import  format_parsing,  messages_parse
-from app.core.memory.agent.langgraph_graph.write_graph import make_write_graph, long_term_storage
-
-from app.core.memory.agent.models.write_aggregate_model import WriteAggregateModel
-from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_
-from app.core.memory.agent.utils.redis_tool import write_store
-from app.core.memory.agent.utils.redis_tool import count_store
-from app.core.memory.agent.utils.template_tools import TemplateService
-from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.db import get_db_context, get_db
-from app.repositories.memory_short_repository import LongTermMemoryRepository
-from app.schemas.memory_agent_schema import AgentMemory_Long_Term
-from app.services.memory_konwledges_server import write_rag
-from app.services.task_service import get_task_memory_write_result
-from app.tasks import write_message_task
-from app.utils.config_utils import resolve_config_id
-logger = get_agent_logger(__name__)
-template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
-
-async def write_rag_agent(end_user_id, user_message, ai_message, user_rag_memory_id):
-    # RAG 模式：组合消息为字符串格式（保持原有逻辑）
-    combined_message = f"user: {user_message}\nassistant: {ai_message}"
-    await write_rag(end_user_id, combined_message, user_rag_memory_id)
-    logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}')
-async def write(storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id,
-                actual_config_id, long_term_messages=[]):
-    """
-    写入记忆（支持结构化消息）
-
-    Args:
-        storage_type: 存储类型 (neo4j/rag)
-        end_user_id: 终端用户ID
-        user_message: 用户消息内容
-        ai_message: AI 回复内容
-        user_rag_memory_id: RAG 记忆ID
-        actual_end_user_id: 实际用户ID
-        actual_config_id: 配置ID
-
-    逻辑说明：
-    - RAG 模式：组合 user_message 和 ai_message 为字符串格式，保持原有逻辑不变
-    - Neo4j 模式：使用结构化消息列表
-      1. 如果 user_message 和 ai_message 都不为空：创建配对消息 [user, assistant]
-      2. 如果只有 user_message：创建单条用户消息 [user]（用于历史记忆场景）
-      3. 每条消息会被转换为独立的 Chunk，保留 speaker 字段
-    """
-
-    db = next(get_db())
-    try:
-        actual_config_id = resolve_config_id(actual_config_id, db)
-        # Neo4j 模式：使用结构化消息列表
-        structured_messages = []
-
-        # 始终添加用户消息（如果不为空）
-        if isinstance(user_message, str) and user_message.strip() != "":
-            structured_messages.append({"role": "user", "content": user_message})
-
-        # 只有当 AI 回复不为空时才添加 assistant 消息
-        if isinstance(ai_message, str) and ai_message.strip() != "":
-            structured_messages.append({"role": "assistant", "content": ai_message})
-
-        # 如果提供了 long_term_messages，使用它替代 structured_messages
-        if long_term_messages and isinstance(long_term_messages, list):
-            structured_messages = long_term_messages
-        elif long_term_messages and isinstance(long_term_messages, str):
-            # 如果是 JSON 字符串，先解析
-            try:
-                structured_messages = json.loads(long_term_messages)
-            except json.JSONDecodeError:
-                logger.error(f"Failed to parse long_term_messages as JSON: {long_term_messages}")
-
-        # 如果没有消息，直接返回
-        if not structured_messages:
-            logger.warning(f"No messages to write for user {actual_end_user_id}")
-            return
-
-        logger.info(
-            f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}")
-        write_id = write_message_task.delay(
-            actual_end_user_id,  # end_user_id: 用户ID
-            structured_messages,  # message: JSON 字符串格式的消息列表
-            str(actual_config_id),  # config_id: 配置ID字符串
-            storage_type,  # storage_type: "neo4j"
-            user_rag_memory_id or ""  # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用）
-        )
-        logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
-        write_status = get_task_memory_write_result(str(write_id))
-        logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
-    finally:
-        db.close()
-
-async def term_memory_save(long_term_messages,actual_config_id,end_user_id,type,scope):
-    with get_db_context() as db_session:
-        repo = LongTermMemoryRepository(db_session)
-
-
-        from app.core.memory.agent.utils.redis_tool import write_store
-        result = write_store.get_session_by_userid(end_user_id)
-        if type==AgentMemory_Long_Term.STRATEGY_CHUNK or AgentMemory_Long_Term.STRATEGY_AGGREGATE:
-            data = await format_parsing(result, "dict")
-            chunk_data = data[:scope]
-            if len(chunk_data)==scope:
-                repo.upsert(end_user_id, chunk_data)
-                logger.info(f'---------写入短长期-----------')
-        else:
-            long_time_data = write_store.find_user_recent_sessions(end_user_id, 5)
-            long_messages = await messages_parse(long_time_data)
-            repo.upsert(end_user_id, long_messages)
-            logger.info(f'写入短长期：')
-
-
-
-'''根据窗口'''
-async def window_dialogue(end_user_id,langchain_messages,memory_config,scope):
-    '''
-    根据窗口获取redis数据,写入neo4j：
-     Args:
-        end_user_id: 终端用户ID
-        memory_config: 内存配置对象
-        langchain_messages：原始数据LIST
-        scope：窗口大小
-    '''
-    scope=scope
-    is_end_user_id = count_store.get_sessions_count(end_user_id)
-    if is_end_user_id is not False:
-        is_end_user_id = count_store.get_sessions_count(end_user_id)[0]
-        redis_messages = count_store.get_sessions_count(end_user_id)[1]
-    if is_end_user_id and int(is_end_user_id) != int(scope):
-        is_end_user_id += 1
-        langchain_messages += redis_messages
-        count_store.update_sessions_count(end_user_id, is_end_user_id, langchain_messages)
-    elif int(is_end_user_id) == int(scope):
-        logger.info('写入长期记忆NEO4J')
-        formatted_messages = (redis_messages)
-        # 获取 config_id（如果 memory_config 是对象，提取 config_id；否则直接使用）
-        if hasattr(memory_config, 'config_id'):
-            config_id = memory_config.config_id
-        else:
-            config_id = memory_config
-        
-        await write(AgentMemory_Long_Term.STORAGE_NEO4J, end_user_id, "", "", None, end_user_id,
-                config_id, formatted_messages)
-        count_store.update_sessions_count(end_user_id, 1, langchain_messages)
-    else:
-        count_store.save_sessions_count(end_user_id, 1, langchain_messages)
-
-
-"""根据时间"""
-async def memory_long_term_storage(end_user_id,memory_config,time):
-    '''
-    根据时间获取redis数据,写入neo4j：
-     Args:
-        end_user_id: 终端用户ID
-        memory_config: 内存配置对象
-    '''
-    long_time_data = write_store.find_user_recent_sessions(end_user_id, time)
-    format_messages = (long_time_data)
-    messages=[]
-    memory_config=memory_config.config_id
-    for i in format_messages:
-        message=json.loads(i['Query'])
-        messages+= message
-    if format_messages!=[]:
-        await write(AgentMemory_Long_Term.STORAGE_NEO4J, end_user_id, "", "", None, end_user_id,
-                    memory_config, messages)
-'''聚合判断'''
-async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config) -> dict:
-    """
-    聚合判断函数：判断输入句子和历史消息是否描述同一事件
-    
-    Args:
-        end_user_id: 终端用户ID
-        ori_messages: 原始消息列表，格式如 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
-        memory_config: 内存配置对象
-    """
-
-    try:
-        # 1. 获取历史会话数据（使用新方法）
-        result = write_store.get_all_sessions_by_end_user_id(end_user_id)
-        history = await format_parsing(result)
-        if not result:
-            history = []
-        else:
-            history = await format_parsing(result)
-        json_schema = WriteAggregateModel.model_json_schema()
-        template_service = TemplateService(template_root)
-        system_prompt = await template_service.render_template(
-            template_name='write_aggregate_judgment.jinja2',
-            operation_name='aggregate_judgment',
-            history=history,
-            sentence=ori_messages,
-            json_schema=json_schema
-        )
-        with get_db_context() as db_session:
-            factory = MemoryClientFactory(db_session)
-            llm_client = factory.get_llm_client(memory_config.llm_model_id)
-            messages = [
-                {
-                    "role": "user",
-                    "content": system_prompt
-                }
-            ]
-            structured = await llm_client.response_structured(
-                messages=messages,
-                response_model=WriteAggregateModel
-            )
-        output_value = structured.output
-        if isinstance(output_value, list):
-            output_value = [
-                {"role": msg.role, "content": msg.content} 
-                for msg in output_value
-            ]
-
-        result_dict = {
-            "is_same_event": structured.is_same_event,
-            "output": output_value
-        }
-        if not structured.is_same_event:
-            logger.info(result_dict)
-            await write("neo4j", end_user_id, "", "", None, end_user_id,
-                        memory_config.config_id, output_value)
-        return result_dict
-        
-    except Exception as e:
-        print(f"[aggregate_judgment] 发生错误: {e}")
-        import traceback
-        traceback.print_exc()
-        
-        return {
-            "is_same_event": False,
-            "output": ori_messages,
-            "messages": ori_messages,
-            "history": history if 'history' in locals() else [],
-            "error": str(e)
-        }

api/app/core/memory/agent/langgraph_graph/state/__init__.py

@@ -0,0 +1,13 @@
+"""LangGraph state management utilities."""
+
+from app.core.memory.agent.langgraph_graph.state.extractors import (
+    extract_search_switch,
+    extract_tool_call_id,
+    extract_content_payload,
+)
+
+__all__ = [
+    "extract_search_switch",
+    "extract_tool_call_id",
+    "extract_content_payload",
+]

api/app/core/memory/agent/langgraph_graph/state/extractors.py

@@ -0,0 +1,179 @@
+"""
+State extraction utilities for type-safe access to LangGraph state values.
+
+This module provides utility functions for extracting values from LangGraph state
+dictionaries with proper error handling and sensible defaults.
+"""
+
+import json
+import logging
+from typing import Any, Optional
+
+logger = logging.getLogger(__name__)
+
+def extract_search_switch(state: dict) -> Optional[str]:
+    """
+    Extract search_switch from state or messages.
+    """
+
+    search_switch = state.get("search_switch")
+
+    if search_switch is not None:
+        return str(search_switch)
+
+    # Try to extract from messages
+    messages = state.get("messages", [])
+    if not messages:
+        return None
+
+    # 从最新的消息开始查找
+    for message in reversed(messages):
+        # 尝试从 tool_calls 中提取
+        if hasattr(message, "tool_calls") and message.tool_calls:
+            for tool_call in message.tool_calls:
+                if isinstance(tool_call, dict):
+                    # 从 tool_call 的 args 中提取
+                    if "args" in tool_call and isinstance(tool_call["args"], dict):
+                        search_switch = tool_call["args"].get("search_switch")
+                        if search_switch is not None:
+                            return str(search_switch)
+                    # 直接从 tool_call 中提取
+                    search_switch = tool_call.get("search_switch")
+                    if search_switch is not None:
+                        return str(search_switch)
+
+        # 尝试从 content 中提取（如果是 JSON 格式）
+        if hasattr(message, "content"):
+            try:
+                import json
+                if isinstance(message.content, str):
+                    content_data = json.loads(message.content)
+                    if isinstance(content_data, dict):
+                        search_switch = content_data.get("search_switch")
+                        if search_switch is not None:
+                            return str(search_switch)
+            except (json.JSONDecodeError, ValueError):
+                pass
+
+    return None
+
+
+def extract_tool_call_id(message: Any) -> str:
+    """
+    Extract tool call ID from message using structured attributes.
+    
+    This function extracts the tool call ID from a message object, handling both
+    direct attribute access and tool_calls list structures.
+    
+    Args:
+        message: Message object (typically ToolMessage or AIMessage)
+        
+    Returns:
+        Tool call ID as string
+        
+    Raises:
+        ValueError: If tool call ID cannot be extracted
+        
+    Examples:
+        >>> message = ToolMessage(content="...", tool_call_id="call_123")
+        >>> extract_tool_call_id(message)
+        'call_123'
+    """
+    # Try direct attribute access for ToolMessage
+    if hasattr(message, "tool_call_id"):
+        tool_call_id = message.tool_call_id
+        if tool_call_id:
+            return str(tool_call_id)
+    
+    # Try extracting from tool_calls list for AIMessage
+    if hasattr(message, "tool_calls") and message.tool_calls:
+        tool_call = message.tool_calls[0]
+        if isinstance(tool_call, dict) and "id" in tool_call:
+            return str(tool_call["id"])
+    
+    # Try extracting from id attribute
+    if hasattr(message, "id"):
+        message_id = message.id
+        if message_id:
+            return str(message_id)
+    
+    # If all else fails, raise an error
+    raise ValueError(f"Could not extract tool call ID from message: {type(message)}")
+
+
+def extract_content_payload(message: Any) -> Any:
+    """
+    Extract content payload from ToolMessage, parsing JSON if needed.
+    
+    This function extracts the content from a message and attempts to parse it as JSON
+    if it appears to be a JSON string. It handles various message formats and provides
+    sensible fallbacks.
+    
+    Args:
+        message: Message object (typically ToolMessage)
+        
+    Returns:
+        Parsed content (dict, list, or str)
+        
+    Examples:
+        >>> message = ToolMessage(content='{"key": "value"}')
+        >>> extract_content_payload(message)
+        {'key': 'value'}
+        
+        >>> message = ToolMessage(content='plain text')
+        >>> extract_content_payload(message)
+        'plain text'
+    """
+    # Extract raw content
+    # For ToolMessages (responses from tools), extract from content
+    if hasattr(message, "content"):
+        raw_content = message.content
+        logger.info(f"extract_content_payload: raw_content type={type(raw_content)}, value={str(raw_content)[:500]}")
+        
+        # Handle MCP content format: [{'type': 'text', 'text': '...'}]
+        if isinstance(raw_content, list):
+            for block in raw_content:
+                if isinstance(block, dict) and block.get('type') == 'text':
+                    raw_content = block.get('text', '')
+                    logger.info(f"extract_content_payload: extracted text from MCP format: {str(raw_content)[:300]}")
+                    break
+        
+        # If content is empty and this is an AIMessage with tool_calls,
+        # extract from args (this handles the initial tool call from content_input)
+        if not raw_content and hasattr(message, "tool_calls") and message.tool_calls:
+            tool_call = message.tool_calls[0]
+            if isinstance(tool_call, dict) and "args" in tool_call:
+                return tool_call["args"]
+    else:
+        raw_content = str(message)
+    
+    # If content is already a dict or list, return it directly
+    if isinstance(raw_content, (dict, list)):
+        logger.info(f"extract_content_payload: returning raw dict/list with keys={list(raw_content.keys()) if isinstance(raw_content, dict) else 'list'}")
+        return raw_content
+    
+    # Try to parse as JSON
+    if isinstance(raw_content, str):
+        # First, try direct JSON parsing
+        try:
+            parsed = json.loads(raw_content)
+            logger.info(f"extract_content_payload: parsed JSON, keys={list(parsed.keys()) if isinstance(parsed, dict) else 'list'}")
+            return parsed
+        except (json.JSONDecodeError, ValueError):
+            pass
+        
+        # If that fails, try to extract JSON from the string
+        # This handles cases where the content is embedded in a larger string
+        import re
+        json_candidates = re.findall(r'[\[{].*[\]}]', raw_content, flags=re.DOTALL)
+        for candidate in json_candidates:
+            try:
+                parsed = json.loads(candidate)
+                logger.info(f"extract_content_payload: parsed JSON from candidate, keys={list(parsed.keys()) if isinstance(parsed, dict) else 'list'}")
+                return parsed
+            except (json.JSONDecodeError, ValueError):
+                continue
+    
+    # If all parsing attempts fail, return the raw content
+    logger.info(f"extract_content_payload: returning raw content (parsing failed)")
+    return raw_content

api/app/core/memory/agent/langgraph_graph/tools/tool.py

@@ -1,321 +0,0 @@
-import asyncio
-import json
-from datetime import datetime, timedelta
-
-
-from langchain.tools import tool
-from pydantic import BaseModel, Field
-
-
-from app.core.memory.src.search import (
-    search_by_temporal,
-    search_by_keyword_temporal,
-)
-
-def extract_tool_message_content(response):
-    """从agent响应中提取ToolMessage内容和工具名称"""
-    messages = response.get('messages', [])
-
-    for message in messages:
-        if hasattr(message, 'tool_call_id') and hasattr(message, 'content'):
-            # 这是一个ToolMessage
-            tool_content = message.content
-            tool_name = None
-
-            # 尝试获取工具名称
-            if hasattr(message, 'name'):
-                tool_name = message.name
-            elif hasattr(message, 'tool_name'):
-                tool_name = message.tool_name
-
-            try:
-                # 解析JSON内容
-                parsed_content = json.loads(tool_content)
-                return {
-                    'tool_name': tool_name,
-                    'content': parsed_content
-                }
-            except json.JSONDecodeError:
-                # 如果不是JSON格式，直接返回内容
-                return {
-                    'tool_name': tool_name,
-                    'content': tool_content
-                }
-
-    return None
-
-
-class TimeRetrievalInput(BaseModel):
-    """时间检索工具的输入模式"""
-    context: str = Field(description="用户输入的查询内容")
-    end_user_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果")
-
-def create_time_retrieval_tool(end_user_id: str):
-    """
-    创建一个带有特定end_user_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements)
-    """
-    
-    def clean_temporal_result_fields(data):
-        """
-        清理时间搜索结果中不需要的字段，并修改结构
-        
-        Args:
-            data: 要清理的数据
-            
-        Returns:
-            清理后的数据
-        """
-        # 需要过滤的字段列表
-        fields_to_remove = {
-            'id', 'apply_id', 'user_id', 'chunk_id', 'created_at', 
-            'valid_at', 'invalid_at', 'statement_ids'
-        }
-        
-        if isinstance(data, dict):
-            cleaned = {}
-            for key, value in data.items():
-                if key == 'statements' and isinstance(value, dict) and 'statements' in value:
-                    # 将 statements: {"statements": [...]} 改为 time_search: {"statements": [...]}
-                    cleaned_value = clean_temporal_result_fields(value)
-                    # 进一步将内部的 statements 改为 time_search
-                    if 'statements' in cleaned_value:
-                        cleaned['results'] = {
-                            'time_search': cleaned_value['statements']
-                        }
-                    else:
-                        cleaned['results'] = cleaned_value
-                elif key not in fields_to_remove:
-                    cleaned[key] = clean_temporal_result_fields(value)
-            return cleaned
-        elif isinstance(data, list):
-            return [clean_temporal_result_fields(item) for item in data]
-        else:
-            return data
-    
-    @tool
-    def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, end_user_id_param: str = None, clean_output: bool = True) -> str:
-        """
-        优化的时间检索工具，只结合时间范围搜索（同步版本），自动过滤不需要的元数据字段
-        显式接收参数：
-        - context: 查询上下文内容
-        - start_date: 开始时间（可选，格式：YYYY-MM-DD）
-        - end_date: 结束时间（可选，格式：YYYY-MM-DD）
-        - end_user_id_param: 组ID（可选，用于覆盖默认组ID）
-        - clean_output: 是否清理输出中的元数据字段
-        -end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
-        """
-        async def _async_search():
-            # 使用传入的参数或默认值
-            actual_end_user_id = end_user_id_param or end_user_id
-            actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d")
-            actual_start_date = start_date or (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")
-            
-            # 基本时间搜索
-            results = await search_by_temporal(
-                end_user_id=actual_end_user_id,
-                start_date=actual_start_date,
-                end_date=actual_end_date,
-                limit=10
-            )
-            
-            # 清理结果中不需要的字段
-            if clean_output:
-                cleaned_results = clean_temporal_result_fields(results)
-            else:
-                cleaned_results = results
-
-            return json.dumps(cleaned_results, ensure_ascii=False, indent=2)
-        
-        return asyncio.run(_async_search())
-
-    @tool
-    def KeywordTimeRetrieval(context: str, days_back: int = 7, start_date: str = None, end_date: str = None, clean_output: bool = True) -> str:
-        """
-        优化的关键词时间检索工具，结合关键词和时间范围搜索（同步版本），自动过滤不需要的元数据字段
-        显式接收参数：
-        - context: 查询内容
-        - days_back: 向前搜索的天数，默认7天
-        - start_date: 开始时间（可选，格式：YYYY-MM-DD）
-        - end_date: 结束时间（可选，格式：YYYY-MM-DD）
-        - clean_output: 是否清理输出中的元数据字段
-        - end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
-        """
-        async def _async_search():
-            actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d")
-            actual_start_date = start_date or (datetime.now() - timedelta(days=days_back)).strftime("%Y-%m-%d")
-
-            # 关键词时间搜索
-            results = await search_by_keyword_temporal(
-                query_text=context,
-                end_user_id=end_user_id,
-                start_date=actual_start_date,
-                end_date=actual_end_date,
-                limit=15
-            )
-            
-            # 清理结果中不需要的字段
-            if clean_output:
-                cleaned_results = clean_temporal_result_fields(results)
-            else:
-                cleaned_results = results
-
-            return json.dumps(cleaned_results, ensure_ascii=False, indent=2)
-
-        return asyncio.run(_async_search())
-    
-    return TimeRetrievalWithGroupId
-
-
-def create_hybrid_retrieval_tool_async(memory_config, **search_params):
-    """
-    创建混合检索工具，使用run_hybrid_search进行混合检索，优化输出格式并过滤不需要的字段
-    
-    Args:
-        memory_config: 内存配置对象
-        **search_params: 搜索参数，包含end_user_id, limit, include等
-    """
-    
-    def clean_result_fields(data):
-        """
-        递归清理结果中不需要的字段
-        
-        Args:
-            data: 要清理的数据（可能是字典、列表或其他类型）
-            
-        Returns:
-            清理后的数据
-        """
-        # 需要过滤的字段列表
-        # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
-        fields_to_remove = {
-            'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids', 
-            'expired_at', 'created_at', 'chunk_id', 'id', 'apply_id', 
-            'user_id', 'statement_ids', 'updated_at',"chunk_ids"  ,"fact_summary"
-        }
-        
-        if isinstance(data, dict):
-            # 对字典进行清理
-            cleaned = {}
-            for key, value in data.items():
-                if key not in fields_to_remove:
-                    cleaned[key] = clean_result_fields(value)  # 递归清理嵌套数据
-            return cleaned
-        elif isinstance(data, list):
-            # 对列表中的每个元素进行清理
-            return [clean_result_fields(item) for item in data]
-        else:
-            # 其他类型直接返回
-            return data
-    
-    @tool
-    async def HybridSearch(
-        context: str, 
-        search_type: str = "hybrid",
-        limit: int = 10,
-        end_user_id: str = None,
-        rerank_alpha: float = 0.6,
-        use_forgetting_rerank: bool = False,
-        use_llm_rerank: bool = False,
-        clean_output: bool = True  # 新增：是否清理输出字段
-    ) -> str:
-        """
-        优化的混合检索工具，支持关键词、向量和混合搜索，自动过滤不需要的元数据字段
-        
-        Args:
-            context: 查询内容
-            search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
-            limit: 结果数量限制
-            end_user_id: 组ID，用于过滤搜索结果
-            rerank_alpha: 重排序权重参数
-            use_forgetting_rerank: 是否使用遗忘重排序
-            use_llm_rerank: 是否使用LLM重排序
-            clean_output: 是否清理输出中的元数据字段
-        """
-        try:
-            # 导入run_hybrid_search函数
-            from app.core.memory.src.search import run_hybrid_search
-            
-            # 合并参数，优先使用传入的参数
-            final_params = {
-                "query_text": context,
-                "search_type": search_type,
-                "end_user_id": end_user_id or search_params.get("end_user_id"),
-                "limit": limit or search_params.get("limit", 10),
-                "include": search_params.get("include", ["summaries", "statements", "chunks", "entities"]),
-                "output_path": None,  # 不保存到文件
-                "memory_config": memory_config,
-                "rerank_alpha": rerank_alpha,
-                "use_forgetting_rerank": use_forgetting_rerank,
-                "use_llm_rerank": use_llm_rerank
-            }
-            
-            # 执行混合检索
-            raw_results = await run_hybrid_search(**final_params)
-            
-            # 清理结果中不需要的字段
-            if clean_output:
-                cleaned_results = clean_result_fields(raw_results)
-            else:
-                cleaned_results = raw_results
-            
-            # 格式化返回结果
-            formatted_results = {
-                "search_query": context,
-                "search_type": search_type,
-                "results": cleaned_results
-            }
-            
-            return json.dumps(formatted_results, ensure_ascii=False, indent=2, default=str)
-            
-        except Exception as e:
-            error_result = {
-                "error": f"混合检索失败: {str(e)}",
-                "search_query": context,
-                "search_type": search_type,
-                "timestamp": datetime.now().isoformat()
-            }
-            return json.dumps(error_result, ensure_ascii=False, indent=2)
-    
-    return HybridSearch
-
-
-def create_hybrid_retrieval_tool_sync(memory_config, **search_params):
-    """
-    创建同步版本的混合检索工具，优化输出格式并过滤不需要的字段
-    
-    Args:
-        memory_config: 内存配置对象
-        **search_params: 搜索参数
-    """
-    @tool
-    def HybridSearchSync(
-        context: str, 
-        search_type: str = "hybrid",
-        limit: int = 10,
-        end_user_id: str = None,
-        clean_output: bool = True
-    ) -> str:
-        """
-        优化的混合检索工具（同步版本），自动过滤不需要的元数据字段
-        
-        Args:
-            context: 查询内容
-            search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
-            limit: 结果数量限制
-            end_user_id: 组ID，用于过滤搜索结果
-            clean_output: 是否清理输出中的元数据字段
-        """
-        async def _async_search():
-            # 创建异步工具并执行
-            async_tool = create_hybrid_retrieval_tool_async(memory_config, **search_params)
-            return await async_tool.ainvoke({
-                "context": context,
-                "search_type": search_type,
-                "limit": limit,
-                "end_user_id": end_user_id,
-                "clean_output": clean_output
-            })
-        
-        return asyncio.run(_async_search())
-    
-    return HybridSearchSync

api/app/core/memory/agent/langgraph_graph/tools/write_tool.py

@@ -1,72 +0,0 @@
-import json
-
-from langchain_core.messages import HumanMessage, AIMessage
-async def format_parsing(messages: list,type:str='string'):
-    """
-    格式化解析消息列表
-    
-    Args:
-        messages: 消息列表
-        type: 返回类型 ('string' 或 'dict')
-        
-    Returns:
-        格式化后的消息列表
-    """
-    result = []
-    user=[]
-    ai=[]
-
-    for message in messages:
-        hstory_messages = message['messages']
-        for history_messag in hstory_messages.strip().splitlines():
-            history_messag = json.loads(history_messag)
-            for content in history_messag:
-                role = content['role']
-                content = content['content']
-                if type == "string":
-                    if role == 'human' or role=="user":
-                        content = '用户:' + content
-                    else:
-                        content = 'AI:' + content
-                    result.append(content)
-                if type == "dict" :
-                    if role == 'human'  or role=="user":
-                        user.append( content)
-                    else:
-                        ai.append(content)
-    if type == "dict":
-        for key,values in zip(user,ai):
-            result.append({key:values})
-    return result
-
-async def messages_parse(messages: list | dict):
-    user=[]
-    ai=[]
-    database=[]
-    for message in messages:
-        Query = message['Query']
-        Query = json.loads(Query)
-        for data in Query:
-            role = data['role']
-            if role == "human":
-                user.append(data['content'])
-            if role == "ai":
-                ai.append(data['content'])
-    for key, values in zip(user, ai):
-        database.append({key, values})
-    return  database
-
-
-async def agent_chat_messages(user_content,ai_content):
-    messages = [
-        {
-            "role": "user",
-            "content": f"{user_content}"
-        },
-        {
-            "role": "assistant",
-            "content": f"{ai_content}"
-        }
-
-    ]
-    return messages

api/app/core/memory/agent/langgraph_graph/write_graph.py

@@ -3,18 +3,17 @@ import json
 import sys
 import warnings
 from contextlib import asynccontextmanager
-from langgraph.constants import END, START
-from langgraph.graph import StateGraph
 
-from app.db import get_db, get_db_context
 from app.core.logging_config import get_agent_logger
 from app.core.memory.agent.utils.llm_tools import WriteState
-from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node
-from app.schemas.memory_agent_schema import AgentMemory_Long_Term
-from app.services.memory_config_service import MemoryConfigService
-
+from app.schemas.memory_config_schema import MemoryConfig
+from langchain_core.messages import AIMessage
+from langgraph.constants import END, START
+from langgraph.graph import StateGraph
+from langgraph.prebuilt import ToolNode
 
 warnings.filterwarnings("ignore", category=RuntimeWarning)
+
 logger = get_agent_logger(__name__)
 
 if sys.platform.startswith("win"):
@@ -22,83 +21,60 @@ if sys.platform.startswith("win"):
 
 
 @asynccontextmanager
-async def make_write_graph():
+async def make_write_graph(user_id, tools, apply_id, group_id, memory_config: MemoryConfig):
     """
     Create a write graph workflow for memory operations.
-
+    
     Args:
         user_id: User identifier
         tools: MCP tools loaded from session
         apply_id: Application identifier
-        end_user_id: Group identifier
+        group_id: Group identifier
         memory_config: MemoryConfig object containing all configuration
     """
+    logger.info("Loading MCP tools: %s", [t.name for t in tools])
+    logger.info(f"Using memory_config: {memory_config.config_name} (id={memory_config.config_id})")
+
+    data_write_tool = next((t for t in tools if t.name == "Data_write"), None)
+
+    if not data_write_tool:
+        logger.error("Data_write tool not found", exc_info=True)
+        raise ValueError("Data_write tool not found")
+
+    write_node = ToolNode([data_write_tool])
+
+    async def call_model(state):
+        messages = state["messages"]
+        last_message = messages[-1]
+        content = last_message[1] if isinstance(last_message, tuple) else last_message.content
+
+        # Call Data_write directly with memory_config
+        write_params = {
+            "content": content,
+            "apply_id": apply_id,
+            "group_id": group_id,
+            "user_id": user_id,
+            "memory_config": memory_config,
+        }
+        logger.debug(f"Passing memory_config to Data_write: {memory_config.config_id}")
+
+        write_result = await data_write_tool.ainvoke(write_params)
+
+        if isinstance(write_result, dict):
+            result_content = write_result.get("data", str(write_result))
+        else:
+            result_content = str(write_result)
+        logger.info("Write content: %s", result_content)
+        return {"messages": [AIMessage(content=result_content)]}
+
     workflow = StateGraph(WriteState)
+    workflow.add_node("content_input", call_model)
     workflow.add_node("save_neo4j", write_node)
-    workflow.add_edge(START, "save_neo4j")
+    workflow.add_edge(START, "content_input")
+    workflow.add_edge("content_input", "save_neo4j")
     workflow.add_edge("save_neo4j", END)
 
     graph = workflow.compile()
 
+
     yield graph
-
-async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[],memory_config:str='',end_user_id:str='',scope:int=6):
-    from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue,aggregate_judgment
-    from app.core.memory.agent.utils.redis_tool import write_store
-    write_store.save_session_write(end_user_id,  (langchain_messages))
-    # 获取数据库会话
-    with get_db_context() as db_session:
-        config_service = MemoryConfigService(db_session)
-        memory_config = config_service.load_memory_config(
-            config_id=memory_config,  # 改为整数
-            service_name="MemoryAgentService"
-        )
-        if long_term_type=='chunk':
-            '''方案一:对话窗口6轮对话'''
-            await window_dialogue(end_user_id,langchain_messages,memory_config,scope)
-        if long_term_type=='time':
-            """时间"""
-            await memory_long_term_storage(end_user_id, memory_config,5)
-        if  long_term_type=='aggregate':
-            """方案三：聚合判断"""
-            await aggregate_judgment(end_user_id, langchain_messages, memory_config)
-
-
-
-async def write_long_term(storage_type,end_user_id,message_chat,aimessages,user_rag_memory_id,actual_config_id):
-    from app.core.memory.agent.langgraph_graph.routing.write_router import write_rag_agent
-    from app.core.memory.agent.langgraph_graph.routing.write_router import term_memory_save
-    from app.core.memory.agent.langgraph_graph.tools.write_tool import  agent_chat_messages
-    if storage_type == AgentMemory_Long_Term.STORAGE_RAG:
-        await write_rag_agent(end_user_id, message_chat, aimessages, user_rag_memory_id)
-    else:
-        # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
-        CHUNK = AgentMemory_Long_Term.STRATEGY_CHUNK
-        SCOPE = AgentMemory_Long_Term.DEFAULT_SCOPE
-        long_term_messages = await agent_chat_messages(message_chat, aimessages)
-        await long_term_storage(long_term_type=CHUNK, langchain_messages=long_term_messages,
-                                memory_config=actual_config_id, end_user_id=end_user_id, scope=SCOPE)
-        await term_memory_save(long_term_messages, actual_config_id, end_user_id, CHUNK, scope=SCOPE)
-
-# async def main():
-#     """主函数 - 运行工作流"""
-#     langchain_messages = [
-#     {
-#       "role": "user",
-#       "content": "今天周五去爬山"
-#     },
-#     {
-#       "role": "assistant",
-#       "content": "好耶"
-#     }
-#
-#   ]
-#     end_user_id = '837fee1b-04a2-48ee-94d7-211488908940'  # 组ID
-#     memory_config="08ed205c-0f05-49c3-8e0c-a580d28f5fd4"
-#     await long_term_storage(long_term_type="chunk",langchain_messages=langchain_messages,memory_config=memory_config,end_user_id=end_user_id,scope=2)
-#
-#
-#
-# if __name__ == "__main__":
-#     import asyncio
-#     asyncio.run(main())

api/app/core/memory/agent/mcp_server/__init__.py

@@ -0,0 +1,28 @@
+"""
+MCP Server package for memory agent.
+
+This package provides the FastMCP server implementation with context-based
+dependency injection for tool functions.
+
+Package structure:
+- server: FastMCP server initialization and context setup
+- tools: MCP tool implementations
+- models: Pydantic response models
+- services: Business logic services
+"""
+# from app.core.memory.agent.mcp_server.server import (
+#     mcp,
+#     initialize_context,
+#     main,
+#     get_context_resource
+# )
+
+# # Import tools to register them (but don't export them)
+# from app.core.memory.agent.mcp_server import tools
+
+# __all__ = [
+#     'mcp',
+#     'initialize_context',
+#     'main',
+#     'get_context_resource',
+# ]

api/app/core/memory/agent/mcp_server/mcp_instance.py

@@ -0,0 +1,11 @@
+"""
+MCP Server Instance
+
+This module contains the FastMCP server instance that is shared across all modules.
+It's in a separate file to avoid circular import issues.
+"""
+from mcp.server.fastmcp import FastMCP
+
+# Initialize FastMCP server instance
+# This instance is shared across all tool modules
+mcp = FastMCP('data_flow')

api/app/core/memory/agent/mcp_server/models/verification_models.py

@@ -0,0 +1,14 @@
+"""Pydantic models for verification operations."""
+
+from typing import List, Optional, Dict, Any
+from pydantic import BaseModel, Field
+
+
+class VerificationResult(BaseModel):
+    """Result model for verification operation."""
+    
+    query: str
+    expansion_issue: List[Dict[str, Any]]
+    split_result: str
+    reason: Optional[str] = None
+    history: List[Dict[str, Any]] = Field(default_factory=list)

api/app/core/memory/agent/mcp_server/server.py

@@ -0,0 +1,159 @@
+"""
+MCP Server initialization with FastMCP context setup.
+
+This module initializes the FastMCP server and registers shared resources
+in the context for dependency injection into tool functions.
+"""
+import os
+import sys
+
+from app.core.config import settings
+from app.core.logging_config import get_agent_logger
+from app.core.memory.agent.mcp_server.mcp_instance import mcp
+from app.core.memory.agent.mcp_server.services.search_service import SearchService
+from app.core.memory.agent.mcp_server.services.session_service import SessionService
+from app.core.memory.agent.mcp_server.services.template_service import TemplateService
+from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_
+from app.core.memory.agent.utils.redis_tool import store
+
+logger = get_agent_logger(__name__)
+
+
+def get_context_resource(ctx, resource_name: str):
+    """
+    Helper function to retrieve a resource from the FastMCP context.
+    
+    Args:
+        ctx: FastMCP Context object (passed to tool functions)
+        resource_name: Name of the resource to retrieve
+        
+    Returns:
+        The requested resource
+        
+    Raises:
+        AttributeError: If the resource doesn't exist
+        
+    Example:
+        @mcp.tool()
+        async def my_tool(ctx: Context):
+            template_service = get_context_resource(ctx, 'template_service')
+            llm_client = get_context_resource(ctx, 'llm_client')
+    """
+    if not hasattr(ctx, 'fastmcp') or ctx.fastmcp is None:
+        raise RuntimeError("Context does not have fastmcp attribute")
+    
+    if not hasattr(ctx.fastmcp, resource_name):
+        raise AttributeError(
+            f"Resource '{resource_name}' not found in context. "
+            f"Available resources: {[k for k in dir(ctx.fastmcp) if not k.startswith('_')]}"
+        )
+    
+    return getattr(ctx.fastmcp, resource_name)
+
+
+def initialize_context():
+    """
+    Initialize and register shared resources in FastMCP context.
+    
+    This function sets up all shared resources that will be available
+    to tool functions via dependency injection through the context parameter.
+    
+    Resources are stored as attributes on the FastMCP instance and can be
+    accessed via ctx.fastmcp in tool functions.
+    
+    Resources registered:
+    - session_store: RedisSessionStore for session management
+    - llm_client: LLM client for structured API calls
+    - app_settings: Application settings (renamed to avoid conflict with FastMCP settings)
+    - template_service: Service for template rendering
+    - search_service: Service for hybrid search
+    - session_service: Service for session operations
+    """
+    try:
+        # Register Redis session store
+        logger.info("Registering session_store in context")
+        mcp.session_store = store
+        
+        # Note: LLM client is NOT loaded at server startup
+        # It should be loaded dynamically when needed, with config_id passed explicitly
+        # to make_write_graph or make_read_graph functions
+        logger.info("LLM client will be loaded dynamically with config_id when needed")
+        mcp.llm_client = None  # Placeholder - actual client loaded per-request with config_id
+        
+        # Register application settings (renamed to avoid conflict with FastMCP's settings)
+        logger.info("Registering app_settings in context")
+        mcp.app_settings = settings
+        
+        # Register template service
+        template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
+        # logger.info(f"Registering template_service in context with root: {template_root}")
+        template_service = TemplateService(template_root)
+        mcp.template_service = template_service
+        
+        # Register search service
+        # logger.info("Registering search_service in context")
+        search_service = SearchService()
+        mcp.search_service = search_service
+        
+        # Register session service
+        # logger.info("Registering session_service in context")
+        session_service = SessionService(store)
+        mcp.session_service = session_service
+        
+        # logger.info("All context resources registered successfully")
+        
+    except Exception as e:
+        logger.error(f"Failed to initialize context: {e}", exc_info=True)
+        raise
+
+
+def main():
+    """
+    Main entry point for the MCP server.
+    
+    Initializes context and starts the server with SSE transport.
+    """
+    try:
+        logger.info("Starting MCP server initialization")
+        # Initialize context resources
+        initialize_context()
+        
+        # Import and register tools (imports trigger tool registration)
+        from app.core.memory.agent.mcp_server.tools import (  # noqa: F401
+            data_tools,
+            problem_tools,
+            retrieval_tools,
+            summary_tools,
+            verification_tools,
+        )
+        
+        # Tools are registered via imports above
+        
+        # Get MCP port from environment (default: 8081)
+        mcp_port = int(os.getenv("MCP_PORT", "8081"))
+        logger.info(f"Starting MCP server on {settings.SERVER_IP}:{mcp_port} with SSE transport")
+        
+        # Configure DNS rebinding protection for Docker container compatibility
+        from mcp.server.fastmcp.server import TransportSecuritySettings
+        
+        # Disable DNS rebinding protection to allow Docker container hostnames
+        # This allows containers to connect using service names like 'mcp-server'
+        mcp.settings.transport_security = TransportSecuritySettings(
+            enable_dns_rebinding_protection=False,
+        )
+        logger.info("DNS rebinding protection: disabled for Docker container compatibility")
+        
+        # logger.info(f"Starting MCP server on {settings.SERVER_IP}:{mcp_port} with SSE transport")
+        
+        # Run the server with SSE transport for HTTP connections
+        import uvicorn
+        app = mcp.sse_app()
+        uvicorn.run(app, host=settings.SERVER_IP, port=mcp_port, log_level="info")
+        
+    except Exception as e:
+        logger.error(f"Failed to start MCP server: {e}", exc_info=True)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

api/app/core/memory/agent/mcp_server/services/parameter_builder.py

@@ -4,19 +4,22 @@ Parameter Builder for constructing tool call arguments.
 This service provides tool-specific parameter transformation logic
 to build correct arguments for each tool type.
 """
+
 from typing import Any, Dict, Optional
+
 from app.core.logging_config import get_agent_logger
+from app.schemas.memory_config_schema import MemoryConfig
 
 logger = get_agent_logger(__name__)
 
 
 class ParameterBuilder:
     """Service for building tool call arguments based on tool type."""
-    
+
     def __init__(self):
         """Initialize the parameter builder."""
         logger.info("ParameterBuilder initialized")
-    
+
     def build_tool_args(
         self,
         tool_name: str,
@@ -24,9 +27,10 @@ class ParameterBuilder:
         tool_call_id: str,
         search_switch: str,
         apply_id: str,
-        end_user_id: str,
+        group_id: str,
+        memory_config: MemoryConfig,
         storage_type: Optional[str] = None,
-        user_rag_memory_id: Optional[str] = None
+        user_rag_memory_id: Optional[str] = None,
     ) -> Dict[str, Any]:
         """
         Build tool arguments based on tool type.
@@ -44,7 +48,8 @@ class ParameterBuilder:
             tool_call_id: Extracted tool call identifier
             search_switch: Search routing parameter
             apply_id: Application identifier
-            end_user_id: Group identifier
+            group_id: Group identifier
+            memory_config: MemoryConfig object containing all configuration
             storage_type: Storage type for the workspace (optional)
             user_rag_memory_id: User RAG memory ID for knowledge base retrieval (optional)
             
@@ -55,18 +60,19 @@ class ParameterBuilder:
         base_args = {
             "usermessages": tool_call_id,
             "apply_id": apply_id,
-            "end_user_id": end_user_id
+            "group_id": group_id,
+            "memory_config": memory_config,
         }
-        
+
         # Always add storage_type and user_rag_memory_id (with defaults if None)
         base_args["storage_type"] = storage_type if storage_type is not None else ""
         base_args["user_rag_memory_id"] = user_rag_memory_id if user_rag_memory_id is not None else ""
         
         # Tool-specific argument construction
-        if tool_name in ["Verify","Summary", "Summary_fails",'Retrieve_Summary']:
-            # Verify expects dict context
+        if tool_name in ["Verify", "Summary", "Summary_fails", "Retrieve_Summary", "Problem_Extension"]:
+            # These tools expect dict context
             return {
-                "context": content if isinstance(content, dict) else {},
+                "context": content if isinstance(content, dict) else {"content": content},
                 **base_args
             }
 

api/app/core/memory/agent/mcp_server/services/search_service.py

@@ -4,21 +4,31 @@ Search Service for executing hybrid search and processing results.
 This service provides clean search result processing with content extraction
 and deduplication.
 """
-from typing import List, Tuple, Optional
+
+from typing import TYPE_CHECKING, List, Optional, Tuple
 
 from app.core.logging_config import get_agent_logger
 from app.core.memory.src.search import run_hybrid_search
 from app.core.memory.utils.data.text_utils import escape_lucene_query
 
+if TYPE_CHECKING:
+    from app.schemas.memory_config_schema import MemoryConfig
 
 logger = get_agent_logger(__name__)
 
 
 class SearchService:
     """Service for executing hybrid search and processing results."""
-    
-    def __init__(self):
-        """Initialize the search service."""
+
+    def __init__(self, memory_config: "MemoryConfig" = None):
+        """
+        Initialize the search service.
+        
+        Args:
+            memory_config: Optional MemoryConfig for embedding model configuration.
+                          If not provided, must be passed to execute_hybrid_search.
+        """
+        self.memory_config = memory_config
         logger.info("SearchService initialized")
     
     def extract_content_from_result(self, result: dict) -> str:
@@ -91,51 +101,61 @@ class SearchService:
     
     async def execute_hybrid_search(
         self,
-        end_user_id: str,
+        group_id: str,
         question: str,
-        limit: int = 5,
+        limit: int = 15,
         search_type: str = "hybrid",
         include: Optional[List[str]] = None,
-        rerank_alpha: float = 0.4,
+        rerank_alpha: float = 0.6,
+        activation_boost_factor: float = 0.8,
         output_path: str = "search_results.json",
         return_raw_results: bool = False,
-        memory_config = None
+        memory_config: "MemoryConfig" = None,
     ) -> Tuple[str, str, Optional[dict]]:
         """
-        Execute hybrid search and return clean content.
+        Execute hybrid search with two-stage ranking.
+        
+        Stage 1: Filter by content relevance (BM25 + Embedding)
+        Stage 2: Rerank by activation values (ACTR)
         
         Args:
-            end_user_id: Group identifier for filtering results
+            group_id: Group identifier for filtering
             question: Search query text
-            limit: Maximum number of results to return (default: 5)
-            search_type: Type of search - "hybrid", "keyword", or "embedding" (default: "hybrid")
-            include: List of result types to include (default: ["statements", "chunks", "entities", "summaries"])
-            rerank_alpha: Weight for BM25 scores in reranking (default: 0.4)
-            output_path: Path to save search results (default: "search_results.json")
-            return_raw_results: If True, also return the raw search results as third element (default: False)
-            memory_config: Memory configuration object (required)
+            limit: Max results per category (default: 15)
+            search_type: "hybrid", "keyword", or "embedding" (default: "hybrid")
+            include: Result types (default: ["statements", "chunks", "entities", "summaries"])
+            rerank_alpha: BM25 weight (default: 0.6)
+            activation_boost_factor: Activation impact on memory strength (default: 0.8)
+            output_path: JSON output path (default: "search_results.json")
+            return_raw_results: Return full metadata (default: False)
+            memory_config: MemoryConfig for embedding model
         
         Returns:
-            Tuple of (clean_content, cleaned_query, raw_results)
-            raw_results is None if return_raw_results=False
+            Tuple[str, str, Optional[dict]]: (clean_content, cleaned_query, raw_results)
         """
         if include is None:
             include = ["statements", "chunks", "entities", "summaries"]
-        
+
+        # Use provided memory_config or fall back to instance config
+        config = memory_config or self.memory_config
+        if not config:
+            raise ValueError("memory_config is required for search - either pass it to __init__ or execute_hybrid_search")
+
         # Clean query
         cleaned_query = self.clean_query(question)
-        
+
         try:
-            # Execute search
+            # Execute search using memory_config
             answer = await run_hybrid_search(
                 query_text=cleaned_query,
                 search_type=search_type,
-                end_user_id=end_user_id,
+                group_id=group_id,
                 limit=limit,
                 include=include,
                 output_path=output_path,
-                memory_config=memory_config,
-                rerank_alpha=rerank_alpha
+                memory_config=config,
+                rerank_alpha=rerank_alpha,
+                activation_boost_factor=activation_boost_factor,
             )
             
             # Extract results based on search type and include parameter
@@ -186,7 +206,7 @@ class SearchService:
             
         except Exception as e:
             logger.error(
-                f"Search failed for query '{question}' in group '{end_user_id}': {e}",
+                f"Search failed for query '{question}' in group '{group_id}': {e}",
                 exc_info=True
             )
             # Return empty results on failure

api/app/core/memory/agent/mcp_server/services/session_service.py

@@ -59,7 +59,7 @@ class SessionService:
         self,
         user_id: str,
         apply_id: str,
-        end_user_id: str
+        group_id: str
     ) -> List[dict]:
         """
         Retrieve conversation history from Redis.
@@ -67,20 +67,20 @@ class SessionService:
         Args:
             user_id: User identifier
             apply_id: Application identifier
-            end_user_id: Group identifier
+            group_id: Group identifier
             
         Returns:
             List of conversation history items with Query and Answer keys
             Returns empty list if no history found or on error
         """
         try:
-            history = self.store.find_user_apply_group(user_id, apply_id, end_user_id)
+            history = self.store.find_user_apply_group(user_id, apply_id, group_id)
             
             # Validate history structure
             if not isinstance(history, list):
                 logger.warning(
                     f"Invalid history format for user {user_id}, "
-                    f"apply {apply_id}, group {end_user_id}: expected list, got {type(history)}"
+                    f"apply {apply_id}, group {group_id}: expected list, got {type(history)}"
                 )
                 return []
             
@@ -89,7 +89,7 @@ class SessionService:
         except Exception as e:
             logger.error(
                 f"Failed to retrieve history for user {user_id}, "
-                f"apply {apply_id}, group {end_user_id}: {e}",
+                f"apply {apply_id}, group {group_id}: {e}",
                 exc_info=True
             )
             # Return empty list on error to allow execution to continue
@@ -100,7 +100,7 @@ class SessionService:
         user_id: str,
         query: str,
         apply_id: str,
-        end_user_id: str,
+        group_id: str,
         ai_response: str
     ) -> Optional[str]:
         """
@@ -110,7 +110,7 @@ class SessionService:
             user_id: User identifier
             query: User query/message
             apply_id: Application identifier
-            end_user_id: Group identifier
+            group_id: Group identifier
             ai_response: AI response/answer
             
         Returns:
@@ -131,7 +131,7 @@ class SessionService:
                 userid=user_id,
                 messages=query,
                 apply_id=apply_id,
-                end_user_id=end_user_id,
+                group_id=group_id,
                 aimessages=ai_response
             )
             
@@ -152,7 +152,7 @@ class SessionService:
         Duplicates are identified by matching:
         - sessionid
         - user_id (id field)
-        - end_user_id
+        - group_id
         - messages
         - aimessages
         

api/app/core/memory/agent/mcp_server/services/template_service.py

@@ -3,22 +3,12 @@ Template Service for loading and rendering Jinja2 templates.
 
 This service provides centralized template management with caching and error handling.
 """
-
 import os
 from functools import lru_cache
+from typing import Optional
+from jinja2 import Environment, FileSystemLoader, Template, TemplateNotFound
 
-from jinja2 import (
-    Environment,
-    FileSystemLoader,
-    Template,
-    TemplateNotFound,
-)
-
-from app.core.logging_config import (
-    get_agent_logger,
-    log_prompt_rendering,
-)
-
+from app.core.logging_config import get_agent_logger, log_prompt_rendering
 
 
 logger = get_agent_logger(__name__)

api/app/core/memory/agent/mcp_server/tools/__init__.py

@@ -0,0 +1,27 @@
+"""
+MCP Tools module.
+
+This module contains all MCP tool implementations organized by functionality.
+
+Tools are organized into the following modules:
+- problem_tools: Question segmentation and extension
+- retrieval_tools: Database and context retrieval
+- verification_tools: Data verification
+- summary_tools: Summarization and summary retrieval
+- data_tools: Data type differentiation and writing
+"""
+
+# Import all tool modules to register them with the MCP server
+from . import problem_tools
+from . import retrieval_tools
+from . import verification_tools
+from . import summary_tools
+from . import data_tools
+
+__all__ = [
+    'problem_tools',
+    'retrieval_tools',
+    'verification_tools',
+    'summary_tools',
+    'data_tools',
+]

api/app/core/memory/agent/mcp_server/tools/data_tools.py

@@ -0,0 +1,155 @@
+"""
+Data Tools for data type differentiation and writing.
+
+This module contains MCP tools for distinguishing data types and writing data.
+"""
+
+import os
+
+from app.core.logging_config import get_agent_logger
+from app.core.memory.agent.mcp_server.mcp_instance import mcp
+from app.core.memory.agent.mcp_server.models.retrieval_models import (
+    DistinguishTypeResponse,
+)
+from app.core.memory.agent.mcp_server.server import get_context_resource
+from app.core.memory.agent.utils.write_tools import write
+from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
+from app.db import get_db_context
+from app.schemas.memory_config_schema import MemoryConfig
+from mcp.server.fastmcp import Context
+
+logger = get_agent_logger(__name__)
+
+
+@mcp.tool()
+async def Data_type_differentiation(
+    ctx: Context,
+    context: str,
+    memory_config: MemoryConfig,
+) -> dict:
+    """
+    Distinguish the type of data (read or write).
+    
+    Args:
+        ctx: FastMCP context for dependency injection
+        context: Text to analyze for type differentiation
+        memory_config: MemoryConfig object containing LLM configuration
+        
+    Returns:
+        dict: Contains 'context' with the original text and 'type' field
+    """
+    try:
+        # Extract services from context
+        template_service = get_context_resource(ctx, 'template_service')
+        
+        # Get LLM client from memory_config using factory pattern
+        with get_db_context() as db:
+            factory = MemoryClientFactory(db)
+            llm_client = factory.get_llm_client_from_config(memory_config)
+        
+        # Render template
+        try:
+            system_prompt = await template_service.render_template(
+                template_name='distinguish_types_prompt.jinja2',
+                operation_name='status_typle',
+                user_query=context
+            )
+        except Exception as e:
+            logger.error(
+                f"Template rendering failed for Data_type_differentiation: {e}",
+                exc_info=True
+            )
+            return {
+                "type": "error",
+                "message": f"Prompt rendering failed: {str(e)}"
+            }
+
+        # Call LLM with structured response
+        try:
+            structured = await llm_client.response_structured(
+                messages=[{"role": "system", "content": system_prompt}],
+                response_model=DistinguishTypeResponse
+            )
+            
+            result = structured.model_dump()
+            
+            # Add context to result
+            result["context"] = context
+            
+            return result
+            
+        except Exception as e:
+            logger.error(
+                f"LLM call failed for Data_type_differentiation: {e}",
+                exc_info=True
+            )
+            return {
+                "context": context,
+                "type": "error",
+                "message": f"LLM call failed: {str(e)}"
+            }
+            
+    except Exception as e:
+        logger.error(
+            f"Data_type_differentiation failed: {e}",
+            exc_info=True
+        )
+        return {
+            "context": context,
+            "type": "error",
+            "message": str(e)
+        }
+
+
+@mcp.tool()
+async def Data_write(
+    ctx: Context,
+    content: str,
+    user_id: str,
+    apply_id: str,
+    group_id: str,
+    memory_config: MemoryConfig,
+) -> dict:
+    """
+    Write data to the database/file system.
+    
+    Args:
+        ctx: FastMCP context for dependency injection
+        content: Data content to write
+        user_id: User identifier
+        apply_id: Application identifier
+        group_id: Group identifier
+        memory_config: MemoryConfig object containing all configuration
+        
+    Returns:
+        dict: Contains 'status', 'saved_to', and 'data' fields
+    """
+    try:
+        # Ensure output directory exists
+        os.makedirs("data_output", exist_ok=True)
+        file_path = os.path.join("data_output", "user_data.csv")
+
+        # Write data - clients are constructed inside write() from memory_config
+        await write(
+            content=content,
+            user_id=user_id,
+            apply_id=apply_id,
+            group_id=group_id,
+            memory_config=memory_config,
+        )
+        logger.info(f"Write completed successfully! Config: {memory_config.config_name}")
+
+        return {
+            "status": "success",
+            "saved_to": file_path,
+            "data": content,
+            "config_id": memory_config.config_id,
+            "config_name": memory_config.config_name,
+        }
+
+    except Exception as e:
+        logger.error(f"Data_write failed: {e}", exc_info=True)
+        return {
+            "status": "error",
+            "message": str(e),
+        }

api/app/core/memory/agent/mcp_server/tools/problem_tools.py

@@ -0,0 +1,304 @@
+"""
+Problem Tools for question segmentation and extension.
+
+This module contains MCP tools for breaking down and extending user questions.
+LLM clients are constructed from MemoryConfig when needed.
+"""
+
+import json
+import time
+
+from app.core.logging_config import get_agent_logger, log_time
+from app.core.memory.agent.mcp_server.mcp_instance import mcp
+from app.core.memory.agent.mcp_server.models.problem_models import (
+    ProblemBreakdownResponse,
+    ProblemExtensionResponse,
+)
+from app.core.memory.agent.mcp_server.server import get_context_resource
+from app.core.memory.agent.utils.messages_tool import Problem_Extension_messages_deal
+from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
+from app.db import get_db_context
+from app.schemas.memory_config_schema import MemoryConfig
+from mcp.server.fastmcp import Context
+
+logger = get_agent_logger(__name__)
+
+
+@mcp.tool()
+async def Split_The_Problem(
+    ctx: Context,
+    sentence: str,
+    sessionid: str,
+    messages_id: str,
+    apply_id: str,
+    group_id: str,
+    memory_config: MemoryConfig,
+) -> dict:
+    """
+    Segment the dialogue or sentence into sub-problems.
+    
+    Args:
+        ctx: FastMCP context for dependency injection
+        sentence: Original sentence to split
+        sessionid: Session identifier
+        messages_id: Message identifier
+        apply_id: Application identifier
+        group_id: Group identifier
+        memory_config: MemoryConfig object containing all configuration
+        
+    Returns:
+        dict: Contains 'context' (JSON string of split results) and 'original' sentence
+    """
+    start = time.time()
+
+    try:
+        # Extract services from context
+        template_service = get_context_resource(ctx, "template_service")
+        session_service = get_context_resource(ctx, "session_service")
+
+        # Get LLM client from memory_config
+        with get_db_context() as db:
+            factory = MemoryClientFactory(db)
+            llm_client = factory.get_llm_client_from_config(memory_config)
+        
+        # Extract user ID from session
+        user_id = session_service.resolve_user_id(sessionid)
+        
+        # Get conversation history
+        history = await session_service.get_history(user_id, apply_id, group_id)
+        # Override with empty list for now (as in original)
+        history = []
+        
+        # Render template
+        try:
+            system_prompt = await template_service.render_template(
+                template_name='problem_breakdown_prompt.jinja2',
+                operation_name='split_the_problem',
+                history=history,
+                sentence=sentence
+            )
+        except Exception as e:
+            logger.error(
+                f"Template rendering failed for Split_The_Problem: {e}",
+                exc_info=True
+            )
+            return {
+                "context": json.dumps([], ensure_ascii=False),
+                "original": sentence,
+                "error": f"Prompt rendering failed: {str(e)}"
+            }
+        
+        # Call LLM with structured response
+        try:
+            structured = await llm_client.response_structured(
+                messages=[{"role": "system", "content": system_prompt}],
+                response_model=ProblemBreakdownResponse
+            )
+            
+            # Handle RootModel response with .root attribute access
+            if structured is None:
+                # LLM returned None, use empty list as fallback
+                split_result = json.dumps([], ensure_ascii=False)
+            elif hasattr(structured, 'root') and structured.root is not None:
+                split_result = json.dumps(
+                    [item.model_dump() for item in structured.root],
+                    ensure_ascii=False
+                )
+            elif isinstance(structured, list):
+                # Fallback: treat structured itself as the list
+                split_result = json.dumps(
+                    [item.model_dump() for item in structured],
+                    ensure_ascii=False
+                )
+            else:
+                # Last resort: use empty list
+                split_result = json.dumps([], ensure_ascii=False)
+                
+        except Exception as e:
+            logger.error(
+                f"LLM call failed for Split_The_Problem: {e}",
+                exc_info=True
+            )
+            split_result = json.dumps([], ensure_ascii=False)
+        
+        logger.info("Problem splitting")
+        logger.info(f"Problem split result: {split_result}")
+        
+        # Emit intermediate output for frontend
+        result = {
+            "context": split_result,
+            "original": sentence,
+            "_intermediate": {
+                "type": "problem_split",
+                "data": json.loads(split_result) if split_result else [],
+                "original_query": sentence
+            }
+        }
+        
+        return result
+        
+    except Exception as e:
+        logger.error(
+            f"Split_The_Problem failed: {e}",
+            exc_info=True
+        )
+        return {
+            "context": json.dumps([], ensure_ascii=False),
+            "original": sentence,
+            "error": str(e)
+        }
+        
+    finally:
+        # Log execution time
+        end = time.time()
+        try:
+            duration = end - start
+        except Exception:
+            duration = 0.0
+        log_time('Problem splitting', duration)
+
+
+@mcp.tool()
+async def Problem_Extension(
+    ctx: Context,
+    context: dict,
+    usermessages: str,
+    apply_id: str,
+    group_id: str,
+    memory_config: MemoryConfig,
+    storage_type: str = "",
+    user_rag_memory_id: str = "",
+) -> dict:
+    """
+    Extend the problem with additional sub-questions.
+    
+    Args:
+        ctx: FastMCP context for dependency injection
+        context: Dictionary containing split problem results
+        usermessages: User messages identifier
+        apply_id: Application identifier
+        group_id: Group identifier
+        memory_config: MemoryConfig object containing all configuration
+        storage_type: Storage type for the workspace (optional)
+        user_rag_memory_id: User RAG memory identifier (optional)
+        
+    Returns:
+        dict: Contains 'context' (aggregated questions) and 'original' question
+    """
+    start = time.time()
+
+    try:
+        # Extract services from context
+        template_service = get_context_resource(ctx, "template_service")
+        session_service = get_context_resource(ctx, "session_service")
+
+        # Get LLM client from memory_config
+        with get_db_context() as db:
+            factory = MemoryClientFactory(db)
+            llm_client = factory.get_llm_client_from_config(memory_config)
+        
+        # Resolve session ID from usermessages
+        from app.core.memory.agent.utils.messages_tool import Resolve_username
+        sessionid = Resolve_username(usermessages)
+        
+        # Get conversation history
+        history = await session_service.get_history(sessionid, apply_id, group_id)
+        # Override with empty list for now (as in original)
+        history = []
+        
+        # Process context to extract questions
+        extent_quest, original = await Problem_Extension_messages_deal(context)
+        
+        # Format questions for template rendering
+        questions_formatted = []
+        for msg in extent_quest:
+            if msg.get("role") == "user":
+                questions_formatted.append(msg.get("content", ""))
+        
+        # Render template
+        try:
+            system_prompt = await template_service.render_template(
+                template_name='Problem_Extension_prompt.jinja2',
+                operation_name='problem_extension',
+                history=history,
+                questions=questions_formatted
+            )
+        except Exception as e:
+            logger.error(
+                f"Template rendering failed for Problem_Extension: {e}",
+                exc_info=True
+            )
+            return {
+                "context": {},
+                "original": original,
+                "error": f"Prompt rendering failed: {str(e)}"
+            }
+        
+        # Call LLM with structured response
+        try:
+            response_content = await llm_client.response_structured(
+                messages=[{"role": "system", "content": system_prompt}],
+                response_model=ProblemExtensionResponse
+            )
+            
+            # Aggregate results by original question
+            aggregated_dict = {}
+            for item in response_content.root:
+                key = getattr(item, "original_question", None) or (
+                    item.get("original_question") if isinstance(item, dict) else None
+                )
+                value = getattr(item, "extended_question", None) or (
+                    item.get("extended_question") if isinstance(item, dict) else None
+                )
+                if not key or not value:
+                    continue
+                aggregated_dict.setdefault(key, []).append(value)
+                
+        except Exception as e:
+            logger.error(
+                f"LLM call failed for Problem_Extension: {e}",
+                exc_info=True
+            )
+            aggregated_dict = {}
+        
+        logger.info("Problem extension")
+        logger.info(f"Problem extension result: {aggregated_dict}")
+        
+        # Emit intermediate output for frontend
+        result = {
+            "context": aggregated_dict,
+            "original": original,
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id,
+            "_intermediate": {
+                "type": "problem_extension",
+                "data": aggregated_dict,
+                "original_query": original,
+                "storage_type": storage_type,
+                "user_rag_memory_id": user_rag_memory_id
+            }
+        }
+        
+        return result
+        
+    except Exception as e:
+        logger.error(
+            f"Problem_Extension failed: {e}",
+            exc_info=True
+        )
+        return {
+            "context": {},
+            "original": context.get("original", ""),
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id,
+            "error": str(e)
+        }
+        
+    finally:
+        # Log execution time
+        end = time.time()
+        try:
+            duration = end - start
+        except Exception:
+            duration = 0.0
+        log_time('Problem extension', duration)

api/app/core/memory/agent/mcp_server/tools/retrieval_tools.py

@@ -0,0 +1,294 @@
+"""
+Retrieval Tools for database and context retrieval.
+
+This module contains MCP tools for retrieving data using hybrid search.
+"""
+
+import os
+import time
+
+from app.core.logging_config import get_agent_logger, log_time
+from app.core.memory.agent.mcp_server.mcp_instance import mcp
+from app.core.memory.agent.mcp_server.server import get_context_resource
+from app.core.memory.agent.utils.llm_tools import (
+    deduplicate_entries,
+    merge_to_key_value_pairs,
+)
+from app.core.memory.agent.utils.messages_tool import Retriev_messages_deal
+from app.core.rag.nlp.search import knowledge_retrieval
+from app.schemas.memory_config_schema import MemoryConfig
+from dotenv import load_dotenv
+from mcp.server.fastmcp import Context
+
+load_dotenv()
+logger = get_agent_logger(__name__)
+
+
+@mcp.tool()
+async def Retrieve(
+    ctx: Context,
+    context,
+    usermessages: str,
+    apply_id: str,
+    group_id: str,
+    memory_config: MemoryConfig,
+    storage_type: str = "",
+    user_rag_memory_id: str = "",
+) -> dict:
+    """
+    Retrieve data from the database using hybrid search.
+    
+    Args:
+        ctx: FastMCP context for dependency injection
+        context: Dictionary or string containing query information
+        usermessages: User messages identifier
+        apply_id: Application identifier
+        group_id: Group identifier
+        memory_config: MemoryConfig object containing all configuration
+        storage_type: Storage type for the workspace (e.g., 'rag', 'vector')
+        user_rag_memory_id: User RAG memory identifier
+        
+    Returns:
+        dict: Contains 'context' with Query and Expansion_issue results
+    """
+    kb_config = {
+        "knowledge_bases": [
+            {
+                "kb_id": user_rag_memory_id,
+                "similarity_threshold": 0.7,
+                "vector_similarity_weight": 0.5,
+                "top_k": 10,
+                "retrieve_type": "participle"
+            }
+        ],
+        "merge_strategy": "weight",
+        "reranker_id": os.getenv('reranker_id'),
+        "reranker_top_k": 10
+    }
+    start = time.time()
+    logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
+    logger.info(f"Retrieve: context type={type(context)}, context={str(context)[:500]}")
+    
+    try:
+        # Extract services from context
+        search_service = get_context_resource(ctx, 'search_service')
+        
+        databases_anser = []
+        
+        # Handle both dict and string context
+        if isinstance(context, dict):
+            # Process dict context with extended questions
+            all_items = []
+            logger.info(f"Retrieve: context keys={list(context.keys())}")
+            content, original = await Retriev_messages_deal(context)
+            logger.info(f"Retrieve: after Retriev_messages_deal - content_type={type(content)}, content={str(content)[:300]}")
+            logger.info(f"Retrieve: original='{original[:100] if original else 'EMPTY'}'")
+            
+            if not original:
+                logger.warning(f"Retrieve: original query is empty! context={context}")
+            
+            # Extract all query items from content
+            # content is like {original_question: [extended_questions...], ...}
+            for key, values in content.items():
+                if isinstance(values, list):
+                    all_items.extend(values)
+                elif isinstance(values, str):
+                    all_items.append(values)
+                elif values is not None:
+                    # Fallback: convert non-empty non-list values to string
+                    all_items.append(str(values))
+            
+            # Execute search for each question
+            for idx, question in enumerate(all_items):
+                try:
+                    # Prepare search parameters based on storage type
+                    search_params = {
+                        "group_id": group_id,
+                        "question": question,
+                        "return_raw_results": True
+                    }
+
+                    # Add storage-specific parameters
+                    if storage_type == "rag" and user_rag_memory_id:
+                        retrieve_chunks_result = knowledge_retrieval(question, kb_config,[str(group_id)])
+                        try:
+                            retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
+                            clean_content = '\n\n'.join(retrieval_knowledge)
+                            cleaned_query=question
+                            raw_results=clean_content
+                            logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
+                        except:
+                            clean_content = ''
+                            raw_results=''
+                            cleaned_query = question
+                            logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
+                    else:
+                        clean_content, cleaned_query, raw_results = await search_service.execute_hybrid_search(
+                            **search_params, memory_config=memory_config
+                        )
+
+                    databases_anser.append({
+                        "Query_small": cleaned_query,
+                        "Result_small": clean_content,
+                        "_intermediate": {
+                            "type": "search_result",
+                            "query": cleaned_query,
+                            "raw_results": raw_results,  
+                            "index": idx + 1,
+                            "total": len(all_items)
+                        }
+                    })
+                except Exception as e:
+                    logger.error(
+                        f"Retrieve: hybrid_search failed for question '{question}': {e}",
+                        exc_info=True
+                    )
+                    # Continue with empty result for this question
+                    databases_anser.append({
+                        "Query_small": question,
+                        "Result_small": ""
+                    })
+            
+            # Build initial database data structure
+            databases_data = {
+                "Query": original,
+                "Expansion_issue": databases_anser
+            }
+            
+            # Collect intermediate outputs before deduplication
+            intermediate_outputs = []
+            for item in databases_anser:
+                if '_intermediate' in item:
+                    intermediate_outputs.append(item['_intermediate'])
+            
+            # Deduplicate and merge results
+            deduplicated_data = deduplicate_entries(databases_data['Expansion_issue'])
+            deduplicated_data_merged = merge_to_key_value_pairs(
+                deduplicated_data,
+                'Query_small',
+                'Result_small'
+            )
+            
+            # Restructure for Verify/Retrieve_Summary compatibility
+            keys, val = [], []
+            for item in deduplicated_data_merged:
+                for items_key, items_value in item.items():
+                    keys.append(items_key)
+                    val.append(items_value)
+            
+            send_verify = []
+            for i, j in zip(keys, val, strict=False):
+                send_verify.append({
+                    "Query_small": i,
+                    "Answer_Small": j
+                })
+            
+            dup_databases = {
+                "Query": original,
+                "Expansion_issue": send_verify,
+                "_intermediate_outputs": intermediate_outputs  # Preserve intermediate outputs
+            }
+            
+            logger.info(f"Collected {len(intermediate_outputs)} intermediate outputs from search results")
+            
+        else:
+            # Handle string context (simple query)
+            query = str(context).strip()
+            
+            try:
+                # Prepare search parameters based on storage type
+                search_params = {
+                    "group_id": group_id,
+                    "question": query,
+                    "return_raw_results": True
+                }
+                
+                # Add storage-specific parameters
+                if storage_type == "rag" and user_rag_memory_id:
+                    retrieve_chunks_result = knowledge_retrieval(query, kb_config,[str(group_id)])
+                    try:
+                        retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
+                        clean_content = '\n\n'.join(retrieval_knowledge)
+                        cleaned_query = query
+                        raw_results = clean_content
+                        logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
+                    except:
+                        clean_content = ''
+                        raw_results = ''
+                        cleaned_query = query
+                        logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
+                else:
+                    clean_content, cleaned_query, raw_results = await search_service.execute_hybrid_search(
+                        **search_params, memory_config=memory_config
+                    )
+                # Keep structure for Verify/Retrieve_Summary compatibility
+                dup_databases = {
+                    "Query": cleaned_query,
+                    "Expansion_issue": [{
+                        "Query_small": cleaned_query,
+                        "Answer_Small": clean_content,
+                        "_intermediate": {
+                            "type": "search_result",
+                            "query": cleaned_query,
+                            "raw_results": raw_results,
+                            "index": 1,
+                            "total": 1
+                        }
+                    }]
+                }
+            except Exception as e:
+                logger.error(
+                    f"Retrieve: hybrid_search failed for query '{query}': {e}",
+                    exc_info=True
+                )
+                # Return empty results on failure
+                dup_databases = {
+                    "Query": query,
+                    "Expansion_issue": []
+                }
+        
+        logger.info(
+            f"Retrieval: {storage_type}--{user_rag_memory_id}--Query={dup_databases.get('Query', '')}, "
+            f"Expansion_issue count={len(dup_databases.get('Expansion_issue', []))}"
+        )
+        
+        # Build result with intermediate outputs
+        result = {
+            "context": dup_databases,
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id
+        }
+        
+        # Add intermediate outputs list if they exist
+        intermediate_outputs = dup_databases.get('_intermediate_outputs', [])
+        if intermediate_outputs:
+            result['_intermediates'] = intermediate_outputs
+            logger.info(f"Adding {len(intermediate_outputs)} intermediate outputs to result")
+        else:
+            logger.warning("No intermediate outputs found in dup_databases")
+        
+        return result
+        
+    except Exception as e:
+        logger.error(
+            f"Retrieve failed: {e}",
+            exc_info=True
+        )
+        return {
+            "context": {
+                "Query": "",
+                "Expansion_issue": []
+            },
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id,
+            "error": str(e)
+        }
+        
+    finally:
+        # Log execution time
+        end = time.time()
+        try:
+            duration = end - start
+        except Exception:
+            duration = 0.0
+        log_time('Retrieval', duration)

api/app/core/memory/agent/mcp_server/tools/summary_tools.py

@@ -0,0 +1,640 @@
+"""
+Summary Tools for data summarization.
+
+This module contains MCP tools for summarizing retrieved data and generating responses.
+LLM clients are constructed from MemoryConfig when needed.
+"""
+
+import json
+import os
+import re
+import time
+
+from app.core.logging_config import get_agent_logger, log_time
+from app.core.memory.agent.mcp_server.mcp_instance import mcp
+from app.core.memory.agent.mcp_server.models.summary_models import (
+    RetrieveSummaryResponse,
+    SummaryResponse,
+)
+from app.core.memory.agent.mcp_server.server import get_context_resource
+from app.core.memory.agent.utils.messages_tool import (
+    Resolve_username,
+    Summary_messages_deal,
+)
+from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
+from app.core.rag.nlp.search import knowledge_retrieval
+from app.db import get_db_context
+from app.schemas.memory_config_schema import MemoryConfig
+from dotenv import load_dotenv
+from mcp.server.fastmcp import Context
+
+load_dotenv()
+logger = get_agent_logger(__name__)
+
+
+@mcp.tool()
+async def Summary(
+    ctx: Context,
+    context: str,
+    usermessages: str,
+    apply_id: str,
+    group_id: str,
+    memory_config: MemoryConfig,
+    storage_type: str = "",
+    user_rag_memory_id: str = "",
+) -> dict:
+    """
+    Summarize the verified data.
+    
+    Args:
+        ctx: FastMCP context for dependency injection
+        context: JSON string containing verified data
+        usermessages: User messages identifier
+        apply_id: Application identifier
+        group_id: Group identifier
+        memory_config: MemoryConfig object containing all configuration
+        storage_type: Storage type for the workspace (optional)
+        user_rag_memory_id: User RAG memory identifier (optional)
+        
+    Returns:
+        dict: Contains 'status' and 'summary_result'
+    """
+    start = time.time()
+
+    try:
+        # Extract services from context
+        template_service = get_context_resource(ctx, "template_service")
+        session_service = get_context_resource(ctx, "session_service")
+
+        # Get LLM client from memory_config
+        with get_db_context() as db:
+            factory = MemoryClientFactory(db)
+            llm_client = factory.get_llm_client_from_config(memory_config)
+        
+        # Resolve session ID
+        sessionid = Resolve_username(usermessages)
+        
+        # Process context to extract answer and query
+        answer_small, query = await Summary_messages_deal(context)
+
+        
+        start_time= time.time()
+        history = await session_service.get_history(sessionid, apply_id, group_id)
+        end_time=time.time()
+        logger.info(f"Retrieve_Summary-REDIS搜索：{end_time - start_time}")
+        data = {
+            "query": query,
+            "history": history,
+            "retrieve_info": answer_small
+        }
+
+    except Exception as e:
+        logger.error(
+            f"Summary: initialization failed: {e}",
+            exc_info=True
+        )
+        return {
+            "status": "error",
+            "summary_result": "信息不足，无法回答"
+        }
+
+    try:
+        # Render template
+        system_prompt = await template_service.render_template(
+            template_name='summary_prompt.jinja2',
+            operation_name='summary',
+            data=data,
+            query=query
+        )
+    except Exception as e:
+        logger.error(
+            f"Template rendering failed for Summary: {e}",
+            exc_info=True
+        )
+        return {
+            "status": "error",
+            "message": f"Prompt rendering failed: {str(e)}"
+        }
+
+    try:
+        # Call LLM with structured response
+        structured = await llm_client.response_structured(
+            messages=[{"role": "system", "content": system_prompt}],
+            response_model=SummaryResponse
+        )
+
+        aimessages = structured.query_answer or ""
+
+    except Exception as e:
+        logger.error(
+            f"LLM call failed for Summary: {e}",
+            exc_info=True
+        )
+        aimessages = ""
+
+    try:
+        # Save session
+        if aimessages != "":
+            await session_service.save_session(
+            user_id=sessionid,
+            query=query,
+            apply_id=apply_id,
+            group_id=group_id,
+            ai_response=aimessages
+        )
+        logger.info(f"sessionid: {aimessages} 写入成功")
+    except Exception as e:
+        logger.error(
+            f"sessionid: {sessionid} 写入失败，错误信息：{str(e)}",
+            exc_info=True
+        )
+        return {
+            "status": "error",
+            "message": str(e)
+        }
+
+    # Cleanup duplicate sessions
+    await session_service.cleanup_duplicates()
+
+    # Use fallback if empty
+    if aimessages == '':
+        aimessages = '信息不足，无法回答'
+
+    logger.info(f"Summary after verification: {aimessages}")
+
+    # Log execution time
+    end = time.time()
+    try:
+        duration = end - start
+    except Exception:
+        duration = 0.0
+    log_time('Summary', duration)
+
+    return {
+        "status": "success",
+        "summary_result": aimessages,
+        "storage_type": storage_type,
+        "user_rag_memory_id": user_rag_memory_id
+    }
+
+
+@mcp.tool()
+async def Retrieve_Summary(
+    ctx: Context,
+    context: dict,
+    usermessages: str,
+    apply_id: str,
+    group_id: str,
+    memory_config: MemoryConfig,
+    storage_type: str = "",
+    user_rag_memory_id: str = "",
+) -> dict:
+    """
+    Summarize data directly from retrieval results.
+
+    Args:
+        ctx: FastMCP context for dependency injection
+        context: Dictionary containing Query and Expansion_issue from Retrieve
+        usermessages: User messages identifier
+        apply_id: Application identifier
+        group_id: Group identifier
+        memory_config: MemoryConfig object containing all configuration
+        storage_type: Storage type for the workspace (optional)
+        user_rag_memory_id: User RAG memory identifier (optional)
+
+    Returns:
+        dict: Contains 'status' and 'summary_result'
+    """
+    start = time.time()
+
+    try:
+        # Extract services from context
+        template_service = get_context_resource(ctx, "template_service")
+        session_service = get_context_resource(ctx, "session_service")
+
+        # Get LLM client from memory_config
+        with get_db_context() as db:
+            factory = MemoryClientFactory(db)
+            llm_client = factory.get_llm_client_from_config(memory_config)
+
+        # Resolve session ID
+        sessionid = Resolve_username(usermessages)
+
+
+
+        # Handle both 'content' and 'context' keys (LangGraph uses 'content')
+        logger.debug(f"Retrieve_Summary: raw context type={type(context)}, keys={list(context.keys()) if isinstance(context, dict) else 'N/A'}")
+        
+        if isinstance(context, dict):
+            if "content" in context:
+                inner = context["content"]
+                # If it's a JSON string, parse it
+                if isinstance(inner, str):
+                    try:
+                        parsed = json.loads(inner)
+                        logger.info("Retrieve_Summary: successfully parsed JSON")
+                    except json.JSONDecodeError:
+                        # Try unescaping first
+                        try:
+                            unescaped = inner.encode('utf-8').decode('unicode_escape')
+                            parsed = json.loads(unescaped)
+                            logger.info("Retrieve_Summary: parsed after unescaping")
+                        except (json.JSONDecodeError, UnicodeDecodeError) as e:
+                            logger.error(
+                                f"Retrieve_Summary: parsing failed even after unescape: {e}"
+                            )
+                            context_dict = {"Query": "", "Expansion_issue": []}
+                            parsed = None
+
+                    if parsed:
+                        # Check if parsed has 'context' wrapper
+                        if isinstance(parsed, dict) and "context" in parsed:
+                            context_dict = parsed["context"]
+                        else:
+                            context_dict = parsed
+                elif isinstance(inner, dict):
+                    context_dict = inner
+                else:
+                    context_dict = {"Query": "", "Expansion_issue": []}
+            elif "context" in context:
+                context_dict = context["context"] if isinstance(context["context"], dict) else context
+            else:
+                context_dict = context
+        else:
+            context_dict = {"Query": "", "Expansion_issue": []}
+
+        query = context_dict.get("Query", "")
+        expansion_issue = context_dict.get("Expansion_issue", [])
+        
+        logger.debug(f"Retrieve_Summary: query='{query}', expansion_issue count={len(expansion_issue)}")
+        logger.debug(f"Retrieve_Summary: expansion_issue={expansion_issue[:2] if expansion_issue else 'empty'}")
+
+        # Extract retrieve_info from expansion_issue
+        retrieve_info = []
+        for item in expansion_issue:
+            # Check for both Answer_Small and Answer_Small (typo) for backward compatibility
+            answer = None
+            if isinstance(item, dict):
+                if "Answer_Small" in item:
+                    answer = item["Answer_Small"]
+
+
+                if answer is not None:
+                    # Handle both string and list formats
+                    if isinstance(answer, list):
+                        # Join list of characters/strings into a single string
+                        retrieve_info.append(''.join(str(x) for x in answer))
+                    elif isinstance(answer, str):
+                        retrieve_info.append(answer)
+                    else:
+                        retrieve_info.append(str(answer))
+
+        # Join all retrieve_info into a single string
+        retrieve_info_str = '\n\n'.join(retrieve_info) if retrieve_info else ""
+
+        start_time=time.time()
+        history = await session_service.get_history(sessionid, apply_id, group_id)
+        # Override with empty list for now (as in original)
+        end_time=time.time()
+        logger.info(f"Retrieve_Summary-REDIS搜索：{end_time - start_time}")
+    except Exception as e:
+        logger.error(
+            f"Retrieve_Summary: initialization failed: {e}",
+            exc_info=True
+        )
+        return {
+            "status": "error",
+            "summary_result": "信息不足，无法回答"
+        }
+
+    try:
+        # Render template
+        system_prompt = await template_service.render_template(
+            template_name='Retrieve_Summary_prompt.jinja2',
+            operation_name='retrieve_summary',
+            query=query,
+            history=history,
+            retrieve_info=retrieve_info_str
+        )
+    except Exception as e:
+        logger.error(
+            f"Template rendering failed for Retrieve_Summary: {e}",
+            exc_info=True
+        )
+        return {
+            "status": "error",
+            "message": f"Prompt rendering failed: {str(e)}"
+        }
+
+    try:
+        # Call LLM with structured response
+        structured = await llm_client.response_structured(
+            messages=[{"role": "system", "content": system_prompt}],
+            response_model=RetrieveSummaryResponse
+        )
+
+        # Handle case where structured response might be None or incomplete
+        if structured and hasattr(structured, 'data') and structured.data:
+            aimessages = structured.data.query_answer or ""
+        else:
+            logger.warning("Structured response is None or incomplete, using default message")
+            aimessages = "信息不足，无法回答"
+
+
+        # Check for insufficient information response
+        if '信息不足，无法回答' not  in str(aimessages) or str(aimessages)!="":
+            # Save session
+            await session_service.save_session(
+                user_id=sessionid,
+                query=query,
+                apply_id=apply_id,
+                group_id=group_id,
+                ai_response=aimessages
+            )
+            logger.info(f"sessionid: {aimessages} 写入成功")
+    except Exception as e:
+        logger.error(
+            f"Retrieve_Summary: LLM call failed: {e}",
+            exc_info=True
+        )
+        aimessages = ""
+    # Cleanup duplicate sessions
+    await session_service.cleanup_duplicates()
+
+    # Use fallback if empty
+    if aimessages == '':
+        aimessages = '信息不足，无法回答'
+
+    logger.info(f"Summary after retrieval: {aimessages}")
+
+    # Log execution time
+    end = time.time()
+    try:
+        duration = end - start
+    except Exception:
+        duration = 0.0
+    log_time('Retrieval summary', duration)
+
+    # Emit intermediate output for frontend
+    return {
+        "status": "success",
+        "summary_result": aimessages,
+        "storage_type": storage_type,
+        "user_rag_memory_id": user_rag_memory_id,
+        "_intermediate": {
+            "type": "retrieval_summary",
+            "summary": aimessages,
+            "query": query,
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id
+        }
+    }
+
+
+@mcp.tool()
+async def Input_Summary(
+    ctx: Context,
+    context: str,
+    usermessages: str,
+    search_switch: str,
+    apply_id: str,
+    group_id: str,
+    memory_config: MemoryConfig,
+    storage_type: str = "",
+    user_rag_memory_id: str = "",
+) -> dict:
+    """
+    Generate a quick summary for direct input without verification.
+
+    Args:
+        ctx: FastMCP context for dependency injection
+        context: String containing the input sentence
+        usermessages: User messages identifier
+        search_switch: Search switch value for routing ('2' for summaries only)
+        apply_id: Application identifier
+        group_id: Group identifier
+        memory_config: MemoryConfig object containing all configuration
+        storage_type: Storage type for the workspace (e.g., 'rag', 'vector')
+        user_rag_memory_id: User RAG memory identifier
+
+    Returns:
+        dict: Contains 'query_answer' with the summary result
+    """
+    start = time.time()
+    logger.info(f"Input_Summary: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
+
+    try:
+        # Extract services from context
+        session_service = get_context_resource(ctx, "session_service")
+        search_service = get_context_resource(ctx, "search_service")
+
+        # Resolve session ID
+        sessionid = Resolve_username(usermessages) or ""
+        sessionid = sessionid.replace('call_id_', '')
+
+        start_time=time.time()
+        history = await session_service.get_history(
+            str(sessionid),
+            str(apply_id),
+            str(group_id)
+        )
+        end_time=time.time()
+        logger.info(f"Input_Summary-REDIS搜索：{end_time - start_time}")
+        # Override with empty list for now (as in original)
+
+        # Log the raw context for debugging
+        logger.info(f"Input_Summary: Received context type={type(context)}, value={context[:200] if isinstance(context, str) else context}")
+
+        # Extract sentence from context
+        # Context can be a string or might contain the sentence in various formats
+        try:
+            # Try to parse as JSON first
+            if isinstance(context, str) and (context.startswith('{') or context.startswith('[')):
+                try:
+                    import json
+                    context_dict = json.loads(context)
+                    if isinstance(context_dict, dict):
+                        query = context_dict.get('sentence', context_dict.get('content', context))
+                    else:
+                        query = context
+                except json.JSONDecodeError:
+                    # Not valid JSON, try regex
+                    match = re.search(r"'sentence':\s*['\"]?(.*?)['\"]?\s*,", context)
+                    query = match.group(1) if match else context
+            else:
+                query = context
+        except Exception as e:
+            logger.warning(f"Failed to extract query from context: {e}")
+            query = context
+
+        # Clean query
+        query = str(query).strip().strip("\"'")
+
+        logger.debug(f"Input_Summary: Extracted query='{query}' from context type={type(context)}")
+
+        # Execute search based on search_switch and storage_type
+        try:
+            logger.info(f"search_switch: {search_switch}, storage_type: {storage_type}")
+
+            # Prepare search parameters based on storage type
+            search_params = {
+                "group_id": group_id,
+                "question": query,
+                "return_raw_results": True
+            }
+
+            # Add storage-specific parameters
+
+            # Retrieval
+            if search_switch == '2':
+                search_params["include"] = ["summaries"]
+                if storage_type == "rag" and user_rag_memory_id:
+                    raw_results = []
+                    retrieve_info = ""
+                    kb_config={
+                        "knowledge_bases": [
+                            {
+                                "kb_id": user_rag_memory_id,
+                                "similarity_threshold": 0.7,
+                                "vector_similarity_weight": 0.5,
+                                "top_k": 10,
+                                "retrieve_type": "participle"
+                            }
+                        ],
+                        "merge_strategy": "weight",
+                        "reranker_id":os.getenv('reranker_id'),
+                        "reranker_top_k": 10
+                    }
+
+                    retrieve_chunks_result = knowledge_retrieval(query, kb_config,[str(group_id)])
+                    try:
+                        retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
+                        retrieve_info = '\n\n'.join(retrieval_knowledge)
+                        raw_results=[retrieve_info]
+                        logger.info(f"Input_Summary: Using RAG storage with memory_id={user_rag_memory_id}")
+                    except:
+                        retrieve_info=''
+                        raw_results=['']
+                        logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
+                else:
+                    retrieve_info, question, raw_results = await search_service.execute_hybrid_search(
+                        **search_params, memory_config=memory_config
+                    )
+                logger.info("Input_Summary: Using summary for retrieval")
+            else:
+                retrieve_info, question, raw_results = await search_service.execute_hybrid_search(
+                    **search_params, memory_config=memory_config
+                )
+
+        except Exception as e:
+            logger.error(
+                f"Input_Summary: hybrid_search failed, using empty results: {e}",
+                exc_info=True
+            )
+            retrieve_info, question, raw_results = "", query, []
+
+        # Return retrieved information directly without LLM processing
+        # Use the raw retrieved info as the answer
+        aimessages = retrieve_info if retrieve_info else "信息不足，无法回答"
+
+        logger.info(f"Quick answer (no LLM): {storage_type}--{user_rag_memory_id}--{aimessages[:500]}...")
+
+        # Emit intermediate output for frontend
+        return {
+            "status": "success",
+            "summary_result": aimessages,
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id,
+            "_intermediate": {
+                "type": "input_summary",
+                "title": "快速答案",
+                "summary": aimessages,
+                "query": query,
+                "raw_results": raw_results,
+                "search_mode": "quick_search",
+                "storage_type": storage_type,
+                "user_rag_memory_id": user_rag_memory_id
+            }
+        }
+
+    except Exception as e:
+        logger.error(
+            f"Input_Summary failed: {e}",
+            exc_info=True
+        )
+        return {
+            "status": "fail",
+            "summary_result": "信息不足，无法回答",
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id,
+            "error": str(e)
+        }
+
+    finally:
+        # Log execution time
+        end = time.time()
+        try:
+            duration = end - start
+        except Exception:
+            duration = 0.0
+        log_time('Retrieval', duration)
+
+
+@mcp.tool()
+async def Summary_fails(
+    ctx: Context,
+    context: str,
+    usermessages: str,
+    apply_id: str,
+    group_id: str,
+    storage_type: str = "",
+    user_rag_memory_id: str = ""
+) -> dict:
+    """
+    Handle workflow failure when summary cannot be generated.
+
+    Args:
+        ctx: FastMCP context for dependency injection
+        context: Failure context string
+        usermessages: User messages identifier
+        apply_id: Application identifier
+        group_id: Group identifier
+        storage_type: Storage type for the workspace (optional)
+        user_rag_memory_id: User RAG memory identifier (optional)
+
+    Returns:
+        dict: Contains 'query_answer' with failure message
+    """
+    try:
+        # Extract services from context
+        session_service = get_context_resource(ctx, 'session_service')
+
+        # Parse session ID from usermessages
+        usermessages_parts = usermessages.split('_')[1:]
+        sessionid = '_'.join(usermessages_parts[:-1])
+
+        # Cleanup duplicate sessions
+        await session_service.cleanup_duplicates()
+
+        logger.info("没有相关数据")
+        logger.debug(f"Summary_fails called with apply_id: {apply_id}, group_id: {group_id}")
+        
+        return {
+            "status": "success",
+            "summary_result": "没有相关数据",
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id
+        }
+        
+    except Exception as e:
+        logger.error(
+            f"Summary_fails failed: {e}",
+            exc_info=True
+        )
+        return {
+            "status": "fail",
+            "summary_result": "没有相关数据",
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id,
+            "error": str(e)
+        }

api/app/core/memory/agent/mcp_server/tools/verification_tools.py

@@ -0,0 +1,174 @@
+"""
+Verification Tools for data verification.
+
+This module contains MCP tools for verifying retrieved data.
+"""
+import time
+
+from app.core.logging_config import get_agent_logger, log_time
+from app.core.memory.agent.mcp_server.mcp_instance import mcp
+from app.core.memory.agent.mcp_server.server import get_context_resource
+from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_
+from app.core.memory.agent.utils.messages_tool import (
+    Resolve_username,
+    Retrieve_verify_tool_messages_deal,
+    Verify_messages_deal,
+)
+from app.core.memory.agent.utils.verify_tool import VerifyTool
+from app.schemas.memory_config_schema import MemoryConfig
+from jinja2 import Template
+from mcp.server.fastmcp import Context
+
+logger = get_agent_logger(__name__)
+
+
+@mcp.tool()
+async def Verify(
+    ctx: Context,
+    context: dict,
+    usermessages: str,
+    apply_id: str,
+    group_id: str,
+    memory_config: MemoryConfig,
+    storage_type: str = "",
+    user_rag_memory_id: str = ""
+) -> dict:
+    """
+    Verify the retrieved data.
+    
+    Args:
+        ctx: FastMCP context for dependency injection
+        context: Dictionary containing query and expansion issues
+        usermessages: User messages identifier
+        apply_id: Application identifier
+        group_id: Group identifier
+        memory_config: MemoryConfig object containing all configuration
+        storage_type: Storage type for the workspace (optional)
+        user_rag_memory_id: User RAG memory identifier (optional)
+        
+    Returns:
+        dict: Contains 'status' and 'verified_data' with verification results
+    """
+    start = time.time()
+
+
+    try:
+        # Extract services from context
+        session_service = get_context_resource(ctx, 'session_service')
+        
+        # Load verification prompt template
+        file_path = PROJECT_ROOT_ + '/agent/utils/prompt/split_verify_prompt.jinja2'
+        
+        # Read template file directly (VerifyTool expects raw template content)
+        from app.core.memory.agent.utils.messages_tool import read_template_file
+        system_prompt = await read_template_file(file_path)
+
+
+        
+        # Resolve session ID
+        sessionid = Resolve_username(usermessages)
+        
+        # Get conversation history
+        history = await session_service.get_history(sessionid, apply_id, group_id)
+
+        template = Template(system_prompt)
+        system_prompt = template.render(history=history, sentence=context)
+        
+        # Process context to extract query and results
+        Query_small, Result_small, query = await Verify_messages_deal(context)
+        
+        # Build query list for verification
+        query_list = []
+        for query_small, anser in zip(Query_small, Result_small, strict=False):
+            query_list.append({
+                'Query_small': query_small,
+                'Answer_Small': anser
+            })
+        
+        messages = {
+            "Query": query,
+            "Expansion_issue": query_list
+        }
+
+
+        
+        # Call verification workflow with LLM model ID from memory_config
+        verify_tool = VerifyTool(
+            system_prompt=system_prompt,
+            verify_data=messages,
+            llm_model_id=str(memory_config.llm_model_id)
+        )
+        verify_result = await verify_tool.verify()
+        
+        # Parse LLM verification result with error handling
+        try:
+            messages_deal = await Retrieve_verify_tool_messages_deal(
+                verify_result,
+                history,
+                query
+            )
+        except Exception as e:
+            logger.error(
+                f"Retrieve_verify_tool_messages_deal parsing failed: {e}",
+                exc_info=True
+            )
+            # Fallback to avoid 500 errors
+            messages_deal = {
+                "data": {
+                    "query": query,
+                    "expansion_issue": []
+                },
+                "split_result": "failed",
+                "reason": str(e),
+                "history": history,
+            }
+        
+        logger.info(f"Verification result: {messages_deal}")
+        
+        # Emit intermediate output for frontend
+        return {
+            "status": "success",
+            "verified_data": messages_deal,
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id,
+            "_intermediate": {
+                "type": "verification",
+                "title": "Data Verification",
+                "result": messages_deal.get("split_result", "unknown"),
+                "reason": messages_deal.get("reason", ""),
+                "query": query,
+                "verified_count": len(query_list),
+                "storage_type": storage_type,
+                "user_rag_memory_id": user_rag_memory_id
+            }
+        }
+        
+    except Exception as e:
+        logger.error(
+            f"Verify failed: {e}",
+            exc_info=True
+        )
+        return {
+            "status": "error",
+            "message": str(e),
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id,
+            "verified_data": {
+                "data": {
+                    "query": "",
+                    "expansion_issue": []
+                },
+                "split_result": "failed",
+                "reason": str(e),
+                "history": [],
+            }
+        }
+        
+    finally:
+        # Log execution time
+        end = time.time()
+        try:
+            duration = end - start
+        except Exception:
+            duration = 0.0
+        log_time('Verification', duration)

api/app/core/memory/agent/models/verification_models.py

@@ -1,32 +0,0 @@
-"""Pydantic models for verification operations."""
-
-from typing import List, Optional, Dict, Any
-from pydantic import BaseModel, Field
-
-
-class VerificationItem(BaseModel):
-    """Individual verification item for a query-answer pair."""
-    
-    query_small: str = Field(..., description="子问题")
-    answer_small: str = Field(..., description="子问题的回答")
-    status: str = Field(..., description="验证状态：True 或 False")
-    query_answer: str = Field(..., description="问题的答案（与 answer_small 相同）")
-
-
-class VerificationResult(BaseModel):
-    """Result model for verification operation."""
-    
-    query: str = Field(..., description="原始查询问题")
-    history: List[Dict[str, Any]] = Field(default_factory=list, description="历史对话记录")
-    expansion_issue: List[VerificationItem] = Field(
-        default_factory=list, 
-        description="验证后的数据列表，包含所有通过验证的问答对"
-    )
-    split_result: str = Field(
-        ..., 
-        description="验证结果状态：success（expansion_issue 非空）或 failed（expansion_issue 为空）"
-    )
-    reason: Optional[str] = Field(
-        None, 
-        description="验证结果的说明和分析"
-    )

api/app/core/memory/agent/models/write_aggregate_model.py

@@ -1,28 +0,0 @@
-"""Pydantic models for write aggregate judgment operations."""
-
-from typing import List, Union
-from pydantic import BaseModel, Field
-
-
-class MessageItem(BaseModel):
-    """Individual message item in conversation."""
-    
-    role: str = Field(..., description="角色：user 或 assistant")
-    content: str = Field(..., description="消息内容")
-
-
-class WriteAggregateResponse(BaseModel):
-    """Response model for aggregate judgment containing judgment result and output."""
-    
-    is_same_event: bool = Field(
-        ..., 
-        description="是否是同一事件。True表示是同一事件，False表示不同事件"
-    )
-    output: Union[List[MessageItem], bool] = Field(
-        ..., 
-        description="如果is_same_event为True，返回False；如果is_same_event为False，返回消息列表"
-    )
-
-
-# 为了保持向后兼容，保留旧的类名作为别名
-WriteAggregateModel = WriteAggregateResponse

api/app/core/memory/agent/multimodal/oss_picture.py

@@ -0,0 +1,114 @@
+import os
+import sys
+import traceback
+
+import requests
+
+# from qcloud_cos import CosConfig, CosS3Client
+# from qcloud_cos.cos_exception import CosClientError, CosServiceError
+
+# from config.paths import BASE_DIR
+BASE_DIR = os.path.dirname(os.path.realpath(sys.argv[0]))
+
+class OSSUploader:
+    """对象存储文件上传工具类"""
+
+    def __init__(self, env):
+        api = {
+            "test": "https://testlingqi.redbearai.com/api/user/file/common/upload/v2/anon",
+            "prod": "https://lingqi.redbearai.com/api/user/file/common/upload/v2/anon"
+        }
+        self.api = api.get(env, "https://testlingqi.redbearai.com/api/user/file/common/upload/v2/anon")
+        self.privacy = "false"
+        self.headers = {
+            "User-Agent": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
+                          'AppleWebKit/537.36 (KHTML, like Gecko)'
+                          ' Chrome/133.0.6833.84 Safari/537.36'
+        }
+
+    @staticmethod
+    def _generate_object_key(file_path, prefix='xhs_'):
+        """
+        生成对象存储的Key
+
+        :param file_path: 本地文件路径
+        :param prefix: 存储前缀，用于分类存储
+        :return: 生成的对象Key
+        """
+        # 文件md5值.后缀名
+        filename = os.path.basename(file_path)
+        filename = f"{filename}"
+
+        # 组合成完整的对象Key
+        return f"{prefix}{filename}"
+
+    def upload_image(self, file_name, prefix='jd_'):
+        """
+        上传文件到COS并返回可访问的URL
+
+        :param file_url: 文件路径
+        :param file_name: 文件名称
+        :param media_type: 文件类型
+        :param prefix: 存储前缀，用于分类存储
+        :return: 文件访问URL
+        """
+        # 检查文件是否存在
+
+
+
+        file_path = os.path.join(BASE_DIR, file_name)
+
+        # response = requests.get(url, headers=self.headers, stream=True)
+
+        # if response.status_code == 200:
+        #     with open(file_path, "wb") as f:
+        #         for chunk in response.iter_content(1024):  # 分块写入，避免内存占用过大
+        #             f.write(chunk)
+        # else:
+        #     raise Exception(f"文件下载失败,{file_name}")
+
+        # 生成对象Key
+        object_key = self._generate_object_key(file_path, prefix +file_name.split('.')[-1])
+
+        try:
+            upload_response = requests.post(
+                self.api,
+                data={
+                    "privacy": self.privacy,
+                    "fileName": object_key,
+                }
+            )
+
+            if upload_response.status_code != 200:
+                raise Exception('上传接口请求失败')
+            resp = upload_response.json()
+            name = resp["data"]["name"]
+            file_url = resp["data"]["path"]
+            policy = resp["data"]["policy"]
+            with open(file_path, 'rb') as f:
+                oss_push_resp = requests.post(
+                    policy["host"],
+                    files={
+                        "key": policy["dir"],
+                        "OSSAccessKeyId": policy["accessid"],
+                        "name": name,
+                        "policy": policy["policy"],
+                        "success_action_status": 200,
+                        "signature": policy["signature"],
+                        "file": f,
+                    }
+                )
+                if oss_push_resp.status_code == 200:
+                    return file_url
+            raise Exception("OSS上传失败")
+        except Exception:
+            raise Exception(f"上传失败: \n{traceback.format_exc()}")
+        finally:
+            print('success')
+            # os.remove(file_path)
+
+
+if __name__ == '__main__':
+    cos_uploader = OSSUploader("prod")
+    url =cos_uploader.upload_image('./example01.jpg')
+    print(url)

api/app/core/memory/agent/multimodal/speech_model.py

@@ -0,0 +1,121 @@
+import asyncio
+import re
+
+from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_, picture_model_requests,Picture_recognize, Voice_recognize
+from app.core.memory.agent.utils.messages_tool import read_template_file
+
+import requests
+import json
+import os
+import time
+# file_urls = [
+#     "https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_female2.wav",
+#     "https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_male2.wav",
+# ]
+class Vico_recognition:
+    def __init__(self,file_urls):
+        self.api_key=''
+        self.backend_model_name=''
+        self.api_base=''
+        self.file_urls=file_urls
+
+    # 提交文件转写任务，包含待转写文件url列表
+    async  def submit_task(self) -> str:
+        self.api_key, self.backend_model_name, self.api_base =await Voice_recognize()
+
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+            "X-DashScope-Async": "enable",
+        }
+        data = {
+            "model": self.backend_model_name,
+            "input": {"file_urls": self.file_urls},
+            "parameters": {
+                "channel_id": [0],
+                "vocabulary_id": "vocab-Xxxx",
+            },
+        }
+        # 录音文件转写服务url
+        service_url = (
+            "https://dashscope.aliyuncs.com/api/v1/services/audio/asr/transcription"
+        )
+        response = requests.post(
+            service_url, headers=headers, data=json.dumps(data)
+        )
+
+        # 打印响应内容
+        if response.status_code == 200:
+            return response.json()["output"]["task_id"]
+        else:
+            print("task failed!")
+            print(response.json())
+            return None
+
+    async def download_transcription_result(self, transcription_url):
+        """
+        Args:
+            transcription_url (str): 转写结果文件URL
+        Returns:
+            dict: 转写结果内容
+        """
+        try:
+            response = requests.get(transcription_url)
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            print(f"下载转写结果失败: {e}")
+            return None
+
+    # 循环查询任务状态直到成功
+    async def wait_for_complete(self,task_id):
+        self.api_key, self.backend_model_name, self.api_base = await Voice_recognize()
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+            "X-DashScope-Async": "enable",
+        }
+
+        pending = True
+        while pending:
+            # 查询任务状态服务url
+            service_url = f"https://dashscope.aliyuncs.com/api/v1/tasks/{task_id}"
+            response = requests.post(
+                service_url, headers=headers
+            )
+            if response.status_code == 200:
+                status = response.json()['output']['task_status']
+                if status == 'SUCCEEDED':
+                    print("task succeeded!")
+                    pending = False
+                    return response.json()['output']['results']
+                elif status == 'RUNNING' or status == 'PENDING':
+                    pass
+                else:
+                    print("task failed!")
+                    pending = False
+            else:
+                print("query failed!")
+                pending = False
+            time.sleep(0.1)
+    async def run(self):
+        self.api_key, self.backend_model_name, self.api_base = await Voice_recognize()
+        task_id=await self.submit_task()
+        result=await self.wait_for_complete(task_id)
+        result_context=[]
+        for  i in result:
+            transcription_url=i['transcription_url']
+            print(f"转写URL: {transcription_url}")
+
+            # 下载并打印转写内容
+            content = await self.download_transcription_result(transcription_url)
+            if content:
+                content=json.dumps(content, indent=2, ensure_ascii=False)
+                context=re.findall(r'"text": "(.*?)"', content)
+                result_context.append(context[0])
+        result=''.join(result_context)
+        return (result)
+
+
+
+

api/app/core/memory/agent/services/optimized_llm_service.py

@@ -1,277 +0,0 @@
-"""
-优化的LLM服务类，用于压缩和统一LLM调用
-"""
-
-import asyncio
-from typing import Any, Dict, List, Optional, Type, TypeVar, Union
-from pydantic import BaseModel
-from sqlalchemy.orm import Session
-
-from app.core.logging_config import get_agent_logger
-from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.core.memory.llm_tools.openai_client import OpenAIClient
-
-T = TypeVar('T', bound=BaseModel)
-
-logger = get_agent_logger(__name__)
-
-
-class OptimizedLLMService:
-    """
-    优化的LLM服务类，提供统一的LLM调用接口
-    
-    特性：
-    1. 客户端复用 - 避免重复创建LLM客户端
-    2. 批量处理 - 支持并发处理多个请求
-    3. 错误处理 - 统一的错误处理和降级策略
-    4. 性能优化 - 缓存和连接池优化
-    """
-    
-    def __init__(self, db_session: Session):
-        self.db_session = db_session
-        self.client_factory = MemoryClientFactory(db_session)
-        self._client_cache: Dict[str, OpenAIClient] = {}
-        
-    def _get_cached_client(self, llm_model_id: str) -> OpenAIClient:
-        """获取缓存的LLM客户端，避免重复创建"""
-        if llm_model_id not in self._client_cache:
-            self._client_cache[llm_model_id] = self.client_factory.get_llm_client(llm_model_id)
-        return self._client_cache[llm_model_id]
-    
-    async def structured_response(
-        self,
-        llm_model_id: str,
-        system_prompt: str,
-        response_model: Type[T],
-        user_message: Optional[str] = None,
-        fallback_value: Optional[Any] = None
-    ) -> T:
-        """
-        统一的结构化响应接口
-        
-        Args:
-            llm_model_id: LLM模型ID
-            system_prompt: 系统提示词
-            response_model: 响应模型类
-            user_message: 用户消息（可选）
-            fallback_value: 失败时的降级值
-            
-        Returns:
-            结构化响应对象
-        """
-        try:
-            llm_client = self._get_cached_client(llm_model_id)
-            
-            messages = [{"role": "system", "content": system_prompt}]
-            if user_message:
-                messages.append({"role": "user", "content": user_message})
-            
-            logger.debug(f"LLM调用: model={llm_model_id}, prompt_length={len(system_prompt)}")
-            
-            structured = await llm_client.response_structured(
-                messages=messages,
-                response_model=response_model
-            )
-            
-            if structured is None:
-                logger.warning(f"LLM返回None，使用降级值")
-                return self._create_fallback_response(response_model, fallback_value)
-            
-            return structured
-            
-        except Exception as e:
-            logger.error(f"结构化响应失败: {e}", exc_info=True)
-            return self._create_fallback_response(response_model, fallback_value)
-    
-    async def batch_structured_response(
-        self,
-        llm_model_id: str,
-        requests: List[Dict[str, Any]],
-        response_model: Type[T],
-        max_concurrent: int = 5
-    ) -> List[T]:
-        """
-        批量处理结构化响应
-        
-        Args:
-            llm_model_id: LLM模型ID
-            requests: 请求列表，每个请求包含system_prompt等参数
-            response_model: 响应模型类
-            max_concurrent: 最大并发数
-            
-        Returns:
-            结构化响应列表
-        """
-        semaphore = asyncio.Semaphore(max_concurrent)
-        
-        async def process_single_request(request: Dict[str, Any]) -> T:
-            async with semaphore:
-                return await self.structured_response(
-                    llm_model_id=llm_model_id,
-                    system_prompt=request.get('system_prompt', ''),
-                    response_model=response_model,
-                    user_message=request.get('user_message'),
-                    fallback_value=request.get('fallback_value')
-                )
-        
-        tasks = [process_single_request(req) for req in requests]
-        return await asyncio.gather(*tasks)
-    
-    async def simple_response(
-        self,
-        llm_model_id: str,
-        system_prompt: str,
-        user_message: Optional[str] = None,
-        fallback_message: str = "信息不足，无法回答"
-    ) -> str:
-        """
-        简单的文本响应接口
-        
-        Args:
-            llm_model_id: LLM模型ID
-            system_prompt: 系统提示词
-            user_message: 用户消息（可选）
-            fallback_message: 失败时的降级消息
-            
-        Returns:
-            响应文本
-        """
-        try:
-            llm_client = self._get_cached_client(llm_model_id)
-            
-            messages = [{"role": "system", "content": system_prompt}]
-            if user_message:
-                messages.append({"role": "user", "content": user_message})
-            
-            response = await llm_client.response(messages=messages)
-            
-            if not response or not response.strip():
-                return fallback_message
-            
-            return response.strip()
-            
-        except Exception as e:
-            logger.error(f"简单响应失败: {e}", exc_info=True)
-            return fallback_message
-    
-    def _create_fallback_response(self, response_model: Type[T], fallback_value: Optional[Any]) -> T:
-        """创建降级响应"""
-        try:
-            if fallback_value is not None:
-                if isinstance(fallback_value, response_model):
-                    return fallback_value
-                elif isinstance(fallback_value, dict):
-                    return response_model(**fallback_value)
-            
-            # 尝试创建空的响应模型
-            if hasattr(response_model, 'root'):
-                # RootModel类型
-                return response_model([])
-            else:
-                # 普通BaseModel类型
-                return response_model()
-                
-        except Exception as e:
-            logger.error(f"创建降级响应失败: {e}")
-            # 最后的降级策略
-            if hasattr(response_model, 'root'):
-                return response_model([])
-            else:
-                return response_model()
-    
-    def clear_cache(self):
-        """清理客户端缓存"""
-        self._client_cache.clear()
-
-
-class LLMServiceMixin:
-    """
-    LLM服务混入类，为节点提供便捷的LLM调用方法
-    """
-    
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._llm_service: Optional[OptimizedLLMService] = None
-    
-    def get_llm_service(self, db_session: Session) -> OptimizedLLMService:
-        """获取LLM服务实例"""
-        if self._llm_service is None:
-            self._llm_service = OptimizedLLMService(db_session)
-        return self._llm_service
-    
-    async def call_llm_structured(
-        self,
-        state: Dict[str, Any],
-        db_session: Session,
-        system_prompt: str,
-        response_model: Type[T],
-        user_message: Optional[str] = None,
-        fallback_value: Optional[Any] = None
-    ) -> T:
-        """
-        便捷的结构化LLM调用方法
-        
-        Args:
-            state: 状态字典，包含memory_config
-            db_session: 数据库会话
-            system_prompt: 系统提示词
-            response_model: 响应模型类
-            user_message: 用户消息（可选）
-            fallback_value: 失败时的降级值
-            
-        Returns:
-            结构化响应对象
-        """
-        memory_config = state.get('memory_config')
-        if not memory_config:
-            raise ValueError("State中缺少memory_config")
-        
-        llm_model_id = memory_config.llm_model_id
-        if not llm_model_id:
-            raise ValueError("Memory config中缺少llm_model_id")
-        
-        llm_service = self.get_llm_service(db_session)
-        return await llm_service.structured_response(
-            llm_model_id=llm_model_id,
-            system_prompt=system_prompt,
-            response_model=response_model,
-            user_message=user_message,
-            fallback_value=fallback_value
-        )
-    
-    async def call_llm_simple(
-        self,
-        state: Dict[str, Any],
-        db_session: Session,
-        system_prompt: str,
-        user_message: Optional[str] = None,
-        fallback_message: str = "信息不足，无法回答"
-    ) -> str:
-        """
-        便捷的简单LLM调用方法
-        
-        Args:
-            state: 状态字典，包含memory_config
-            db_session: 数据库会话
-            system_prompt: 系统提示词
-            user_message: 用户消息（可选）
-            fallback_message: 失败时的降级消息
-            
-        Returns:
-            响应文本
-        """
-        memory_config = state.get('memory_config')
-        if not memory_config:
-            raise ValueError("State中缺少memory_config")
-        
-        llm_model_id = memory_config.llm_model_id
-        if not llm_model_id:
-            raise ValueError("Memory config中缺少llm_model_id")
-        
-        llm_service = self.get_llm_service(db_session)
-        return await llm_service.simple_response(
-            llm_model_id=llm_model_id,
-            system_prompt=system_prompt,
-            user_message=user_message,
-            fallback_message=fallback_message
-        )

api/app/core/memory/agent/utils/__init__.py

@@ -0,0 +1,7 @@
+"""Agent utilities."""
+
+from app.core.memory.agent.utils.multimodal import MultimodalProcessor
+
+__all__ = [
+    "MultimodalProcessor",
+]

api/app/core/memory/agent/utils/get_dialogs.py

@@ -9,59 +9,62 @@ from app.core.memory.models.message_models import DialogData, ConversationContex
 
 async def get_chunked_dialogs(
         chunker_strategy: str = "RecursiveChunker",
-        end_user_id: str = "group_1",
-        messages: list = None,
+        group_id: str = "group_1",
+        user_id: str = "user1",
+        apply_id: str = "applyid",
+        content: str = "这是用户的输入",
         ref_id: str = "wyl_20251027",
         config_id: str = None
 ) -> List[DialogData]:
-    """Generate chunks from structured messages using the specified chunker strategy.
+    """Generate chunks from all test data entries using the specified chunker strategy.
 
     Args:
         chunker_strategy: The chunking strategy to use (default: RecursiveChunker)
-        end_user_id: Group identifier
-        messages: Structured message list [{"role": "user", "content": "..."}, ...]
+        group_id: Group identifier
+        user_id: User identifier
+        apply_id: Application identifier
+        content: Dialog content
         ref_id: Reference identifier
         config_id: Configuration ID for processing
 
     Returns:
-        List of DialogData objects with generated chunks
+        List of DialogData objects with generated chunks for each test entry
     """
-    from app.core.logging_config import get_agent_logger
-    logger = get_agent_logger(__name__)
+    dialog_data_list = []
+    messages = []
 
-    if not messages or not isinstance(messages, list) or len(messages) == 0:
-        raise ValueError("messages parameter must be a non-empty list")
+    messages.append(ConversationMessage(role="用户", msg=content))
 
-    conversation_messages = []
-
-    for idx, msg in enumerate(messages):
-        if not isinstance(msg, dict) or 'role' not in msg or 'content' not in msg:
-            raise ValueError(f"Message {idx} format error: must contain 'role' and 'content' fields")
-
-        role = msg['role']
-        content = msg['content']
-
-        if role not in ['user', 'assistant']:
-            raise ValueError(f"Message {idx} role must be 'user' or 'assistant', got: {role}")
-
-        if content.strip():
-            conversation_messages.append(ConversationMessage(role=role, msg=content.strip()))
-
-    if not conversation_messages:
-        raise ValueError("Message list cannot be empty after filtering")
-
-    conversation_context = ConversationContext(msgs=conversation_messages)
+    # Create DialogData
+    conversation_context = ConversationContext(msgs=messages)
+    # Create DialogData with group_id based on the entry's id for uniqueness
     dialog_data = DialogData(
         context=conversation_context,
         ref_id=ref_id,
-        end_user_id=end_user_id,
+        group_id=group_id,
+        user_id=user_id,
+        apply_id=apply_id,
         config_id=config_id
     )
-
+    # Create DialogueChunker and process the dialogue
     chunker = DialogueChunker(chunker_strategy)
     extracted_chunks = await chunker.process_dialogue(dialog_data)
     dialog_data.chunks = extracted_chunks
 
-    logger.info(f"DialogData created with {len(extracted_chunks)} chunks")
+    dialog_data_list.append(dialog_data)
 
-    return [dialog_data]
+    # Convert to dict with datetime serialized
+    def serialize_datetime(obj):
+        if isinstance(obj, datetime):
+            return obj.isoformat()
+        raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")
+
+    combined_output = [dd.model_dump() for dd in dialog_data_list]
+
+    print(dialog_data_list)
+
+    # with open(os.path.join(os.path.dirname(__file__), "chunker_test_output.txt"), "w", encoding="utf-8") as f:
+    #     json.dump(combined_output, f, ensure_ascii=False, indent=4, default=serialize_datetime)
+
+
+    return dialog_data_list

api/app/core/memory/agent/utils/llm_client_pool.py

@@ -1,56 +0,0 @@
-
-import asyncio
-from typing import Dict, Optional
-from app.core.memory.utils.llm.llm_utils import get_llm_client_fast
-from app.db import get_db
-from app.core.logging_config import get_agent_logger
-
-logger = get_agent_logger(__name__)
-
-class LLMClientPool:
-    """LLM客户端连接池"""
-    
-    def __init__(self, max_size: int = 5):
-        self.max_size = max_size
-        self.pools: Dict[str, asyncio.Queue] = {}
-        self.active_clients: Dict[str, int] = {}
-        
-    async def get_client(self, llm_model_id: str):
-        """获取LLM客户端"""
-        if llm_model_id not in self.pools:
-            self.pools[llm_model_id] = asyncio.Queue(maxsize=self.max_size)
-            self.active_clients[llm_model_id] = 0
-            
-        pool = self.pools[llm_model_id]
-        
-        try:
-            # 尝试从池中获取客户端
-            client = pool.get_nowait()
-            logger.debug(f"从池中获取LLM客户端: {llm_model_id}")
-            return client
-        except asyncio.QueueEmpty:
-            # 池为空，创建新客户端
-            if self.active_clients[llm_model_id] < self.max_size:
-                db_session = next(get_db())
-                client = get_llm_client_fast(llm_model_id, db_session)
-                self.active_clients[llm_model_id] += 1
-                logger.debug(f"创建新LLM客户端: {llm_model_id}")
-                return client
-            else:
-                # 等待可用客户端
-                logger.debug(f"等待LLM客户端可用: {llm_model_id}")
-                return await pool.get()
-                
-    async def return_client(self, llm_model_id: str, client):
-        """归还LLM客户端到池中"""
-        if llm_model_id in self.pools:
-            try:
-                self.pools[llm_model_id].put_nowait(client)
-                logger.debug(f"归还LLM客户端到池: {llm_model_id}")
-            except asyncio.QueueFull:
-                # 池已满，丢弃客户端
-                self.active_clients[llm_model_id] -= 1
-                logger.debug(f"池已满，丢弃LLM客户端: {llm_model_id}")
-
-# 全局客户端池
-llm_client_pool = LLMClientPool()

api/app/core/memory/agent/utils/llm_tools.py

@@ -1,84 +1,82 @@
+import asyncio
+import json
+import logging
 import os
 from collections import defaultdict
-from pathlib import Path
 from typing import Annotated, TypedDict
 
+from app.core.memory.agent.utils.messages_tool import read_template_file
+from app.core.memory.utils.config.config_utils import (
+    get_picture_config,
+    get_voice_config,
+)
+
+# Removed global variable imports - use dependency injection instead
+from dotenv import load_dotenv
 from langchain_core.messages import AnyMessage
 from langgraph.graph import add_messages
+from openai import OpenAI
 
-PROJECT_ROOT_ = str(Path(__file__).resolve().parents[3])
+PROJECT_ROOT_ = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+logger = logging.getLogger(__name__)
 
+load_dotenv()
+
+
+async def picture_model_requests(image_url):
+    '''
+
+    Args:
+        image_url:
+    Returns:
+
+    '''
+    file_path = PROJECT_ROOT_ + '/agent/utils/prompt/Template_for_image_recognition_prompt.jinja2 '
+    system_prompt = await read_template_file(file_path)
+    result = await Picture_recognize(image_url,system_prompt)
+    return (result)
 class WriteState(TypedDict):
     '''
     Langgrapg Writing TypedDict
     '''
     messages: Annotated[list[AnyMessage], add_messages]
-    end_user_id: str
+    user_id:str
+    apply_id:str
+    group_id:str
     errors: list[dict]  # Track errors: [{"tool": "tool_name", "error": "message"}]
-    memory_config: object
-    write_result: dict
-    data: str
-    language: str  # 语言类型 ("zh" 中文, "en" 英文)
 
 class ReadState(TypedDict):
-    """
-    LangGraph 工作流状态定义
-
-    Attributes:
-        messages: 消息列表，支持自动追加
-        loop_count: 遍历次数
-        search_switch: 搜索类型开关
-        end_user_id: 组标识
-        config_id: 配置ID，用于过滤结果
-        data: 从content_input_node传递的内容数据
-        spit_data: 从Split_The_Problem传递的分解结果
-        tool_calls: 工具调用请求列表
-        tool_results: 工具执行结果列表
-        memory_config: 内存配置对象
-    """
-    messages: Annotated[list[AnyMessage], add_messages]  # 消息追加模式
-    loop_count: int
+    '''
+       Langgrapg READING TypedDict
+       name:
+       id:user id
+       loop_count:Traverse times
+       search_switch：type
+       config_id: configuration id for filtering results
+       errors: list of errors that occurred during workflow execution
+       '''
+    messages: Annotated[list[AnyMessage], add_messages] #消息追加的模式增加消息
+    name: str
+    id: str
+    loop_count:int
     search_switch: str
-    end_user_id: str
+    user_id: str
+    apply_id: str
+    group_id: str
     config_id: str
-    data: str  # 新增字段用于传递内容
-    spit_data: dict  # 新增字段用于传递问题分解结果
-    problem_extension:dict
-    storage_type: str
-    user_rag_memory_id: str
-    llm_id: str
-    embedding_id: str
-    memory_config: object  # 新增字段用于传递内存配置对象
-    retrieve:dict
-    RetrieveSummary: dict
-    InputSummary: dict
-    verify: dict
-    SummaryFails: dict
-    summary: dict
+    errors: list[dict]  # Track errors: [{"tool": "tool_name", "error": "message"}]
+
+
 class COUNTState:
-    """
-    工作流对话检索内容计数器
-
-    用于记录工作流对话检索内容没有正确消息召回遍历的次数。
-    """
-
+    '''
+    The number of times the workflow dialogue retrieval content has no correct message recall traversal
+    '''
     def __init__(self, limit: int = 5):
-        """
-        初始化计数器
-
-        Args:
-            limit: 最大计数限制，默认为5
-        """
         self.total: int = 0  # 当前累加值
         self.limit: int = limit  # 最大上限
 
-    def add(self, value: int = 1) -> None:
-        """
-        累加数字，如果达到上限就保持最大值
-
-        Args:
-            value: 要累加的值，默认为1
-        """
+    def add(self, value: int = 1):
+        """累加数字，如果达到上限就保持最大值"""
         self.total += value
         print(f"[COUNTState] 当前值: {self.total}")
         if self.total >= self.limit:
@@ -86,19 +84,21 @@ class COUNTState:
             self.total = self.limit  # 达到上限不再增加
 
     def get_total(self) -> int:
-        """
-        获取当前累加值
-
-        Returns:
-            当前累加值
-        """
+        """获取当前累加值"""
         return self.total
 
-    def reset(self) -> None:
+    def reset(self):
         """手动重置累加值"""
         self.total = 0
         print("[COUNTState] 已重置为 0")
 
+
+def merge_to_key_value_pairs(data, query_key, result_key):
+    grouped = defaultdict(list)
+    for item in data:
+        grouped[item[query_key]].append(item[result_key])
+    return [{key: values} for key, values in grouped.items()]
+
 def deduplicate_entries(entries):
     seen = set()
     deduped = []
@@ -109,37 +109,70 @@ def deduplicate_entries(entries):
             deduped.append(entry)
     return deduped
 
-def merge_to_key_value_pairs(data, query_key, result_key):
-    grouped = defaultdict(list)
-    for item in data:
-        grouped[item[query_key]].append(item[result_key])
-    return [{key: values} for key, values in grouped.items()]
 
 
-def convert_extended_question_to_question(data):
+async def Picture_recognize(image_path, PROMPT_TICKET_EXTRACTION, picture_model_name: str) -> str:
     """
-    递归地将数据中的 extended_question 字段转换为 question 字段
-
+    Updated to eliminate global variables in favor of explicit parameters.
+    
     Args:
-        data: 要转换的数据（可能是字典、列表或其他类型）
-
-    Returns:
-        转换后的数据
+        image_path: Path to image file
+        PROMPT_TICKET_EXTRACTION: Extraction prompt
+        picture_model_name: Picture model name (required, no longer from global variables)
     """
-    if isinstance(data, dict):
-        # 创建新字典来存储转换后的数据
-        converted = {}
-        for key, value in data.items():
-            if key == 'extended_question':
-                # 将 extended_question 转换为 question
-                converted['question'] = convert_extended_question_to_question(value)
-            else:
-                # 递归处理其他字段
-                converted[key] = convert_extended_question_to_question(value)
-        return converted
-    elif isinstance(data, list):
-        # 递归处理列表中的每个元素
-        return [convert_extended_question_to_question(item) for item in data]
-    else:
-        # 其他类型直接返回
-        return data
+    try:
+        model_config = get_picture_config(picture_model_name)
+    except Exception as e:
+            err = f"LLM配置不可用：{str(e)}。请检查 config.json 和 runtime.json。"
+            logger.error(err)
+            return err
+    api_key = os.getenv(model_config["api_key"])  # 从环境变量读取对应后端的 API key
+    backend_model_name = model_config["llm_name"].split("/")[-1]
+    api_base=model_config['api_base']
+
+    logger.info(f"model_name: {backend_model_name}")
+    logger.info(f"api_key set: {'yes' if api_key else 'no'}")
+    logger.info(f"base_url: {model_config['api_base']}")
+
+    client = OpenAI(
+        api_key=api_key, base_url=api_base,
+    )
+    completion = client.chat.completions.create(
+        model=backend_model_name,
+        messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url":image_path,
+                        },
+                        {"type": "text",
+                         "text": PROMPT_TICKET_EXTRACTION}
+                    ]
+                }
+            ])
+    picture_text = completion.choices[0].message.content
+    picture_text = picture_text.replace('```json', '').replace('```', '')
+    picture_text = json.loads(picture_text)
+    return (picture_text['statement'])
+
+async def Voice_recognize(voice_model_name: str):
+    """
+    Updated to eliminate global variables in favor of explicit parameters.
+    
+    Args:
+        voice_model_name: Voice model name (required, no longer from global variables)
+    """
+    try:
+        model_config = get_voice_config(voice_model_name)
+    except Exception as e:
+            err = f"LLM配置不可用：{str(e)}。请检查 config.json 和 runtime.json。"
+            logger.error(err)
+            return err
+    api_key = os.getenv(model_config["api_key"])  # 从环境变量读取对应后端的 API key
+    backend_model_name = model_config["llm_name"].split("/")[-1]
+    api_base = model_config['api_base']
+    return api_key,backend_model_name,api_base
+
+

api/app/core/memory/agent/utils/mcp_tools.py

@@ -0,0 +1,33 @@
+import os
+from app.core.config import settings
+
+def get_mcp_server_config():
+    """
+    Get the MCP server configuration.
+    
+    Uses MCP_SERVER_URL environment variable if set (for Docker),
+    otherwise falls back to SERVER_IP and MCP_PORT (for local development).
+    """
+    # Get MCP port from environment (default: 8081)
+    mcp_port = os.getenv("MCP_PORT", "8081")
+    
+    # In Docker: MCP_SERVER_URL=http://mcp-server:8081
+    # In local dev: uses SERVER_IP (127.0.0.1 or localhost)
+    mcp_server_url = os.getenv("MCP_SERVER_URL")
+    
+    if mcp_server_url:
+        # Docker environment: use full URL from environment
+        base_url = mcp_server_url
+    else:
+        # Local development: build URL from SERVER_IP and MCP_PORT
+        base_url = f"http://{settings.SERVER_IP}:{mcp_port}"
+    
+    mcp_server_config = {
+        "data_flow": {
+            "url": f"{base_url}/sse",
+            "transport": "sse",
+            "timeout": 15000,
+            "sse_read_timeout": 15000,
+        }
+    }
+    return mcp_server_config

api/app/core/memory/agent/utils/messages_tool.py

@@ -0,0 +1,260 @@
+import json
+import logging
+import re
+from typing import Any, List
+
+from app.core.logging_config import get_agent_logger
+from langchain_core.messages import AnyMessage
+
+logger = get_agent_logger(__name__)
+
+
+def _to_openai_messages(msgs: List[AnyMessage]) -> List[dict]:
+    out = []
+    for m in msgs:
+        if hasattr(m, "content"):
+            out.append({"role": "user", "content": getattr(m, "content", "")})
+        elif isinstance(m, dict) and "role" in m and "content" in m:
+            out.append(m)
+        else:
+            out.append({"role": "user", "content": str(m)})
+    return out
+
+
+def _extract_content(resp: Any) -> str:
+    """Extract LLM content and sanitize to raw JSON/text.
+
+    - Supports both object and dict response shapes.
+    - Removes leading role labels (e.g., "Assistant:").
+    - Strips Markdown code fences like ```json ... ```.
+    - Attempts to isolate the first valid JSON array/object block when extra text is present.
+    """
+
+    def _to_text(r: Any) -> str:
+        try:
+            # 对象形式: resp.choices[0].message.content
+            if hasattr(r, "choices") and getattr(r, "choices", None):
+                msg = r.choices[0].message
+                if hasattr(msg, "content"):
+                    return msg.content
+                if isinstance(msg, dict) and "content" in msg:
+                    return msg["content"]
+            # 字典形式: resp["choices"][0]["message"]["content"]
+            if isinstance(r, dict):
+                return r.get("choices", [{}])[0].get("message", {}).get("content", "")
+        except Exception:
+            pass
+        return str(r)
+
+    def _clean_text(text: str) -> str:
+        s = str(text).strip()
+        # 移除可能的角色前缀
+        s = re.sub(r"^\s*(Assistant|assistant)\s*:\s*", "", s)
+        # 提取 ```json ... ``` 代码块
+        m = re.search(r"```json\s*(.*?)\s*```", s, flags=re.S | re.I)
+        if m:
+            s = m.group(1).strip()
+        # 如果仍然包含多余文本，尝试截取第一个 JSON 数组/对象片段
+        if not (s.startswith("{") or s.startswith("[")):
+            left = s.find("[")
+            right = s.rfind("]")
+            if left != -1 and right != -1 and right > left:
+                s = s[left:right + 1].strip()
+            else:
+                left = s.find("{")
+                right = s.rfind("}")
+                if left != -1 and right != -1 and right > left:
+                    s = s[left:right + 1].strip()
+        return s
+
+    raw = _to_text(resp)
+    return _clean_text(raw)
+
+def Resolve_username(usermessages):
+    '''
+    Extract username
+    Args:
+        usermessages: user name
+
+    Returns:
+
+    '''
+    usermessages = usermessages.split('_')[1:]
+    sessionid = '_'.join(usermessages[:-1])
+    return sessionid
+
+
+# TODO: USE app.core.memory.src.utils.render_template instead
+async def read_template_file(template_path: str) -> str:
+    """
+    读取模板文件
+
+    Args:
+        template_path: 模板文件路径
+
+    Returns:
+        模板内容字符串
+
+    Note:
+        建议使用 app.core.memory.utils.template_render 中的统一模板渲染功能
+    """
+    try:
+        with open(template_path, "r", encoding="utf-8") as f:
+            return f.read()
+    except FileNotFoundError:
+        logger.error(f"模板文件未找到: {template_path}")
+        raise
+    except IOError as e:
+        logger.error(f"读取模板文件失败: {template_path}, 错误: {str(e)}", exc_info=True)
+        raise
+
+
+async def Problem_Extension_messages_deal(context):
+    '''
+    Extract data
+    Args:
+        context:
+    Returns:
+    '''
+    extent_quest = []
+    original = context.get('original', '')
+    messages = context.get('context', '')
+    
+    # Handle empty or non-string messages
+    if not messages:
+        return extent_quest, original
+    
+    if isinstance(messages, str):
+        try:
+            messages = json.loads(messages)
+        except json.JSONDecodeError:
+            # If JSON parsing fails, return empty list
+            return extent_quest, original
+    
+    if isinstance(messages, list):
+        for message in messages:
+            question = message.get('question', '')
+            type = message.get('type', '')
+            extent_quest.append({"role": "user", "content": f"问题:{question}；问题类型：{type}"})
+
+    return extent_quest, original
+
+
+async def Retriev_messages_deal(context):
+    '''
+    Extract data
+    Args:
+        context:
+    Returns:
+    '''
+    logger.info(f"Retriev_messages_deal input: type={type(context)}, value={str(context)[:500]}")
+    
+    if isinstance(context, dict):
+        logger.info(f"Retriev_messages_deal: context is dict with keys={list(context.keys())}")
+        if 'context' in context or 'original' in context:
+            content = context.get('context', {})
+            original = context.get('original', '')
+            logger.info(f"Retriev_messages_deal output: content_type={type(content)}, content={str(content)[:300]}, original='{original[:50] if original else ''}'")
+            return content, original
+    
+    # Return empty defaults if context is not a dict or doesn't have expected keys
+    logger.warning(f"Retriev_messages_deal: context missing expected keys, returning empty defaults")
+    return {}, ''
+
+async  def Verify_messages_deal(context):
+    '''
+    Extract data
+    Args:
+        context:
+    Returns:
+    '''
+
+    query = context['context']['Query']
+    Query_small_list = context['context']['Expansion_issue']
+    Result_small = []
+    Query_small = []
+    for i in Query_small_list:
+        Result_small.append(i['Answer_Small'][0])
+        Query_small.append(i['Query_small'])
+    return Query_small, Result_small, query
+
+
+async def Summary_messages_deal(context):
+    '''
+    Extract data
+    Args:
+        context:
+    Returns:
+    '''
+    messages = str(context).replace('\\n', '').replace('\n', '').replace('\\', '')
+    query = re.findall(r'"query": (.*?),', messages)[0]
+    query = query.replace('[', '').replace(']', '').strip()
+    matches = re.findall(r'"answer_small"\s*:\s*"(\[.*?\])"', messages)
+    answer_small_texts = []
+    for m in matches:
+        try:
+            parsed = json.loads(m)
+            for item in parsed:
+                answer_small_texts.append(item.strip().replace('\\', '').replace('[', '').replace(']', ''))
+        except Exception:
+            answer_small_texts.append(m.strip().replace('\\', '').replace('[', '').replace(']', ''))
+
+    return answer_small_texts, query
+
+
+async def VerifyTool_messages_deal(context):
+    '''
+    Extract data
+    Args:
+        context:
+    Returns:
+    '''
+    messages = str(context).replace('\\n', '').replace('\n', '').replace('\\', '')
+    content_messages = messages.split('"context":')[1].replace('""', '"')
+    messages = str(content_messages).split("name='Retrieve'")[0]
+    query = re.findall('"Query": "(.*?)"', messages)[0]
+    Query_small = re.findall('"Query_small": "(.*?)"', messages)
+    Result_small = re.findall('"Result_small": "(.*?)"', messages)
+    return Query_small, Result_small, query
+
+
+async def Retrieve_Summary_messages_deal(context):
+    pass
+
+
+async def Retrieve_verify_tool_messages_deal(context, history, query):
+    '''
+    Extract data
+    Args:
+        context:
+    Returns:
+    '''
+    results = []
+    # 统一转为字符串，避免 None 或非字符串导致正则报错
+    text = str(context)
+    blocks = re.findall(r'\{(.*?)\}', text, flags=re.S)
+    for block in blocks:
+        query_small = re.search(r'"Query_small"\s*:\s*"([^"]*)"', block)
+        answer_small = re.search(r'"Answer_Small"\s*:\s*(\[[^\]]*\])', block)
+        status = re.search(r'"status"\s*:\s*"([^"]*)"', block)
+        query_answer = re.search(r'"Query_answer"\s*:\s*"([^"]*)"', block)
+
+        results.append({
+            "query_small": query_small.group(1) if query_small else None,
+            "answer_small": answer_small.group(1) if answer_small else None,
+            # 将缺失的 status 统一为空字符串，后续用字符串判定，避免 NoneType 错误
+            "status": status.group(1) if status else "",
+            "query_answer": query_answer.group(1) if query_answer else None
+        })
+    result = []
+    for r in results:
+        # 统一按字符串判定状态，兼容大小写和缺失情况
+        status_str = str(r.get('status', '')).strip().lower()
+        if status_str == 'false':
+            continue
+        else:
+            result.append(r)
+    split_result = 'failed' if not result else 'success'
+    result = {"data": {"query": query, "expansion_issue": result}, "split_result": split_result, "reason": "",
+              "history": history}
+    return result

api/app/core/memory/agent/utils/messages_tools.py

@@ -1,194 +0,0 @@
-from typing import List, Dict, Any
-from app.core.logging_config import get_agent_logger
-
-logger = get_agent_logger(__name__)
-async def read_template_file(template_path: str) -> str:
-    """
-    读取模板文件
-
-    Args:
-        template_path: 模板文件路径
-
-    Returns:
-        模板内容字符串
-
-    Note:
-        建议使用 app.core.memory.utils.template_render 中的统一模板渲染功能
-    """
-    try:
-        with open(template_path, "r", encoding="utf-8") as f:
-            return f.read()
-    except FileNotFoundError:
-        logger.error(f"模板文件未找到: {template_path}")
-        raise
-    except IOError as e:
-        logger.error(f"读取模板文件失败: {template_path}, 错误: {str(e)}", exc_info=True)
-        raise
-
-def reorder_output_results(results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-    """
-    重新排序输出结果，将 retrieval_summary 类型的数据放到最后面
-
-    Args:
-        results: 原始输出结果列表
-
-    Returns:
-        重新排序后的结果列表
-    """
-    retrieval_summaries = []
-    other_results = []
-
-    # 分离 retrieval_summary 和其他类型的结果
-    for result in results:
-        if 'summary' in result.get('type'):
-            retrieval_summaries.append(result)
-        else:
-            other_results.append(result)
-
-    # 将 retrieval_summary 放到最后
-    return other_results + retrieval_summaries
-
-def optimize_search_results(intermediate_outputs):
-    """
-    优化检索结果，合并多个搜索结果，过滤空结果，统一格式
-
-    Args:
-        intermediate_outputs: 原始的中间输出列表
-
-    Returns:
-        优化后的检索结果列表
-    """
-    optimized_results = []
-
-    for item in intermediate_outputs:
-        if not item or item == [] or item == {}:
-            continue
-
-        # 检查是否是搜索结果类型
-        if isinstance(item, dict) and item.get('type') == 'search_result':
-            raw_results = item.get('raw_results', {})
-
-            # 如果 raw_results 为空，跳过
-            if not raw_results or raw_results == [] or raw_results == {}:
-                continue
-
-            # 创建优化后的结果结构
-            optimized_item = {
-                "type": "search_result",
-                "title": f"检索结果 ({item.get('index', 1)}/{item.get('total', 1)})",
-                "query": item.get('query', ''),
-                "raw_results": {},
-                "index": item.get('index', 1),
-                "total": item.get('total', 1)
-            }
-
-            # 合并所有搜索结果类型到一个 raw_results 中
-            merged_raw_results = {}
-
-            # 处理 time_search
-            if 'time_search' in raw_results and raw_results['time_search']:
-                merged_raw_results['time_search'] = raw_results['time_search']
-
-            # 处理 keyword_search
-            if 'keyword_search' in raw_results and raw_results['keyword_search']:
-                merged_raw_results['keyword_search'] = raw_results['keyword_search']
-
-            # 处理 embedding_search
-            if 'embedding_search' in raw_results and raw_results['embedding_search']:
-                merged_raw_results['embedding_search'] = raw_results['embedding_search']
-
-            # 处理 combined_summary
-            if 'combined_summary' in raw_results and raw_results['combined_summary']:
-                merged_raw_results['combined_summary'] = raw_results['combined_summary']
-
-            # 处理 reranked_results
-            if 'reranked_results' in raw_results and raw_results['reranked_results']:
-                merged_raw_results['reranked_results'] = raw_results['reranked_results']
-
-            # 如果合并后的结果不为空，添加到优化结果中
-            if merged_raw_results:
-                optimized_item['raw_results'] = merged_raw_results
-                optimized_results.append(optimized_item)
-        else:
-            # 非搜索结果类型，直接添加
-            optimized_results.append(item)
-
-    return optimized_results
-
-
-def merge_multiple_search_results(intermediate_outputs):
-    """
-    将多个搜索结果合并为一个统一的搜索结果
-
-    Args:
-        intermediate_outputs: 原始的中间输出列表
-
-    Returns:
-        合并后的结果列表
-    """
-    search_results = []
-    other_results = []
-
-    # 分离搜索结果和其他结果
-    for item in intermediate_outputs:
-        if isinstance(item, dict) and item.get('type') == 'search_result':
-            raw_results = item.get('raw_results', {})
-            # 只保留有内容的搜索结果
-            if raw_results and raw_results != [] and raw_results != {}:
-                search_results.append(item)
-        else:
-            other_results.append(item)
-
-    # 如果没有搜索结果，返回原始结果
-    if not search_results:
-        return intermediate_outputs
-
-    # 如果只有一个搜索结果，优化格式后返回
-    if len(search_results) == 1:
-        optimized = optimize_search_results(search_results)
-        return other_results + optimized
-
-    # 合并多个搜索结果
-    merged_raw_results = {}
-    all_queries = []
-
-    for result in search_results:
-        query = result.get('query', '')
-        if query:
-            all_queries.append(query)
-
-        raw_results = result.get('raw_results', {})
-
-        # 合并各种搜索类型的结果
-        for search_type in ['time_search', 'keyword_search', 'embedding_search', 'combined_summary',
-                            'reranked_results']:
-            if search_type in raw_results and raw_results[search_type]:
-                if search_type not in merged_raw_results:
-                    merged_raw_results[search_type] = raw_results[search_type]
-                else:
-                    # 如果是字典类型，需要合并
-                    if isinstance(raw_results[search_type], dict) and isinstance(merged_raw_results[search_type], dict):
-                        for key, value in raw_results[search_type].items():
-                            if key not in merged_raw_results[search_type]:
-                                merged_raw_results[search_type][key] = value
-                            elif isinstance(value, list) and isinstance(merged_raw_results[search_type][key], list):
-                                merged_raw_results[search_type][key].extend(value)
-                    elif isinstance(raw_results[search_type], list):
-                        if isinstance(merged_raw_results[search_type], list):
-                            merged_raw_results[search_type].extend(raw_results[search_type])
-                        else:
-                            merged_raw_results[search_type] = raw_results[search_type]
-
-    # 创建合并后的结果
-    if merged_raw_results:
-        merged_result = {
-            "type": "search_result",
-            "title": f"合并检索结果 (共{len(search_results)}个查询)",
-            "query": " | ".join(all_queries),
-            "raw_results": merged_raw_results,
-            "index": 1,
-            "total": 1
-        }
-        return other_results + [merged_result]
-
-    return other_results

api/app/core/memory/agent/utils/model_tool.py

@@ -0,0 +1,38 @@
+
+
+# project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+# sys.path.insert(0, project_root)
+
+# load_dotenv()
+
+# async def llm_client_chat(messages: List[dict]) -> str:
+#     """使用 OpenAI 兼容接口进行对话，返回内容字符串。"""
+#     try:
+#         cfg = get_model_config(SELECTED_LLM_ID)
+#         rb_config = RedBearModelConfig(
+#         model_name=cfg["model_name"],
+#         provider=cfg["provider"],
+#         api_key=cfg["api_key"],
+#         base_url=cfg["base_url"],
+#     )
+#         client = OpenAIClient(model_config=rb_config, type_="chat")
+
+#     except Exception as e:
+#         logger.error(f"获取模型配置失败：{e}")
+#         err = f"获取模型配置失败：{str(e)}。请检查!!!"
+#         return err
+#     try:
+#         response = await client.chat(messages)
+#         print(f"model_tool's llm_client_chat response ======>:\n {response}")
+#         return _extract_content(response)
+#         # return _extract_content(result)
+#     except Exception as e:
+#         logger.error(f"LLM调用失败：{str(e)}。请检查 model_name、api_key、api_base 是否正确。")
+#     return f"LLM调用失败：{str(e)}。请检查 model_name、api_key、api_base 是否正确。"
+
+# async def main(image_url):
+#     await llm_client_chat(image_url)
+#
+# # 运行主函数
+# asyncio.run(main(['https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_male2.wav']))
+#

api/app/core/memory/agent/utils/multimodal.py

@@ -0,0 +1,131 @@
+"""
+Multimodal input processor for handling image and audio content.
+
+This module provides utilities for detecting and processing multimodal inputs
+(images and audio files) by converting them to text using appropriate models.
+"""
+
+import logging
+from typing import List
+
+from app.core.memory.agent.multimodal.speech_model import Vico_recognition
+from app.core.memory.agent.utils.llm_tools import picture_model_requests
+
+logger = logging.getLogger(__name__)
+
+
+class MultimodalProcessor:
+    """
+    Processor for handling multimodal inputs (images and audio).
+    
+    This class detects image and audio file paths in input content and converts
+    them to text using appropriate recognition models.
+    """
+    
+    # Supported file extensions
+    IMAGE_EXTENSIONS = ['.jpg', '.png']
+    AUDIO_EXTENSIONS = [
+        'aac', 'amr', 'avi', 'flac', 'flv', 'm4a', 'mkv', 'mov',
+        'mp3', 'mp4', 'mpeg', 'ogg', 'opus', 'wav', 'webm', 'wma', 'wmv'
+    ]
+    
+    def __init__(self):
+        """Initialize the multimodal processor."""
+        pass
+    
+    def is_image(self, content: str) -> bool:
+        """
+        Check if content is an image file path.
+        
+        Args:
+            content: Input string to check
+            
+        Returns:
+            True if content ends with a supported image extension
+            
+        Examples:
+            >>> processor = MultimodalProcessor()
+            >>> processor.is_image("photo.jpg")
+            True
+            >>> processor.is_image("document.pdf")
+            False
+        """
+        if not isinstance(content, str):
+            return False
+        
+        content_lower = content.lower()
+        return any(content_lower.endswith(ext) for ext in self.IMAGE_EXTENSIONS)
+    
+    def is_audio(self, content: str) -> bool:
+        """
+        Check if content is an audio file path.
+        
+        Args:
+            content: Input string to check
+            
+        Returns:
+            True if content ends with a supported audio extension
+            
+        Examples:
+            >>> processor = MultimodalProcessor()
+            >>> processor.is_audio("recording.mp3")
+            True
+            >>> processor.is_audio("video.mp4")
+            True
+            >>> processor.is_audio("document.txt")
+            False
+        """
+        if not isinstance(content, str):
+            return False
+        
+        content_lower = content.lower()
+        return any(content_lower.endswith(f'.{ext}') for ext in self.AUDIO_EXTENSIONS)
+    
+    async def process_input(self, content: str) -> str:
+        """
+        Process input content, converting images/audio to text if needed.
+        
+        This method detects if the input is an image or audio file and converts
+        it to text using the appropriate recognition model. If processing fails
+        or the content is not multimodal, it returns the original content.
+        
+        Args:
+            content: Input string (may be file path or regular text)
+            
+        Returns:
+            Text content (original or converted from image/audio)
+            
+        Examples:
+            >>> processor = MultimodalProcessor()
+            >>> await processor.process_input("photo.jpg")
+            "Recognized text from image..."
+            
+            >>> await processor.process_input("Hello world")
+            "Hello world"
+        """
+        if not isinstance(content, str):
+            logger.warning(f"[MultimodalProcessor] Content is not a string: {type(content)}")
+            return str(content)
+        
+        try:
+            # Check for image input
+            if self.is_image(content):
+                logger.info(f"[MultimodalProcessor] Detected image input: {content}")
+                result = await picture_model_requests(content)
+                logger.info(f"[MultimodalProcessor] Image recognition result: {result[:100]}...")
+                return result
+            
+            # Check for audio input
+            if self.is_audio(content):
+                logger.info(f"[MultimodalProcessor] Detected audio input: {content}")
+                result = await Vico_recognition([content]).run()
+                logger.info(f"[MultimodalProcessor] Audio recognition result: {result[:100]}...")
+                return result
+        
+        except Exception as e:
+            logger.error(f"[MultimodalProcessor] Error processing multimodal input: {e}", exc_info=True)
+            logger.info("[MultimodalProcessor] Falling back to original content")
+            return content
+        
+        # Return original content if not multimodal
+        return content