Merge pull request #141 from SuanmoSuanyangTechnology/fix/web_zy

Fix/web zy

.gitignore

@@ -21,17 +21,13 @@ examples/
 
 # Temporary outputs
 .DS_Store
-.hypothesis/
 time.log
 celerybeat-schedule.db
 search_results.json
-redbear-mem-metrics/
-pitch-deck/
 
 api/migrations/versions
 tmp
 files
-powers/ 
 
 # Exclude dep files
 huggingface.co/
@@ -39,5 +35,3 @@ nltk_data/
 tika-server*.jar*
 cl100k_base.tiktoken
 libssl*.deb
-
-sandbox/lib/seccomp_redbear/target

README.md

@@ -226,8 +226,8 @@ REDIS_PORT=6379
 REDIS_DB=1 
 
 # Celery (Using Redis as broker)
-REDIS_DB_CELERY_BROKER=1
-REDIS_DB_CELERY_BACKEND=2
+BROKER_URL=redis://127.0.0.1:6379/0
+RESULT_BACKEND=redis://127.0.0.1:6379/0
 
 # JWT Secret Key (Formation method: openssl rand -hex 32)
 SECRET_KEY=your-secret-key-here
@@ -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

README_CN.md

@@ -201,8 +201,8 @@ REDIS_PORT=6379
 REDIS_DB=1 
 
 # Celery (使用Redis作为broker)
-REDIS_DB_CELERY_BROKER=1
-REDIS_DB_CELERY_BACKEND=2
+BROKER_URL=redis://127.0.0.1:6379/0
+RESULT_BACKEND=redis://127.0.0.1:6379/0
 
 # JWT密钥 (生成方式: openssl rand -hex 32)
 SECRET_KEY=your-secret-key-here

api/Dockerfile

@@ -45,8 +45,7 @@ RUN --mount=type=cache,id=mem_apt,target=/var/cache/apt,sharing=locked \
     apt install -y libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev && \
     apt install -y libjemalloc-dev && \
     apt install -y python3-pip pipx nginx unzip curl wget git vim less && \
-    apt install -y ghostscript && \
-    apt install -y libmagic1
+    apt install -y ghostscript
 
 RUN if [ "$NEED_MIRROR" == "1" ]; then \
         pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple && \

api/alembic.ini

@@ -60,12 +60,7 @@ version_path_separator = os  # Use os.pathsep. Default configuration used for ne
 # are written from script.py.mako
 # output_encoding = utf-8
 
-# Database connection URL - DO NOT hardcode credentials here!
-# Connection string is set dynamically from environment variables in migrations/env.py
-# Required env vars: DB_USER, DB_PASSWORD, DB_HOST, DB_PORT, DB_NAME
-# Example: postgresql://user:password@localhost:5432/dbname
-; sqlalchemy.url = postgresql://user:password@host:port/dbname
-sqlalchemy.url = driver://user:password@host:port/dbname
+sqlalchemy.url = postgresql://user:password@localhost/dbname
 
 
 [post_write_hooks]

api/app/aioRedis.py

@@ -1,16 +1,16 @@
+import os
 import asyncio
 import json
 import logging
 from typing import Dict, Any, Optional
-
 import redis.asyncio as redis
 from redis.asyncio import ConnectionPool
-
 from app.core.config import settings
 
 # 设置日志记录器
 logger = logging.getLogger(__name__)
 
+
 # 创建连接池
 pool = ConnectionPool.from_url(
     f"redis://{settings.REDIS_HOST}:{settings.REDIS_PORT}",
@@ -21,7 +21,6 @@ pool = ConnectionPool.from_url(
 )
 aio_redis = redis.StrictRedis(connection_pool=pool)
 
-
 async def get_redis_connection():
     """获取Redis连接"""
     try:
@@ -30,8 +29,7 @@ async def get_redis_connection():
         logger.error(f"Redis连接失败: {str(e)}")
         return None
 
-
-async def aio_redis_set(key: str, val: str | dict, expire: int = None):
+async def aio_redis_set(key: str, val: str|dict, expire: int = None):
     """设置Redis键值
     
     Args:
@@ -42,7 +40,7 @@ async def aio_redis_set(key: str, val: str | dict, expire: int = None):
     try:
         if isinstance(val, dict):
             val = json.dumps(val, ensure_ascii=False)
-
+        
         if expire is not None:
             # 设置带过期时间的键值
             await aio_redis.set(key, val, ex=expire)
@@ -52,7 +50,6 @@ async def aio_redis_set(key: str, val: str | dict, expire: int = None):
     except Exception as e:
         logger.error(f"Redis set错误: {str(e)}")
 
-
 async def aio_redis_get(key: str):
     """获取Redis键值"""
     try:
@@ -61,7 +58,6 @@ async def aio_redis_get(key: str):
         logger.error(f"Redis get错误: {str(e)}")
         return None
 
-
 async def aio_redis_delete(key: str):
     """删除Redis键"""
     try:
@@ -70,7 +66,6 @@ async def aio_redis_delete(key: str):
         logger.error(f"Redis delete错误: {str(e)}")
         return None
 
-
 async def aio_redis_publish(channel: str, message: Dict[str, Any]) -> bool:
     """发布消息到Redis频道"""
     try:
@@ -83,10 +78,9 @@ async def aio_redis_publish(channel: str, message: Dict[str, Any]) -> bool:
         logger.error(f"Redis发布错误: {str(e)}")
         return False
 
-
 class RedisSubscriber:
     """Redis订阅器"""
-
+    
     def __init__(self, channel: str):
         self.channel = channel
         self.conn = None
@@ -94,25 +88,25 @@ class RedisSubscriber:
         self.is_closed = False
         self._queue = asyncio.Queue()
         self._task = None
-
+    
     async def start(self):
         """开始订阅"""
         if self.is_closed or self._task:
             return
-
+            
         self._task = asyncio.create_task(self._receive_messages())
         logger.info(f"开始订阅: {self.channel}")
-
+    
     async def _receive_messages(self):
         """接收消息"""
         try:
             self.conn = await get_redis_connection()
             if not self.conn:
                 return
-
+                
             self.pubsub = self.conn.pubsub()
             await self.pubsub.subscribe(self.channel)
-
+            
             while not self.is_closed:
                 try:
                     message = await self.pubsub.get_message(ignore_subscribe_messages=True, timeout=0.01)
@@ -133,7 +127,7 @@ class RedisSubscriber:
         finally:
             await self._queue.put(None)
             await self._cleanup()
-
+    
     async def _cleanup(self):
         """清理资源"""
         if self.pubsub:
@@ -147,7 +141,7 @@ class RedisSubscriber:
                 await self.conn.close()
             except Exception:
                 pass
-
+    
     async def get_message(self) -> Optional[Dict[str, Any]]:
         """获取消息"""
         if self.is_closed:
@@ -159,7 +153,7 @@ class RedisSubscriber:
         except Exception as e:
             logger.error(f"获取消息错误: {str(e)}")
             return None
-
+    
     async def close(self):
         """关闭订阅器"""
         if self.is_closed:
@@ -169,33 +163,32 @@ class RedisSubscriber:
             self._task.cancel()
         await self._cleanup()
 
-
 class RedisPubSubManager:
     """Redis发布订阅管理器"""
-
+    
     def __init__(self):
         self.subscribers = {}
-
+    
     async def publish(self, channel: str, message: Dict[str, Any]) -> bool:
         return await aio_redis_publish(channel, message)
-
+    
     def get_subscriber(self, channel: str) -> RedisSubscriber:
         if channel in self.subscribers:
             subscriber = self.subscribers[channel]
             if not subscriber.is_closed:
                 return subscriber
-
+        
         subscriber = RedisSubscriber(channel)
         self.subscribers[channel] = subscriber
         return subscriber
-
+    
     def cancel_subscription(self, channel: str) -> bool:
         if channel in self.subscribers:
             asyncio.create_task(self.subscribers[channel].close())
             del self.subscribers[channel]
             return True
         return False
-
+    
     def cancel_all_subscriptions(self) -> int:
         count = len(self.subscribers)
         for subscriber in self.subscribers.values():
@@ -203,6 +196,6 @@ class RedisPubSubManager:
         self.subscribers.clear()
         return count
 
-
 # 全局实例
 pubsub_manager = RedisPubSubManager()
+

api/app/cache/__init__.py

@@ -1,10 +0,0 @@
-"""
-Cache 缓存模块
-
-提供各种缓存功能的统一入口
-"""
-from .memory import InterestMemoryCache
-
-__all__ = [
-    "InterestMemoryCache",
-]

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 .interest_memory import InterestMemoryCache
-from .activity_stats_cache import ActivityStatsCache
-
-__all__ = [
-    "InterestMemoryCache",
-    "ActivityStatsCache",
-]

api/app/cache/memory/activity_stats_cache.py

@@ -1,124 +0,0 @@
-"""
-Recent Activity Stats Cache
-
-记忆提取活动统计缓存模块
-用于缓存每次记忆提取流程的统计数据，按 workspace_id 存储，24小时后释放
-查询命令：cache:memory:activity_stats:by_workspace:7de31a97-40a6-4fc0-b8d3-15c89f523843
-"""
-import json
-import logging
-from typing import Optional, Dict, Any
-from datetime import datetime
-
-from app.aioRedis import aio_redis
-
-logger = logging.getLogger(__name__)
-
-# 缓存过期时间：24小时
-ACTIVITY_STATS_CACHE_EXPIRE = 86400
-
-
-class ActivityStatsCache:
-    """记忆提取活动统计缓存类"""
-
-    PREFIX = "cache:memory:activity_stats"
-
-    @classmethod
-    def _get_key(cls, workspace_id: str) -> str:
-        """生成 Redis key
-
-        Args:
-            workspace_id: 工作空间ID
-
-        Returns:
-            完整的 Redis key
-        """
-        return f"{cls.PREFIX}:by_workspace:{workspace_id}"
-
-    @classmethod
-    async def set_activity_stats(
-        cls,
-        workspace_id: str,
-        stats: Dict[str, Any],
-        expire: int = ACTIVITY_STATS_CACHE_EXPIRE,
-    ) -> bool:
-        """设置记忆提取活动统计缓存
-
-        Args:
-            workspace_id: 工作空间ID
-            stats: 统计数据，格式：
-                {
-                    "chunk_count": int,
-                    "statements_count": int,
-                    "triplet_entities_count": int,
-                    "triplet_relations_count": int,
-                    "temporal_count": int,
-                }
-            expire: 过期时间（秒），默认24小时
-
-        Returns:
-            是否设置成功
-        """
-        try:
-            key = cls._get_key(workspace_id)
-            payload = {
-                "stats": stats,
-                "generated_at": datetime.now().isoformat(),
-                "workspace_id": workspace_id,
-                "cached": True,
-            }
-            value = json.dumps(payload, ensure_ascii=False)
-            await aio_redis.set(key, value, ex=expire)
-            logger.info(f"设置活动统计缓存成功: {key}, 过期时间: {expire}秒")
-            return True
-        except Exception as e:
-            logger.error(f"设置活动统计缓存失败: {e}", exc_info=True)
-            return False
-
-    @classmethod
-    async def get_activity_stats(
-        cls,
-        workspace_id: str,
-    ) -> Optional[Dict[str, Any]]:
-        """获取记忆提取活动统计缓存
-
-        Args:
-            workspace_id: 工作空间ID
-
-        Returns:
-            统计数据字典，缓存不存在或已过期返回 None
-        """
-        try:
-            key = cls._get_key(workspace_id)
-            value = await aio_redis.get(key)
-            if value:
-                payload = json.loads(value)
-                logger.info(f"命中活动统计缓存: {key}")
-                return payload
-            logger.info(f"活动统计缓存不存在或已过期: {key}")
-            return None
-        except Exception as e:
-            logger.error(f"获取活动统计缓存失败: {e}", exc_info=True)
-            return None
-
-    @classmethod
-    async def delete_activity_stats(
-        cls,
-        workspace_id: str,
-    ) -> bool:
-        """删除记忆提取活动统计缓存
-
-        Args:
-            workspace_id: 工作空间ID
-
-        Returns:
-            是否删除成功
-        """
-        try:
-            key = cls._get_key(workspace_id)
-            result = await aio_redis.delete(key)
-            logger.info(f"删除活动统计缓存: {key}, 结果: {result}")
-            return result > 0
-        except Exception as e:
-            logger.error(f"删除活动统计缓存失败: {e}", exc_info=True)
-            return False

api/app/cache/memory/interest_memory.py

@@ -1,122 +0,0 @@
-"""
-Interest Distribution Cache
-
-兴趣分布缓存模块
-用于缓存用户的兴趣分布标签数据，避免重复调用模型生成
-"""
-import json
-import logging
-from typing import Optional, List, Dict, Any
-from datetime import datetime
-
-from app.aioRedis import aio_redis
-
-logger = logging.getLogger(__name__)
-
-# 缓存过期时间：24小时
-INTEREST_CACHE_EXPIRE = 86400
-
-
-class InterestMemoryCache:
-    """兴趣分布缓存类"""
-
-    PREFIX = "cache:memory:interest_distribution"
-
-    @classmethod
-    def _get_key(cls, end_user_id: str, language: str) -> str:
-        """生成 Redis key
-
-        Args:
-            end_user_id: 用户ID
-            language: 语言类型
-
-        Returns:
-            完整的 Redis key
-        """
-        return f"{cls.PREFIX}:by_user:{end_user_id}:{language}"
-
-    @classmethod
-    async def set_interest_distribution(
-        cls,
-        end_user_id: str,
-        language: str,
-        data: List[Dict[str, Any]],
-        expire: int = INTEREST_CACHE_EXPIRE,
-    ) -> bool:
-        """设置用户兴趣分布缓存
-
-        Args:
-            end_user_id: 用户ID
-            language: 语言类型
-            data: 兴趣分布列表，格式 [{"name": "...", "frequency": ...}, ...]
-            expire: 过期时间（秒），默认24小时
-
-        Returns:
-            是否设置成功
-        """
-        try:
-            key = cls._get_key(end_user_id, language)
-            payload = {
-                "data": data,
-                "generated_at": datetime.now().isoformat(),
-                "cached": True,
-            }
-            value = json.dumps(payload, ensure_ascii=False)
-            await aio_redis.set(key, value, ex=expire)
-            logger.info(f"设置兴趣分布缓存成功: {key}, 过期时间: {expire}秒")
-            return True
-        except Exception as e:
-            logger.error(f"设置兴趣分布缓存失败: {e}", exc_info=True)
-            return False
-
-    @classmethod
-    async def get_interest_distribution(
-        cls,
-        end_user_id: str,
-        language: str,
-    ) -> Optional[List[Dict[str, Any]]]:
-        """获取用户兴趣分布缓存
-
-        Args:
-            end_user_id: 用户ID
-            language: 语言类型
-
-        Returns:
-            兴趣分布列表，缓存不存在或已过期返回 None
-        """
-        try:
-            key = cls._get_key(end_user_id, language)
-            value = await aio_redis.get(key)
-            if value:
-                payload = json.loads(value)
-                logger.info(f"命中兴趣分布缓存: {key}")
-                return payload.get("data")
-            logger.info(f"兴趣分布缓存不存在或已过期: {key}")
-            return None
-        except Exception as e:
-            logger.error(f"获取兴趣分布缓存失败: {e}", exc_info=True)
-            return None
-
-    @classmethod
-    async def delete_interest_distribution(
-        cls,
-        end_user_id: str,
-        language: str,
-    ) -> bool:
-        """删除用户兴趣分布缓存
-
-        Args:
-            end_user_id: 用户ID
-            language: 语言类型
-
-        Returns:
-            是否删除成功
-        """
-        try:
-            key = cls._get_key(end_user_id, language)
-            result = await aio_redis.delete(key)
-            logger.info(f"删除兴趣分布缓存: {key}, 结果: {result}")
-            return result > 0
-        except Exception as e:
-            logger.error(f"删除兴趣分布缓存失败: {e}", exc_info=True)
-            return False

api/app/celery_app.py

@@ -1,60 +1,40 @@
 import os
-import platform
 from datetime import timedelta
 from urllib.parse import quote
 
-from celery import Celery
-from celery.schedules import crontab
-
 from app.core.config import settings
-from app.core.logging_config import get_logger
-
-logger = get_logger(__name__)
-
-# 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，由 CELERY_BROKER_DB 指定）
-# backend: 结果存储（使用 Redis DB，由 CELERY_BACKEND_DB 指定）
-# NOTE: 不要在 .env 中设置 BROKER_URL / RESULT_BACKEND / CELERY_BROKER / CELERY_BACKEND，
-#       这些名称会被 Celery CLI 的 Click 框架劫持，详见 docs/celery-env-bug-report.md
-
-# Build canonical broker/backend URLs and force them into os.environ so that
-# Celery's Settings.broker_url property (which checks CELERY_BROKER_URL first)
-# cannot be overridden by stray env vars.
-# See: https://github.com/celery/celery/issues/4284
-_broker_url = f"redis://:{quote(settings.REDIS_PASSWORD)}@{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.REDIS_DB_CELERY_BROKER}"
-_backend_url = f"redis://:{quote(settings.REDIS_PASSWORD)}@{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.REDIS_DB_CELERY_BACKEND}"
-os.environ["CELERY_BROKER_URL"] = _broker_url
-os.environ["CELERY_RESULT_BACKEND"] = _backend_url
-# Neutralize legacy Celery env vars that can be hijacked by Celery's CLI/Click
-# integration and accidentally override our canonical URLs.
-os.environ.pop("BROKER_URL", None)
-os.environ.pop("RESULT_BACKEND", None)
-os.environ.pop("CELERY_BROKER", None)
-os.environ.pop("CELERY_BACKEND", None)
-
+# broker: 任务队列（使用 Redis DB 0）
+# backend: 结果存储（使用 Redis DB 10）
 celery_app = Celery(
     "redbear_tasks",
-    broker=_broker_url,
-    backend=_backend_url,
+    broker=f"redis://:{quote(settings.REDIS_PASSWORD)}@{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.CELERY_BROKER}",
+    backend=f"redis://:{quote(settings.REDIS_PASSWORD)}@{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.CELERY_BACKEND}",
 )
 
-logger.info(
-    "Celery app initialized",
-    extra={
-        "broker": _broker_url.replace(quote(settings.REDIS_PASSWORD), "***"),
-        "backend": _backend_url.replace(quote(settings.REDIS_PASSWORD), "***"),
-    },
-)
-# 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(
@@ -63,78 +43,58 @@ celery_app.conf.update(
     accept_content=['json'],
     result_serializer='json',
     
-    # # 时区
-    # timezone='Asia/Shanghai',
-    # enable_utc=False,
+    # 时区
+    timezone='Asia/Shanghai',
+    enable_utc=True,
     
     # 任务追踪
     task_track_started=True,
     task_ignore_result=False,
-
+    
     # 超时设置
-    task_time_limit=3600,  # 60分钟硬超时
-    task_soft_time_limit=3000,  # 50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
-
+    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 - 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,
-
-    # 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'},
-        'app.tasks.write_perceptual_memory': {'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.tasks.write_all_workspaces_memory_task': {'queue': 'periodic_tasks'},
-        'app.tasks.update_implicit_emotions_storage': {'queue': 'periodic_tasks'},
-        'app.tasks.init_implicit_emotions_for_users': {'queue': 'periodic_tasks'},
-        'app.tasks.init_interest_distribution_for_users': {'queue': 'periodic_tasks'},
-        'app.tasks.init_community_clustering_for_users': {'queue': 'periodic_tasks'},
-    },
+    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
+    
+    # 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
-memory_increment_schedule = crontab(hour=settings.MEMORY_INCREMENT_HOUR, minute=settings.MEMORY_INCREMENT_MINUTE)
+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)
-workspace_reflection_schedule = timedelta(seconds=settings.WORKSPACE_REFLECTION_INTERVAL_SECONDS)
-forgetting_cycle_schedule = timedelta(hours=settings.FORGETTING_CYCLE_INTERVAL_HOURS)
-implicit_emotions_update_schedule = crontab(
-    hour=settings.IMPLICIT_EMOTIONS_UPDATE_HOUR,
-    minute=settings.IMPLICIT_EMOTIONS_UPDATE_MINUTE,
-)
+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
 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,
@@ -152,16 +112,16 @@ beat_schedule_config = {
             "config_id": None,  # 使用默认配置，可以通过环境变量配置
         },
     },
-    "write-all-workspaces-memory": {
-        "task": "app.tasks.write_all_workspaces_memory_task",
-        "schedule": memory_increment_schedule,
-        "args": (),
-    },
-    "update-implicit-emotions-storage": {
-        "task": "app.tasks.update_implicit_emotions_storage",
-        "schedule": implicit_emotions_update_schedule,
-        "args": (),
-    },
 }
 
+# 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",
+        "schedule": memory_increment_schedule,
+        "kwargs": {
+            "workspace_id": settings.DEFAULT_WORKSPACE_ID,
+        },
+    }
+
 celery_app.conf.beat_schedule = beat_schedule_config

api/app/celery_worker.py

@@ -3,14 +3,8 @@ 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
 
-__all__ = ['celery_app']
+__all__ = ['celery_app']

api/app/config/__init__.py

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

api/app/config/default_ontology_config.py

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

api/app/config/default_ontology_initializer.py

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

api/app/controllers/__init__.py

@@ -13,26 +13,19 @@ from . import (
     document_controller,
     emotion_config_controller,
     emotion_controller,
-    end_user_controller,
     file_controller,
-    file_storage_controller,
     home_page_controller,
-    i18n_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,
@@ -45,9 +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
@@ -64,8 +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)
@@ -82,6 +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)
@@ -93,10 +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)
-manager_router.include_router(i18n_controller.router)
-manager_router.include_router(end_user_controller.router)
 
 __all__ = ["manager_router"]

api/app/controllers/app_controller.py

@@ -1,16 +1,13 @@
 import uuid
-import io
 from typing import Optional, Annotated
 
-import yaml
-from fastapi import APIRouter, Depends, Path, Form, UploadFile, File
+from fastapi import APIRouter, Depends, Path
 from fastapi.responses import StreamingResponse
 from sqlalchemy.orm import Session
-from urllib.parse import quote
 
 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
@@ -20,14 +17,11 @@ from app.repositories.end_user_repository import EndUserRepository
 from app.schemas import app_schema
 from app.schemas.response_schema import PageData, PageMeta
 from app.schemas.workflow_schema import WorkflowConfig as WorkflowConfigSchema
-from app.schemas.workflow_schema import WorkflowConfigUpdate, WorkflowImportSave
+from app.schemas.workflow_schema import WorkflowConfigUpdate
 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.app_statistics_service import AppStatisticsService
-from app.services.workflow_import_service import WorkflowImportService
 from app.services.workflow_service import WorkflowService, get_workflow_service
-from app.services.app_dsl_service import AppDslService
 
 router = APIRouter(prefix="/apps", tags=["Apps"])
 logger = get_business_logger()
@@ -53,7 +47,6 @@ def list_apps(
         status: str | None = None,
         search: str | None = None,
         include_shared: bool = True,
-        shared_only: bool = False,
         page: int = 1,
         pagesize: int = 10,
         ids: Optional[str] = None,
@@ -71,7 +64,7 @@ def list_apps(
 
     # 当 ids 存在且不为 None 时，根据 ids 获取应用
     if ids is not None:
-        app_ids = [app_id.strip() for app_id in ids.split(',') if app_id.strip()]
+        app_ids = [id.strip() for id in ids.split(',') if id.strip()]
         items_orm = app_service.get_apps_by_ids(db, app_ids, workspace_id)
         items = [service._convert_to_schema(app, workspace_id) for app in items_orm]
         return success(data=items)
@@ -85,7 +78,6 @@ def list_apps(
         status=status,
         search=search,
         include_shared=include_shared,
-        shared_only=shared_only,
         page=page,
         pagesize=pagesize,
     )
@@ -95,37 +87,6 @@ def list_apps(
     return success(data=PageData(page=meta, items=items))
 
 
-@router.get("/my-shared-out", summary="列出本工作空间主动分享出去的记录")
-@cur_workspace_access_guard()
-def list_my_shared_out(
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """列出本工作空间主动分享给其他工作空间的所有记录（我的共享）"""
-    workspace_id = current_user.current_workspace_id
-    service = app_service.AppService(db)
-    shares = service.list_my_shared_out(workspace_id=workspace_id)
-    data = [app_schema.AppShare.model_validate(s) for s in shares]
-    return success(data=data)
-
-
-@router.delete("/share/{target_workspace_id}", summary="取消对某工作空间的所有应用分享")
-@cur_workspace_access_guard()
-def unshare_all_apps_to_workspace(
-        target_workspace_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """Cancel all app shares from current workspace to a target workspace."""
-    workspace_id = current_user.current_workspace_id
-    service = app_service.AppService(db)
-    count = service.unshare_all_apps_to_workspace(
-        target_workspace_id=target_workspace_id,
-        workspace_id=workspace_id
-    )
-    return success(msg=f"已取消 {count} 个应用的分享", data={"count": count})
-
-
 @router.get("/{app_id}", summary="获取应用详情")
 @cur_workspace_access_guard()
 def get_app(
@@ -194,7 +155,6 @@ def delete_app(
 def copy_app(
         app_id: uuid.UUID,
         new_name: Optional[str] = None,
-        payload: app_schema.CopyAppRequest = None,
         db: Session = Depends(get_db),
         current_user=Depends(get_current_user),
 ):
@@ -206,8 +166,6 @@ def copy_app(
     - 不影响原应用
     """
     workspace_id = current_user.current_workspace_id
-    # body takes precedence over query param for backward compatibility
-    new_name = (payload.new_name if payload else None) or new_name
     logger.info(
         "用户请求复制应用",
         extra={
@@ -257,27 +215,6 @@ def get_agent_config(
     return success(data=app_schema.AgentConfig.model_validate(cfg))
 
 
-@router.get("/{app_id}/opening", summary="获取应用开场白配置")
-@cur_workspace_access_guard()
-def get_opening(
-        app_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """返回开场白文本和预设问题，供前端对话界面初始化时展示"""
-    workspace_id = current_user.current_workspace_id
-    cfg = app_service.get_agent_config(db, app_id=app_id, workspace_id=workspace_id)
-    features = cfg.features or {}
-    if hasattr(features, "model_dump"):
-        features = features.model_dump()
-    opening = features.get("opening_statement", {})
-    return success(data=app_schema.OpeningResponse(
-        enabled=opening.get("enabled", False),
-        statement=opening.get("statement"),
-        suggested_questions=opening.get("suggested_questions", []),
-    ))
-
-
 @router.post("/{app_id}/publish", summary="发布应用（生成不可变快照）")
 @cur_workspace_access_guard()
 def publish_app(
@@ -359,8 +296,7 @@ def share_app(
         app_id=app_id,
         target_workspace_ids=payload.target_workspace_ids,
         user_id=current_user.id,
-        workspace_id=workspace_id,
-        permission=payload.permission
+        workspace_id=workspace_id
     )
 
     data = [app_schema.AppShare.model_validate(s) for s in shares]
@@ -391,32 +327,6 @@ def unshare_app(
     return success(msg="应用分享已取消")
 
 
-@router.patch("/{app_id}/share/{target_workspace_id}", summary="更新共享权限")
-@cur_workspace_access_guard()
-def update_share_permission(
-        app_id: uuid.UUID,
-        target_workspace_id: uuid.UUID,
-        payload: app_schema.UpdateSharePermissionRequest,
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """更新共享权限（readonly <-> editable）
-
-    - 只能修改自己工作空间应用的共享权限
-    """
-    workspace_id = current_user.current_workspace_id
-
-    service = app_service.AppService(db)
-    share = service.update_share_permission(
-        app_id=app_id,
-        target_workspace_id=target_workspace_id,
-        permission=payload.permission,
-        workspace_id=workspace_id
-    )
-
-    return success(data=app_schema.AppShare.model_validate(share))
-
-
 @router.get("/{app_id}/shares", summary="列出应用的分享记录")
 @cur_workspace_access_guard()
 def list_app_shares(
@@ -440,46 +350,6 @@ def list_app_shares(
     return success(data=data)
 
 
-@router.delete("/shared/{source_workspace_id}", summary="批量移除某来源工作空间的所有共享应用")
-@cur_workspace_access_guard()
-def remove_all_shared_apps_from_workspace(
-        source_workspace_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """Remove all shared apps from a specific source workspace (recipient operation)."""
-    workspace_id = current_user.current_workspace_id
-    service = app_service.AppService(db)
-    count = service.remove_all_shared_apps_from_workspace(
-        source_workspace_id=source_workspace_id,
-        workspace_id=workspace_id
-    )
-    return success(msg=f"已移除 {count} 个共享应用", data={"count": count})
-
-
-@router.delete("/{app_id}/shared", summary="移除共享给我的应用")
-@cur_workspace_access_guard()
-def remove_shared_app(
-        app_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """被共享者从自己的工作空间移除共享应用
-
-    - 不会删除源应用，只删除共享记录
-    - 只能移除共享给自己工作空间的应用
-    """
-    workspace_id = current_user.current_workspace_id
-
-    service = app_service.AppService(db)
-    service.remove_shared_app(
-        app_id=app_id,
-        workspace_id=workspace_id
-    )
-
-    return success(msg="已移除共享应用")
-
-
 @router.post("/{app_id}/draft/run", summary="试运行 Agent（使用当前草稿配置）")
 @cur_workspace_access_guard()
 async def draft_run(
@@ -520,13 +390,13 @@ async def draft_run(
     # 提前验证和准备（在流式响应开始前完成）
     from app.services.app_service import AppService
     from app.services.multi_agent_service import MultiAgentService
-    from app.models import AgentConfig, ModelConfig, AppRelease
+    from app.models import AgentConfig, ModelConfig
     from sqlalchemy import select
     from app.core.exceptions import BusinessException
-    from app.services.draft_run_service import AgentRunService
+    from app.services.draft_run_service import DraftRunService
 
     service = AppService(db)
-    draft_service = AgentRunService(db)
+    draft_service = DraftRunService(db)
 
     # 1. 验证应用
     app = service._get_app_or_404(app_id)
@@ -537,12 +407,11 @@ async def draft_run(
     service._validate_app_accessible(app, workspace_id)
 
     if payload.user_id is None:
-        # 先获取 app 的 workspace_id
         end_user_repo = EndUserRepository(db)
         new_end_user = end_user_repo.get_or_create_end_user(
             app_id=app_id,
-            workspace_id=app.workspace_id,
             other_id=str(current_user.id),
+            original_user_id=str(current_user.id)  # Save original user_id to other_id
         )
         payload.user_id = str(new_end_user.id)
 
@@ -559,29 +428,18 @@ async def draft_run(
         service._check_agent_config(app_id)
 
         # 2. 获取 Agent 配置
-        # 共享应用：从最新发布版本读配置快照，而非草稿
-        is_shared = app.workspace_id != workspace_id
-        if is_shared:
-            if not app.current_release_id:
-                raise BusinessException("该应用尚未发布，无法使用", BizCode.AGENT_CONFIG_MISSING)
-            release = db.get(AppRelease, app.current_release_id)
-            if not release:
-                raise BusinessException("发布版本不存在", BizCode.AGENT_CONFIG_MISSING)
-            agent_cfg = service._agent_config_from_release(release)
-            model_config = db.get(ModelConfig, release.default_model_config_id) if release.default_model_config_id else None
-        else:
-            stmt = select(AgentConfig).where(AgentConfig.app_id == app_id)
-            agent_cfg = db.scalars(stmt).first()
-            if not agent_cfg:
-                raise BusinessException("Agent 配置不存在", BizCode.AGENT_CONFIG_MISSING)
+        stmt = select(AgentConfig).where(AgentConfig.app_id == app_id)
+        agent_cfg = db.scalars(stmt).first()
+        if not agent_cfg:
+            raise BusinessException("Agent 配置不存在", BizCode.AGENT_CONFIG_MISSING)
 
-            # 3. 获取模型配置
-            model_config = None
-            if agent_cfg.default_model_config_id:
-                model_config = db.get(ModelConfig, agent_cfg.default_model_config_id)
-                if not model_config:
-                    from app.core.exceptions import ResourceNotFoundException
-                    raise ResourceNotFoundException("模型配置", str(agent_cfg.default_model_config_id))
+        # 3. 获取模型配置
+        model_config = None
+        if agent_cfg.default_model_config_id:
+            model_config = db.get(ModelConfig, agent_cfg.default_model_config_id)
+            if not model_config:
+                from app.core.exceptions import ResourceNotFoundException
+                raise ResourceNotFoundException("模型配置", str(agent_cfg.default_model_config_id))
 
         # 流式返回
         if payload.stream:
@@ -596,8 +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
 
@@ -618,13 +475,12 @@ 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)
             }
         )
 
-        from app.services.draft_run_service import AgentRunService
-        draft_service = AgentRunService(db)
+        from app.services.draft_run_service import DraftRunService
+        draft_service = DraftRunService(db)
         result = await draft_service.run(
             agent_config=agent_cfg,
             model_config=model_config,
@@ -634,8 +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(
@@ -737,17 +592,7 @@ async def draft_run(
             msg="多 Agent 任务执行成功"
         )
     elif app.type == AppType.WORKFLOW:  # 工作流
-        # 共享应用：从最新发布版本读配置快照，而非草稿
-        is_shared = app.workspace_id != workspace_id
-        if is_shared:
-            if not app.current_release_id:
-                raise BusinessException("该应用尚未发布，无法使用", BizCode.AGENT_CONFIG_MISSING)
-            release = db.get(AppRelease, app.current_release_id)
-            if not release:
-                raise BusinessException("发布版本不存在", BizCode.AGENT_CONFIG_MISSING)
-            config = service._workflow_config_from_release(release)
-        else:
-            config = workflow_service.check_config(app_id)
+        config = workflow_service.check_config(app_id)
         # 3. 流式返回
         if payload.stream:
             logger.debug(
@@ -816,11 +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")
@@ -890,16 +730,6 @@ async def draft_run_compare(
         raise BusinessException("只有 Agent 类型应用支持试运行", BizCode.APP_TYPE_NOT_SUPPORTED)
     service._validate_app_accessible(app, workspace_id)
 
-    if payload.user_id is None:
-        # 先获取 app 的 workspace_id
-        end_user_repo = EndUserRepository(db)
-        new_end_user = end_user_repo.get_or_create_end_user(
-            app_id=app_id,
-            workspace_id=app.workspace_id,
-            other_id=str(current_user.id),
-        )
-        payload.user_id = str(new_end_user.id)
-
     # 2. 获取 Agent 配置
     from sqlalchemy import select
     from app.models import AgentConfig
@@ -945,33 +775,25 @@ async def draft_run_compare(
             "conversation_id": model_item.conversation_id  # 传递每个模型的 conversation_id
         })
 
-    # 从 features 中读取功能开关（与 draft_run 保持一致）
-    features_config: dict = agent_cfg.features or {}
-    if hasattr(features_config, 'model_dump'):
-        features_config = features_config.model_dump()
-    web_search_feature = features_config.get("web_search", {})
-    web_search = isinstance(web_search_feature, dict) and web_search_feature.get("enabled", False)
-
     # 流式返回
     if payload.stream:
         async def event_generator():
-            from app.services.draft_run_service import AgentRunService
-            draft_service = AgentRunService(db)
+            from app.services.draft_run_service import DraftRunService
+            draft_service = DraftRunService(db)
             async for event in draft_service.run_compare_stream(
                     agent_config=agent_cfg,
                     models=model_configs,
                     message=payload.message,
                     workspace_id=workspace_id,
                     conversation_id=payload.conversation_id,
-                    user_id=payload.user_id,
+                    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,
-                    web_search=web_search,
+                    web_search=True,
                     memory=True,
                     parallel=payload.parallel,
-                    timeout=payload.timeout or 60,
-                    files=payload.files
+                    timeout=payload.timeout or 60
             ):
                 yield event
 
@@ -986,23 +808,22 @@ async def draft_run_compare(
         )
 
     # 非流式返回
-    from app.services.draft_run_service import AgentRunService
-    draft_service = AgentRunService(db)
+    from app.services.draft_run_service import DraftRunService
+    draft_service = DraftRunService(db)
     result = await draft_service.run_compare(
         agent_config=agent_cfg,
         models=model_configs,
         message=payload.message,
         workspace_id=workspace_id,
         conversation_id=payload.conversation_id,
-        user_id=payload.user_id,
+        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,
-        web_search=web_search,
+        web_search=True,
         memory=True,
         parallel=payload.parallel,
-        timeout=payload.timeout or 60,
-        files=payload.files
+        timeout=payload.timeout or 60
     )
 
     logger.info(
@@ -1046,187 +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}/workflow/export")
-@cur_workspace_access_guard()
-async def export_workflow_config(
-        app_id: uuid.UUID,
-        db: Annotated[Session, Depends(get_db)],
-        current_user: Annotated[User, Depends(get_current_user)]
-):
-    """导出工作流配置为YAML文件"""
-    workflow_service = WorkflowService(db)
-
-    return success(data={
-        "content": workflow_service.export_workflow_dsl(app_id=app_id),
-    })
-
-
-@router.post("/workflow/import")
-@cur_workspace_access_guard()
-async def import_workflow_config(
-        file: UploadFile = File(...),
-        platform: str = Form(...),
-        app_id: str = Form(None),
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-
-):
-    """从YAML内容导入工作流配置"""
-    if not file.filename.lower().endswith((".yaml", ".yml")):
-        return fail(msg="Only yaml file is allowed", code=BizCode.BAD_REQUEST)
-
-    raw_text = (await file.read()).decode("utf-8")
-    import_service = WorkflowImportService(db)
-    config = yaml.safe_load(raw_text)
-    result = await import_service.upload_config(platform, config)
-    return success(data=result)
-
-
-@router.post("/workflow/import/save")
-@cur_workspace_access_guard()
-async def save_workflow_import(
-        data: WorkflowImportSave,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    import_service = WorkflowImportService(db)
-    app = await import_service.save_workflow(
-        user_id=current_user.id,
-        workspace_id=current_user.current_workspace_id,
-        temp_id=data.temp_id,
-        name=data.name,
-        description=data.description,
-    )
-    return success(data=app_schema.App.model_validate(app))
-
-
-@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）
-        db: 数据库连接
-        current_user: 当前用户
-
-    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）
-        db: 数据库连接
-        current_user: 当前用户
-    
-    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)
-
-
-@router.get("/{app_id}/export", summary="导出应用配置为 YAML 文件")
-@cur_workspace_access_guard()
-async def export_app(
-        app_id: uuid.UUID,
-        db: Annotated[Session, Depends(get_db)],
-        current_user: Annotated[User, Depends(get_current_user)],
-        release_id: Optional[uuid.UUID] = None
-):
-    """导出 agent / multi_agent / workflow 应用配置为 YAML 文件流。
-    release_id: 指定发布版本id，不传则导出当前草稿配置。
-    """
-    yaml_str, filename = AppDslService(db).export_dsl(app_id, release_id)
-    encoded = quote(filename, safe=".")
-    yaml_bytes = yaml_str.encode("utf-8")
-    file_stream = io.BytesIO(yaml_bytes)
-    file_stream.seek(0)
-    return StreamingResponse(
-        file_stream,
-        media_type="application/octet-stream; charset=utf-8",
-        headers={"Content-Disposition": f"attachment; filename={encoded}",
-                 "Content-Length": str(len(yaml_bytes))}
-    )
-
-
-@router.post("/import", summary="从 YAML 文件导入应用")
-@cur_workspace_access_guard()
-async def import_app(
-        file: UploadFile = File(...),
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """从 YAML 文件导入 agent / multi_agent / workflow 应用。
-    跨空间/跨租户导入时，模型/工具/知识库会按名称匹配，匹配不到则置空并返回 warnings。
-    """
-    if not file.filename.lower().endswith((".yaml", ".yml")):
-        return fail(msg="仅支持 YAML 文件", code=BizCode.BAD_REQUEST)
-
-    raw = (await file.read()).decode("utf-8")
-    dsl = yaml.safe_load(raw)
-    if not dsl or "app" not in dsl:
-        return fail(msg="YAML 格式无效，缺少 app 字段", code=BizCode.BAD_REQUEST)
-
-    new_app, warnings = AppDslService(db).import_dsl(
-        dsl=dsl,
-        workspace_id=current_user.current_workspace_id,
-        tenant_id=current_user.tenant_id,
-        user_id=current_user.id,
-    )
-    return success(
-        data={"app": app_schema.App.model_validate(new_app), "warnings": warnings},
-        msg="应用导入成功" + ("，但部分资源需手动配置" if warnings else "")
-    )

api/app/controllers/auth_controller.py

@@ -1,5 +1,4 @@
 from datetime import datetime, timedelta, timezone
-from typing import Callable
 from fastapi import APIRouter, Depends
 from sqlalchemy.orm import Session
 
@@ -17,7 +16,6 @@ from app.core.exceptions import BusinessException
 from app.core.error_codes import BizCode
 from app.dependencies import get_current_user, oauth2_scheme
 from app.models.user_model import User
-from app.i18n.dependencies import get_translator
 
 # 获取专用日志器
 auth_logger = get_auth_logger()
@@ -28,8 +26,7 @@ router = APIRouter(tags=["Authentication"])
 @router.post("/token", response_model=ApiResponse)
 async def login_for_access_token(
     form_data: TokenRequest,
-    db: Session = Depends(get_db),
-    t: Callable = Depends(get_translator)
+    db: Session = Depends(get_db)
 ):
     """用户登录获取token"""
     auth_logger.info(f"用户登录请求: {form_data.email}")
@@ -43,10 +40,10 @@ async def login_for_access_token(
         invite_info = workspace_service.validate_invite_token(db, form_data.invite)
         
         if not invite_info.is_valid:
-            raise BusinessException(t("auth.invite.invalid"), code=BizCode.BAD_REQUEST)
+            raise BusinessException("邀请码无效或已过期", code=BizCode.BAD_REQUEST)
         
         if invite_info.email != form_data.email:
-            raise BusinessException(t("auth.invite.email_mismatch"), code=BizCode.BAD_REQUEST)        
+            raise BusinessException("邀请邮箱与登录邮箱不匹配", code=BizCode.BAD_REQUEST)        
         auth_logger.info(f"邀请码验证成功: workspace={invite_info.workspace_name}")
         try:
             # 尝试认证用户
@@ -64,7 +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
@@ -72,7 +68,7 @@ async def login_for_access_token(
             elif e.code == BizCode.PASSWORD_ERROR:
                 # 用户存在但密码错误
                 auth_logger.warning(f"接受邀请失败，密码验证错误: {form_data.email}")
-                raise BusinessException(t("auth.invite.password_verification_failed"), BizCode.LOGIN_FAILED)
+                raise BusinessException("接受邀请失败，密码验证错误", BizCode.LOGIN_FAILED)
             else:
                 # 其他认证失败情况，直接抛出
                 raise
@@ -85,7 +81,7 @@ async def login_for_access_token(
         except BusinessException as e:
             
             # 其他认证失败情况，直接抛出
-            raise BusinessException(e.message, BizCode.LOGIN_FAILED)
+            raise BusinessException(e.message,BizCode.LOGIN_FAILED)
 
     # 创建 tokens
     access_token, access_token_id = security.create_access_token(subject=user.id)
@@ -113,15 +109,14 @@ async def login_for_access_token(
             expires_at=access_expires_at,
             refresh_expires_at=refresh_expires_at
         ),
-        msg=t("auth.login.success")
+        msg="登录成功"
     )
 
 
 @router.post("/refresh", response_model=ApiResponse)
 async def refresh_token(
     refresh_request: RefreshTokenRequest,
-    db: Session = Depends(get_db),
-    t: Callable = Depends(get_translator)
+    db: Session = Depends(get_db)
 ):
     """刷新token"""
     auth_logger.info("收到token刷新请求")
@@ -129,18 +124,18 @@ async def refresh_token(
     # 验证 refresh token
     userId = security.verify_token(refresh_request.refresh_token, "refresh")
     if not userId:
-        raise BusinessException(t("auth.token.invalid_refresh_token"), code=BizCode.TOKEN_INVALID)
+        raise BusinessException("无效的refresh token", code=BizCode.TOKEN_INVALID)
     
     # 检查用户是否存在
     user = auth_service.get_user_by_id(db, userId)
     if not user:
-        raise BusinessException(t("auth.user.not_found"), code=BizCode.USER_NOT_FOUND)
+        raise BusinessException("用户不存在", code=BizCode.USER_NOT_FOUND)
     
     # 检查 refresh token 黑名单
     if settings.ENABLE_SINGLE_SESSION:
         refresh_token_id = security.get_token_id(refresh_request.refresh_token)
         if refresh_token_id and await SessionService.is_token_blacklisted(refresh_token_id):
-            raise BusinessException(t("auth.token.refresh_token_blacklisted"), code=BizCode.TOKEN_BLACKLISTED)
+            raise BusinessException("Refresh token已失效", code=BizCode.TOKEN_BLACKLISTED)
     
     # 生成新 tokens
     new_access_token, new_access_token_id = security.create_access_token(subject=user.id)
@@ -171,7 +166,7 @@ async def refresh_token(
             expires_at=access_expires_at,
             refresh_expires_at=refresh_expires_at
         ),
-        msg=t("auth.token.refresh_success")
+        msg="token刷新成功"
     )
 
 
@@ -179,15 +174,14 @@ async def refresh_token(
 async def logout(
     token: str = Depends(oauth2_scheme),
     current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
-    t: Callable = Depends(get_translator)
+    db: Session = Depends(get_db)
 ):
     """登出当前用户：加入token黑名单并清理会话"""
     auth_logger.info(f"用户 {current_user.username} 请求登出")
     
     token_id = security.get_token_id(token)
     if not token_id:
-        raise BusinessException(t("auth.token.invalid"), code=BizCode.TOKEN_INVALID)
+        raise BusinessException("无效的access token", code=BizCode.TOKEN_INVALID)
 
     # 加入黑名单
     await SessionService.blacklist_token(token_id)
@@ -197,5 +191,5 @@ async def logout(
         await SessionService.clear_user_session(current_user.username)
 
     auth_logger.info(f"用户 {current_user.username} 登出成功")
-    return success(msg=t("auth.logout.success"))
+    return success(msg="登出成功")
 

api/app/controllers/chunk_controller.py

@@ -441,14 +441,14 @@ async def retrieve_chunks(
     # 1 participle search, 2 semantic search, 3 hybrid search
     match retrieve_data.retrieve_type:
         case chunk_schema.RetrieveType.PARTICIPLE:
-            rs = vector_service.search_by_full_text(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.similarity_threshold, file_names_filter=retrieve_data.file_names_filter)
+            rs = vector_service.search_by_full_text(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.similarity_threshold)
             return success(data=rs, msg="retrieval successful")
         case chunk_schema.RetrieveType.SEMANTIC:
-            rs = vector_service.search_by_vector(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.vector_similarity_weight, file_names_filter=retrieve_data.file_names_filter)
+            rs = vector_service.search_by_vector(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.vector_similarity_weight)
             return success(data=rs, msg="retrieval successful")
         case _:
-            rs1 = vector_service.search_by_vector(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.vector_similarity_weight, file_names_filter=retrieve_data.file_names_filter)
-            rs2 = vector_service.search_by_full_text(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.similarity_threshold, file_names_filter=retrieve_data.file_names_filter)
+            rs1 = vector_service.search_by_vector(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.vector_similarity_weight)
+            rs2 = vector_service.search_by_full_text(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.similarity_threshold)
             # Efficient deduplication
             seen_ids = set()
             unique_rs = []

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(
@@ -208,112 +193,63 @@ async def get_emotion_health(
 
 
 
-# @router.post("/check-data", response_model=ApiResponse)
-# async def check_emotion_data_exists(
-#     request: EmotionSuggestionsRequest,
-#     db: Session = Depends(get_db),
-#     current_user: User = Depends(get_current_user),
-# ):
-#     """检查用户情绪建议数据是否存在
-
-#     Args:
-#         request: 包含 end_user_id
-#         db: 数据库会话
-#         current_user: 当前用户
-
-#     Returns:
-#         数据存在状态
-#     """
-#     try:
-#         api_logger.info(
-#             f"检查用户情绪建议数据是否存在: {request.end_user_id}",
-#             extra={"end_user_id": request.end_user_id}
-#         )
-
-#         # 从数据库获取建议
-#         data = await emotion_service.get_cached_suggestions(
-#             end_user_id=request.end_user_id,
-#             db=db
-#         )
-
-#         if data is None:
-#             api_logger.info(f"用户 {request.end_user_id} 的情绪建议数据不存在")
-#             return fail(
-#                 BizCode.NOT_FOUND,
-#                 "情绪建议数据不存在，请点击右上角刷新进行初始化",
-#                 {"exists": False}
-#             )
-
-#         api_logger.info(f"用户 {request.end_user_id} 的情绪建议数据存在")
-#         return success(data={"exists": True}, msg="情绪建议数据已存在")
-
-#     except Exception as e:
-#         api_logger.error(
-#             f"检查情绪建议数据失败: {str(e)}",
-#             extra={"end_user_id": request.end_user_id},
-#             exc_info=True
-#         )
-#         raise HTTPException(
-#             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-#             detail=f"检查情绪建议数据失败: {str(e)}"
-#         )
-
-
 @router.post("/suggestions", response_model=ApiResponse)
 async def get_emotion_suggestions(
     request: EmotionSuggestionsRequest,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
-    """获取个性化情绪建议（从数据库读取）
-
+    """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:
-        存储的个性化情绪建议响应
+        缓存的个性化情绪建议响应
     """
     try:
         api_logger.info(
-            f"用户 {current_user.username} 请求获取个性化情绪建议",
+            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
             }
         )
-
-        # 从数据库获取建议
+        
+        # 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
             api_logger.info(
-                f"用户 {request.end_user_id} 的建议数据不存在",
-                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}
             )
-            return success(
-                data={"exists": False},
-                msg="情绪建议数据不存在，请点击右上角刷新进行初始化"
+            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",
+            "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")
-
+        
+        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(
@@ -325,62 +261,83 @@ 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并保存到数据库）
-
+    """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
-        )
-
-        # 保存到数据库
-        await emotion_service.save_suggestions_cache(
-            end_user_id=request.end_user_id,
-            suggestions_data=data,
+            end_user_id=request.group_id,
             db=db
         )
-
+        
+        # 保存到缓存
+        await emotion_service.save_suggestions_cache(
+            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(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             detail=f"生成个性化建议失败: {str(e)}"
-        )
+        )

api/app/controllers/end_user_controller.py

@@ -1,48 +0,0 @@
-"""End User 管理接口 - 无需认证"""
-
-from app.core.logging_config import get_business_logger
-from app.core.response_utils import success
-from app.db import get_db
-from app.repositories.end_user_repository import EndUserRepository
-from app.schemas.memory_api_schema import (
-    CreateEndUserRequest,
-    CreateEndUserResponse,
-)
-from fastapi import APIRouter, Depends
-from sqlalchemy.orm import Session
-
-router = APIRouter(prefix="/end_users", tags=["End Users"])
-logger = get_business_logger()
-
-
-@router.post("")
-async def create_end_user(
-    data: CreateEndUserRequest,
-    db: Session = Depends(get_db),
-):
-    """
-    Create an end user.
-    
-    Creates a new end user for the given workspace.
-    If an end user with the same other_id already exists in the workspace,
-    returns the existing one.
-    """
-    logger.info(f"Create end user request - other_id: {data.other_id}, workspace_id: {data.workspace_id}")
-
-    end_user_repo = EndUserRepository(db)
-    end_user = end_user_repo.get_or_create_end_user(
-        app_id=None,
-        workspace_id=data.workspace_id,
-        other_id=data.other_id,
-    )
-
-    logger.info(f"End user ready: {end_user.id}")
-
-    result = {
-        "id": str(end_user.id),
-        "other_id": end_user.other_id or "",
-        "other_name": end_user.other_name or "",
-        "workspace_id": str(end_user.workspace_id),
-    }
-
-    return success(data=CreateEndUserResponse(**result).model_dump(), msg="End user created successfully")

api/app/controllers/file_storage_controller.py

@@ -1,655 +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, Request, 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"]
-)
-
-
-def _match_scheme(request: Request, url: str) -> str:
-    """
-    将 presigned URL 的协议替换为与当前请求一致的协议（http/https）。
-    解决反向代理场景下 presigned URL 协议与请求协议不匹配的问题。
-    """
-    incoming_scheme = request.headers.get("x-forwarded-proto") or request.url.scheme
-    if url.startswith("http://") and incoming_scheme == "https":
-        return "https://" + url[7:]
-    if url.startswith("https://") and incoming_scheme == "http":
-        return "http://" + url[8:]
-    return url
-
-
-@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(
-    request: Request,
-    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)
-            presigned_url = _match_scheme(request, presigned_url)
-            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(
-    request: Request,
-    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)
-            url = _match_scheme(request, url)
-
-        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(
-    request: Request,
-    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)
-            presigned_url = _match_scheme(request, presigned_url)
-            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(
-    request: Request,
-    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)
-            presigned_url = _match_scheme(request, presigned_url)
-            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/i18n_controller.py

@@ -1,833 +0,0 @@
-"""
-I18n Management API Controller
-
-This module provides management APIs for:
-- Language management (list, get, add, update languages)
-- Translation management (get, update, reload translations)
-"""
-
-from fastapi import APIRouter, Depends, HTTPException, status
-from sqlalchemy.orm import Session
-from typing import Callable, Optional
-
-from app.core.logging_config import get_api_logger
-from app.core.response_utils import success
-from app.db import get_db
-from app.dependencies import get_current_user, get_current_superuser
-from app.i18n.dependencies import get_translator
-from app.i18n.service import get_translation_service
-from app.models.user_model import User
-from app.schemas.i18n_schema import (
-    LanguageInfo,
-    LanguageListResponse,
-    LanguageCreateRequest,
-    LanguageUpdateRequest,
-    TranslationResponse,
-    TranslationUpdateRequest,
-    MissingTranslationsResponse,
-    ReloadResponse
-)
-from app.schemas.response_schema import ApiResponse
-
-api_logger = get_api_logger()
-
-router = APIRouter(
-    prefix="/i18n",
-    tags=["I18n Management"],
-)
-
-
-# ============================================================================
-# Language Management APIs
-# ============================================================================
-
-@router.get("/languages", response_model=ApiResponse)
-def get_languages(
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_user)
-):
-    """
-    Get list of all supported languages.
-    
-    Returns:
-        List of language information including code, name, and status
-    """
-    api_logger.info(f"Get languages request from user: {current_user.username}")
-    
-    from app.core.config import settings
-    translation_service = get_translation_service()
-    
-    # Get available locales from translation service
-    available_locales = translation_service.get_available_locales()
-    
-    # Build language info list
-    languages = []
-    for locale in available_locales:
-        is_default = locale == settings.I18N_DEFAULT_LANGUAGE
-        is_enabled = locale in settings.I18N_SUPPORTED_LANGUAGES
-        
-        # Get native names
-        native_names = {
-            "zh": "中文（简体）",
-            "en": "English",
-            "ja": "日本語",
-            "ko": "한국어",
-            "fr": "Français",
-            "de": "Deutsch",
-            "es": "Español"
-        }
-        
-        language_info = LanguageInfo(
-            code=locale,
-            name=f"{locale.upper()}",
-            native_name=native_names.get(locale, locale),
-            is_enabled=is_enabled,
-            is_default=is_default
-        )
-        languages.append(language_info)
-    
-    response = LanguageListResponse(languages=languages)
-    
-    api_logger.info(f"Returning {len(languages)} languages")
-    return success(data=response.dict(), msg=t("common.success.retrieved"))
-
-
-@router.get("/languages/{locale}", response_model=ApiResponse)
-def get_language(
-    locale: str,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_user)
-):
-    """
-    Get information about a specific language.
-    
-    Args:
-        locale: Language code (e.g., 'zh', 'en')
-        
-    Returns:
-        Language information
-    """
-    api_logger.info(f"Get language info request: locale={locale}, user={current_user.username}")
-    
-    from app.core.config import settings
-    translation_service = get_translation_service()
-    
-    # Check if locale exists
-    available_locales = translation_service.get_available_locales()
-    if locale not in available_locales:
-        api_logger.warning(f"Language not found: {locale}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=t("i18n.language.not_found", locale=locale)
-        )
-    
-    # Build language info
-    is_default = locale == settings.I18N_DEFAULT_LANGUAGE
-    is_enabled = locale in settings.I18N_SUPPORTED_LANGUAGES
-    
-    native_names = {
-        "zh": "中文（简体）",
-        "en": "English",
-        "ja": "日本語",
-        "ko": "한국어",
-        "fr": "Français",
-        "de": "Deutsch",
-        "es": "Español"
-    }
-    
-    language_info = LanguageInfo(
-        code=locale,
-        name=f"{locale.upper()}",
-        native_name=native_names.get(locale, locale),
-        is_enabled=is_enabled,
-        is_default=is_default
-    )
-    
-    api_logger.info(f"Returning language info for: {locale}")
-    return success(data=language_info.dict(), msg=t("common.success.retrieved"))
-
-
-@router.post("/languages", response_model=ApiResponse)
-def add_language(
-    request: LanguageCreateRequest,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Add a new language (admin only).
-    
-    Note: This endpoint validates the request but actual language addition
-    requires creating translation files in the locales directory.
-    
-    Args:
-        request: Language creation request
-        
-    Returns:
-        Success message
-    """
-    api_logger.info(
-        f"Add language request: code={request.code}, admin={current_user.username}"
-    )
-    
-    from app.core.config import settings
-    translation_service = get_translation_service()
-    
-    # Check if language already exists
-    available_locales = translation_service.get_available_locales()
-    if request.code in available_locales:
-        api_logger.warning(f"Language already exists: {request.code}")
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=t("i18n.language.already_exists", locale=request.code)
-        )
-    
-    # Note: Actual language addition requires creating translation files
-    # This endpoint serves as a validation and documentation point
-    
-    api_logger.info(
-        f"Language addition validated: {request.code}. "
-        "Translation files need to be created manually."
-    )
-    
-    return success(
-        msg=t(
-            "i18n.language.add_instructions",
-            locale=request.code,
-            dir=settings.I18N_CORE_LOCALES_DIR
-        )
-    )
-
-
-@router.put("/languages/{locale}", response_model=ApiResponse)
-def update_language(
-    locale: str,
-    request: LanguageUpdateRequest,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Update language configuration (admin only).
-    
-    Note: This endpoint validates the request but actual configuration
-    changes require updating environment variables or config files.
-    
-    Args:
-        locale: Language code
-        request: Language update request
-        
-    Returns:
-        Success message
-    """
-    api_logger.info(
-        f"Update language request: locale={locale}, admin={current_user.username}"
-    )
-    
-    translation_service = get_translation_service()
-    
-    # Check if language exists
-    available_locales = translation_service.get_available_locales()
-    if locale not in available_locales:
-        api_logger.warning(f"Language not found: {locale}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=t("i18n.language.not_found", locale=locale)
-        )
-    
-    # Note: Actual configuration changes require updating settings
-    # This endpoint serves as a validation and documentation point
-    
-    api_logger.info(
-        f"Language update validated: {locale}. "
-        "Configuration changes require environment variable updates."
-    )
-    
-    return success(msg=t("i18n.language.update_instructions", locale=locale))
-
-
-# ============================================================================
-# Translation Management APIs
-# ============================================================================
-
-@router.get("/translations", response_model=ApiResponse)
-def get_all_translations(
-    locale: Optional[str] = None,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_user)
-):
-    """
-    Get all translations for all or specific locale.
-    
-    Args:
-        locale: Optional locale filter
-        
-    Returns:
-        All translations organized by locale and namespace
-    """
-    api_logger.info(
-        f"Get all translations request: locale={locale}, user={current_user.username}"
-    )
-    
-    translation_service = get_translation_service()
-    
-    if locale:
-        # Get translations for specific locale
-        available_locales = translation_service.get_available_locales()
-        if locale not in available_locales:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail=t("i18n.language.not_found", locale=locale)
-            )
-        
-        translations = {
-            locale: translation_service._cache.get(locale, {})
-        }
-    else:
-        # Get all translations
-        translations = translation_service._cache
-    
-    response = TranslationResponse(translations=translations)
-    
-    api_logger.info(f"Returning translations for: {locale or 'all locales'}")
-    return success(data=response.dict(), msg=t("common.success.retrieved"))
-
-
-@router.get("/translations/{locale}", response_model=ApiResponse)
-def get_locale_translations(
-    locale: str,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_user)
-):
-    """
-    Get all translations for a specific locale.
-    
-    Args:
-        locale: Language code
-        
-    Returns:
-        All translations for the locale organized by namespace
-    """
-    api_logger.info(
-        f"Get locale translations request: locale={locale}, user={current_user.username}"
-    )
-    
-    translation_service = get_translation_service()
-    
-    # Check if locale exists
-    available_locales = translation_service.get_available_locales()
-    if locale not in available_locales:
-        api_logger.warning(f"Language not found: {locale}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=t("i18n.language.not_found", locale=locale)
-        )
-    
-    translations = translation_service._cache.get(locale, {})
-    
-    api_logger.info(f"Returning {len(translations)} namespaces for locale: {locale}")
-    return success(data={"locale": locale, "translations": translations}, msg=t("common.success.retrieved"))
-
-
-@router.get("/translations/{locale}/{namespace}", response_model=ApiResponse)
-def get_namespace_translations(
-    locale: str,
-    namespace: str,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_user)
-):
-    """
-    Get translations for a specific namespace in a locale.
-    
-    Args:
-        locale: Language code
-        namespace: Translation namespace (e.g., 'common', 'auth')
-        
-    Returns:
-        Translations for the specified namespace
-    """
-    api_logger.info(
-        f"Get namespace translations request: locale={locale}, "
-        f"namespace={namespace}, user={current_user.username}"
-    )
-    
-    translation_service = get_translation_service()
-    
-    # Check if locale exists
-    available_locales = translation_service.get_available_locales()
-    if locale not in available_locales:
-        api_logger.warning(f"Language not found: {locale}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=t("i18n.language.not_found", locale=locale)
-        )
-    
-    # Get namespace translations
-    locale_translations = translation_service._cache.get(locale, {})
-    namespace_translations = locale_translations.get(namespace, {})
-    
-    if not namespace_translations:
-        api_logger.warning(f"Namespace not found: {namespace} in locale: {locale}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=t("i18n.namespace.not_found", namespace=namespace, locale=locale)
-        )
-    
-    api_logger.info(
-        f"Returning translations for namespace: {namespace} in locale: {locale}"
-    )
-    return success(
-        data={
-            "locale": locale,
-            "namespace": namespace,
-            "translations": namespace_translations
-        },
-        msg=t("common.success.retrieved")
-    )
-
-
-@router.put("/translations/{locale}/{key:path}", response_model=ApiResponse)
-def update_translation(
-    locale: str,
-    key: str,
-    request: TranslationUpdateRequest,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Update a single translation (admin only).
-    
-    Note: This endpoint validates the request but actual translation updates
-    require modifying translation files in the locales directory.
-    
-    Args:
-        locale: Language code
-        key: Translation key (format: "namespace.key.subkey")
-        request: Translation update request
-        
-    Returns:
-        Success message
-    """
-    api_logger.info(
-        f"Update translation request: locale={locale}, key={key}, "
-        f"admin={current_user.username}"
-    )
-    
-    translation_service = get_translation_service()
-    
-    # Check if locale exists
-    available_locales = translation_service.get_available_locales()
-    if locale not in available_locales:
-        api_logger.warning(f"Language not found: {locale}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=t("i18n.language.not_found", locale=locale)
-        )
-    
-    # Validate key format
-    if "." not in key:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=t("i18n.translation.invalid_key_format", key=key)
-        )
-    
-    # Note: Actual translation updates require modifying JSON files
-    # This endpoint serves as a validation and documentation point
-    
-    api_logger.info(
-        f"Translation update validated: {locale}/{key}. "
-        "Translation files need to be updated manually."
-    )
-    
-    return success(
-        msg=t("i18n.translation.update_instructions", locale=locale, key=key)
-    )
-
-
-@router.get("/translations/missing", response_model=ApiResponse)
-def get_missing_translations(
-    locale: Optional[str] = None,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_user)
-):
-    """
-    Get list of missing translations.
-    
-    Compares translations across locales to find missing keys.
-    
-    Args:
-        locale: Optional locale to check (defaults to checking all non-default locales)
-        
-    Returns:
-        List of missing translation keys
-    """
-    api_logger.info(
-        f"Get missing translations request: locale={locale}, user={current_user.username}"
-    )
-    
-    from app.core.config import settings
-    translation_service = get_translation_service()
-    
-    default_locale = settings.I18N_DEFAULT_LANGUAGE
-    available_locales = translation_service.get_available_locales()
-    
-    # Get default locale translations as reference
-    default_translations = translation_service._cache.get(default_locale, {})
-    
-    # Collect all keys from default locale
-    def collect_keys(data, prefix=""):
-        keys = []
-        for key, value in data.items():
-            full_key = f"{prefix}.{key}" if prefix else key
-            if isinstance(value, dict):
-                keys.extend(collect_keys(value, full_key))
-            else:
-                keys.append(full_key)
-        return keys
-    
-    default_keys = set()
-    for namespace, translations in default_translations.items():
-        namespace_keys = collect_keys(translations, namespace)
-        default_keys.update(namespace_keys)
-    
-    # Find missing keys in target locale(s)
-    missing_by_locale = {}
-    
-    target_locales = [locale] if locale else [
-        loc for loc in available_locales if loc != default_locale
-    ]
-    
-    for target_locale in target_locales:
-        if target_locale not in available_locales:
-            continue
-        
-        target_translations = translation_service._cache.get(target_locale, {})
-        target_keys = set()
-        
-        for namespace, translations in target_translations.items():
-            namespace_keys = collect_keys(translations, namespace)
-            target_keys.update(namespace_keys)
-        
-        missing_keys = default_keys - target_keys
-        if missing_keys:
-            missing_by_locale[target_locale] = sorted(list(missing_keys))
-    
-    response = MissingTranslationsResponse(missing_translations=missing_by_locale)
-    
-    total_missing = sum(len(keys) for keys in missing_by_locale.values())
-    api_logger.info(f"Found {total_missing} missing translations across {len(missing_by_locale)} locales")
-    
-    return success(data=response.dict(), msg=t("common.success.retrieved"))
-
-
-@router.post("/reload", response_model=ApiResponse)
-def reload_translations(
-    locale: Optional[str] = None,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Trigger hot reload of translation files (admin only).
-    
-    Args:
-        locale: Optional locale to reload (defaults to reloading all locales)
-        
-    Returns:
-        Reload status and statistics
-    """
-    api_logger.info(
-        f"Reload translations request: locale={locale or 'all'}, "
-        f"admin={current_user.username}"
-    )
-    
-    from app.core.config import settings
-    
-    if not settings.I18N_ENABLE_HOT_RELOAD:
-        api_logger.warning("Hot reload is disabled in configuration")
-        raise HTTPException(
-            status_code=status.HTTP_403_FORBIDDEN,
-            detail=t("i18n.reload.disabled")
-        )
-    
-    translation_service = get_translation_service()
-    
-    try:
-        # Reload translations
-        translation_service.reload(locale)
-        
-        # Get statistics
-        available_locales = translation_service.get_available_locales()
-        reloaded_locales = [locale] if locale else available_locales
-        
-        response = ReloadResponse(
-            success=True,
-            reloaded_locales=reloaded_locales,
-            total_locales=len(available_locales)
-        )
-        
-        api_logger.info(
-            f"Successfully reloaded translations for: {', '.join(reloaded_locales)}"
-        )
-        
-        return success(data=response.dict(), msg=t("i18n.reload.success"))
-        
-    except Exception as e:
-        api_logger.error(f"Failed to reload translations: {str(e)}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=t("i18n.reload.failed", error=str(e))
-        )
-
-
-# ============================================================================
-# Performance Monitoring APIs
-# ============================================================================
-
-@router.get("/metrics", response_model=ApiResponse)
-def get_metrics(
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Get i18n performance metrics (admin only).
-    
-    Returns:
-        Performance metrics including:
-        - Request counts
-        - Missing translations
-        - Timing statistics
-        - Locale usage
-        - Error counts
-    """
-    api_logger.info(f"Get metrics request: admin={current_user.username}")
-    
-    translation_service = get_translation_service()
-    metrics = translation_service.get_metrics_summary()
-    
-    api_logger.info("Returning i18n metrics")
-    return success(data=metrics, msg=t("common.success.retrieved"))
-
-
-@router.get("/metrics/cache", response_model=ApiResponse)
-def get_cache_stats(
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Get cache statistics (admin only).
-    
-    Returns:
-        Cache statistics including:
-        - Hit/miss rates
-        - LRU cache performance
-        - Loaded locales
-        - Memory usage
-    """
-    api_logger.info(f"Get cache stats request: admin={current_user.username}")
-    
-    translation_service = get_translation_service()
-    cache_stats = translation_service.get_cache_stats()
-    memory_usage = translation_service.get_memory_usage()
-    
-    data = {
-        "cache": cache_stats,
-        "memory": memory_usage
-    }
-    
-    api_logger.info("Returning cache statistics")
-    return success(data=data, msg=t("common.success.retrieved"))
-
-
-@router.get("/metrics/prometheus")
-def get_prometheus_metrics(
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Get metrics in Prometheus format (admin only).
-    
-    Returns:
-        Prometheus-formatted metrics as plain text
-    """
-    api_logger.info(f"Get Prometheus metrics request: admin={current_user.username}")
-    
-    from app.i18n.metrics import get_metrics
-    metrics = get_metrics()
-    prometheus_output = metrics.export_prometheus()
-    
-    from fastapi.responses import PlainTextResponse
-    return PlainTextResponse(content=prometheus_output)
-
-
-@router.post("/metrics/reset", response_model=ApiResponse)
-def reset_metrics(
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Reset all metrics (admin only).
-    
-    Returns:
-        Success message
-    """
-    api_logger.info(f"Reset metrics request: admin={current_user.username}")
-    
-    from app.i18n.metrics import get_metrics
-    metrics = get_metrics()
-    metrics.reset()
-    
-    translation_service = get_translation_service()
-    translation_service.cache.reset_stats()
-    
-    api_logger.info("Metrics reset completed")
-    return success(msg=t("i18n.metrics.reset_success"))
-
-
-# ============================================================================
-# Missing Translation Logging and Reporting APIs
-# ============================================================================
-
-@router.get("/logs/missing", response_model=ApiResponse)
-def get_missing_translation_logs(
-    locale: Optional[str] = None,
-    limit: Optional[int] = 100,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Get missing translation logs (admin only).
-    
-    Returns logged missing translations with context information.
-    
-    Args:
-        locale: Optional locale filter
-        limit: Maximum number of entries to return (default: 100)
-        
-    Returns:
-        Missing translation logs with context
-    """
-    api_logger.info(
-        f"Get missing translation logs request: locale={locale}, "
-        f"limit={limit}, admin={current_user.username}"
-    )
-    
-    translation_service = get_translation_service()
-    translation_logger = translation_service.translation_logger
-    
-    # Get missing translations
-    missing_translations = translation_logger.get_missing_translations(locale)
-    
-    # Get missing with context
-    missing_with_context = translation_logger.get_missing_with_context(locale, limit)
-    
-    # Get statistics
-    statistics = translation_logger.get_statistics()
-    
-    data = {
-        "missing_translations": missing_translations,
-        "recent_context": missing_with_context,
-        "statistics": statistics
-    }
-    
-    api_logger.info(
-        f"Returning {statistics['total_missing']} missing translations"
-    )
-    return success(data=data, msg=t("common.success.retrieved"))
-
-
-@router.get("/logs/missing/report", response_model=ApiResponse)
-def generate_missing_translation_report(
-    locale: Optional[str] = None,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Generate a comprehensive missing translation report (admin only).
-    
-    Args:
-        locale: Optional locale filter
-        
-    Returns:
-        Comprehensive report with missing translations and statistics
-    """
-    api_logger.info(
-        f"Generate missing translation report request: locale={locale}, "
-        f"admin={current_user.username}"
-    )
-    
-    translation_service = get_translation_service()
-    translation_logger = translation_service.translation_logger
-    
-    # Generate report
-    report = translation_logger.generate_report(locale)
-    
-    api_logger.info(
-        f"Generated report with {report['total_missing']} missing translations"
-    )
-    return success(data=report, msg=t("common.success.retrieved"))
-
-
-@router.post("/logs/missing/export", response_model=ApiResponse)
-def export_missing_translations(
-    locale: Optional[str] = None,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Export missing translations to JSON file (admin only).
-    
-    Args:
-        locale: Optional locale filter
-        
-    Returns:
-        Export status and file path
-    """
-    api_logger.info(
-        f"Export missing translations request: locale={locale}, "
-        f"admin={current_user.username}"
-    )
-    
-    from datetime import datetime
-    translation_service = get_translation_service()
-    translation_logger = translation_service.translation_logger
-    
-    # Generate filename with timestamp
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    locale_suffix = f"_{locale}" if locale else "_all"
-    output_file = f"logs/i18n/missing_translations{locale_suffix}_{timestamp}.json"
-    
-    # Export to file
-    translation_logger.export_to_json(output_file)
-    
-    api_logger.info(f"Missing translations exported to: {output_file}")
-    return success(
-        data={"file_path": output_file},
-        msg=t("i18n.logs.export_success", file=output_file)
-    )
-
-
-@router.delete("/logs/missing", response_model=ApiResponse)
-def clear_missing_translation_logs(
-    locale: Optional[str] = None,
-    t: Callable = Depends(get_translator),
-    current_user: User = Depends(get_current_superuser)
-):
-    """
-    Clear missing translation logs (admin only).
-    
-    Args:
-        locale: Optional locale to clear (clears all if not specified)
-        
-    Returns:
-        Success message
-    """
-    api_logger.info(
-        f"Clear missing translation logs request: locale={locale or 'all'}, "
-        f"admin={current_user.username}"
-    )
-    
-    translation_service = get_translation_service()
-    translation_logger = translation_service.translation_logger
-    
-    # Clear logs
-    translation_logger.clear(locale)
-    
-    api_logger.info(f"Cleared missing translation logs for: {locale or 'all locales'}")
-    return success(msg=t("i18n.logs.clear_success"))

api/app/controllers/implicit_memory_controller.py

@@ -122,52 +122,10 @@ def validate_confidence_threshold(threshold: float) -> None:
         raise ValueError("confidence_threshold must be between 0.0 and 1.0")
 
 
-@router.get("/check-data/{end_user_id}", response_model=ApiResponse)
-@cur_workspace_access_guard()
-async def check_user_data_exists(
-    end_user_id: str,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user)
-) -> ApiResponse:
-    """
-    检查用户画像数据是否存在
-    
-    Args:
-        end_user_id: 目标用户ID
-        
-    Returns:
-        数据存在状态
-    """
-    api_logger.info(f"检查用户画像数据是否存在: {end_user_id}")
-    
-    try:
-        # Validate inputs
-        validate_user_id(end_user_id)
-        
-        # Create service with user-specific config
-        service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
-        
-        # Get cached profile
-        cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
-        
-        if cached_profile is None:
-            api_logger.info(f"用户 {end_user_id} 的画像数据不存在")
-            return success(
-                data={"exists": False},
-                msg="画像数据不存在，请点击右上角刷新进行初始化"
-            )
-        
-        api_logger.info(f"用户 {end_user_id} 的画像数据存在")
-        return success(data={"exists": True}, msg="画像数据已存在")
-        
-    except Exception as e:
-        return handle_implicit_memory_error(e, "检查画像数据", end_user_id)
-
-
-@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"),
@@ -179,7 +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
@@ -188,21 +146,25 @@ 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} 的画像数据不存在")
-            return fail(BizCode.NOT_FOUND, "", "")
+            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
+            return fail(
+                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
         preferences = cached_profile.get("preferences", [])
@@ -230,17 +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)
@@ -249,42 +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} 的画像数据不存在")
-            return fail(BizCode.NOT_FOUND, "", "")
+            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
+            return fail(
+                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)
@@ -293,42 +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} 的画像数据不存在")
-            return fail(BizCode.NOT_FOUND, "", "")
+            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
+            return fail(
+                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"),
@@ -339,7 +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)
@@ -347,21 +317,25 @@ 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} 的画像数据不存在")
-            return fail(BizCode.NOT_FOUND, "", "")
+            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
+            return fail(
+                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
         habits = cached_profile.get("habits", [])
@@ -394,11 +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

@@ -1,465 +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, mcp_market_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"
-        )
-    if page * pagesize > 100:
-        api_logger.warning(f"Paging parameters exceed ModelScope limit: page={page}, pagesize={pagesize}")
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"The maximum number of MCP services can view is 100. Please visit the ModelScope MCP Plaza."
-        )
-
-    # 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}")
-        return success(msg='The mcp market config does not exist or access is denied')
-
-    # 3. Execute paged query
-    token = db_mcp_market_config.token
-    if not token:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="MCP market config token is not configured"
-        )
-    api = MCPApi()
-    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"Failed 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
-        }
-    }
-    # 5. Update mck_market.mcp_count
-    db_mcp_market = mcp_market_service.get_mcp_market_by_id(db, mcp_market_id=db_mcp_market_config.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={db_mcp_market_config.mcp_market_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The mcp market does not exist or access is denied"
-        )
-    db_mcp_market.mcp_count = total
-    db.commit()
-    db.refresh(db_mcp_market)
-    return success(data=result, msg="Query of mcp servers list successful")
-
-
-@router.get("/operational_mcp_servers", response_model=ApiResponse)
-async def get_operational_mcp_servers(
-        mcp_market_config_id: uuid.UUID,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    Query the operational mcp servers list in pages
-    - Support keyword search for name,author,owner
-    - Return paging metadata + operational mcp server list
-    """
-    api_logger.info(
-        f"Query operational mcp server list: tenant_id={current_user.tenant_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}")
-        return success(msg='The mcp market config does not exist or access is denied')
-
-    # 2. Execute paged query
-    token = db_mcp_market_config.token
-    if not token:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="MCP market config token is not configured"
-        )
-    api = MCPApi()
-    api.login(token)
-
-    url = f'{api.mcp_base_url}/operational'
-    headers = api.builder_headers(api.headers)
-
-    try:
-        cookies = api.get_cookies(access_token=token, cookies_required=True)
-        r = api.session.get(url, headers=headers, cookies=cookies)
-        raise_for_http_status(r)
-    except requests.exceptions.RequestException as e:
-        api_logger.error(f"Failed to get operational MCP servers: {str(e)}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Failed to get operational 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]
-
-    # 3. Return structured response
-    return success(data=mcp_server_list, msg="Query of operational 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}")
-        return success(msg='The mcp market config does not exist or access is denied')
-
-    # 2. Get detailed information for a specific MCP Server
-    token = db_mcp_market_config.token
-    if not token:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="MCP market config token is not configured"
-        )
-    api = MCPApi()
-    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. Validate token can access ModelScope MCP market
-        if not create_data.token:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail="Token is required to access ModelScope MCP market"
-            )
-        try:
-            api = MCPApi()
-            api.login(create_data.token)
-            body = {'filter': {}, 'page_number': 1, 'page_size': 1, 'search': None}
-            cookies = api.get_cookies(create_data.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 Exception as e:
-            api_logger.warning(f"Token validation failed for ModelScope MCP market: {str(e)}")
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"Unable to access ModelScope MCP market with the provided token: {str(e)}"
-            )
-        # 2. 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}"
-            )
-        # 2. verify token
-        create_data.status = 1
-        try:
-            api = MCPApi()
-            token = create_data.token
-            api.login(token)
-
-            body = {
-                'filter': {},
-                'page_number': 1,
-                'page_size': 20,
-                'search': ""
-            }
-            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"Failed to get MCP servers: {str(e)}")
-            create_data.status = 0
-        # 3. create mcp_market_config
-        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}")
-            return success(msg='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}")
-            return success(msg='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}")
-        return success(msg='The mcp market config does not exist or access is denied')
-
-    # 2. Validate new token if provided
-    if update_data.token is not None:
-        try:
-            api = MCPApi()
-            api.login(update_data.token)
-            body = {'filter': {}, 'page_number': 1, 'page_size': 1, 'search': None}
-            cookies = api.get_cookies(update_data.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 Exception as e:
-            api_logger.warning(f"Token validation failed for ModelScope MCP market: {str(e)}")
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"Unable to access ModelScope MCP market with the provided token: {str(e)}"
-            )
-
-    # 3. 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)}")
-
-    # 4. 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)}"
-        )
-
-    # 5. 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}")
-            return success(msg='The mcp market config does not exist or access is denied')
-
-        # 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

@@ -1,17 +1,8 @@
 from typing import List, Optional
 
-from dotenv import load_dotenv
-from fastapi import APIRouter, Depends, File, Form, Query, UploadFile, Header
-from sqlalchemy.orm import Session
-from starlette.responses import StreamingResponse
-
-from app.cache.memory.interest_memory import InterestMemoryCache
 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.memory.agent.utils.redis_tool import store
-from app.core.memory.agent.utils.session_tools import SessionService
 from app.core.rag.llm.cv_model import QWenCV
 from app.core.response_utils import fail, success
 from app.db import get_db
@@ -24,6 +15,10 @@ 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
+from sqlalchemy.orm import Session
+from starlette.responses import StreamingResponse
 
 load_dotenv()
 api_logger = get_api_logger()
@@ -38,7 +33,7 @@ router = APIRouter(
 
 @router.get("/health/status", response_model=ApiResponse)
 async def get_health_status(
-        current_user: User = Depends(get_current_user)
+    current_user: User = Depends(get_current_user)
 ):
     """
     Get latest health status written by Celery periodic task
@@ -56,9 +51,8 @@ async def get_health_status(
 
 @router.get("/download_log")
 async def download_log(
-        log_type: str = Query("file", regex="^(file|transmission)$",
-                              description="日志类型: file=完整文件, transmission=实时流式传输"),
-        current_user: User = Depends(get_current_user)
+    log_type: str = Query("file", regex="^(file|transmission)$", description="日志类型: file=完整文件, transmission=实时流式传输"),
+    current_user: User = Depends(get_current_user)
 ):
     """
     Download or stream agent service log file
@@ -77,16 +71,16 @@ async def download_log(
         - transmission mode: StreamingResponse with SSE
     """
     api_logger.info(f"Log download requested with log_type={log_type}")
-
+    
     # Validate log_type parameter (FastAPI Query regex already validates, but explicit check for clarity)
     if log_type not in ["file", "transmission"]:
         api_logger.warning(f"Invalid log_type parameter: {log_type}")
         return fail(
-            BizCode.BAD_REQUEST,
-            "无效的log_type参数",
+            BizCode.BAD_REQUEST, 
+            "无效的log_type参数", 
             "log_type必须是'file'或'transmission'"
         )
-
+    
     # Route to appropriate mode
     if log_type == "file":
         # File mode: Return complete log file content
@@ -121,28 +115,23 @@ async def download_log(
 @router.post("/writer_service", response_model=ApiResponse)
 @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)
+    user_input: Write_UserInput,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
 ):
     """
     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(
         db=db,
@@ -151,7 +140,7 @@ async def write_server(
     )
     if storage_type is None: storage_type = 'neo4j'
     user_rag_memory_id = ''
-
+    
     # 如果 storage_type 是 rag，必须确保有有效的 user_rag_memory_id
     if storage_type == 'rag':
         if workspace_id:
@@ -163,27 +152,22 @@ async def write_server(
             if knowledge:
                 user_rag_memory_id = str(knowledge.id)
             else:
-                api_logger.warning(
-                    f"未找到名为 'USER_RAG_MERORY' 的知识库，workspace_id: {workspace_id}，将使用 neo4j 存储")
+                api_logger.warning(f"未找到名为 'USER_RAG_MERORY' 的知识库，workspace_id: {workspace_id}，将使用 neo4j 存储")
                 storage_type = 'neo4j'
         else:
             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
+            storage_type, 
+            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
@@ -199,29 +183,23 @@ async def write_server(
 @router.post("/writer_service_async", response_model=ApiResponse)
 @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)
+    user_input: Write_UserInput,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
 ):
     """
     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(
@@ -241,15 +219,12 @@ 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}")
-
+        
         return success(data={"task_id": task.id}, msg="写入任务已提交")
     except Exception as e:
         api_logger.error(f"Async write operation failed: {str(e)}")
@@ -259,9 +234,9 @@ async def write_server_async(
 @router.post("/read_service", response_model=ApiResponse)
 @cur_workspace_access_guard()
 async def read_server(
-        user_input: UserInput,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
+    user_input: UserInput,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
 ):
     """
     Read service endpoint - processes read operations synchronously
@@ -272,14 +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,
@@ -294,14 +271,12 @@ async def read_server(
             name="USER_RAG_MERORY",
             workspace_id=workspace_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}")
+        if knowledge: user_rag_memory_id = str(knowledge.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,
@@ -310,23 +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
@@ -342,10 +300,9 @@ async def read_server(
 @router.post("/file", response_model=ApiResponse)
 async def file_update(
         files: List[UploadFile] = File(..., description="要上传的文件"),
-        model_id: str = Form(..., description="模型ID"),
+        model_id:str = Form(..., description="模型ID"),
         metadata: Optional[str] = Form(None, description="文件元数据 (JSON格式)"),
-        current_user: User = Depends(get_current_user),
-        db: Session = Depends(get_db),
+        current_user: User = Depends(get_current_user)
 ):
     """
     文件上传接口 - 支持图片识别
@@ -358,6 +315,9 @@ async def file_update(
     Returns:
         文件处理结果
     """
+
+    db_gen = get_db()             # get_db 通常是一个生成器
+    db = next(db_gen)
     api_logger.info(f"File upload requested, file count: {len(files)}")
     config = ModelConfigService.get_model_by_id(db=db, model_id=model_id)
     apiConfig: ModelApiKey = config.api_keys[0]
@@ -366,7 +326,7 @@ async def file_update(
         for file in files:
             api_logger.debug(f"Processing file: {file.filename}, content_type: {file.content_type}")
             content = await file.read()
-
+            
             if file.content_type and file.content_type.startswith("image/"):
                 vision_model = QWenCV(
                     key=apiConfig.api_key,
@@ -380,12 +340,12 @@ async def file_update(
             else:
                 api_logger.warning(f"Unsupported file type: {file.content_type}")
                 file_content.append(f"[不支持的文件类型: {file.content_type}]")
-
+        
         result_text = ';'.join(file_content)
         api_logger.info(f"File processing completed, result length: {len(result_text)}")
-
+        
         return success(data=result_text, msg="转换文本成功")
-
+        
     except Exception as e:
         api_logger.error(f"File processing failed: {str(e)}", exc_info=True)
         return fail(BizCode.INTERNAL_ERROR, "转换文本失败", str(e))
@@ -422,7 +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}")
@@ -435,8 +395,8 @@ async def read_server_async(
 
 @router.get("/read_result/", response_model=ApiResponse)
 async def get_read_task_result(
-        task_id: str,
-        current_user: User = Depends(get_current_user)
+    task_id: str,
+    current_user: User = Depends(get_current_user)
 ):
     """
     Get the status and result of an async read task
@@ -457,7 +417,7 @@ async def get_read_task_result(
     try:
         result = task_service.get_task_memory_read_result(task_id)
         status = result.get("status")
-
+        
         if status == "SUCCESS":
             # 任务成功完成
             task_result = result.get("result", {})
@@ -466,7 +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
                     },
@@ -475,7 +435,7 @@ async def get_read_task_result(
             else:
                 # 旧格式：直接返回结果
                 return success(data=task_result, msg="查询任务已完成")
-
+                
         elif status == "FAILURE":
             # 任务失败
             error_info = result.get("result", "Unknown error")
@@ -484,7 +444,7 @@ async def get_read_task_result(
             else:
                 error_msg = str(error_info)
             return fail(BizCode.INTERNAL_ERROR, "查询任务失败", error_msg)
-
+            
         elif status in ["PENDING", "STARTED"]:
             # 任务进行中
             return success(
@@ -504,7 +464,7 @@ async def get_read_task_result(
                 },
                 msg=f"任务状态: {status}"
             )
-
+            
     except Exception as e:
         api_logger.error(f"Read task status check failed: {str(e)}", exc_info=True)
         return fail(BizCode.INTERNAL_ERROR, "任务状态查询失败", str(e))
@@ -512,8 +472,8 @@ async def get_read_task_result(
 
 @router.get("/write_result/", response_model=ApiResponse)
 async def get_write_task_result(
-        task_id: str,
-        current_user: User = Depends(get_current_user)
+    task_id: str,
+    current_user: User = Depends(get_current_user)
 ):
     """
     Get the status and result of an async write task
@@ -534,7 +494,7 @@ async def get_write_task_result(
     try:
         result = task_service.get_task_memory_write_result(task_id)
         status = result.get("status")
-
+        
         if status == "SUCCESS":
             # 任务成功完成
             task_result = result.get("result", {})
@@ -543,7 +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
                     },
@@ -552,7 +512,7 @@ async def get_write_task_result(
             else:
                 # 旧格式：直接返回结果
                 return success(data=task_result, msg="写入任务已完成")
-
+                
         elif status == "FAILURE":
             # 任务失败
             error_info = result.get("result", "Unknown error")
@@ -561,7 +521,7 @@ async def get_write_task_result(
             else:
                 error_msg = str(error_info)
             return fail(BizCode.INTERNAL_ERROR, "写入任务失败", error_msg)
-
+            
         elif status in ["PENDING", "STARTED"]:
             # 任务进行中
             return success(
@@ -581,7 +541,7 @@ async def get_write_task_result(
                 },
                 msg=f"任务状态: {status}"
             )
-
+            
     except Exception as e:
         api_logger.error(f"Write task status check failed: {str(e)}", exc_info=True)
         return fail(BizCode.INTERNAL_ERROR, "任务状态查询失败", str(e))
@@ -589,38 +549,23 @@ async def get_write_task_result(
 
 @router.post("/status_type", response_model=ApiResponse)
 async def status_type(
-        user_input: Write_UserInput,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
+    user_input: Write_UserInput,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
 ):
     """
     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
         )
@@ -634,21 +579,26 @@ async def status_type(
 
 @router.get("/stats/types", response_model=ApiResponse)
 async def get_knowledge_type_stats_api(
-        end_user_id: Optional[str] = Query(None, description="用户ID（可选）"),
-        only_active: bool = Query(True, description="仅统计有效记录(status=1)"),
-        current_user: User = Depends(get_current_user),
-        db: Session = Depends(get_db),
+    end_user_id: Optional[str] = Query(None, description="用户ID（可选）"),
+    only_active: bool = Query(True, description="仅统计有效记录(status=1)"),
+    current_user: User = Depends(get_current_user)
 ):
     """
-    统计当前空间下各知识库类型的数量，包含 General | Web | Third-party | Folder。
+    统计当前空间下各知识库类型的数量，包含 General | Web | Third-party | Folder | memory。
     会对缺失类型补 0，返回字典形式。
     可选按状态过滤。
     - 知识库类型根据当前用户的 current_workspace_id 过滤
-    - 如果用户没有当前工作空间，对应的统计返回 0
+    - 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}")
+    api_logger.info(f"Knowledge type stats requested for workspace_id: {current_user.current_workspace_id}, end_user_id: {end_user_id}")
     try:
+        from app.db import get_db
+        
+        # 获取数据库会话
+        db_gen = get_db()
+        db = next(db_gen)
+        
         # 调用service层函数
         result = await memory_agent_service.get_knowledge_type_stats(
             end_user_id=end_user_id,
@@ -656,70 +606,45 @@ async def get_knowledge_type_stats_api(
             current_workspace_id=current_user.current_workspace_id,
             db=db
         )
-
+        
         return success(data=result, msg="获取知识库类型统计成功")
     except Exception as e:
         api_logger.error(f"Knowledge type stats failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "获取知识库类型统计失败", str(e))
 
 
-@router.get("/analytics/interest_distribution/by_user", response_model=ApiResponse)
-async def get_interest_distribution_by_user_api(
-        end_user_id: str = Query(..., description="用户ID（必填）"),
-        limit: int = Query(5, le=5, description="返回兴趣标签数量限制，最多5个"),
-        language_type: str = Header(default=None, alias="X-Language-Type"),
-        current_user: User = Depends(get_current_user),
-        db: Session = Depends(get_db),
+@router.get("/analytics/hot_memory_tags/by_user", response_model=ApiResponse)
+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)
 ):
     """
-    获取指定用户的兴趣分布标签
-    
-    与热门标签不同，此接口专注于识别用户的兴趣活动（运动、爱好、学习、创作等），
-    过滤掉纯物品、工具、地点等不代表用户主动参与活动的名词。
+    获取指定用户的热门记忆标签
     
     返回格式：
     [
-        {"name": "兴趣活动名", "frequency": 频次},
+        {"name": "标签名", "frequency": 频次},
         ...
     ]
     """
-    language = get_language_from_header(language_type)
-    api_logger.info(f"Interest distribution by user requested: end_user_id={end_user_id}, language={language}")
+    api_logger.info(f"Hot memory tags by user requested: end_user_id={end_user_id}")
     try:
-        # 优先读取缓存
-        cached = await InterestMemoryCache.get_interest_distribution(
+        result = await memory_agent_service.get_hot_memory_tags_by_user(
             end_user_id=end_user_id,
-            language=language,
+            limit=limit
         )
-        if cached is not None:
-            api_logger.info(f"Interest distribution cache hit: end_user_id={end_user_id}")
-            return success(data=cached, msg="获取兴趣分布标签成功")
-
-        # 缓存未命中，调用模型生成
-        result = await memory_agent_service.get_interest_distribution_by_user(
-            end_user_id=end_user_id,
-            limit=limit,
-            language=language
-        )
-
-        # 写入缓存，24小时过期
-        await InterestMemoryCache.set_interest_distribution(
-            end_user_id=end_user_id,
-            language=language,
-            data=result,
-        )
-
-        return success(data=result, msg="获取兴趣分布标签成功")
+        return success(data=result, msg="获取热门记忆标签成功")
     except Exception as e:
-        api_logger.error(f"Interest distribution by user failed: {str(e)}")
-        return fail(BizCode.INTERNAL_ERROR, "获取兴趣分布标签失败", str(e))
+        api_logger.error(f"Hot memory tags by user failed: {str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "获取热门记忆标签失败", str(e))
 
 
 @router.get("/analytics/user_profile", response_model=ApiResponse)
 async def get_user_profile_api(
-        end_user_id: Optional[str] = Query(None, description="用户ID（可选）"),
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
+    end_user_id: Optional[str] = Query(None, description="用户ID（可选）"),
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
 ):
     """
     获取用户详情，包含：
@@ -757,17 +682,17 @@ async def get_user_profile_api(
 # ):
 #     """
 #     Get parsed API documentation (Public endpoint - no authentication required)
-
+    
 #     Args:
 #         file_path: Optional path to API docs file. If None, uses default path.
-
+    
 #     Returns:
 #         Parsed API documentation including title, meta info, and sections
 #     """
 #     api_logger.info(f"API docs requested, file_path: {file_path or 'default'}")
 #     try:
 #         result = await memory_agent_service.get_api_docs(file_path)
-
+        
 #         if result.get("success"):
 #             return success(msg=result["msg"], data=result["data"])
 #         else:
@@ -783,9 +708,9 @@ async def get_user_profile_api(
 
 @router.get("/end_user/{end_user_id}/connected_config", response_model=ApiResponse)
 async def get_end_user_connected_config(
-        end_user_id: str,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
+    end_user_id: str,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
 ):
     """
     获取终端用户关联的记忆配置
@@ -804,9 +729,9 @@ async def get_end_user_connected_config(
     from app.services.memory_agent_service import (
         get_end_user_connected_config as get_config,
     )
-
+    
     api_logger.info(f"Getting connected config for end_user: {end_user_id}")
-
+    
     try:
         result = get_config(end_user_id, db)
         return success(data=result, msg="获取终端用户关联配置成功")
@@ -815,4 +740,4 @@ async def get_end_user_connected_config(
         return fail(BizCode.NOT_FOUND, str(e))
     except Exception as e:
         api_logger.error(f"Failed to get end user connected config: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "获取终端用户关联配置失败", str(e))
+        return fail(BizCode.INTERNAL_ERROR, "获取终端用户关联配置失败", str(e))

api/app/controllers/memory_dashboard_controller.py

@@ -1,16 +1,15 @@
 from fastapi import APIRouter, Depends, HTTPException, status, Query
-from pydantic import BaseModel, Field
 from sqlalchemy.orm import Session
 from typing import Optional
 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
 from app.services.memory_agent_service import get_end_users_connected_configs_batch
-from app.services.app_statistics_service import AppStatisticsService
 from app.core.logging_config import get_api_logger
 
 # 获取API专用日志器
@@ -41,7 +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)}"
+        )
 
 
 
@@ -51,158 +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}
-    
-    # 触发按需初始化：为 implicit_emotions_storage 中没有记录的用户异步生成数据
-    try:
-        from app.celery_app import celery_app as _celery_app
-        _celery_app.send_task(
-            "app.tasks.init_implicit_emotions_for_users",
-            kwargs={"end_user_ids": end_user_ids},
-        )
-        _celery_app.send_task(
-            "app.tasks.init_interest_distribution_for_users",
-            kwargs={"end_user_ids": end_user_ids},
-        )
-        api_logger.info(f"已触发按需初始化任务，候选用户数: {len(end_user_ids)}")
-    except Exception as e:
-        api_logger.warning(f"触发按需初始化任务失败（不影响主流程）: {e}")
-
-    # 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)}")
-
-    # 触发社区聚类补全任务（异步，不阻塞接口响应）
-    # 对有 ExtractedEntity 但无 Community 节点的存量用户自动补跑全量聚类
-    try:
-        from app.tasks import init_community_clustering_for_users
-        init_community_clustering_for_users.delay(end_user_ids=end_user_ids)
-        api_logger.info(f"已触发社区聚类补全任务，候选用户数: {len(end_user_ids)}")
-    except Exception as e:
-        api_logger.warning(f"触发社区聚类补全任务失败（不影响主流程）: {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")
 
@@ -412,15 +363,14 @@ def get_current_user_rag_total_num(
 @router.get("/rag_content", response_model=ApiResponse)
 def get_rag_content(
     end_user_id: str = Query(..., description="宿主ID"),
-    page: int = Query(1, gt=0, description="页码，从1开始"),
-    pagesize: int = Query(15, gt=0, le=100, description="每页返回记录数"),
+    limit: int = Query(15, description="返回记录数"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
     """
-    获取当前宿主知识库中的chunk内容（分页）
+    获取当前宿主知识库中的chunk内容
     """
-    data = memory_dashboard_service.get_rag_content(end_user_id, page, pagesize, db, current_user)
+    data = memory_dashboard_service.get_rag_content(end_user_id, limit, db, current_user)
     return success(data=data, msg="宿主RAGchunk数据获取成功")
 
 
@@ -433,18 +383,26 @@ async def get_chunk_summary_tag(
     current_user: User = Depends(get_current_user),
 ):
     """
-    读取RAG摘要、标签和人物形象（纯读库，不触发生成）。
-
+    获取chunk总结、提取的标签和人物形象
+    
     返回格式：
     {
-        "summary": "用户摘要",
-        "tags": [{"tag": "标签1", "frequency": 5}, ...],
-        "personas": ["产品设计师", ...],
-        "generated": true/false  // false表示尚未生产，请调用 /generate_rag_profile
+        "summary": "chunk内容的总结",
+        "tags": [
+            {"tag": "标签1", "frequency": 5},
+            {"tag": "标签2", "frequency": 3},
+            ...
+        ],
+        "personas": [
+            "产品设计师",
+            "旅行爱好者",
+            "摄影发烧友",
+            ...
+        ]
     }
     """
-    api_logger.info(f"用户 {current_user.username} 读取宿主 {end_user_id} 的RAG摘要/标签/人物形象")
-
+    api_logger.info(f"用户 {current_user.username} 请求获取宿主 {end_user_id} 的chunk摘要、标签和人物形象")
+    
     data = await memory_dashboard_service.get_chunk_summary_and_tags(
         end_user_id=end_user_id,
         limit=limit,
@@ -452,8 +410,9 @@ async def get_chunk_summary_tag(
         db=db,
         current_user=current_user
     )
-
-    return success(data=data, msg="获取成功")
+    
+    api_logger.info(f"成功获取chunk摘要、{len(data.get('tags', []))} 个标签和 {len(data.get('personas', []))} 个人物形象")
+    return success(data=data, msg="chunk摘要、标签和人物形象获取成功")
 
 
 @router.get("/chunk_insight", response_model=ApiResponse)
@@ -464,64 +423,29 @@ async def get_chunk_insight(
     current_user: User = Depends(get_current_user),
 ):
     """
-    读取RAG洞察报告（纯读库，不触发生成）。
-
+    获取chunk的洞察内容
+    
     返回格式：
     {
-        "insight": "总体概述",
-        "behavior_pattern": "行为模式",
-        "key_findings": "关键发现",
-        "growth_trajectory": "成长轨迹",
-        "generated": true/false  // false表示尚未生产，请调用 /generate_rag_profile
+        "insight": "对chunk内容的深度洞察分析"
     }
     """
-    api_logger.info(f"用户 {current_user.username} 读取宿主 {end_user_id} 的RAG洞察")
-
+    api_logger.info(f"用户 {current_user.username} 请求获取宿主 {end_user_id} 的chunk洞察")
+    
     data = await memory_dashboard_service.get_chunk_insight(
         end_user_id=end_user_id,
         limit=limit,
         db=db,
         current_user=current_user
     )
-
-    return success(data=data, msg="获取成功")
-
-
-class GenerateRagProfileRequest(BaseModel):
-    end_user_id: str = Field(..., description="宿主ID")
-    limit: int = Field(15, description="参与生成的chunk数量上限")
-    max_tags: int = Field(10, description="最大标签数量")
-
-
-@router.post("/generate_rag_profile", response_model=ApiResponse)
-async def generate_rag_profile(
-    body: GenerateRagProfileRequest,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-):
-    """
-    生产接口：为RAG存储模式的宿主全量重新生成完整画像并持久化到end_user表。
-    每次请求都会重新生成，覆盖已有数据。
-    """
-    api_logger.info(f"用户 {current_user.username} 触发RAG画像生产: end_user_id={body.end_user_id}")
-
-    data = await memory_dashboard_service.generate_rag_profile(
-        end_user_id=body.end_user_id,
-        limit=body.limit,
-        max_tags=body.max_tags,
-        db=db,
-        current_user=current_user,
-    )
-
-    api_logger.info(f"RAG画像生产完成: {data}")
-    return success(data=data, msg="RAG画像生产完成")
+    
+    api_logger.info("成功获取chunk洞察")
+    return success(data=data, msg="chunk洞察获取成功")
 
 
 @router.get("/dashboard_data", response_model=ApiResponse)
 async def dashboard_data(
     end_user_id: Optional[str] = Query(None, description="可选的用户ID"),
-    start_date: Optional[int] = Query(None, description="开始时间戳（毫秒）"),
-    end_date: Optional[int] = Query(None, description="结束时间戳（毫秒）"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
@@ -556,15 +480,6 @@ async def dashboard_data(
     workspace_id = current_user.current_workspace_id
     api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {workspace_id} 的dashboard整合数据")
     
-    # 如果没有提供时间范围，默认使用最近30天
-    if start_date is None or end_date is None:
-        from datetime import datetime, timedelta
-        end_dt = datetime.now()
-        start_dt = end_dt - timedelta(days=30)
-        end_date = int(end_dt.timestamp() * 1000)
-        start_date = int(start_dt.timestamp() * 1000)
-        api_logger.info(f"使用默认时间范围: {start_dt} 到 {end_dt}")
-    
     # 获取 storage_type，如果为 None 则使用默认值
     storage_type = workspace_service.get_workspace_storage_type(
         db=db,
@@ -603,12 +518,9 @@ async def dashboard_data(
                 )
                 neo4j_data["total_memory"] = total_memory_data.get("total_memory_count", 0)
                 # total_app: 统计当前空间下的所有app数量
-                # 包含自有app + 被分享给本工作空间的app
-                from app.services import app_service as _app_svc
-                _, total_app = _app_svc.AppService(db).list_apps(
-                    workspace_id=workspace_id, include_shared=True, pagesize=1
-                )
-                neo4j_data["total_app"] = total_app
+                from app.repositories import app_repository
+                apps_orm = app_repository.get_apps_by_workspace_id(db, workspace_id)
+                neo4j_data["total_app"] = len(apps_orm)
                 api_logger.info(f"成功获取记忆总量: {neo4j_data['total_memory']}, 应用数量: {neo4j_data['total_app']}")
             except Exception as e:
                 api_logger.warning(f"获取记忆总量失败: {str(e)}")
@@ -628,22 +540,17 @@ async def dashboard_data(
             except Exception as e:
                 api_logger.warning(f"获取知识库类型统计失败: {str(e)}")
             
-            # 3. 获取API调用统计（total_api_call）
+            # 3. 获取API调用增量（total_api_call，转换为整数）
             try:
-                # 使用 AppStatisticsService 获取真实的API调用统计
-                app_stats_service = AppStatisticsService(db)
-                api_stats = app_stats_service.get_workspace_api_statistics(
+                api_increment = memory_dashboard_service.get_workspace_api_increment(
+                    db=db,
                     workspace_id=workspace_id,
-                    start_date=start_date,
-                    end_date=end_date
+                    current_user=current_user
                 )
-                # 计算总调用次数
-                total_api_calls = sum(item.get("total_calls", 0) for item in api_stats)
-                neo4j_data["total_api_call"] = total_api_calls
-                api_logger.info(f"成功获取API调用统计: {neo4j_data['total_api_call']}")
+                neo4j_data["total_api_call"] = api_increment
+                api_logger.info(f"成功获取API调用增量: {neo4j_data['total_api_call']}")
             except Exception as e:
-                api_logger.error(f"获取API调用统计失败: {str(e)}")
-                neo4j_data["total_api_call"] = 0
+                api_logger.warning(f"获取API调用增量失败: {str(e)}")
             
             result["neo4j_data"] = neo4j_data
             api_logger.info("成功获取neo4j_data")
@@ -659,8 +566,8 @@ async def dashboard_data(
             
             # 获取RAG相关数据
             try:
-                # total_memory: 只统计用户知识库（permission_id='Memory'）的chunk数
-                total_chunk = memory_dashboard_service.get_rag_user_kb_total_chunk(db, current_user)
+                # total_memory: 使用 total_chunk（总chunk数）
+                total_chunk = memory_dashboard_service.get_rag_total_chunk(db, current_user)
                 rag_data["total_memory"] = total_chunk
                 
                 # total_app: 统计当前空间下的所有app数量
@@ -672,23 +579,10 @@ async def dashboard_data(
                 total_kb = memory_dashboard_service.get_rag_total_kb(db, current_user)
                 rag_data["total_knowledge"] = total_kb
                 
-                # total_api_call: 使用 AppStatisticsService 获取真实的API调用统计
-                try:
-                    app_stats_service = AppStatisticsService(db)
-                    api_stats = app_stats_service.get_workspace_api_statistics(
-                        workspace_id=workspace_id,
-                        start_date=start_date,
-                        end_date=end_date
-                    )
-                    # 计算总调用次数
-                    total_api_calls = sum(item.get("total_calls", 0) for item in api_stats)
-                    rag_data["total_api_call"] = total_api_calls
-                    api_logger.info(f"成功获取RAG模式API调用统计: {rag_data['total_api_call']}")
-                except Exception as e:
-                    api_logger.warning(f"获取RAG模式API调用统计失败，使用默认值: {str(e)}")
-                    rag_data["total_api_call"] = 0
+                # total_api_call: 固定值
+                rag_data["total_api_call"] = 1024
                 
-                api_logger.info(f"成功获取RAG相关数据: memory={total_chunk}, app={len(apps_orm)}, knowledge={total_kb}, api_calls={rag_data['total_api_call']}")
+                api_logger.info(f"成功获取RAG相关数据: memory={total_chunk}, app={len(apps_orm)}, knowledge={total_kb}")
             except Exception as e:
                 api_logger.warning(f"获取RAG相关数据失败: {str(e)}")
             

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

@@ -1,35 +1,16 @@
-"""
-Memory Reflection Controller
-
-This module provides REST API endpoints for managing memory reflection configurations
-and operations. It handles reflection engine setup, configuration management, and
-execution of self-reflection processes across memory systems.
-
-Key Features:
-- Reflection configuration management (save, retrieve, update)
-- Workspace-wide reflection execution across multiple applications
-- Individual configuration-based reflection runs
-- Multi-language support for reflection outputs
-- Integration with Neo4j memory storage and LLM models
-- Comprehensive error handling and logging
-"""
-
 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 (
@@ -38,19 +19,13 @@ 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 environment variables for configuration
 load_dotenv()
-
-# Initialize API logger for request tracking and debugging
 api_logger = get_api_logger()
 
-# Configure router with prefix and tags for API organization
 router = APIRouter(
     prefix="/memory",
     tags=["Memory"],
@@ -63,74 +38,65 @@ async def save_reflection_config(
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
-    """
-    Save reflection configuration to memory config table
+    """Save reflection configuration to data_comfig table"""
     
-    Persists reflection engine configuration settings to the data_config table,
-    including reflection parameters, model settings, and evaluation criteria.
-    Validates configuration parameters and ensures data consistency.
+
     
-    Args:
-        request: Memory reflection configuration data including:
-            - config_id: Configuration identifier to update
-            - reflection_enabled: Whether reflection is enabled
-            - reflection_period_in_hours: Reflection execution interval
-            - reflexion_range: Scope of reflection (partial/all)
-            - baseline: Reflection strategy (time/fact/hybrid)
-            - reflection_model_id: LLM model for reflection operations
-            - memory_verify: Enable memory verification checks
-            - quality_assessment: Enable quality assessment evaluation
-        current_user: Authenticated user saving the configuration
-        db: Database session for data operations
-    
-    Returns:
-        dict: Success response with saved reflection configuration data
-        
-    Raises:
-        HTTPException 400: If config_id is missing or parameters are invalid
-        HTTPException 500: If configuration save operation fails
-        
-    Database Operations:
-        - Updates memory_config table with reflection settings
-        - Commits transaction and refreshes entity
-        - Maintains configuration consistency
-    """
     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}")
 
-        # Update reflection configuration in database
-        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,
+        }
 
-        # Commit transaction and refresh entity
+
+
+        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")
         
@@ -150,119 +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:
-    """
-    Start reflection functionality for all matching applications in workspace
-    
-    Initiates reflection processes across all applications within the user's current
-    workspace that have valid memory configurations. Processes each application's
-    configurations and associated end users, executing reflection operations
-    with proper error isolation and transaction management.
-    
-    This endpoint serves as a workspace-wide reflection orchestrator, ensuring
-    that reflection failures for individual users don't affect other operations.
-    
-    Args:
-        current_user: Authenticated user initiating workspace reflection
-        db: Database session for configuration queries
-    
-    Returns:
-        dict: Success response with reflection results for all processed applications:
-            - app_id: Application identifier
-            - config_id: Memory configuration identifier
-            - end_user_id: End user identifier
-            - reflection_result: Individual reflection operation result
-    
-    Processing Logic:
-        1. Retrieve all applications in the current workspace
-        2. Filter applications with valid memory configurations
-        3. For each configuration, find matching releases
-        4. Execute reflection for each end user with isolated transactions
-        5. Aggregate results with error handling per user
-    
-    Error Handling:
-        - Individual user reflection failures are isolated
-        - Failed operations are logged and included in results
-        - Database transactions are isolated per user to prevent cascading failures
-        - Comprehensive error reporting for debugging
-    
-    Raises:
-        HTTPException 500: If workspace reflection initialization fails
-        
-    Performance Notes:
-        - Uses independent database sessions for each user operation
-        - Prevents transaction failures from affecting other users
-        - Comprehensive logging for operation tracking
-    """
+    """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}")
 
-        # Use independent database session to get workspace app details, avoiding transaction failures
-        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 = []
         
-        # Process each application in the workspace
         for data in result['apps_detailed_info']:
-            # Skip applications without configurations
-            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']
-
-            # Execute reflection for each configuration and user combination
-            for config in memory_configs:
-                config_id_str = str(config['config_id'])
-
-                # Find all releases matching this configuration
-                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
-
-                # Execute reflection for each user - using independent database sessions
-                for user in end_users:
-                    api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config_id_str}")
-
-                    # Create independent database session for each user to avoid transaction failure impact
-                    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")
 
@@ -276,73 +171,42 @@ 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:
-    """
-    Query reflection configuration information by config_id
-    
-    Retrieves detailed reflection configuration settings from the memory_config
-    table for a specific configuration ID. Provides comprehensive reflection
-    parameters including model settings, evaluation criteria, and operational flags.
-    
-    Args:
-        config_id: Configuration identifier (UUID or integer) to query
-        current_user: Authenticated user making the request
-        db: Database session for data operations
-    
-    Returns:
-        dict: Success response with detailed reflection configuration:
-            - config_id: Resolved configuration identifier
-            - reflection_enabled: Whether reflection is enabled for this config
-            - reflection_period_in_hours: Reflection execution interval
-            - reflexion_range: Scope of reflection operations (partial/all)
-            - baseline: Reflection strategy (time/fact/hybrid)
-            - reflection_model_id: LLM model identifier for reflection
-            - memory_verify: Memory verification flag
-            - quality_assessment: Quality assessment flag
-    
-    Database Operations:
-        - Queries memory_config table by resolved config_id
-        - Retrieves all reflection-related configuration fields
-        - Resolves configuration ID for consistent formatting
-    
-    Raises:
-        HTTPException 404: If configuration with specified ID is not found
-        HTTPException 500: If configuration query operation fails
-        
-    ID Resolution:
-        - Supports both UUID and integer config_id formats
-        - Automatically resolves to appropriate internal format
-        - Maintains consistency across different ID representations
-    """
-    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)
         
-        # Build response data with comprehensive configuration details
+        # 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")
-
-        api_logger.info(f"Successfully queried reflection config, config_id: {config_id}")
-        return success(data=reflection_config, msg="Reflection configuration query successful")
         
+
     except HTTPException:
-        # Re-raise HTTP exceptions without modification
+        # 重新抛出HTTP异常
         raise
     except Exception as e:
         api_logger.error(f"查询反思配置失败: {str(e)}")
@@ -353,72 +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:
-    """
-    Execute reflection engine with specified configuration
-    
-    Runs the reflection engine using configuration parameters from the database.
-    Validates model availability, sets up the reflection engine with proper
-    configuration, and executes the reflection process with multi-language support.
-    
-    This endpoint provides a test run capability for reflection configurations,
-    allowing users to validate their reflection settings and see results before
-    deploying to production environments.
-    
-    Args:
-        config_id: Configuration identifier (UUID or integer) for reflection settings
-        language_type: Language preference header for output localization (optional)
-        current_user: Authenticated user executing the reflection
-        db: Database session for configuration queries
-    
-    Returns:
-        dict: Success response with reflection execution results including:
-            - baseline: Reflection strategy used
-            - source_data: Input data processed
-            - memory_verifies: Memory verification results (if enabled)
-            - quality_assessments: Quality assessment results (if enabled)
-            - reflexion_data: Generated reflection insights and solutions
-    
-    Configuration Validation:
-        - Verifies configuration exists in database
-        - Validates LLM model availability
-        - Falls back to default model if specified model is unavailable
-        - Ensures all required parameters are properly set
-    
-    Reflection Engine Setup:
-        - Creates ReflectionConfig with database parameters
-        - Initializes Neo4j connector for memory access
-        - Sets up ReflectionEngine with validated model
-        - Configures language preferences for output
-    
-    Error Handling:
-        - Model validation with fallback to default
-        - Configuration validation and error reporting
-        - Comprehensive logging for debugging
-        - Graceful handling of missing configurations
-    
-    Raises:
-        HTTPException 404: If configuration is not found
-        HTTPException 500: If reflection execution fails
-        
-    Performance Notes:
-        - Direct database query for configuration retrieval
-        - Model validation to prevent runtime failures
-        - Efficient reflection engine initialization
-        - Language-aware output processing
-    """
-    # Use centralized language validation for consistent localization
-    language = get_language_from_header(language_type)
+    """Activate the reflection function for all matching applications in the workspace"""
 
     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)
-    
-    # Query reflection configuration using MemoryConfigRepository
-    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,
@@ -427,23 +238,22 @@ async def reflection_run(
 
     api_logger.info(f"成功查询反思配置，config_id: {config_id}")
 
-    # Validate model ID existence
+    # 验证模型ID是否存在
     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)}")
             # 可以设置为None，让反思引擎使用默认模型
             model_id = None
 
-    # Create reflection configuration with database parameters
     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,
@@ -451,13 +261,11 @@ async def reflection_run(
         model_id=model_id,
         language_type=language_type
     )
-    
-    # Initialize Neo4j connector and reflection engine
     connector = Neo4jConnector()
     engine = ReflectionEngine(
         config=config,
         neo4j_connector=connector,
-        llm_client=model_id  # Pass validated model_id
+        llm_client=model_id  # 传入验证后的 model_id
     )
 
     result=await (engine.reflection_run())

api/app/controllers/memory_short_term_controller.py

@@ -1,40 +1,18 @@
-"""
-Memory Short Term Controller
-
-This module provides REST API endpoints for managing short-term and long-term memory
-data retrieval and analysis. It handles memory system statistics, data aggregation,
-and provides comprehensive memory insights for end users.
-
-Key Features:
-- Short-term memory data retrieval and statistics
-- Long-term memory data aggregation
-- Entity count integration
-- Multi-language response support
-- Memory system analytics and reporting
-"""
-
-from typing import Optional
-
-from dotenv import load_dotenv
-from fastapi import APIRouter, Depends, Header, HTTPException, status
-from sqlalchemy.orm import Session
-
-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
 from app.dependencies import get_current_user
 from app.models.user_model import User
-from app.services.memory_short_service import LongService, ShortService
+
 from app.services.memory_storage_service import search_entity
-
-# Load environment variables for configuration
+from app.services.memory_short_service import ShortService,LongService
+from dotenv import load_dotenv
+from sqlalchemy.orm import Session
+from typing import Optional
 load_dotenv()
-
-# Initialize API logger for request tracking and debugging
 api_logger = get_api_logger()
 
-# Configure router with prefix and tags for API organization
 router = APIRouter(
     prefix="/memory/short",
     tags=["Memory"],
@@ -42,77 +20,24 @@ 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),
 ):
-    """
-    Retrieve comprehensive short-term and long-term memory statistics
-    
-    Provides a comprehensive overview of memory system data for a specific end user,
-    including short-term memory entries, long-term memory aggregations, entity counts,
-    and retrieval statistics. Supports multi-language responses based on request headers.
-    
-    This endpoint serves as a central dashboard for memory system analytics, combining
-    data from multiple memory subsystems to provide a holistic view of user memory state.
-    
-    Args:
-        end_user_id: Unique identifier for the end user whose memory data to retrieve
-        language_type: Language preference header for response localization (optional)
-        current_user: Authenticated user making the request (injected by dependency)
-        db: Database session for data operations (injected by dependency)
-    
-    Returns:
-        dict: Success response containing comprehensive memory statistics:
-            - short_term: List of short-term memory entries with detailed data
-            - long_term: List of long-term memory aggregations and summaries
-            - entity: Count of entities associated with the end user
-            - retrieval_number: Total count of short-term memory retrievals
-            - long_term_number: Total count of long-term memory entries
-    
-    Response Structure:
-        {
-            "code": 200,
-            "msg": "Short-term memory system data retrieved successfully",
-            "data": {
-                "short_term": [...],      # Short-term memory entries
-                "long_term": [...],       # Long-term memory data
-                "entity": 42,             # Entity count
-                "retrieval_number": 156,  # Short-term retrieval count
-                "long_term_number": 23    # Long-term memory count
-            }
-        }
-    
-    Raises:
-        HTTPException: If end_user_id is invalid or data retrieval fails
-        
-    Performance Notes:
-        - Combines multiple service calls for comprehensive data
-        - Entity search is performed asynchronously for better performance
-        - Response time depends on memory data volume for the specified user
-    """
-    # Use centralized language validation for consistent localization
-    language = get_language_from_header(language_type)
-    
-    # Retrieve short-term memory data and statistics
-    short_term = ShortService(end_user_id, db)
-    short_result = short_term.get_short_databasets()  # Get short-term memory entries
-    short_count = short_term.get_short_count()        # Get short-term retrieval count
+    # 获取短期记忆数据
+    short_term=ShortService(end_user_id)
+    short_result=short_term.get_short_databasets()
+    short_count=short_term.get_short_count()
 
-    # Retrieve long-term memory data and aggregations
-    long_term = LongService(end_user_id, db)
-    long_result = long_term.get_long_databasets()     # Get long-term memory entries
+    long_term=LongService(end_user_id)
+    long_result=long_term.get_long_databasets()
 
-    # Get entity count for the specified end user
     entity_result = await search_entity(end_user_id)
-    
-    # Compile comprehensive memory statistics response
     result = {
-        'short_term': short_result,                    # Short-term memory entries
-        'long_term': long_result,                      # Long-term memory data
-        'entity': entity_result.get('num', 0),        # Entity count (default to 0 if not found)
-        "retrieval_number": short_count,               # Short-term retrieval statistics
-        "long_term_number": len(long_result)          # Long-term memory entry count
+        'short_term': short_result,
+        'long_term': long_result,
+        'entity': entity_result.get('num', 0),
+        "retrieval_number":short_count,
+        "long_term_number":len(long_result)
     }
 
     return success(data=result, msg="短期记忆系统数据获取成功")

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, JSONResponse
-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
@@ -85,9 +141,9 @@ def create_config(
     payload: ConfigParamsCreate,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
-    x_language_type: Optional[str] = Header(None, alias="X-Language-Type"),
-) -> 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")
@@ -100,125 +156,46 @@ def create_config(
         svc = DataConfigService(db)
         result = svc.create(payload)
         return success(data=result, msg="创建成功")
-    except ValueError as e:
-        err_str = str(e)
-        if err_str.startswith("DUPLICATE_CONFIG_NAME:"):
-            config_name = err_str.split(":", 1)[1]
-            api_logger.warning(f"重复的配置名称 '{config_name}' 在工作空间 {workspace_id}")
-            lang = get_language_from_header(x_language_type)
-            if lang == "en":
-                msg = fail(BizCode.BAD_REQUEST, "Config name already exists", f"A config named \"{config_name}\" already exists in the current workspace. Please use a different name.")
-            else:
-                msg = fail(BizCode.BAD_REQUEST, "配置名称已存在", f"当前工作空间下已存在名为「{config_name}」的记忆配置，请使用其他名称")
-            return JSONResponse(status_code=400, content=msg)
-        api_logger.error(f"Create config failed: {err_str}")
-        return fail(BizCode.INTERNAL_ERROR, "创建配置失败", err_str)
     except Exception as e:
-        from sqlalchemy.exc import IntegrityError
-        if isinstance(e, IntegrityError) and "uq_workspace_config_name" in str(getattr(e, 'orig', '')):
-            api_logger.warning(f"重复的配置名称 '{payload.config_name}' 在工作空间 {workspace_id}")
-            lang = get_language_from_header(x_language_type)
-            if lang == "en":
-                msg = fail(BizCode.BAD_REQUEST, "Config name already exists", f"A config named \"{payload.config_name}\" already exists in the current workspace. Please use a different name.")
-            else:
-                msg = fail(BizCode.BAD_REQUEST, "配置名称已存在", f"当前工作空间下已存在名为「{payload.config_name}」的记忆配置，请使用其他名称")
-            return JSONResponse(status_code=400, content=msg)
         api_logger.error(f"Create config failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "创建配置失败", str(e))
 
 
 @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)
@@ -234,9 +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")
@@ -258,12 +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")
@@ -282,7 +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
@@ -304,22 +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",
@@ -328,8 +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(
@@ -442,7 +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)
@@ -451,106 +437,39 @@ 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),
-) -> dict:
-    workspace_id = str(current_user.current_workspace_id) if current_user.current_workspace_id else None
-    api_logger.info(f"Recent activity stats requested: workspace_id={workspace_id}")
+    ) -> dict:
+    api_logger.info("Recent activity stats requested")
     try:
-        result = await analytics_recent_activity_stats(workspace_id=workspace_id)
+        result = await analytics_recent_activity_stats()
         return success(data=result, msg="查询成功")
     except Exception as e:
         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

@@ -8,7 +8,6 @@ from app.core.response_utils import success
 from app.db import get_db
 from app.dependencies import get_current_user
 from app.models import User
-from app.schemas import conversation_schema
 from app.schemas.response_schema import ApiResponse
 from app.services.conversation_service import ConversationService
 
@@ -21,18 +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)
 ):
@@ -40,7 +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.
 
@@ -54,7 +53,7 @@ def get_conversations(
     """
     conversation_service = ConversationService(db)
     conversations = conversation_service.get_user_conversations(
-        end_user_id
+        group_id
     )
     return success(data=[
         {
@@ -64,7 +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),
@@ -91,13 +90,17 @@ def get_messages(
         conversation_id,
     )
     messages = [
-        conversation_schema.Message.model_validate(message)
+        {
+            "role": message.role,
+            "content": message.content,
+            "created_at": int(message.created_at.timestamp() * 1000),
+        }
         for message in messages_obj
     ]
     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,
@@ -368,14 +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)
-
-    if model_data.is_active:
-        active_keys = ModelApiKeyService.get_api_keys_by_model(db=db, model_config_id=model_id, is_active=model_data.is_active)
-        if not active_keys:
-            raise BusinessException("请先为该模型配置可用的 API Key", BizCode.INVALID_PARAMETER)
     
     try:
         api_logger.debug(f"开始更新模型配置: model_id={model_id}")
@@ -441,55 +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,
-            capability=api_key_data.capability,
-            is_omni=api_key_data.is_omni
-        )
-        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,
@@ -504,12 +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)}")
@@ -611,3 +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

@@ -1,663 +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, Header
-from sqlalchemy.orm import Session
-
-from app.core.error_codes import BizCode
-from app.core.logging_config import get_api_logger, get_business_logger
-from app.core.response_utils import fail, success
-from app.db import get_db
-from app.dependencies import get_current_user
-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
-from app.repositories.ontology_class_repository import OntologyClassRepository
-
-
-api_logger = get_api_logger()
-business_logger = get_business_logger()
-
-
-def _get_dummy_ontology_service(db: Session) -> OntologyService:
-    """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,
-    pagesize: 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）
-        pagesize: 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}, pagesize={pagesize}"
-    )
-    
-    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 pagesize is not None and pagesize < 1:
-                api_logger.warning(f"Invalid pagesize: {pagesize}")
-                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pagesize中的一个，返回错误
-            if (page is not None and pagesize is None) or (page is None and pagesize is not None):
-                api_logger.warning(f"Incomplete pagination params: page={page}, pagesize={pagesize}")
-                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 pagesize is not None:
-                start_idx = (page - 1) * pagesize
-                end_idx = start_idx + pagesize
-                scenes = scenes[start_idx:end_idx]
-            
-            # 构建响应
-            items = []
-            for scene in scenes:
-                entity_type = [cls.class_name for cls in scene.classes[:3]] if scene.classes else None
-                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,
-                    is_system_default=scene.is_system_default
-                ))
-            
-            # 构建响应（包含分页信息）
-            if page is not None and pagesize is not None:
-                hasnext = (page * pagesize) < total
-                pagination_info = PaginationInfo(
-                    page=page,
-                    pagesize=pagesize,
-                    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）
-            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 pagesize is not None and pagesize < 1:
-                api_logger.warning(f"Invalid pagesize: {pagesize}")
-                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pagesize中的一个，返回错误
-            if (page is not None and pagesize is None) or (page is None and pagesize is not None):
-                api_logger.warning(f"Incomplete pagination params: page={page}, pagesize={pagesize}")
-                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, pagesize)
-            
-            # 构建响应
-            items = []
-            for scene in scenes:
-                entity_type = [cls.class_name for cls in scene.classes[:3]] if scene.classes else None
-                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,
-                    is_system_default=scene.is_system_default
-                ))
-            
-            # 构建响应（包含分页信息）
-            if page is not None and pagesize is not None:
-                hasnext = (page * pagesize) < total
-                pagination_info = PaginationInfo(
-                    page=page,
-                    pagesize=pagesize,
-                    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),
-    x_language_type: Optional[str] = None
-):
-    """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 - 先检查重名
-            class_data = classes_data[0]
-            existing = OntologyClassRepository(db).get_by_name(class_data["class_name"], request.scene_id)
-            if existing:
-                raise ValueError(f"DUPLICATE_CLASS_NAME:{class_data['class_name']}")
-            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:
-        err_str = str(e)
-        if err_str.startswith("DUPLICATE_CLASS_NAME:"):
-            class_name = err_str.split(":", 1)[1]
-            api_logger.warning(f"Duplicate class name '{class_name}' in scene {request.scene_id}")
-            from app.core.language_utils import get_language_from_header
-            from fastapi.responses import JSONResponse
-            lang = get_language_from_header(x_language_type)
-            if lang == "en":
-                msg = fail(BizCode.BAD_REQUEST, "Class name already exists", f"A class named \"{class_name}\" already exists in this scene. Please use a different name.")
-            else:
-                msg = fail(BizCode.BAD_REQUEST, "类型名称已存在", f"当前场景下已存在名为「{class_name}」的类型，请使用其他名称")
-            return JSONResponse(status_code=400, content=msg)
-        api_logger.warning(f"Validation error in class creation: {err_str}")
-        return fail(BizCode.BAD_REQUEST, "请求参数无效", err_str)
-
-    except RuntimeError as e:
-        err_str = str(e)
-        if "UniqueViolation" in err_str or "uq_scene_class_name" in err_str:
-            api_logger.warning(f"Duplicate class name in scene {request.scene_id}")
-            from app.core.language_utils import get_language_from_header
-            from fastapi.responses import JSONResponse
-            lang = get_language_from_header(x_language_type)
-            class_name = request.classes[0].class_name if request.classes else ""
-            if lang == "en":
-                msg = fail(BizCode.BAD_REQUEST, "Class name already exists", f"A class named \"{class_name}\" already exists in this scene. Please use a different name.")
-            else:
-                msg = fail(BizCode.BAD_REQUEST, "类型名称已存在", f"当前场景下已存在名为「{class_name}」的类型，请使用其他名称")
-            return JSONResponse(status_code=400, content=msg)
-        api_logger.error(f"Runtime error in class creation: {err_str}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "类型创建失败", err_str)
-        
-    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")
-        
-        # 检查是否为系统默认类型
-        class_repo = OntologyClassRepository(db)
-        ontology_class = class_repo.get_by_id(class_uuid)
-        if ontology_class and ontology_class.is_system_default:
-            business_logger.warning(
-                f"尝试修改系统默认类型: user_id={current_user.id}, "
-                f"class_id={class_id}, class_name={ontology_class.class_name}"
-            )
-            return fail(
-                BizCode.BAD_REQUEST,
-                "系统默认类型不可修改",
-                "该类型为系统预设类型，不允许修改"
-            )
-        
-        # 创建Service
-        service = _get_dummy_ontology_service(db)
-        
-        # 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")
-        
-        # 检查是否为系统默认类型
-        class_repo = OntologyClassRepository(db)
-        ontology_class = class_repo.get_by_id(class_uuid)
-        if ontology_class and ontology_class.is_system_default:
-            business_logger.warning(
-                f"尝试删除系统默认类型: user_id={current_user.id}, "
-                f"class_id={class_id}, class_name={ontology_class.class_name}"
-            )
-            return fail(
-                BizCode.BAD_REQUEST,
-                "系统默认类型不可删除",
-                "该类型为系统预设类型，不允许删除"
-            )
-        
-        # 创建Service
-        service = _get_dummy_ontology_service(db)
-        
-        # 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,
-            is_system_default=scene.is_system_default,
-            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

@@ -2,33 +2,24 @@ import hashlib
 import json
 import uuid
 from typing import Annotated
-
 from fastapi import APIRouter, Depends, Query, Request
 from fastapi.responses import StreamingResponse
 from sqlalchemy.orm import Session
 
-from app.core.error_codes import BizCode
-from app.core.exceptions import BusinessException
 from app.core.logging_config import get_business_logger
-from app.core.response_utils import success, fail
-from app.db import get_db, get_db_read
+from app.core.response_utils import success
+from app.db import get_db
 from app.dependencies import get_share_user_id, ShareTokenData
-from app.models.app_model import AppType
 from app.repositories import knowledge_repository
-from app.repositories.end_user_repository import EndUserRepository
-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
-from app.services.app_chat_service import AppChatService, get_app_chat_service
-from app.services.app_service import AppService
 from app.services.auth_service import create_access_token
 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.workflow_service import WorkflowService
-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.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
 
 router = APIRouter(prefix="/public/share", tags=["Public Share"])
 logger = get_business_logger()
@@ -74,10 +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
 
@@ -122,9 +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
 
@@ -146,9 +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
 
@@ -168,11 +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
 
@@ -192,6 +183,7 @@ def get_embed_code(
     return success(data=embed_code)
 
 
+
 # ---------- 会话管理接口 ----------
 
 @router.get(
@@ -199,11 +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
 
@@ -213,16 +205,15 @@ def list_conversations(
     logger.debug(f"share_data:{share_data.user_id}")
     other_id = share_data.user_id
     service = SharedChatService(db)
-    share, release = service.get_release_by_share_token(share_data.share_token, password)
+    share, release = service._get_release_by_share_token(share_data.share_token, password)
+    from app.repositories.end_user_repository import EndUserRepository
     end_user_repo = EndUserRepository(db)
-    app_service = AppService(db)
-    app = app_service._get_app_or_404(share.app_id)
     new_end_user = end_user_repo.get_or_create_end_user(
-        app_id=share.app_id,
-        workspace_id=app.workspace_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(
         share_token=share_data.share_token,
         user_id=str(new_end_user.id),
@@ -242,10 +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)
@@ -275,10 +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
 
@@ -301,39 +292,37 @@ async def chat(
 
     # 提前验证和准备（在流式响应开始前完成）
     # 这样可以确保错误能正确返回，而不是在流式响应中间出错
-
+    from app.models.app_model import AppType
     try:
+        from app.core.exceptions import BusinessException
+        from app.core.error_codes import BizCode
+        from app.services.app_service import AppService
         # 验证分享链接和密码
-        share, release = service.get_release_by_share_token(share_token, password)
+        share, release = service._get_release_by_share_token(share_token, password)
 
         # # Create end_user_id by concatenating app_id with user_id
         # end_user_id = f"{share.app_id}_{user_id}"
 
         # Store end_user_id in database with original user_id
+        from app.repositories.end_user_repository import EndUserRepository
         end_user_repo = EndUserRepository(db)
-        app_service = AppService(db)
-        app = app_service._get_app_or_404(share.app_id)
-        workspace_id = app.workspace_id
         new_end_user = end_user_repo.get_or_create_end_user(
             app_id=share.app_id,
-            workspace_id=workspace_id,
             other_id=other_id,
-            original_user_id=user_id
+            original_user_id=user_id  # Save original user_id to other_id
         )
         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）
-        # app = db.query(App).filter(
-        #     App.id == appid,
-        #     App.is_active.is_(True)
-        # ).first()
-        # if not app:
-        #     raise BusinessException("应用不存在", BizCode.APP_NOT_FOUND)
+        # 直接通过 SQLAlchemy 查询 app
+        from app.models.app_model import App
+        app = db.query(App).filter(App.id == appid).first()
+        if not app:
+            raise BusinessException("应用不存在", BizCode.APP_NOT_FOUND)
 
-        # workspace_id = app.workspace_id
+        workspace_id = app.workspace_id
 
         # 直接从 workspace 获取 storage_type（公开分享场景无需权限检查）
         storage_type = workspace_service.get_workspace_storage_type_without_auth(
@@ -366,12 +355,12 @@ async def chat(
         app_type = release.app.type if release.app else None
 
         # 根据应用类型验证配置
-        if app_type == AppType.AGENT:
+        if app_type == "agent":
             # Agent 类型：验证模型配置
             model_config_id = release.default_model_config_id
             if not model_config_id:
                 raise BusinessException("Agent 应用未配置模型", BizCode.AGENT_CONFIG_MISSING)
-        elif app_type == AppType.MULTI_AGENT:
+        elif app_type == "multi_agent":
             # Multi-Agent 类型：验证多 Agent 配置
             config = release.config or {}
             if not config.get("sub_agents"):
@@ -436,17 +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
 
@@ -483,8 +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:
@@ -494,15 +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
 
@@ -574,29 +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", {})
@@ -617,19 +599,18 @@ async def chat(
 
         # 多 Agent 非流式返回
         result = await app_chat_service.workflow_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
             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
+            workspace_id=workspace_id
         )
         logger.debug(
             "工作流试运行返回结果",
@@ -645,38 +626,6 @@ async def chat(
         # return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
 
     else:
+        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)
-
-
-@router.get("/config", summary="获取应用启动配置")
-async def config_query(
-        password: str = Query(None, description="访问密码"),
-        share_data: ShareTokenData = Depends(get_share_user_id),
-        db: Session = Depends(get_db),
-):
-    share_service = SharedChatService(db)
-    share_token = share_data.share_token
-    share, release = share_service.get_release_by_share_token(share_token, password)
-    if release.app.type == AppType.WORKFLOW:
-        workflow_service = WorkflowService(db)
-        content = {
-            "app_type": release.app.type,
-            "variables": workflow_service.get_start_node_variables(release.config),
-            "memory":  workflow_service.is_memory_enable(release.config),
-            "features": release.config.get("features")
-        }
-    elif release.app.type == AppType.AGENT:
-        content = {
-            "app_type": release.app.type,
-            "variables": release.config.get("variables"),
-            "features": release.config.get("features")
-        }
-    elif release.app.type == AppType.MULTI_AGENT:
-        content = {
-            "app_type": release.app.type,
-            "variables": [],
-            "features": release.config.get("features")
-        }
-    else:
-        return fail(msg="Unsupported app type", code=BizCode.APP_TYPE_NOT_SUPPORTED)
-    return success(data=content)

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")
@@ -74,33 +73,33 @@ 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)
 
+    other_id = payload.user_id
     app = app_service.get_app(api_key_auth.resource_id, api_key_auth.workspace_id)
     other_id = payload.user_id
     workspace_id = app.workspace_id
     end_user_repo = EndUserRepository(db)
     new_end_user = end_user_repo.get_or_create_end_user(
         app_id=app.id,
-        workspace_id=workspace_id,
         other_id=other_id,
+        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,
@@ -134,8 +133,7 @@ async def chat(
         app_id=app.id,
         workspace_id=workspace_id,
         user_id=end_user_id,
-        is_draft=False,
-        conversation_id=payload.conversation_id
+        is_draft=False
     )
 
     if app_type == AppType.AGENT:
@@ -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
 
@@ -236,20 +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,
-                        public=True
+
+                    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", {})
@@ -273,17 +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,
-            files=payload.files,
-            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",
@@ -300,3 +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

@@ -6,7 +6,6 @@ from app.core.response_utils import success
 from app.db import get_db
 from app.schemas.api_key_schema import ApiKeyAuth
 from app.schemas.memory_api_schema import (
-    ListConfigsResponse,
     MemoryReadRequest,
     MemoryReadResponse,
     MemoryWriteRequest,
@@ -32,16 +31,15 @@ async def write_memory_api_service(
     request: Request,
     api_key_auth: ApiKeyAuth = None,
     db: Session = Depends(get_db),
-    message: str = Body(..., description="Message content"),
+    payload: MemoryWriteRequest = Body(..., embed=False),
+
 ):
     """
     Write memory to storage.
     
     Stores memory content for the specified end user using the Memory API Service.
     """
-    body = await request.json()
-    payload = MemoryWriteRequest(**body)
-    logger.info(f"Memory write request - end_user_id: {payload.end_user_id}, workspace_id: {api_key_auth.workspace_id}")
+    logger.info(f"Memory write request - end_user_id: {payload.end_user_id}")
     
     memory_api_service = MemoryAPIService(db)
     
@@ -64,15 +62,13 @@ async def read_memory_api_service(
     request: Request,
     api_key_auth: ApiKeyAuth = None,
     db: Session = Depends(get_db),
-    message: str = Body(..., description="Query message"),
+    payload: MemoryReadRequest = Body(..., embed=False),
 ):
     """
     Read memory from storage.
     
     Queries and retrieves memories for the specified end user with context-aware responses.
     """
-    body = await request.json()
-    payload = MemoryReadRequest(**body)
     logger.info(f"Memory read request - end_user_id: {payload.end_user_id}")
     
     memory_api_service = MemoryAPIService(db)
@@ -89,27 +85,3 @@ async def read_memory_api_service(
     
     logger.info(f"Memory read successful for end_user: {payload.end_user_id}")
     return success(data=MemoryReadResponse(**result).model_dump(), msg="Memory read successfully")
-
-
-@router.get("/configs")
-@require_api_key(scopes=["memory"])
-async def list_memory_configs(
-    request: Request,
-    api_key_auth: ApiKeyAuth = None,
-    db: Session = Depends(get_db),
-):
-    """
-    List all memory configs for the workspace.
-    
-    Returns all available memory configurations associated with the authorized workspace.
-    """
-    logger.info(f"List configs request - workspace_id: {api_key_auth.workspace_id}")
-
-    memory_api_service = MemoryAPIService(db)
-
-    result = memory_api_service.list_memory_configs(
-        workspace_id=api_key_auth.workspace_id,
-    )
-
-    logger.info(f"Listed {result['total']} configs for workspace: {api_key_auth.workspace_id}")
-    return success(data=ListConfigsResponse(**result).model_dump(), msg="Configs listed successfully")

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/tool_controller.py

@@ -3,11 +3,8 @@ from typing import Optional
 
 from fastapi import APIRouter, Depends, HTTPException, Query
 from sqlalchemy.orm import Session
-
-from app.core.error_codes import BizCode
 from app.schemas.tool_schema import (
-    ToolCreateRequest, ToolUpdateRequest, ToolExecuteRequest, ParseSchemaRequest,
-    CustomToolTestRequest, ToolActiveUpdate
+    ToolCreateRequest, ToolUpdateRequest, ToolExecuteRequest, ParseSchemaRequest, CustomToolTestRequest
 )
 
 from app.core.response_utils import success
@@ -17,7 +14,6 @@ from app.models import User
 from app.models.tool_model import ToolType, ToolStatus, AuthType
 from app.services.tool_service import ToolService
 from app.schemas.response_schema import ApiResponse
-from app.core.exceptions import BusinessException
 
 router = APIRouter(prefix="/tools", tags=["Tool System"])
 
@@ -101,13 +97,7 @@ async def create_tool(
 ):
     """创建工具"""
     try:
-        # 将 MCP 来源字段合并进 config
-        if request.tool_type == ToolType.MCP:
-            for key in ("source_channel", "market_id", "market_config_id", "mcp_service_id"):
-                val = getattr(request, key, None)
-                if val is not None:
-                    request.config[key] = val
-        tool_id = await service.create_tool(
+        tool_id = service.create_tool(
             name=request.name,
             tool_type=request.tool_type,
             tenant_id=current_user.tenant_id,
@@ -117,8 +107,6 @@ async def create_tool(
             tags=request.tags
         )
         return success(data={"tool_id": tool_id}, msg="工具创建成功")
-    except BusinessException as e:
-        raise HTTPException(status_code=400, detail=e.message)
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     except Exception as e:
@@ -159,7 +147,7 @@ async def delete_tool(
         current_user: User = Depends(get_current_user),
         service: ToolService = Depends(get_tool_service)
 ):
-    """删除工具（逻辑删除，is_active=False）"""
+    """删除工具"""
     try:
         success_flag = service.delete_tool(tool_id, current_user.tenant_id)
         if not success_flag:
@@ -171,30 +159,6 @@ async def delete_tool(
         raise HTTPException(status_code=500, detail=str(e))
 
 
-@router.patch("/{tool_id}/active", response_model=ApiResponse)
-async def set_tool_active(
-        tool_id: str,
-        request: ToolActiveUpdate,
-        current_user: User = Depends(get_current_user),
-        service: ToolService = Depends(get_tool_service)
-):
-    """设置工具可用状态（启用/禁用）
-
-    - is_active=true: 启用工具
-    - is_active=false: 禁用工具（等同于删除，但可恢复）
-    """
-    try:
-        success_flag = service.set_tool_active(tool_id, current_user.tenant_id, request.is_active)
-        if not success_flag:
-            raise HTTPException(status_code=404, detail="工具不存在")
-        action = "启用" if request.is_active else "禁用"
-        return success(msg=f"工具已{action}")
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e))
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
 @router.post("/execution/execute", response_model=ApiResponse)
 async def execute_tool(
         request: ToolExecuteRequest,
@@ -252,10 +216,8 @@ async def sync_mcp_tools(
     try:
         result = await service.sync_mcp_tools(tool_id, current_user.tenant_id)
         if not result.get("success", False):
-            raise BusinessException(result.get("message", "工具列表同步失败"), BizCode.BAD_REQUEST)
+            raise HTTPException(status_code=400, detail=result.get("message", "同步失败"))
         return success(data=result, msg="MCP工具列表同步完成")
-    except BusinessException:
-        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -278,10 +240,8 @@ async def test_tool_connection(
             # 普通连接测试
             result = await service.test_connection(tool_id, current_user.tenant_id)
         if result["success"] is False:
-            raise BusinessException(result["message"], BizCode.SERVICE_UNAVAILABLE)
+            raise HTTPException(status_code=400, detail=result["message"])
         return success(data=result, msg="连接测试完成")
-    except BusinessException:
-        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 

api/app/controllers/user_controller.py

@@ -1,26 +1,16 @@
 from fastapi import APIRouter, Depends
 from sqlalchemy.orm import Session
 import uuid
-from typing import Callable
 
-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
-from app.i18n.dependencies import get_translator
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -35,8 +25,7 @@ router = APIRouter(
 def create_superuser(
     user: user_schema.UserCreate,
     db: Session = Depends(get_db),
-    current_superuser: User = Depends(get_current_superuser),
-    t: Callable = Depends(get_translator)
+    current_superuser: User = Depends(get_current_superuser)
 ):
     """创建超级管理员（仅超级管理员可访问）"""
     api_logger.info(f"超级管理员创建请求: {user.username}, email: {user.email}")
@@ -45,7 +34,7 @@ def create_superuser(
     api_logger.info(f"超级管理员创建成功: {result.username} (ID: {result.id})")
     
     result_schema = user_schema.User.model_validate(result)
-    return success(data=result_schema, msg=t("users.create.superuser_success"))
+    return success(data=result_schema, msg="超级管理员创建成功")
 
 
 @router.delete("/{user_id}", response_model=ApiResponse)
@@ -53,7 +42,6 @@ def delete_user(
     user_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    t: Callable = Depends(get_translator)
 ):
     """停用用户（软删除）"""
     api_logger.info(f"用户停用请求: user_id={user_id}, 操作者: {current_user.username}")
@@ -61,14 +49,13 @@ def delete_user(
         db=db, user_id_to_deactivate=user_id, current_user=current_user
     )
     api_logger.info(f"用户停用成功: {result.username} (ID: {result.id})")
-    return success(msg=t("users.delete.deactivate_success"))
+    return success(msg="用户停用成功")
 
 @router.post("/{user_id}/activate", response_model=ApiResponse)
 def activate_user(
     user_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    t: Callable = Depends(get_translator)
 ):
     """激活用户"""
     api_logger.info(f"用户激活请求: user_id={user_id}, 操作者: {current_user.username}")
@@ -79,14 +66,13 @@ def activate_user(
     api_logger.info(f"用户激活成功: {result.username} (ID: {result.id})")
     
     result_schema = user_schema.User.model_validate(result)
-    return success(data=result_schema, msg=t("users.activate.success"))
+    return success(data=result_schema, msg="用户激活成功")
 
 
 @router.get("", response_model=ApiResponse)
 def get_current_user_info(
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    t: Callable = Depends(get_translator)
 ):
     """获取当前用户信息"""
     api_logger.info(f"当前用户信息请求: {current_user.username}")
@@ -106,12 +92,12 @@ 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
     
     api_logger.info(f"当前用户信息获取成功: {result.username}, 角色: {result_schema.role}, 工作空间: {result_schema.current_workspace_name}")
-    return success(data=result_schema, msg=t("users.info.get_success"))
+    return success(data=result_schema, msg="用户信息获取成功")
 
 
 @router.get("/superusers", response_model=ApiResponse)
@@ -119,7 +105,6 @@ def get_tenant_superusers(
     include_inactive: bool = False,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_superuser),
-    t: Callable = Depends(get_translator)
 ):
     """获取当前租户下的超管账号列表（仅超级管理员可访问）"""
     api_logger.info(f"获取租户超管列表请求: {current_user.username}")
@@ -132,8 +117,7 @@ def get_tenant_superusers(
     api_logger.info(f"租户超管列表获取成功: count={len(superusers)}")
     
     superusers_schema = [user_schema.User.model_validate(u) for u in superusers]
-    return success(data=superusers_schema, msg=t("users.list.superusers_success"))
-
+    return success(data=superusers_schema, msg="租户超管列表获取成功")
 
 
 @router.get("/{user_id}", response_model=ApiResponse)
@@ -141,7 +125,6 @@ def get_user_info_by_id(
     user_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    t: Callable = Depends(get_translator)
 ):
     """根据用户ID获取用户信息"""
     api_logger.info(f"获取用户信息请求: user_id={user_id}, 操作者: {current_user.username}")
@@ -152,7 +135,7 @@ def get_user_info_by_id(
     api_logger.info(f"用户信息获取成功: {result.username}")
     
     result_schema = user_schema.User.model_validate(result)
-    return success(data=result_schema, msg=t("users.info.get_success"))
+    return success(data=result_schema, msg="用户信息获取成功")
 
 
 @router.put("/change-password", response_model=ApiResponse)
@@ -160,7 +143,6 @@ async def change_password(
     request: ChangePasswordRequest,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    t: Callable = Depends(get_translator)
 ):
     """修改当前用户密码"""
     api_logger.info(f"用户密码修改请求: {current_user.username}")
@@ -173,7 +155,7 @@ async def change_password(
         current_user=current_user
     )
     api_logger.info(f"用户密码修改成功: {current_user.username}")
-    return success(msg=t("auth.password.change_success"))
+    return success(msg="密码修改成功")
 
 
 @router.put("/admin/change-password", response_model=ApiResponse)
@@ -181,7 +163,6 @@ async def admin_change_password(
     request: AdminChangePasswordRequest,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_superuser),
-    t: Callable = Depends(get_translator)
 ):
     """超级管理员修改指定用户的密码"""
     api_logger.info(f"管理员密码修改请求: 管理员 {current_user.username} 修改用户 {request.user_id}")
@@ -196,107 +177,7 @@ async def admin_change_password(
     # 根据是否生成了随机密码来构造响应
     if request.new_password:
         api_logger.info(f"管理员密码修改成功: 用户 {request.user_id}")
-        return success(msg=t("auth.password.change_success"))
+        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=t("auth.password.reset_success"))
-
-
-@router.post("/verify_pwd", response_model=ApiResponse)
-def verify_pwd(
-    request: VerifyPasswordRequest,
-    current_user: User = Depends(get_current_user),
-    t: Callable = Depends(get_translator)
-):
-    """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(t("users.errors.password_verification_failed"), code=BizCode.VALIDATION_FAILED)
-    return success(data={"valid": is_valid}, msg=t("common.success.retrieved"))
-
-
-@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),
-    t: Callable = Depends(get_translator)
-):
-    """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=t("users.email.code_sent"))
-
-
-@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),
-    t: Callable = Depends(get_translator)
-):
-    """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=t("users.email.change_success"))
-
-
-
-@router.get("/me/language", response_model=ApiResponse)
-def get_current_user_language(
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-    t: Callable = Depends(get_translator)
-):
-    """获取当前用户的语言偏好"""
-    api_logger.info(f"获取用户语言偏好: {current_user.username}")
-    
-    language = user_service.get_user_language_preference(
-        db=db,
-        user_id=current_user.id,
-        current_user=current_user
-    )
-    
-    api_logger.info(f"用户语言偏好获取成功: {current_user.username}, language={language}")
-    return success(
-        data=user_schema.LanguagePreferenceResponse(language=language),
-        msg=t("users.language.get_success")
-    )
-
-
-@router.put("/me/language", response_model=ApiResponse)
-def update_current_user_language(
-    request: user_schema.LanguagePreferenceRequest,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-    t: Callable = Depends(get_translator)
-):
-    """设置当前用户的语言偏好"""
-    api_logger.info(f"更新用户语言偏好: {current_user.username}, language={request.language}")
-    
-    updated_user = user_service.update_user_language_preference(
-        db=db,
-        user_id=current_user.id,
-        language=request.language,
-        current_user=current_user
-    )
-    
-    api_logger.info(f"用户语言偏好更新成功: {current_user.username}, language={request.language}")
-    return success(
-        data=user_schema.LanguagePreferenceResponse(language=updated_user.preferred_language),
-        msg=t("users.language.update_success")
-    )
+        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
@@ -17,12 +16,11 @@ from app.services.user_memory_service import (
     UserMemoryService,
     analytics_memory_types,
     analytics_graph_data,
-    analytics_community_graph_data,
 )
 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,
@@ -74,7 +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:
@@ -83,26 +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}")
@@ -118,7 +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:
@@ -127,14 +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
@@ -142,27 +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": []
@@ -182,9 +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")
 
@@ -192,7 +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(
@@ -279,6 +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')}")
@@ -296,42 +271,6 @@ async def get_graph_data_api(
         return fail(BizCode.INTERNAL_ERROR, "图数据查询失败", str(e))
 
 
-@router.get("/analytics/community_graph", response_model=ApiResponse)
-async def get_community_graph_data_api(
-    end_user_id: str,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
-) -> dict:
-    workspace_id = current_user.current_workspace_id
-
-    if workspace_id is None:
-        api_logger.warning(f"用户 {current_user.username} 尝试查询社区图谱但未选择工作空间")
-        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"社区图谱查询请求: end_user_id={end_user_id}, user={current_user.username}, "
-        f"workspace={workspace_id}"
-    )
-
-    try:
-        result = await analytics_community_graph_data(db=db, end_user_id=end_user_id)
-
-        if "message" in result and result["statistics"]["total_nodes"] == 0:
-            api_logger.warning(f"社区图谱查询返回空结果: {result.get('message')}")
-            return success(data=result, msg=result.get("message", "查询成功"))
-
-        api_logger.info(
-            f"成功获取社区图谱: end_user_id={end_user_id}, "
-            f"nodes={result['statistics']['total_nodes']}, "
-            f"edges={result['statistics']['total_edges']}"
-        )
-        return success(data=result, msg="查询成功")
-
-    except Exception as e:
-        api_logger.error(f"社区图谱查询失败: end_user_id={end_user_id}, error={str(e)}")
-        return fail(BizCode.INTERNAL_ERROR, "社区图谱查询失败", str(e))
-
-
 @router.get("/read_end_user/profile", response_model=ApiResponse)
 async def get_end_user_profile(
     end_user_id: str,
@@ -339,13 +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")
@@ -363,6 +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,
@@ -394,11 +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")
@@ -408,44 +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/controllers/workspace_controller.py

@@ -1,7 +1,7 @@
 import uuid
 from typing import List, Optional
 
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, status
+from fastapi import APIRouter, Depends, HTTPException, Query, status
 from sqlalchemy.orm import Session
 
 from app.core.logging_config import get_api_logger
@@ -14,12 +14,6 @@ from app.dependencies import (
     get_current_user,
     workspace_access_guard,
 )
-from app.i18n.dependencies import get_current_language, get_translator
-from app.i18n.serializers import (
-    WorkspaceSerializer,
-    WorkspaceMemberSerializer,
-    WorkspaceInviteSerializer
-)
 from app.models.tenant_model import Tenants
 from app.models.user_model import User
 from app.models.workspace_model import InviteStatus
@@ -71,9 +65,7 @@ def get_workspaces(
     include_current: bool = Query(True, description="是否包含当前工作空间"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    current_tenant: Tenants = Depends(get_current_tenant),
-    language: str = Depends(get_current_language),
-    t: callable = Depends(get_translator)
+    current_tenant: Tenants = Depends(get_current_tenant)
 ):
     """获取当前租户下用户参与的所有工作空间
 
@@ -96,50 +88,25 @@ def get_workspaces(
         )
 
     api_logger.info(f"成功获取 {len(workspaces)} 个工作空间")
-    
-    # 使用序列化器添加国际化字段
-    serializer = WorkspaceSerializer()
-    workspaces_data = [WorkspaceResponse.model_validate(w).model_dump() for w in workspaces]
-    workspaces_i18n = serializer.serialize_list(workspaces_data, language)
-    
-    return success(data=workspaces_i18n, msg=t("workspace.list_retrieved"))
+    workspaces_schema = [WorkspaceResponse.model_validate(w) for w in workspaces]
+    return success(data=workspaces_schema, msg="工作空间列表获取成功")
 
 
 @router.post("", response_model=ApiResponse)
 def create_workspace(
     workspace: WorkspaceCreate,
-    language_type: str = Header(default="zh", alias="X-Language-Type"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_superuser),
-    language: str = Depends(get_current_language),
-    t: callable = Depends(get_translator)
 ):
     """创建新的工作空间"""
-    from app.core.language_utils import get_language_from_header
-    
-    # 验证并获取语言参数
-    language = get_language_from_header(language_type)
-    
-    api_logger.info(
-        f"用户 {current_user.username} 请求创建工作空间: {workspace.name}, "
-        f"language={language}"
-    )
+    api_logger.info(f"用户 {current_user.username} 请求创建工作空间: {workspace.name}")
 
     result = workspace_service.create_workspace(
-        db=db, workspace=workspace, user=current_user, language=language
-    )
+        db=db, workspace=workspace, user=current_user)
 
-    api_logger.info(
-        f"工作空间创建成功 - 名称: {workspace.name}, ID: {result.id}, "
-        f"创建者: {current_user.username}, language={language}"
-    )
-    
-    # 使用序列化器添加国际化字段
-    serializer = WorkspaceSerializer()
-    result_data = WorkspaceResponse.model_validate(result).model_dump()
-    result_i18n = serializer.serialize(result_data, language)
-    
-    return success(data=result_i18n, msg=t("workspace.created"))
+    api_logger.info(f"工作空间创建成功 - 名称: {workspace.name}, ID: {result.id}, 创建者: {current_user.username}")
+    result_schema = WorkspaceResponse.model_validate(result)
+    return success(data=result_schema, msg="工作空间创建成功")
 
 @router.put("", response_model=ApiResponse)
 @cur_workspace_access_guard()
@@ -147,8 +114,6 @@ def update_workspace(
     workspace: WorkspaceUpdate,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    language: str = Depends(get_current_language),
-    t: callable = Depends(get_translator)
 ):
     """更新工作空间"""
     workspace_id = current_user.current_workspace_id
@@ -161,21 +126,14 @@ def update_workspace(
         user=current_user,
     )
     api_logger.info(f"工作空间更新成功 - ID: {workspace_id}, 用户: {current_user.username}")
-    
-    # 使用序列化器添加国际化字段
-    serializer = WorkspaceSerializer()
-    result_data = WorkspaceResponse.model_validate(result).model_dump()
-    result_i18n = serializer.serialize(result_data, language)
-    
-    return success(data=result_i18n, msg=t("workspace.updated"))
+    result_schema = WorkspaceResponse.model_validate(result)
+    return success(data=result_schema, msg="工作空间更新成功")
 
 @router.get("/members", response_model=ApiResponse)
 @cur_workspace_access_guard()
 def get_cur_workspace_members(
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    language: str = Depends(get_current_language),
-    t: callable = Depends(get_translator)
 ):
     """获取工作空间成员列表（关系序列化）"""
     api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {current_user.current_workspace_id} 的成员列表")
@@ -186,14 +144,8 @@ def get_cur_workspace_members(
         user=current_user,
     )
     api_logger.info(f"工作空间成员列表获取成功 - ID: {current_user.current_workspace_id}, 数量: {len(members)}")
-    
-    # 转换为表格项并使用序列化器添加国际化字段
     table_items = _convert_members_to_table_items(members)
-    serializer = WorkspaceMemberSerializer()
-    members_data = [item.model_dump() for item in table_items]
-    members_i18n = serializer.serialize_list(members_data, language)
-    
-    return success(data=members_i18n, msg=t("workspace.members.list_retrieved"))
+    return success(data=table_items, msg="工作空间成员列表获取成功")
 
 
 @router.put("/members", response_model=ApiResponse)
@@ -203,7 +155,6 @@ def update_workspace_members(
     updates: List[WorkspaceMemberUpdate],
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    t: callable = Depends(get_translator)
 ):
     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} 的成员角色")
@@ -214,7 +165,7 @@ def update_workspace_members(
         user=current_user,
     )
     api_logger.info(f"工作空间成员角色更新成功 - ID: {workspace_id}, 数量: {len(members)}")
-    return success(msg=t("workspace.members.role_updated"))
+    return success(msg="成员角色更新成功")
 
 
 @router.delete("/members/{member_id}", response_model=ApiResponse)
@@ -223,7 +174,6 @@ def delete_workspace_member(
     member_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    t: callable = Depends(get_translator)
 ):
     workspace_id = current_user.current_workspace_id
     api_logger.info(f"用户 {current_user.username} 请求删除工作空间 {workspace_id} 的成员 {member_id}")
@@ -235,7 +185,7 @@ def delete_workspace_member(
         user=current_user,
     )
     api_logger.info(f"工作空间成员删除成功 - ID: {workspace_id}, 成员: {member_id}")
-    return success(msg=t("workspace.members.deleted"))
+    return success(msg="成员删除成功")
 
 
 # 创建空间协作邀请
@@ -245,8 +195,6 @@ def create_workspace_invite(
     invite_data: WorkspaceInviteCreate,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    language: str = Depends(get_current_language),
-    t: callable = Depends(get_translator)
 ):
     """创建工作空间邀请"""
     workspace_id = current_user.current_workspace_id
@@ -259,12 +207,7 @@ def create_workspace_invite(
         user=current_user
     )
     api_logger.info(f"工作空间邀请创建成功 - 工作空间: {workspace_id}, 邮箱: {invite_data.email}")
-    
-    # 使用序列化器添加国际化字段
-    serializer = WorkspaceInviteSerializer()
-    result_i18n = serializer.serialize(result, language)
-    
-    return success(data=result_i18n, msg=t("workspace.invites.created"))
+    return success(data=result, msg="邀请创建成功")
 
 
 @router.get("/invites", response_model=ApiResponse)
@@ -276,8 +219,6 @@ def get_workspace_invites(
     offset: int = Query(0, ge=0),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    language: str = Depends(get_current_language),
-    t: callable = Depends(get_translator)
 ):
     """获取工作空间邀请列表"""
     workspace_id = current_user.current_workspace_id
@@ -292,30 +233,18 @@ def get_workspace_invites(
         offset=offset
     )
     api_logger.info(f"成功获取 {len(invites)} 个邀请记录")
-    
-    # 使用序列化器添加国际化字段
-    serializer = WorkspaceInviteSerializer()
-    invites_i18n = serializer.serialize_list(invites, language)
-    
-    return success(data=invites_i18n, msg=t("workspace.invites.list_retrieved"))
+    return success(data=invites, msg="邀请列表获取成功")
 
 
 @public_router.get("/invites/validate/{token}", response_model=ApiResponse)
 def get_workspace_invite_info(
     token: str,
     db: Session = Depends(get_db),
-    language: str = Depends(get_current_language),
-    t: callable = Depends(get_translator)
 ):
     """获取工作空间邀请用户信息（无需认证）"""
     result = workspace_service.validate_invite_token(db=db, token=token)
     api_logger.info(f"工作空间邀请验证成功 - 邀请: {token}")
-    
-    # 使用序列化器添加国际化字段
-    serializer = WorkspaceInviteSerializer()
-    result_i18n = serializer.serialize(result, language)
-    
-    return success(data=result_i18n, msg=t("workspace.invites.validated"))
+    return success(data=result, msg="邀请验证成功")
 
 
 @router.delete("/invites/{invite_id}", response_model=ApiResponse)
@@ -325,8 +254,6 @@ def revoke_workspace_invite(
     invite_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    language: str = Depends(get_current_language),
-    t: callable = Depends(get_translator)
 ):
     """撤销工作空间邀请"""
     workspace_id = current_user.current_workspace_id
@@ -339,12 +266,7 @@ def revoke_workspace_invite(
         user=current_user
     )
     api_logger.info(f"工作空间邀请撤销成功 - 邀请: {invite_id}")
-    
-    # 使用序列化器添加国际化字段
-    serializer = WorkspaceInviteSerializer()
-    result_i18n = serializer.serialize(result, language)
-    
-    return success(data=result_i18n, msg=t("workspace.invites.revoked"))
+    return success(data=result, msg="邀请撤销成功")
 
 # ==================== 公开邀请接口（无需认证） ====================
 
@@ -367,7 +289,6 @@ def switch_workspace(
     workspace_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    t: callable = Depends(get_translator)
 ):
     """切换工作空间"""
     api_logger.info(f"用户 {current_user.username} 请求切换工作空间为 {workspace_id}")
@@ -378,7 +299,7 @@ def switch_workspace(
         user=current_user,
     )
     api_logger.info(f"成功切换工作空间为 {workspace_id}")
-    return success(msg=t("workspace.switched"))
+    return success(msg="工作空间切换成功")
 
 
 @router.get("/storage", response_model=ApiResponse)
@@ -386,7 +307,6 @@ def switch_workspace(
 def get_workspace_storage_type(
         db: Session = Depends(get_db),
         current_user: User = Depends(get_current_user),
-        t: callable = Depends(get_translator)
 ):
     """获取当前工作空间的存储类型"""
     workspace_id = current_user.current_workspace_id
@@ -398,7 +318,7 @@ def get_workspace_storage_type(
         user=current_user
     )
     api_logger.info(f"成功获取工作空间 {workspace_id} 的存储类型: {storage_type}")
-    return success(data={"storage_type": storage_type}, msg=t("workspace.storage.type_retrieved"))
+    return success(data={"storage_type": storage_type}, msg="存储类型获取成功")
 
 
 @router.get("/workspace_models", response_model=ApiResponse)
@@ -406,8 +326,6 @@ def get_workspace_storage_type(
 def workspace_models_configs(
         db: Session = Depends(get_db),
         current_user: User = Depends(get_current_user),
-        language: str = Depends(get_current_language),
-        t: callable = Depends(get_translator)
 ):
     """获取当前工作空间的模型配置（llm, embedding, rerank）"""
     workspace_id = current_user.current_workspace_id
@@ -423,14 +341,14 @@ def workspace_models_configs(
         api_logger.warning(f"工作空间 {workspace_id} 不存在或无权访问")
         raise HTTPException(
             status_code=status.HTTP_404_NOT_FOUND,
-            detail=t("workspace.not_found")
+            detail="工作空间不存在或无权访问"
         )
 
     api_logger.info(
         f"成功获取工作空间 {workspace_id} 的模型配置: "
         f"llm={configs.get('llm')}, embedding={configs.get('embedding')}, rerank={configs.get('rerank')}"
     )
-    return success(data=WorkspaceModelsConfig.model_validate(configs), msg=t("workspace.models.config_retrieved"))
+    return success(data=WorkspaceModelsConfig.model_validate(configs), msg="模型配置获取成功")
 
 
 @router.put("/workspace_models", response_model=ApiResponse)
@@ -439,7 +357,6 @@ def update_workspace_models_configs(
         models_update: WorkspaceModelsUpdate,
         db: Session = Depends(get_db),
         current_user: User = Depends(get_current_user),
-        t: callable = Depends(get_translator)
 ):
     """更新当前工作空间的模型配置（llm, embedding, rerank）"""
     workspace_id = current_user.current_workspace_id
@@ -456,5 +373,5 @@ def update_workspace_models_configs(
         f"成功更新工作空间 {workspace_id} 的模型配置: "
         f"llm={updated_workspace.llm}, embedding={updated_workspace.embedding}, rerank={updated_workspace.rerank}"
     )
-    return success(data=WorkspaceModelsConfig.model_validate(updated_workspace), msg=t("workspace.models.config_updated"))
+    return success(data=WorkspaceModelsConfig.model_validate(updated_workspace), msg="模型配置更新成功")
 

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

@@ -7,18 +7,23 @@ 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, ModelProvider
+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
@@ -29,19 +34,16 @@ logger = get_business_logger()
 class LangChainAgent:
 
     def __init__(
-            self,
-            model_name: str,
-            api_key: str,
-            provider: str = "openai",
-            api_base: Optional[str] = None,
-            is_omni: bool = False,
-            temperature: float = 0.7,
-            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
+        self,
+        model_name: str,
+        api_key: str,
+        provider: str = "openai",
+        api_base: Optional[str] = None,
+        temperature: float = 0.7,
+        max_tokens: int = 2000,
+        system_prompt: Optional[str] = None,
+        tools: Optional[Sequence[BaseTool]] = None,
+        streaming: bool = False
     ):
         """初始化 LangChain Agent
 
@@ -54,37 +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.is_omni = is_omni
-        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(
@@ -92,7 +70,6 @@ class LangChainAgent:
             provider=provider,
             api_key=api_key,
             base_url=api_base,
-            is_omni=is_omni,
             extra_params={
                 "temperature": temperature,
                 "max_tokens": max_tokens,
@@ -109,14 +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
         )
 
@@ -128,92 +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
+        self,
+        message: str,
+        history: Optional[List[Dict[str, str]]] = None,
+        context: Optional[str] = None
     ) -> List[BaseMessage]:
         """准备消息列表
 
@@ -221,7 +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
@@ -244,49 +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
+    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
 
-    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 [ModelProvider.BEDROCK, ModelProvider.OPENAI, ModelProvider.XINFERENCE,
-        #                               ModelProvider.GPUSTACK] or (
-        #         self.provider.lower() == ModelProvider.DASHSCOPE and self.is_omni)):
-        #     # Anthropic/Bedrock/Xinference/Gpustack/Openai: {"type": "text", "text": "..."}
-        #     content_parts = [{"type": "text", "text": text}]
-        # else:
-        #     # 通义千问等: {"text": "..."}
-        #     content_parts = [{"type": "text", "text": text}]
-        content_parts = [{"type": "text", "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
+    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:
+            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,
@@ -297,8 +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
 
@@ -310,7 +205,7 @@ class LangChainAgent:
         Returns:
             Dict: 包含 content 和元数据的字典
         """
-        message_chat = message
+        message_chat= message
         start_time = time.time()
         actual_config_id = config_id
         # If config_id is None, try to get from end_user's connected config
@@ -330,11 +225,34 @@ class LangChainAgent:
             except Exception as e:
                 logger.warning(f"Failed to get db session: {e}")
         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)}')
+        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",
@@ -342,86 +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,
@@ -429,7 +286,7 @@ class LangChainAgent:
                 "usage": {
                     "prompt_tokens": 0,
                     "completion_tokens": 0,
-                    "total_tokens": total_tokens
+                    "total_tokens": 0
                 }
             }
 
@@ -448,16 +305,15 @@ class LangChainAgent:
             raise
 
     async def chat_stream(
-            self,
-            message: str,
-            history: Optional[List[Dict[str, str]]] = None,
-            context: Optional[str] = None,
-            end_user_id: Optional[str] = None,
-            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
+        self,
+        message: str,
+        history: Optional[List[Dict[str, str]]] = None,
+        context: Optional[str] = None,
+        end_user_id:Optional[str] = None,
+        config_id: Optional[str] = None,
+        storage_type:Optional[str] = None,
+        user_rag_memory_id:Optional[str] = None,
+        memory_flag: Optional[bool] = True
     ) -> AsyncGenerator[str, None]:
         """执行流式对话
 
@@ -491,13 +347,32 @@ class LangChainAgent:
             except Exception as e:
                 logger.warning(f"Failed to get db session: {e}")
 
-            # 注意：不在这里写入用户消息，等 AI 回复后一起写入
+        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()
+
+            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
@@ -505,106 +380,47 @@ 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}
+                    {"messages": messages},
+                    version="v2"
                 ):
                     chunk_count += 1
                     kind = event.get("event")
-
+                    
                     # 处理所有可能的流式事件
                     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
-
+                    
                     # 记录工具调用（可选）
                     elif kind == "on_tool_start":
                         logger.debug(f"工具调用开始: {event.get('name')}")
                     elif kind == "on_tool_end":
                         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
@@ -618,3 +434,5 @@ class LangChainAgent:
             logger.info("=" * 80)
             logger.info("chat_stream 方法执行结束")
             logger.info("=" * 80)
+
+

api/app/core/config.py

@@ -1,33 +1,14 @@
+import json
 import os
 from pathlib import Path
-from typing import Annotated, Optional
+from typing import Any, Dict, Optional
 
 from dotenv import load_dotenv
-from pydantic import Field, TypeAdapter
 
 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", "")
@@ -91,30 +72,9 @@ 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", "")
-    S3_ENDPOINT_URL: str = os.getenv("S3_ENDPOINT_URL", "")
 
     # 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)
@@ -157,49 +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")
-
-    # ========================================================================
-    # Internationalization (i18n) Configuration
-    # ========================================================================
-    # Default language for API responses
-    I18N_DEFAULT_LANGUAGE: str = os.getenv("I18N_DEFAULT_LANGUAGE", "zh")
-    
-    # Supported languages (comma-separated)
-    I18N_SUPPORTED_LANGUAGES: list[str] = [
-        lang.strip()
-        for lang in os.getenv("I18N_SUPPORTED_LANGUAGES", "zh,en").split(",")
-        if lang.strip()
-    ]
-    
-    # Core locales directory (community edition)
-    # Use absolute path to work from any working directory
-    I18N_CORE_LOCALES_DIR: str = os.getenv(
-        "I18N_CORE_LOCALES_DIR",
-        os.path.join(os.path.dirname(os.path.dirname(__file__)), "locales")
-    )
-    
-    # Premium locales directory (enterprise edition, optional)
-    I18N_PREMIUM_LOCALES_DIR: Optional[str] = os.getenv("I18N_PREMIUM_LOCALES_DIR", None)
-    
-    # Enable translation cache
-    I18N_ENABLE_TRANSLATION_CACHE: bool = os.getenv("I18N_ENABLE_TRANSLATION_CACHE", "true").lower() == "true"
-    
-    # LRU cache size for hot translations
-    I18N_LRU_CACHE_SIZE: int = int(os.getenv("I18N_LRU_CACHE_SIZE", "1000"))
-    
-    # Enable hot reload of translation files
-    I18N_ENABLE_HOT_RELOAD: bool = os.getenv("I18N_ENABLE_HOT_RELOAD", "false").lower() == "true"
-    
-    # Fallback language when translation is missing
-    I18N_FALLBACK_LANGUAGE: str = os.getenv("I18N_FALLBACK_LANGUAGE", "zh")
-    
-    # Log missing translations
-    I18N_LOG_MISSING_TRANSLATIONS: bool = os.getenv("I18N_LOG_MISSING_TRANSLATIONS", "true").lower() == "true"
-
     # 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")
@@ -228,45 +144,18 @@ class Settings:
     LOG_FILE_MAX_SIZE_MB: int = int(os.getenv("LOG_FILE_MAX_SIZE_MB", "10"))  # 10MB
 
     # Celery configuration (internal)
-    # NOTE: 变量名不以 CELERY_ 开头，避免被 Celery CLI 的前缀匹配机制劫持
-    # 详见 docs/celery-env-bug-report.md
-    # 默认使用 Redis DB 3 (broker) 和 DB 4 (backend)，与业务缓存 (DB 1/2) 隔离
-    # 多人共用同一 Redis 时，每位开发者应在 .env 中配置不同的 DB 编号避免任务互相干扰
-    REDIS_DB_CELERY_BROKER: int = int(os.getenv("REDIS_DB_CELERY_BROKER", "3"))
-    REDIS_DB_CELERY_BACKEND: int = int(os.getenv("REDIS_DB_CELERY_BACKEND", "4"))
-
-    # 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", "")
-
+    CELERY_BROKER: int = int(os.getenv("CELERY_BROKER", "1"))
+    CELERY_BACKEND: int = int(os.getenv("CELERY_BACKEND", "2"))
     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"))
+    DEFAULT_WORKSPACE_ID: Optional[str] = os.getenv("DEFAULT_WORKSPACE_ID", None)
     REFLECTION_INTERVAL_TIME: Optional[str] = int(os.getenv("REFLECTION_INTERVAL_TIME", 30))
 
     # Memory Cache Regeneration Configuration
     MEMORY_CACHE_REGENERATION_HOURS: int = int(os.getenv("MEMORY_CACHE_REGENERATION_HOURS", "24"))
 
-    # Celery Beat Schedule Configuration (定时任务执行频率)
-    MEMORY_INCREMENT_HOUR: int = TypeAdapter(
-        Annotated[int, Field(ge=0, le=23, description="cron hour [0, 23]")]
-    ).validate_python(int(os.getenv("MEMORY_INCREMENT_HOUR", "2")))
-    MEMORY_INCREMENT_MINUTE: int = TypeAdapter(
-        Annotated[int, Field(ge=0, le=59, description="cron minute [0, 59]")]
-    ).validate_python(int(os.getenv("MEMORY_INCREMENT_MINUTE", "0")))
-    WORKSPACE_REFLECTION_INTERVAL_SECONDS: int = TypeAdapter(
-        Annotated[int, Field(ge=1, description="reflection interval in seconds, must be >= 1")]
-    ).validate_python(int(os.getenv("WORKSPACE_REFLECTION_INTERVAL_SECONDS", "30")))
-    FORGETTING_CYCLE_INTERVAL_HOURS: int = TypeAdapter(
-        Annotated[int, Field(ge=1, description="forgetting cycle interval in hours, must be >= 1")]
-    ).validate_python(int(os.getenv("FORGETTING_CYCLE_INTERVAL_HOURS", "24")))
-    
-    IMPLICIT_EMOTIONS_UPDATE_HOUR: int = int(os.getenv("IMPLICIT_EMOTIONS_UPDATE_HOUR", "2"))
-    # implicit_emotions_update: 每天几分执行（分钟，0-59）
-    IMPLICIT_EMOTIONS_UPDATE_MINUTE: int = int(os.getenv("IMPLICIT_EMOTIONS_UPDATE_MINUTE", "0"))  
     # Memory Module Configuration (internal)
-    
     MEMORY_OUTPUT_DIR: str = os.getenv("MEMORY_OUTPUT_DIR", "logs/memory-output")
     MEMORY_CONFIG_DIR: str = os.getenv("MEMORY_CONFIG_DIR", "app/core/memory")
 
@@ -277,37 +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_IMPORT_CACHE_TIMEOUT: int = int(os.getenv("WORKFLOW_IMPORT_CACHE_TIMEOUT", 1800))
     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", "api/app/core/memory/ontology_services/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

@@ -1,45 +0,0 @@
-from app.core.memory.agent.utils.llm_tools import ReadState, WriteState
-from app.schemas.memory_agent_schema import AgentMemoryDataset
-
-
-def content_input_node(state: ReadState) -> ReadState:
-    """
-    Start node - Extract content and maintain state information
-
-    Extracts the content from the first message in the state and returns it
-    as the data field while preserving all other state information.
-
-    Args:
-        state: ReadState containing messages and other state data
-
-    Returns:
-        ReadState: Updated state with extracted content in data field
-    """
-
-    content = state['messages'][0].content if state.get('messages') else ''
-    # Return content and maintain all state information
-    for pronoun in AgentMemoryDataset.PRONOUN:
-        content = content.replace(pronoun, AgentMemoryDataset.NAME)
-
-    return {"data": content}
-
-
-def content_input_write(state: WriteState) -> WriteState:
-    """
-    Start node - Extract content and maintain state information for write operations
-
-    Extracts the content from the first message in the state for write operations.
-
-    Args:
-        state: WriteState containing messages and other state data
-
-    Returns:
-        WriteState: Updated state with extracted content in data field
-    """
-
-    content = state['messages'][0].content if state.get('messages') else ''
-    # Return content and maintain all state information
-    for pronoun in AgentMemoryDataset.PRONOUN:
-        content = content.replace(pronoun, AgentMemoryDataset.NAME)
-
-    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

@@ -1,282 +0,0 @@
-import json
-import os
-import time
-
-from app.core.logging_config import get_agent_logger
-from app.core.memory.agent.models.problem_models import ProblemExtensionResponse
-from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
-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_context
-
-template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
-logger = get_agent_logger(__name__)
-
-
-class ProblemNodeService(LLMServiceMixin):
-    """
-    Problem processing node service class
-    
-    Handles problem decomposition and extension operations using LLM services.
-    Inherits from LLMServiceMixin to provide structured LLM calling capabilities.
-    
-    Attributes:
-        template_service: Service for rendering Jinja2 templates
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.template_service = TemplateService(template_root)
-
-
-# Create global service instance
-problem_service = ProblemNodeService()
-
-
-async def Split_The_Problem(state: ReadState) -> ReadState:
-    """
-    Problem decomposition node
-    
-    Breaks down complex user queries into smaller, more manageable sub-problems.
-    Uses LLM to analyze the input and generate structured problem decomposition
-    with question types and reasoning.
-    
-    Args:
-        state: ReadState containing user input and configuration
-        
-    Returns:
-        ReadState: Updated state with problem decomposition results
-    """
-    # 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
-        with get_db_context() as db_session:
-            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)}")
-
-        # Validate structured response
-        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
-        )
-
-        # Provide more detailed error information
-        error_details = {
-            "error_type": type(e).__name__,
-            "error_message": str(e),
-            "content_length": len(content),
-            "llm_model_id": str(memory_config.llm_model_id) if memory_config else None
-        }
-
-        logger.error(f"Split_The_Problem error details: {error_details}")
-
-        # Create default empty result
-        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
-            }
-        }
-
-    # Return updated state including spit_context field
-    return {"spit_data": result}
-
-
-async def Problem_Extension(state: ReadState) -> ReadState:
-    """
-    Problem extension node
-    
-    Extends the decomposed problems from Split_The_Problem node by generating
-    additional related questions and organizing them by original question.
-    Uses LLM to create comprehensive question extensions for better memory retrieval.
-    
-    Args:
-        state: ReadState containing decomposed problems and configuration
-        
-    Returns:
-        ReadState: Updated state with extended problem results
-    """
-    # Get original data and decomposition results
-    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
-        with get_db_context() as db_session:
-            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)}")
-
-        # Validate structured response
-        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
-        )
-
-        # Provide more detailed error information
-        error_details = {
-            "error_type": type(e).__name__,
-            "error_message": str(e),
-            "questions_count": len(databasets),
-            "llm_model_id": str(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

@@ -1,509 +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.core.memory.agent.langgraph_graph.tools.tool import (
-    create_hybrid_retrieval_tool_sync,
-    create_time_retrieval_tool,
-    extract_tool_message_content,
-)
-from app.core.memory.agent.services.search_service import SearchService
-from app.core.memory.agent.utils.llm_tools import (
-    ReadState,
-    deduplicate_entries,
-    merge_to_key_value_pairs,
-)
-from app.core.rag.nlp.search import knowledge_retrieval
-from app.db import get_db_context
-from app.schemas import model_schema
-from app.services.memory_config_service import MemoryConfigService
-from app.services.model_service import ModelConfigService
-
-logger = get_agent_logger(__name__)
-
-
-async def rag_config(state):
-    """
-    Configure RAG (Retrieval-Augmented Generation) settings
-    
-    Creates configuration for knowledge base retrieval including similarity thresholds,
-    weights, and reranker settings.
-    
-    Args:
-        state: Current state containing user_rag_memory_id
-        
-    Returns:
-        dict: RAG configuration dictionary
-    """
-    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):
-    """
-    Retrieve knowledge using RAG approach
-    
-    Performs knowledge retrieval from configured knowledge bases using the
-    provided question and returns formatted results.
-    
-    Args:
-        state: Current state containing configuration
-        question: Question to search for
-        
-    Returns:
-        tuple: (retrieval_knowledge, clean_content, cleaned_query, raw_results)
-    """
-    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:
-    """
-    Get LLM configuration information from state
-    
-    Retrieves model configuration details including model ID and tenant ID
-    from the memory configuration in the current state.
-    
-    Args:
-        state: ReadState containing memory configuration
-        
-    Returns:
-        ReadState: Model configuration as Pydantic model
-    """
-    memory_config = state.get('memory_config', None)
-    model_id = memory_config.llm_model_id
-    tenant_id = memory_config.tenant_id
-
-    # Use existing memory_config instead of re-querying database
-    # or use thread-safe database access
-    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:
-    """
-    Simplified database search result cleaning function
-    
-    Processes and cleans search results from various sources including
-    reranked results and time-based search results. Extracts text content
-    from structured data and returns as formatted string.
-    
-    Args:
-        data: Search result data (can be string, dict, or other types)
-        
-    Returns:
-        str: Cleaned content string
-    """
-    try:
-        # Parse JSON string
-        if isinstance(data, str):
-            try:
-                data = json.loads(data)
-            except json.JSONDecodeError:
-                return data
-
-        if not isinstance(data, dict):
-            return str(data)
-
-        # Get result data
-        # 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)
-
-        # Collect all content
-        content_list = []
-
-        # Process reranked results
-        reranked = results.get('reranked_results', {})
-        if reranked:
-            for category in ['summaries', 'communities', 'statements', 'chunks', 'entities']:
-                items = reranked.get(category, [])
-                if isinstance(items, list):
-                    content_list.extend(items)
-        # Process time search results
-        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)
-
-        # Extract text content，对 community 按 name 去重（多次 tool 调用会产生重复）
-        text_parts = []
-        seen_community_names = set()
-        for item in content_list:
-            if isinstance(item, dict):
-                # community 节点用 name 去重
-                if 'member_count' in item or 'core_entities' in item:
-                    community_name = item.get('name') or item.get('id', '')
-                    if community_name in seen_community_names:
-                        continue
-                    seen_community_names.add(community_name)
-                text = item.get('statement') or item.get('content') or item.get('summary', '')
-                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:
-    """
-    Retrieve information using simplified search approach
-    
-    Processes extended problems from previous nodes and performs retrieval
-    using either RAG or hybrid search based on storage type. Handles concurrent
-    processing of multiple questions and deduplicates results.
-    
-    Args:
-        state: ReadState containing problem extensions and configuration
-        
-    Returns:
-        ReadState: Updated state with retrieval results and intermediate outputs
-    """
-
-    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}")
-
-    # Create async task to process individual questions
-    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)
-                }
-            }
-
-    # Process all questions concurrently
-    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:
-    """
-    Advanced retrieve function using LangChain agents and tools
-    
-    Uses LangChain agents with specialized retrieval tools (time-based and hybrid)
-    to perform sophisticated information retrieval. Supports both RAG and traditional
-    memory storage approaches with concurrent processing and result deduplication.
-    
-    Args:
-        state: ReadState containing problem extensions and configuration
-        
-    Returns:
-        ReadState: Updated state with retrieval results and intermediate outputs
-    """
-    # Get end_user_id from state
-    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,
-        "include": ["summaries", "statements", "chunks", "entities", "communities"],
-    }
-    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}"
-    )
-
-    # Create async task to process individual questions
-    import asyncio
-
-    # Define semaphore at module level to limit maximum concurrency
-    SEMAPHORE = asyncio.Semaphore(5)  # Limit to maximum 5 concurrent database operations
-
-    async def process_question(idx, question):
-        async with SEMAPHORE:  # Limit concurrency
-            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
-                    # Use asyncio to run synchronous agent.invoke in thread pool
-                    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)
-
-                        # 社区展开：从 tool 返回结果中提取命中的 community，
-                        # 沿 BELONGS_TO_COMMUNITY 关系拉取关联 Statement 追加到 clean_content
-                        _expanded_stmts_to_write = []
-                        try:
-                            results_dict = raw_results.get('results', {}) if isinstance(raw_results, dict) else {}
-                            reranked = results_dict.get('reranked_results', {})
-                            community_hits = reranked.get('communities', [])
-                            if not community_hits:
-                                community_hits = results_dict.get('communities', [])
-                            if community_hits:
-                                from app.core.memory.agent.services.search_service import expand_communities_to_statements
-                                _expanded_stmts_to_write, new_texts = await expand_communities_to_statements(
-                                    community_results=community_hits,
-                                    end_user_id=end_user_id,
-                                    existing_content=clean_content,
-                                )
-                                if new_texts:
-                                    clean_content = clean_content + '\n' + '\n'.join(new_texts)
-                        except Exception as parse_err:
-                            logger.warning(f"[Retrieve] 解析社区命中结果失败，跳过展开: {parse_err}")
-
-                        try:
-                            raw_results = raw_results['results']
-                            # 写回展开结果，接口返回中可见（已在 helper 中清洗过字段）
-                            if _expanded_stmts_to_write and isinstance(raw_results, dict):
-                                raw_results.setdefault('reranked_results', {})['expanded_statements'] = _expanded_stmts_to_write
-                        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)
-                    }
-                }
-
-    # Process all questions concurrently
-    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

@@ -1,524 +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.core.rag.nlp.search import knowledge_retrieval
-from app.db import get_db_context
-
-template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
-logger = get_agent_logger(__name__)
-
-
-class SummaryNodeService(LLMServiceMixin):
-    """
-    Summary node service class
-    
-    Handles summary generation operations using LLM services. Inherits from 
-    LLMServiceMixin to provide structured LLM calling capabilities for 
-    generating summaries from retrieved information.
-    
-    Attributes:
-        template_service: Service for rendering Jinja2 templates
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.template_service = TemplateService(template_root)
-
-
-# Create global service instance
-summary_service = SummaryNodeService()
-
-
-async def rag_config(state):
-    """
-    Configure RAG (Retrieval-Augmented Generation) settings for summary operations
-    
-    Creates configuration for knowledge base retrieval including similarity thresholds,
-    weights, and reranker settings specifically for summary generation.
-    
-    Args:
-        state: Current state containing user_rag_memory_id
-        
-    Returns:
-        dict: RAG configuration dictionary with knowledge base settings
-    """
-    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):
-    """
-    Retrieve knowledge using RAG approach for summary generation
-    
-    Performs knowledge retrieval from configured knowledge bases using the
-    provided question and returns formatted results for summary processing.
-    
-    Args:
-        state: Current state containing configuration
-        question: Question to search for in knowledge base
-        
-    Returns:
-        tuple: (retrieval_knowledge, clean_content, cleaned_query, raw_results)
-            - retrieval_knowledge: List of retrieved knowledge chunks
-            - clean_content: Formatted content string
-            - cleaned_query: Processed query string
-            - raw_results: Raw retrieval results
-    """
-    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 summary_history(state: ReadState) -> ReadState:
-    """
-    Retrieve conversation history for summary context
-    
-    Gets the conversation history for the current user to provide context
-    for summary generation operations.
-    
-    Args:
-        state: ReadState containing end_user_id
-        
-    Returns:
-        ReadState: Conversation history data
-    """
-    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:
-    """
-    Enhanced summary_llm function with better error handling and data validation
-    
-    Generates summaries using LLM with structured output. Includes fallback mechanisms
-    for handling LLM failures and provides robust error recovery.
-    
-    Args:
-        state: ReadState containing current context
-        history: Conversation history for context
-        retrieve_info: Retrieved information to summarize
-        template_name: Jinja2 template name for prompt generation
-        operation_name: Type of operation (summary, input_summary, retrieve_summary)
-        response_model: Pydantic model for structured output
-        search_mode: Search mode flag ("0" for simple, "1" for complex)
-        
-    Returns:
-        str: Generated summary text or fallback message
-    """
-    data = state.get("data", '')
-
-    # Build system prompt
-    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:
-        # Use optimized LLM service for structured output
-        with get_db_context() as db_session:
-            structured = await summary_service.call_llm_structured(
-                state=state,
-                db_session=db_session,
-                system_prompt=system_prompt,
-                response_model=response_model,
-                fallback_value=None
-            )
-        # Validate structured response
-        if structured is None:
-            logger.warning("LLM返回None，使用默认回答")
-            return "信息不足，无法回答"
-
-        # Extract answer based on operation type
-        if operation_name == "summary":
-            aimessages = getattr(structured, 'query_answer', None) or "信息不足，无法回答"
-        else:
-            # Handle 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("结构化响应缺少data字段")
-                aimessages = "信息不足，无法回答"
-
-        # Validate answer is not empty
-        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)
-
-        # Try unstructured output as 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():
-                # Simple response cleaning
-                cleaned_response = response.strip()
-                # Remove possible JSON markers
-                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:
-    """
-    Save summary results to Redis session storage
-    
-    Stores the generated summary and user query in Redis for session management
-    and conversation history tracking.
-    
-    Args:
-        state: ReadState containing user and query information
-        aimessages: Generated summary message to save
-        
-    Returns:
-        ReadState: Updated state after saving to Redis
-    """
-    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:
-    """
-    Format summary results for different output types
-    
-    Creates structured output formats for both input summary and retrieval summary
-    operations, including metadata and intermediate results for frontend display.
-    
-    Args:
-        state: ReadState containing storage and user information
-        aimessages: Generated summary message
-        raw_results: Raw search/retrieval results
-        
-    Returns:
-        tuple: (input_summary, retrieve_summary) formatted result dictionaries
-    """
-    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:
-    """
-    Generate quick input summary from retrieved information
-    
-    Performs fast retrieval and generates a quick summary response for user queries.
-    This function prioritizes speed by only searching summary nodes and provides
-    immediate feedback to users.
-    
-    Args:
-        state: ReadState containing user query, storage configuration, and context
-        
-    Returns:
-        ReadState: Dictionary containing summary results with status and metadata
-    """
-    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", "communities"]  # MemorySummary 和 Community 同为高维度概括节点
-    }
-
-    try:
-        if storage_type != "rag":
-            retrieve_info, question, raw_results = await SearchService().execute_hybrid_search(
-                **search_params,
-                memory_config=memory_config,
-                expand_communities=False,  # 路径 "2" 只需要 community 的 summary 文本，不展开到 Statement
-            )
-            # 调试：打印 community 检索结果数量
-            if raw_results and isinstance(raw_results, dict):
-                reranked = raw_results.get('reranked_results', {})
-                community_hits = reranked.get('communities', [])
-                logger.debug(f"[Input_Summary] community 命中数: {len(community_hits)}, "
-                             f"summary 命中数: {len(reranked.get('summaries', []))}")
-        else:
-            retrieval_knowledge, retrieve_info, question, raw_results = await rag_knowledge(state, data)
-    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:
-    """
-    Generate comprehensive summary from retrieved expansion issues
-    
-    Processes retrieved expansion issues and generates a detailed summary using LLM.
-    This function handles complex retrieval results and provides comprehensive answers
-    based on expanded query results.
-    
-    Args:
-        state: ReadState containing retrieve data with expansion issues
-        
-    Returns:
-        ReadState: Dictionary containing comprehensive summary results
-    """
-    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)
-
-    # Fixed coroutine call - await first, then access return value
-    summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
-    summary = summary_result[1]
-    return {"summary": summary}
-
-
-async def Summary(state: ReadState) -> ReadState:
-    """
-    Generate final comprehensive summary from verified data
-    
-    Creates the final summary using verified expansion issues and conversation history.
-    This function processes verified data to generate the most comprehensive and
-    accurate response to user queries.
-    
-    Args:
-        state: ReadState containing verified data and query information
-        
-    Returns:
-        ReadState: Dictionary containing final summary results
-    """
-    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)
-
-    # Fixed coroutine call - await first, then access return value
-    summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
-    summary = summary_result[1]
-    return {"summary": summary}
-
-
-async def Summary_fails(state: ReadState) -> ReadState:
-    """
-    Generate fallback summary when normal summary process fails
-    
-    Provides a fallback summary generation mechanism when the standard summary
-    process encounters errors or fails to produce satisfactory results. Uses
-    a specialized failure template to handle edge cases.
-    
-    Args:
-        state: ReadState containing verified data and failure context
-        
-    Returns:
-        ReadState: Dictionary containing fallback summary results
-    """
-    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

@@ -1,184 +0,0 @@
-import asyncio
-import os
-
-from app.core.logging_config import get_agent_logger
-from app.core.memory.agent.models.verification_models import VerificationResult
-from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
-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_context
-
-template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
-logger = get_agent_logger(__name__)
-
-
-class VerificationNodeService(LLMServiceMixin):
-    """
-    Verification node service class
-    
-    Handles data verification operations using LLM services. Inherits from 
-    LLMServiceMixin to provide structured LLM calling capabilities for 
-    verifying and validating retrieved information.
-    
-    Attributes:
-        template_service: Service for rendering Jinja2 templates
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.template_service = TemplateService(template_root)
-
-
-# Create global service instance
-verification_service = VerificationNodeService()
-
-
-async def Verify_prompt(state: ReadState, messages_deal: VerificationResult):
-    """
-    Process verification results and generate output format
-    
-    Transforms VerificationResult objects into structured output format suitable
-    for frontend consumption. Handles conversion of VerificationItem objects to
-    dictionary format and adds metadata for tracking.
-    
-    Args:
-        state: ReadState containing storage and user configuration
-        messages_deal: VerificationResult containing verification outcomes
-        
-    Returns:
-        dict: Formatted verification result with status and metadata
-    """
-    storage_type = state.get('storage_type', '')
-    user_rag_memory_id = state.get('user_rag_memory_id', '')
-    data = state.get('data', '')
-
-    # Convert VerificationItem objects to dictionary list
-    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")
-
-        # Generate JSON schema to guide LLM output format
-        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:
-            # Add asyncio.wait_for timeout wrapper to prevent infinite waiting
-            # Timeout set to 150 seconds (slightly longer than LLM config's 120 seconds)
-
-            with get_db_context() as db_session:
-                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 second timeout
-                )
-            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

@@ -1,67 +0,0 @@
-from app.cache.memory.interest_memory import InterestMemoryCache
-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}")
-
-        # 写入 neo4j 成功后，删除该用户的兴趣分布缓存，确保下次请求重新生成
-        for lang in ["zh", "en"]:
-            deleted = await InterestMemoryCache.delete_interest_distribution(
-                end_user_id=end_user_id,
-                language=lang,
-            )
-            if deleted:
-                logger.info(f"Invalidated interest distribution cache: end_user_id={end_user_id}, language={lang}")
-
-        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

@@ -1,86 +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
 
-from app.db import get_db
-from app.services.memory_config_service import MemoryConfigService
+logger = get_agent_logger(__name__)
 
-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,
-)
+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}
+
+
+def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]:
+    messages = state["messages"]
+
+    # 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():
+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 and return a LangGraph workflow for memory reading operations
+    Create a read graph workflow for memory operations.
     
-    Builds a state graph workflow that handles memory retrieval, problem analysis,
-    verification, and summarization. The workflow includes nodes for content input,
-    problem splitting, retrieval, verification, and various summary operations.
-    
-    Yields:
-        StateGraph: Compiled LangGraph workflow for memory reading
-        
-    Raises:
-        Exception: If workflow creation fails
+    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)
     """
-    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)
+    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,
+    )
 
-        # Add edges to define workflow flow
-        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)
+    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,
+    )
 
-        '''-----'''
-        # workflow.add_edge("Retrieve", END)
+    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,
+    )
 
-        # Compile workflow
-        graph = workflow.compile()
-        yield graph
+    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,
+    )
 
-    except Exception as e:
-        print(f"创建工作流失败: {e}")
-        raise
-    finally:
-        print("工作流创建完成")
+    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,
+    )
+
+    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

@@ -1,64 +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 = get_agent_logger(__name__)
+logger = logging.getLogger(__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

@@ -1,304 +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.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 count_store
-from app.core.memory.agent.utils.redis_tool import write_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
-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):
-    """
-    Write messages to RAG storage system
-
-    Combines user and AI messages into a single string format and stores them
-    in the RAG (Retrieval-Augmented Generation) knowledge base for future retrieval.
-
-    Args:
-        end_user_id: User identifier for the conversation
-        user_message: User's input message content
-        ai_message: AI's response message content
-        user_rag_memory_id: RAG memory identifier for storage location
-    """
-    # RAG mode: combine messages into string format (maintain original logic)
-    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=None
-):
-    """
-    Write memory with structured message support
-
-    Handles memory writing operations for different storage types (Neo4j/RAG).
-    Supports both individual message pairs and batch long-term message processing.
-
-    Args:
-        storage_type: Storage type identifier ("neo4j" or "rag")
-        end_user_id: Terminal user identifier
-        user_message: User message content
-        ai_message: AI response content
-        user_rag_memory_id: RAG memory identifier
-        actual_end_user_id: Actual user identifier for storage
-        actual_config_id: Configuration identifier
-        long_term_messages: Optional list of structured messages for batch processing
-
-    Logic explanation:
-    - RAG mode: Combines user_message and ai_message into string format, maintains original logic
-    - Neo4j mode: Uses structured message lists
-      1. If both user_message and ai_message are not empty: Creates paired messages [user, assistant]
-      2. If only user_message exists: Creates single user message [user] (for historical memory scenarios)
-      3. Each message is converted to independent Chunk, preserving speaker field
-    """
-
-    if long_term_messages is None:
-        long_term_messages = []
-    with get_db_context() as db:
-        actual_config_id = resolve_config_id(actual_config_id, db)
-        # Neo4j mode: Use structured message lists
-        structured_messages = []
-
-        # Always add user message (if not empty)
-        if isinstance(user_message, str) and user_message.strip() != "":
-            structured_messages.append({"role": "user", "content": user_message})
-
-        # Only add assistant message when AI reply is not empty
-        if isinstance(ai_message, str) and ai_message.strip() != "":
-            structured_messages.append({"role": "assistant", "content": ai_message})
-
-        # If long_term_messages provided, use it to replace 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):
-            # If it's a JSON string, parse it first
-            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}")
-
-        # If no messages, return directly
-        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: User ID
-            structured_messages,  # message: JSON string format message list
-            str(actual_config_id),  # config_id: Configuration ID string
-            storage_type,  # storage_type: "neo4j"
-            user_rag_memory_id or ""  # user_rag_memory_id: RAG memory ID (not used in Neo4j mode)
-        )
-        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}')
-
-
-async def term_memory_save(long_term_messages, actual_config_id, end_user_id, type, scope):
-    """
-    Save long-term memory data to database
-
-    Handles the storage of long-term memory data based on different strategies
-    (chunk-based or aggregate-based) and manages the transition from short-term
-    to long-term memory storage.
-
-    Args:
-        long_term_messages: Long-term message data to be saved
-        actual_config_id: Configuration identifier for memory settings
-        end_user_id: User identifier for memory association
-        type: Memory storage strategy type (STRATEGY_CHUNK or STRATEGY_AGGREGATE)
-        scope: Scope/window size for memory processing
-    """
-    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：')
-
-
-"""Window-based dialogue processing"""
-
-
-async def window_dialogue(end_user_id, langchain_messages, memory_config, scope):
-    """
-    Process dialogue based on window size and write to Neo4j
-
-    Manages conversation data based on a sliding window approach. When the window
-    reaches the specified scope size, it triggers long-term memory storage to Neo4j.
-
-    Args:
-        end_user_id: Terminal user identifier
-        memory_config: Memory configuration object containing settings
-        langchain_messages: Original message data list
-        scope: Window size determining when to trigger long-term storage
-    """
-    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)
-        # Get config_id (if memory_config is an object, extract config_id; otherwise use directly)
-        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)
-
-
-"""Time-based memory processing"""
-
-
-async def memory_long_term_storage(end_user_id, memory_config, time):
-    """
-    Process memory storage based on time intervals and write to Neo4j
-
-    Retrieves Redis data based on time intervals and writes it to Neo4j for
-    long-term storage. This function handles time-based memory consolidation.
-
-    Args:
-        end_user_id: Terminal user identifier
-        memory_config: Memory configuration object containing settings
-        time: Time interval for data retrieval
-    """
-    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)
-
-
-async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config) -> dict:
-    """
-    Aggregation judgment function: determine if input sentence and historical messages describe the same event
-
-    Uses LLM-based analysis to determine whether new messages should be aggregated with existing
-    historical data or stored as separate events. This helps optimize memory storage and retrieval.
-    
-    Args:
-        end_user_id: Terminal user identifier
-        ori_messages: Original message list, format like [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
-        memory_config: Memory configuration object containing LLM settings
-
-    Returns:
-        dict: Aggregation judgment result containing is_same_event flag and processed output
-    """
-    history = None
-    try:
-        # 1. Get historical session data (using new method)
-        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

@@ -1,405 +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):
-    """
-    Extract ToolMessage content and tool names from agent response
-
-    Parses agent response messages to extract tool execution results and metadata.
-    Handles JSON parsing and provides structured access to tool output data.
-
-    Args:
-        response: Agent response dictionary containing messages
-
-    Returns:
-        dict: Dictionary containing tool_name and parsed content, or None if no tool message found
-            - tool_name: Name of the executed tool
-            - content: Parsed tool execution result (JSON or raw text)
-    """
-    messages = response.get('messages', [])
-
-    for message in messages:
-        if hasattr(message, 'tool_call_id') and hasattr(message, 'content'):
-            # This is a ToolMessage
-            tool_content = message.content
-            tool_name = None
-
-            # Try to get tool name
-            if hasattr(message, 'name'):
-                tool_name = message.name
-            elif hasattr(message, 'tool_name'):
-                tool_name = message.tool_name
-
-            try:
-                # Parse JSON content
-                parsed_content = json.loads(tool_content)
-                return {
-                    'tool_name': tool_name,
-                    'content': parsed_content
-                }
-            except json.JSONDecodeError:
-                # If not JSON format, return content directly
-                return {
-                    'tool_name': tool_name,
-                    'content': tool_content
-                }
-
-    return None
-
-
-class TimeRetrievalInput(BaseModel):
-    """
-    Input schema for time retrieval tool
-
-    Defines the expected input parameters for time-based retrieval operations.
-    Used for validation and documentation of tool parameters.
-
-    Attributes:
-        context: User input query content for search
-        end_user_id: Group ID for filtering search results, defaults to test user
-    """
-    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):
-    """
-    Create a TimeRetrieval tool with specific end_user_id (synchronous version) for searching statements by time range
-
-    Creates a specialized time-based retrieval tool that searches for statements within
-    specified time ranges. Includes field cleaning functionality to remove unnecessary
-    metadata from search results.
-
-    Args:
-        end_user_id: User identifier for scoping search results
-
-    Returns:
-        function: Configured TimeRetrievalWithGroupId tool function
-    """
-
-    def clean_temporal_result_fields(data):
-        """
-        Clean unnecessary fields from temporal search results and modify structure
-
-        Removes metadata fields that are not needed for end-user consumption and
-        restructures the response format for better usability.
-        
-        Args:
-            data: Data to be cleaned (dict, list, or other types)
-            
-        Returns:
-            Cleaned data with unnecessary fields removed
-        """
-        # List of fields to filter out
-        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:
-                    # Change statements: {"statements": [...]} to time_search: {"statements": [...]}
-                    cleaned_value = clean_temporal_result_fields(value)
-                    # Further change internal statements to 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:
-        """
-        Optimized time retrieval tool, combines time range search only (synchronous version), automatically filters unnecessary metadata fields
-
-        Performs time-based search operations with automatic metadata filtering. Supports
-        flexible date range specification and provides clean, user-friendly output.
-
-        Explicit parameters:
-        - context: Query context content
-        - start_date: Start time (optional, format: YYYY-MM-DD)
-        - end_date: End time (optional, format: YYYY-MM-DD)
-        - end_user_id_param: Group ID (optional, overrides default group ID)
-        - clean_output: Whether to clean metadata fields from output
-        - end_date needs to be obtained based on user description, output format uses strftime("%Y-%m-%d")
-
-        Returns:
-            str: JSON formatted search results with temporal data
-        """
-
-        async def _async_search():
-            # Use passed parameters or default values
-            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")
-            
-            # Basic time search
-            results = await search_by_temporal(
-                end_user_id=actual_end_user_id,
-                start_date=actual_start_date,
-                end_date=actual_end_date,
-                limit=10
-            )
-            
-            # Clean unnecessary fields from results
-            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:
-        """
-        Optimized keyword time retrieval tool, combines keyword and time range search (synchronous version), automatically filters unnecessary metadata fields
-
-        Performs combined keyword and temporal search operations with automatic metadata
-        filtering. Provides more targeted search results by combining content relevance
-        with time-based filtering.
-
-        Explicit parameters:
-        - context: Query content for keyword matching
-        - days_back: Number of days to search backwards, default 7 days
-        - start_date: Start time (optional, format: YYYY-MM-DD)
-        - end_date: End time (optional, format: YYYY-MM-DD)
-        - clean_output: Whether to clean metadata fields from output
-        - end_date needs to be obtained based on user description, output format uses strftime("%Y-%m-%d")
-
-        Returns:
-            str: JSON formatted search results combining keyword and temporal data
-        """
-
-        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")
-
-            # Keyword time search
-            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
-            )
-            
-            # Clean unnecessary fields from results
-            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):
-    """
-    Create hybrid retrieval tool using run_hybrid_search for hybrid retrieval, optimize output format and filter unnecessary fields
-
-    Creates an advanced hybrid search tool that combines multiple search strategies
-    (keyword, vector, hybrid) with automatic result cleaning and formatting.
-    
-    Args:
-        memory_config: Memory configuration object containing LLM and search settings
-        **search_params: Search parameters including end_user_id, limit, include, etc.
-
-    Returns:
-        function: Configured HybridSearch tool function with async capabilities
-    """
-
-    def clean_result_fields(data):
-        """
-        Recursively clean unnecessary fields from results
-
-        Removes metadata fields that are not needed for end-user consumption,
-        improving readability and reducing response size.
-        
-        Args:
-            data: Data to be cleaned (can be dict, list, or other types)
-            
-        Returns:
-            Cleaned data with unnecessary fields removed
-        """
-        # List of fields to filter out
-        # TODO: fact_summary functionality temporarily disabled, will be enabled after future development
-        fields_to_remove = {
-            'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids',
-            'expired_at', 'created_at', 'chunk_id', 'apply_id',
-            'user_id', 'statement_ids', 'updated_at', "chunk_ids", "fact_summary"
-        }
-        # 注意：'id' 字段保留，community 展开时需要用 community id 查询成员 statements
-
-        if isinstance(data, dict):
-            # Clean dictionary
-            cleaned = {}
-            for key, value in data.items():
-                if key not in fields_to_remove:
-                    cleaned[key] = clean_result_fields(value)  # Recursively clean nested data
-            return cleaned
-        elif isinstance(data, list):
-            # Clean each element in list
-            return [clean_result_fields(item) for item in data]
-        else:
-            # Return other types directly
-            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  # New: whether to clean output fields
-    ) -> str:
-        """
-        Optimized hybrid retrieval tool, supports keyword, vector and hybrid search, automatically filters unnecessary metadata fields
-
-        Provides comprehensive search capabilities combining multiple search strategies
-        with intelligent result ranking and automatic metadata filtering for clean output.
-        
-        Args:
-            context: Query content for search
-            search_type: Search type ('keyword', 'embedding', 'hybrid')
-            limit: Result quantity limit
-            end_user_id: Group ID for filtering search results
-            rerank_alpha: Reranking weight parameter for result scoring
-            use_forgetting_rerank: Whether to use forgetting-based reranking
-            use_llm_rerank: Whether to use LLM-based reranking
-            clean_output: Whether to clean metadata fields from output
-
-        Returns:
-            str: JSON formatted comprehensive search results
-        """
-        try:
-            # Import run_hybrid_search function
-            from app.core.memory.src.search import run_hybrid_search
-            
-            # Merge parameters, prioritize passed parameters
-            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", "communities"]),
-                "output_path": None,  # Don't save to file
-                "memory_config": memory_config,
-                "rerank_alpha": rerank_alpha,
-                "use_forgetting_rerank": use_forgetting_rerank,
-                "use_llm_rerank": use_llm_rerank
-            }
-            
-            # Execute hybrid retrieval
-            raw_results = await run_hybrid_search(**final_params)
-            
-            # Clean unnecessary fields from results
-            if clean_output:
-                cleaned_results = clean_result_fields(raw_results)
-            else:
-                cleaned_results = raw_results
-            
-            # Format return results
-            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):
-    """
-    Create synchronous version of hybrid retrieval tool, optimize output format and filter unnecessary fields
-
-    Creates a synchronous wrapper around the async hybrid search functionality,
-    making it compatible with synchronous tool execution environments.
-    
-    Args:
-        memory_config: Memory configuration object containing search settings
-        **search_params: Search parameters for configuration
-
-    Returns:
-        function: Configured HybridSearchSync tool function
-    """
-
-    @tool
-    def HybridSearchSync(
-            context: str,
-            search_type: str = "hybrid",
-            limit: int = 10,
-            end_user_id: str = None,
-            clean_output: bool = True
-    ) -> str:
-        """
-        Optimized hybrid retrieval tool (synchronous version), automatically filters unnecessary metadata fields
-
-        Provides the same hybrid search capabilities as the async version but in a
-        synchronous execution context. Automatically handles async-to-sync conversion.
-        
-        Args:
-            context: Query content for search
-            search_type: Search type ('keyword', 'embedding', 'hybrid')
-            limit: Result quantity limit
-            end_user_id: Group ID for filtering search results
-            clean_output: Whether to clean metadata fields from output
-
-        Returns:
-            str: JSON formatted search results
-        """
-
-        async def _async_search():
-            # Create async tool and execute
-            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

@@ -1,106 +0,0 @@
-import json
-
-from langchain_core.messages import HumanMessage, AIMessage
-
-
-async def format_parsing(messages: list, type: str = 'string'):
-    """
-    Format and parse message lists into different output types
-
-    Processes message lists from storage and converts them into either string format
-    or dictionary format based on the specified type parameter. Handles JSON parsing
-    and role-based message organization.
-    
-    Args:
-        messages: List of message objects from storage containing message data
-        type: Return type specification ('string' for text format, 'dict' for key-value pairs)
-        
-    Returns:
-        list: Formatted message list in the specified format
-            - 'string': List of formatted text messages with role prefixes
-            - 'dict': List of dictionaries mapping user messages to AI responses
-    """
-    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):
-    """
-    Parse messages from storage format into user-AI conversation pairs
-
-    Extracts and organizes conversation data from stored message format,
-    separating user and AI messages and pairing them for database storage.
-
-    Args:
-        messages: List or dictionary containing stored message data with Query fields
-
-    Returns:
-        list: List of dictionaries containing user-AI message pairs for database storage
-    """
-    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):
-    """
-    Create structured chat message format for agent conversations
-
-    Formats user and AI content into a standardized message structure suitable
-    for agent processing and storage. Creates role-based message objects.
-
-    Args:
-        user_content: User's message content string
-        ai_content: AI's response content string
-
-    Returns:
-        list: List of structured message dictionaries with role and content fields
-    """
-    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

@@ -3,17 +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"):
@@ -21,112 +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):
-    """
-    Handle long-term memory storage with different strategies
-
-    Supports multiple storage strategies including chunk-based, time-based,
-    and aggregate judgment approaches for long-term memory persistence.
-
-    Args:
-        long_term_type: Storage strategy type ('chunk', 'time', 'aggregate')
-        langchain_messages: List of messages to store
-        memory_config: Memory configuration identifier
-        end_user_id: User group identifier
-        scope: Scope parameter for chunk-based storage (default: 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 == AgentMemory_Long_Term.STRATEGY_CHUNK:
-            '''Strategy 1: Dialogue window with 6 rounds of conversation'''
-            await window_dialogue(end_user_id, langchain_messages, memory_config, scope)
-        if long_term_type == AgentMemory_Long_Term.STRATEGY_TIME:
-            """Time-based strategy"""
-            await memory_long_term_storage(end_user_id, memory_config, AgentMemory_Long_Term.TIME_SCOPE)
-        if long_term_type == AgentMemory_Long_Term.STRATEGY_AGGREGATE:
-            """Strategy 3: Aggregate judgment"""
-            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):
-    """
-    Write long-term memory with different storage types
-
-    Handles both RAG-based storage and traditional memory storage approaches.
-    For traditional storage, uses chunk-based strategy with paired user-AI messages.
-
-    Args:
-        storage_type: Type of storage (RAG or traditional)
-        end_user_id: User group identifier
-        message_chat: User message content
-        aimessages: AI response messages
-        user_rag_memory_id: RAG memory identifier
-        actual_config_id: Actual configuration 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 reply writing (user messages and AI replies paired, written as complete dialogue at once)
-        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

@@ -0,0 +1,216 @@
+"""
+Search Service for executing hybrid search and processing results.
+
+This service provides clean search result processing with content extraction
+and deduplication.
+"""
+
+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, 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:
+        """
+        Extract only meaningful content from search results, dropping all metadata.
+        
+        Extraction rules by node type:
+        - Statements: extract 'statement' field
+        - Entities: extract 'name' and 'fact_summary' fields
+        - Summaries: extract 'content' field
+        - Chunks: extract 'content' field
+        
+        Args:
+            result: Search result dictionary
+            
+        Returns:
+            Clean content string without metadata
+        """
+        if not isinstance(result, dict):
+            return str(result)
+        
+        content_parts = []
+        
+        # Statements: extract statement field
+        if 'statement' in result and result['statement']:
+            content_parts.append(result['statement'])
+        
+        # Summaries/Chunks: extract content field
+        if 'content' in result and result['content']:
+            content_parts.append(result['content'])
+        
+        # Entities: extract name and fact_summary (commented out in original)
+        # if 'name' in result and result['name']:
+        #     content_parts.append(result['name'])
+        #     if result.get('fact_summary'):
+        #         content_parts.append(result['fact_summary'])
+        
+        # Return concatenated content or empty string
+        return '\n'.join(content_parts) if content_parts else ""
+    
+    def clean_query(self, query: str) -> str:
+        """
+        Clean and escape query text for Lucene.
+        
+        - Removes wrapping quotes
+        - Removes newlines and carriage returns
+        - Applies Lucene escaping
+        
+        Args:
+            query: Raw query string
+            
+        Returns:
+            Cleaned and escaped query string
+        """
+        q = str(query).strip()
+        
+        # Remove wrapping quotes
+        if (q.startswith("'") and q.endswith("'")) or (
+            q.startswith('"') and q.endswith('"')
+        ):
+            q = q[1:-1]
+        
+        # Remove newlines and carriage returns
+        q = q.replace('\r', ' ').replace('\n', ' ').strip()
+        
+        # Apply Lucene escaping
+        q = escape_lucene_query(q)
+        
+        return q
+    
+    async def execute_hybrid_search(
+        self,
+        group_id: str,
+        question: str,
+        limit: int = 15,
+        search_type: str = "hybrid",
+        include: Optional[List[str]] = None,
+        rerank_alpha: float = 0.6,
+        activation_boost_factor: float = 0.8,
+        output_path: str = "search_results.json",
+        return_raw_results: bool = False,
+        memory_config: "MemoryConfig" = None,
+    ) -> Tuple[str, str, Optional[dict]]:
+        """
+        Execute hybrid search with two-stage ranking.
+        
+        Stage 1: Filter by content relevance (BM25 + Embedding)
+        Stage 2: Rerank by activation values (ACTR)
+        
+        Args:
+            group_id: Group identifier for filtering
+            question: Search query text
+            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[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 using memory_config
+            answer = await run_hybrid_search(
+                query_text=cleaned_query,
+                search_type=search_type,
+                group_id=group_id,
+                limit=limit,
+                include=include,
+                output_path=output_path,
+                memory_config=config,
+                rerank_alpha=rerank_alpha,
+                activation_boost_factor=activation_boost_factor,
+            )
+            
+            # Extract results based on search type and include parameter
+            # Prioritize summaries as they contain synthesized contextual information
+            answer_list = []
+            
+            # For hybrid search, use reranked_results
+            if search_type == "hybrid":
+                reranked_results = answer.get('reranked_results', {})
+                
+                # Priority order: summaries first (most contextual), then statements, chunks, entities
+                priority_order = ['summaries', 'statements', 'chunks', 'entities']
+                
+                for category in priority_order:
+                    if category in include and category in reranked_results:
+                        category_results = reranked_results[category]
+                        if isinstance(category_results, list):
+                            answer_list.extend(category_results)
+            else:
+                # For keyword or embedding search, results are directly in answer dict
+                # Apply same priority order
+                priority_order = ['summaries', 'statements', 'chunks', 'entities']
+                
+                for category in priority_order:
+                    if category in include and category in answer:
+                        category_results = answer[category]
+                        if isinstance(category_results, list):
+                            answer_list.extend(category_results)
+            
+            # Extract clean content from all results
+            content_list = [
+                self.extract_content_from_result(ans) 
+                for ans in answer_list
+            ]
+
+            
+            # Filter out empty strings and join with newlines
+            clean_content = '\n'.join([c for c in content_list if c])
+            
+            # Log first 200 chars
+            logger.info(f"检索接口搜索结果==>>:{clean_content[:200]}...")
+            
+            # Return raw results if requested
+            if return_raw_results:
+                return clean_content, cleaned_query, answer
+            else:
+                return clean_content, cleaned_query, None
+            
+        except Exception as e:
+            logger.error(
+                f"Search failed for query '{question}' in group '{group_id}': {e}",
+                exc_info=True
+            )
+            # Return empty results on failure
+            if return_raw_results:
+                return "", cleaned_query, {}
+            else:
+                return "", cleaned_query, None

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)