Merge pull request #1033 from SuanmoSuanyangTechnology/release/v0.3.2

Release/v0.3.2

.github/workflows/release-notify-wechat.yml

@@ -0,0 +1,164 @@
+name: Release Notify Workflow
+
+on:
+  pull_request:
+    types: [closed]
+
+jobs:
+  notify:
+    if: >
+      github.event.pull_request.merged == true &&
+      startsWith(github.event.pull_request.base.ref, 'release')
+    runs-on: ubuntu-latest
+
+    steps:
+      # 防止 GitHub HEAD 未同步
+      - run: sleep 3
+
+      # 1️⃣ 获取分支 HEAD
+      - name: Get HEAD
+        id: head
+        run: |
+          HEAD_SHA=$(curl -s \
+            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
+            https://api.github.com/repos/${{ github.repository }}/git/ref/heads/${{ github.event.pull_request.base.ref }} \
+            | jq -r '.object.sha')
+          echo "head_sha=$HEAD_SHA" >> $GITHUB_OUTPUT
+
+      # 2️⃣ 判断是否最终PR
+      - name: Check Latest
+        id: check
+        run: |
+          if [ "${{ github.event.pull_request.merge_commit_sha }}" = "${{ steps.head.outputs.head_sha }}" ]; then
+            echo "ok=true" >> $GITHUB_OUTPUT
+          else
+            echo "ok=false" >> $GITHUB_OUTPUT
+          fi
+
+      # 3️⃣ 尝试从 PR body 提取 Sourcery 摘要
+      - name: Extract Sourcery Summary
+        if: steps.check.outputs.ok == 'true'
+        id: sourcery
+        env:
+          PR_BODY: ${{ github.event.pull_request.body }}
+        run: |
+          python3 << 'PYEOF'
+          import os, re
+
+          body = os.environ.get("PR_BODY", "") or ""
+          match = re.search(
+              r"## Summary by Sourcery\s*\n(.*?)(?=\n## |\Z)",
+              body,
+              re.DOTALL
+          )
+
+          if match:
+              summary = match.group(1).strip()
+              found = "true"
+          else:
+              summary = ""
+              found = "false"
+
+          with open("sourcery_summary.txt", "w", encoding="utf-8") as f:
+              f.write(summary)
+
+          with open(os.environ["GITHUB_OUTPUT"], "a") as gh:
+              gh.write(f"found={found}\n")
+              gh.write("summary<<EOF\n")
+              gh.write(summary + "\n")
+              gh.write("EOF\n")
+          PYEOF
+
+      # 4️⃣ Fallback: 获取 commits + 通义千问总结
+      - name: Get Commits
+        if: steps.check.outputs.ok == 'true' && steps.sourcery.outputs.found == 'false'
+        run: |
+          curl -s \
+            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
+            ${{ github.event.pull_request.commits_url }} \
+            | jq -r '.[].commit.message' | head -n 20 > commits.txt
+
+      - name: AI Summary (Qwen Fallback)
+        if: steps.check.outputs.ok == 'true' && steps.sourcery.outputs.found == 'false'
+        id: qwen
+        env:
+          DASHSCOPE_API_KEY: ${{ secrets.DASHSCOPE_API_KEY }}
+        run: |
+          python3 << 'PYEOF'
+          import json, os, urllib.request
+
+          with open("commits.txt", "r") as f:
+              commits = f.read().strip()
+
+          prompt = "请用中文总结以下代码提交，输出3-5条要点，面向测试人员。直接输出编号列表，不要输出标题或前言：\n" + commits
+          payload = {"model": "qwen-plus", "input": {"prompt": prompt}}
+          data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
+
+          req = urllib.request.Request(
+              "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation",
+              data=data,
+              headers={
+                  "Authorization": "Bearer " + os.environ["DASHSCOPE_API_KEY"],
+                  "Content-Type": "application/json"
+              }
+          )
+          resp = urllib.request.urlopen(req)
+          result = json.loads(resp.read().decode())
+          summary = result.get("output", {}).get("text", "AI 摘要生成失败")
+
+          with open(os.environ["GITHUB_OUTPUT"], "a") as gh:
+              gh.write("summary<<EOF\n")
+              gh.write(summary + "\n")
+              gh.write("EOF\n")
+          PYEOF
+
+      # 5️⃣ 企业微信通知（Markdown）
+      - name: Notify WeChat
+        if: steps.check.outputs.ok == 'true'
+        env:
+          WECHAT_WEBHOOK: ${{ secrets.WECHAT_WEBHOOK }}
+          BRANCH: ${{ github.event.pull_request.base.ref }}
+          AUTHOR: ${{ github.event.pull_request.user.login }}
+          PR_TITLE: ${{ github.event.pull_request.title }}
+          PR_URL: ${{ github.event.pull_request.html_url }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+          MERGE_SHA: ${{ github.event.pull_request.merge_commit_sha }}
+          SOURCERY_FOUND: ${{ steps.sourcery.outputs.found }}
+          SOURCERY_SUMMARY: ${{ steps.sourcery.outputs.summary }}
+          QWEN_SUMMARY: ${{ steps.qwen.outputs.summary }}
+        run: |
+          python3 << 'PYEOF'
+          import json, os, urllib.request
+
+          if os.environ.get("SOURCERY_FOUND") == "true":
+              label = "Summary by Sourcery"
+              summary = os.environ.get("SOURCERY_SUMMARY", "")
+          else:
+              label = "AI变更摘要"
+              summary = os.environ.get("QWEN_SUMMARY", "AI 摘要生成失败")
+
+          pr_number = os.environ.get("PR_NUMBER", "")
+          short_sha = os.environ.get("MERGE_SHA", "")[:7]
+
+          content = (
+            "## 🚀 Release 发布通知\n"
+            "> <20> **分支**: " + os.environ["BRANCH"] + "\n"
+            "> 👤 **提交人**: " + os.environ["AUTHOR"] + "\n"
+            "> 📝 **标题**: " + os.environ["PR_TITLE"] + "\n"
+            "> 🔢 **PR编号**: #" + pr_number + "\n"
+            "> 🔖 **Commit**: " + short_sha + "\n\n"
+            "### 🧠 " + label + "\n" +
+            summary + "\n\n"
+            "---\n"
+            "🔗 [查看PR详情](" + os.environ["PR_URL"] + ")"
+          )
+          payload = {"msgtype": "markdown", "markdown": {"content": content}}
+          data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
+          req = urllib.request.Request(
+            os.environ["WECHAT_WEBHOOK"],
+            data=data,
+            headers={"Content-Type": "application/json"}
+          )
+          resp = urllib.request.urlopen(req)
+          print(resp.read().decode())
+          PYEOF

.github/workflows/sync-to-gitee.yml

@@ -0,0 +1,33 @@
+name: Sync to Gitee
+
+on:
+  push:
+    branches:
+      - '**' # All branchs
+    tags:
+      - '**'      # All version tags (v1.0.0, etc.)
+
+jobs:
+  sync:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout Source Code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Sync to Gitee
+        run: |
+          GITEE_URL="https://${{ secrets.GITEE_USERNAME }}:${{ secrets.GITEE_TOKEN }}@gitee.com/hangzhou-hongxiong-intelligent_1/MemoryBear.git"
+          git remote add gitee "$GITEE_URL"
+
+          # 遍历并推送所有分支
+          for branch in $(git branch -r | grep -v HEAD | sed 's/origin\///'); do
+            echo "Syncing branch: $branch"
+            git push -f gitee "origin/$branch:refs/heads/$branch"
+          done
+
+          # 推送所有标签
+          echo "Syncing tags..."
+          git push gitee --tags --force

.gitignore

@@ -18,16 +18,22 @@ examples/
 .kiro
 .vscode
 .idea
+.claude
 
 # Temporary outputs
 .DS_Store
+.hypothesis/
 time.log
 celerybeat-schedule.db
 search_results.json
+redbear-mem-metrics/
+redbear-mem-benchmark/
+pitch-deck/
 
 api/migrations/versions
 tmp
 files
+powers/ 
 
 # Exclude dep files
 huggingface.co/
@@ -36,5 +42,4 @@ tika-server*.jar*
 cl100k_base.tiktoken
 libssl*.deb
 
-sandbox/lib/seccomp_python/target
-sandbox/lib/seccomp_nodejs/target
+sandbox/lib/seccomp_redbear/target

README.md

@@ -2,6 +2,10 @@
 
 # MemoryBear empowers AI with human-like memory capabilities
 
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
+[![Python](https://img.shields.io/badge/Python-3.12+-green?logo=python&logoColor=white)](https://www.python.org/)
+[![Gitee Sync](https://img.shields.io/github/actions/workflow/status/SuanmoSuanyangTechnology/MemoryBear/sync-to-gitee.yml?label=Gitee%20Sync&logo=gitee&logoColor=white)](https://github.com/SuanmoSuanyangTechnology/MemoryBear/actions/workflows/sync-to-gitee.yml)
+
 [中文](./README_CN.md) | English
 
 ### [Installation Guide](#memorybear-installation-guide)
@@ -226,8 +230,8 @@ REDIS_PORT=6379
 REDIS_DB=1 
 
 # Celery (Using Redis as broker)
-BROKER_URL=redis://127.0.0.1:6379/0
-RESULT_BACKEND=redis://127.0.0.1:6379/0
+REDIS_DB_CELERY_BROKER=1
+REDIS_DB_CELERY_BACKEND=2
 
 # JWT Secret Key (Formation method: openssl rand -hex 32)
 SECRET_KEY=your-secret-key-here

README_CN.md

@@ -2,6 +2,10 @@
 
 # MemoryBear 让AI拥有如同人类一样的记忆
 
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
+[![Python](https://img.shields.io/badge/Python-3.12+-green?logo=python&logoColor=white)](https://www.python.org/)
+[![Gitee Sync](https://img.shields.io/github/actions/workflow/status/SuanmoSuanyangTechnology/MemoryBear/sync-to-gitee.yml?label=Gitee%20Sync&logo=gitee&logoColor=white)](https://github.com/SuanmoSuanyangTechnology/MemoryBear/actions/workflows/sync-to-gitee.yml)
+
 中文 | [English](./README.md)
 
 ### [安装教程](#memorybear安装教程)
@@ -201,8 +205,8 @@ REDIS_PORT=6379
 REDIS_DB=1 
 
 # Celery (使用Redis作为broker)
-BROKER_URL=redis://127.0.0.1:6379/0
-RESULT_BACKEND=redis://127.0.0.1:6379/0
+REDIS_DB_CELERY_BROKER=1
+REDIS_DB_CELERY_BACKEND=2
 
 # JWT密钥 (生成方式: openssl rand -hex 32)
 SECRET_KEY=your-secret-key-here

api/Dockerfile

@@ -45,7 +45,8 @@ 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 ghostscript && \
+    apt install -y libmagic1
 
 RUN if [ "$NEED_MIRROR" == "1" ]; then \
         pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple && \

api/alembic.ini

@@ -60,7 +60,12 @@ version_path_separator = os  # Use os.pathsep. Default configuration used for ne
 # are written from script.py.mako
 # output_encoding = utf-8
 
-sqlalchemy.url = postgresql://user:password@localhost/dbname
+# 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
 
 
 [post_write_hooks]

api/app/aioRedis.py

@@ -1,16 +1,18 @@
-import os
 import asyncio
 import json
 import logging
+import os
+import threading
 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}",
@@ -21,6 +23,51 @@ pool = ConnectionPool.from_url(
 )
 aio_redis = redis.StrictRedis(connection_pool=pool)
 
+_REDIS_URL = f"redis://{settings.REDIS_HOST}:{settings.REDIS_PORT}"
+
+# Thread-local storage for connection pools.
+# Each thread (and each forked process) gets its own pool to avoid
+# "Future attached to a different loop" errors in Celery --pool=threads
+# and stale connections after fork in --pool=prefork.
+_thread_local = threading.local()
+
+
+def get_thread_safe_redis() -> redis.StrictRedis:
+    """Return a Redis client whose connection pool is bound to the current
+    thread, process **and** event loop.
+
+    The pool is recreated when:
+    - The PID changes (fork, Celery --pool=prefork)
+    - The thread has no pool yet (Celery --pool=threads)
+    - The previously-cached event loop has been closed (Celery tasks call
+      ``_shutdown_loop_gracefully`` which closes the loop after each run)
+    """
+    current_pid = os.getpid()
+    cached_loop = getattr(_thread_local, "loop", None)
+    loop_stale = cached_loop is not None and cached_loop.is_closed()
+
+    if not hasattr(_thread_local, "pool") \
+            or getattr(_thread_local, "pid", None) != current_pid \
+            or loop_stale:
+        _thread_local.pid = current_pid
+        # Python 3.10+: get_event_loop() raises RuntimeError in threads
+        # where no loop has been set yet (e.g. Celery --pool=threads).
+        try:
+            _thread_local.loop = asyncio.get_event_loop()
+        except RuntimeError:
+            _thread_local.loop = None
+        _thread_local.pool = ConnectionPool.from_url(
+            _REDIS_URL,
+            db=settings.REDIS_DB,
+            password=settings.REDIS_PASSWORD,
+            decode_responses=True,
+            max_connections=5,
+            health_check_interval=30,
+        )
+
+    return redis.StrictRedis(connection_pool=_thread_local.pool)
+
+
 async def get_redis_connection():
     """获取Redis连接"""
     try:
@@ -29,7 +76,8 @@ 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:
@@ -40,16 +88,15 @@ 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)
         else:
-            # 设置永久键值
             await aio_redis.set(key, val)
     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:
@@ -58,6 +105,7 @@ 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:
@@ -66,6 +114,7 @@ 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:
@@ -78,9 +127,10 @@ 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
@@ -88,25 +138,25 @@ 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)
@@ -127,7 +177,7 @@ class RedisSubscriber:
         finally:
             await self._queue.put(None)
             await self._cleanup()
-    
+
     async def _cleanup(self):
         """清理资源"""
         if self.pubsub:
@@ -141,7 +191,7 @@ 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:
@@ -153,7 +203,7 @@ 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:
@@ -163,32 +213,33 @@ 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():
@@ -196,6 +247,6 @@ class RedisPubSubManager:
         self.subscribers.clear()
         return count
 
+
 # 全局实例
 pubsub_manager = RedisPubSubManager()
-

api/app/cache/__init__.py

@@ -3,9 +3,8 @@ Cache 缓存模块
 
 提供各种缓存功能的统一入口
 """
-from .memory import EmotionMemoryCache, ImplicitMemoryCache
+from .memory import InterestMemoryCache
 
 __all__ = [
-    "EmotionMemoryCache",
-    "ImplicitMemoryCache",
+    "InterestMemoryCache",
 ]

api/app/cache/memory/__init__.py

@@ -3,10 +3,10 @@ Memory 缓存模块
 
 提供记忆系统相关的缓存功能
 """
-from .emotion_memory import EmotionMemoryCache
-from .implicit_memory import ImplicitMemoryCache
+from .interest_memory import InterestMemoryCache
+from .activity_stats_cache import ActivityStatsCache
 
 __all__ = [
-    "EmotionMemoryCache",
-    "ImplicitMemoryCache",
+    "InterestMemoryCache",
+    "ActivityStatsCache",
 ]

api/app/cache/memory/activity_stats_cache.py

@@ -0,0 +1,124 @@
+"""
+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 get_thread_safe_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 get_thread_safe_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 get_thread_safe_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 get_thread_safe_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/emotion_memory.py

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

api/app/cache/memory/implicit_memory.py

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

api/app/cache/memory/interest_memory.py

@@ -0,0 +1,122 @@
+"""
+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 get_thread_safe_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 get_thread_safe_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 get_thread_safe_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 get_thread_safe_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

@@ -1,20 +1,59 @@
 import os
 import platform
+import re
 from datetime import timedelta
 from urllib.parse import quote
 
-from app.core.config import settings
 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__)
+
+
+def _mask_url(url: str) -> str:
+    """隐藏 URL 中的密码部分，适用于 redis:// 和 amqp:// 等协议"""
+    return re.sub(r'(://[^:]*:)[^@]+(@)', r'\1***\2', url)
+
+
+# macOS fork() safety - must be set before any Celery initialization
+if platform.system() == 'Darwin':
+    os.environ.setdefault('OBJC_DISABLE_INITIALIZE_FORK_SAFETY', 'YES')
 
 # 创建 Celery 应用实例
-# broker: 任务队列（使用 Redis DB 0）
-# backend: 结果存储（使用 Redis DB 10）
+# broker: 优先使用环境变量 CELERY_BROKER_URL（支持 amqp:// 等任意协议），
+#         未配置则回退到 Redis 方案
+# backend: 结果存储（使用 Redis）
+# 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
+
+_broker_url = os.getenv("CELERY_BROKER_URL") or \
+              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)
+
 celery_app = Celery(
     "redbear_tasks",
-    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}",
+    broker=_broker_url,
+    backend=_backend_url,
 )
 
+logger.info(
+    "Celery app initialized",
+    extra={
+        "broker": _mask_url(_broker_url),
+        "backend": _mask_url(_backend_url),
+    },
+)
 # Default queue for unrouted tasks
 celery_app.conf.task_default_queue = 'memory_tasks'
 
@@ -28,50 +67,70 @@ celery_app.conf.update(
     task_serializer='json',
     accept_content=['json'],
     result_serializer='json',
-    
-    # 时区
-    timezone='Asia/Shanghai',
-    enable_utc=True,
-    
+
+    # # 时区
+    # timezone='Asia/Shanghai',
+    # enable_utc=False,
+
     # 任务追踪
     task_track_started=True,
     task_ignore_result=False,
-    
+
     # 超时设置
-    task_time_limit=1800,  # 30分钟硬超时
-    task_soft_time_limit=1500,  # 25分钟软超时
-    
+    task_time_limit=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
-    
+    worker_redirect_stdouts_level='INFO',  # stdout/print → INFO instead of WARNING
+
     # 结果过期时间
     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'},
-        
+
+        # 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'},
+
+        # Clustering tasks → memory_tasks queue (使用相同的 worker，避免 macOS fork 问题)
+        'app.tasks.run_incremental_clustering': {'queue': 'memory_tasks'},
+
+        # Metadata extraction → memory_tasks queue
+        'app.tasks.extract_user_metadata': {'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'},
-        
-        # Beat/periodic tasks → document_tasks queue (prefork worker)
-        'app.tasks.workspace_reflection_task': {'queue': 'document_tasks'},
-        'app.tasks.regenerate_memory_cache': {'queue': 'document_tasks'},
-        'app.tasks.run_forgetting_cycle_task': {'queue': 'document_tasks'},
-        'app.controllers.memory_storage_controller.search_all': {'queue': 'document_tasks'},
+        'app.core.rag.tasks.sync_knowledge_for_kb': {'queue': 'document_tasks'},
+
+        # GraphRAG tasks → graphrag_tasks queue (独立队列，避免阻塞文档解析)
+        'app.core.rag.tasks.build_graphrag_for_kb': {'queue': 'graphrag_tasks'},
+        'app.core.rag.tasks.build_graphrag_for_document': {'queue': 'graphrag_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'},
     },
 )
 
@@ -79,10 +138,14 @@ celery_app.conf.update(
 celery_app.autodiscover_tasks(['app'])
 
 # Celery Beat schedule for periodic tasks
-memory_increment_schedule = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS)
+memory_increment_schedule = crontab(hour=settings.MEMORY_INCREMENT_HOUR, minute=settings.MEMORY_INCREMENT_MINUTE)
 memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS)
-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
+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,
+)
 
 # 构建定时任务配置
 beat_schedule_config = {
@@ -103,16 +166,16 @@ 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_task_scheduler.py

@@ -0,0 +1,500 @@
+import hashlib
+import json
+import os
+import socket
+import threading
+import time
+import uuid
+
+import redis
+
+from app.core.config import settings
+from app.core.logging_config import get_named_logger
+from app.celery_app import celery_app
+
+logger = get_named_logger("task_scheduler")
+
+# per-user queue scheduler:uq:{user_id}
+USER_QUEUE_PREFIX = "scheduler:uq:"
+# User Collection of Pending Messages
+ACTIVE_USERS = "scheduler:active_users"
+# Set of users that can dispatch (ready signal)
+READY_SET = "scheduler:ready_users"
+# Metadata of tasks that have been dispatched and are pending completion
+PENDING_HASH = "scheduler:pending_tasks"
+# Dynamic Sharding: Instance Registry
+REGISTRY_KEY = "scheduler:instances"
+
+TASK_TIMEOUT = 7800  # Task timeout (seconds), considered lost if exceeded
+HEARTBEAT_INTERVAL = 10  # Heartbeat interval (seconds)
+INSTANCE_TTL = 30  # Instance timeout (seconds)
+
+LUA_ATOMIC_LOCK = """
+local dispatch_lock = KEYS[1]
+local lock_key = KEYS[2]
+local instance_id = ARGV[1]
+local dispatch_ttl = tonumber(ARGV[2])
+local lock_ttl = tonumber(ARGV[3])
+
+if redis.call('SET', dispatch_lock, instance_id, 'NX', 'EX', dispatch_ttl) == false then
+    return 0
+end
+
+if redis.call('EXISTS', lock_key) == 1 then
+    redis.call('DEL', dispatch_lock)
+    return -1
+end
+
+redis.call('SET', lock_key, 'dispatching', 'EX', lock_ttl)
+return 1
+"""
+
+LUA_SAFE_DELETE = """
+if redis.call('GET', KEYS[1]) == ARGV[1] then
+    return redis.call('DEL', KEYS[1])
+end
+return 0
+"""
+
+
+def stable_hash(value: str) -> int:
+    return int.from_bytes(
+        hashlib.md5(value.encode("utf-8")).digest(),
+        "big"
+    )
+
+
+def health_check_server(scheduler_ref):
+    import uvicorn
+    from fastapi import FastAPI
+
+    health_app = FastAPI()
+
+    @health_app.get("/")
+    def health():
+        return scheduler_ref.health()
+
+    port = int(os.environ.get("SCHEDULER_HEALTH_PORT", "8001"))
+    threading.Thread(
+        target=uvicorn.run,
+        kwargs={
+            "app": health_app,
+            "host": "0.0.0.0",
+            "port": port,
+            "log_config": None,
+        },
+        daemon=True,
+    ).start()
+    logger.info("[Health] Server started at http://0.0.0.0:%s", port)
+
+
+class RedisTaskScheduler:
+    def __init__(self):
+        self.redis = redis.Redis(
+            host=settings.REDIS_HOST,
+            port=settings.REDIS_PORT,
+            db=settings.REDIS_DB_CELERY_BACKEND,
+            password=settings.REDIS_PASSWORD,
+            decode_responses=True,
+        )
+        self.running = False
+        self.dispatched = 0
+        self.errors = 0
+
+        self.instance_id = f"{socket.gethostname()}-{os.getpid()}"
+        self._shard_index = 0
+        self._shard_count = 1
+        self._last_heartbeat = 0.0
+
+    def push_task(self, task_name, user_id, params):
+        try:
+            msg_id = str(uuid.uuid4())
+            msg = json.dumps({
+                "msg_id": msg_id,
+                "task_name": task_name,
+                "user_id": user_id,
+                "params": json.dumps(params),
+            })
+
+            lock_key = f"{task_name}:{user_id}"
+            queue_key = f"{USER_QUEUE_PREFIX}{user_id}"
+
+            pipe = self.redis.pipeline()
+            pipe.rpush(queue_key, msg)
+            pipe.sadd(ACTIVE_USERS, user_id)
+            pipe.set(
+                f"task_tracker:{msg_id}",
+                json.dumps({"status": "QUEUED", "task_id": None}),
+                ex=86400,
+            )
+            pipe.execute()
+
+            if not self.redis.exists(lock_key):
+                self.redis.sadd(READY_SET, user_id)
+
+            logger.info("Task pushed: msg_id=%s task=%s user=%s", msg_id, task_name, user_id)
+            return msg_id
+        except Exception as e:
+            logger.error("Push task exception %s", e, exc_info=True)
+            raise
+
+    def get_task_status(self, msg_id: str) -> dict:
+        raw = self.redis.get(f"task_tracker:{msg_id}")
+        if raw is None:
+            return {"status": "NOT_FOUND"}
+
+        tracker = json.loads(raw)
+        status = tracker["status"]
+        task_id = tracker.get("task_id")
+        result_content = tracker.get("result") or {}
+
+        if status == "DISPATCHED" and task_id:
+            result_raw = self.redis.get(f"celery-task-meta-{task_id}")
+            if result_raw:
+                result_data = json.loads(result_raw)
+                status = result_data.get("status", status)
+                result_content = result_data.get("result")
+
+        return {"status": status, "task_id": task_id, "result": result_content}
+
+    def _cleanup_finished(self):
+        pending = self.redis.hgetall(PENDING_HASH)
+        if not pending:
+            return
+
+        now = time.time()
+        task_ids = list(pending.keys())
+
+        pipe = self.redis.pipeline()
+        for task_id in task_ids:
+            pipe.get(f"celery-task-meta-{task_id}")
+        results = pipe.execute()
+
+        cleanup_pipe = self.redis.pipeline()
+        has_cleanup = False
+        ready_user_ids = set()
+
+        for task_id, raw_result in zip(task_ids, results):
+            try:
+                meta = json.loads(pending[task_id])
+                lock_key = meta["lock_key"]
+                dispatched_at = meta.get("dispatched_at", 0)
+                age = now - dispatched_at
+
+                should_cleanup = False
+                result_data = {}
+
+                if raw_result is not None:
+                    result_data = json.loads(raw_result)
+                    if result_data.get("status") in ("SUCCESS", "FAILURE", "REVOKED"):
+                        should_cleanup = True
+                        logger.info(
+                            "Task finished: %s state=%s", task_id,
+                            result_data.get("status"),
+                        )
+                elif age > TASK_TIMEOUT:
+                    should_cleanup = True
+                    logger.warning(
+                        "Task expired or lost: %s age=%.0fs, force cleanup",
+                        task_id, age,
+                    )
+
+                if should_cleanup:
+                    final_status = (
+                        result_data.get("status", "UNKNOWN") if result_data else "EXPIRED"
+                    )
+
+                    self.redis.eval(LUA_SAFE_DELETE, 1, lock_key, task_id)
+
+                    cleanup_pipe.hdel(PENDING_HASH, task_id)
+
+                    tracker_msg_id = meta.get("msg_id")
+                    if tracker_msg_id:
+                        cleanup_pipe.set(
+                            f"task_tracker:{tracker_msg_id}",
+                            json.dumps({
+                                "status": final_status,
+                                "task_id": task_id,
+                                "result": result_data.get("result") or {},
+                            }),
+                            ex=86400,
+                        )
+                    has_cleanup = True
+
+                    parts = lock_key.split(":", 1)
+                    if len(parts) == 2:
+                        ready_user_ids.add(parts[1])
+
+            except Exception as e:
+                logger.error("Cleanup error for %s: %s", task_id, e, exc_info=True)
+                self.errors += 1
+
+        if has_cleanup:
+            cleanup_pipe.execute()
+
+        if ready_user_ids:
+            self.redis.sadd(READY_SET, *ready_user_ids)
+
+    def _heartbeat(self):
+        now = time.time()
+        if now - self._last_heartbeat < HEARTBEAT_INTERVAL:
+            return
+        self._last_heartbeat = now
+
+        self.redis.hset(REGISTRY_KEY, self.instance_id, str(now))
+
+        all_instances = self.redis.hgetall(REGISTRY_KEY)
+
+        alive = []
+        dead = []
+        for iid, ts in all_instances.items():
+            if now - float(ts) < INSTANCE_TTL:
+                alive.append(iid)
+            else:
+                dead.append(iid)
+
+        if dead:
+            pipe = self.redis.pipeline()
+            for iid in dead:
+                pipe.hdel(REGISTRY_KEY, iid)
+            pipe.execute()
+            logger.info("Cleaned dead instances: %s", dead)
+
+        alive.sort()
+        self._shard_count = max(len(alive), 1)
+        self._shard_index = (
+            alive.index(self.instance_id) if self.instance_id in alive else 0
+        )
+        logger.debug(
+            "Shard: %s/%s (instance=%s, alive=%d)",
+            self._shard_index, self._shard_count,
+            self.instance_id, len(alive),
+        )
+
+    def _is_mine(self, user_id: str) -> bool:
+        if self._shard_count <= 1:
+            return True
+        return stable_hash(user_id) % self._shard_count == self._shard_index
+
+    def _dispatch(self, msg_id, msg_data) -> bool:
+        user_id = msg_data["user_id"]
+        task_name = msg_data["task_name"]
+        params = json.loads(msg_data.get("params", "{}"))
+
+        lock_key = f"{task_name}:{user_id}"
+        dispatch_lock = f"dispatch:{msg_id}"
+
+        result = self.redis.eval(
+            LUA_ATOMIC_LOCK, 2,
+            dispatch_lock, lock_key,
+            self.instance_id, str(300), str(3600),
+        )
+
+        if result == 0:
+            return False
+        if result == -1:
+            return False
+
+        try:
+            task = celery_app.send_task(task_name, kwargs=params)
+        except Exception as e:
+            pipe = self.redis.pipeline()
+            pipe.delete(dispatch_lock)
+            pipe.delete(lock_key)
+            pipe.execute()
+            self.errors += 1
+            logger.error(
+                "send_task failed for %s:%s msg=%s: %s",
+                task_name, user_id, msg_id, e, exc_info=True,
+            )
+            return False
+
+        try:
+            pipe = self.redis.pipeline()
+            pipe.set(lock_key, task.id, ex=3600)
+            pipe.hset(PENDING_HASH, task.id, json.dumps({
+                "lock_key": lock_key,
+                "dispatched_at": time.time(),
+                "msg_id": msg_id,
+            }))
+            pipe.delete(dispatch_lock)
+            pipe.set(
+                f"task_tracker:{msg_id}",
+                json.dumps({"status": "DISPATCHED", "task_id": task.id}),
+                ex=86400,
+            )
+            pipe.execute()
+        except Exception as e:
+            logger.error(
+                "Post-dispatch state update failed for %s: %s",
+                task.id, e, exc_info=True,
+            )
+            self.errors += 1
+
+        self.dispatched += 1
+        logger.info("Task dispatched: %s (msg=%s)", task.id, msg_id)
+        return True
+
+    def _process_batch(self, user_ids):
+        if not user_ids:
+            return
+
+        pipe = self.redis.pipeline()
+        for uid in user_ids:
+            pipe.lindex(f"{USER_QUEUE_PREFIX}{uid}", 0)
+        heads = pipe.execute()
+
+        candidates = []  # (user_id, msg_dict)
+        empty_users = []
+
+        for uid, head in zip(user_ids, heads):
+            if head is None:
+                empty_users.append(uid)
+            else:
+                try:
+                    candidates.append((uid, json.loads(head)))
+                except (json.JSONDecodeError, TypeError) as e:
+                    logger.error("Bad message in queue for user %s: %s", uid, e)
+                    self.redis.lpop(f"{USER_QUEUE_PREFIX}{uid}")
+
+        if empty_users:
+            pipe = self.redis.pipeline()
+            for uid in empty_users:
+                pipe.srem(ACTIVE_USERS, uid)
+            pipe.execute()
+
+        if not candidates:
+            return
+
+        for uid, msg in candidates:
+            if self._dispatch(msg["msg_id"], msg):
+                self.redis.lpop(f"{USER_QUEUE_PREFIX}{uid}")
+
+    def schedule_loop(self):
+        self._heartbeat()
+        self._cleanup_finished()
+
+        pipe = self.redis.pipeline()
+        pipe.smembers(READY_SET)
+        pipe.delete(READY_SET)
+        results = pipe.execute()
+        ready_users = results[0] or set()
+
+        my_users = [uid for uid in ready_users if self._is_mine(uid)]
+
+        if not my_users:
+            time.sleep(0.5)
+            return
+
+        self._process_batch(my_users)
+        time.sleep(0.1)
+
+    def _full_scan(self):
+        cursor = 0
+        ready_batch = []
+        while True:
+            cursor, user_ids = self.redis.sscan(
+                ACTIVE_USERS, cursor=cursor, count=1000,
+            )
+            if user_ids:
+                my_users = [uid for uid in user_ids if self._is_mine(uid)]
+                if my_users:
+                    pipe = self.redis.pipeline()
+                    for uid in my_users:
+                        pipe.lindex(f"{USER_QUEUE_PREFIX}{uid}", 0)
+                    heads = pipe.execute()
+
+                    for uid, head in zip(my_users, heads):
+                        if head is None:
+                            continue
+                        try:
+                            msg = json.loads(head)
+                            lock_key = f"{msg['task_name']}:{uid}"
+                            ready_batch.append((uid, lock_key))
+                        except (json.JSONDecodeError, TypeError):
+                            continue
+
+            if cursor == 0:
+                break
+
+        if not ready_batch:
+            return
+
+        pipe = self.redis.pipeline()
+        for _, lock_key in ready_batch:
+            pipe.exists(lock_key)
+        lock_exists = pipe.execute()
+
+        ready_uids = [
+            uid for (uid, _), locked in zip(ready_batch, lock_exists)
+            if not locked
+        ]
+
+        if ready_uids:
+            self.redis.sadd(READY_SET, *ready_uids)
+            logger.info("Full scan found %d ready users", len(ready_uids))
+
+    def run_server(self):
+        health_check_server(self)
+        self.running = True
+
+        last_full_scan = 0.0
+        full_scan_interval = 30.0
+
+        logger.info(
+            "Scheduler started: instance=%s", self.instance_id,
+        )
+
+        while True:
+            try:
+                self.schedule_loop()
+
+                now = time.time()
+                if now - last_full_scan > full_scan_interval:
+                    self._full_scan()
+                    last_full_scan = now
+
+            except Exception as e:
+                logger.error("Scheduler exception %s", e, exc_info=True)
+                self.errors += 1
+                time.sleep(5)
+
+    def health(self) -> dict:
+        return {
+            "running": self.running,
+            "active_users": self.redis.scard(ACTIVE_USERS),
+            "ready_users": self.redis.scard(READY_SET),
+            "pending_tasks": self.redis.hlen(PENDING_HASH),
+            "dispatched": self.dispatched,
+            "errors": self.errors,
+            "shard": f"{self._shard_index}/{self._shard_count}",
+            "instance": self.instance_id,
+        }
+
+    def shutdown(self):
+        logger.info("Scheduler shutting down: instance=%s", self.instance_id)
+        self.running = False
+        try:
+            self.redis.hdel(REGISTRY_KEY, self.instance_id)
+        except Exception as e:
+            logger.error("Shutdown cleanup error: %s", e)
+
+
+scheduler: RedisTaskScheduler | None = None
+if scheduler is None:
+    scheduler = RedisTaskScheduler()
+
+if __name__ == "__main__":
+    import signal
+    import sys
+
+
+    def _signal_handler(signum, frame):
+        scheduler.shutdown()
+        sys.exit(0)
+
+
+    signal.signal(signal.SIGTERM, _signal_handler)
+    signal.signal(signal.SIGINT, _signal_handler)
+
+    scheduler.run_server()

api/app/celery_worker.py

@@ -2,6 +2,8 @@
 Celery Worker 入口点
 用于启动 Celery Worker: celery -A app.celery_worker worker --loglevel=info
 """
+from celery.signals import worker_process_init
+
 from app.celery_app import celery_app
 from app.core.logging_config import LoggingConfig, get_logger
 
@@ -13,4 +15,39 @@ logger.info("Celery worker logging initialized")
 # 导入任务模块以注册任务
 import app.tasks
 
-__all__ = ['celery_app']
+
+@worker_process_init.connect
+def _reinit_db_pool(**kwargs):
+    """
+    prefork 子进程启动时重建被 fork 污染的资源。
+    
+    fork() 后子进程继承了父进程的：
+    1. SQLAlchemy 连接池 — 多进程共享 TCP socket 导致 DB 连接损坏
+    2. ThreadPoolExecutor — fork 后线程状态不确定，第二个任务会死锁
+    """
+    # 重建 DB 连接池
+    from app.db import engine
+    engine.dispose()
+    logger.info("DB connection pool disposed for forked worker process")
+
+    # 重建模块级 ThreadPoolExecutor（fork 后线程池不可用）
+    try:
+        from app.core.rag.deepdoc.parser import figure_parser
+        from concurrent.futures import ThreadPoolExecutor
+        figure_parser.shared_executor = ThreadPoolExecutor(max_workers=10)
+        logger.info("figure_parser.shared_executor recreated")
+    except Exception as e:
+        logger.warning(f"Failed to recreate figure_parser.shared_executor: {e}")
+
+    try:
+        from app.core.rag.utils import libre_office
+        from concurrent.futures import ThreadPoolExecutor
+        import os
+        max_workers = os.cpu_count() * 2 if os.cpu_count() else 4
+        libre_office.executor = ThreadPoolExecutor(max_workers=max_workers)
+        logger.info("libre_office.executor recreated")
+    except Exception as e:
+        logger.warning(f"Failed to recreate libre_office.executor: {e}")
+
+
+__all__ = ['celery_app']

api/app/config/__init__.py

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

api/app/config/default_free_plan.py

@@ -0,0 +1,77 @@
+"""
+社区版默认免费套餐配置
+当无法从 SaaS 版获取 premium 模块时，使用此配置作为兜底
+
+可通过环境变量覆盖配额配置，格式：QUOTA_<QUOTA_NAME>
+例如：QUOTA_END_USER_QUOTA=100
+"""
+
+import os
+
+
+def _get_quota_from_env():
+    """从环境变量获取配额配置"""
+    quota_keys = [
+        "workspace_quota",
+        "skill_quota",
+        "app_quota",
+        "knowledge_capacity_quota",
+        "memory_engine_quota",
+        "end_user_quota",
+        "ontology_project_quota",
+        "model_quota",
+        "api_ops_rate_limit",
+    ]
+    quotas = {}
+    for key in quota_keys:
+        env_key = f"QUOTA_{key.upper()}"
+        env_value = os.getenv(env_key)
+        if env_value is not None:
+            try:
+                quotas[key] = float(env_value) if '.' in env_value else int(env_value)
+            except ValueError:
+                pass
+    return quotas
+
+
+def _build_default_free_plan():
+    """构建默认免费套餐配置"""
+    base = {
+        "name": "记忆体验版",
+        "name_en": "Memory Experience",
+        "category": "saas_personal",
+        "tier_level": 0,
+        "version": "1.0",
+        "status": True,
+        "price": 0,
+        "billing_cycle": "permanent_free",
+        "core_value": "感受永久记忆",
+        "core_value_en": "Experience Permanent Memory",
+        "tech_support": "社群交流",
+        "tech_support_en": "Community Support",
+        "sla_compliance": "无",
+        "sla_compliance_en": "None",
+        "page_customization": "无",
+        "page_customization_en": "None",
+        "theme_color": "#64748B",
+        "quotas": {
+            "workspace_quota": 1,
+            "skill_quota": 5,
+            "app_quota": 2,
+            "knowledge_capacity_quota": 0.3,
+            "memory_engine_quota": 1,
+            "end_user_quota": 10,
+            "ontology_project_quota": 3,
+            "model_quota": 1,
+            "api_ops_rate_limit": 50,
+        },
+    }
+
+    env_quotas = _get_quota_from_env()
+    if env_quotas:
+        base["quotas"].update(env_quotas)
+
+    return base
+
+
+DEFAULT_FREE_PLAN = _build_default_free_plan()

api/app/config/default_ontology_config.py

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

api/app/config/default_ontology_initializer.py

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

api/app/controllers/__init__.py

@@ -8,6 +8,7 @@ from fastapi import APIRouter
 from . import (
     api_key_controller,
     app_controller,
+    app_log_controller,
     auth_controller,
     chunk_controller,
     document_controller,
@@ -16,17 +17,22 @@ from . import (
     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,
@@ -39,12 +45,10 @@ from . import (
     upload_controller,
     user_controller,
     user_memory_controllers,
-    workflow_controller,
     workspace_controller,
-    memory_forget_controller,
-    home_page_controller,
-    memory_perceptual_controller,
-    memory_working_controller,
+    ontology_controller,
+    skill_controller,
+    tenant_subscription_controller,
 )
 
 # 创建管理端 API 路由器
@@ -61,10 +65,13 @@ 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)
 manager_router.include_router(app_controller.router)
+manager_router.include_router(app_log_controller.router)
 manager_router.include_router(upload_controller.router)
 manager_router.include_router(memory_agent_controller.router)
 manager_router.include_router(memory_dashboard_controller.router)
@@ -77,7 +84,6 @@ 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)
@@ -90,5 +96,10 @@ 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(tenant_subscription_controller.router)
+manager_router.include_router(tenant_subscription_controller.public_router)
 
 __all__ = ["manager_router"]

api/app/controllers/api_key_controller.py

@@ -167,6 +167,8 @@ def update_api_key(
 
         return success(data=api_key_schema.ApiKey.model_validate(api_key), msg="API Key 更新成功")
 
+    except BusinessException:
+        raise
     except Exception as e:
         logger.error(f"未知错误: {str(e)}", extra={
             "api_key_id": str(api_key_id),

api/app/controllers/app_controller.py

@@ -1,9 +1,12 @@
 import uuid
+import io
 from typing import Optional, Annotated
 
-from fastapi import APIRouter, Depends, Path
+import yaml
+from fastapi import APIRouter, Depends, Path, Form, UploadFile, File
 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
@@ -17,11 +20,15 @@ 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
+from app.schemas.workflow_schema import WorkflowConfigUpdate, WorkflowImportSave
 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
+from app.core.quota_stub import check_app_quota
 
 router = APIRouter(prefix="/apps", tags=["Apps"])
 logger = get_business_logger()
@@ -29,6 +36,7 @@ logger = get_business_logger()
 
 @router.post("", summary="创建应用（可选创建 Agent 配置）")
 @cur_workspace_access_guard()
+@check_app_quota
 def create_app(
         payload: app_schema.AppCreate,
         db: Session = Depends(get_db),
@@ -47,6 +55,7 @@ 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,
@@ -58,16 +67,42 @@ def list_apps(
     - 默认包含本工作空间的应用和分享给本工作空间的应用
     - 设置 include_shared=false 可以只查看本工作空间的应用
     - 当提供 ids 参数时，按逗号分割获取指定应用，不分页
+    - search 参数支持：应用名称模糊搜索、API Key 精确搜索
     """
+    from sqlalchemy import select as sa_select
+    from app.models.api_key_model import ApiKey
+
     workspace_id = current_user.current_workspace_id
     service = app_service.AppService(db)
 
-    # 当 ids 存在且不为 None 时，根据 ids 获取应用
+    # 通过 search 参数搜索：支持应用名称模糊搜索和 API Key 精确搜索
+    if search:
+        search = search.strip()
+        # 尝试作为 API Key 精确匹配（API Key 通常较长）
+        if len(search) >= 10:
+            matched_id = db.execute(
+                sa_select(ApiKey.resource_id).where(
+                    ApiKey.workspace_id == workspace_id,
+                    ApiKey.api_key == search,
+                    ApiKey.resource_id.isnot(None),
+                )
+            ).scalar_one_or_none()
+            if matched_id:
+                # 找到 API Key，直接返回关联的应用
+                ids = str(matched_id)
+
+    # 当 ids 存在时，根据 ids 获取应用（不分页）
     if ids is not None:
-        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)
+        app_ids = [app_id.strip() for app_id in ids.split(',') if app_id.strip()]
+        if app_ids:
+            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]
+            # 返回标准分页格式
+            meta = PageMeta(page=1, pagesize=len(items), total=len(items), hasnext=False)
+            return success(data=PageData(page=meta, items=items))
+        # ids 为空时，返回空列表
+        meta = PageMeta(page=1, pagesize=0, total=0, hasnext=False)
+        return success(data=PageData(page=meta, items=[]))
 
     # 正常分页查询
     items_orm, total = app_service.list_apps(
@@ -78,6 +113,7 @@ def list_apps(
         status=status,
         search=search,
         include_shared=include_shared,
+        shared_only=shared_only,
         page=page,
         pagesize=pagesize,
     )
@@ -87,6 +123,37 @@ 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(
@@ -152,9 +219,11 @@ def delete_app(
 
 @router.post("/{app_id}/copy", summary="复制应用")
 @cur_workspace_access_guard()
+@check_app_quota
 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),
 ):
@@ -166,6 +235,8 @@ 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={
@@ -201,6 +272,19 @@ def update_agent_config(
     return success(data=app_schema.AgentConfig.model_validate(cfg))
 
 
+@router.get("/{app_id}/model/parameters/default", summary="获取 Agent 模型参数默认配置")
+@cur_workspace_access_guard()
+def get_agent_model_parameters(
+        app_id: uuid.UUID,
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    workspace_id = current_user.current_workspace_id
+    service = AppService(db)
+    model_parameters = service.get_default_model_parameters(app_id=app_id)
+    return success(data=model_parameters, msg="获取 Agent 模型参数默认配置")
+
+
 @router.get("/{app_id}/config", summary="获取 Agent 配置")
 @cur_workspace_access_guard()
 def get_agent_config(
@@ -215,6 +299,36 @@ 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
+
+    # 根据应用类型获取 features
+    from app.models.app_model import App as AppModel
+    app = db.get(AppModel, app_id)
+    if app and app.type == "workflow":
+        cfg = app_service.get_workflow_config(db=db, app_id=app_id, workspace_id=workspace_id)
+        features = cfg.features or {}
+    else:
+        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(
@@ -296,7 +410,8 @@ def share_app(
         app_id=app_id,
         target_workspace_ids=payload.target_workspace_ids,
         user_id=current_user.id,
-        workspace_id=workspace_id
+        workspace_id=workspace_id,
+        permission=payload.permission
     )
 
     data = [app_schema.AppShare.model_validate(s) for s in shares]
@@ -327,6 +442,32 @@ 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(
@@ -350,6 +491,46 @@ 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(
@@ -390,13 +571,13 @@ 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
+    from app.models import AgentConfig, ModelConfig, AppRelease
     from sqlalchemy import select
     from app.core.exceptions import BusinessException
-    from app.services.draft_run_service import DraftRunService
+    from app.services.draft_run_service import AgentRunService
 
     service = AppService(db)
-    draft_service = DraftRunService(db)
+    draft_service = AgentRunService(db)
 
     # 1. 验证应用
     app = service._get_app_or_404(app_id)
@@ -407,11 +588,12 @@ 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)
 
@@ -428,18 +610,29 @@ async def draft_run(
         service._check_agent_config(app_id)
 
         # 2. 获取 Agent 配置
-        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)
+        # 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)
 
-        # 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:
@@ -454,7 +647,8 @@ 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
+                        user_rag_memory_id=user_rag_memory_id,
+                        files=payload.files  # 传递多模态文件
                 ):
                     yield event
 
@@ -475,12 +669,13 @@ 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_variables": bool(payload.variables),
+                "has_files": bool(payload.files)
             }
         )
 
-        from app.services.draft_run_service import DraftRunService
-        draft_service = DraftRunService(db)
+        from app.services.draft_run_service import AgentRunService
+        draft_service = AgentRunService(db)
         result = await draft_service.run(
             agent_config=agent_cfg,
             model_config=model_config,
@@ -490,7 +685,8 @@ 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
+            user_rag_memory_id=user_rag_memory_id,
+            files=payload.files  # 传递多模态文件
         )
 
         logger.debug(
@@ -592,7 +788,17 @@ async def draft_run(
             msg="多 Agent 任务执行成功"
         )
     elif app.type == AppType.WORKFLOW:  # 工作流
-        config = workflow_service.check_config(app_id)
+        # 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)
         # 3. 流式返回
         if payload.stream:
             logger.debug(
@@ -735,6 +941,16 @@ 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
@@ -780,25 +996,33 @@ 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 DraftRunService
-            draft_service = DraftRunService(db)
+            from app.services.draft_run_service import AgentRunService
+            draft_service = AgentRunService(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 or str(current_user.id),
+                    user_id=payload.user_id,
                     variables=payload.variables,
                     storage_type=storage_type,
                     user_rag_memory_id=user_rag_memory_id,
-                    web_search=True,
+                    web_search=web_search,
                     memory=True,
                     parallel=payload.parallel,
-                    timeout=payload.timeout or 60
+                    timeout=payload.timeout or 60,
+                    files=payload.files
             ):
                 yield event
 
@@ -813,22 +1037,23 @@ async def draft_run_compare(
         )
 
     # 非流式返回
-    from app.services.draft_run_service import DraftRunService
-    draft_service = DraftRunService(db)
+    from app.services.draft_run_service import AgentRunService
+    draft_service = AgentRunService(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 or str(current_user.id),
+        user_id=payload.user_id,
         variables=payload.variables,
         storage_type=storage_type,
         user_rag_memory_id=user_rag_memory_id,
-        web_search=True,
+        web_search=web_search,
         memory=True,
         parallel=payload.parallel,
-        timeout=payload.timeout or 60
+        timeout=payload.timeout or 60,
+        files=payload.files
     )
 
     logger.info(
@@ -870,10 +1095,73 @@ async def update_workflow_config(
         current_user: Annotated[User, Depends(get_current_user)]
 ):
     workspace_id = current_user.current_workspace_id
+    if payload.variables:
+        from app.services.workflow_service import WorkflowService
+        resolved = await WorkflowService(db)._resolve_variables_file_defaults(
+            [v.model_dump() for v in payload.variables]
+        )
+        # Patch default values back into VariableDefinition objects
+        for var_def, resolved_def in zip(payload.variables, resolved):
+            var_def.default = resolved_def.get("default", var_def.default)
     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()
+@check_app_quota
+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(
@@ -884,12 +1172,14 @@ def get_app_statistics(
         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
@@ -901,15 +1191,153 @@ def get_app_statistics(
         - total_tokens: 总token消耗
     """
     workspace_id = current_user.current_workspace_id
-    
-    from app.services.app_statistics_service import AppStatisticsService
     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),
+        app_id: Optional[str] = Form(None),
+):
+    """从 YAML 文件导入 agent / multi_agent / workflow 应用。
+    传入 app_id 时覆盖该应用的配置（类型必须一致），否则创建新应用。
+    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)
+
+    target_app_id = uuid.UUID(app_id) if app_id else None
+    # 仅新建应用时检查配额，覆盖已有应用时跳过
+    if target_app_id is None:
+        from app.core.quota_manager import _check_quota
+        _check_quota(db, current_user.tenant_id, "app_quota", "app", workspace_id=current_user.current_workspace_id)
+    result_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,
+        app_id=target_app_id,
+    )
+    return success(
+        data={"app": app_schema.App.model_validate(result_app), "warnings": warnings},
+        msg="应用导入成功" + ("，但部分资源需手动配置" if warnings else "")
+    )
+
+
+@router.get("/citations/{document_id}/download", summary="下载引用文档原始文件")
+async def download_citation_file(
+        document_id: uuid.UUID = Path(..., description="引用文档ID"),
+        db: Session = Depends(get_db),
+):
+    """
+    下载引用文档的原始文件。
+    仅当应用功能特性 citation.allow_download=true 时，前端才会展示此下载链接。
+    路由本身不做权限校验，由业务层通过 allow_download 开关控制入口。
+    """
+    import os
+    from fastapi import HTTPException, status as http_status
+    from fastapi.responses import FileResponse
+    from app.core.config import settings
+    from app.models.document_model import Document
+    from app.models.file_model import File as FileModel
+
+    doc = db.query(Document).filter(Document.id == document_id).first()
+    if not doc:
+        raise HTTPException(status_code=http_status.HTTP_404_NOT_FOUND, detail="文档不存在")
+
+    file_record = db.query(FileModel).filter(FileModel.id == doc.file_id).first()
+    if not file_record:
+        raise HTTPException(status_code=http_status.HTTP_404_NOT_FOUND, detail="原始文件不存在")
+
+    file_path = os.path.join(
+        settings.FILE_PATH,
+        str(file_record.kb_id),
+        str(file_record.parent_id),
+        f"{file_record.id}{file_record.file_ext}"
+    )
+    if not os.path.exists(file_path):
+        raise HTTPException(status_code=http_status.HTTP_404_NOT_FOUND, detail="文件未找到")
+
+    encoded_name = quote(doc.file_name)
+    return FileResponse(
+        path=file_path,
+        filename=doc.file_name,
+        media_type="application/octet-stream",
+        headers={"Content-Disposition": f"attachment; filename*=UTF-8''{encoded_name}"}
+    )

api/app/controllers/app_log_controller.py

@@ -0,0 +1,110 @@
+"""应用日志（消息记录）接口"""
+import uuid
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy.orm import Session
+
+from app.core.logging_config import get_business_logger
+from app.core.response_utils import success
+from app.db import get_db
+from app.dependencies import get_current_user, cur_workspace_access_guard
+from app.schemas.app_log_schema import AppLogConversation, AppLogConversationDetail, AppLogMessage
+from app.schemas.response_schema import PageData, PageMeta
+from app.services.app_service import AppService
+from app.services.app_log_service import AppLogService
+
+router = APIRouter(prefix="/apps", tags=["App Logs"])
+logger = get_business_logger()
+
+
+@router.get("/{app_id}/logs", summary="应用日志 - 会话列表")
+@cur_workspace_access_guard()
+def list_app_logs(
+        app_id: uuid.UUID,
+        page: int = Query(1, ge=1),
+        pagesize: int = Query(20, ge=1, le=100),
+        is_draft: Optional[bool] = Query(None, description="是否草稿会话（不传则返回全部）"),
+        keyword: Optional[str] = Query(None, description="搜索关键词（匹配消息内容）"),
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """查看应用下所有会话记录（分页）
+
+    - is_draft 不传则返回所有会话（草稿 + 正式）
+    - is_draft=True 只返回草稿会话
+    - is_draft=False 只返回发布会话
+    - 支持按 keyword 搜索（匹配消息内容）
+    - 按最新更新时间倒序排列
+    """
+    workspace_id = current_user.current_workspace_id
+
+    # 验证应用访问权限
+    app_service = AppService(db)
+    app = app_service.get_app(app_id, workspace_id)
+
+    # 使用 Service 层查询
+    log_service = AppLogService(db)
+    conversations, total = log_service.list_conversations(
+        app_id=app_id,
+        workspace_id=workspace_id,
+        page=page,
+        pagesize=pagesize,
+        is_draft=is_draft,
+        keyword=keyword,
+        app_type=app.type,
+    )
+
+    items = [AppLogConversation.model_validate(c) for c in conversations]
+    meta = PageMeta(page=page, pagesize=pagesize, total=total, hasnext=(page * pagesize) < total)
+
+    return success(data=PageData(page=meta, items=items))
+
+
+@router.get("/{app_id}/logs/{conversation_id}", summary="应用日志 - 会话消息详情")
+@cur_workspace_access_guard()
+def get_app_log_detail(
+        app_id: uuid.UUID,
+        conversation_id: uuid.UUID,
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """查看某会话的完整消息记录
+
+    - 返回会话基本信息 + 所有消息（按时间正序）
+    - 消息 meta_data 包含模型名、token 用量等信息
+    - 所有人（包括共享者和被共享者）都只能查看自己的会话详情
+    """
+    workspace_id = current_user.current_workspace_id
+
+    # 验证应用访问权限
+    app_service = AppService(db)
+    app = app_service.get_app(app_id, workspace_id)
+
+    # 使用 Service 层查询
+    log_service = AppLogService(db)
+    conversation, messages, node_executions_map = log_service.get_conversation_detail(
+        app_id=app_id,
+        conversation_id=conversation_id,
+        workspace_id=workspace_id,
+        app_type=app.type
+    )
+
+    # 构建基础会话信息（不经过 ORM relationship）
+    base = AppLogConversation.model_validate(conversation)
+
+    # 单独处理 messages，避免触发 SQLAlchemy relationship 校验
+    if messages and isinstance(messages[0], AppLogMessage):
+        # 工作流：已经是 AppLogMessage 实例
+        msg_list = messages
+    else:
+        # Agent：ORM Message 对象逐个转换
+        msg_list = [AppLogMessage.model_validate(m) for m in messages]
+
+    detail = AppLogConversationDetail(
+        **base.model_dump(),
+        messages=msg_list,
+        node_executions_map=node_executions_map,
+    )
+
+    return success(data=detail)

api/app/controllers/auth_controller.py

@@ -1,4 +1,5 @@
 from datetime import datetime, timedelta, timezone
+from typing import Callable
 from fastapi import APIRouter, Depends
 from sqlalchemy.orm import Session
 
@@ -16,6 +17,7 @@ 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()
@@ -26,7 +28,8 @@ router = APIRouter(tags=["Authentication"])
 @router.post("/token", response_model=ApiResponse)
 async def login_for_access_token(
     form_data: TokenRequest,
-    db: Session = Depends(get_db)
+    db: Session = Depends(get_db),
+    t: Callable = Depends(get_translator)
 ):
     """用户登录获取token"""
     auth_logger.info(f"用户登录请求: {form_data.email}")
@@ -40,35 +43,38 @@ 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("邀请码无效或已过期", code=BizCode.BAD_REQUEST)
+            raise BusinessException(t("auth.invite.invalid"), code=BizCode.BAD_REQUEST)
         
         if invite_info.email != form_data.email:
-            raise BusinessException("邀请邮箱与登录邮箱不匹配", code=BizCode.BAD_REQUEST)        
+            raise BusinessException(t("auth.invite.email_mismatch"), 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
             user = auth_service.authenticate_user_or_raise(db, form_data.email, form_data.password)
             auth_logger.info(f"用户认证成功: {user.email} (ID: {user.id})")
             if form_data.invite:
-                auth_service.bind_workspace_with_invite(db=db,
-                user=user,        
-                invite_token=form_data.invite,
-                workspace_id=invite_info.workspace_id)
+                auth_service.bind_workspace_with_invite(
+                    db=db,
+                    user=user,
+                    invite_token=form_data.invite,
+                    workspace_id=invite_info.workspace_id
+                )
         except BusinessException as e:
         # 用户不存在且有邀请码，尝试注册
             if e.code == BizCode.USER_NOT_FOUND:
                 auth_logger.info(f"用户不存在，使用邀请码注册: {form_data.email}")
                 user = auth_service.register_user_with_invite(
-                db=db,
-                email=form_data.email,
-                password=form_data.password,
-                invite_token=form_data.invite,
-                workspace_id=invite_info.workspace_id
-            )
+                    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
+                )
             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("接受邀请失败，密码验证错误", BizCode.LOGIN_FAILED)
+                raise BusinessException(t("auth.invite.password_verification_failed"), BizCode.LOGIN_FAILED)
             else:
                 # 其他认证失败情况，直接抛出
                 raise
@@ -81,7 +87,7 @@ 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)
@@ -109,14 +115,15 @@ async def login_for_access_token(
             expires_at=access_expires_at,
             refresh_expires_at=refresh_expires_at
         ),
-        msg="登录成功"
+        msg=t("auth.login.success")
     )
 
 
 @router.post("/refresh", response_model=ApiResponse)
 async def refresh_token(
     refresh_request: RefreshTokenRequest,
-    db: Session = Depends(get_db)
+    db: Session = Depends(get_db),
+    t: Callable = Depends(get_translator)
 ):
     """刷新token"""
     auth_logger.info("收到token刷新请求")
@@ -124,18 +131,18 @@ async def refresh_token(
     # 验证 refresh token
     userId = security.verify_token(refresh_request.refresh_token, "refresh")
     if not userId:
-        raise BusinessException("无效的refresh token", code=BizCode.TOKEN_INVALID)
+        raise BusinessException(t("auth.token.invalid_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("用户不存在", code=BizCode.USER_NOT_FOUND)
+        raise BusinessException(t("auth.user.not_found"), code=BizCode.USER_NO_ACCESS)
     
     # 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("Refresh token已失效", code=BizCode.TOKEN_BLACKLISTED)
+            raise BusinessException(t("auth.token.refresh_token_blacklisted"), code=BizCode.TOKEN_BLACKLISTED)
     
     # 生成新 tokens
     new_access_token, new_access_token_id = security.create_access_token(subject=user.id)
@@ -166,7 +173,7 @@ async def refresh_token(
             expires_at=access_expires_at,
             refresh_expires_at=refresh_expires_at
         ),
-        msg="token刷新成功"
+        msg=t("auth.token.refresh_success")
     )
 
 
@@ -174,14 +181,15 @@ async def refresh_token(
 async def logout(
     token: str = Depends(oauth2_scheme),
     current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db)
+    db: Session = Depends(get_db),
+    t: Callable = Depends(get_translator)
 ):
     """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("无效的access token", code=BizCode.TOKEN_INVALID)
+        raise BusinessException(t("auth.token.invalid"), code=BizCode.TOKEN_INVALID)
 
     # 加入黑名单
     await SessionService.blacklist_token(token_id)
@@ -191,5 +199,5 @@ 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="登出成功")
+    return success(msg=t("auth.logout.success"))
 

api/app/controllers/chunk_controller.py

@@ -23,6 +23,7 @@ from app.models.user_model import User
 from app.schemas import chunk_schema
 from app.schemas.response_schema import ApiResponse
 from app.services import knowledge_service, document_service, file_service, knowledgeshare_service
+from app.services.model_service import ModelApiKeyService
 
 # Obtain a dedicated API logger
 api_logger = get_api_logger()
@@ -441,14 +442,14 @@ 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)
-            return success(data=rs, msg="retrieval successful")
+            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)
+            return success(data=jsonable_encoder(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)
-            return success(data=rs, msg="retrieval successful")
+            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)
+            return success(data=jsonable_encoder(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)
-            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)
+            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)
             # Efficient deduplication
             seen_ids = set()
             unique_rs = []
@@ -456,22 +457,24 @@ async def retrieve_chunks(
                 if doc.metadata["doc_id"] not in seen_ids:
                     seen_ids.add(doc.metadata["doc_id"])
                     unique_rs.append(doc)
-            rs = vector_service.rerank(query=retrieve_data.query, docs=unique_rs, top_k=retrieve_data.top_k)
+            rs = vector_service.rerank(query=retrieve_data.query, docs=unique_rs, top_k=retrieve_data.top_k) if unique_rs else []
             if retrieve_data.retrieve_type == chunk_schema.RetrieveType.Graph:
                 kb_ids = [str(kb_id) for kb_id in private_kb_ids]
                 workspace_ids = [str(workspace_id) for workspace_id in private_workspace_ids]
+                llm_key = ModelApiKeyService.get_available_api_key(db, db_knowledge.llm_id)
+                emb_key = ModelApiKeyService.get_available_api_key(db, db_knowledge.embedding_id)
                 # Prepare to configure chat_mdl、embedding_model、vision_model information
                 chat_model = Base(
-                    key=db_knowledge.llm.api_keys[0].api_key,
-                    model_name=db_knowledge.llm.api_keys[0].model_name,
-                    base_url=db_knowledge.llm.api_keys[0].api_base
+                    key=llm_key.api_key,
+                    model_name=llm_key.model_name,
+                    base_url=llm_key.api_base
                 )
                 embedding_model = OpenAIEmbed(
-                    key=db_knowledge.embedding.api_keys[0].api_key,
-                    model_name=db_knowledge.embedding.api_keys[0].model_name,
-                    base_url=db_knowledge.embedding.api_keys[0].api_base
+                    key=emb_key.api_key,
+                    model_name=emb_key.model_name,
+                    base_url=emb_key.api_base
                 )
-                doc = kg_retriever.retrieval(question=retrieve_data.query, workspace_ids=workspace_ids, kb_ids= kb_ids, emb_mdl=embedding_model, llm=chat_model)
+                doc = kg_retriever.retrieval(question=retrieve_data.query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
                 if doc:
                     rs.insert(0, doc)
             return success(data=jsonable_encoder(rs), msg="retrieval successful")

api/app/controllers/document_controller.py

@@ -314,8 +314,10 @@ async def parse_documents(
         )
 
         # 4. Check if the file exists
+        api_logger.debug(f"Constructed file path: {file_path}")
+        api_logger.debug(f"File metadata - kb_id: {db_file.kb_id}, parent_id: {db_file.parent_id}, file_id: {db_file.id}, extension: {db_file.file_ext}")
         if not os.path.exists(file_path):
-            api_logger.warning(f"File not found (possibly deleted): file_path={file_path}")
+            api_logger.error(f"File not found (possibly deleted): file_path={file_path}, file_id={db_file.id}, document_id={document_id}")
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
                 detail="File not found (possibly deleted)"

api/app/controllers/emotion_controller.py

@@ -11,6 +11,7 @@ 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
@@ -45,11 +46,14 @@ emotion_service = EmotionAnalyticsService()
 @router.post("/tags", response_model=ApiResponse)
 async def get_emotion_tags(
     request: EmotionTagsRequest,
-    language_type: str = Header(default="zh", alias="X-Language-Type"),
+    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={
@@ -57,7 +61,8 @@ async def get_emotion_tags(
                 "emotion_type": request.emotion_type,
                 "start_date": request.start_date,
                 "end_date": request.end_date,
-                "limit": request.limit
+                "limit": request.limit,
+                "language_type": language
             }
         )
 
@@ -67,7 +72,8 @@ async def get_emotion_tags(
             emotion_type=request.emotion_type,
             start_date=request.start_date,
             end_date=request.end_date,
-            limit=request.limit
+            limit=request.limit,
+            language=language
         )
 
         api_logger.info(
@@ -97,11 +103,14 @@ async def get_emotion_tags(
 @router.post("/wordcloud", response_model=ApiResponse)
 async def get_emotion_wordcloud(
     request: EmotionWordcloudRequest,
-    language_type: str = Header(default="zh", alias="X-Language-Type"),
+    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={
@@ -144,11 +153,14 @@ async def get_emotion_wordcloud(
 @router.post("/health", response_model=ApiResponse)
 async def get_emotion_health(
     request: EmotionHealthRequest,
-    language_type: str = Header(default="zh", alias="X-Language-Type"),
+    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(
@@ -174,7 +186,7 @@ async def get_emotion_health(
             "情绪健康指数获取成功",
             extra={
                 "end_user_id": request.end_user_id,
-                "health_score": data.get("health_score", 0),
+                "health_score": data.get("health_score") or 0,
                 "level": data.get("level", "未知")
             }
         )
@@ -196,14 +208,64 @@ 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,
-    language_type: str = Header(default="zh", alias="X-Language-Type"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
-    """获取个性化情绪建议（从缓存读取）
+    """获取个性化情绪建议（从数据库读取）
 
     Args:
         request: 包含 end_user_id 和可选的 config_id
@@ -211,44 +273,42 @@ async def get_emotion_suggestions(
         current_user: 当前用户
 
     Returns:
-        缓存的个性化情绪建议响应
+        存储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（缓存）",
+            f"用户 {current_user.username} 请求获取个性化情绪建议",
             extra={
                 "end_user_id": request.end_user_id,
                 "config_id": request.config_id
             }
         )
 
-        # 从缓存获取建议
+        # 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:
-            # 缓存不存在或已过期
             api_logger.info(
-                f"用户 {request.end_user_id} 的建议缓存不存在或已过期",
+                f"用户 {request.end_user_id} 的建议数据不存在",
                 extra={"end_user_id": request.end_user_id}
             )
-            return fail(
-                BizCode.NOT_FOUND,
-                "建议缓存不存在或已过期，请右上角刷新生成新建议",
-                ""
+            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"
             )
 
         api_logger.info(
-            "个性化建议获取成功（缓存）",
+            "个性化建议获取成功",
             extra={
                 "end_user_id": request.end_user_id,
                 "suggestions_count": len(data.get("suggestions", []))
             }
         )
 
-        return success(data=data, msg="个性化建议获取成功（缓存）")
+        return success(data=data, msg="个性化建议获取成功")
 
     except Exception as e:
         api_logger.error(
@@ -265,11 +325,11 @@ async def get_emotion_suggestions(
 @router.post("/generate_suggestions", response_model=ApiResponse)
 async def generate_emotion_suggestions(
     request: EmotionGenerateSuggestionsRequest,
-    language_type: str = Header(default="zh", alias="X-Language-Type"),
+    language_type: str = Header(default=None, alias="X-Language-Type"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
-    """生成个性化情绪建议（调用LLM并缓存）
+    """生成个性化情绪建议（调用LLM并保存到数据库）
 
     Args:
         request: 包含 end_user_id
@@ -280,6 +340,9 @@ async def generate_emotion_suggestions(
         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)
+        
         api_logger.info(
             f"用户 {current_user.username} 请求生成个性化情绪建议",
             extra={
@@ -290,15 +353,15 @@ async def generate_emotion_suggestions(
         # 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
+            db=db,
+            language=language
         )
 
-        # 保存到缓存
+        # 保存到数据库
         await emotion_service.save_suggestions_cache(
             end_user_id=request.end_user_id,
             suggestions_data=data,
-            db=db,
-            expires_hours=24
+            db=db
         )
 
         api_logger.info(
@@ -320,4 +383,4 @@ async def generate_emotion_suggestions(
         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/file_controller.py

@@ -19,6 +19,7 @@ from app.models.user_model import User
 from app.schemas import file_schema, document_schema
 from app.schemas.response_schema import ApiResponse
 from app.services import file_service, document_service
+from app.core.quota_stub import check_knowledge_capacity_quota
 
 
 # Obtain a dedicated API logger
@@ -131,6 +132,7 @@ async def create_folder(
 
 
 @router.post("/file", response_model=ApiResponse)
+@check_knowledge_capacity_quota
 async def upload_file(
         kb_id: uuid.UUID,
         parent_id: uuid.UUID,

api/app/controllers/file_storage_controller.py

@@ -14,8 +14,11 @@ Routes:
 import os
 import uuid
 from typing import Any
+import httpx
+import mimetypes
+from urllib.parse import urlparse, unquote
 
-from fastapi import APIRouter, Depends, File, HTTPException, UploadFile, status
+from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile, status
 from fastapi.responses import FileResponse, RedirectResponse
 from sqlalchemy.orm import Session
 
@@ -29,7 +32,7 @@ from app.core.storage_exceptions import (
     StorageUploadError,
 )
 from app.db import get_db
-from app.dependencies import get_current_user
+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
@@ -47,6 +50,19 @@ router = APIRouter(
 )
 
 
+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(...),
@@ -78,7 +94,7 @@ async def upload_file(
 
     if file_size > settings.MAX_FILE_SIZE:
         raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
+            status_code=status.HTTP_413_CONTENT_TOO_LARGE,
             detail=f"The file size exceeds the {settings.MAX_FILE_SIZE} byte limit"
         )
 
@@ -143,8 +159,238 @@ async def upload_file(
     )
 
 
+@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)
+    
+    # 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/info-by-url", response_model=ApiResponse)
+async def get_file_info_by_url(
+        url: str,
+):
+    """
+    Get file information by network URL (no authentication required).
+
+    Fetches file metadata from a remote URL via HTTP HEAD request.
+    Falls back to GET request if HEAD is not supported.
+    Returns file type, name, and size.
+
+    Args:
+        url: The network URL of the file.
+
+    Returns:
+        ApiResponse with file information.
+    """
+    api_logger.info(f"File info by URL request: url={url}")
+
+    try:
+        async with httpx.AsyncClient(timeout=10.0) as client:
+            # Try HEAD request first
+            response = await client.head(url, follow_redirects=True)
+
+            # If HEAD fails, try GET request (some servers don't support HEAD)
+            if response.status_code != 200:
+                api_logger.info(f"HEAD request failed with {response.status_code}, trying GET request")
+                response = await client.get(url, follow_redirects=True)
+                
+                if response.status_code != 200:
+                    api_logger.error(f"Failed to fetch file info: HTTP {response.status_code}")
+                    raise HTTPException(
+                        status_code=status.HTTP_400_BAD_REQUEST,
+                        detail=f"Unable to access file: HTTP {response.status_code}"
+                    )
+
+            # Get file size from Content-Length header or actual content
+            file_size = response.headers.get("Content-Length")
+            if file_size:
+                file_size = int(file_size)
+            elif hasattr(response, 'content'):
+                file_size = len(response.content)
+            else:
+                file_size = None
+
+            # Get content type from Content-Type header
+            content_type = response.headers.get("Content-Type", "application/octet-stream")
+            # Remove charset and other parameters from content type
+            content_type = content_type.split(';')[0].strip()
+
+            # Extract filename from Content-Disposition or URL
+            file_name = None
+            content_disposition = response.headers.get("Content-Disposition")
+            if content_disposition and "filename=" in content_disposition:
+                parts = content_disposition.split("filename=")
+                if len(parts) > 1:
+                    file_name = parts[1].strip('"').strip("'")
+
+            if not file_name:
+                parsed_url = urlparse(url)
+                file_name = unquote(os.path.basename(parsed_url.path)) or "unknown"
+
+            # Extract file extension from filename
+            _, file_ext = os.path.splitext(file_name)
+            
+            # If no extension found, infer from content type
+            if not file_ext:
+                ext = mimetypes.guess_extension(content_type)
+                if ext:
+                    file_ext = ext
+                    file_name = f"{file_name}{file_ext}"
+
+            api_logger.info(f"File info retrieved: name={file_name}, size={file_size}, type={content_type}")
+
+            return success(
+                data={
+                    "url": url,
+                    "file_name": file_name,
+                    "file_ext": file_ext.lower() if file_ext else "",
+                    "file_size": file_size,
+                    "content_type": content_type,
+                },
+                msg="File information retrieved successfully"
+            )
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        api_logger.error(f"Unexpected error: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Failed to retrieve file information: {str(e)}"
+        )
+
+
 @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),
@@ -192,6 +438,7 @@ async def download_file(
     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:
@@ -265,6 +512,7 @@ async def delete_file(
 
 @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,
@@ -326,8 +574,13 @@ async def get_file_url(
             # 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)
+            # For remote storage (OSS/S3), get presigned URL with forced download
+            url = await storage_service.get_file_url(
+                file_key,
+                expires=expires,
+                file_name=file_metadata.file_name,
+            )
+            url = _match_scheme(request, url)
 
         api_logger.info(f"Generated file URL: file_id={file_id}")
         return success(
@@ -347,8 +600,54 @@ async def get_file_url(
         )
 
 
+@router.get("/files/{file_id}/public-url", response_model=ApiResponse)
+async def get_permanent_file_url(
+    file_id: uuid.UUID,
+    db: Session = Depends(get_db),
+    storage_service: FileStorageService = Depends(get_file_storage_service),
+):
+    """
+    获取文件的永久公开 URL（无过期时间）。
+
+    - 本地存储：返回 API 永久访问地址（基于 FILE_LOCAL_SERVER_URL 配置）
+    - 远程存储（OSS/S3）：返回 bucket 公读地址（需 bucket 已配置公共读权限）
+    """
+    file_metadata = db.query(FileMetadata).filter(FileMetadata.id == file_id).first()
+    if not file_metadata:
+        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 isinstance(storage, LocalStorage):
+            url = f"{settings.FILE_LOCAL_SERVER_URL}/storage/permanent/{file_id}"
+        else:
+            url = await storage.get_permanent_url(file_key)
+            if not url:
+                raise HTTPException(status_code=status.HTTP_501_NOT_IMPLEMENTED,
+                                    detail="Permanent URL not supported for current storage backend")
+
+        api_logger.info(f"Generated permanent URL: file_id={file_id}")
+        return success(
+            data={"url": url, "expires_in": None, "permanent": True, "file_name": file_metadata.file_name},
+            msg="Permanent file URL generated successfully"
+        )
+    except HTTPException:
+        raise
+    except Exception as e:
+        api_logger.error(f"Failed to generate permanent URL: {e}")
+        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+                            detail=f"Failed to generate permanent 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 = "",
@@ -420,6 +719,7 @@ async def public_download_file(
         # 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}")
@@ -431,6 +731,7 @@ async def public_download_file(
 
 @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),
@@ -489,7 +790,8 @@ async def permanent_download_file(
         # 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 = await storage_service.get_file_url(file_key, expires=604800, file_name=file_metadata.file_name)
+            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}")
@@ -497,3 +799,44 @@ async def permanent_download_file(
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
                 detail=f"Failed to retrieve file: {str(e)}"
             )
+
+
+@router.get("/files/{file_id}/status", response_model=ApiResponse)
+async def get_file_status(
+    file_id: uuid.UUID,
+    db: Session = Depends(get_db),
+):
+    """
+    Get file upload/processing status (no authentication required).
+    
+    This endpoint is used to check if a file (e.g., TTS audio) is ready.
+    Returns status: pending, completed, or failed.
+    
+    Args:
+        file_id: The UUID of the file.
+        db: Database session.
+    
+    Returns:
+        ApiResponse with file status and metadata.
+    """
+    api_logger.info(f"File status 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"
+        )
+    
+    return success(
+        data={
+            "file_id": str(file_id),
+            "status": file_metadata.status,
+            "file_name": file_metadata.file_name,
+            "file_size": file_metadata.file_size,
+            "content_type": file_metadata.content_type,
+        },
+        msg="File status retrieved successfully"
+    )

api/app/controllers/home_page_controller.py

@@ -3,9 +3,10 @@ from sqlalchemy.orm import Session
 
 from app.core.config import settings
 from app.core.response_utils import success
-from app.db import get_db
+from app.db import get_db, SessionLocal
 from app.dependencies import get_current_user
 from app.models.user_model import User
+from app.repositories.home_page_repository import HomePageRepository
 from app.schemas.response_schema import ApiResponse
 from app.services.home_page_service import HomePageService
 
@@ -31,9 +32,32 @@ def get_workspace_list(
 
 @router.get("/version", response_model=ApiResponse)
 def get_system_version():
-    """获取系统版本号+说明"""
-    current_version = settings.SYSTEM_VERSION
-    version_info = HomePageService.load_version_introduction(current_version)
+    """获取系统版本号 + 说明"""
+    current_version = None
+    version_info = None
+    
+    # 1️⃣ 优先从数据库获取最新已发布的版本
+    try:
+        db = SessionLocal()
+        try:
+            current_version, version_info = HomePageRepository.get_latest_version_introduction(db)
+        finally:
+            db.close()
+    except Exception as e:
+        pass
+    
+    # 2️⃣ 降级：使用环境变量中的版本号
+    if not current_version:
+        current_version = settings.SYSTEM_VERSION
+        version_info = HomePageService.load_version_introduction(current_version)
+    
+    # 3️⃣ 如果数据库和 JSON 都没有，返回基本信息
+    if not version_info:
+        version_info = {
+            "introduction": {"codeName": "", "releaseDate": "", "upgradePosition": "", "coreUpgrades": []},
+            "introduction_en": {"codeName": "", "releaseDate": "", "upgradePosition": "", "coreUpgrades": []}
+        }
+    
     return success(
         data={
             "version": current_version,

api/app/controllers/i18n_controller.py

@@ -0,0 +1,833 @@
+"""
+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

@@ -122,6 +122,48 @@ 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)
 @cur_workspace_access_guard()
 async def get_preference_tags(
@@ -159,12 +201,8 @@ async def get_preference_tags(
         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} 的画像缓存不存在或已过期")
-            return fail(
-                BizCode.NOT_FOUND,
-                "画像缓存不存在或已过期，请右上角刷新生成新画像",
-                ""
-            )
+            api_logger.info(f"用户 {end_user_id} 的画像数据不存在")
+            return fail(BizCode.NOT_FOUND, "", "")
         
         # Extract preferences from cache
         preferences = cached_profile.get("preferences", [])
@@ -230,12 +268,8 @@ async def get_dimension_portrait(
         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} 的画像缓存不存在或已过期")
-            return fail(
-                BizCode.NOT_FOUND,
-                "画像缓存不存在或已过期，请右上角刷新生成新画像",
-                ""
-            )
+            api_logger.info(f"用户 {end_user_id} 的画像数据不存在")
+            return fail(BizCode.NOT_FOUND, "", "")
         
         # Extract portrait from cache
         portrait = cached_profile.get("portrait", {})
@@ -278,12 +312,8 @@ async def get_interest_area_distribution(
         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} 的画像缓存不存在或已过期")
-            return fail(
-                BizCode.NOT_FOUND,
-                "画像缓存不存在或已过期，请右上角刷新生成新画像",
-                ""
-            )
+            api_logger.info(f"用户 {end_user_id} 的画像数据不存在")
+            return fail(BizCode.NOT_FOUND, "", "")
         
         # Extract interest areas from cache
         interest_areas = cached_profile.get("interest_areas", {})
@@ -330,12 +360,8 @@ async def get_behavior_habits(
         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} 的画像缓存不存在或已过期")
-            return fail(
-                BizCode.NOT_FOUND,
-                "画像缓存不存在或已过期，请右上角刷新生成新画像",
-                ""
-            )
+            api_logger.info(f"用户 {end_user_id} 的画像数据不存在")
+            return fail(BizCode.NOT_FOUND, "", "")
         
         # Extract habits from cache
         habits = cached_profile.get("habits", [])

api/app/controllers/knowledge_controller.py

@@ -9,13 +9,16 @@ 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
+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 knowledge_model
@@ -24,6 +27,7 @@ from app.schemas import knowledge_schema
 from app.schemas.response_schema import ApiResponse
 from app.services import knowledge_service, document_service
 from app.services.model_service import ModelConfigService
+from app.core.quota_stub import check_knowledge_capacity_quota
 
 # Obtain a dedicated API logger
 api_logger = get_api_logger()
@@ -176,6 +180,7 @@ async def get_knowledges(
 
 
 @router.post("/knowledge", response_model=ApiResponse)
+@check_knowledge_capacity_quota
 async def create_knowledge(
         create_data: knowledge_schema.KnowledgeCreate,
         db: Session = Depends(get_db),
@@ -349,6 +354,7 @@ async def delete_knowledge(
         # 2. Soft-delete knowledge base
         api_logger.debug(f"Perform a soft delete: {db_knowledge.name} (ID: {knowledge_id})")
         db_knowledge.status = 2
+        db_knowledge.updated_at = datetime.datetime.now()
         db.commit()
         api_logger.info(f"The knowledge base has been successfully deleted: {db_knowledge.name} (ID: {knowledge_id})")
         return success(msg="The knowledge base has been successfully deleted")
@@ -484,3 +490,99 @@ 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

@@ -0,0 +1,474 @@
+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)
+        headers=api.builder_headers(api.headers)
+        headers['Authorization'] = f'Bearer {token}'
+        r = api.session.put(
+            url=api.mcp_base_url,
+            headers=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)
+    headers['Authorization'] = f'Bearer {token}'
+
+    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)
+            headers = api.builder_headers(api.headers)
+            headers['Authorization'] = f'Bearer {create_data.token}'
+            r = api.session.put(url=api.mcp_base_url, headers=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)
+            headers = api.builder_headers(api.headers)
+            headers['Authorization'] = f'Bearer {token}'
+            r = api.session.put(
+                url=api.mcp_base_url,
+                headers=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)
+            headers = api.builder_headers(api.headers)
+            headers['Authorization'] = f'Bearer {update_data.token}'
+            r = api.session.put(url=api.mcp_base_url, headers=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

@@ -0,0 +1,262 @@
+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

@@ -1,26 +1,32 @@
 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.memory.enums import SearchStrategy, Neo4jNodeType
+from app.core.memory.memory_service import MemoryService
 from app.core.rag.llm.cv_model import QWenCV
 from app.core.response_utils import fail, success
 from app.db import get_db
 from app.dependencies import cur_workspace_access_guard, get_current_user
 from app.models import ModelApiKey
 from app.models.user_model import User
-from app.core.memory.agent.utils.session_tools import SessionService
-from app.core.memory.agent.utils.redis_tool import store
-from app.repositories import knowledge_repository, WorkspaceRepository
+from app.repositories import knowledge_repository
 from app.schemas.memory_agent_schema import UserInput, Write_UserInput
 from app.schemas.response_schema import ApiResponse
 from app.services import task_service, workspace_service
 from app.services.memory_agent_service import MemoryAgentService
+from app.services.memory_agent_service import get_end_user_connected_config as get_config
 from app.services.model_service import ModelConfigService
-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
 
 load_dotenv()
 api_logger = get_api_logger()
@@ -35,7 +41,7 @@ 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
@@ -53,8 +59,9 @@ 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
@@ -73,16 +80,16 @@ 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
@@ -114,136 +121,150 @@ async def download_log(
             return fail(BizCode.INTERNAL_ERROR, "启动日志流式传输失败", str(e))
 
 
-@router.post("/writer_service", response_model=ApiResponse)
-@cur_workspace_access_guard()
-async def write_server(
-    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
-    
-    Returns:
-        Response with write operation status
-    """
-    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}")
-    
-    # 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,
-        user=current_user
-    )
-    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:
-            knowledge = knowledge_repository.get_knowledge_by_name(
-                db=db,
-                name="USER_RAG_MERORY",
-                workspace_id=workspace_id
-            )
-            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")
-                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}")
-    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,
-            config_id,
-            db,
-            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
-        if hasattr(e, 'exceptions'):
-            error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions]
-            detailed_error = "; ".join(error_messages)
-            api_logger.error(f"Write operation error (TaskGroup): {detailed_error}", exc_info=True)
-            return fail(BizCode.INTERNAL_ERROR, "写入失败", detailed_error)
-        api_logger.error(f"Write operation error: {str(e)}", exc_info=True)
-        return fail(BizCode.INTERNAL_ERROR, "写入失败", str(e))
-
-
-@router.post("/writer_service_async", response_model=ApiResponse)
-@cur_workspace_access_guard()
-async def write_server_async(
-    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
-    
-    Returns:
-        Task ID for tracking async operation
-        Use GET /memory/write_result/{task_id} to check task status and get result
-    """
-    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}")
-
-    # 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,
-        user=current_user
-    )
-    if storage_type is None: storage_type = 'neo4j'
-    user_rag_memory_id = ''
-    if workspace_id:
-
-        knowledge = knowledge_repository.get_knowledge_by_name(
-            db=db,
-            name="USER_RAG_MERORY",
-            workspace_id=workspace_id
-        )
-        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]
-        )
-        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)}")
-        return fail(BizCode.INTERNAL_ERROR, "写入失败", str(e))
+# @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)
+# ):
+#     """
+#     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
+#
+#     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}")
+#
+#     # 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,
+#         user=current_user
+#     )
+#     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:
+#             knowledge = knowledge_repository.get_knowledge_by_name(
+#                 db=db,
+#                 name="USER_RAG_MERORY",
+#                 workspace_id=workspace_id
+#             )
+#             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")
+#                 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}")
+#     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,
+#             config_id,
+#             db,
+#             storage_type,
+#             user_rag_memory_id,
+#             language
+#         )
+#
+#         return success(data=result, msg="写入成功")
+#     except BaseException as e:
+#         # Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup
+#         if hasattr(e, 'exceptions'):
+#             error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions]
+#             detailed_error = "; ".join(error_messages)
+#             api_logger.error(f"Write operation error (TaskGroup): {detailed_error}", exc_info=True)
+#             return fail(BizCode.INTERNAL_ERROR, "写入失败", detailed_error)
+#         api_logger.error(f"Write operation error: {str(e)}", exc_info=True)
+#         return fail(BizCode.INTERNAL_ERROR, "写入失败", str(e))
+#
+#
+# @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)
+# ):
+#     """
+#     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
+#
+#     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}")
+#
+#     # 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,
+#         user=current_user
+#     )
+#     if storage_type is None: storage_type = 'neo4j'
+#     user_rag_memory_id = ''
+#     if workspace_id:
+#
+#         knowledge = knowledge_repository.get_knowledge_by_name(
+#             db=db,
+#             name="USER_RAG_MERORY",
+#             workspace_id=workspace_id
+#         )
+#         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]
+#         )
+#         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)}")
+#         return fail(BizCode.INTERNAL_ERROR, "写入失败", str(e))
 
 
 @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
@@ -278,35 +299,94 @@ async def read_server(
         )
         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}")
-    try:
-        result = await memory_agent_service.read_memory(
-            user_input.end_user_id,
-            user_input.message,
-            user_input.history,
-            user_input.search_switch,
-            config_id,
-            db,
-            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(
+    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}")
+    try:
+        # result = await memory_agent_service.read_memory(
+        #     user_input.end_user_id,
+        #     user_input.message,
+        #     user_input.history,
+        #     user_input.search_switch,
+        #     config_id,
+        #     db,
+        #     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
+        memory_config = get_config(user_input.end_user_id, db)
+        service = MemoryService(
+            db,
+            memory_config["memory_config_id"],
+            end_user_id=user_input.end_user_id
+        )
+        search_result = await service.read(
+            user_input.message,
+            SearchStrategy(user_input.search_switch)
+        )
+        intermediate_outputs = []
+        sub_queries = set()
+        for memory in search_result.memories:
+            sub_queries.add(str(memory.query))
+        if user_input.search_switch in [SearchStrategy.DEEP, SearchStrategy.NORMAL]:
+            intermediate_outputs.append({
+                "type": "problem_split",
+                "title": "问题拆分",
+                "data": [
+                    {
+                        "id": f"Q{idx+1}",
+                        "question": question
+                    }
+                    for idx, question in enumerate(sub_queries)
+                ]
+            })
+        perceptual_data = [
+            memory.data
+            for memory in search_result.memories
+            if memory.source == Neo4jNodeType.PERCEPTUAL
+        ]
+
+        intermediate_outputs.append({
+            "type": "perceptual_retrieve",
+            "title": "感知记忆检索",
+            "data": perceptual_data,
+            "total": len(perceptual_data),
+        })
+        intermediate_outputs.append({
+            "type": "search_result",
+            "title": f"合并检索结果 (共{len(sub_queries)}个查询,{len(search_result.memories)}条结果)",
+            "result": search_result.content,
+            "raw_result": search_result.memories,
+            "total": len(search_result.memories),
+        })
+        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,
+                retrieve_info=search_result.content,
+                history=[],
+                query=user_input.message,
                 config_id=config_id,
                 db=db
-            )
-            if "信息不足，无法回答" in result['answer']:
-                result['answer']=retrieve_info
+            ),
+            "intermediate_outputs": intermediate_outputs
+        }
+
         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
@@ -322,9 +402,10 @@ 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)
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db),
 ):
     """
     文件上传接口 - 支持图片识别
@@ -337,9 +418,6 @@ 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]
@@ -348,7 +426,7 @@ 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,
@@ -362,12 +440,12 @@ 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))
@@ -417,8 +495,8 @@ 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
@@ -439,7 +517,7 @@ 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", {})
@@ -457,7 +535,7 @@ 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")
@@ -466,7 +544,7 @@ 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(
@@ -486,7 +564,7 @@ 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))
@@ -494,8 +572,8 @@ 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
@@ -516,7 +594,7 @@ 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", {})
@@ -534,7 +612,7 @@ 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")
@@ -543,7 +621,7 @@ 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(
@@ -563,7 +641,7 @@ 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))
@@ -571,9 +649,9 @@ 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)
@@ -616,26 +694,21 @@ 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)
+        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),
 ):
     """
-    统计当前空间下各知识库类型的数量，包含 General | Web | Third-party | Folder | memory。
+    统计当前空间下各知识库类型的数量，包含 General | Web | Third-party | Folder。
     会对缺失类型补 0，返回字典形式。
     可选按状态过滤。
     - 知识库类型根据当前用户的 current_workspace_id 过滤
-    - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (end_user_id) 过滤
-    - 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
+    - 如果用户没有当前工作空间，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,
@@ -643,59 +716,70 @@ 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/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）"),
-    language_type: str = Header(default="zh", alias="X-Language-Type"),
-    limit: int = Query(20, description="返回标签数量限制"),
-    current_user: User = Depends(get_current_user),
-    db: Session=Depends(get_db),
+@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),
 ):
     """
-    获取指定用户的热门记忆标签
+    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。
     
     返回格式：
     [
-        {"name": "标签名", "frequency": 频次},
+        {"name": "兴趣活动名", "frequency": 频次},
         ...
     ]
     """
-
-    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"Hot memory tags by user requested: end_user_id={end_user_id}")
+    language = get_language_from_header(language_type)
+    api_logger.info(f"Interest distribution by user requested: end_user_id={end_user_id}, language={language}")
     try:
-        result = await memory_agent_service.get_hot_memory_tags_by_user(
+        # 优先读取缓存
+        cached = await InterestMemoryCache.get_interest_distribution(
             end_user_id=end_user_id,
-            language_type=language_type,
-            model_id=model_id,
-            limit=limit
+            language=language,
         )
-        return success(data=result, msg="获取热门记忆标签成功")
+        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")
     except Exception as 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))
+        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))
 
 
 @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：
@@ -733,17 +817,17 @@ 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:
@@ -759,9 +843,9 @@ 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
@@ -777,12 +861,9 @@ async def get_end_user_connected_config(
     Returns:
         包含 memory_config_id 和相关信息的响应
     """
-    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")
@@ -791,4 +872,4 @@ 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

@@ -1,4 +1,7 @@
+import asyncio
+import uuid
 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
@@ -9,6 +12,7 @@ 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
@@ -45,64 +49,64 @@ def get_workspace_total_end_users(
 
 @router.get("/end_users", response_model=ApiResponse)
 async def get_workspace_end_users(
+    workspace_id: Optional[uuid.UUID] = Query(None, description="工作空间ID（可选，默认当前用户工作空间）"),
+    keyword: Optional[str] = Query(None, description="搜索关键词（同时模糊匹配 other_name 和 id）"),
+    page: int = Query(1, ge=1, description="页码，从1开始"),
+    pagesize: int = Query(10, ge=1, description="每页数量"),
     db: Session = Depends(get_db),
     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）
-    
-    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"}
-    }
+    获取工作空间的宿主列表（分页查询，支持模糊搜索）
+
+    返回工作空间下的宿主列表，支持分页查询和模糊搜索。
+    通过 keyword 参数同时模糊匹配 other_name 和 id 字段。
+
+    Args:
+        workspace_id: 工作空间ID（可选，默认当前用户工作空间）
+        keyword: 搜索关键词（可选，同时模糊匹配 other_name 和 id）
+        page: 页码（从1开始，默认1）
+        pagesize: 每页数量（默认10）
+        db: 数据库会话
+        current_user: 当前用户
+
+    Returns:
+        ApiResponse: 包含宿主列表和分页信息
     """
-    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)}")
-    
+    # 如果未提供 workspace_id，使用当前用户的工作空间
+    if workspace_id is None:
+        workspace_id = current_user.current_workspace_id
     # 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(
+    api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {workspace_id} 的宿主列表, 类型: {current_workspace_type}")
+
+    # 获取分页的 end_users
+    end_users_result = memory_dashboard_service.get_workspace_end_users_paginated(
         db=db,
         workspace_id=workspace_id,
-        current_user=current_user
+        current_user=current_user,
+        page=page,
+        pagesize=pagesize,
+        keyword=keyword
     )
+
+    end_users = end_users_result.get("items", [])
+    total = end_users_result.get("total", 0)
+
     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")
-    
+        api_logger.info(f"工作空间下没有宿主或当前页无数据: total={total}, page={page}")
+        return success(data={
+            "items": [],
+            "page": {
+                "page": page,
+                "pagesize": pagesize,
+                "total": total,
+                "hasnext": (page * pagesize) < total
+            }
+        }, msg="宿主列表获取成功")
+
     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）"""
@@ -114,7 +118,7 @@ async def get_workspace_end_users(
         except Exception as e:
             api_logger.error(f"批量获取记忆配置失败: {str(e)}")
             return {}
-    
+
     async def get_memory_nums():
         """获取记忆数量"""
         if current_workspace_type == "rag":
@@ -128,38 +132,45 @@ async def get_workspace_end_users(
             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))}
-        
+            # Neo4j 模式：批量查询（简化版本，只返回total）
+            try:
+                batch_result = await memory_storage_service.search_all_batch(end_user_ids)
+                return {uid: {"total": count} for uid, count in batch_result.items()}
+            except Exception as e:
+                api_logger.error(f"批量获取 Neo4j 记忆数量失败: {str(e)}")
+                return {uid: {"total": 0} for uid in 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 = []
+
+    # 构建结果列表
+    items = []
     for end_user in end_users:
         user_id = str(end_user.id)
         config_info = memory_configs_map.get(user_id, {})
-        result.append({
+        items.append({
             'end_user': {
                 'id': user_id,
                 'other_name': end_user.other_name
@@ -170,14 +181,27 @@ async def get_workspace_end_users(
                 "memory_config_name": config_info.get("memory_config_name")
             }
         })
-    
-    # 写入缓存（30秒过期）
+
+    # 触发社区聚类补全任务（异步，不阻塞接口响应）
     try:
-        await aio_redis_set(cache_key, json.dumps(result), expire=30)
+        from app.tasks import init_community_clustering_for_users
+        init_community_clustering_for_users.delay(end_user_ids=end_user_ids, workspace_id=str(workspace_id))
+        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"Redis 缓存写入失败: {str(e)}")
-    
-    api_logger.info(f"成功获取 {len(end_users)} 个宿主记录")
+        api_logger.warning(f"触发社区聚类补全任务失败（不影响主流程）: {str(e)}")
+
+    # 构建分页响应
+    result = {
+        "items": items,
+        "page": {
+            "page": page,
+            "pagesize": pagesize,
+            "total": total,
+            "hasnext": (page * pagesize) < total
+        }
+    }
+
+    api_logger.info(f"成功获取 {len(end_users)} 个宿主记录，总计 {total} 条")
     return success(data=result, msg="宿主列表获取成功")
 
 
@@ -386,14 +410,15 @@ 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"),
-    limit: int = Query(15, description="返回记录数"),
+    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"),
     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
+    获取当前宿主知识库中的chunk内容（分页）
     """
-    data = memory_dashboard_service.get_rag_content(end_user_id, limit, db, current_user)
+    data = memory_dashboard_service.get_rag_content(end_user_id, page, pagesize, db, current_user)
     return success(data=data, msg="宿主RAGchunk数据获取成功")
 
 
@@ -406,26 +431,18 @@ async def get_chunk_summary_tag(
     current_user: User = Depends(get_current_user),
 ):
     """
-    获取chunk总结、提取的标签和人物形象
-    
+    读取RAG摘要、标签和人物形象（纯读库，不触发生成）。
+
     返回格式：
     {
-        "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",
-            ...
-        ]
+        "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
     }
     """
-    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")
-    
+    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")
+
     data = await memory_dashboard_service.get_chunk_summary_and_tags(
         end_user_id=end_user_id,
         limit=limit,
@@ -433,9 +450,8 @@ async def get_chunk_summary_tag(
         db=db,
         current_user=current_user
     )
-    
-    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")
+
+    return success(data=data, msg="获取成功")
 
 
 @router.get("/chunk_insight", response_model=ApiResponse)
@@ -446,29 +462,64 @@ async def get_chunk_insight(
     current_user: User = Depends(get_current_user),
 ):
     """
-    获取chunk的洞察内容
-    
+    读取RAG洞察报告（纯读库，不触发生成）。
+
     返回格式：
     {
-        "insight": "对chunk内容的深度洞察分析"
+        "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
     }
     """
-    api_logger.info(f"用户 {current_user.username} 请求获取宿主 {end_user_id} 的chunk洞察")
-    
+    api_logger.info(f"用户 {current_user.username} 读取宿主 {end_user_id} 的RAG洞察")
+
     data = await memory_dashboard_service.get_chunk_insight(
         end_user_id=end_user_id,
         limit=limit,
         db=db,
         current_user=current_user
     )
-    
-    api_logger.info("成功获取chunk洞察")
-    return success(data=data, msg="chunk洞察获取成功")
+
+    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")
 
 
 @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),
 ):
@@ -503,6 +554,15 @@ 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,
@@ -531,7 +591,7 @@ async def dashboard_data(
                 "total_api_call": None
             }
             
-            # 1. 获取记忆总量（total_memory）
+            # 1. 获取记忆总量（total_memory）—— neo4j 独有逻辑：查询 neo4j 存储节点
             try:
                 total_memory_data = await memory_dashboard_service.get_workspace_total_memory_count(
                     db=db,
@@ -540,41 +600,33 @@ async def dashboard_data(
                     end_user_id=end_user_id
                 )
                 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
-                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']}")
+                api_logger.info(f"成功获取记忆总量: {neo4j_data['total_memory']}")
             except Exception as e:
                 api_logger.warning(f"获取记忆总量失败: {str(e)}")
             
-            # 2. 获取知识库类型统计（total_knowledge）
-            try:
-                from app.services.memory_agent_service import MemoryAgentService 
-                memory_agent_service = MemoryAgentService()
-                knowledge_stats = await memory_agent_service.get_knowledge_type_stats(
-                    end_user_id=end_user_id,
-                    only_active=True,
-                    current_workspace_id=workspace_id,
-                    db=db
-                )
-                neo4j_data["total_knowledge"] = knowledge_stats.get("total", 0)
-                api_logger.info(f"成功获取知识库类型统计total: {neo4j_data['total_knowledge']}")
-            except Exception as e:
-                api_logger.warning(f"获取知识库类型统计失败: {str(e)}")
+            # 2. 获取共享统计数据（total_app、total_knowledge、total_api_call）
+            common_stats = memory_dashboard_service.get_dashboard_common_stats(db, workspace_id)
+            neo4j_data.update(common_stats)
+            api_logger.info(f"成功获取共享统计: app={common_stats['total_app']}, knowledge={common_stats['total_knowledge']}, api_call={common_stats['total_api_call']}")
             
-            # 3. 获取API调用增量（total_api_call，转换为整数）
+            # 计算昨日对比
             try:
-                api_increment = memory_dashboard_service.get_workspace_api_increment(
+                changes = memory_dashboard_service.get_dashboard_yesterday_changes(
                     db=db,
                     workspace_id=workspace_id,
-                    current_user=current_user
+                    storage_type=storage_type,
+                    today_data=neo4j_data
                 )
-                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']}")
+                neo4j_data.update(changes)
             except Exception as e:
-                api_logger.warning(f"获取API调用增量失败: {str(e)}")
-            
+                api_logger.warning(f"计算neo4j昨日对比失败: {str(e)}")
+                neo4j_data.update({
+                    "total_memory_change": None,
+                    "total_app_change": None,
+                    "total_knowledge_change": None,
+                    "total_api_call_change": None,
+                })
+
             result["neo4j_data"] = neo4j_data
             api_logger.info("成功获取neo4j_data")
         
@@ -587,28 +639,37 @@ async def dashboard_data(
                 "total_api_call": None
             }
             
-            # 获取RAG相关数据
+            # 1. 获取记忆总量（total_memory）—— rag 独有逻辑：查询 document 表的 chunk_num
             try:
-                # total_memory: 使用 total_chunk（总chunk数）
-                total_chunk = memory_dashboard_service.get_rag_total_chunk(db, current_user)
+                total_chunk = memory_dashboard_service.get_rag_user_kb_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
-                from app.repositories import app_repository
-                apps_orm = app_repository.get_apps_by_workspace_id(db, workspace_id)
-                rag_data["total_app"] = len(apps_orm)
-                
-                # total_knowledge: 使用 total_kb（总知识库数）
-                total_kb = memory_dashboard_service.get_rag_total_kb(db, current_user)
-                rag_data["total_knowledge"] = total_kb
-                
-                # 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_logger.info(f"成功获取RAG记忆总量: {total_chunk}")
             except Exception as e:
-                api_logger.warning(f"获取RAG相关数据失败: {str(e)}")
+                api_logger.warning(f"获取RAG记忆总量失败: {str(e)}")
             
+            # 2. 获取共享统计数据（total_app、total_knowledge、total_api_call）
+            common_stats = memory_dashboard_service.get_dashboard_common_stats(db, workspace_id)
+            rag_data.update(common_stats)
+            api_logger.info(f"成功获取共享统计: app={common_stats['total_app']}, knowledge={common_stats['total_knowledge']}, api_call={common_stats['total_api_call']}")
+            
+            # 计算昨日对比
+            try:
+                changes = memory_dashboard_service.get_dashboard_yesterday_changes(
+                    db=db,
+                    workspace_id=workspace_id,
+                    storage_type=storage_type,
+                    today_data=rag_data
+                )
+                rag_data.update(changes)
+            except Exception as e:
+                api_logger.warning(f"计算RAG昨日对比失败: {str(e)}")
+                rag_data.update({
+                    "total_memory_change": None,
+                    "total_app_change": None,
+                    "total_knowledge_change": None,
+                    "total_api_call_change": None,
+                })
+
             result["rag_data"] = rag_data
             api_logger.info("成功获取rag_data")
         

api/app/controllers/memory_episodic_controller.py

@@ -3,9 +3,10 @@
 包含情景记忆总览和详情查询接口
 """
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, Header
 
 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
@@ -14,6 +15,7 @@ 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
 
@@ -84,6 +86,7 @@ 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:
     """
@@ -111,6 +114,11 @@ 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_explicit_controller.py

@@ -4,7 +4,9 @@
 处理显性记忆相关的API接口，包括情景记忆和语义记忆的查询。
 """
 
-from fastapi import APIRouter, Depends
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Query
 
 from app.core.logging_config import get_api_logger
 from app.core.response_utils import success, fail
@@ -69,6 +71,140 @@ async def get_explicit_memory_overview_api(
         return fail(BizCode.INTERNAL_ERROR, "显性记忆总览查询失败", str(e))
 
 
+@router.get("/episodics", response_model=ApiResponse)
+async def get_episodic_memory_list_api(
+    end_user_id: str = Query(..., description="end user ID"),
+    page: int = Query(1, gt=0, description="page number, starting from 1"),
+    pagesize: int = Query(10, gt=0, le=100, description="number of items per page, max 100"),
+    start_date: Optional[int] = Query(None, description="start timestamp (ms)"),
+    end_date: Optional[int] = Query(None, description="end timestamp (ms)"),
+    episodic_type: str = Query("all", description="episodic type ：all/conversation/project_work/learning/decision/important_event"),
+    current_user: User = Depends(get_current_user),
+) -> dict:
+    """
+    获取情景记忆分页列表
+
+    返回指定用户的情景记忆列表，支持分页、时间范围筛选和情景类型筛选。
+
+    Args:
+        end_user_id: 终端用户ID（必填）
+        page: 页码（从1开始，默认1）
+        pagesize: 每页数量（默认10，最大100）
+        start_date: 开始时间戳（可选，毫秒），自动扩展到当天 00:00:00
+        end_date: 结束时间戳（可选，毫秒），自动扩展到当天 23:59:59
+        episodic_type: 情景类型筛选（可选，默认all）
+        current_user: 当前用户
+
+    Returns:
+        ApiResponse: 包含情景记忆分页列表
+
+    Examples:
+        - 基础分页查询：GET /episodics?end_user_id=xxx&page=1&pagesize=5
+          返回第1页，每页5条数据
+        - 按时间范围筛选：GET /episodics?end_user_id=xxx&page=1&pagesize=5&start_date=1738684800000&end_date=1738771199000
+          返回指定时间范围内的数据
+        - 按情景类型筛选：GET /episodics?end_user_id=xxx&page=1&pagesize=5&episodic_type=important_event
+          返回类型为"重要事件"的数据
+
+    Notes:
+        - start_date 和 end_date 必须同时提供或同时不提供
+        - start_date 不能大于 end_date
+        - episodic_type 可选值：all, conversation, project_work, learning, decision, important_event
+        - total 为该用户情景记忆总数（不受筛选条件影响）
+        - page.total 为筛选后的总条数
+    """
+    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} 尝试查询情景记忆列表但未选择工作空间")
+        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"情景记忆分页查询: end_user_id={end_user_id}, "
+        f"start_date={start_date}, end_date={end_date}, episodic_type={episodic_type}, "
+        f"page={page}, pagesize={pagesize}, username={current_user.username}"
+    )
+
+    # 1. 参数校验
+    if page < 1 or pagesize < 1:
+        api_logger.warning(f"分页参数错误: page={page}, pagesize={pagesize}")
+        return fail(BizCode.INVALID_PARAMETER, "分页参数必须大于0")
+
+    valid_episodic_types = ["all", "conversation", "project_work", "learning", "decision", "important_event"]
+    if episodic_type not in valid_episodic_types:
+        api_logger.warning(f"无效的情景类型参数: {episodic_type}")
+        return fail(BizCode.INVALID_PARAMETER, f"无效的情景类型参数，可选值：{', '.join(valid_episodic_types)}")
+
+    # 时间戳参数校验
+    if (start_date is not None and end_date is None) or (end_date is not None and start_date is None):
+        return fail(BizCode.INVALID_PARAMETER, "start_date和end_date必须同时提供")
+
+    if start_date is not None and end_date is not None and start_date > end_date:
+        return fail(BizCode.INVALID_PARAMETER, "start_date不能大于end_date")
+
+    # 2. 执行查询
+    try:
+        result = await memory_explicit_service.get_episodic_memory_list(
+            end_user_id=end_user_id,
+            page=page,
+            pagesize=pagesize,
+            start_date=start_date,
+            end_date=end_date,
+            episodic_type=episodic_type,
+        )
+        api_logger.info(
+            f"情景记忆分页查询成功: end_user_id={end_user_id}, "
+            f"total={result['total']}, 返回={len(result['items'])}条"
+        )
+    except Exception as e:
+        api_logger.error(f"情景记忆分页查询失败: end_user_id={end_user_id}, error={str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "情景记忆分页查询失败", str(e))
+
+    # 3. 返回结构化响应
+    return success(data=result, msg="查询成功")
+
+@router.get("/semantics", response_model=ApiResponse)
+async def get_semantic_memory_list_api(
+    end_user_id: str = Query(..., description="终端用户ID"),
+    current_user: User = Depends(get_current_user),
+) -> dict:
+    """
+    获取语义记忆列表
+
+    返回指定用户的全量语义记忆列表。
+
+    Args:
+        end_user_id: 终端用户ID（必填）
+        current_user: 当前用户
+
+    Returns:
+        ApiResponse: 包含语义记忆全量列表
+    """
+    workspace_id = current_user.current_workspace_id
+
+    if workspace_id is None:
+        api_logger.warning(f"用户 {current_user.username} 尝试查询语义记忆列表但未选择工作空间")
+        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"语义记忆列表查询: end_user_id={end_user_id}, username={current_user.username}"
+    )
+
+    try:
+        result = await memory_explicit_service.get_semantic_memory_list(
+            end_user_id=end_user_id
+        )
+        api_logger.info(
+            f"语义记忆列表查询成功: end_user_id={end_user_id}, total={len(result)}"
+        )
+    except Exception as e:
+        api_logger.error(f"语义记忆列表查询失败: end_user_id={end_user_id}, error={str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "语义记忆列表查询失败", str(e))
+
+    return success(data=result, msg="查询成功")
+
+
 @router.post("/details", response_model=ApiResponse)
 async def get_explicit_memory_details_api(
     request: ExplicitMemoryDetailsRequest,

api/app/controllers/memory_forget_controller.py

@@ -31,6 +31,7 @@ from app.schemas.memory_storage_schema import (
     ForgettingCurveRequest,
     ForgettingCurveResponse,
     ForgettingCurvePoint,
+    PendingNodesResponse,
 )
 from app.schemas.response_schema import ApiResponse
 from app.services.memory_forget_service import MemoryForgetService
@@ -308,6 +309,100 @@ async def get_forgetting_stats(
         return fail(BizCode.INTERNAL_ERROR, "获取遗忘引擎统计失败", str(e))
 
 
+@router.get("/pending-nodes", response_model=ApiResponse)
+async def get_pending_nodes(
+    end_user_id: str,
+    page: int = 1,
+    pagesize: int = 10,
+    current_user: User = Depends(get_current_user),
+    db: Session = Depends(get_db)
+):
+    """
+    获取待遗忘节点列表（独立分页接口）
+
+    查询满足遗忘条件的节点（激活值低于阈值且最后访问时间超过最小天数）。
+    此接口独立分页，与 /stats 接口分离。
+
+    Args:
+        end_user_id: 组ID（即 end_user_id，必填）
+        page: 页码（从1开始，默认1）
+        pagesize: 每页数量（默认10）
+        current_user: 当前用户
+        db: 数据库会话
+
+    Returns:
+        ApiResponse: 包含待遗忘节点列表和分页信息的响应
+
+    Examples:
+        - 第1页，每页10条：GET /memory/forget-memory/pending-nodes?end_user_id=xxx&page=1&pagesize=10
+        - 第2页，每页20条：GET /memory/forget-memory/pending-nodes?end_user_id=xxx&page=2&pagesize=20
+
+    Notes:
+        - page 从1开始，pagesize 必须大于0
+        - 返回格式：{"items": [...], "page": {"page": 1, "pagesize": 10, "total": 100, "hasnext": true}}
+    """
+    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} 尝试获取待遗忘节点但未选择工作空间")
+        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
+
+    # 验证 end_user_id 必填
+    if not end_user_id:
+        api_logger.warning(f"用户 {current_user.username} 尝试获取待遗忘节点但未提供 end_user_id")
+        return fail(BizCode.INVALID_PARAMETER, "end_user_id 不能为空", "end_user_id is required")
+
+    # 通过 end_user_id 获取关联的 config_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)
+        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.debug(f"通过 end_user_id={end_user_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")
+    except Exception as e:
+        api_logger.error(f"获取终端用户配置时发生错误: {str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "获取终端用户配置失败", str(e))
+
+    # 验证分页参数
+    if page < 1:
+        return fail(BizCode.INVALID_PARAMETER, "page 必须大于等于1", "page < 1")
+    if pagesize < 1:
+        return fail(BizCode.INVALID_PARAMETER, "pagesize 必须大于等于1", "pagesize < 1")
+
+    api_logger.info(
+        f"用户 {current_user.username} 在工作空间 {workspace_id} 请求获取待遗忘节点: "
+        f"end_user_id={end_user_id}, page={page}, pagesize={pagesize}"
+    )
+
+    try:
+        # 调用服务层获取待遗忘节点列表
+        result = await forget_service.get_pending_nodes(
+            db=db,
+            end_user_id=end_user_id,
+            config_id=config_id,
+            page=page,
+            pagesize=pagesize
+        )
+
+        # 构建响应
+        response_data = PendingNodesResponse(**result)
+
+        return success(data=response_data.model_dump(), msg="查询成功")
+
+    except Exception as e:
+        api_logger.error(f"获取待遗忘节点列表失败: {str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "获取待遗忘节点列表失败", str(e))
+
+
 @router.post("/forgetting_curve", response_model=ApiResponse)
 async def get_forgetting_curve(
     request: ForgettingCurveRequest,

api/app/controllers/memory_reflection_controller.py

@@ -1,8 +1,25 @@
+"""
+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,
@@ -27,9 +44,13 @@ 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"],
@@ -42,7 +63,38 @@ async def save_reflection_config(
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
-    """Save reflection configuration to data_comfig table"""
+    """
+    Save reflection configuration to memory config 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)
@@ -51,9 +103,9 @@ async def save_reflection_config(
                 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,
@@ -66,6 +118,7 @@ async def save_reflection_config(
             quality_assessment=request.quality_assessment
         )
 
+        # Commit transaction and refresh entity
         db.commit()
         db.refresh(memory_config)
 
@@ -102,51 +155,114 @@ async def start_workspace_reflection(
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
-    """Activate the reflection function for all matching applications in the workspace"""
+    """
+    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
+    """
     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}")
 
-        service = WorkspaceAppService(db)
-        result = service.get_workspace_apps_detailed(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)
         
         reflection_results = []
         
+        # Process each application in the workspace
         for data in result['apps_detailed_info']:
-            if data['memory_configs'] == []: 
+            # Skip applications without configurations
+            if not data['memory_configs']:
+                api_logger.debug(f"应用 {data['id']} 没有memory_configs，跳过")
                 continue
-                
+
             releases = data['releases']
             memory_configs = data['memory_configs']
             end_users = data['end_users']
-            
-            for base, config, user in zip(releases, memory_configs, end_users):
-                # 安全地转换为整数，处理空字符串和None的情况
-                print(base['config'])
-                try:
-                    base_config = int(base['config']) if base['config'] else 0
-                    config_id = int(config['config_id']) if config['config_id'] else 0
-                except (ValueError, TypeError):
-                    api_logger.warning(f"无效的配置ID: base['config']={base.get('config')}, config['config_id']={config.get('config_id')}")
+
+            # 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
-                
-                if base_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_text_reflection(
-                        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
-                    })
+
+                # 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)}"
+                                }
+                            })
 
         return success(data=reflection_results, msg="反思配置成功")
 
@@ -164,14 +280,51 @@ async def start_reflection_configs(
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db),
 ) -> dict:
-    """通过config_id查询memory_config表中的反思配置信息"""
+    """
+    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)
     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)
-        # 构建返回数据
+        
+        # Build response data with comprehensive configuration details
         reflection_config = {
             "config_id": memory_config_id,
             "reflection_enabled": result.enable_self_reflexion,
@@ -184,10 +337,12 @@ async def start_reflection_configs(
         }
         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:
-        # 重新抛出HTTP异常
+        # Re-raise HTTP exceptions without modification
         raise
     except Exception as e:
         api_logger.error(f"查询反思配置失败: {str(e)}")
@@ -199,15 +354,70 @@ async def start_reflection_configs(
 @router.get("/reflection/run")
 async def reflection_run(
     config_id: UUID|int,
-    language_type: str = Header(default="zh", alias="X-Language-Type"),
+    language_type: str = Header(default=None, alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
-    """Activate the reflection function for all matching applications in the workspace"""
+    """
+    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)
 
     api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
     config_id = resolve_config_id(config_id, db)
-    # 使用MemoryConfigRepository查询反思配置
+    
+    # Query reflection configuration using MemoryConfigRepository
     result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id)
     if not result:
         raise HTTPException(
@@ -217,7 +427,7 @@ async def reflection_run(
 
     api_logger.info(f"成功查询反思配置，config_id: {config_id}")
 
-    # 验证模型ID是否存在
+    # Validate model ID existence
     model_id = result.reflection_model_id
     if model_id:
         try:
@@ -228,6 +438,7 @@ async def reflection_run(
             # 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,
@@ -240,11 +451,13 @@ 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  # 传入验证后的 model_id
+        llm_client=model_id  # Pass validated model_id
     )
 
     result=await (engine.reflection_run())

api/app/controllers/memory_short_term_controller.py

@@ -1,18 +1,40 @@
-from fastapi import APIRouter, Depends, HTTPException, status,Header
+"""
+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 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
-from app.services.memory_short_service import ShortService,LongService
-from dotenv import load_dotenv
-from sqlalchemy.orm import Session
-from typing import Optional
+
+# 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/short",
     tags=["Memory"],
@@ -20,25 +42,77 @@ router = APIRouter(
 @router.get("/short_term")
 async def short_term_configs(
         end_user_id: str,
-        language_type:str = Header(default="zh", alias="X-Language-Type"),
+        language_type:str = Header(default=None, alias="X-Language-Type"),
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db),
 ):
-    # 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 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
 
-    long_term=LongService(end_user_id)
-    long_result=long_term.get_long_databasets()
+    # 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
 
+    # 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,
-        'long_term': long_result,
-        'entity': entity_result.get('num', 0),
-        "retrieval_number":short_count,
-        "long_term_number":len(long_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
     }
 
     return success(data=result, msg="短期记忆系统数据获取成功")

api/app/controllers/memory_storage_controller.py

@@ -1,8 +1,12 @@
-import os
 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.response_utils import fail, success
 from app.db import get_db
@@ -11,7 +15,6 @@ from app.models.user_model import User
 from app.schemas.memory_storage_schema import (
     ConfigKey,
     ConfigParamsCreate,
-    ConfigParamsDelete,
     ConfigPilotRun,
     ConfigUpdate,
     ConfigUpdateExtracted,
@@ -23,7 +26,7 @@ from app.services.memory_storage_service import (
     analytics_hot_memory_tags,
     analytics_recent_activity_stats,
     kb_type_distribution,
-    search_all,
+    search_all_batch,
     search_chunk,
     search_detials,
     search_dialogue,
@@ -31,7 +34,8 @@ from app.services.memory_storage_service import (
     search_entity,
     search_statement,
 )
-from fastapi import APIRouter, Depends
+from app.core.quota_stub import check_memory_engine_quota
+from fastapi import APIRouter, Depends, Header
 from fastapi.responses import StreamingResponse
 from sqlalchemy.orm import Session
 
@@ -51,8 +55,8 @@ router = APIRouter(
 
 @router.get("/info", response_model=ApiResponse)
 async def get_storage_info(
-    storage_id: str,
-    current_user: User = Depends(get_current_user)
+        storage_id: str,
+        current_user: User = Depends(get_current_user)
 ):
     """
     Example wrapper endpoint - retrieves storage information
@@ -72,82 +76,20 @@ 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
+@router.post("/create_config", response_model=ApiResponse)  # 创建配置文件，其他参数默认
+@check_memory_engine_quota
 def create_config(
-    payload: ConfigParamsCreate,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
-    ) -> dict:
+        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:
     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")
         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} 请求创建配置: {payload.config_name}")
     try:
         # 将 workspace_id 注入到 payload 中（保持为 UUID 类型）
@@ -155,46 +97,130 @@ 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,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
-    ) -> dict:
+        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）"),
+        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）
+    """
     workspace_id = current_user.current_workspace_id
-    config_id=resolve_config_id(config_id, db)
+    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: {config_id}")
+
+    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}"
+    )
+
     try:
-        svc = DataConfigService(db)
-        result = svc.delete(ConfigParamsDelete(config_id=config_id))
-        return success(data=result, msg="删除成功")
+        # 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"]}
+        )
+
     except Exception as e:
-        api_logger.error(f"Delete config failed: {str(e)}")
+        api_logger.error(f"Delete config failed: {str(e)}", exc_info=True)
         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:
+        payload: ConfigUpdate,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db),
+) -> 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)
@@ -207,17 +233,17 @@ def update_config(
 
 @router.post("/update_config_extracted", response_model=ApiResponse)  # 更新数据库中的部分内容 所有业务字段均可选
 def update_config_extracted(
-    payload: ConfigUpdateExtracted,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
-    ) -> dict:
+        payload: ConfigUpdateExtracted,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db),
+) -> 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")
         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} 请求更新提取配置: {payload.config_id}")
     try:
         svc = DataConfigService(db)
@@ -232,19 +258,19 @@ def update_config_extracted(
 # 遗忘引擎配置接口已迁移到 memory_forget_controller.py
 # 使用新接口: /api/memory/forget/read_config 和 /api/memory/forget/update_config
 
-@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
+@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,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
-    ) -> dict:
+        config_id: UUID | int,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db),
+) -> 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")
         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} 请求读取提取配置: {config_id}")
     try:
         svc = DataConfigService(db)
@@ -254,18 +280,19 @@ def read_config_extracted(
         api_logger.error(f"Read config extracted failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "查询配置失败", str(e))
 
-@router.get("/read_all_config", response_model=ApiResponse) # 读取所有配置文件列表
+
+@router.get("/read_all_config", response_model=ApiResponse)  # 读取所有配置文件列表
 def read_all_config(
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
-    ) -> dict:
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db),
+) -> 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} 尝试查询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} 请求读取所有配置")
     try:
         svc = DataConfigService(db)
@@ -279,18 +306,23 @@ def read_all_config(
 
 @router.post("/pilot_run", response_model=None)
 async def pilot_run(
-    payload: ConfigPilotRun,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
+        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"dialogue_text_length={len(payload.dialogue_text)}, "
+        f"custom_text_length={len(payload.custom_text) if payload.custom_text else 0}"
     )
     payload.config_id = resolve_config_id(payload.config_id, db)
     svc = DataConfigService(db)
     return StreamingResponse(
-        svc.pilot_run_stream(payload),
+        svc.pilot_run_stream(payload, language=language),
         media_type="text/event-stream",
         headers={
             "Cache-Control": "no-cache",
@@ -299,15 +331,14 @@ async def pilot_run(
         },
     )
 
-"""
-以下为搜索与分析接口，直接挂载到同一 router，统一响应为 ApiResponse。
-"""
+
+# ==================== Search & Analytics ====================
 
 @router.get("/search/kb_type_distribution", response_model=ApiResponse)
 async def get_kb_type_distribution(
-    end_user_id: Optional[str] = None,
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
+        end_user_id: Optional[str] = None,
+        current_user: User = Depends(get_current_user),
+) -> dict:
     api_logger.info(f"KB type distribution requested for end_user_id: {end_user_id}")
     try:
         result = await kb_type_distribution(end_user_id)
@@ -316,12 +347,12 @@ async def get_kb_type_distribution(
         api_logger.error(f"KB type distribution failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "知识库类型分布查询失败", str(e))
 
-    
+
 @router.get("/search/dialogue", response_model=ApiResponse)
 async def search_dialogues_num(
-    end_user_id: Optional[str] = None,
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
+        end_user_id: Optional[str] = None,
+        current_user: User = Depends(get_current_user),
+) -> dict:
     api_logger.info(f"Search dialogue requested for end_user_id: {end_user_id}")
     try:
         result = await search_dialogue(end_user_id)
@@ -333,9 +364,9 @@ async def search_dialogues_num(
 
 @router.get("/search/chunk", response_model=ApiResponse)
 async def search_chunks_num(
-    end_user_id: Optional[str] = None,
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
+        end_user_id: Optional[str] = None,
+        current_user: User = Depends(get_current_user),
+) -> dict:
     api_logger.info(f"Search chunk requested for end_user_id: {end_user_id}")
     try:
         result = await search_chunk(end_user_id)
@@ -347,9 +378,9 @@ async def search_chunks_num(
 
 @router.get("/search/statement", response_model=ApiResponse)
 async def search_statements_num(
-    end_user_id: Optional[str] = None,
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
+        end_user_id: Optional[str] = None,
+        current_user: User = Depends(get_current_user),
+) -> dict:
     api_logger.info(f"Search statement requested for end_user_id: {end_user_id}")
     try:
         result = await search_statement(end_user_id)
@@ -361,9 +392,9 @@ async def search_statements_num(
 
 @router.get("/search/entity", response_model=ApiResponse)
 async def search_entities_num(
-    end_user_id: Optional[str] = None,
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
+        end_user_id: Optional[str] = None,
+        current_user: User = Depends(get_current_user),
+) -> dict:
     api_logger.info(f"Search entity requested for end_user_id: {end_user_id}")
     try:
         result = await search_entity(end_user_id)
@@ -375,12 +406,15 @@ async def search_entities_num(
 
 @router.get("/search", response_model=ApiResponse)
 async def search_all_num(
-    end_user_id: Optional[str] = None,
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
+        end_user_id: Optional[str] = None,
+        current_user: User = Depends(get_current_user),
+) -> dict:
     api_logger.info(f"Search all requested for end_user_id: {end_user_id}")
     try:
-        result = await search_all(end_user_id)
+        if not end_user_id:
+            return success(data={"total": 0}, msg="查询成功")
+        batch_result = await search_all_batch([end_user_id])
+        result = {"total": batch_result.get(end_user_id, 0)}
         return success(data=result, msg="查询成功")
     except Exception as e:
         api_logger.error(f"Search all failed: {str(e)}")
@@ -389,9 +423,9 @@ async def search_all_num(
 
 @router.get("/search/detials", response_model=ApiResponse)
 async def search_entities_detials(
-    end_user_id: Optional[str] = None,
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
+        end_user_id: Optional[str] = None,
+        current_user: User = Depends(get_current_user),
+) -> dict:
     api_logger.info(f"Search details requested for end_user_id: {end_user_id}")
     try:
         result = await search_detials(end_user_id)
@@ -403,9 +437,9 @@ async def search_entities_detials(
 
 @router.get("/search/edges", response_model=ApiResponse)
 async def search_entity_edges(
-    end_user_id: Optional[str] = None,
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
+        end_user_id: Optional[str] = None,
+        current_user: User = Depends(get_current_user),
+) -> dict:
     api_logger.info(f"Search edges requested for end_user_id: {end_user_id}")
     try:
         result = await search_edges(end_user_id)
@@ -415,14 +449,12 @@ async def search_entity_edges(
         return fail(BizCode.INTERNAL_ERROR, "边查询失败", str(e))
 
 
-
-
 @router.get("/analytics/hot_memory_tags", response_model=ApiResponse)
 async def get_hot_memory_tags_api(
-    limit: int = 10,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
+        limit: int = 10,
+        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）
     
@@ -433,17 +465,18 @@ async def get_hot_memory_tags_api(
     - 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}")
-    
+
     try:
         # 尝试从Redis缓存获取
-        from app.aioRedis import aio_redis_get, aio_redis_set
         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}")
@@ -452,11 +485,11 @@ async def get_hot_memory_tags_api(
                 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:
@@ -466,9 +499,9 @@ async def get_hot_memory_tags_api(
         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))
@@ -476,8 +509,8 @@ async def get_hot_memory_tags_api(
 
 @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:
+        current_user: User = Depends(get_current_user),
+) -> dict:
     """
     清除热门标签缓存
     
@@ -487,12 +520,12 @@ async def clear_hot_memory_tags_cache(
     - 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]:
@@ -501,12 +534,12 @@ async def clear_hot_memory_tags_cache(
             if result:
                 cleared_count += 1
                 api_logger.info(f"Cleared cache for key: {cache_key}")
-        
+
         return success(
-            data={"cleared_count": cleared_count}, 
+            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))
@@ -514,13 +547,13 @@ async def clear_hot_memory_tags_cache(
 
 @router.get("/analytics/recent_activity_stats", response_model=ApiResponse)
 async def get_recent_activity_stats_api(
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
-    api_logger.info("Recent activity stats requested")
+        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}")
     try:
-        result = await analytics_recent_activity_stats()
+        result = await analytics_recent_activity_stats(workspace_id=workspace_id)
         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))
-

api/app/controllers/memory_working_controller.py

@@ -8,6 +8,7 @@ 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
 
@@ -32,35 +33,47 @@ def get_memory_count(
 @router.get("/{end_user_id}/conversations", response_model=ApiResponse)
 def get_conversations(
         end_user_id: uuid.UUID,
+        page: int = 1,
+        pagesize: int = 20,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     """
-    Retrieve all conversations for the current user in a specific group.
+    Retrieve conversations for the current user in a specific group with pagination.
 
     Args:
         end_user_id (UUID): The group identifier.
+        page (int): Page number (1-based). Defaults to 1.
+        pagesize (int): Number of items per page. Defaults to 20.
         current_user (User, optional): The authenticated user.
         db (Session, optional): SQLAlchemy session.
 
     Returns:
-        ApiResponse: Contains a list of conversation IDs.
-
-    Notes:
-        - Initializes the ConversationService with the current DB session.
-        - Returns only conversation IDs for lightweight response.
-        - Logs can be added to trace requests in production.
+        ApiResponse: Contains a paginated list of conversations.
     """
+    page = max(1, page)
+    page_size = max(1, min(pagesize, 100))  # Limit page size between 1 and 100
     conversation_service = ConversationService(db)
-    conversations = conversation_service.get_user_conversations(
-        end_user_id
+    conversations, total = conversation_service.get_user_conversations(
+        end_user_id,
+        page=page,
+        page_size=page_size
     )
-    return success(data=[
-        {
-            "id": conversation.id,
-            "title": conversation.title
-        } for conversation in conversations
-    ], msg="get conversations success")
+    return success(data={
+        "items": [
+            {
+                "id": conversation.id,
+                "title": conversation.title
+            } for conversation in conversations
+        ],
+        "total": total,
+        "page": {
+            "page": page,
+            "pagesize": page_size,
+            "total": total,
+            "hasnext": (page * page_size) < total
+        },
+    }, msg="get conversations success")
 
 
 @router.get("/{end_user_id}/messages", response_model=ApiResponse)
@@ -90,11 +103,7 @@ def get_messages(
         conversation_id,
     )
     messages = [
-        {
-            "role": message.role,
-            "content": message.content,
-            "created_at": int(message.created_at.timestamp() * 1000),
-        }
+        conversation_schema.Message.model_validate(message)
         for message in messages_obj
     ]
     return success(data=messages, msg="get conversation history success")

api/app/controllers/model_controller.py

@@ -15,6 +15,7 @@ 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.core.logging_config import get_api_logger
+from app.core.quota_stub import check_model_quota, check_model_activation_quota
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -42,6 +43,7 @@ def get_model_strategies():
 @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）"),
+        capability: Optional[list[str]] = Query(None, description="能力筛选（支持多个，如 ?capability=chat 或 ?capability=chat, 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"),
@@ -74,10 +76,21 @@ def get_model_list(
             unique_flat_type = list(dict.fromkeys(flat_type))
             type_list = [ModelType(t.lower()) for t in unique_flat_type]
 
+        capability_list = []
+        if capability is not None:
+            flat_capability = []
+            for item in capability:
+                split_items = [c.strip() for c in item.split(', ') if c.strip()]
+                flat_capability.extend(split_items)
+
+            unique_flat_capability = list(dict.fromkeys(flat_capability))
+            capability_list = unique_flat_capability
+
         api_logger.error(f"获取模型type_list: {type_list}")
         query = model_schema.ModelConfigQuery(
             type=type_list,
             provider=provider,
+            capability=capability_list,
             is_active=is_active,
             is_public=is_public,
             search=search,
@@ -291,6 +304,7 @@ async def create_model(
 
 
 @router.post("/composite", response_model=ApiResponse)
+@check_model_quota
 async def create_composite_model(
     model_data: model_schema.CompositeModelCreate,
     db: Session = Depends(get_db),
@@ -317,6 +331,7 @@ async def create_composite_model(
 
 
 @router.put("/composite/{model_id}", response_model=ApiResponse)
+@check_model_activation_quota
 async def update_composite_model(
     model_id: uuid.UUID,
     model_data: model_schema.CompositeModelCreate,
@@ -328,7 +343,7 @@ async def update_composite_model(
     
     try:
         if model_data.type is not None:
-            raise BusinessException("不允许更改模型类型和供应商", BizCode.INVALID_PARAMETER)
+            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})")
         
@@ -368,6 +383,14 @@ 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}")
@@ -466,7 +489,9 @@ async def create_model_api_key_by_provider(
             config=api_key_data.config,
             is_active=api_key_data.is_active,
             priority=api_key_data.priority,
-            model_config_ids=model_config_ids
+            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/app/controllers/ontology_secondary_routes.py

@@ -0,0 +1,663 @@
+# -*- 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 uuid
 import json
+import uuid
 
 from fastapi import APIRouter, Depends, Path
 from sqlalchemy.orm import Session
@@ -8,9 +8,13 @@ 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.models.prompt_optimizer_model import RoleType
-from app.schemas.prompt_optimizer_schema import PromptOptMessage, PromptOptModelSet, CreateSessionResponse, \
-    OptimizePromptResponse, SessionHistoryResponse, SessionMessage
+from app.schemas.prompt_optimizer_schema import (
+    PromptOptMessage,
+    CreateSessionResponse,
+    SessionHistoryResponse,
+    SessionMessage,
+    PromptSaveRequest
+)
 from app.schemas.response_schema import ApiResponse
 from app.services.prompt_optimizer_service import PromptOptimizerService
 
@@ -116,13 +120,15 @@ async def get_prompt_opt(
                     session_id=session_id,
                     user_id=current_user.id,
                     current_prompt=data.current_prompt,
-                    user_require=data.message
+                    user_require=data.message,
+                    skill=data.skill
             ):
                 # chunk 是 prompt 的增量内容
-                yield f"event:message\ndata: {json.dumps(chunk)}\n\n"
+                yield f"event:message\ndata: {json.dumps(chunk, ensure_ascii=False)}\n\n"
         except Exception as e:
             yield f"event:error\ndata: {json.dumps(
-                {"error": str(e)}
+                {"error": str(e)},
+                ensure_ascii=False
             )}\n\n"
         yield "event:end\ndata: {}\n\n"
 
@@ -135,3 +141,109 @@ 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

@@ -2,25 +2,34 @@ 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
+from app.core.quota_manager import check_end_user_quota
+from app.core.response_utils import success, fail
 from app.db import get_db, get_db_read
 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.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, \
+from app.services.workflow_service import WorkflowService
+from app.models.file_metadata_model import FileMetadata
+from app.utils.app_config_utils import workflow_config_4_app_release, \
     agent_config_4_app_release, multi_agent_config_4_app_release
 
 router = APIRouter(prefix="/public/share", tags=["Public Share"])
@@ -206,15 +215,27 @@ 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)
-    from app.repositories.end_user_repository import EndUserRepository
+    share, release = service.get_release_by_share_token(share_data.share_token, password)
     end_user_repo = EndUserRepository(db)
+    app_service = AppService(db)
+    app = app_service._get_app_or_404(share.app_id)
+    workspace_id = app.workspace_id
+
+    # 仅在新建终端用户时检查配额
+    existing_end_user = end_user_repo.get_end_user_by_other_id(workspace_id=workspace_id, other_id=other_id)
+    if existing_end_user is None:
+        from app.core.quota_manager import _check_quota
+        from app.models.workspace_model import Workspace
+        ws = db.query(Workspace).filter(Workspace.id == workspace_id).first()
+        if ws:
+            _check_quota(db, ws.tenant_id, "end_user_quota", "end_user", workspace_id=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
     )
     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),
@@ -251,8 +272,41 @@ def get_conversation(
     conv_service = ConversationService(db)
     messages = conv_service.get_messages(conversation_id)
 
-    # 构建响应
-    conv_dict = conversation_schema.Conversation.model_validate(conversation).model_dump()
+    file_ids = []
+    message_file_id_map = {}
+
+    # 第一次遍历：解析 audio_url，收集所有有效的 file_id
+    for idx, m in enumerate(messages):
+        if m.role == "assistant" and m.meta_data:
+            audio_url = m.meta_data.get("audio_url")
+            if not audio_url:
+                continue
+            try:
+                file_id = uuid.UUID(audio_url.rstrip("/").split("/")[-1])
+            except (ValueError, IndexError):
+                # audio_url 无法解析为 UUID，标记为 unknown
+                m.meta_data["audio_status"] = "unknown"
+                continue
+
+            file_ids.append(file_id)
+            message_file_id_map[idx] = file_id
+
+    # 批量查询所有相关的 FileMetadata
+    file_status_map = {}
+    if file_ids:
+        file_metas = (
+            db.query(FileMetadata)
+            .filter(FileMetadata.id.in_(set(file_ids)))
+            .all()
+        )
+        file_status_map = {fm.id: fm.status for fm in file_metas}
+
+    # 第二次遍历：将查询结果映射回消息
+    for idx, file_id in message_file_id_map.items():
+        m = messages[idx]
+        m.meta_data["audio_status"] = file_status_map.get(file_id, "unknown")
+
+    conv_dict = conversation_schema.Conversation.model_validate(conversation).model_dump(mode="json")
     conv_dict["messages"] = [
         conversation_schema.Message.model_validate(m) for m in messages
     ]
@@ -293,40 +347,61 @@ 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
+
+        # 仅在新建终端用户时检查配额，已有用户复用不受限制
+        existing_end_user = end_user_repo.get_end_user_by_other_id(workspace_id=workspace_id, other_id=other_id)
+        logger.info(f"终端用户配额检查: workspace_id={workspace_id}, other_id={other_id}, existing={existing_end_user is not None}")
+        if existing_end_user is None:
+            from app.core.quota_manager import _check_quota
+            from app.models.workspace_model import Workspace
+            ws = db.query(Workspace).filter(Workspace.id == workspace_id).first()
+            if ws:
+                logger.info(f"新终端用户，执行配额检查: tenant_id={ws.tenant_id}")
+                _check_quota(db, ws.tenant_id, "end_user_quota", "end_user", workspace_id=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  # Save original user_id to other_id
+            original_user_id=user_id
         )
+
+        # Only extract and set memory_config_id when the end user doesn't have one yet
+        if not new_end_user.memory_config_id:
+            from app.services.memory_config_service import MemoryConfigService
+            memory_config_service = MemoryConfigService(db)
+            memory_config_id, _ = memory_config_service.extract_memory_config_id(release.type, release.config or {})
+            if memory_config_id:
+                new_end_user.memory_config_id = memory_config_id
+                db.commit()
+                db.refresh(new_end_user)
         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）
-        from app.models.app_model import App
-        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)
+        # 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)
 
-        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(
@@ -359,12 +434,12 @@ 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 == "agent":
+        if app_type == AppType.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 == "multi_agent":
+        elif app_type == AppType.MULTI_AGENT:
             # Multi-Agent 类型：验证多 Agent 配置
             config = release.config or {}
             if not config.get("sub_agents"):
@@ -402,31 +477,10 @@ async def chat(
         # 流式返回
         agent_config = agent_config_4_app_release(release)
 
-        if payload.stream:
-            # async def event_generator():
-            #     async for event in service.chat_stream(
-            #         share_token=share_token,
-            #         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,
-            #         password=password,
-            #         web_search=payload.web_search,
-            #         memory=payload.memory,
-            #         storage_type=storage_type,
-            #         user_rag_memory_id=user_rag_memory_id
-            #     ):
-            #         yield event
+        if not (agent_config.model_parameters.get("deep_thinking", False) and payload.thinking):
+            agent_config.model_parameters["deep_thinking"] = False
 
-            # return StreamingResponse(
-            #     event_generator(),
-            #     media_type="text/event-stream",
-            #     headers={
-            #         "Cache-Control": "no-cache",
-            #         "Connection": "keep-alive",
-            #         "X-Accel-Buffering": "no"
-            #     }
-            # )
+        if payload.stream:
             async def event_generator():
                 async for event in app_chat_service.agnet_chat_stream(
                         message=payload.message,
@@ -438,7 +492,8 @@ async def chat(
                         memory=payload.memory,
                         storage_type=storage_type,
                         user_rag_memory_id=user_rag_memory_id,
-                        workspace_id=workspace_id
+                        workspace_id=workspace_id,
+                        files=payload.files  # 传递多模态文件
                 ):
                     yield event
 
@@ -451,20 +506,6 @@ async def chat(
                     "X-Accel-Buffering": "no"
                 }
             )
-        # 非流式返回
-        # result = await service.chat(
-        #     share_token=share_token,
-        #     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,
-        #     password=password,
-        #     web_search=payload.web_search,
-        #     memory=payload.memory,
-        #     storage_type=storage_type,
-        #     user_rag_memory_id=user_rag_memory_id
-        # )
-        # return success(data=conversation_schema.ChatResponse(**result))
         result = await app_chat_service.agnet_chat(
             message=payload.message,
             conversation_id=conversation.id,  # 使用已创建的会话 ID
@@ -475,7 +516,8 @@ async def chat(
             memory=payload.memory,
             storage_type=storage_type,
             user_rag_memory_id=user_rag_memory_id,
-            workspace_id=workspace_id
+            workspace_id=workspace_id,
+            files=payload.files  # 传递多模态文件
         )
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
     elif app_type == AppType.MULTI_AGENT:
@@ -522,48 +564,6 @@ async def chat(
         )
 
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
-        # 多 Agent 流式返回
-        # if payload.stream:
-        #     async def event_generator():
-        #         async for event in service.multi_agent_chat_stream(
-        #             share_token=share_token,
-        #             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,
-        #             password=password,
-        #             web_search=payload.web_search,
-        #             memory=payload.memory,
-        #                 storage_type=storage_type,
-        #                 user_rag_memory_id=user_rag_memory_id
-        #         ):
-        #             yield event
-
-        #     return StreamingResponse(
-        #         event_generator(),
-        #         media_type="text/event-stream",
-        #         headers={
-        #             "Cache-Control": "no-cache",
-        #             "Connection": "keep-alive",
-        #             "X-Accel-Buffering": "no"
-        #         }
-        #     )
-
-        # # 多 Agent 非流式返回
-        # result = await service.multi_agent_chat(
-        #     share_token=share_token,
-        #     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,
-        #     password=password,
-        #     web_search=payload.web_search,
-        #     memory=payload.memory,
-        #     storage_type=storage_type,
-        #     user_rag_memory_id=user_rag_memory_id
-        # )
-
-        # return success(data=conversation_schema.ChatResponse(**result))
     elif app_type == AppType.WORKFLOW:
         config = workflow_config_4_app_release(release)
         if not config.id:
@@ -578,6 +578,7 @@ 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,
+                        files=payload.files,
                         config=config,
                         web_search=payload.web_search,
                         memory=payload.memory,
@@ -585,7 +586,8 @@ async def chat(
                         user_rag_memory_id=user_rag_memory_id,
                         app_id=release.app_id,
                         workspace_id=workspace_id,
-                        release_id=release.id
+                        release_id=release.id,
+                        public=True
                 ):
                     event_type = event.get("event", "message")
                     event_data = event.get("data", {})
@@ -606,11 +608,11 @@ 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,
@@ -634,6 +636,40 @@ 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"),
+            "memory": release.config.get("memory", {}).get("enabled"),
+            "features": release.config.get("features"),
+            "model_parameters": release.config.get("model_parameters")
+        }
+    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/__init__.py

@@ -4,7 +4,18 @@
 认证方式: API Key
 """
 from fastapi import APIRouter
-from . import app_api_controller, rag_api_knowledge_controller, rag_api_document_controller, rag_api_file_controller, rag_api_chunk_controller, memory_api_controller
+
+from . import (
+    app_api_controller,
+    end_user_api_controller,
+    memory_api_controller,
+    memory_config_api_controller,
+    rag_api_chunk_controller,
+    rag_api_document_controller,
+    rag_api_file_controller,
+    rag_api_knowledge_controller,
+    user_memory_api_controller,
+)
 
 # 创建 V1 API 路由器
 service_router = APIRouter()
@@ -16,5 +27,8 @@ service_router.include_router(rag_api_document_controller.router)
 service_router.include_router(rag_api_file_controller.router)
 service_router.include_router(rag_api_chunk_controller.router)
 service_router.include_router(memory_api_controller.router)
+service_router.include_router(end_user_api_controller.router)
+service_router.include_router(memory_config_api_controller.router)
+service_router.include_router(user_memory_api_controller.router)
 
 __all__ = ["service_router"]

api/app/controllers/service/app_api_controller.py

@@ -12,18 +12,19 @@ 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.models.app_release_model import AppRelease
 from app.repositories import knowledge_repository
 from app.repositories.end_user_repository import EndUserRepository
 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.conversation_service import ConversationService, get_conversation_service
-from app.utils.app_config_utils import dict_to_multi_agent_config, workflow_config_4_app_release, agent_config_4_app_release, multi_agent_config_4_app_release
 from app.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
 
 router = APIRouter(prefix="/app", tags=["V1 - App API"])
 logger = get_business_logger()
@@ -34,6 +35,7 @@ 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")
@@ -60,46 +62,68 @@ async def list_apps():
 #     return success(data={"received": True}, msg="消息已接收")
 
 
-def _checkAppConfig(app: App):
-    if app.type == AppType.AGENT:
-        if not app.current_release.config:
+def _checkAppConfig(release: AppRelease):
+    if release.type == AppType.AGENT:
+        if not release.config:
             raise BusinessException("Agent 应用未配置模型", BizCode.AGENT_CONFIG_MISSING)
-    elif app.type == AppType.MULTI_AGENT:
-        if not app.current_release.config:
+    elif release.type == AppType.MULTI_AGENT:
+        if not release.config:
             raise BusinessException("Multi-Agent 应用未配置模型", BizCode.AGENT_CONFIG_MISSING)
-    elif app.type == AppType.WORKFLOW:
-        if not app.current_release.config:
+    elif release.type == AppType.WORKFLOW:
+        if not release.config:
             raise BusinessException("工作流应用未配置模型", BizCode.AGENT_CONFIG_MISSING)
     else:
-        raise BusinessException("不支持的应用类型", BizCode.AGENT_CONFIG_MISSING)
+        raise BusinessException("不支持的应用类型", BizCode.APP_TYPE_NOT_SUPPORTED)
+
 
 @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"),
 ):
+    """
+    Agent/Workflow 聊天接口
+
+    - 不传 version：使用当前生效版本（current_release，回滚后为回滚目标版本）
+    - 传 version=release_id：使用指定版本uuid的历史快照，例如 {"version": "{{release_id}}"}
+    """
     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)
+
+    # 版本切换：指定 release_id 时查找对应历史快照，否则使用当前激活版本
+    if payload.version is not None:
+        active_release = app_service.get_release_by_id(app.id, payload.version)
+    else:
+        active_release = app.current_release
     other_id = payload.user_id
-    workspace_id = app.workspace_id
+    workspace_id = api_key_auth.workspace_id
     end_user_repo = EndUserRepository(db)
+
+    # 仅在新建终端用户时检查配额，已有用户复用不受限制
+    existing_end_user = end_user_repo.get_end_user_by_other_id(workspace_id=workspace_id, other_id=other_id)
+    if existing_end_user is None:
+        from app.core.quota_manager import _check_quota
+        from app.models.workspace_model import Workspace
+        ws = db.query(Workspace).filter(Workspace.id == workspace_id).first()
+        if ws:
+            _check_quota(db, ws.tenant_id, "end_user_quota", "end_user", workspace_id=workspace_id)
+
     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,
@@ -126,36 +150,43 @@ async def chat(
             storage_type = 'neo4j'
     app_type = app.type
     # check app config
-    _checkAppConfig(app)
+    _checkAppConfig(active_release)
 
     # 获取或创建会话（提前验证）
     conversation = conversation_service.create_or_get_conversation(
         app_id=app.id,
         workspace_id=workspace_id,
         user_id=end_user_id,
-        is_draft=False
+        is_draft=False,
+        conversation_id=payload.conversation_id
     )
 
     if app_type == AppType.AGENT:
 
         # print("="*50)
         # print(app.current_release.default_model_config_id)
-        agent_config = agent_config_4_app_release(app.current_release)
+        agent_config = agent_config_4_app_release(active_release)
         # print(agent_config.default_model_config_id)
+
+        # thinking 开关：仅当 agent 配置了 deep_thinking 且请求 thinking=True 时才启用
+        if not (agent_config.model_parameters.get("deep_thinking", False) and payload.thinking):
+            agent_config.model_parameters["deep_thinking"] = False
+
         # 流式返回
         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
+                        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
                 ):
                     yield event
 
@@ -175,30 +206,31 @@ 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
+            workspace_id=workspace_id,
+            files=payload.files  # 传递多模态文件
         )
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
     elif app_type == AppType.MULTI_AGENT:
         # 多 Agent 流式返回
-        config = multi_agent_config_4_app_release(app.current_release)
+        config = multi_agent_config_4_app_release(active_release)
         if payload.stream:
             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
 
@@ -228,23 +260,24 @@ async def chat(
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
     elif app_type == AppType.WORKFLOW:
         # 多 Agent 流式返回
-        config = workflow_config_4_app_release(app.current_release)
+        config = workflow_config_4_app_release(active_release)
         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,
-                    config=config,
-                    web_search=web_search,
-                    memory=memory,
-                    storage_type=storage_type,
-                    user_rag_memory_id=user_rag_memory_id,
-                    app_id=app.id,
-                    workspace_id=workspace_id,
-                    release_id=app.current_release.id,
+                        message=payload.message,
+                        conversation_id=conversation.id,  # 使用已创建的会话 ID
+                        user_id=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=active_release.id,
+                        public=True
                 ):
                     event_type = event.get("event", "message")
                     event_data = event.get("data", {})
@@ -263,7 +296,7 @@ async def chat(
                 }
             )
 
-        # 多 Agent 非流式返回
+        # workflow 非流式返回
         result = await app_chat_service.workflow_chat(
 
             message=payload.message,
@@ -275,9 +308,10 @@ async def chat(
             memory=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
+            release_id=active_release.id
         )
         logger.debug(
             "工作流试运行返回结果",
@@ -291,7 +325,4 @@ async def chat(
             msg="工作流任务执行成功"
         )
     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)
-

api/app/controllers/service/end_user_api_controller.py

@@ -0,0 +1,173 @@
+"""End User 服务接口 - 基于 API Key 认证"""
+
+import uuid
+
+from fastapi import APIRouter, Body, Depends, Request
+from sqlalchemy.orm import Session
+
+from app.controllers import user_memory_controllers
+from app.core.api_key_auth import require_api_key
+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.quota_stub import check_end_user_quota
+from app.core.response_utils import success
+from app.db import get_db
+from app.repositories.end_user_repository import EndUserRepository
+from app.schemas.api_key_schema import ApiKeyAuth
+from app.schemas.end_user_info_schema import EndUserInfoUpdate
+from app.schemas.memory_api_schema import CreateEndUserRequest, CreateEndUserResponse
+from app.services import api_key_service
+from app.services.memory_config_service import MemoryConfigService
+
+router = APIRouter(prefix="/end_user", tags=["V1 - End User API"])
+logger = get_business_logger()
+
+
+def _get_current_user(api_key_auth: ApiKeyAuth, db: Session):
+    """Build a current_user object from API key auth
+
+    Args:
+        api_key_auth: Validated API key auth info
+        db: Database session
+
+    Returns:
+        User object with current_workspace_id set
+    """
+    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 current_user
+
+
+@router.post("/create")
+@require_api_key(scopes=["memory"])
+@check_end_user_quota
+async def create_end_user(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(None, description="Request body"),
+):
+    """
+    Create or retrieve an end user for the workspace.
+
+    Creates a new end user and connects it to a memory configuration.
+    If an end user with the same other_id already exists in the workspace,
+    returns the existing one.
+
+    Optionally accepts a memory_config_id to connect the end user to a specific
+    memory configuration. If not provided, falls back to the workspace default config.
+    Optionally accepts an app_id to bind the end user to a specific app.
+    """
+    body = await request.json()
+    payload = CreateEndUserRequest(**body)
+    workspace_id = api_key_auth.workspace_id
+
+    logger.info("Create end user request - other_id: %s, workspace_id: %s", payload.other_id, workspace_id)
+
+    # Resolve memory_config_id: explicit > workspace default
+    memory_config_id = None
+    config_service = MemoryConfigService(db)
+
+    if payload.memory_config_id:
+        try:
+            memory_config_id = uuid.UUID(payload.memory_config_id)
+        except ValueError:
+            raise BusinessException(
+                f"Invalid memory_config_id format: {payload.memory_config_id}",
+                BizCode.INVALID_PARAMETER
+            )
+        config = config_service.get_config_with_fallback(memory_config_id, workspace_id)
+        if not config:
+            raise BusinessException(
+                f"Memory config not found: {payload.memory_config_id}",
+                BizCode.MEMORY_CONFIG_NOT_FOUND
+            )
+        memory_config_id = config.config_id
+    else:
+        default_config = config_service.get_workspace_default_config(workspace_id)
+        if default_config:
+            memory_config_id = default_config.config_id
+            logger.info(f"Using workspace default memory config: {memory_config_id}")
+        else:
+            logger.warning(f"No default memory config found for workspace: {workspace_id}")
+
+    # Resolve app_id: explicit from payload, otherwise None
+    app_id = None
+    if payload.app_id:
+        try:
+            app_id = uuid.UUID(payload.app_id)
+        except ValueError:
+            raise BusinessException(
+                f"Invalid app_id format: {payload.app_id}",
+                BizCode.INVALID_PARAMETER
+            )
+
+    end_user_repo = EndUserRepository(db)
+    end_user = end_user_repo.get_or_create_end_user_with_config(
+        app_id=app_id,
+        workspace_id=workspace_id,
+        other_id=payload.other_id,
+        memory_config_id=memory_config_id,
+        other_name=payload.other_name,
+    )
+    end_user.other_name = payload.other_name  
+    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),
+        "memory_config_id": str(end_user.memory_config_id) if end_user.memory_config_id else None,
+    }
+
+    return success(data=CreateEndUserResponse(**result).model_dump(), msg="End user created successfully")
+
+
+@router.get("/info")
+@require_api_key(scopes=["memory"])
+async def get_end_user_info(
+    request: Request,
+    end_user_id: str,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    Get end user info.
+
+    Retrieves the info record (aliases, meta_data, etc.) for the specified end user.
+    Delegates to the manager-side controller for shared logic.
+    """
+    current_user = _get_current_user(api_key_auth, db)
+    return await user_memory_controllers.get_end_user_info(
+        end_user_id=end_user_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+@router.post("/info/update")
+@require_api_key(scopes=["memory"])
+async def update_end_user_info(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(None, description="Request body"),
+):
+    """
+    Update end user info.
+
+    Updates the info record (other_name, aliases, meta_data) for the specified end user.
+    Delegates to the manager-side controller for shared logic.
+    """
+    body = await request.json()
+    payload = EndUserInfoUpdate(**body)
+
+    current_user = _get_current_user(api_key_auth, db)
+    return await user_memory_controllers.update_end_user_info(
+        info_update=payload,
+        current_user=current_user,
+        db=db,
+    )

api/app/controllers/service/memory_api_controller.py

@@ -1,49 +1,84 @@
 """Memory 服务接口 - 基于 API Key 认证"""
 
+from fastapi import APIRouter, Body, Depends, Query, Request
+from sqlalchemy.orm import Session
+
+from app.celery_task_scheduler import scheduler
 from app.core.api_key_auth import require_api_key
 from app.core.logging_config import get_business_logger
+from app.core.quota_stub import check_end_user_quota
 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 (
     MemoryReadRequest,
     MemoryReadResponse,
+    MemoryReadSyncResponse,
     MemoryWriteRequest,
     MemoryWriteResponse,
+    MemoryWriteSyncResponse,
 )
 from app.services.memory_api_service import MemoryAPIService
-from fastapi import APIRouter, Body, Depends, Request
-from sqlalchemy.orm import Session
 
 router = APIRouter(prefix="/memory", tags=["V1 - Memory API"])
 logger = get_business_logger()
 
 
+def _sanitize_task_result(result: dict) -> dict:
+    """Make Celery task result JSON-serializable.
+
+    Converts UUID and other non-serializable values to strings.
+
+    Args:
+        result: Raw task result dict from task_service
+
+    Returns:
+        JSON-safe dict
+    """
+    import uuid as _uuid
+    from datetime import datetime
+
+    def _convert(obj):
+        if isinstance(obj, dict):
+            return {k: _convert(v) for k, v in obj.items()}
+        if isinstance(obj, list):
+            return [_convert(i) for i in obj]
+        if isinstance(obj, _uuid.UUID):
+            return str(obj)
+        if isinstance(obj, datetime):
+            return obj.isoformat()
+        return obj
+
+    return _convert(result)
+
+
 @router.get("")
 async def get_memory_info():
     """获取记忆服务信息（占位）"""
     return success(data={}, msg="Memory API - Coming Soon")
 
 
-@router.post("/write_api_service")
+@router.post("/write")
 @require_api_key(scopes=["memory"])
-async def write_memory_api_service(
+async def write_memory(
     request: Request,
     api_key_auth: ApiKeyAuth = None,
     db: Session = Depends(get_db),
-    payload: MemoryWriteRequest = Body(..., embed=False),
-
+    message: str = Body(..., description="Message content"),
 ):
     """
-    Write memory to storage.
-    
-    Stores memory content for the specified end user using the Memory API Service.
+    Submit a memory write task.
+
+    Validates the end user, then dispatches the write to a Celery background task
+    with per-user fair locking. Returns a task_id for status polling.
     """
-    logger.info(f"Memory write request - end_user_id: {payload.end_user_id}, tenant_id: {api_key_auth.tenant_id}")
-    
+    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}")
+
     memory_api_service = MemoryAPIService(db)
-    
-    result = await memory_api_service.write_memory(
+
+    result = memory_api_service.write_memory(
         workspace_id=api_key_auth.workspace_id,
         end_user_id=payload.end_user_id,
         message=payload.message,
@@ -51,29 +86,52 @@ async def write_memory_api_service(
         storage_type=payload.storage_type,
         user_rag_memory_id=payload.user_rag_memory_id,
     )
-    
-    logger.info(f"Memory write successful for end_user: {payload.end_user_id}")
-    return success(data=MemoryWriteResponse(**result).model_dump(), msg="Memory written successfully")
+
+    logger.info(f"Memory write task submitted: task_id: {result['task_id']} end_user_id: {payload.end_user_id}")
+    return success(data=MemoryWriteResponse(**result).model_dump(), msg="Memory write task submitted")
 
 
-@router.post("/read_api_service")
+@router.get("/write/status")
 @require_api_key(scopes=["memory"])
-async def read_memory_api_service(
+async def get_write_task_status(
+    request: Request,
+    task_id: str = Query(..., description="Celery task ID"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    Check the status of a memory write task.
+
+    Returns the current status and result (if completed) of a previously submitted write task.
+    """
+    logger.info(f"Write task status check - task_id: {task_id}")
+
+    result = scheduler.get_task_status(task_id)
+
+    return success(data=_sanitize_task_result(result), msg="Task status retrieved")
+
+
+@router.post("/read")
+@require_api_key(scopes=["memory"])
+async def read_memory(
     request: Request,
     api_key_auth: ApiKeyAuth = None,
     db: Session = Depends(get_db),
-    payload: MemoryReadRequest = Body(..., embed=False),
+    message: str = Body(..., description="Query message"),
 ):
     """
-    Read memory from storage.
-    
-    Queries and retrieves memories for the specified end user with context-aware responses.
+    Submit a memory read task.
+
+    Validates the end user, then dispatches the read to a Celery background task.
+    Returns a task_id for status polling.
     """
+    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)
-    
-    result = await memory_api_service.read_memory(
+
+    result = memory_api_service.read_memory(
         workspace_id=api_key_auth.workspace_id,
         end_user_id=payload.end_user_id,
         message=payload.message,
@@ -82,6 +140,95 @@ async def read_memory_api_service(
         storage_type=payload.storage_type,
         user_rag_memory_id=payload.user_rag_memory_id,
     )
-    
-    logger.info(f"Memory read successful for end_user: {payload.end_user_id}")
-    return success(data=MemoryReadResponse(**result).model_dump(), msg="Memory read successfully")
+
+    logger.info(f"Memory read task submitted: task_id={result['task_id']}, end_user_id: {payload.end_user_id}")
+    return success(data=MemoryReadResponse(**result).model_dump(), msg="Memory read task submitted")
+
+
+@router.get("/read/status")
+@require_api_key(scopes=["memory"])
+async def get_read_task_status(
+    request: Request,
+    task_id: str = Query(..., description="Celery task ID"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    Check the status of a memory read task.
+
+    Returns the current status and result (if completed) of a previously submitted read task.
+    """
+    logger.info(f"Read task status check - task_id: {task_id}")
+
+    from app.services.task_service import get_task_memory_read_result
+    result = get_task_memory_read_result(task_id)
+
+    return success(data=_sanitize_task_result(result), msg="Task status retrieved")
+
+
+@router.post("/write/sync")
+@require_api_key(scopes=["memory"])
+@check_end_user_quota
+async def write_memory_sync(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(..., description="Message content"),
+):
+    """
+    Write memory synchronously.
+
+    Blocks until the write completes and returns the result directly.
+    For async processing with task polling, use /write instead.
+    """
+    body = await request.json()
+    payload = MemoryWriteRequest(**body)
+    logger.info(f"Memory write (sync) request - end_user_id: {payload.end_user_id}")
+
+    memory_api_service = MemoryAPIService(db)
+
+    result = await memory_api_service.write_memory_sync(
+        workspace_id=api_key_auth.workspace_id,
+        end_user_id=payload.end_user_id,
+        message=payload.message,
+        config_id=payload.config_id,
+        storage_type=payload.storage_type,
+        user_rag_memory_id=payload.user_rag_memory_id,
+    )
+
+    logger.info(f"Memory write (sync) successful for end_user: {payload.end_user_id}")
+    return success(data=MemoryWriteSyncResponse(**result).model_dump(), msg="Memory written successfully")
+
+
+@router.post("/read/sync")
+@require_api_key(scopes=["memory"])
+async def read_memory_sync(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(..., description="Query message"),
+):
+    """
+    Read memory synchronously.
+
+    Blocks until the read completes and returns the answer directly.
+    For async processing with task polling, use /read instead.
+    """
+    body = await request.json()
+    payload = MemoryReadRequest(**body)
+    logger.info(f"Memory read (sync) request - end_user_id: {payload.end_user_id}")
+
+    memory_api_service = MemoryAPIService(db)
+
+    result = await memory_api_service.read_memory_sync(
+        workspace_id=api_key_auth.workspace_id,
+        end_user_id=payload.end_user_id,
+        message=payload.message,
+        search_switch=payload.search_switch,
+        config_id=payload.config_id,
+        storage_type=payload.storage_type,
+        user_rag_memory_id=payload.user_rag_memory_id,
+    )
+
+    logger.info(f"Memory read (sync) successful for end_user: {payload.end_user_id}")
+    return success(data=MemoryReadSyncResponse(**result).model_dump(), msg="Memory read successfully")

api/app/controllers/service/memory_config_api_controller.py

@@ -0,0 +1,491 @@
+"""Memory Config 服务接口 - 基于 API Key 认证"""
+
+from typing import Optional
+import uuid
+
+from fastapi import APIRouter, Body, Depends, Header, Query, Request
+from fastapi.encoders import jsonable_encoder
+from sqlalchemy.orm import Session
+
+from app.controllers import memory_storage_controller
+from app.controllers import memory_forget_controller
+from app.controllers import ontology_controller
+from app.controllers import emotion_config_controller
+from app.controllers import memory_reflection_controller
+from app.schemas.memory_storage_schema import ForgettingConfigUpdateRequest
+from app.controllers.emotion_config_controller import EmotionConfigUpdate
+from app.schemas.memory_reflection_schemas import Memory_Reflection
+from app.core.api_key_auth import require_api_key
+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
+from app.db import get_db
+from app.repositories.memory_config_repository import MemoryConfigRepository
+from app.schemas.api_key_schema import ApiKeyAuth
+from app.schemas.memory_api_schema import (
+    ConfigUpdateExtractedRequest,
+    ConfigUpdateRequest,
+    ListConfigsResponse,
+    ConfigCreateRequest,
+    ConfigUpdateForgettingRequest,
+    EmotionConfigUpdateRequest,
+    ReflectionConfigUpdateRequest,
+)
+from app.schemas.memory_storage_schema import (
+    ConfigUpdate, 
+    ConfigUpdateExtracted,
+    ConfigParamsCreate,
+)
+from app.services import api_key_service
+from app.services.memory_api_service import MemoryAPIService
+from app.utils.config_utils import resolve_config_id
+
+router = APIRouter(prefix="/memory_config", tags=["V1 - Memory Config API"])
+logger = get_business_logger()
+
+
+def _get_current_user(api_key_auth: ApiKeyAuth, db: Session):
+    """Build a current_user object from API key auth
+
+    Args:
+        api_key_auth: Validated API key auth info
+        db: Database session
+
+    Returns:
+        User object with current_workspace_id set
+    """
+    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 current_user
+
+
+def _verify_config_ownership(config_id:str, workspace_id:uuid.UUID, db:Session):
+    """Verify that the config belongs to the workspace.
+    
+      Args: 
+          config_id: The ID of the config to verify
+          workspace_id: The workspace ID tocheck against
+          db: Database session for querying
+        Raises:
+            BusinessException: If the config does not exist or does not belong to the workspace
+    """
+    try:
+        resolved_id = resolve_config_id(config_id, db)
+    except ValueError as e:
+        raise BusinessException(
+            message=f"Invalid config_id: {e}",
+            code=BizCode.INVALID_PARAMETER,
+        )
+    config = MemoryConfigRepository.get_by_id(db, resolved_id)
+    if not config or config.workspace_id != workspace_id:
+        raise BusinessException(
+            message="Config not found or access denied",
+            code=BizCode.MEMORY_CONFIG_NOT_FOUND,
+        )
+
+# @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")
+
+@router.get("/read_all_config")
+@require_api_key(scopes=["memory"])
+async def read_all_config(
+    request:Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    List all memory configs with full details (enhanced version).
+
+    Returns complete config fields for the authorized workspace.
+    No config_id ownership check needed — results are filtered by workspace.
+    """
+    logger.info(f"V1 get all configs (full) - workspace: {api_key_auth.workspace_id}")
+
+    current_user = _get_current_user(api_key_auth, db)
+
+    return memory_storage_controller.read_all_config(
+        current_user=current_user,
+        db=db,
+    )
+
+@router.get("/scenes/simple")
+@require_api_key(scopes=["memory"])
+async def get_ontology_scenes(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    Get available ontology scenes for the workspace.
+
+    Returns a simple list of scene_id and scene_name for dropdown selection.
+    Used before creating a memory config to choose which ontology scene to associate.
+    """
+    logger.info(f"V1 get scenes - workspace: {api_key_auth.workspace_id}")
+
+    current_user = _get_current_user(api_key_auth, db)
+
+    return await ontology_controller.get_scenes_simple(
+        db=db,
+        current_user=current_user,
+    )
+
+@router.get("/read_config_extracted")
+@require_api_key(scopes=["memory"])
+async def read_config_extracted(
+    request: Request,
+    config_id: str = Query(..., description="config_id"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    Get extraction engine config details for a specific config.
+
+    Only configs belonging to the authorized workspace can be queried.
+    """
+    logger.info(f"V1 read extracted config - config_id: {config_id}, workspace: {api_key_auth.workspace_id}")
+
+    _verify_config_ownership(config_id, api_key_auth.workspace_id, db)
+
+    current_user = _get_current_user(api_key_auth, db)
+
+    return memory_storage_controller.read_config_extracted(
+        config_id = config_id,
+        current_user = current_user,
+        db = db,
+    )
+
+@router.get("/read_config_forgetting")
+@require_api_key(scopes=["memory"])
+async def read_config_forgetting(
+    request: Request,
+    config_id: str = Query(..., description="config_id"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    Get forgetting settings for a specific memory config.
+
+    Only configs belonging to the authorized workspace can be queried.
+    """
+    logger.info(f"V1 read forgetting config - config_id: {config_id}, workspace: {api_key_auth.workspace_id}")
+
+    _verify_config_ownership(config_id, api_key_auth.workspace_id, db)
+
+    current_user = _get_current_user(api_key_auth, db)
+
+    result = await memory_forget_controller.read_forgetting_config(
+        config_id = config_id,
+        current_user = current_user,
+        db = db,
+    )
+    return jsonable_encoder(result)
+
+
+
+@router.get("/read_config_emotion")
+@require_api_key(scopes=["memory"])
+async def read_config_emotion(
+    request: Request,
+    config_id: str = Query(..., description="config_id"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    Get emotion engine config details for a specific config.
+
+    Only configs belonging to the authorized workspace can be queried.
+    """
+    logger.info(f"V1 read emotion config - config_id: {config_id}, workspace: {api_key_auth.workspace_id}")
+
+    _verify_config_ownership(config_id, api_key_auth.workspace_id, db)
+
+    current_user = _get_current_user(api_key_auth, db)
+
+    return jsonable_encoder(emotion_config_controller.get_emotion_config(
+        config_id=config_id,
+        db=db,
+        current_user=current_user,
+    ))
+
+@router.get("/read_config_reflection")
+@require_api_key(scopes=["memory"])
+async def read_config_reflection(
+    request: Request,
+    config_id: str = Query(..., description="config_id"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    Get reflection engine config details for a specific config.
+
+    Only configs belonging to the authorized workspace can be queried.
+    """
+    logger.info(f"V1 read reflection config - config_id: {config_id}, workspace: {api_key_auth.workspace_id}")
+
+    _verify_config_ownership(config_id, api_key_auth.workspace_id, db)
+
+    current_user = _get_current_user(api_key_auth, db)
+
+    return jsonable_encoder(await memory_reflection_controller.start_reflection_configs(
+        config_id=config_id,
+        current_user=current_user,
+        db=db,
+    ))
+
+
+@router.post("/create_config")
+@require_api_key(scopes=["memory"])
+async def create_memory_config(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(None, description="Request body"),
+    x_language_type: Optional[str] = Header(None, alias="X-Language-Type"),
+):
+    """
+    Create a new memory config for the workspace.
+
+    The config will be associated with the workspace of the API Key.
+    config_name is required, other fields are optional.
+    """
+    body = await request.json()
+    payload = ConfigCreateRequest(**body)
+
+    logger.info(f"V1 create config - workspace: {api_key_auth.workspace_id}, config_name: {payload.config_name}")
+    
+    # 构造管理端 Schema，workspace_id 从 API Key 注入
+    current_user = _get_current_user(api_key_auth, db)
+    mgmt_payload = ConfigParamsCreate(
+        config_name=payload.config_name,
+        config_desc=payload.config_desc or "",
+        scene_id=payload.scene_id,
+        llm_id=payload.llm_id,
+        embedding_id=payload.embedding_id,
+        rerank_id=payload.rerank_id,
+        reflection_model_id=payload.reflection_model_id,
+        emotion_model_id=payload.emotion_model_id,
+    )
+    #将返回数据中UUID序列化处理
+    result =memory_storage_controller.create_config(
+        payload=mgmt_payload,
+        current_user=current_user,
+        db=db,
+        x_language_type=x_language_type,
+    )
+    return jsonable_encoder(result)
+
+@router.put("/update_config")
+@require_api_key(scopes=["memory"])
+async def update_memory_config(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(None, description="Request body"),
+):
+    """
+    Update memory config basic info (name, description, scene).
+
+    Requires API Key with 'memory' scope
+    Only configs belonging to the authorized workspace can be updated.
+    """
+    body = await request.json()
+    payload = ConfigUpdateRequest(**body)
+    
+    logger.info(f"V1 update config - config_id: {payload.config_id}, workspace: {api_key_auth.workspace_id}")
+
+    _verify_config_ownership(payload.config_id, api_key_auth.workspace_id, db)
+
+    current_user = _get_current_user(api_key_auth, db)
+    mgmt_payload = ConfigUpdate(
+        config_id = payload.config_id,
+        config_name = payload.config_name,
+        config_desc = payload.config_desc,
+        scene_id = payload.scene_id,
+    )
+
+    return memory_storage_controller.update_config(
+        payload = mgmt_payload,
+        current_user = current_user,
+        db = db,
+    )
+
+@router.put("/update_config_extracted")
+@require_api_key(scopes=["memory"])
+async def update_memory_config_extracted(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(None, description="Request body"),
+):
+   """
+    update memory config extraction engine config (models, thresholds, chunking, pruning, etc.).
+
+    Requires API Key with 'memory' scope.
+    Only configs belonging to the authorized workspace can be updated.
+   """
+   body = await request.json()
+   payload = ConfigUpdateExtractedRequest(**body)
+
+   logger.info(f"V1 update extracted config - config_id: {payload.config_id}, workspace: {api_key_auth.workspace_id}")
+
+   #校验权限
+   _verify_config_ownership(payload.config_id, api_key_auth.workspace_id, db)
+
+   current_user = _get_current_user(api_key_auth, db)
+   update_fields = payload.model_dump(exclude_unset=True)
+   mgmt_payload = ConfigUpdateExtracted(**update_fields)
+
+   return memory_storage_controller.update_config_extracted(
+        payload = mgmt_payload,
+        current_user = current_user,
+        db = db,
+   )
+
+@router.put("/update_config_forgetting")
+@require_api_key(scopes=["memory"])
+async def update_memory_config_forgetting(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(None, description="Request body"),
+):
+   """
+    update memory config forgetting settings (forgetting strategy, parameters, etc.).
+
+    Requires API Key with 'memory' scope.
+    Only configs belonging to the authorized workspace can be updated.
+   """
+   body = await request.json()
+   payload = ConfigUpdateForgettingRequest(**body)
+
+   logger.info(f"V1 update forgetting config - config_id: {payload.config_id}, workspace: {api_key_auth.workspace_id}")
+
+   #校验权限
+   _verify_config_ownership(payload.config_id, api_key_auth.workspace_id, db)
+
+   current_user = _get_current_user(api_key_auth, db)
+   update_fields = payload.model_dump(exclude_unset=True)
+   mgmt_payload = ForgettingConfigUpdateRequest(**update_fields)
+
+   #将返回数据中UUID序列化处理
+   result = await memory_forget_controller.update_forgetting_config(
+        payload = mgmt_payload,
+        current_user = current_user,
+        db = db,
+   )
+   return jsonable_encoder(result)
+
+@router.put("/update_config_emotion")
+@require_api_key(scopes=["memory"])
+async def update_config_emotion(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(None, description="Request body"),
+):
+    """
+    Update emotion engine config (full update).
+
+    All fields except emotion_model_id are required.
+    Only configs belonging to the authorized workspace can be updated.
+    """
+    body = await request.json()
+    payload = EmotionConfigUpdateRequest(**body)
+
+    logger.info(f"V1 update emotion config - config_id: {payload.config_id}, workspace: {api_key_auth.workspace_id}")
+
+    _verify_config_ownership(payload.config_id, api_key_auth.workspace_id, db)
+
+    current_user = _get_current_user(api_key_auth, db)
+    update_fields = payload.model_dump(exclude_unset=True)
+    mgmt_payload = EmotionConfigUpdate(**update_fields)
+    return jsonable_encoder(emotion_config_controller.update_emotion_config(
+        config=mgmt_payload,
+        db=db,
+        current_user=current_user,
+    ))
+
+@router.put("/update_config_reflection")
+@require_api_key(scopes=["memory"])
+async def update_config_reflection(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(None, description="Request body"),
+):
+    """
+    Update reflection engine config (full update).
+
+    All fields are required.
+    Only configs belonging to the authorized workspace can be updated.
+    """
+    body = await request.json()
+    payload = ReflectionConfigUpdateRequest(**body)
+
+    logger.info(f"V1 update reflection config - config_id: {payload.config_id}, workspace: {api_key_auth.workspace_id}")
+
+    _verify_config_ownership(payload.config_id, api_key_auth.workspace_id, db)
+
+    current_user = _get_current_user(api_key_auth, db)
+    update_fields = payload.model_dump(exclude_unset=True)
+    mgmt_payload = Memory_Reflection(**update_fields)
+
+    return jsonable_encoder(await memory_reflection_controller.save_reflection_config(
+        request=mgmt_payload,
+        current_user=current_user,
+        db=db,
+    ))
+
+@router.delete("/delete_config")
+@require_api_key(scopes=["memory"])
+async def delete_memory_config(
+    config_id: str,
+    request: Request,
+    force: bool = Query(False, description="是否强制删除（即使有终端用户正在使用）"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    Delete a memory config.
+
+    - Default configs cannot be deleted.
+    - If end users are connected and force=False, returns a warning.
+    - If force=True, clears end user references and deletes the config.
+
+    Only configs belonging to the authorized workspace can be deleted.
+    """
+    logger.info(f"V1 delete config - config_id: {config_id}, force: {force}, workspace: {api_key_auth.workspace_id}")
+
+    _verify_config_ownership(config_id, api_key_auth.workspace_id, db)
+
+    current_user = _get_current_user(api_key_auth, db)
+
+    return memory_storage_controller.delete_config(
+        config_id=config_id,
+        force=force,
+        current_user=current_user,
+        db=db,
+    )

api/app/controllers/service/rag_api_knowledge_controller.py

@@ -246,3 +246,73 @@ 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/service/user_memory_api_controller.py

@@ -0,0 +1,230 @@
+"""User Memory 服务接口 — 基于 API Key 认证
+
+包装 user_memory_controllers.py 和 memory_agent_controller.py 中的内部接口，
+提供基于 API Key 认证的对外服务:
+1./analytics/graph_data - 知识图谱数据接口
+2./analytics/community_graph - 社区图谱接口
+3./analytics/node_statistics - 记忆节点统计接口
+4./analytics/user_summary - 用户摘要接口
+5./analytics/memory_insight - 记忆洞察接口
+6./analytics/interest_distribution - 兴趣分布接口
+7./analytics/end_user_info - 终端用户信息接口
+8./analytics/generate_cache - 缓存生成接口
+
+
+路由前缀: /memory
+子路径: /analytics/...
+最终路径: /v1/memory/analytics/...
+认证方式: API Key (@require_api_key)
+"""
+
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Header, Query, Request, Body
+from sqlalchemy.orm import Session
+
+from app.core.api_key_auth import require_api_key
+from app.core.api_key_utils import get_current_user_from_api_key, validate_end_user_in_workspace
+from app.core.logging_config import get_business_logger
+from app.db import get_db
+from app.schemas.api_key_schema import ApiKeyAuth
+from app.schemas.memory_storage_schema import GenerateCacheRequest
+
+# 包装内部服务 controller
+from app.controllers import user_memory_controllers, memory_agent_controller
+
+router = APIRouter(prefix="/memory", tags=["V1 - User Memory API"])
+logger = get_business_logger()
+
+
+# ==================== 知识图谱 ====================
+
+
+@router.get("/analytics/graph_data")
+@require_api_key(scopes=["memory"])
+async def get_graph_data(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    node_types: Optional[str] = Query(None, description="Comma-separated node types filter"),
+    limit: int = Query(100, description="Max nodes to return (auto-capped at 1000 in service layer)"),
+    depth: int = Query(1, description="Graph traversal depth (auto-capped at 3 in service layer)"),
+    center_node_id: Optional[str] = Query(None, description="Center node for subgraph"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get knowledge graph data (nodes + edges) for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_graph_data_api(
+        end_user_id=end_user_id,
+        node_types=node_types,
+        limit=limit,
+        depth=depth,
+        center_node_id=center_node_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+@router.get("/analytics/community_graph")
+@require_api_key(scopes=["memory"])
+async def get_community_graph(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get community clustering graph for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_community_graph_data_api(
+        end_user_id=end_user_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+# ==================== 节点统计 ====================
+
+
+@router.get("/analytics/node_statistics")
+@require_api_key(scopes=["memory"])
+async def get_node_statistics(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get memory node type statistics for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_node_statistics_api(
+        end_user_id=end_user_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+# ==================== 用户摘要 & 洞察 ====================
+
+
+@router.get("/analytics/user_summary")
+@require_api_key(scopes=["memory"])
+async def get_user_summary(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    language_type: str = Header(default=None, alias="X-Language-Type"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get cached user summary for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_user_summary_api(
+        end_user_id=end_user_id,
+        language_type=language_type,
+        current_user=current_user,
+        db=db,
+    )
+
+
+@router.get("/analytics/memory_insight")
+@require_api_key(scopes=["memory"])
+async def get_memory_insight(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get cached memory insight report for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_memory_insight_report_api(
+        end_user_id=end_user_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+# ==================== 兴趣分布 ====================
+
+
+@router.get("/analytics/interest_distribution")
+@require_api_key(scopes=["memory"])
+async def get_interest_distribution(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    limit: int = Query(5, le=5, description="Max interest tags to return"),
+    language_type: str = Header(default=None, alias="X-Language-Type"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get interest distribution tags for an end user."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await memory_agent_controller.get_interest_distribution_by_user_api(
+        end_user_id=end_user_id,
+        limit=limit,
+        language_type=language_type,
+        current_user=current_user,
+        db=db,
+    )
+
+
+# ==================== 终端用户信息 ====================
+
+
+@router.get("/analytics/end_user_info")
+@require_api_key(scopes=["memory"])
+async def get_end_user_info(
+    request: Request,
+    end_user_id: str = Query(..., description="End user ID"),
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """Get end user basic information (name, aliases, metadata)."""
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+    validate_end_user_in_workspace(db, end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.get_end_user_info(
+        end_user_id=end_user_id,
+        current_user=current_user,
+        db=db,
+    )
+
+
+# ==================== 缓存生成 ====================
+
+
+@router.post("/analytics/generate_cache")
+@require_api_key(scopes=["memory"])
+async def generate_cache(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(None, description="Request body"),
+    language_type: str = Header(default=None, alias="X-Language-Type"),
+):
+    """Trigger cache generation (user summary + memory insight) for an end user or all workspace users."""
+    body = await request.json()
+    cache_request = GenerateCacheRequest(**body)
+
+    current_user = get_current_user_from_api_key(db, api_key_auth)
+
+    if cache_request.end_user_id:
+        validate_end_user_in_workspace(db, cache_request.end_user_id, api_key_auth.workspace_id)
+
+    return await user_memory_controllers.generate_cache_api(
+        request=cache_request,
+        language_type=language_type,
+        current_user=current_user,
+        db=db,
+    )
+
+

api/app/controllers/skill_controller.py

@@ -0,0 +1,87 @@
+"""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
+from app.core.quota_stub import check_skill_quota
+
+router = APIRouter(prefix="/skills", tags=["Skills"])
+
+
+@router.post("", summary="创建技能")
+@check_skill_quota
+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/tenant_subscription_controller.py

@@ -0,0 +1,173 @@
+"""
+租户套餐查询接口（普通用户可访问）
+"""
+import datetime
+from typing import Callable, Optional
+
+from fastapi import APIRouter, Depends
+from fastapi.responses import JSONResponse
+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.i18n.dependencies import get_translator
+from app.models.user_model import User
+from app.schemas.response_schema import ApiResponse
+
+logger = get_api_logger()
+
+router = APIRouter(prefix="/tenant", tags=["Tenant"])
+public_router = APIRouter(tags=["Tenant"])
+
+
+@router.get("/subscription", response_model=ApiResponse, summary="获取当前用户所属租户的套餐信息")
+async def get_my_tenant_subscription(
+    current_user: User = Depends(get_current_user),
+    db: Session = Depends(get_db),
+    t: Callable = Depends(get_translator),
+):
+    """
+    获取当前登录用户所属租户的有效套餐订阅信息。
+    包含套餐名称、版本、配额、到期时间等。
+    """
+    try:
+        from premium.platform_admin.package_plan_service import TenantSubscriptionService
+
+        if not current_user.tenant:
+            return JSONResponse(status_code=404, content=fail(code=404, msg="用户未关联租户"))
+
+        tenant_id = current_user.tenant.id
+        svc = TenantSubscriptionService(db)
+        sub = svc.get_subscription(tenant_id)
+
+        if not sub:
+            # 无订阅记录时，兜底返回免费套餐信息
+            free_plan = svc.plan_repo.get_free_plan()
+            if not free_plan:
+                return success(data=None, msg="暂无有效套餐")
+            return success(data={
+                "subscription_id": None,
+                "tenant_id": str(tenant_id),
+                "package_plan_id": str(free_plan.id),
+                "package_version": free_plan.version,
+                "package_plan": {
+                    "id": str(free_plan.id),
+                    "name": free_plan.name,
+                    "name_en": free_plan.name_en,
+                    "version": free_plan.version,
+                    "category": free_plan.category,
+                    "tier_level": free_plan.tier_level,
+                    "price": float(free_plan.price) if free_plan.price is not None else 0.0,
+                    "billing_cycle": free_plan.billing_cycle,
+                    "core_value": free_plan.core_value,
+                    "core_value_en": free_plan.core_value_en,
+                    "tech_support": free_plan.tech_support,
+                    "tech_support_en": free_plan.tech_support_en,
+                    "sla_compliance": free_plan.sla_compliance,
+                    "sla_compliance_en": free_plan.sla_compliance_en,
+                    "page_customization": free_plan.page_customization,
+                    "page_customization_en": free_plan.page_customization_en,
+                    "theme_color": free_plan.theme_color,
+                },
+                "started_at": None,
+                "expired_at": None,
+                "status": "active",
+                "quotas": free_plan.quotas or {},
+                "created_at": int(datetime.datetime.utcnow().timestamp() * 1000),
+                "updated_at": int(datetime.datetime.utcnow().timestamp() * 1000),
+            }, msg="免费套餐")
+
+        return success(data=svc.build_response(sub))
+
+    except ModuleNotFoundError:
+        # 社区版无 premium 模块，从配置文件读取免费套餐
+        if not current_user.tenant:
+            return JSONResponse(status_code=404, content=fail(code=404, msg="用户未关联租户"))
+
+        from app.config.default_free_plan import DEFAULT_FREE_PLAN
+
+        plan = DEFAULT_FREE_PLAN
+        response_data = {
+            "subscription_id": None,
+            "tenant_id": str(current_user.tenant.id),
+            "package_plan_id": None,
+            "package_version": plan["version"],
+            "package_plan": {
+                "id": None,
+                "name": plan["name"],
+                "name_en": plan.get("name_en"),
+                "version": plan["version"],
+                "category": plan["category"],
+                "tier_level": plan["tier_level"],
+                "price": float(plan["price"]),
+                "billing_cycle": plan["billing_cycle"],
+                "core_value": plan.get("core_value"),
+                "core_value_en": plan.get("core_value_en"),
+                "tech_support": plan.get("tech_support"),
+                "tech_support_en": plan.get("tech_support_en"),
+                "sla_compliance": plan.get("sla_compliance"),
+                "sla_compliance_en": plan.get("sla_compliance_en"),
+                "page_customization": plan.get("page_customization"),
+                "page_customization_en": plan.get("page_customization_en"),
+                "theme_color": plan.get("theme_color"),
+            },
+            "started_at": None,
+            "expired_at": None,
+            "status": "active",
+            "quotas": plan["quotas"],
+            "created_at": int(datetime.datetime.utcnow().timestamp() * 1000),
+            "updated_at": int(datetime.datetime.utcnow().timestamp() * 1000),
+        }
+        return success(data=response_data, msg="社区版免费套餐")
+
+    except Exception as e:
+        logger.error(f"获取租户套餐信息失败: {e}", exc_info=True)
+        return JSONResponse(status_code=500, content=fail(code=500, msg="获取套餐信息失败"))
+
+
+@public_router.get("/package-plans", response_model=ApiResponse, summary="获取套餐列表（公开）")
+async def list_package_plans_public(
+    category: Optional[str] = None,
+    status: Optional[bool] = None,
+    search: Optional[str] = None,
+    db: Session = Depends(get_db),
+):
+    """
+    公开接口，无需鉴权。
+    SaaS 版从数据库读取套餐列表；社区版降级返回 default_free_plan.py 中的免费套餐。
+    """
+    try:
+        from premium.platform_admin.package_plan_service import PackagePlanService
+        from premium.platform_admin.package_plan_schema import PackagePlanResponse
+        svc = PackagePlanService(db)
+        result = svc.get_list(page=1, size=9999, category=category, status=status, search=search)
+        return success(data=[PackagePlanResponse.model_validate(p).model_dump(mode="json") for p in result["items"]])
+    except ModuleNotFoundError:
+        from app.config.default_free_plan import DEFAULT_FREE_PLAN
+        plan = DEFAULT_FREE_PLAN
+        return success(data=[{
+            "id": None,
+            "name": plan["name"],
+            "name_en": plan.get("name_en"),
+            "version": plan["version"],
+            "category": plan["category"],
+            "tier_level": plan["tier_level"],
+            "price": float(plan["price"]),
+            "billing_cycle": plan["billing_cycle"],
+            "core_value": plan.get("core_value"),
+            "core_value_en": plan.get("core_value_en"),
+            "tech_support": plan.get("tech_support"),
+            "tech_support_en": plan.get("tech_support_en"),
+            "sla_compliance": plan.get("sla_compliance"),
+            "sla_compliance_en": plan.get("sla_compliance_en"),
+            "page_customization": plan.get("page_customization"),
+            "page_customization_en": plan.get("page_customization_en"),
+            "theme_color": plan.get("theme_color"),
+            "status": plan.get("status", True),
+            "quotas": plan["quotas"],
+        }])
+    except Exception as e:
+        logger.error(f"获取套餐列表失败: {e}", exc_info=True)
+        return JSONResponse(status_code=500, content=fail(code=500, msg="获取套餐列表失败"))

api/app/controllers/tool_controller.py

@@ -3,8 +3,11 @@ 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
+    ToolCreateRequest, ToolUpdateRequest, ToolExecuteRequest, ParseSchemaRequest,
+    CustomToolTestRequest, ToolActiveUpdate
 )
 
 from app.core.response_utils import success
@@ -14,6 +17,7 @@ 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"])
 
@@ -72,6 +76,8 @@ async def get_tool_methods(
         if methods is None:
             raise HTTPException(status_code=404, detail="工具不存在")
         return success(data=methods, msg="获取工具方法成功")
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -97,7 +103,13 @@ async def create_tool(
 ):
     """创建工具"""
     try:
-        tool_id = service.create_tool(
+        # 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(
             name=request.name,
             tool_type=request.tool_type,
             tenant_id=current_user.tenant_id,
@@ -107,8 +119,12 @@ 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 HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -137,6 +153,8 @@ async def update_tool(
         return success(msg="工具更新成功")
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -147,7 +165,7 @@ async def delete_tool(
         current_user: User = Depends(get_current_user),
         service: ToolService = Depends(get_tool_service)
 ):
-    """删除工具"""
+    """删除工具（逻辑删除，is_active=False）"""
     try:
         success_flag = service.delete_tool(tool_id, current_user.tenant_id)
         if not success_flag:
@@ -155,6 +173,34 @@ async def delete_tool(
         return success(msg="工具删除成功")
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
+    except HTTPException:
+        raise
+    except Exception as e:
+        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 HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -187,6 +233,8 @@ async def execute_tool(
             },
             msg="工具执行完成"
         )
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -203,6 +251,8 @@ async def parse_openapi_schema(
         if result["success"] is False:
             raise HTTPException(status_code=400, detail=result["message"])
         return success(data=result, msg="Schema解析完成")
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -216,8 +266,10 @@ 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 HTTPException(status_code=400, detail=result.get("message", "同步失败"))
+            raise BusinessException(result.get("message", "工具列表同步失败"), BizCode.BAD_REQUEST)
         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))
 
@@ -240,8 +292,10 @@ 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 HTTPException(status_code=400, detail=result["message"])
+            raise BusinessException(result["message"], BizCode.SERVICE_UNAVAILABLE)
         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

@@ -1,16 +1,26 @@
 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
+from app.schemas.user_schema import (
+    ChangePasswordRequest,
+    AdminChangePasswordRequest,
+    SendEmailCodeRequest,
+    VerifyEmailCodeRequest,
+    VerifyPasswordRequest)
 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()
@@ -25,7 +35,8 @@ router = APIRouter(
 def create_superuser(
     user: user_schema.UserCreate,
     db: Session = Depends(get_db),
-    current_superuser: User = Depends(get_current_superuser)
+    current_superuser: User = Depends(get_current_superuser),
+    t: Callable = Depends(get_translator)
 ):
     """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}")
@@ -34,7 +45,7 @@ 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="超级管理员创建成功")
+    return success(data=result_schema, msg=t("users.create.superuser_success"))
 
 
 @router.delete("/{user_id}", response_model=ApiResponse)
@@ -42,6 +53,7 @@ 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}")
@@ -49,13 +61,14 @@ 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="用户停用成功")
+    return success(msg=t("users.delete.deactivate_success"))
 
 @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}")
@@ -66,13 +79,14 @@ 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="用户激活成功")
+    return success(data=result_schema, msg=t("users.activate.success"))
 
 
 @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}")
@@ -92,12 +106,27 @@ 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:
+            if ws.workspace_id == current_user.current_workspace_id and ws.is_active:
                 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="用户信息获取成功")
+
+    # 设置权限：如果用户来自 SSO Source，则使用该 Source 的 permissions；否则返回 "all" 表示拥有所有权限
+    if current_user.external_source:
+        try:
+            from premium.sso.models import SSOSource
+            source = db.query(SSOSource).filter(SSOSource.source_code == current_user.external_source).first()
+            if source and source.permissions:
+                result_schema.permissions = source.permissions
+            else:
+                result_schema.permissions = []
+        except ModuleNotFoundError:
+            result_schema.permissions = []
+    else:
+        result_schema.permissions = ["all"]
+
+    return success(data=result_schema, msg=t("users.info.get_success"))
 
 
 @router.get("/superusers", response_model=ApiResponse)
@@ -105,6 +134,7 @@ 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}")
@@ -117,7 +147,7 @@ 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="租户超管列表获取成功")
+    return success(data=superusers_schema, msg=t("users.list.superusers_success"))
 
 
 @router.get("/{user_id}", response_model=ApiResponse)
@@ -125,6 +155,7 @@ 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}")
@@ -135,7 +166,7 @@ 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="用户信息获取成功")
+    return success(data=result_schema, msg=t("users.info.get_success"))
 
 
 @router.put("/change-password", response_model=ApiResponse)
@@ -143,6 +174,7 @@ 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}")
@@ -155,7 +187,7 @@ 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="密码修改成功")
+    return success(msg=t("auth.password.change_success"))
 
 
 @router.put("/admin/change-password", response_model=ApiResponse)
@@ -163,6 +195,7 @@ 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}")
@@ -177,7 +210,107 @@ 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="密码修改成功")
+        return success(msg=t("auth.password.change_success"))
     else:
         api_logger.info(f"管理员密码重置成功: 用户 {request.user_id}, 随机密码已生成")
-        return success(data=generated_password, msg="密码重置成功")
+        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")
+    )

api/app/controllers/user_memory_controllers.py

@@ -5,26 +5,29 @@
 from typing import Optional
 import datetime
 from sqlalchemy.orm import Session
-from fastapi import APIRouter, Depends,Header
+from fastapi import APIRouter, Depends, Header
 
 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
 from app.core.api_key_utils import timestamp_to_datetime
-from app.services.memory_base_service import Translation_English
 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.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,
+from app.repositories.end_user_repository import EndUserRepository
+from app.schemas.end_user_info_schema import (
+    EndUserInfoResponse,
+    EndUserInfoCreate,
+    EndUserInfoUpdate,
 )
 from app.models.end_user_model import EndUser
 from app.dependencies import get_current_user
@@ -44,10 +47,9 @@ router = APIRouter(
 
 @router.get("/analytics/memory_insight/report", response_model=ApiResponse)
 async def get_memory_insight_report_api(
-    end_user_id: str,
-    language_type: str = Header(default="zh", alias="X-Language-Type"),
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
+        end_user_id: str,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db),
 ) -> dict:
     """
     获取缓存的记忆洞察报告
@@ -55,18 +57,10 @@ async def get_memory_insight_report_api(
     此接口仅查询数据库中已缓存的记忆洞察数据，不执行生成操作。
     如需生成新的洞察报告，请使用专门的生成接口。
     """
-    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"记忆洞察报告查询请求: 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_memory_insight(db, end_user_id,model_id,language_type)
+        result = await user_memory_service.get_cached_memory_insight(db, end_user_id)
 
         if result["is_cached"]:
             api_logger.info(f"成功返回缓存的记忆洞察报告: end_user_id={end_user_id}")
@@ -81,17 +75,24 @@ 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="zh", alias="X-Language-Type"),
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
+        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:
     """
     获取缓存的用户摘要
 
     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)
@@ -103,7 +104,7 @@ async def get_user_summary_api(
     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_type)
+        result = await user_memory_service.get_cached_user_summary(db, end_user_id, model_id, language)
 
         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}")
@@ -118,16 +119,24 @@ async def get_user_summary_api(
 
 @router.post("/analytics/generate_cache", response_model=ApiResponse)
 async def generate_cache_api(
-    request: GenerateCacheRequest,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
+        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:
     """
     手动触发缓存生成
 
     - 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
@@ -139,7 +148,7 @@ async def generate_cache_api(
 
     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'}"
+        f"end_user_id={end_user_id if end_user_id else '全部用户'}, language={language}"
     )
 
     try:
@@ -148,10 +157,12 @@ async def generate_cache_api(
             api_logger.info(f"开始为单个用户生成缓存: end_user_id={end_user_id}")
 
             # 生成记忆洞察
-            insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id)
+            insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id,
+                                                                                  language=language)
 
             # 生成用户摘要
-            summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id)
+            summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id,
+                                                                                  language=language)
 
             # 构建响应
             result = {
@@ -185,7 +196,7 @@ 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)
+            result = await user_memory_service.generate_cache_for_workspace(db, workspace_id, language=language)
 
             # 记录统计信息
             api_logger.info(
@@ -202,9 +213,9 @@ async def generate_cache_api(
 
 @router.get("/analytics/node_statistics", response_model=ApiResponse)
 async def get_node_statistics_api(
-    end_user_id: str,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
+        end_user_id: str,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db),
 ) -> dict:
     workspace_id = current_user.current_workspace_id
 
@@ -213,7 +224,8 @@ async def get_node_statistics_api(
         api_logger.warning(f"用户 {current_user.username} 尝试查询节点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")
 
-    api_logger.info(f"记忆类型统计请求: end_user_id={end_user_id}, user={current_user.username}, workspace={workspace_id}")
+    api_logger.info(
+        f"记忆类型统计请求: end_user_id={end_user_id}, user={current_user.username}, workspace={workspace_id}")
 
     try:
         # 调用新的记忆类型统计函数
@@ -221,21 +233,23 @@ async def get_node_statistics_api(
 
         # 计算总数用于日志
         total_count = sum(item["count"] for item in result)
-        api_logger.info(f"成功获取记忆类型统计: end_user_id={end_user_id}, 总记忆数={total_count}, 类型数={len(result)}")
+        api_logger.info(
+            f"成功获取记忆类型统计: end_user_id={end_user_id}, 总记忆数={total_count}, 类型数={len(result)}")
         return success(data=result, msg="查询成功")
     except Exception as e:
         api_logger.error(f"记忆类型查询失败: end_user_id={end_user_id}, error={str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "记忆类型查询失败", str(e))
 
+
 @router.get("/analytics/graph_data", response_model=ApiResponse)
 async def get_graph_data_api(
-    end_user_id: str,
-    node_types: Optional[str] = None,
-    limit: int = 100,
-    depth: int = 1,
-    center_node_id: Optional[str] = None,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
+        end_user_id: str,
+        node_types: Optional[str] = None,
+        limit: int = 100,
+        depth: int = 1,
+        center_node_id: Optional[str] = None,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db),
 ) -> dict:
     workspace_id = current_user.current_workspace_id
 
@@ -289,107 +303,165 @@ async def get_graph_data_api(
         return fail(BizCode.INTERNAL_ERROR, "图数据查询失败", str(e))
 
 
-@router.get("/read_end_user/profile", response_model=ApiResponse)
-async def get_end_user_profile(
-    end_user_id: str,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
+@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
-    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")
+        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"用户信息查询请求: end_user_id={end_user_id}, user={current_user.username}, "
+        f"社区图谱查询请求: end_user_id={end_user_id}, user={current_user.username}, "
         f"workspace={workspace_id}"
     )
 
     try:
-        # 查询终端用户
-        end_user = db.query(EndUser).filter(EndUser.id == end_user_id).first()
+        result = await analytics_community_graph_data(db=db, end_user_id=end_user_id)
 
-        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,
-            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
+        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']}"
         )
-
-        api_logger.info(f"成功获取用户信息: end_user_id={end_user_id}")
-        return success(data=UserMemoryService.convert_profile_to_dict_with_timestamp(profile_data), msg="查询成功")
+        return success(data=result, msg="查询成功")
 
     except Exception as e:
-        api_logger.error(f"用户信息查询失败: end_user_id={end_user_id}, error={str(e)}")
-        return fail(BizCode.INTERNAL_ERROR, "用户信息查询失败", str(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))
 
+#=======================终端用户信息接口=======================
 
-@router.post("/updated_end_user/profile", response_model=ApiResponse)
-async def update_end_user_profile(
-    profile_update: EndUserProfileUpdate,
+@router.get("/end_user_info", response_model=ApiResponse)
+async def get_end_user_info(
+    end_user_id: str,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
     """
-    更新终端用户的基本信息
+    查询终端用户信息记录
 
-    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。
+    根据 end_user_id 查询单条终端用户信息记录。
     """
     workspace_id = current_user.current_workspace_id
-    end_user_id = profile_update.end_user_id
 
-    # 验证工作空间
     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")
+        api_logger.warning(f"用户 {current_user.username} 尝试查询终端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")
 
     api_logger.info(
-        f"用户信息更新请求: end_user_id={end_user_id}, user={current_user.username}, "
+        f"查询终端用户信息请求: end_user_id={end_user_id}, user={current_user.username}, "
         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)
+    # 校验 end_user 是否属于当前工作空间
+    end_user_repo = EndUserRepository(db)
+    end_user = end_user_repo.get_end_user_by_id(end_user_id)
+    if end_user is None:
+        return fail(BizCode.USER_NOT_FOUND, "终端用户不存在", "end_user not found")
+    if str(end_user.workspace_id) != str(workspace_id):
+        api_logger.warning(
+            f"用户 {current_user.username} 尝试查询不属于工作空间 {workspace_id} 的终端用户 {end_user_id}"
+        )
+        return fail(BizCode.PERMISSION_DENIED, "该终端用户不属于当前工作空间", "end_user workspace mismatch")
+
+    result = user_memory_service.get_end_user_info(db, end_user_id)
 
     if result["success"]:
-        api_logger.info(f"成功更新用户信息: end_user_id={end_user_id}")
+        api_logger.info(f"成功查询终端用户信息: end_user_id={end_user_id}")
+        return success(data=result["data"], msg="查询成功")
+    else:
+        error_msg = result["error"]
+        api_logger.error(f"查询终端用户信息失败: end_user_id={end_user_id}, error={error_msg}")
+        
+        if error_msg == "终端用户信息记录不存在":
+            return fail(BizCode.USER_NOT_FOUND, "终端用户信息记录不存在", error_msg)
+        elif error_msg == "无效的终端用户ID格式":
+            return fail(BizCode.INVALID_USER_ID, "无效的终端用户ID格式", error_msg)
+        else:
+            return fail(BizCode.INTERNAL_ERROR, "查询终端用户信息失败", error_msg)
+
+
+@router.post("/end_user_info/updated", response_model=ApiResponse)
+async def update_end_user_info(
+    info_update: EndUserInfoUpdate,
+    current_user: User = Depends(get_current_user),
+    db: Session = Depends(get_db),
+) -> dict:
+    """
+    更新终端用户信息记录
+
+    根据 end_user_id 更新终端用户信息记录，支持批量更新多个别名。
+    
+    示例请求体：
+    {
+      "end_user_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
+      "other_name": "张三1",
+      "aliases": ["小张", "张工"],
+      "meta_data": {"position": "工程师", "department": "技术部"}
+    }
+    """
+    workspace_id = current_user.current_workspace_id
+    end_user_id = info_update.end_user_id
+
+    if workspace_id is None:
+        api_logger.warning(f"用户 {current_user.username} 尝试更新终端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")
+
+    api_logger.info(
+        f"更新终端用户信息请求: end_user_id={end_user_id}, user={current_user.username}, "
+        f"workspace={workspace_id}"
+    )
+
+    # 校验 end_user 是否属于当前工作空间
+    end_user_repo = EndUserRepository(db)
+    end_user = end_user_repo.get_end_user_by_id(end_user_id)
+    if end_user is None:
+        return fail(BizCode.USER_NOT_FOUND, "终端用户不存在", "end_user not found")
+    if str(end_user.workspace_id) != str(workspace_id):
+        api_logger.warning(
+            f"用户 {current_user.username} 尝试更新不属于工作空间 {workspace_id} 的终端用户 {end_user_id}"
+        )
+        return fail(BizCode.PERMISSION_DENIED, "该终端用户不属于当前工作空间", "end_user workspace mismatch")
+
+    # 获取更新数据（排除 end_user_id）
+    update_data = info_update.model_dump(exclude_unset=True, exclude={'end_user_id'})
+    
+    result = user_memory_service.update_end_user_info(db, end_user_id, update_data)
+
+    if result["success"]:
+        api_logger.info(f"成功更新终端用户信息: 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}")
+        api_logger.error(f"终端用户信息更新失败: 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)
+        if error_msg == "终端用户信息记录不存在":
+            return fail(BizCode.USER_NOT_FOUND, "终端用户信息记录不存在", error_msg)
+        elif error_msg == "无效的终端用户ID格式":
+            return fail(BizCode.INVALID_USER_ID, "无效的终端用户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)
+            return fail(BizCode.INTERNAL_ERROR, "终端用户信息更新失败", error_msg)
 
 @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="zh", alias="X-Language-Type"),
-                                      current_user: User = Depends(get_current_user),
-                                      db: Session = Depends(get_db),
-                                      ):
-    workspace_id=current_user.current_workspace_id
+async def memory_space_timeline_of_shared_memories(
+        id: str, label: str,
+        language_type: str = Header(default=None, alias="X-Language-Type"),
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db),
+):
+    # 使用集中化的语言校验
+    language = get_language_from_header(language_type)
+
+    workspace_id = current_user.current_workspace_id
     workspace_repo = WorkspaceRepository(db)
     workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
 
@@ -398,14 +470,16 @@ async def memory_space_timeline_of_shared_memories(id: str, label: str,language_
     else:
         model_id = None
     MemoryEntity = MemoryEntityService(id, label)
-    timeline_memories_result = await MemoryEntity.get_timeline_memories_server(model_id, language_type)
+    timeline_memories_result = await MemoryEntity.get_timeline_memories_server(model_id, language)
 
     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,
-                                      current_user: User = Depends(get_current_user),
-                                      db: Session = Depends(get_db),
-                                      ):
+                                              current_user: User = Depends(get_current_user),
+                                              db: Session = Depends(get_db),
+                                              ):
     try:
         api_logger.info(f"关系演变查询请求: id={id}, table={label}, user={current_user.username}")
 

api/app/controllers/workflow_controller.py

@@ -1,610 +0,0 @@
-"""
-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.is_(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.is_(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.is_(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.is_(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.is_(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.is_(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.is_(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, HTTPException, Query, status
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, status
 from sqlalchemy.orm import Session
 
 from app.core.logging_config import get_api_logger
@@ -14,6 +14,12 @@ 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
@@ -29,6 +35,7 @@ from app.schemas.workspace_schema import (
     WorkspaceUpdate,
 )
 from app.services import workspace_service
+from app.core.quota_stub import check_workspace_quota
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -65,7 +72,9 @@ 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)
+    current_tenant: Tenants = Depends(get_current_tenant),
+    language: str = Depends(get_current_language),
+    t: callable = Depends(get_translator)
 ):
     """获取当前租户下用户参与的所有工作空间
 
@@ -88,25 +97,51 @@ def get_workspaces(
         )
 
     api_logger.info(f"成功获取 {len(workspaces)} 个工作空间")
-    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")
+    
+    # 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"))
 
 
 @router.post("", response_model=ApiResponse)
+@check_workspace_quota
 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"""
-    api_logger.info(f"用户 {current_user.username} 请求创建工作空间: {workspace.name}")
+    from app.core.language_utils import get_language_from_header
+    
+    # 验证并获取语言参数
+    language = get_language_from_header(language_type)
+    
+    api_logger.info(
+        f"用户 {current_user.username} 请求创建工作空间: {workspace.name}, "
+        f"language={language}"
+    )
 
     result = workspace_service.create_workspace(
-        db=db, workspace=workspace, user=current_user)
+        db=db, workspace=workspace, user=current_user, language=language
+    )
 
-    api_logger.info(f"工作空间创建成功 - 名称: {workspace.name}, ID: {result.id}, 创建者: {current_user.username}")
-    result_schema = WorkspaceResponse.model_validate(result)
-    return success(data=result_schema, msg="工作空间创建成功")
+    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"))
 
 @router.put("", response_model=ApiResponse)
 @cur_workspace_access_guard()
@@ -114,6 +149,8 @@ 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
@@ -126,14 +163,21 @@ 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}")
-    result_schema = WorkspaceResponse.model_validate(result)
-    return success(data=result_schema, msg="工作空间更新成功")
+    
+    # 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"))
 
 @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")
@@ -144,8 +188,14 @@ 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)
-    return success(data=table_items, msg="工作空间成员列表获取成功")
+    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"))
 
 
 @router.put("/members", response_model=ApiResponse)
@@ -155,6 +205,7 @@ 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")
@@ -165,27 +216,28 @@ 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="成员角色更新成功")
+    return success(msg=t("workspace.members.role_updated"))
 
 
 @router.delete("/members/{member_id}", response_model=ApiResponse)
 @cur_workspace_access_guard()
-def delete_workspace_member(
+async 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}")
 
-    workspace_service.delete_workspace_member(
+    await workspace_service.delete_workspace_member(
         db=db,
         workspace_id=workspace_id,
         member_id=member_id,
         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="成员删除成功")
+    return success(msg=t("workspace.members.deleted"))
 
 
 # 创建空间协作邀请
@@ -195,6 +247,8 @@ 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
@@ -207,7 +261,12 @@ 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}")
-    return success(data=result, msg="邀请创建成功")
+    
+    # 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"))
 
 
 @router.get("/invites", response_model=ApiResponse)
@@ -219,6 +278,8 @@ 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
@@ -233,18 +294,30 @@ def get_workspace_invites(
         offset=offset
     )
     api_logger.info(f"成功获取 {len(invites)} 个邀请记录")
-    return success(data=invites, msg="邀请列表获取成功")
+    
+    # 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"))
 
 
 @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}")
-    return success(data=result, msg="邀请验证成功")
+    
+    # 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"))
 
 
 @router.delete("/invites/{invite_id}", response_model=ApiResponse)
@@ -254,6 +327,8 @@ 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
@@ -266,7 +341,12 @@ 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}")
-    return success(data=result, msg="邀请撤销成功")
+    
+    # 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"))
 
 # ==================== 公开邀请接口（无需认证） ====================
 
@@ -289,6 +369,7 @@ 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}")
@@ -299,7 +380,7 @@ def switch_workspace(
         user=current_user,
     )
     api_logger.info(f"成功切换工作空间为 {workspace_id}")
-    return success(msg="工作空间切换成功")
+    return success(msg=t("workspace.switched"))
 
 
 @router.get("/storage", response_model=ApiResponse)
@@ -307,6 +388,7 @@ 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
@@ -318,7 +400,7 @@ 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="存储类型获取成功")
+    return success(data={"storage_type": storage_type}, msg=t("workspace.storage.type_retrieved"))
 
 
 @router.get("/workspace_models", response_model=ApiResponse)
@@ -326,6 +408,8 @@ 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
@@ -341,14 +425,14 @@ 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="工作空间不存在或无权访问"
+            detail=t("workspace.not_found")
         )
 
     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="模型配置获取成功")
+    return success(data=WorkspaceModelsConfig.model_validate(configs), msg=t("workspace.models.config_retrieved"))
 
 
 @router.put("/workspace_models", response_model=ApiResponse)
@@ -357,6 +441,7 @@ 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
@@ -373,5 +458,5 @@ 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="模型配置更新成功")
+    return success(data=WorkspaceModelsConfig.model_validate(updated_workspace), msg=t("workspace.models.config_updated"))
 

api/app/core/__init__.py

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

api/app/core/agent/agent_middleware.py

@@ -0,0 +1,162 @@
+"""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

@@ -7,28 +7,18 @@ 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 langchain.agents import create_agent
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
+from langchain_core.tools import BaseTool
+from langgraph.errors import GraphRecursionError
 
-from app.db import get_db
 from app.core.logging_config import get_business_logger
-from app.core.memory.agent.utils.redis_tool import store
 from app.core.models import RedBearLLM, RedBearModelConfig
 from app.models.models_model import ModelType
-from app.repositories.memory_short_repository import LongTermMemoryRepository
-from app.services.memory_agent_service import (
-    get_end_user_connected_config,
-)
-from app.services.memory_konwledges_server import write_rag
-from app.services.task_service import get_task_memory_write_result
-from app.tasks import write_message_task
-from langchain.agents import create_agent
-from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
-from langchain_core.tools import BaseTool
-
-from app.utils.config_utils import resolve_config_id
 
 logger = get_business_logger()
 
@@ -36,16 +26,23 @@ logger = get_business_logger()
 class LangChainAgent:
 
     def __init__(
-        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
+            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
+            deep_thinking: bool = False,  # 是否启用深度思考模式
+            thinking_budget_tokens: Optional[int] = None,  # 深度思考 token 预算
+            json_output: bool = False,  # 是否强制 JSON 输出
+            capability: Optional[List[str]] = None  # 模型能力列表，用于校验是否支持深度思考
     ):
         """初始化 LangChain Agent
 
@@ -58,28 +55,71 @@ 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（默认 True）
+            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）
         """
         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
 
-        # 创建 RedBearLLM（支持多提供商）
+        # 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"
+
+        # ChatTongyi 要求 messages 含 'json' 字样才能使用 response_format
+        # 在 system prompt 中注入 JSON 要求
+        from app.models.models_model import ModelProvider
+        if json_output and (
+            (provider.lower() == ModelProvider.DASHSCOPE and not is_omni)
+            or provider.lower() == ModelProvider.VOLCANO
+            # 有工具时 response_format 会被移除，所有 provider 都需要 system prompt 注入保证 JSON 输出
+            or bool(tools)
+        ):
+            self.system_prompt += "\n请以JSON格式输出。"
+
+        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，capability 校验由 RedBearModelConfig 统一处理
         model_config = RedBearModelConfig(
             model_name=model_name,
             provider=provider,
             api_key=api_key,
             base_url=api_base,
+            is_omni=is_omni,
+            capability=capability,
+            deep_thinking=deep_thinking,
+            thinking_budget_tokens=thinking_budget_tokens,
+            json_output=json_output,
             extra_params={
                 "temperature": temperature,
                 "max_tokens": max_tokens,
-                "streaming": streaming  # 使用参数控制流式
+                "streaming": streaming
             }
         )
 
         self.llm = RedBearLLM(model_config, type=ModelType.CHAT)
+        # 从经过校验的 config 读取实际生效的能力开关
+        self.deep_thinking = model_config.deep_thinking
+        self.json_output = model_config.json_output
 
         # 获取底层模型用于真正的流式调用
         self._underlying_llm = self.llm._model if hasattr(self.llm, '_model') else self.llm
@@ -88,11 +128,14 @@ 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=self.tools if self.tools else None,
+            tools=wrapped_tools,
             system_prompt=self.system_prompt
         )
 
@@ -104,17 +147,92 @@ 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
+            self,
+            message: str,
+            history: Optional[List[Dict[str, str]]] = None,
+            context: Optional[str] = None,
+            files: Optional[List[Dict[str, Any]]] = None
     ) -> List[BaseMessage]:
         """准备消息列表
 
@@ -122,14 +240,12 @@ 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
         """
-        messages = []
-
-        # 添加系统提示词
-        messages.append(SystemMessage(content=self.system_prompt))
+        messages: list = []
 
         # 添加历史消息
         if history:
@@ -144,117 +260,94 @@ class LangChainAgent:
         if context:
             user_content = f"参考信息：\n{context}\n\n用户问题：\n{user_content}"
 
-        messages.append(HumanMessage(content=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))
 
         return messages
-# TODO 乐力齐 - 累积多组对话批量写入功能已禁用
-    # 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,
-    #                     end_user_id=end_user_end,
-    #                     aimessages=aimessages
-    #                 )
-    #     store.delete_duplicate_sessions()
-    #     # logger.info(f'Redis_Agent:{end_user_end};{session_id}')
-    #     return session_id
 
-# TODO 乐力齐 - 累积多组对话批量写入功能已禁用
-    # async def term_memory_redis_read(self,end_user_end):
-    #     end_user_end = f"Term_{end_user_end}"
-    #     history = store.find_user_apply_group(end_user_end, end_user_end, end_user_end)
-    #     # logger.info(f'Redis_Agent:{end_user_end};{history}')
-    #     messagss_list=[]
-    #     retrieved_content=[]
-    #     for messages in history:
-    #         query = messages.get("Query")
-    #         aimessages = messages.get("Answer")
-    #         messagss_list.append(f'用户:{query}。AI回复:{aimessages}')
-    #         retrieved_content.append({query: aimessages})
-    #     return messagss_list,retrieved_content
-    async def write(self, storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id):
+    @staticmethod
+    def _extract_tokens_from_message(msg) -> int:
+        """从 AIMessage 或类似对象中提取 total_tokens，兼容多种 provider 格式
+
+        支持的格式：
+        - response_metadata.token_usage.total_tokens (OpenAI/ChatOpenAI)
+        - response_metadata.usage.total_tokens (部分 provider)
+        - usage_metadata.total_tokens (LangChain 新版)
         """
-        写入记忆（支持结构化消息）
+        total = 0
+        # 1. response_metadata
+        response_meta = getattr(msg, "response_metadata", None)
+        if response_meta and isinstance(response_meta, dict):
+            # 尝试 token_usage 路径
+            token_usage = response_meta.get("token_usage") or response_meta.get("usage", {})
+            if isinstance(token_usage, dict):
+                total = token_usage.get("total_tokens", 0)
+        # 2. usage_metadata（LangChain 新版 AIMessage 属性）
+        if not total:
+            usage_meta = getattr(msg, "usage_metadata", None)
+            if usage_meta:
+                if isinstance(usage_meta, dict):
+                    total = usage_meta.get("total_tokens", 0)
+                else:
+                    total = getattr(usage_meta, "total_tokens", 0)
+        return total or 0
 
+    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:
-            storage_type: 存储类型 (neo4j/rag)
-            end_user_id: 终端用户ID
-            user_message: 用户消息内容
-            ai_message: AI 回复内容
-            user_rag_memory_id: RAG 记忆ID
-            actual_end_user_id: 实际用户ID
-            actual_config_id: 配置ID
-
-        逻辑说明：
-        - RAG 模式：组合 user_message 和 ai_message 为字符串格式，保持原有逻辑不变
-        - Neo4j 模式：使用结构化消息列表
-          1. 如果 user_message 和 ai_message 都不为空：创建配对消息 [user, assistant]
-          2. 如果只有 user_message：创建单条用户消息 [user]（用于历史记忆场景）
-          3. 每条消息会被转换为独立的 Chunk，保留 speaker 字段
+            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}]
 
-        db = next(get_db())
-        try:
-            actual_config_id=resolve_config_id(actual_config_id, db)
+        # 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)
 
-            if storage_type == "rag":
-                # RAG 模式：组合消息为字符串格式（保持原有逻辑）
-                combined_message = f"user: {user_message}\nassistant: {ai_message}"
-                await write_rag(end_user_id, combined_message, user_rag_memory_id)
-                logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}')
-            else:
-                # Neo4j 模式：使用结构化消息列表
-                structured_messages = []
+        logger.debug(
+            f"构建多模态消息: provider={self.provider}, "
+            f"parts={len(content_parts)}, "
+            f"files={len(files)}"
+        )
 
-                # 始终添加用户消息（如果不为空）
-                if user_message:
-                    structured_messages.append({"role": "user", "content": user_message})
+        return content_parts
 
-                # 只有当 AI 回复不为空时才添加 assistant 消息
-                if ai_message:
-                    structured_messages.append({"role": "assistant", "content": ai_message})
+    @staticmethod
+    def _extract_reasoning_content(msg) -> str:
+        """从 AIMessage 中提取深度思考内容（reasoning_content）
 
-                # 如果没有消息，直接返回
-                if not structured_messages:
-                    logger.warning(f"No messages to write for user {actual_end_user_id}")
-                    return
+        所有 provider 统一通过 additional_kwargs.reasoning_content 传递：
+        - DeepSeek-R1 / QwQ: 原生字段
+        - Volcano (Doubao-thinking): 由 VolcanoChatOpenAI 从 delta.reasoning_content 注入
+        """
+        additional = getattr(msg, "additional_kwargs", None) or {}
+        return additional.get("reasoning_content") or additional.get("reasoning", "")
 
-                # 调用 Celery 任务，传递结构化消息列表
-                # 数据流：
-                # 1. structured_messages 传递给 write_message_task
-                # 2. write_message_task 调用 memory_agent_service.write_memory
-                # 3. write_memory 调用 write_tools.write，传递 messages 参数
-                # 4. write_tools.write 调用 get_chunked_dialogs，传递 messages 参数
-                # 5. get_chunked_dialogs 为每条消息创建独立的 Chunk，设置 speaker 字段
-                # 6. 每个 Chunk 保存到 Neo4j，包含 speaker 字段
-                logger.info(f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}")
-                write_id = write_message_task.delay(
-                    actual_end_user_id,  # end_user_id: 用户ID
-                    structured_messages,  # message: 结构化消息列表 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
-                    actual_config_id,    # config_id: 配置ID
-                    storage_type,        # storage_type: "neo4j"
-                    user_rag_memory_id   # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用）
-                )
-                logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
-                write_status = get_task_memory_write_result(str(write_id))
-                logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
-        finally:
-            db.close()
     async def chat(
             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,  # 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
     ) -> Dict[str, Any]:
         """执行对话
 
@@ -262,59 +355,15 @@ class LangChainAgent:
             message: 用户消息
             history: 历史消息列表 [{"role": "user/assistant", "content": "..."}]
             context: 上下文信息（如知识库检索结果）
+            files: 多模态文件
 
         Returns:
             Dict: 包含 content 和元数据的字典
         """
-        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
-        if actual_config_id is None and end_user_id:
-            try:
-                from app.services.memory_agent_service import (
-                    get_end_user_connected_config,
-                )
-                db = next(get_db())
-                try:
-                    connected_config = get_end_user_connected_config(end_user_id, db)
-                    actual_config_id = connected_config.get("memory_config_id")
-                except Exception as e:
-                    logger.warning(f"Failed to get connected config for end_user {end_user_id}: {e}")
-                finally:
-                    db.close()
-            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)}')
-# # TODO 乐力齐，在长短期记忆存储的时候再使用此代码
-#         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()
-
-#                 # 长期记忆写入（
-#                 await self.write(storage_type, actual_end_user_id, history_term_memory, "", user_rag_memory_id, actual_end_user_id, actual_config_id)
-#             # 注意：不在这里写入用户消息，等 AI 回复后一起写入
         try:
-            # 准备消息列表
-            messages = self._prepare_messages(message, history, context)
+            # 准备消息列表（支持多模态）
+            messages = self._prepare_messages(message, history, context, files)
 
             logger.debug(
                 "准备调用 LangChain Agent",
@@ -322,27 +371,84 @@ class LangChainAgent:
                     "has_context": bool(context),
                     "has_history": bool(history),
                     "has_tools": bool(self.tools),
-                    "message_count": len(messages)
+                    "has_files": bool(files),
+                    "message_count": len(messages),
+                    "max_iterations": self.max_iterations
                 }
             )
 
             # 统一使用 agent.invoke 调用
-            result = await self.agent.ainvoke({"messages": messages})
+            # 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, GraphRecursionError) 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
+                    }
+                }
 
             # 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
+            reasoning_content = ""
             for msg in reversed(output_messages):
                 if isinstance(msg, AIMessage):
-                    content = msg.content
+                    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]}...")
+                    total_tokens = self._extract_tokens_from_message(msg)
+                    reasoning_content = self._extract_reasoning_content(msg) if self.deep_thinking else ""
                     break
 
+            logger.info(f"最终提取的内容长度: {len(content)}")
+
             elapsed_time = time.time() - start_time
-            if memory_flag:
-                # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
-                await self.write(storage_type, actual_end_user_id, message_chat, content, user_rag_memory_id, actual_end_user_id, actual_config_id)
-                # TODO 乐力齐 - 累积多组对话批量写入功能已禁用
-                # await self.term_memory_save(message_chat, end_user_id, content)
             response = {
                 "content": content,
                 "model": self.model_name,
@@ -350,9 +456,11 @@ class LangChainAgent:
                 "usage": {
                     "prompt_tokens": 0,
                     "completion_tokens": 0,
-                    "total_tokens": 0
+                    "total_tokens": total_tokens
                 }
             }
+            if reasoning_content:
+                response["reasoning_content"] = reasoning_content
 
             logger.debug(
                 "Agent 调用完成",
@@ -369,25 +477,24 @@ 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
-    ) -> AsyncGenerator[str, None]:
+            self,
+            message: str,
+            history: Optional[List[Dict[str, str]]] = None,
+            context: Optional[str] = None,
+            files: Optional[List[Dict[str, Any]]] = None
+    ) -> AsyncGenerator[str | int | dict[str, str], None]:
         """执行流式对话
 
         Args:
             message: 用户消息
             history: 历史消息列表
             context: 上下文信息
+            files: 多模态文件
 
         Yields:
             str: 消息内容块
+            int: token 统计
+            Dict: 深度思考内容 {"type": "reasoning", "content": "..."}
         """
         logger.info("=" * 80)
         logger.info(" chat_stream 方法开始执行")
@@ -395,98 +502,129 @@ class LangChainAgent:
         logger.info(f"  Has tools: {bool(self.tools)}")
         logger.info(f"  Tool count: {len(self.tools) if self.tools else 0}")
         logger.info("=" * 80)
-        message_chat = message
-        actual_config_id = config_id
-        # If config_id is None, try to get from end_user's connected config
-        if actual_config_id is None and end_user_id:
-            try:
-                db = next(get_db())
-                try:
-                    connected_config = get_end_user_connected_config(end_user_id, db)
-                    actual_config_id = connected_config.get("memory_config_id")
-                except Exception as e:
-                    logger.warning(f"Failed to get connected config for end_user {end_user_id}: {e}")
-                finally:
-                    db.close()
-            except Exception as e:
-                logger.warning(f"Failed to get db session: {e}")
-# # TODO 乐力齐
-#         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)}')
-#                     # 长期记忆写入
-#                     await self.write(storage_type, end_user_id, history_term_memory, "", user_rag_memory_id, end_user_id, actual_config_id)
-#                 except Exception as e:
-#                     logger.error(f"Failed to write to long term memory: {e}")
-#                 finally:
-#                     db_for_memory.close()
-
-            # 注意：不在这里写入用户消息，等 AI 回复后一起写入
         try:
-            # 准备消息列表
-            messages = self._prepare_messages(message, history, context)
+            # 准备消息列表（支持多模态）
+            messages = self._prepare_messages(message, history, context, files)
 
             logger.debug(
-                f"准备流式调用，has_tools={bool(self.tools)}, message_count={len(messages)}"
+                f"准备流式调用，has_tools={bool(self.tools)}, has_files={bool(files)}, message_count={len(messages)}"
             )
 
             chunk_count = 0
-            yielded_content = False
 
             # 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 = ''
+            full_reasoning = ''
             try:
+                last_event = {}
                 async for event in self.agent.astream_events(
-                    {"messages": messages},
-                    version="v2"
+                        {"messages": messages},
+                        version="v2",
+                        config={"recursion_limit": self.max_iterations}
                 ):
+                    last_event = event
                     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")
-                        full_content+=chunk.content
-                        if chunk and hasattr(chunk, "content") and chunk.content:
-                            yield chunk.content
-                            yielded_content = True
-                    
+                        if chunk and hasattr(chunk, "content"):
+                            # 提取深度思考内容（仅在启用深度思考时）
+                            if self.deep_thinking:
+                                reasoning_chunk = self._extract_reasoning_content(chunk)
+                                if reasoning_chunk:
+                                    full_reasoning += reasoning_chunk
+                                    yield {"type": "reasoning", "content": reasoning_chunk}
+
+                            # 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
+                            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
+                                        # OpenAI 格式: {"type": "text", "text": "..."}
+                                        elif item.get("type") == "text":
+                                            text = item.get("text", "")
+                                            if text:
+                                                full_content += text
+                                                yield text
+                                    elif isinstance(item, str):
+                                        full_content += item
+                                        yield item
+
                     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") and chunk.content:
-                                full_content+=chunk.content
-                                yield chunk.content
-                                yielded_content = True
+                            if hasattr(chunk, "content"):
+                                # 提取深度思考内容（仅在启用深度思考时）
+                                if self.deep_thinking:
+                                    reasoning_chunk = self._extract_reasoning_content(chunk)
+                                    if reasoning_chunk:
+                                        full_reasoning += reasoning_chunk
+                                        yield {"type": "reasoning", "content": reasoning_chunk}
+
+                                chunk_content = chunk.content
+                                if isinstance(chunk_content, str) and chunk_content:
+                                    full_content += chunk_content
+                                    yield chunk_content
+                                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
+                                            # OpenAI 格式: {"type": "text", "text": "..."}
+                                            elif item.get("type") == "text":
+                                                text = item.get("text", "")
+                                                if text:
+                                                    full_content += text
+                                                    yield text
+                                        elif isinstance(item, str):
+                                            full_content += item
+                                            yield item
                             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")
-                if memory_flag:
-                    # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
-                    await self.write(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id, end_user_id, actual_config_id)
-                    # TODO 乐力齐 - 累积多组对话批量写入功能已禁用
-                    # await self.term_memory_save(message_chat, end_user_id, full_content)
-                
+                # 统计token消耗
+                output_messages = last_event.get("data", {}).get("output", {}).get("messages", [])
+                for msg in reversed(output_messages):
+                    if isinstance(msg, AIMessage):
+                        stream_total_tokens = self._extract_tokens_from_message(msg)
+                        logger.info(f"流式 token 统计: total_tokens={stream_total_tokens}")
+                        yield stream_total_tokens
+                        break
+
+            except GraphRecursionError:
+                logger.warning(
+                    f"Agent 达到最大迭代次数限制 ({self.max_iterations})，模型可能不支持正确的工具调用停止判断"
+                )
+                if not full_content:
+                    yield "抱歉，我在处理您的请求时遇到了问题（已达最大处理步骤限制）。请尝试简化问题或更换模型后重试。"
             except Exception as e:
                 logger.error(f"Agent astream_events 失败: {str(e)}", exc_info=True)
                 raise
@@ -500,5 +638,3 @@ class LangChainAgent:
             logger.info("=" * 80)
             logger.info("chat_stream 方法执行结束")
             logger.info("=" * 80)
-
-

api/app/core/api_key_auth.py

@@ -70,6 +70,8 @@ def require_api_key(
                 })
                 raise BusinessException("API Key 无效或已过期", BizCode.API_KEY_INVALID)
 
+            ApiKeyAuthService.check_app_published(db, api_key_obj)
+
             if scopes:
                 missing_scopes = []
                 for scope in scopes:
@@ -97,7 +99,7 @@ def require_api_key(
             )
 
             rate_limiter = RateLimiterService()
-            is_allowed, error_msg, rate_headers = await rate_limiter.check_all_limits(api_key_obj)
+            is_allowed, error_msg, rate_headers = await rate_limiter.check_all_limits(api_key_obj, db=db)
             if not is_allowed:
                 logger.warning("API Key 限流触发", extra={
                     "api_key_id": str(api_key_obj.id),
@@ -106,10 +108,12 @@ def require_api_key(
                     "error_msg": error_msg
                 })
                 # 根据错误消息判断限流类型
-                if "QPS" in error_msg:
-                    code = BizCode.API_KEY_QPS_LIMIT_EXCEEDED
-                elif "Daily" in error_msg:
+                if "Daily" in error_msg:
                     code = BizCode.API_KEY_DAILY_LIMIT_EXCEEDED
+                elif "Tenant" in error_msg:
+                    code = BizCode.API_KEY_QPS_LIMIT_EXCEEDED  # 租户套餐速率超限，同属 QPS 类
+                elif "QPS" in error_msg:
+                    code = BizCode.API_KEY_QPS_LIMIT_EXCEEDED
                 else:
                     code = BizCode.API_KEY_QUOTA_EXCEEDED
 

api/app/core/api_key_utils.py

@@ -1,8 +1,15 @@
 """API Key 工具函数"""
 import secrets
+import uuid as _uuid
 from typing import Optional, Union
 from datetime import datetime
 
+from sqlalchemy.orm import Session as _Session
+from app.core.error_codes import BizCode as _BizCode
+from app.core.exceptions import BusinessException as _BusinessException
+from app.models.end_user_model import EndUser as _EndUser
+from app.repositories.end_user_repository import EndUserRepository as _EndUserRepository
+
 from app.models.api_key_model import ApiKeyType
 from fastapi import Response
 from fastapi.responses import JSONResponse
@@ -65,3 +72,72 @@ def datetime_to_timestamp(dt: Optional[datetime]) -> Optional[int]:
         return None
 
     return int(dt.timestamp() * 1000)
+
+
+def get_current_user_from_api_key(db: _Session, api_key_auth):
+    """通过 API Key 构造 current_user 对象。
+
+    从 API Key 反查创建者（管理员用户），并设置其 workspace 上下文。
+    与内部接口的 Depends(get_current_user) (JWT) 等价。
+
+    Args:
+        db: 数据库会话
+        api_key_auth: API Key 认证信息（ApiKeyAuth）
+
+    Returns:
+        User ORM 对象，已设置 current_workspace_id
+    """
+    from app.services import api_key_service
+
+    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 current_user
+
+
+def validate_end_user_in_workspace(
+    db: _Session,
+    end_user_id: str,
+    workspace_id,
+) -> _EndUser:
+    """校验 end_user 是否存在且属于指定 workspace。
+
+    Args:
+        db: 数据库会话
+        end_user_id: 终端用户 ID
+        workspace_id: 工作空间 ID（UUID 或字符串均可）
+
+    Returns:
+        EndUser ORM 对象（校验通过时）
+
+    Raises:
+        BusinessException(INVALID_PARAMETER): end_user_id 格式无效
+        BusinessException(USER_NOT_FOUND): end_user 不存在
+        BusinessException(PERMISSION_DENIED): end_user 不属于该 workspace
+    """
+    try:
+        _uuid.UUID(end_user_id)
+    except (ValueError, AttributeError):
+        raise _BusinessException(
+            f"Invalid end_user_id format: {end_user_id}",
+            _BizCode.INVALID_PARAMETER,
+        )
+
+    end_user_repo = _EndUserRepository(db)
+    end_user = end_user_repo.get_end_user_by_id(end_user_id)
+
+    if end_user is None:
+        raise _BusinessException(
+            "End user not found",
+            _BizCode.USER_NOT_FOUND,
+        )
+
+    if str(end_user.workspace_id) != str(workspace_id):
+        raise _BusinessException(
+            "End user does not belong to this workspace",
+            _BizCode.PERMISSION_DENIED,
+        )
+
+    return end_user

api/app/core/config.py

@@ -1,9 +1,9 @@
-import json
 import os
 from pathlib import Path
-from typing import Any, Dict, Optional
+from typing import Annotated, Optional
 
 from dotenv import load_dotenv
+from pydantic import Field, TypeAdapter
 
 load_dotenv()
 
@@ -16,18 +16,18 @@ class Settings:
     # cloud: SaaS 云服务版（全功能，按量计费）
     # enterprise: 企业私有化版（License 控制）
     DEPLOYMENT_MODE: str = os.getenv("DEPLOYMENT_MODE", "community")
-    
+
     # License 配置（企业版）
     LICENSE_FILE: str = os.getenv("LICENSE_FILE", "/etc/app/license.json")
     LICENSE_SERVER_URL: str = os.getenv("LICENSE_SERVER_URL", "https://license.yourcompany.com")
-    
+
     # 计费服务配置（SaaS 版）
     BILLING_SERVICE_URL: str = os.getenv("BILLING_SERVICE_URL", "")
-    
+
     # 基础 URL（用于 SSO 回调等）
     BASE_URL: str = os.getenv("BASE_URL", "http://localhost:8000")
     FRONTEND_URL: str = os.getenv("FRONTEND_URL", "http://localhost:3000")
-    
+
     ENABLE_SINGLE_WORKSPACE: bool = os.getenv("ENABLE_SINGLE_WORKSPACE", "true").lower() == "true"
     # API Keys Configuration
     OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "")
@@ -57,7 +57,6 @@ class Settings:
     REDIS_PORT: int = int(os.getenv("REDIS_PORT", "6379"))
     REDIS_DB: int = int(os.getenv("REDIS_DB", "1"))
     REDIS_PASSWORD: str = os.getenv("REDIS_PASSWORD", "")
-    
 
     # ElasticSearch configuration
     ELASTICSEARCH_HOST: str = os.getenv("ELASTICSEARCH_HOST", "https://127.0.0.1")
@@ -91,13 +90,14 @@ 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"))
+    MAX_FILE_COUNT: int = int(os.getenv("MAX_FILE_COUNT", "20"))
     FILE_PATH: str = os.getenv("FILE_PATH", "/files")
     FILE_URL_EXPIRES: int = int(os.getenv("FILE_URL_EXPIRES", "3600"))
 
@@ -115,6 +115,7 @@ class Settings:
     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 +131,7 @@ 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")
+    FILE_LOCAL_SERVER_URL: str = os.getenv("FILE_LOCAL_SERVER_URL", "http://localhost:8000/api")
 
     # ========================================================================
     # Internal Configuration (not in .env, used by application code)
@@ -157,6 +158,49 @@ 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")
@@ -185,19 +229,47 @@ class Settings:
     LOG_FILE_MAX_SIZE_MB: int = int(os.getenv("LOG_FILE_MAX_SIZE_MB", "10"))  # 10MB
 
     # Celery configuration (internal)
-    CELERY_BROKER: int = int(os.getenv("CELERY_BROKER", "1"))
-    CELERY_BACKEND: int = int(os.getenv("CELERY_BACKEND", "2"))
+    # 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 作为 broker 和 backend，与业务缓存隔离
+    # 如需使用 RabbitMQ，在 .env 中设置 CELERY_BROKER_URL=amqp://user:pass@host:5672/vhost
+    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", "")
+    
+    SANDBOX_URL: str = os.getenv("SANDBOX_URL", "")
 
     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")
 
@@ -210,9 +282,35 @@ class Settings:
     # 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"
+
     # 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

@@ -19,6 +19,7 @@ class BizCode(IntEnum):
     TENANT_NOT_FOUND = 3002
     WORKSPACE_NO_ACCESS = 3003
     WORKSPACE_INVITE_NOT_FOUND = 3004
+    WORKSPACE_ACCESS_DENIED = 3005
     # API Key 管理（3xxx）
     API_KEY_NOT_FOUND = 3007
     API_KEY_DUPLICATE_NAME = 3008
@@ -30,6 +31,9 @@ class BizCode(IntEnum):
     API_KEY_QPS_LIMIT_EXCEEDED = 3014
     API_KEY_DAILY_LIMIT_EXCEEDED = 3015
     API_KEY_QUOTA_EXCEEDED = 3016
+    API_KEY_RATE_LIMIT_EXCEEDED = 3017
+    QUOTA_EXCEEDED = 3018
+    RATE_LIMIT_EXCEEDED = 3019
     # 资源（4xxx）
     NOT_FOUND = 4000
     USER_NOT_FOUND = 4001
@@ -40,12 +44,14 @@ class BizCode(IntEnum):
     FILE_NOT_FOUND = 4006
     APP_NOT_FOUND = 4007
     RELEASE_NOT_FOUND = 4008
+    USER_NO_ACCESS = 4009
 
     # 冲突/状态（5xxx）
     DUPLICATE_NAME = 5001
     RESOURCE_ALREADY_EXISTS = 5002
     VERSION_ALREADY_EXISTS = 5003
     STATE_CONFLICT = 5004
+    RESOURCE_IN_USE = 5005
 
     # 应用发布（6xxx）
     PUBLISH_FAILED = 6001
@@ -60,6 +66,7 @@ class BizCode(IntEnum):
     PERMISSION_DENIED = 6010
     INVALID_CONVERSATION = 6011
     CONFIG_MISSING = 6012
+    APP_NOT_PUBLISHED = 6013
 
     # 模型（7xxx）
     MODEL_CONFIG_INVALID = 7001
@@ -112,8 +119,11 @@ HTTP_MAPPING = {
     BizCode.FORBIDDEN: 403,
     BizCode.TENANT_NOT_FOUND: 400,
     BizCode.WORKSPACE_NO_ACCESS: 403,
+    BizCode.WORKSPACE_INVITE_NOT_FOUND: 400,
+    BizCode.WORKSPACE_ACCESS_DENIED: 403,
     BizCode.NOT_FOUND: 400,
     BizCode.USER_NOT_FOUND: 200,
+    BizCode.USER_NO_ACCESS: 401,
     BizCode.WORKSPACE_NOT_FOUND: 400,
     BizCode.MODEL_NOT_FOUND: 400,
     BizCode.KNOWLEDGE_NOT_FOUND: 400,
@@ -125,6 +135,7 @@ 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,
@@ -148,7 +159,8 @@ HTTP_MAPPING = {
     BizCode.API_KEY_QPS_LIMIT_EXCEEDED: 429,
     BizCode.API_KEY_DAILY_LIMIT_EXCEEDED: 429,
     BizCode.API_KEY_QUOTA_EXCEEDED: 429,
-    
+    BizCode.QUOTA_EXCEEDED: 402,
+
     BizCode.MODEL_CONFIG_INVALID: 400,
     BizCode.API_KEY_MISSING: 400,
     BizCode.PROVIDER_NOT_SUPPORTED: 400,
@@ -177,4 +189,21 @@ HTTP_MAPPING = {
     BizCode.DB_ERROR: 500,
     BizCode.SERVICE_UNAVAILABLE: 503,
     BizCode.RATE_LIMITED: 429,
+    BizCode.RATE_LIMIT_EXCEEDED: 429,
+}
+
+ERROR_CODE_TO_BIZ_CODE = {
+    "QUOTA_EXCEEDED": BizCode.QUOTA_EXCEEDED,
+    "RATE_LIMIT_EXCEEDED": BizCode.RATE_LIMIT_EXCEEDED,
+    "API_KEY_NOT_FOUND": BizCode.API_KEY_NOT_FOUND,
+    "API_KEY_INVALID": BizCode.API_KEY_INVALID,
+    "API_KEY_EXPIRED": BizCode.API_KEY_EXPIRED,
+    "WORKSPACE_NOT_FOUND": BizCode.WORKSPACE_NOT_FOUND,
+    "WORKSPACE_NO_ACCESS": BizCode.WORKSPACE_NO_ACCESS,
+    "PERMISSION_DENIED": BizCode.PERMISSION_DENIED,
+    "TOKEN_EXPIRED": BizCode.TOKEN_EXPIRED,
+    "TOKEN_INVALID": BizCode.TOKEN_INVALID,
+    "VALIDATION_FAILED": BizCode.VALIDATION_FAILED,
+    "INVALID_PARAMETER": BizCode.INVALID_PARAMETER,
+    "MISSING_PARAMETER": BizCode.MISSING_PARAMETER,
 }

api/app/core/language_utils.py

@@ -0,0 +1,82 @@
+# -*- 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

@@ -38,6 +38,56 @@ 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"""
     
@@ -65,6 +115,22 @@ 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,
@@ -80,6 +146,7 @@ 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）
@@ -93,6 +160,7 @@ 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
@@ -461,8 +529,9 @@ def log_time(step_name: str, duration: float, log_file: str = "logs/time.log") -
         # Fallback to console only if file write fails
         print(f"Warning: Could not write to timing log: {e}")
     
-    # Always print to console (backward compatible behavior)
-    print(f"✓ {step_name}: {duration:.2f}s")
+    # Always log at INFO level (avoids Celery treating stdout as WARNING)
+    _timing_logger = logging.getLogger(__name__)
+    _timing_logger.info(f"✓ {step_name}: {duration:.2f}s")
 
 
 def get_agent_logger(name: str = "agent_service", 

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

@@ -1,16 +1,45 @@
 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:
-    """开始节点 - 提取内容并保持状态信息"""
+    """
+    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 ''
-    # 返回内容并保持所有状态信息
+    # 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:
-    """开始节点 - 提取内容并保持状态信息"""
+    """
+    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 ''
-    # 返回内容并保持所有状态信息
-    return {"data": content}
+    # 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/perceptual_retrieve_node.py

@@ -0,0 +1,408 @@
+"""
+Perceptual Memory Retrieval Node & Service
+
+Provides PerceptualSearchService for searching perceptual memories (vision, audio,
+text, conversation) from Neo4j using keyword fulltext + embedding semantic search
+with BM25+embedding fusion reranking.
+
+Also provides the perceptual_retrieve_node for use as a LangGraph node.
+"""
+import asyncio
+import math
+from typing import List, Dict, Any, Optional
+
+from app.core.logging_config import get_agent_logger
+from app.core.memory.agent.utils.llm_tools import ReadState
+from app.core.memory.utils.data.text_utils import escape_lucene_query
+from app.repositories.neo4j.graph_search import (
+    search_perceptual_by_fulltext,
+    search_perceptual_by_embedding,
+)
+from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+
+logger = get_agent_logger(__name__)
+
+
+class PerceptualSearchService:
+    """
+    感知记忆检索服务。
+
+    封装关键词全文检索 + 向量语义检索 + BM25/embedding 融合排序的完整流程。
+    调用方只需提供 query / keywords、end_user_id、memory_config，即可获得
+    格式化并排序后的感知记忆列表和拼接文本。
+
+    Usage:
+        service = PerceptualSearchService(end_user_id=..., memory_config=...)
+        results = await service.search(query="...", keywords=[...], limit=10)
+        # results = {"memories": [...], "content": "...", "keyword_raw": N, "embedding_raw": M}
+    """
+
+    DEFAULT_ALPHA = 0.6
+    DEFAULT_CONTENT_SCORE_THRESHOLD = 0.5
+
+    def __init__(
+            self,
+            end_user_id: str,
+            memory_config: Any,
+            alpha: float = DEFAULT_ALPHA,
+            content_score_threshold: float = DEFAULT_CONTENT_SCORE_THRESHOLD,
+    ):
+        self.end_user_id = end_user_id
+        self.memory_config = memory_config
+        self.alpha = alpha
+        self.content_score_threshold = content_score_threshold
+
+    async def search(
+            self,
+            query: str,
+            keywords: Optional[List[str]] = None,
+            limit: int = 10,
+    ) -> Dict[str, Any]:
+        """
+        执行感知记忆检索（关键词 + 向量并行），融合排序后返回结果。
+
+        对 embedding 命中但 keyword 未命中的结果，补查全文索引获取 BM25 分数，
+        确保所有结果都同时具备 BM25 和 embedding 两个维度的评分。
+
+        Args:
+            query: 原始用户查询（用于向量检索和 BM25 补查）
+            keywords: 关键词列表（用于全文检索），为 None 时使用 [query]
+            limit: 最大返回数量
+
+        Returns:
+            {
+                "memories": [格式化后的记忆 dict, ...],
+                "content": "拼接的纯文本摘要",
+                "keyword_raw": int,
+                "embedding_raw": int,
+            }
+        """
+        if keywords is None:
+            keywords = [query] if query else []
+
+        connector = Neo4jConnector()
+        try:
+            kw_task = self._keyword_search(connector, keywords, limit)
+            emb_task = self._embedding_search(connector, query, limit)
+
+            kw_results, emb_results = await asyncio.gather(
+                kw_task, emb_task, return_exceptions=True
+            )
+            if isinstance(kw_results, Exception):
+                logger.warning(f"[PerceptualSearch] keyword search error: {kw_results}")
+                kw_results = []
+            if isinstance(emb_results, Exception):
+                logger.warning(f"[PerceptualSearch] embedding search error: {emb_results}")
+                emb_results = []
+
+            # 补查 BM25：找出 embedding 命中但 keyword 未命中的 id，
+            # 用原始 query 对这些节点补查全文索引拿 BM25 score
+            kw_ids = {r.get("id") for r in kw_results if r.get("id")}
+            emb_only_ids = {r.get("id") for r in emb_results if r.get("id") and r.get("id") not in kw_ids}
+
+            if emb_only_ids and query:
+                backfill = await self._bm25_backfill(connector, query, emb_only_ids, limit)
+                # 把补查到的 BM25 score 注入到 embedding 结果中
+                backfill_map = {r["id"]: r.get("score", 0) for r in backfill}
+                for r in emb_results:
+                    rid = r.get("id", "")
+                    if rid in backfill_map:
+                        r["bm25_backfill_score"] = backfill_map[rid]
+                logger.info(
+                    f"[PerceptualSearch] BM25 backfill: {len(emb_only_ids)} embedding-only ids, "
+                    f"{len(backfill_map)} got BM25 scores"
+                )
+
+            reranked = self._rerank(kw_results, emb_results, limit)
+
+            memories = []
+            content_parts = []
+            for record in reranked:
+                fmt = self._format_result(record)
+                fmt["score"] = round(record.get("content_score", 0), 4)
+                memories.append(fmt)
+                content_parts.append(self._build_content_text(fmt))
+
+            logger.info(
+                f"[PerceptualSearch] {len(memories)} results after rerank "
+                f"(keyword_raw={len(kw_results)}, embedding_raw={len(emb_results)})"
+            )
+            return {
+                "memories": memories,
+                "content": "\n\n".join(content_parts),
+                "keyword_raw": len(kw_results),
+                "embedding_raw": len(emb_results),
+            }
+        finally:
+            await connector.close()
+
+    async def _bm25_backfill(
+            self,
+            connector: Neo4jConnector,
+            query: str,
+            target_ids: set,
+            limit: int,
+    ) -> List[dict]:
+        """
+        对指定 id 集合补查全文索引 BM25 score。
+
+        用原始 query 查全文索引，只保留 id 在 target_ids 中的结果。
+        """
+        escaped = escape_lucene_query(query)
+        if not escaped.strip():
+            return []
+        try:
+            r = await search_perceptual_by_fulltext(
+                connector=connector, query=escaped,
+                end_user_id=self.end_user_id,
+                limit=limit * 5,  # 多查一些以提高命中率
+            )
+            all_hits = r.get("perceptuals", [])
+            return [h for h in all_hits if h.get("id") in target_ids]
+        except Exception as e:
+            logger.warning(f"[PerceptualSearch] BM25 backfill failed: {e}")
+            return []
+
+    async def _keyword_search(
+            self,
+            connector: Neo4jConnector,
+            keywords: List[str],
+            limit: int,
+    ) -> List[dict]:
+        """并发对每个关键词做全文检索，去重后按 score 降序返回 top N 原始结果。"""
+        seen_ids: set = set()
+        all_results: List[dict] = []
+
+        async def _one(kw: str):
+            escaped = escape_lucene_query(kw)
+            if not escaped.strip():
+                return []
+            r = await search_perceptual_by_fulltext(
+                connector=connector, query=escaped,
+                end_user_id=self.end_user_id, limit=limit,
+            )
+            return r.get("perceptuals", [])
+
+        tasks = [_one(kw) for kw in keywords[:10]]
+        batch = await asyncio.gather(*tasks, return_exceptions=True)
+
+        for result in batch:
+            if isinstance(result, Exception):
+                logger.warning(f"[PerceptualSearch] keyword sub-query error: {result}")
+                continue
+            for rec in result:
+                rid = rec.get("id", "")
+                if rid and rid not in seen_ids:
+                    seen_ids.add(rid)
+                    all_results.append(rec)
+
+        all_results.sort(key=lambda x: float(x.get("score", 0)), reverse=True)
+        return all_results[:limit]
+
+    async def _embedding_search(
+            self,
+            connector: Neo4jConnector,
+            query_text: str,
+            limit: int,
+    ) -> List[dict]:
+        """向量语义检索，返回原始结果（不做阈值过滤）。"""
+        try:
+            from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
+            from app.core.models.base import RedBearModelConfig
+            from app.db import get_db_context
+            from app.services.memory_config_service import MemoryConfigService
+
+            with get_db_context() as db:
+                cfg = MemoryConfigService(db).get_embedder_config(
+                    str(self.memory_config.embedding_model_id)
+                )
+            client = OpenAIEmbedderClient(RedBearModelConfig(**cfg))
+
+            r = await search_perceptual_by_embedding(
+                connector=connector, embedder_client=client,
+                query_text=query_text, end_user_id=self.end_user_id,
+                limit=limit,
+            )
+            return r.get("perceptuals", [])
+        except Exception as e:
+            logger.warning(f"[PerceptualSearch] embedding search failed: {e}")
+            return []
+
+    def _rerank(
+            self,
+            keyword_results: List[dict],
+            embedding_results: List[dict],
+            limit: int,
+    ) -> List[dict]:
+        """BM25 + embedding 融合排序。
+
+        对 embedding 结果中带有 bm25_backfill_score 的条目，
+        将其与 keyword 结果合并后统一归一化，确保 BM25 分数在同一尺度上。
+        """
+        # 把补查的 BM25 score 合并到 keyword_results 中统一归一化
+        emb_backfill_items = []
+        for item in embedding_results:
+            backfill_score = item.get("bm25_backfill_score")
+            if backfill_score is not None and item.get("id"):
+                emb_backfill_items.append({"id": item["id"], "score": backfill_score})
+
+        # 合并后统一归一化 BM25 scores
+        all_bm25_items = keyword_results + emb_backfill_items
+        all_bm25_items = self._normalize_scores(all_bm25_items)
+
+        # 建立 id -> normalized BM25 score 的映射
+        bm25_norm_map: Dict[str, float] = {}
+        for item in all_bm25_items:
+            item_id = item.get("id", "")
+            if item_id:
+                bm25_norm_map[item_id] = float(item.get("normalized_score", 0))
+
+        # 归一化 embedding scores
+        embedding_results = self._normalize_scores(embedding_results)
+
+        # 合并
+        combined: Dict[str, dict] = {}
+        for item in keyword_results:
+            item_id = item.get("id", "")
+            if not item_id:
+                continue
+            combined[item_id] = item.copy()
+            combined[item_id]["bm25_score"] = bm25_norm_map.get(item_id, 0)
+            combined[item_id]["embedding_score"] = 0.0
+
+        for item in embedding_results:
+            item_id = item.get("id", "")
+            if not item_id:
+                continue
+            if item_id in combined:
+                combined[item_id]["embedding_score"] = item.get("normalized_score", 0)
+            else:
+                combined[item_id] = item.copy()
+                combined[item_id]["bm25_score"] = bm25_norm_map.get(item_id, 0)
+                combined[item_id]["embedding_score"] = item.get("normalized_score", 0)
+
+        for item in combined.values():
+            bm25 = float(item.get("bm25_score", 0) or 0)
+            emb = float(item.get("embedding_score", 0) or 0)
+            item["content_score"] = self.alpha * bm25 + (1 - self.alpha) * emb
+
+        results = list(combined.values())
+        before = len(results)
+        results = [r for r in results if r["content_score"] >= self.content_score_threshold]
+        results.sort(key=lambda x: x["content_score"], reverse=True)
+        results = results[:limit]
+
+        logger.info(
+            f"[PerceptualSearch] rerank: merged={before}, after_threshold={len(results)} "
+            f"(alpha={self.alpha}, threshold={self.content_score_threshold})"
+        )
+        return results
+
+    @staticmethod
+    def _normalize_scores(items: List[dict], field: str = "score") -> List[dict]:
+        """Z-score + sigmoid 归一化。"""
+        if not items:
+            return items
+        scores = [float(it.get(field, 0) or 0) for it in items]
+        if len(scores) <= 1:
+            for it in items:
+                it[f"normalized_{field}"] = 1.0
+            return items
+        mean = sum(scores) / len(scores)
+        var = sum((s - mean) ** 2 for s in scores) / len(scores)
+        std = math.sqrt(var)
+        if std == 0:
+            for it in items:
+                it[f"normalized_{field}"] = 1.0
+        else:
+            for it, s in zip(items, scores):
+                z = (s - mean) / std
+                it[f"normalized_{field}"] = 1 / (1 + math.exp(-z))
+        return items
+
+    @staticmethod
+    def _format_result(record: dict) -> dict:
+        return {
+            "id": record.get("id", ""),
+            "perceptual_type": record.get("perceptual_type", ""),
+            "file_name": record.get("file_name", ""),
+            "file_path": record.get("file_path", ""),
+            "summary": record.get("summary", ""),
+            "topic": record.get("topic", ""),
+            "domain": record.get("domain", ""),
+            "keywords": record.get("keywords", []),
+            "created_at": str(record.get("created_at", "")),
+            "file_type": record.get("file_type", ""),
+            "score": record.get("score", 0),
+        }
+
+    @staticmethod
+    def _build_content_text(formatted: dict) -> str:
+        parts = []
+        if formatted["summary"]:
+            parts.append(formatted["summary"])
+        if formatted["topic"]:
+            parts.append(f"[主题: {formatted['topic']}]")
+        if formatted["keywords"]:
+            kw_list = formatted["keywords"]
+            if isinstance(kw_list, list):
+                parts.append(f"[关键词: {', '.join(kw_list)}]")
+        if formatted["file_name"]:
+            parts.append(f"[文件: {formatted['file_name']}]")
+        return " ".join(parts)
+
+
+def _extract_keywords_from_problems(problem_extension: dict) -> List[str]:
+    """Extract search keywords from problem extension results."""
+    keywords = []
+    context = problem_extension.get("context", {})
+    if isinstance(context, dict):
+        for original_q, extended_qs in context.items():
+            keywords.append(original_q)
+            if isinstance(extended_qs, list):
+                keywords.extend(extended_qs)
+    return keywords
+
+
+async def perceptual_retrieve_node(state: ReadState) -> ReadState:
+    """
+    LangGraph node: perceptual memory retrieval.
+
+    Uses PerceptualSearchService to run keyword + embedding search with
+    BM25 fusion reranking, then writes results to state['perceptual_data'].
+    """
+    end_user_id = state.get("end_user_id", "")
+    problem_extension = state.get("problem_extension", {})
+    original_query = state.get("data", "")
+    memory_config = state.get("memory_config", None)
+
+    logger.info(f"Perceptual_Retrieve: start, end_user_id={end_user_id}")
+
+    keywords = _extract_keywords_from_problems(problem_extension)
+    if not keywords:
+        keywords = [original_query] if original_query else []
+
+    logger.info(f"Perceptual_Retrieve: {len(keywords)} keywords extracted")
+
+    service = PerceptualSearchService(
+        end_user_id=end_user_id,
+        memory_config=memory_config,
+    )
+    search_result = await service.search(
+        query=original_query,
+        keywords=keywords,
+        limit=10,
+    )
+
+    result = {
+        "memories": search_result["memories"],
+        "content": search_result["content"],
+        "_intermediate": {
+            "type": "perceptual_retrieve",
+            "title": "感知记忆检索",
+            "data": search_result["memories"],
+            "query": original_query,
+            "result_count": len(search_result["memories"]),
+        },
+    }
+    return {"perceptual_data": result}

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

@@ -1,10 +1,10 @@
-import os
 import json
+import os
 import time
-from app.core.logging_config import get_agent_logger
-from app.db import get_db
 
+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,
@@ -12,27 +12,46 @@ from app.core.memory.agent.utils.llm_tools import (
 from app.core.memory.agent.utils.redis_tool import store
 from app.core.memory.agent.utils.session_tools import SessionService
 from app.core.memory.agent.utils.template_tools import TemplateService
-from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
+from app.db import get_db_context
 
 template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
-db_session = next(get_db())
 logger = get_agent_logger(__name__)
 
 
 class ProblemNodeService(LLMServiceMixin):
-    """问题处理节点服务类"""
+    """
+    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)
 
 
-# 创建全局服务实例
+# Create global service instance
 problem_service = ProblemNodeService()
 
 
 async def Split_The_Problem(state: ReadState) -> ReadState:
-    """问题分解节点"""
+    """
+    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', '')
@@ -53,18 +72,19 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
 
     try:
         # 使用优化的LLM服务
-        structured = await problem_service.call_llm_structured(
-            state=state,
-            db_session=db_session,
-            system_prompt=system_prompt,
-            response_model=ProblemExtensionResponse,
-            fallback_value=[]
-        )
+        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)}")
 
-        # 验证结构化响应
+        # 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)
@@ -106,17 +126,17 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
             exc_info=True
         )
 
-        # 提供更详细的错误信息
+        # Provide more detailed error information
         error_details = {
             "error_type": type(e).__name__,
             "error_message": str(e),
             "content_length": len(content),
-            "llm_model_id": memory_config.llm_model_id if memory_config else None
+            "llm_model_id": str(memory_config.llm_model_id) if memory_config else None
         }
 
         logger.error(f"Split_The_Problem error details: {error_details}")
 
-        # 创建默认的空结果
+        # Create default empty result
         result = {
             "context": json.dumps([], ensure_ascii=False),
             "original": content,
@@ -130,13 +150,25 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
             }
         }
 
-    # 返回更新后的状态，包含spit_context字段
+    # Return updated state including spit_context field
     return {"spit_data": result}
 
 
 async def Problem_Extension(state: ReadState) -> ReadState:
-    """问题扩展节点"""
-    # 获取原始数据和分解结果
+    """
+    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']
@@ -171,17 +203,18 @@ async def Problem_Extension(state: ReadState) -> ReadState:
 
     try:
         # 使用优化的LLM服务
-        response_content = await problem_service.call_llm_structured(
-            state=state,
-            db_session=db_session,
-            system_prompt=system_prompt,
-            response_model=ProblemExtensionResponse,
-            fallback_value=[]
-        )
+        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)}")
 
-        # 验证结构化响应
+        # 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 = {}
@@ -215,12 +248,12 @@ async def Problem_Extension(state: ReadState) -> ReadState:
             exc_info=True
         )
 
-        # 提供更详细的错误信息
+        # Provide more detailed error information
         error_details = {
             "error_type": type(e).__name__,
             "error_message": str(e),
             "questions_count": len(databasets),
-            "llm_model_id": memory_config.llm_model_id if memory_config else None
+            "llm_model_id": str(memory_config.llm_model_id) if memory_config else None
         }
 
         logger.error(f"Problem_Extension error details: {error_details}")
@@ -230,7 +263,6 @@ async def Problem_Extension(state: ReadState) -> ReadState:
     logger.info(f"Problem extension result: {aggregated_dict}")
 
     # Emit intermediate output for frontend
-    print(time.time() - start)
     result = {
         "context": aggregated_dict,
         "original": data,

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

@@ -6,34 +6,41 @@ import os
 # ===== 第三方库 =====
 from langchain.agents import create_agent
 from langchain_openai import ChatOpenAI
+
 from app.core.logging_config import get_agent_logger
-from app.db import get_db, get_db_context
-
-from app.schemas import model_schema
-from app.services.memory_config_service import MemoryConfigService
-from app.services.model_service import ModelConfigService
-
-from app.core.memory.agent.services.search_service import SearchService
-from app.core.memory.agent.utils.llm_tools import (
-    COUNTState,
-    ReadState,
-    deduplicate_entries,
-    merge_to_key_value_pairs,
-)
 from app.core.memory.agent.langgraph_graph.tools.tool import (
     create_hybrid_retrieval_tool_sync,
     create_time_retrieval_tool,
     extract_tool_message_content,
 )
-
+from app.core.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__)
-db = next(get_db())
-
 
 
 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": [
@@ -50,10 +57,25 @@ async def rag_config(state):
         "reranker_top_k": 10
     }
     return kb_config
-async def rag_knowledge(state,question):
+
+
+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",'')
+    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]
@@ -61,22 +83,34 @@ async def rag_knowledge(state,question):
         cleaned_query = question
         raw_results = clean_content
         logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
-    except  Exception :
-        retrieval_knowledge=[]
+    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
+    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
 
-    # 使用现有的 memory_config 而不是重新查询数据库
-    # 或者使用线程安全的数据库访问
+    # 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)
@@ -85,16 +119,20 @@ async def llm_infomation(state: ReadState) -> ReadState:
 
 async def clean_databases(data) -> str:
     """
-    简化的数据库搜索结果清理函数
+    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: 搜索结果数据
+        data: Search result data (can be string, dict, or other types)
         
     Returns:
-        清理后的内容字符串
+        str: Cleaned content string
     """
     try:
-        # 解析JSON字符串
+        # Parse JSON string
         if isinstance(data, str):
             try:
                 data = json.loads(data)
@@ -104,24 +142,24 @@ async def clean_databases(data) -> str:
         if not isinstance(data, dict):
             return str(data)
 
-        # 获取结果数据
+        # 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)
 
-        # 收集所有内容
+        # Collect all content
         content_list = []
-        
-        # 处理重排序结果
+
+        # Process reranked results
         reranked = results.get('reranked_results', {})
         if reranked:
-            for category in ['summaries', 'statements', 'chunks', 'entities']:
+            for category in ['summaries', 'communities', 'statements', 'chunks', 'entities']:
                 items = reranked.get(category, [])
                 if isinstance(items, list):
                     content_list.extend(items)
-        # 处理时间搜索结果
+        # Process time search results
         time_search = results.get('time_search', {})
         if time_search:
             if isinstance(time_search, dict):
@@ -131,17 +169,23 @@ async def clean_databases(data) -> str:
             elif isinstance(time_search, list):
                 content_list.extend(time_search)
 
-        # 提取文本内容
+        # 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):
-                text = item.get('statement') or item.get('content', '')
+                # 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:
@@ -150,24 +194,33 @@ async def clean_databases(data) -> str:
 
 
 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
+    """
 
-    '''
-
-    模型信息
-    '''
-
-    problem_extension=state.get('problem_extension', '')['context']
-    storage_type=state.get('storage_type', '')
-    user_rag_memory_id=state.get('user_rag_memory_id', '')
-    end_user_id=state.get('end_user_id', '')
+    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():
+    original = state.get('data', '')
+    problem_list = []
+    for key, values in problem_extension.items():
         for data in values:
             problem_list.append(data)
     logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
-    # 创建异步任务处理单个问题
+
+    # Create async task to process individual questions
     async def process_question_nodes(idx, question):
         try:
             # Prepare search parameters based on storage type
@@ -213,7 +266,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState:
                 }
             }
 
-    # 并发处理所有问题
+    # 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 = {
@@ -244,7 +297,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState:
 
     send_verify = []
     for i, j in zip(keys, val, strict=False):
-        if j!=['']:
+        if j != ['']:
             send_verify.append({
                 "Query_small": i,
                 "Answer_Small": j
@@ -257,15 +310,26 @@ async def retrieve_nodes(state: ReadState) -> ReadState:
     }
 
     logger.info(f"Collected {len(intermediate_outputs)} intermediate outputs from search results")
-    return {'retrieve':dup_databases}
-
-
+    return {'retrieve': dup_databases}
 
 
 async def retrieve(state: ReadState) -> ReadState:
-    # 从state中获取end_user_id
+    """
+    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()
+    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', '')
@@ -283,6 +347,7 @@ async def retrieve(state: ReadState) -> ReadState:
         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
@@ -296,28 +361,33 @@ async def retrieve(state: ReadState) -> ReadState:
     )
 
     time_retrieval_tool = create_time_retrieval_tool(end_user_id)
-    search_params = { "end_user_id": end_user_id, "return_raw_results": True }
-    hybrid_retrieval=create_hybrid_retrieval_tool_sync(memory_config, **search_params)
+    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],
+        tools=[time_retrieval_tool, hybrid_retrieval],
         system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的end_user_id是: {end_user_id}"
     )
 
-    # 创建异步任务处理单个问题
+    # Create async task to process individual questions
     import asyncio
 
-    # 在模块级别定义信号量，限制最大并发数
-    SEMAPHORE = asyncio.Semaphore(5)  # 限制最多5个并发数据库操作
+    # 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:  # 限制并发
+        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)
+                    retrieval_knowledge, clean_content, cleaned_query, raw_results = await rag_knowledge(state,
+                                                                                                         question)
                 else:
                     cleaned_query = question
-                    # 使用 asyncio 在线程池中运行同步的 agent.invoke
+                    # Use asyncio to run synchronous agent.invoke in thread pool
                     import asyncio
                     response = await asyncio.get_event_loop().run_in_executor(
                         None,
@@ -331,8 +401,32 @@ async def retrieve(state: ReadState) -> ReadState:
                         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 = []
 
@@ -366,7 +460,7 @@ async def retrieve(state: ReadState) -> ReadState:
                     }
                 }
 
-    # 并发处理所有问题
+    # Process all questions concurrently
     import asyncio
     tasks = [process_question(idx, question) for idx, question in enumerate(problem_list)]
     databases_anser = await asyncio.gather(*tasks)
@@ -413,5 +507,3 @@ async def retrieve(state: ReadState) -> ReadState:
     #     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

@@ -1,9 +1,11 @@
-
-
+import asyncio
 import os
 import time
 
 from app.core.logging_config import get_agent_logger, log_time
+from app.core.memory.agent.langgraph_graph.nodes.perceptual_retrieve_node import (
+    PerceptualSearchService,
+)
 from app.core.memory.agent.models.summary_models import (
     RetrieveSummaryResponse,
     SummaryResponse,
@@ -17,34 +19,144 @@ from app.core.memory.agent.utils.llm_tools import (
 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
+from app.core.memory.enums import Neo4jNodeType
+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__)
-db_session = next(get_db())
+
 
 class SummaryNodeService(LLMServiceMixin):
-    """总结节点服务类"""
+    """
+    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)
 
-# 创建全局服务实例
+
+# 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:
+
+async def summary_llm(state: ReadState, history, retrieve_info, template_name, operation_name, response_model,
+                      search_mode) -> str:
     """
-    增强的summary_llm函数，包含更好的错误处理和数据验证
+    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", '')
-    
-    # 构建系统提示词
+
+    # Build system prompt
     if str(search_mode) == "0":
         system_prompt = await summary_service.template_service.render_template(
             template_name=template_name,
@@ -61,40 +173,41 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o
             retrieve_info=retrieve_info
         )
     try:
-        # 使用优化的LLM服务进行结构化输出
-        structured = await summary_service.call_llm_structured(
-            state=state,
-            db_session=db_session,
-            system_prompt=system_prompt,
-            response_model=response_model,
-            fallback_value=None
-        )
-        # 验证结构化响应
+        # 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(f"LLM返回None，使用默认回答")
+            logger.warning("LLM返回None，使用默认回答")
             return "信息不足，无法回答"
-        
-        # 根据操作类型提取答案
+
+        # 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:
-            # 处理RetrieveSummaryResponse
+            # 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(f"结构化响应缺少data字段")
+                logger.warning("结构化响应缺少data字段")
                 aimessages = "信息不足，无法回答"
-        
-        # 验证答案不为空
+
+        # 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)
-        
-        # 尝试非结构化输出作为fallback
+
+        # 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(
@@ -103,24 +216,38 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o
                 system_prompt=system_prompt,
                 fallback_message="信息不足，无法回答"
             )
-            
+
             if response and response.strip():
-                # 简单清理响应
+                # Simple response cleaning
                 cleaned_response = response.strip()
-                # 移除可能的JSON标记
+                # 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:
+
+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(
@@ -132,10 +259,26 @@ async def summary_redis_save(state: ReadState,aimessages) -> ReadState:
     )
     await SessionService(store).cleanup_duplicates()
     logger.info(f"sessionid: {aimessages} 写入成功")
-async def summary_prompt(state: ReadState,aimessages,raw_results) -> ReadState:
-    storage_type=state.get("storage_type",'')
-    user_rag_memory_id=state.get("user_rag_memory_id",'')
-    data=state.get("data", '')
+
+
+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,
@@ -152,14 +295,14 @@ async def summary_prompt(state: ReadState,aimessages,raw_results) -> ReadState:
             "user_rag_memory_id": user_rag_memory_id
         }
     }
-    retrieve={
+    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",
+            "title": "快速检索",
             "summary": aimessages,
             "query": data,
             "storage_type": storage_type,
@@ -167,31 +310,90 @@ async def summary_prompt(state: ReadState,aimessages,raw_results) -> ReadState:
         }
     }
 
-    return input_summary,retrieve
+    return input_summary, retrieve
+
 
 async def Input_Summary(state: ReadState) -> ReadState:
-    start=time.time()
-    storage_type=state.get("storage_type",'')
+    """
+    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", '')
+    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)
+    history = await summary_history(state)
     search_params = {
         "end_user_id": end_user_id,
         "question": data,
         "return_raw_results": True,
-        "include": ["summaries"]  # Only search summary nodes for faster performance
+        "include": [Neo4jNodeType.MEMORYSUMMARY, Neo4jNodeType.COMMUNITY]  # MemorySummary 和 Community 同为高维度概括节点
     }
 
     try:
-        retrieve_info, question, raw_results = await SearchService().execute_hybrid_search(**search_params, memory_config=memory_config)
+        if storage_type != "rag":
+
+            async def _perceptual_search():
+                service = PerceptualSearchService(
+                    end_user_id=end_user_id,
+                    memory_config=memory_config,
+                )
+                return await service.search(query=data, limit=5)
+
+            hybrid_task = SearchService().execute_hybrid_search(
+                **search_params,
+                memory_config=memory_config,
+                expand_communities=False,
+            )
+            perceptual_task = _perceptual_search()
+
+            gather_results = await asyncio.gather(
+                hybrid_task, perceptual_task, return_exceptions=True
+            )
+            hybrid_result = gather_results[0]
+            perceptual_results = gather_results[1]
+
+            # 处理 hybrid search 异常
+            if isinstance(hybrid_result, Exception):
+                raise hybrid_result
+            retrieve_info, question, raw_results = hybrid_result
+
+            # 处理感知记忆结果
+            if isinstance(perceptual_results, Exception):
+                logger.warning(f"[Input_Summary] perceptual search failed: {perceptual_results}")
+                perceptual_results = []
+
+            # 拼接感知记忆内容到 retrieve_info
+            if perceptual_results and isinstance(perceptual_results, dict):
+                perceptual_content = perceptual_results.get("content", "")
+                if perceptual_content:
+                    retrieve_info = f"{retrieve_info}\n\n<history-files>\n{perceptual_content}"
+                    count = len(perceptual_results.get("memories", []))
+                    logger.info(f"[Input_Summary] appended {count} perceptual memories (reranked)")
+
+            # 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 )
+        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)
@@ -199,8 +401,8 @@ async def Input_Summary(state: ReadState) -> ReadState:
         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= {
+        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,
@@ -208,35 +410,58 @@ async def Input_Summary(state: ReadState) -> ReadState:
             "error": str(e)
         }
     end = time.time()
-    try:
-        duration = end - start
-    except Exception:
-        duration = 0.0
+    duration = end - start
     log_time('检索', duration)
-    return {"summary":summary}
+    return {"summary": summary}
 
-async def Retrieve_Summary(state: ReadState)-> ReadState:
-    retrieve=state.get("retrieve", '')
-    history = await summary_history( state)
+
+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:
+    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=[]
+    retrieve = retrieve.get("Expansion_issue", [])
+    start = time.time()
+    retrieve_info_str = []
     for data in retrieve:
-        if data=='':
-            retrieve_info_str=''
+        if data == '':
+            retrieve_info_str = ''
         else:
             for key, value in data.items():
-                if key=='Answer_Small':
+                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)
+    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")
+    # Merge perceptual memory content
+    perceptual_data = state.get("perceptual_data", {})
+    perceptual_content = perceptual_data.get("content", "") if isinstance(perceptual_data, dict) else ""
+    if perceptual_content:
+        retrieve_info_str = f"{retrieve_info_str}\n\n<history-file-input>\n{perceptual_content}</history-file-input>"
+
+    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 == '':
@@ -248,33 +473,52 @@ async def Retrieve_Summary(state: ReadState)-> ReadState:
     except Exception:
         duration = 0.0
     log_time('Retrieval summary', duration)
-    
-    # 修复协程调用 - 先await，然后访问返回值
+
+    # 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}
+    return {"summary": summary}
 
 
-async def Summary(state: ReadState)-> ReadState:
-    start=time.time()
+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=''
+    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':
+            if key == 'answer_small':
                 for i in value:
-                    retrieve_info_str+=i+'\n'
-    history=await summary_history(state)
+                    retrieve_info_str += i + '\n'
+    history = await summary_history(state)
+
+    # Merge perceptual memory content
+    perceptual_data = state.get("perceptual_data", {})
+    perceptual_content = perceptual_data.get("content", "") if isinstance(perceptual_data, dict) else ""
+    if perceptual_content:
+        retrieve_info_str = f"{retrieve_info_str}\n\n<history-file-input>\n{perceptual_content}</history-file-input>"
 
     data = {
         "query": query,
         "history": history,
         "retrieve_info": retrieve_info_str
     }
-    aimessages=await  summary_llm(state,history,data,
-                                  'summary_prompt.jinja2','summary',SummaryResponse,0)
+    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)
@@ -286,14 +530,28 @@ async def Summary(state: ReadState)-> ReadState:
         duration = 0.0
     log_time('Retrieval summary', duration)
 
-    # 修复协程调用 - 先await，然后访问返回值
+    # 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}
+    return {"summary": summary}
 
-async def Summary_fails(state: ReadState)-> ReadState:
-    storage_type=state.get("storage_type", '')
-    user_rag_memory_id=state.get("user_rag_memory_id", '')
+
+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", '')
@@ -304,17 +562,24 @@ async def Summary_fails(state: ReadState)-> ReadState:
             if key == 'answer_small':
                 for i in value:
                     retrieve_info_str += i + '\n'
+
+    # Merge perceptual memory content
+    perceptual_data = state.get("perceptual_data", {})
+    perceptual_content = perceptual_data.get("content", "") if isinstance(perceptual_data, dict) else ""
+    if perceptual_content:
+        retrieve_info_str = f"{retrieve_info_str}\n\n<history-file-input>\n{perceptual_content}</history-file-input>"
+
     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= {
+    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}
+    return {"summary": result}

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

@@ -1,8 +1,9 @@
+import asyncio
 import os
-from app.core.logging_config import get_agent_logger
-from app.db import get_db
 
+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,
@@ -10,29 +11,53 @@ from app.core.memory.agent.utils.llm_tools import (
 from app.core.memory.agent.utils.redis_tool import store
 from app.core.memory.agent.utils.session_tools import SessionService
 from app.core.memory.agent.utils.template_tools import TemplateService
-from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
+from app.db import get_db_context
 
 template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
-db_session = next(get_db())
 logger = get_agent_logger(__name__)
 
+
 class VerificationNodeService(LLMServiceMixin):
-    """验证节点服务类"""
+    """
+    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)
 
-# 创建全局服务实例
+
+# Create global service instance
 verification_service = VerificationNodeService()
 
+
 async def Verify_prompt(state: ReadState, messages_deal: VerificationResult):
-    """处理验证结果并生成输出格式"""
+    """
+    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', '')
-    
-    # 将 VerificationItem 对象转换为字典列表
+
+    # Convert VerificationItem objects to dictionary list
     verified_data = []
     if messages_deal.expansion_issue:
         for item in messages_deal.expansion_issue:
@@ -40,7 +65,7 @@ async def Verify_prompt(state: ReadState, messages_deal: VerificationResult):
                 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,
@@ -58,34 +83,37 @@ async def Verify_prompt(state: ReadState, messages_deal: VerificationResult):
         }
     }
     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'}")
-        
+        logger.info(
+            f"Verify: retrieve data type={type(retrieve)}, keys={retrieve.keys() if isinstance(retrieve, dict) else 'N/A'}")
+
         retrieve_expansion = retrieve.get("Expansion_issue", []) if isinstance(retrieve, dict) else []
         logger.info(f"Verify: Expansion_issue length={len(retrieve_expansion)}")
-        
+
         messages = {
             "Query": content,
             "Expansion_issue": retrieve_expansion
         }
 
         logger.info("Verify: 开始渲染模板")
-        
-        # 生成 JSON schema 以指导 LLM 输出正确格式
+
+        # 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',
@@ -94,29 +122,30 @@ async def Verify(state: ReadState):
             json_schema=json_schema
         )
         logger.info(f"Verify: 模板渲染完成，prompt length={len(system_prompt)}")
-        
+
         # 使用优化的LLM服务，添加超时保护
         logger.info("Verify: 开始调用 LLM")
         try:
-            # 添加 asyncio.wait_for 超时包裹，防止无限等待
-            # 超时时间设置为 150 秒（比 LLM 配置的 120 秒稍长）
-            import asyncio
-            structured = await asyncio.wait_for(
-                verification_service.call_llm_structured(
-                    state=state,
-                    db_session=db_session,
-                    system_prompt=system_prompt,
-                    response_model=VerificationResult,
-                    fallback_value={
-                        "query": content,
-                        "history": history if isinstance(history, list) else [],
-                        "expansion_issue": [],
-                        "split_result": "failed",
-                        "reason": "验证失败或超时"
-                    }
-                ),
-                timeout=150.0  # 150秒超时
-            )
+            # 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")
@@ -127,11 +156,11 @@ async def Verify(state: ReadState):
                 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
@@ -152,4 +181,4 @@ async def Verify(state: ReadState):
                     "user_rag_memory_id": state.get('user_rag_memory_id', '')
                 }
             }
-        }
+        }

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

@@ -1,3 +1,4 @@
+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
@@ -10,7 +11,7 @@ async def write_node(state: WriteState) -> WriteState:
         Write data to the database/file system.
 
         Args:
-            state: WriteState containing messages, end_user_id, and memory_config
+            state: WriteState containing messages, end_user_id, memory_config, and language
 
         Returns:
             dict: Contains 'write_result' with status and data fields
@@ -18,6 +19,7 @@ async def write_node(state: WriteState) -> WriteState:
     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 = []
@@ -35,9 +37,19 @@ async def write_node(state: WriteState) -> WriteState:
             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,

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

@@ -1,22 +1,20 @@
 #!/usr/bin/env python3
+import logging
 from contextlib import asynccontextmanager
 
-from langchain_core.messages import HumanMessage
 from langgraph.constants import START, END
 from langgraph.graph import StateGraph
 
-
-from app.db import get_db
-from app.services.memory_config_service import MemoryConfigService
-
-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.perceptual_retrieve_node import (
+    perceptual_retrieve_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,
+    retrieve_nodes,
 )
 from app.core.memory.agent.langgraph_graph.nodes.summary_nodes import (
     Input_Summary,
@@ -30,12 +28,26 @@ from app.core.memory.agent.langgraph_graph.routing.routers import (
     Retrieve_continue,
     Verify_continue,
 )
+from app.core.memory.agent.utils.llm_tools import ReadState
 
+logger = logging.getLogger(__name__)
 
 
 @asynccontextmanager
 async def make_read_graph():
-    """创建并返回 LangGraph 工作流"""
+    """
+    Create and return a LangGraph workflow for memory reading 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
+    """
     try:
         # Build workflow graph
         workflow = StateGraph(ReadState)
@@ -43,135 +55,34 @@ async def make_read_graph():
         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("Retrieve", retrieve_nodes)
+        # workflow.add_node("Retrieve", retrieve)
+        workflow.add_node("Perceptual_Retrieve", perceptual_retrieve_node)
         workflow.add_node("Verify", Verify)
         workflow.add_node("Retrieve_Summary", Retrieve_Summary)
         workflow.add_node("Summary", Summary)
         workflow.add_node("Summary_fails", Summary_fails)
-        
-        # 添加边
+
+        # 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")
+        # After Problem_Extension, retrieve perceptual memory first, then main Retrieve
+        workflow.add_edge("Problem_Extension", "Perceptual_Retrieve")
+        workflow.add_edge("Perceptual_Retrieve", "Retrieve")
         workflow.add_conditional_edges("Retrieve", Retrieve_continue)
         workflow.add_edge("Retrieve_Summary", END)
         workflow.add_conditional_edges("Verify", Verify_continue)
         workflow.add_edge("Summary_fails", END)
         workflow.add_edge("Summary", END)
 
-
-        '''-----'''
         # workflow.add_edge("Retrieve", END)
-        
-        # 编译工作流
+
+        # Compile workflow
         graph = workflow.compile()
         yield graph
-        
+
     except Exception as e:
-        print(f"创建工作流失败: {e}")
+        logger.error(f"创建工作流失败: {e}")
         raise
-    finally:
-        print("工作流创建完成")
-
-async def main():
-    """主函数 - 运行工作流"""
-    message = "昨天有什么好看的电影"
-    end_user_id = '88a459f5_text09'  # 组ID
-    storage_type = 'neo4j'  # 存储类型
-    search_switch = '1'  # 搜索开关
-    user_rag_memory_id = 'wwwwwwww'  # 用户RAG记忆ID
-
-    # 获取数据库会话
-    db_session = next(get_db())
-    config_service = MemoryConfigService(db_session)
-    memory_config = config_service.load_memory_config(
-        config_id=17,  # 改为整数
-        service_name="MemoryAgentService"
-    )
-    import time
-    start=time.time()
-    try:
-        async with make_read_graph() as graph:
-            config = {"configurable": {"thread_id": end_user_id}}
-            # 初始状态 - 包含所有必要字段
-            initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"end_user_id":end_user_id
-                             ,"storage_type":storage_type,"user_rag_memory_id":user_rag_memory_id,"memory_config":memory_config}
-            # 获取节点更新信息
-            _intermediate_outputs = []
-            summary = ''
-            
-            async for update_event in graph.astream(
-                    initial_state,
-                    stream_mode="updates",
-                    config=config
-            ):
-                for node_name, node_data in update_event.items():
-                    print(f"处理节点: {node_name}")
-                    
-                    # 处理不同Summary节点的返回结构
-                    if 'Summary' in node_name:
-                        if 'InputSummary' in node_data and 'summary_result' in node_data['InputSummary']:
-                            summary = node_data['InputSummary']['summary_result']
-                        elif 'RetrieveSummary' in node_data and 'summary_result' in node_data['RetrieveSummary']:
-                            summary = node_data['RetrieveSummary']['summary_result']
-                        elif 'summary' in node_data and 'summary_result' in node_data['summary']:
-                            summary = node_data['summary']['summary_result']
-                        elif 'SummaryFails' in node_data and 'summary_result' in node_data['SummaryFails']:
-                            summary = node_data['SummaryFails']['summary_result']
-
-                    spit_data = node_data.get('spit_data', {}).get('_intermediate', None)
-                    if spit_data and spit_data != [] and spit_data != {}:
-                        _intermediate_outputs.append(spit_data)
-                    
-                    # Problem_Extension 节点
-                    problem_extension = node_data.get('problem_extension', {}).get('_intermediate', None)
-                    if problem_extension and problem_extension != [] and problem_extension != {}:
-                        _intermediate_outputs.append(problem_extension)
-                    
-                    # Retrieve 节点
-                    retrieve_node = node_data.get('retrieve', {}).get('_intermediate_outputs', None)
-                    if retrieve_node and retrieve_node != [] and retrieve_node != {}:
-                        _intermediate_outputs.extend(retrieve_node)
-                    
-                    # Verify 节点
-                    verify_n = node_data.get('verify', {}).get('_intermediate', None)
-                    if verify_n and verify_n != [] and verify_n != {}:
-                        _intermediate_outputs.append(verify_n)
-
-                    
-                    # Summary 节点
-                    summary_n = node_data.get('summary', {}).get('_intermediate', None)
-                    if summary_n and summary_n != [] and summary_n != {}:
-                        _intermediate_outputs.append(summary_n)
-
-            # # 过滤掉空值
-            # _intermediate_outputs = [item for item in _intermediate_outputs if item and item != [] and item != {}]
-            #
-            # # 优化搜索结果
-            # print("=== 开始优化搜索结果 ===")
-            # optimized_outputs = merge_multiple_search_results(_intermediate_outputs)
-            # result=reorder_output_results(optimized_outputs)
-            # # 保存优化后的结果到文件
-            # with open('_intermediate_outputs_optimized.json', 'w', encoding='utf-8') as f:
-            #     import json
-            #     f.write(json.dumps(result, indent=4, ensure_ascii=False))
-            #
-            print(f"=== 最终摘要 ===")
-            print(summary)
-                
-    except Exception as e:
-        import traceback
-        traceback.print_exc()
-
-    end=time.time()
-    print(100*'y')
-    print(f"总耗时: {end-start}s")
-    print(100*'y')
-
-
-if __name__ == "__main__":
-    import asyncio
-    asyncio.run(main())

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

@@ -1,13 +1,13 @@
-
 from typing import Literal
 
 from app.core.logging_config import get_agent_logger
 from app.core.memory.agent.utils.llm_tools import ReadState, COUNTState
 
-
 logger = get_agent_logger(__name__)
 counter = COUNTState(limit=3)
-def Split_continue(state:ReadState) -> Literal["Split_The_Problem", "Input_Summary"]:
+
+
+def Split_continue(state: ReadState) -> Literal["Split_The_Problem", "Input_Summary"]:
     """
     Determine routing based on search_switch value.
 
@@ -25,6 +25,7 @@ def Split_continue(state:ReadState) -> Literal["Split_The_Problem", "Input_Summa
             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.
@@ -43,8 +44,10 @@ def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]:
         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']
+    status = state.get('verify', '')['status']
     # loop_count = counter.get_total()
     if "success" in status:
         # counter.reset()
@@ -53,7 +56,7 @@ def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "co
         # if loop_count < 2:  # Maximum loop count is 3
         #     return "content_input"
         # else:
-            # counter.reset()
+        # counter.reset()
         return "Summary_fails"
     else:
         # Add default return value to avoid returning None

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

@@ -0,0 +1,295 @@
+import json
+import os
+
+from app.celery_task_scheduler import scheduler
+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.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(
+        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)
+        # )
+        scheduler.push_task(
+            "app.core.memory.agent.write_message",
+            str(actual_end_user_id),
+            {
+                "end_user_id": str(actual_end_user_id),
+                "message": structured_messages,
+                "config_id": str(actual_config_id),
+                "storage_type": storage_type,
+                "user_rag_memory_id": user_rag_memory_id or ""
+            }
+        )
+
+        # 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}')
+
+
+async def term_memory_save(end_user_id, strategy_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:
+        end_user_id: User identifier for memory association
+        strategy_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 not result:
+            logger.warning(f"No write data found for user {end_user_id}")
+            return
+        if strategy_type in [AgentMemory_Long_Term.STRATEGY_CHUNK, 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：')
+
+
+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
+    """
+    is_end_user_has_history = count_store.get_sessions_count(end_user_id)
+    if is_end_user_has_history:
+        end_user_visit_count, redis_messages = is_end_user_has_history
+    else:
+        count_store.save_sessions_count(end_user_id, 1, langchain_messages)
+        return
+    end_user_visit_count += 1
+    if end_user_visit_count < scope:
+        redis_messages.extend(langchain_messages)
+        count_store.update_sessions_count(end_user_id, end_user_visit_count, redis_messages)
+    else:
+        logger.info('写入长期记忆NEO4J')
+        redis_messages.extend(langchain_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
+
+        scheduler.push_task(
+            "app.core.memory.agent.write_message",
+            str(end_user_id),
+            {
+                "end_user_id": str(end_user_id),
+                "message": redis_messages,
+                "config_id": str(config_id),
+                "storage_type": AgentMemory_Long_Term.STORAGE_NEO4J,
+                "user_rag_memory_id": ""
+            }
+        )
+        # write_message_task.delay(
+        #     end_user_id,  # end_user_id: User ID
+        #     redis_messages,  # message: JSON string format message list
+        #     config_id,  # config_id: Configuration ID string
+        #     AgentMemory_Long_Term.STORAGE_NEO4J,  # storage_type: "neo4j"
+        #     ""  # user_rag_memory_id: RAG memory ID (not used in Neo4j mode)
+        # )
+        count_store.update_sessions_count(end_user_id, 0, [])
+
+
+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:
+        logger.error(f"[aggregate_judgment] 发生错误: {e}", exc_info=True)
+
+        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/tools/tool.py

@@ -2,41 +2,53 @@ import asyncio
 import json
 from datetime import datetime, timedelta
 
-
 from langchain.tools import tool
 from pydantic import BaseModel, Field
 
-
 from app.core.memory.src.search import (
     search_by_temporal,
     search_by_keyword_temporal,
 )
 
+
 def extract_tool_message_content(response):
-    """从agent响应中提取ToolMessage内容和工具名称"""
+    """
+    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'):
-            # 这是一个ToolMessage
+            # This is a ToolMessage
             tool_content = message.content
             tool_name = None
 
-            # 尝试获取工具名称
+            # Try to get tool name
             if hasattr(message, 'name'):
                 tool_name = message.name
             elif hasattr(message, 'tool_name'):
                 tool_name = message.tool_name
 
             try:
-                # 解析JSON内容
+                # Parse JSON content
                 parsed_content = json.loads(tool_content)
                 return {
                     'tool_name': tool_name,
                     'content': parsed_content
                 }
             except json.JSONDecodeError:
-                # 如果不是JSON格式，直接返回内容
+                # If not JSON format, return content directly
                 return {
                     'tool_name': tool_name,
                     'content': tool_content
@@ -46,38 +58,61 @@ def extract_tool_message_content(response):
 
 
 class TimeRetrievalInput(BaseModel):
-    """时间检索工具的输入模式"""
+    """
+    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):
     """
-    创建一个带有特定end_user_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements)
+    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):
         """
-        清理时间搜索结果中不需要的字段，并修改结构
+        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: 要清理的数据
+            data: Data to be cleaned (dict, list, or other types)
             
         Returns:
-            清理后的数据
+            Cleaned data with unnecessary fields removed
         """
-        # 需要过滤的字段列表
+        # List of fields to filter out
         fields_to_remove = {
-            'id', 'apply_id', 'user_id', 'chunk_id', 'created_at', 
+            'id', 'apply_id', 'user_id', 'chunk_id', 'created_at',
             'valid_at', 'invalid_at', 'statement_ids'
         }
-        
+
         if isinstance(data, dict):
             cleaned = {}
             for key, value in data.items():
                 if key == 'statements' and isinstance(value, dict) and 'statements' in value:
-                    # 将 statements: {"statements": [...]} 改为 time_search: {"statements": [...]}
+                    # Change statements: {"statements": [...]} to time_search: {"statements": [...]}
                     cleaned_value = clean_temporal_result_fields(value)
-                    # 进一步将内部的 statements 改为 time_search
+                    # Further change internal statements to time_search
                     if 'statements' in cleaned_value:
                         cleaned['results'] = {
                             'time_search': cleaned_value['statements']
@@ -91,26 +126,35 @@ def create_time_retrieval_tool(end_user_id: str):
             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:
+    def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None,
+                                 end_user_id_param: str = None, clean_output: bool = True) -> str:
         """
-        优化的时间检索工具，只结合时间范围搜索（同步版本），自动过滤不需要的元数据字段
-        显式接收参数：
-        - context: 查询上下文内容
-        - start_date: 开始时间（可选，格式：YYYY-MM-DD）
-        - end_date: 结束时间（可选，格式：YYYY-MM-DD）
-        - end_user_id_param: 组ID（可选，用于覆盖默认组ID）
-        - clean_output: 是否清理输出中的元数据字段
-        -end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
+        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():
-            # 使用传入的参数或默认值
+            # 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")
             
-            # 基本时间搜索
+            # Basic time search
             results = await search_by_temporal(
                 end_user_id=actual_end_user_id,
                 start_date=actual_start_date,
@@ -118,33 +162,43 @@ def create_time_retrieval_tool(end_user_id: str):
                 limit=10
             )
             
-            # 清理结果中不需要的字段
+            # 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:
+    def KeywordTimeRetrieval(context: str, days_back: int = 7, start_date: str = None, end_date: str = None,
+                             clean_output: bool = True) -> str:
         """
-        优化的关键词时间检索工具，结合关键词和时间范围搜索（同步版本），自动过滤不需要的元数据字段
-        显式接收参数：
-        - context: 查询内容
-        - days_back: 向前搜索的天数，默认7天
-        - start_date: 开始时间（可选，格式：YYYY-MM-DD）
-        - end_date: 结束时间（可选，格式：YYYY-MM-DD）
-        - clean_output: 是否清理输出中的元数据字段
-        - end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
+        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")
 
-            # 关键词时间搜索
+            # Keyword time search
             results = await search_by_keyword_temporal(
                 query_text=context,
                 end_user_id=end_user_id,
@@ -153,7 +207,7 @@ def create_time_retrieval_tool(end_user_id: str):
                 limit=15
             )
             
-            # 清理结果中不需要的字段
+            # Clean unnecessary fields from results
             if clean_output:
                 cleaned_results = clean_temporal_result_fields(results)
             else:
@@ -162,50 +216,61 @@ def create_time_retrieval_tool(end_user_id: str):
             return json.dumps(cleaned_results, ensure_ascii=False, indent=2)
 
         return asyncio.run(_async_search())
-    
+
     return TimeRetrievalWithGroupId
 
 
 def create_hybrid_retrieval_tool_async(memory_config, **search_params):
     """
-    创建混合检索工具，使用run_hybrid_search进行混合检索，优化输出格式并过滤不需要的字段
+    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: 内存配置对象
-        **search_params: 搜索参数，包含end_user_id, limit, include等
+        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):
         """
-        递归清理结果中不需要的字段
+        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: 要清理的数据（可能是字典、列表或其他类型）
+            data: Data to be cleaned (can be dict, list, or other types)
             
         Returns:
-            清理后的数据
+            Cleaned data with unnecessary fields removed
         """
-        # 需要过滤的字段列表
+        # 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', 'id', 'apply_id', 
-            'user_id', 'statement_ids', 'updated_at',"chunk_ids","fact_summary"
+            '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):
-            # 对字典进行清理
+            # Clean dictionary
             cleaned = {}
             for key, value in data.items():
                 if key not in fields_to_remove:
-                    cleaned[key] = clean_result_fields(value)  # 递归清理嵌套数据
+                    cleaned[key] = clean_result_fields(value)  # Recursively clean nested data
             return cleaned
         elif isinstance(data, list):
-            # 对列表中的每个元素进行清理
+            # Clean each element in list
             return [clean_result_fields(item) for item in data]
         else:
-            # 其他类型直接返回
+            # Return other types directly
             return data
-    
+
     @tool
     async def HybridSearch(
         context: str, 
@@ -215,57 +280,63 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
         rerank_alpha: float = 0.6,
         use_forgetting_rerank: bool = False,
         use_llm_rerank: bool = False,
-        clean_output: bool = True  # 新增：是否清理输出字段
+        clean_output: bool = True  # New: whether to clean output fields
     ) -> str:
         """
-        优化的混合检索工具，支持关键词、向量和混合搜索，自动过滤不需要的元数据字段
+        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: 查询内容
-            search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
-            limit: 结果数量限制
-            end_user_id: 组ID，用于过滤搜索结果
-            rerank_alpha: 重排序权重参数
-            use_forgetting_rerank: 是否使用遗忘重排序
-            use_llm_rerank: 是否使用LLM重排序
-            clean_output: 是否清理输出中的元数据字段
+            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:
-            # 导入run_hybrid_search函数
+            # Import run_hybrid_search function
             from app.core.memory.src.search import run_hybrid_search
             
-            # 合并参数，优先使用传入的参数
+            # 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"]),
-                "output_path": None,  # 不保存到文件
+                "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
             }
             
-            # 执行混合检索
+            # Execute hybrid retrieval
             raw_results = await run_hybrid_search(**final_params)
             
-            # 清理结果中不需要的字段
+            # Clean unnecessary fields from results
             if clean_output:
                 cleaned_results = clean_result_fields(raw_results)
             else:
                 cleaned_results = raw_results
             
-            # 格式化返回结果
+            # 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)}",
@@ -274,38 +345,52 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
                 "timestamp": datetime.now().isoformat()
             }
             return json.dumps(error_result, ensure_ascii=False, indent=2)
-    
+
     return HybridSearch
 
 
 def create_hybrid_retrieval_tool_sync(memory_config, **search_params):
     """
-    创建同步版本的混合检索工具，优化输出格式并过滤不需要的字段
+    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: 内存配置对象
-        **search_params: 搜索参数
+        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
+            context: str,
+            search_type: str = "hybrid",
+            limit: int = 10,
+            end_user_id: str = None,
+            clean_output: bool = True
     ) -> str:
         """
-        优化的混合检索工具（同步版本），自动过滤不需要的元数据字段
+        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: 查询内容
-            search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
-            limit: 结果数量限制
-            end_user_id: 组ID，用于过滤搜索结果
-            clean_output: 是否清理输出中的元数据字段
+            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():
-            # 创建异步工具并执行
+            # Create async tool and execute
             async_tool = create_hybrid_retrieval_tool_async(memory_config, **search_params)
             return await async_tool.ainvoke({
                 "context": context,
@@ -314,7 +399,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params):
                 "end_user_id": end_user_id,
                 "clean_output": clean_output
             })
-        
+
         return asyncio.run(_async_search())
-    
-    return HybridSearchSync
+
+    return HybridSearchSync

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

@@ -0,0 +1,106 @@
+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

@@ -1,93 +1,94 @@
-
-import asyncio
-import sys
 import warnings
-from contextlib import asynccontextmanager
 
-
-from langchain_core.messages import HumanMessage
-from langgraph.constants import END, START
-from langgraph.graph import StateGraph
-
-
-from app.db import get_db
 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.core.memory.agent.langgraph_graph.nodes.data_nodes import content_input_write
+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
+from app.db import get_db_context
+from app.schemas.memory_agent_schema import AgentMemory_Long_Term
 from app.services.memory_config_service import MemoryConfigService
+from app.services.memory_konwledges_server import write_rag
 
 warnings.filterwarnings("ignore", category=RuntimeWarning)
 logger = get_agent_logger(__name__)
 
-if sys.platform.startswith("win"):
-    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
-@asynccontextmanager
-async def make_write_graph():
+
+async def long_term_storage(
+        long_term_type: str,
+        langchain_messages: list,
+        memory_config_id: str,
+        end_user_id: str,
+        scope: int = 6
+):
     """
-    Create a write graph workflow for memory operations.
+    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:
-        user_id: User identifier
-        tools: MCP tools loaded from session
-        apply_id: Application identifier
-        end_user_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
+        long_term_type: Storage strategy type ('chunk', 'time', 'aggregate')
+        langchain_messages: List of messages to store
+        memory_config_id: Memory configuration identifier
+        end_user_id: User group identifier
+        scope: Scope parameter for chunk-based storage (default: 6)
     """
-    # workflow = StateGraph(WriteState)
-    # workflow.add_node("content_input", content_input_write)
-    # workflow.add_node("save_neo4j", write_node)
-    # workflow.add_edge(START, "content_input")
-    # workflow.add_edge("content_input", "save_neo4j")
-    # workflow.add_edge("save_neo4j", END)
-    #
-    # graph = workflow.compile()
-    workflow = StateGraph(WriteState)
-    workflow.add_node("save_neo4j", write_node)
-    workflow.add_edge(START, "save_neo4j")
-    workflow.add_edge("save_neo4j", END)
-
-    graph = workflow.compile()
-
-    yield graph
-
-
-async def main():
-    """主函数 - 运行工作流"""
-    message = "今天周一"
-    end_user_id = 'new_2025test1103'  # 组ID
-
+    if langchain_messages is None:
+        langchain_messages = []
 
+    write_store.save_session_write(end_user_id, langchain_messages)
     # 获取数据库会话
-    db_session = next(get_db())
-    config_service = MemoryConfigService(db_session)
-    memory_config = config_service.load_memory_config(
-        config_id=17,  # 改为整数
-        service_name="MemoryAgentService"
-    )
-    try:
-        async with make_write_graph() as graph:
-            config = {"configurable": {"thread_id": end_user_id}}
-            # 初始状态 - 包含所有必要字段
-            initial_state = {"messages": [HumanMessage(content=message)],  "end_user_id": end_user_id, "memory_config": memory_config}
-
-            # 获取节点更新信息
-            async for update_event in graph.astream(
-                    initial_state,
-                    stream_mode="updates",
-                    config=config
-            ):
-                for node_name, node_data in update_event.items():
-                    if 'save_neo4j'==node_name:
-                        massages=node_data
-            massages=massages.get('write_result')['status']
-            print(massages)  # | 更新数据: {node_data}
-
-    except Exception as e:
-        import traceback
-        traceback.print_exc()
+    with get_db_context() as db_session:
+        config_service = MemoryConfigService(db_session)
+        memory_config = config_service.load_memory_config(
+            config_id=memory_config_id,  # 改为整数
+            service_name="MemoryAgentService"
+        )
+        if long_term_type == AgentMemory_Long_Term.STRATEGY_CHUNK:
+            # 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:
+            # Aggregate judgment
+            await aggregate_judgment(end_user_id, langchain_messages, memory_config)
 
 
-if __name__ == "__main__":
-    import asyncio
-    asyncio.run(main())
+async def write_long_term(
+        storage_type: str,
+        end_user_id: str,
+        messages: list[dict],
+        user_rag_memory_id: str,
+        actual_config_id: str
+):
+    """
+    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
+        messages: message list
+        user_rag_memory_id: RAG memory identifier
+        actual_config_id: Actual configuration ID
+    """
+    from app.core.memory.agent.langgraph_graph.routing.write_router import term_memory_save
+    if storage_type == AgentMemory_Long_Term.STORAGE_RAG:
+        message_content = []
+        for message in messages:
+            message_content.append(f'{message.get("role")}:{message.get("content")}')
+        messages_string = "\n".join(message_content)
+        await write_rag(end_user_id, messages_string, 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
+        await long_term_storage(long_term_type=CHUNK,
+                                langchain_messages=messages,
+                                memory_config_id=actual_config_id,
+                                end_user_id=end_user_id,
+                                scope=SCOPE)
+        await term_memory_save(end_user_id, CHUNK, scope=SCOPE)

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

@@ -0,0 +1,28 @@
+"""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/services/search_service.py

@@ -7,21 +7,88 @@ and deduplication.
 from typing import List, Tuple, Optional
 
 from app.core.logging_config import get_agent_logger
+from app.core.memory.enums import Neo4jNodeType
 from app.core.memory.src.search import run_hybrid_search
 from app.core.memory.utils.data.text_utils import escape_lucene_query
 
-
 logger = get_agent_logger(__name__)
 
+# 需要从展开结果中过滤的字段（含 Neo4j DateTime，不可 JSON 序列化）
+_EXPAND_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'
+}
+
+
+def _clean_expand_fields(obj):
+    """递归过滤展开结果中不可序列化的字段（DateTime 等）。"""
+    if isinstance(obj, dict):
+        return {k: _clean_expand_fields(v) for k, v in obj.items() if k not in _EXPAND_FIELDS_TO_REMOVE}
+    if isinstance(obj, list):
+        return [_clean_expand_fields(i) for i in obj]
+    return obj
+
+
+async def expand_communities_to_statements(
+        community_results: List[dict],
+        end_user_id: str,
+        existing_content: str = "",
+        limit: int = 10,
+) -> Tuple[List[dict], List[str]]:
+    """
+    社区展开 helper：给定命中的 community 列表，拉取关联 Statement。
+
+    - 对展开结果去重（过滤已在 existing_content 中出现的文本）
+    - 过滤不可序列化字段
+    - 返回 (cleaned_expanded_stmts, new_texts)
+      - cleaned_expanded_stmts: 可直接写回 raw_results 的列表
+      - new_texts: 去重后新增的 statement 文本列表，用于追加到 clean_content
+    """
+    community_ids = [r.get("id") for r in community_results if r.get("id")]
+    if not community_ids or not end_user_id:
+        return [], []
+
+    from app.repositories.neo4j.graph_search import search_graph_community_expand
+    from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+
+    connector = Neo4jConnector()
+    try:
+        result = await search_graph_community_expand(
+            connector=connector,
+            community_ids=community_ids,
+            end_user_id=end_user_id,
+            limit=limit,
+        )
+    except Exception as e:
+        logger.warning(f"[expand_communities] 社区展开检索失败，跳过: {e}")
+        return [], []
+    finally:
+        await connector.close()
+
+    expanded_stmts = result.get("expanded_statements", [])
+    if not expanded_stmts:
+        return [], []
+
+    existing_lines = set(existing_content.splitlines())
+    new_texts = [
+        s["statement"] for s in expanded_stmts
+        if s.get("statement") and s["statement"] not in existing_lines
+    ]
+    cleaned = _clean_expand_fields(expanded_stmts)
+    logger.info(
+        f"[expand_communities] 展开 {len(expanded_stmts)} 条 statements，新增 {len(new_texts)} 条，community_ids={community_ids}")
+    return cleaned, new_texts
+
 
 class SearchService:
     """Service for executing hybrid search and processing results."""
-    
+
     def __init__(self):
         """Initialize the search service."""
         logger.info("SearchService initialized")
-    
-    def extract_content_from_result(self, result: dict) -> str:
+
+    def extract_content_from_result(self, result: dict, node_type: str = "") -> str:
         """
         Extract only meaningful content from search results, dropping all metadata.
         
@@ -30,35 +97,50 @@ class SearchService:
         - Entities: extract 'name' and 'fact_summary' fields
         - Summaries: extract 'content' field
         - Chunks: extract 'content' field
+        - Communities: extract 'content' field (c.summary), prefixed with community name
         
         Args:
             result: Search result dictionary
+            node_type: Hint for node type ("community", "summary", etc.)
             
         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']:
+        if Neo4jNodeType.STATEMENT in result and result[Neo4jNodeType.STATEMENT]:
+            content_parts.append(result[Neo4jNodeType.STATEMENT])
+
+        # Community 节点：有 member_count 或 core_entities 字段，或 node_type 明确指定
+        # 用 "[主题：{name}]" 前缀区分，让 LLM 知道这是主题级摘要
+        is_community = (
+                node_type == Neo4jNodeType.COMMUNITY
+                or 'member_count' in result
+                or 'core_entities' in result
+        )
+        if is_community:
+            name = result.get('name', '')
+            content = result.get('content', '')
+            if content:
+                prefix = f"[主题：{name}] " if name else ""
+                content_parts.append(f"{prefix}{content}")
+        elif 'content' in result and result['content']:
+            # Summaries / Chunks
             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.
@@ -74,32 +156,33 @@ class SearchService:
             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.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,
-        end_user_id: str,
-        question: str,
-        limit: int = 5,
-        search_type: str = "hybrid",
-        include: Optional[List[str]] = None,
-        rerank_alpha: float = 0.4,
-        output_path: str = "search_results.json",
-        return_raw_results: bool = False,
-        memory_config = None
+            self,
+            end_user_id: str,
+            question: str,
+            limit: int = 5,
+            search_type: str = "hybrid",
+            include: Optional[List[str]] = None,
+            rerank_alpha: float = 0.4,
+            output_path: str = "search_results.json",
+            return_raw_results: bool = False,
+            memory_config=None,
+            expand_communities: bool = True,
     ) -> Tuple[str, str, Optional[dict]]:
         """
         Execute hybrid search and return clean content.
@@ -114,17 +197,19 @@ class SearchService:
             output_path: Path to save search results (default: "search_results.json")
             return_raw_results: If True, also return the raw search results as third element (default: False)
             memory_config: Memory configuration object (required)
+            expand_communities: If True, expand community hits to member statements (default: True).
+                                 Set to False for quick-summary paths that only need community-level text.
         
         Returns:
             Tuple of (clean_content, cleaned_query, raw_results)
             raw_results is None if return_raw_results=False
         """
         if include is None:
-            include = ["statements", "chunks", "entities", "summaries"]
-        
+            include = [Neo4jNodeType.STATEMENT, Neo4jNodeType.CHUNK, Neo4jNodeType.EXTRACTEDENTITY, Neo4jNodeType.MEMORYSUMMARY, Neo4jNodeType.COMMUNITY]
+
         # Clean query
         cleaned_query = self.clean_query(question)
-        
+
         try:
             # Execute search
             answer = await run_hybrid_search(
@@ -137,18 +222,18 @@ class SearchService:
                 memory_config=memory_config,
                 rerank_alpha=rerank_alpha
             )
-            
+
             # 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']
-                
+
+                # Priority order: summaries first (most contextual), then communities, statements, chunks, entities
+                priority_order = [Neo4jNodeType.STATEMENT, Neo4jNodeType.CHUNK, Neo4jNodeType.EXTRACTEDENTITY, Neo4jNodeType.MEMORYSUMMARY, Neo4jNodeType.COMMUNITY]
+
                 for category in priority_order:
                     if category in include and category in reranked_results:
                         category_results = reranked_results[category]
@@ -157,33 +242,46 @@ class SearchService:
             else:
                 # For keyword or embedding search, results are directly in answer dict
                 # Apply same priority order
-                priority_order = ['summaries', 'statements', 'chunks', 'entities']
-                
+                priority_order = [Neo4jNodeType.STATEMENT, Neo4jNodeType.CHUNK, Neo4jNodeType.EXTRACTEDENTITY, Neo4jNodeType.MEMORYSUMMARY, Neo4jNodeType.COMMUNITY]
+
                 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
-            ]
 
-            
+            # 对命中的 community 节点展开其成员 statements（路径 "0"/"1" 需要，路径 "2" 不需要）
+            if expand_communities and Neo4jNodeType.COMMUNITY in include:
+                community_results = (
+                    answer.get('reranked_results', {}).get(Neo4jNodeType.COMMUNITY.value, [])
+                    if search_type == "hybrid"
+                    else answer.get(Neo4jNodeType.COMMUNITY.value, [])
+                )
+                cleaned_stmts, new_texts = await expand_communities_to_statements(
+                    community_results=community_results,
+                    end_user_id=end_user_id,
+                )
+                answer_list.extend(cleaned_stmts)
+
+            # Extract clean content from all results，按类型传入 node_type 区分 community
+            content_list = []
+            for ans in answer_list:
+                # community 节点有 member_count 或 core_entities 字段
+                ntype = Neo4jNodeType.COMMUNITY if ('member_count' in ans or 'core_entities' in ans) else ""
+                content_list.append(self.extract_content_from_result(ans, node_type=ntype))
+
             # 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 '{end_user_id}': {e}",

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

@@ -11,7 +11,7 @@ async def get_chunked_dialogs(
         chunker_strategy: str = "RecursiveChunker",
         end_user_id: str = "group_1",
         messages: list = None,
-        ref_id: str = "wyl_20251027",
+        ref_id: str = "",
         config_id: str = None
 ) -> List[DialogData]:
     """Generate chunks from structured messages using the specified chunker strategy.
@@ -21,7 +21,7 @@ async def get_chunked_dialogs(
         end_user_id: Group identifier
         messages: Structured message list [{"role": "user", "content": "..."}, ...]
         ref_id: Reference identifier
-        config_id: Configuration ID for processing
+        config_id: Configuration ID for processing (used to load pruning config)
 
     Returns:
         List of DialogData objects with generated chunks
@@ -40,12 +40,13 @@ async def get_chunked_dialogs(
 
         role = msg['role']
         content = msg['content']
+        files = msg.get("file_content", [])
 
         if role not in ['user', 'assistant']:
             raise ValueError(f"Message {idx} role must be 'user' or 'assistant', got: {role}")
 
         if content.strip():
-            conversation_messages.append(ConversationMessage(role=role, msg=content.strip()))
+            conversation_messages.append(ConversationMessage(role=role, msg=content.strip(), files=files))
 
     if not conversation_messages:
         raise ValueError("Message list cannot be empty after filtering")
@@ -57,6 +58,63 @@ async def get_chunked_dialogs(
         end_user_id=end_user_id,
         config_id=config_id
     )
+    
+    # 语义剪枝步骤（在分块之前）
+    try:
+        from app.core.memory.storage_services.extraction_engine.data_preprocessing.data_pruning import SemanticPruner
+        from app.core.memory.models.config_models import PruningConfig
+        from app.db import get_db_context
+        from app.services.memory_config_service import MemoryConfigService
+        from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
+        
+        # 加载剪枝配置
+        pruning_config = None
+        if config_id:
+            try:
+                with get_db_context() as db:
+                    # 使用 MemoryConfigService 加载完整的 MemoryConfig 对象
+                    config_service = MemoryConfigService(db)
+                    memory_config = config_service.load_memory_config(
+                        config_id=config_id,
+                        service_name="semantic_pruning"
+                    )
+                    
+                    if memory_config:
+                        pruning_config = PruningConfig(
+                            pruning_switch=memory_config.pruning_enabled,
+                            pruning_scene=memory_config.pruning_scene or "education",
+                            pruning_threshold=memory_config.pruning_threshold,
+                            scene_id=str(memory_config.scene_id) if memory_config.scene_id else None,
+                            ontology_class_infos=memory_config.ontology_class_infos,
+                        )
+                        logger.info(f"[剪枝] 加载配置: switch={pruning_config.pruning_switch}, scene={pruning_config.pruning_scene}, threshold={pruning_config.pruning_threshold}")
+                        
+                        # 获取LLM客户端用于剪枝
+                        if pruning_config.pruning_switch:
+                            factory = MemoryClientFactory(db)
+                            llm_client = factory.get_llm_client_from_config(memory_config)
+                            
+                            # 执行剪枝 - 使用 prune_dataset 支持消息级剪枝
+                            pruner = SemanticPruner(config=pruning_config, llm_client=llm_client)
+                            original_msg_count = len(dialog_data.context.msgs)
+                            
+                            # 使用 prune_dataset 而不是 prune_dialog
+                            # prune_dataset 会进行消息级剪枝，即使对话整体相关也会删除不重要消息
+                            pruned_dialogs = await pruner.prune_dataset([dialog_data])
+                            
+                            if pruned_dialogs:
+                                dialog_data = pruned_dialogs[0]
+                                remaining_msg_count = len(dialog_data.context.msgs)
+                                deleted_count = original_msg_count - remaining_msg_count
+                                logger.info(f"[剪枝] 完成: 原始{original_msg_count}条 -> 保留{remaining_msg_count}条 (删除{deleted_count}条)")
+                            else:
+                                logger.warning("[剪枝] prune_dataset 返回空列表")
+                        else:
+                            logger.info("[剪枝] 配置中剪枝开关关闭，跳过剪枝")
+            except Exception as e:
+                logger.warning(f"[剪枝] 加载配置失败，跳过剪枝: {e}", exc_info=True)
+    except Exception as e:
+        logger.warning(f"[剪枝] 执行失败，跳过剪枝: {e}", exc_info=True)
 
     chunker = DialogueChunker(chunker_strategy)
     extracted_chunks = await chunker.process_dialogue(dialog_data)

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

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

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

@@ -1,4 +1,3 @@
-import os
 from collections import defaultdict
 from pathlib import Path
 from typing import Annotated, TypedDict
@@ -8,16 +7,19 @@ from langgraph.graph import add_messages
 
 PROJECT_ROOT_ = str(Path(__file__).resolve().parents[3])
 
+
 class WriteState(TypedDict):
-    '''
+    """
     Langgrapg Writing TypedDict
-    '''
+    """
     messages: Annotated[list[AnyMessage], add_messages]
     end_user_id: str
     errors: list[dict]  # Track errors: [{"tool": "tool_name", "error": "message"}]
     memory_config: object
     write_result: dict
     data: str
+    language: str  # 语言类型 ("zh" 中文, "en" 英文)
+
 
 class ReadState(TypedDict):
     """
@@ -42,18 +44,21 @@ class ReadState(TypedDict):
     config_id: str
     data: str  # 新增字段用于传递内容
     spit_data: dict  # 新增字段用于传递问题分解结果
-    problem_extension:dict
+    problem_extension: dict
     storage_type: str
     user_rag_memory_id: str
     llm_id: str
     embedding_id: str
     memory_config: object  # 新增字段用于传递内存配置对象
-    retrieve:dict
+    retrieve: dict
+    perceptual_data: dict
     RetrieveSummary: dict
     InputSummary: dict
     verify: dict
     SummaryFails: dict
     summary: dict
+
+
 class COUNTState:
     """
     工作流对话检索内容计数器
@@ -98,6 +103,7 @@ class COUNTState:
         self.total = 0
         print("[COUNTState] 已重置为 0")
 
+
 def deduplicate_entries(entries):
     seen = set()
     deduped = []
@@ -108,6 +114,7 @@ def deduplicate_entries(entries):
             deduped.append(entry)
     return deduped
 
+
 def merge_to_key_value_pairs(data, query_key, result_key):
     grouped = defaultdict(list)
     for item in data:
@@ -141,4 +148,4 @@ def convert_extended_question_to_question(data):
         return [convert_extended_question_to_question(item) for item in data]
     else:
         # 其他类型直接返回
-        return data
+        return data

api/app/core/memory/agent/utils/prompt/Problem_Extension_prompt.jinja2

@@ -39,6 +39,30 @@
     比如：输入历史信息内容:[{'Query': '4月27日，我和你推荐过一本书，书名是什么？', 'ANswer': '张曼玉推荐了《小王子》'}]
     拆分问题：4月27日，我和你推荐过一本书，书名是什么？，可以拆分为：4月27日，张曼玉推荐过一本书，书名是什么？
 
+## 指代消歧规则（Coreference Resolution）：
+在拆分问题时，必须解析并替换所有指代词和抽象称呼，使问题具体化：
+
+1. **"用户"的消歧**：
+   - "用户是谁？" → 分析历史记录，找出对话发起者的姓名
+   - 如果历史中有"我叫X"、"我的名字是X"、或多次提到某个人物，则"用户"指的就是这个人
+   - 示例：历史中有"老李的原名叫李建国"，则"用户是谁？"应拆分为"李建国是谁？"或"老李（李建国）是谁？"
+
+2. **"我"的消歧**：
+   - "我喜欢什么？" → 从历史中找出对话发起者的姓名，替换为"X喜欢什么？"
+   - 示例：历史中有"张曼玉推荐了《小王子》"，则"我推荐的书是什么？"应拆分为"张曼玉推荐的书是什么？"
+
+3. **"他/她/它"的消歧**：
+   - 从上下文或历史中找出最近提到的同类实体
+   - 示例：历史中有"老李的同事叫他建国哥"，则"他的同事怎么称呼他？"应拆分为"老李的同事怎么称呼他？"
+
+4. **"那个人/这个人"的消歧**：
+   - 从历史中找出最近提到的人物
+   - 示例：历史中有"李建国"，则"那个人的原名是什么？"应拆分为"李建国的原名是什么？"
+
+5. **优先级**：
+   - 如果历史记录中反复出现某个人物（如"老李"、"李建国"、"建国哥"），则"用户"很可能指的就是这个人
+   - 如果无法从历史中确定指代对象，保留原问题，但在reason中说明"无法确定指代对象"
+
 
 
 输出要求：
@@ -71,6 +95,34 @@
     "reason": "输出原问题的关键要素"
   }
 ]
+
+## 指代消歧示例（重要）：
+示例1 - "用户"的消歧：
+输入历史：[{'Query': '老李的原名叫什么？', 'Answer': '李建国'}, {'Query': '老李的同事叫他什么？', 'Answer': '建国哥'}]
+输入问题："用户是谁？"
+输出：
+[
+  {
+    "original_question": "用户是谁？",
+    "extended_question": "李建国是谁？",
+    "type": "单跳",
+    "reason": "历史中反复提到'老李/李建国/建国哥'，'用户'指的就是对话发起者李建国"
+  }
+]
+
+示例2 - "我"的消歧：
+输入历史：[{'Query': '张曼玉推荐了什么书？', 'Answer': '《小王子》'}]
+输入问题："我推荐的书是什么？"
+输出：
+[
+  {
+    "original_question": "我推荐的书是什么？",
+    "extended_question": "张曼玉推荐的书是什么？",
+    "type": "单跳",
+    "reason": "历史中提到张曼玉推荐了书，'我'指的就是张曼玉"
+  }
+]
+
 **Output format**
 **CRITICAL JSON FORMATTING REQUIREMENTS:**
 1. Use only standard ASCII double quotes (") for JSON structure - never use Chinese quotation marks ("") or other Unicode quotes

api/app/core/memory/agent/utils/prompt/problem_breakdown_prompt.jinja2

@@ -27,6 +27,30 @@
     比如：输入历史信息内容:[{'Query': '4月27日，我和你推荐过一本书，书名是什么？', 'ANswer': '张曼玉推荐了《小王子》'}]
     拆分问题：4月27日，我和你推荐过一本书，书名是什么？，可以拆分为：4月27日，张曼玉推荐过一本书，书名是什么？
 
+## 指代消歧规则（Coreference Resolution）：
+在拆分问题时，必须解析并替换所有指代词和抽象称呼，使问题具体化：
+
+1. **"用户"的消歧**：
+   - "用户是谁？" → 分析历史记录，找出对话发起者的姓名
+   - 如果历史中有"我叫X"、"我的名字是X"、或多次提到某个人物（如"老李"、"李建国"），则"用户"指的就是这个人
+   - 示例：历史中反复出现"老李/李建国/建国哥"，则"用户是谁？"应拆分为"李建国是谁？"或"老李（李建国）是谁？"
+
+2. **"我"的消歧**：
+   - "我喜欢什么？" → 从历史中找出对话发起者的姓名，替换为"X喜欢什么？"
+   - 示例：历史中有"张曼玉推荐了《小王子》"，则"我推荐的书是什么？"应拆分为"张曼玉推荐的书是什么？"
+
+3. **"他/她/它"的消歧**：
+   - 从上下文或历史中找出最近提到的同类实体
+   - 示例：历史中有"老李的同事叫他建国哥"，则"他的同事怎么称呼他？"应拆分为"老李的同事怎么称呼他？"
+
+4. **"那个人/这个人"的消歧**：
+   - 从历史中找出最近提到的人物
+   - 示例：历史中有"李建国"，则"那个人的原名是什么？"应拆分为"李建国的原名是什么？"
+
+5. **优先级**：
+   - 如果历史记录中反复出现某个人物（如"老李"、"李建国"、"建国哥"），则"用户"很可能指的就是这个人
+   - 如果无法从历史中确定指代对象，保留原问题，但在reason中说明"无法确定指代对象"
+
 ## 指令：
 你是一个智能数据拆分助手，请根据数据特性判断输入属于哪种类型：
 单跳（Single-hop）
@@ -151,6 +175,34 @@
 ]
 - 必须通过json.loads()的格式支持的形式输出
 - 必须通过json.loads()的格式支持的形式输出,响应必须是与此确切模式匹配的有效JSON对象。不要在JSON之前或之后包含任何文本。
+
+## 指代消歧示例（重要）：
+示例1 - "用户"的消歧：
+输入历史：[{'Query': '老李的原名叫什么？', 'Answer': '李建国'}, {'Query': '老李的同事叫他什么？', 'Answer': '建国哥'}]
+输入问题："用户是谁？"
+输出：
+[
+  {
+    "id": "Q1",
+    "question": "李建国是谁？",
+    "type": "单跳",
+    "reason": "历史中反复提到'老李/李建国/建国哥'，'用户'指的就是对话发起者李建国"
+  }
+]
+
+示例2 - "我"的消歧：
+输入历史：[{'Query': '张曼玉推荐了什么书？', 'Answer': '《小王子》'}]
+输入问题："我推荐的书是什么？"
+输出：
+[
+  {
+    "id": "Q1",
+    "question": "张曼玉推荐的书是什么？",
+    "type": "单跳",
+    "reason": "历史中提到张曼玉推荐了书，'我'指的就是张曼玉"
+  }
+]
+
 - 关键的JSON格式要求
 1.JSON结构仅使用标准ASCII双引号（“）-切勿使用中文引号（“”）或其他Unicode引号
 2.如果提取的语句文本包含引号，请使用反斜杠（\“）正确转义它们

api/app/core/memory/agent/utils/prompt/write_aggregate_judgment.jinja2

@@ -0,0 +1,57 @@
+输入句子：{{sentence}}
+历史消息：{{history}}
+
+# 你的角色
+你是一个擅长事件聚合与语义判断的专家。
+
+# 你的任务
+结合历史消息和输入句子，判断它们是否在描述**同一件事件或同一事件链**。
+
+以下情况视为"同一事件"（需要返回 is_same_event=True, output=False）：
+- 描述的是同一个具体事件或事实
+- 存在明显的因果关系、前后发展关系
+- 是对同一事件的补充、解释、追问或延展
+- 逻辑上属于同一语境下的连续讨论
+
+以下情况视为"不同事件"（需要返回 is_same_event=False, output=消息列表）：
+- 话题不同，事件主体不同
+- 时间、地点、对象明显不同
+- 只是语义相似，但并非同一具体事件
+- 无直接事件、因果或逻辑关联
+
+# 输出规则（非常重要）
+你必须按照以下JSON格式输出：
+
+**如果是同一事件：**
+```json
+{
+  "is_same_event": true,
+  "output": false
+}
+```
+
+**如果不是同一事件：**
+```json
+{
+  "is_same_event": false,
+  "output": [
+    {
+      "role": "user",
+      "content": "输入句子的内容"
+    },
+    {
+      "role": "assistant",
+      "content": "对应的回复内容"
+    }
+  ]
+}
+```
+
+# JSON Schema
+{{json_schema}}
+
+# 注意事项
+- 必须严格按照上述格式输出
+- output 字段：如果是同一事件返回 false，如果不是同一事件返回完整的消息列表
+- 消息列表必须包含 role 和 content 字段
+- 不要输出任何解释、分析或多余内容

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

@@ -0,0 +1,186 @@
+import json
+from typing import Any, List, Dict, Optional
+from datetime import datetime, timedelta
+
+
+def serialize_messages(messages: Any) -> str:
+    """
+    将消息序列化为 JSON 字符串，支持 LangChain 消息对象
+
+    Args:
+        messages: 可以是 list、dict、string 或 LangChain 消息对象列表
+
+    Returns:
+        str: JSON 字符串
+    """
+    if isinstance(messages, str):
+        return messages
+
+    if isinstance(messages, (list, tuple)):
+        # 检查是否是 LangChain 消息对象列表
+        serialized_list = []
+        for msg in messages:
+            if hasattr(msg, 'type') and hasattr(msg, 'content'):
+                # LangChain 消息对象
+                serialized_list.append({
+                    'type': msg.type,
+                    'content': msg.content,
+                    'role': getattr(msg, 'role', msg.type)
+                })
+            elif isinstance(msg, dict):
+                serialized_list.append(msg)
+            else:
+                serialized_list.append(str(msg))
+        return json.dumps(serialized_list, ensure_ascii=False)
+
+    if isinstance(messages, dict):
+        return json.dumps(messages, ensure_ascii=False)
+
+    # 其他类型转为字符串
+    return str(messages)
+
+
+def deserialize_messages(messages_str: str) -> Any:
+    """
+    将 JSON 字符串反序列化为原始格式
+
+    Args:
+        messages_str: JSON 字符串
+
+    Returns:
+        反序列化后的对象（list、dict 或 string）
+    """
+    if not messages_str:
+        return []
+
+    try:
+        return json.loads(messages_str)
+    except (json.JSONDecodeError, TypeError):
+        return messages_str
+
+
+def fix_encoding(text: str) -> str:
+    """
+    修复错误编码的文本
+    
+    Args:
+        text: 需要修复的文本
+        
+    Returns:
+        str: 修复后的文本
+    """
+    if not text or not isinstance(text, str):
+        return text
+    try:
+        # 尝试修复 Latin-1 误编码为 UTF-8 的情况
+        return text.encode('latin-1').decode('utf-8')
+    except (UnicodeDecodeError, UnicodeEncodeError):
+        # 如果修复失败，返回原文本
+        return text
+
+
+def format_session_data(data: Dict[str, Any], include_time: bool = False) -> Dict[str, Any]:
+    """
+    格式化会话数据为统一的输出格式
+    
+    Args:
+        data: 原始会话数据
+        include_time: 是否包含时间字段
+        
+    Returns:
+        Dict: 格式化后的数据 {"Query": "...", "Answer": "...", "starttime": "..."}
+    """
+    result = {
+        "Query": fix_encoding(data.get('messages', '')),
+        "Answer": fix_encoding(data.get('aimessages', ''))
+    }
+    
+    if include_time:
+        result["starttime"] = data.get('starttime', '')
+    
+    return result
+
+
+def filter_by_time_range(items: List[Dict], minutes: int) -> List[Dict]:
+    """
+    根据时间范围过滤数据
+    
+    Args:
+        items: 包含 starttime 字段的数据列表
+        minutes: 时间范围（分钟）
+        
+    Returns:
+        List[Dict]: 过滤后的数据列表
+    """
+    time_threshold = datetime.now() - timedelta(minutes=minutes)
+    time_threshold_str = time_threshold.strftime("%Y-%m-%d %H:%M:%S")
+    
+    filtered_items = []
+    for item in items:
+        starttime = item.get('starttime', '')
+        if starttime and starttime >= time_threshold_str:
+            filtered_items.append(item)
+    
+    return filtered_items
+
+
+def sort_and_limit_results(items: List[Dict], limit: int = 6, 
+                           remove_time: bool = True) -> List[Dict]:
+    """
+    对结果进行排序、限制数量并移除时间字段
+    
+    Args:
+        items: 数据列表
+        limit: 最大返回数量
+        remove_time: 是否移除 starttime 字段
+        
+    Returns:
+        List[Dict]: 处理后的数据列表
+    """
+    # 按时间降序排序（最新的在前）
+    items.sort(key=lambda x: x.get('starttime', ''), reverse=True)
+    
+    # 限制数量
+    result_items = items[:limit]
+    
+    # 移除 starttime 字段
+    if remove_time:
+        for item in result_items:
+            item.pop('starttime', None)
+    
+    # 如果结果少于1条，返回空列表
+    if len(result_items) < 1:
+        return []
+    
+    return result_items
+
+
+def generate_session_key(session_id: str, key_type: str = "session") -> str:
+    """
+    生成 Redis key
+    
+    Args:
+        session_id: 会话ID
+        key_type: key 类型 ("session", "read", "write", "count")
+        
+    Returns:
+        str: Redis key
+    """
+    if key_type == "count":
+        return f"session:count:{session_id}"
+    elif key_type == "write":
+        return f"session:write:{session_id}"
+    elif key_type == "session" or key_type == "read":
+        return f"session:{session_id}"
+    else:
+        return f"session:{session_id}"
+
+
+def get_current_timestamp() -> str:
+    """
+    获取当前时间戳字符串
+    
+    Returns:
+        str: 格式化的时间字符串 "YYYY-MM-DD HH:MM:SS"
+    """
+    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")

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

@@ -1,11 +1,37 @@
 import redis
 import uuid
-from datetime import datetime
 from app.core.config import settings
+from typing import List, Dict, Any, Optional, Union
+
+from app.core.logging_config import get_logger
+from app.core.memory.agent.utils.redis_base import (
+    serialize_messages,
+    deserialize_messages,
+    fix_encoding,
+    format_session_data,
+    filter_by_time_range,
+    sort_and_limit_results,
+    generate_session_key,
+    get_current_timestamp
+)
+
+logger = get_logger(__name__)
 
 
-class RedisSessionStore:
+class RedisWriteStore:
+    """Redis Write 类型存储类，用于管理 save_session_write 相关的数据"""
+
     def __init__(self, host='localhost', port=6379, db=0, password=None, session_id=''):
+        """
+        初始化 Redis 连接
+        
+        Args:
+            host: Redis 主机地址
+            port: Redis 端口
+            db: Redis 数据库编号
+            password: Redis 密码
+            session_id: 会话ID
+        """
         self.r = redis.Redis(
             host=host,
             port=port,
@@ -16,32 +42,439 @@ class RedisSessionStore:
         )
         self.uudi = session_id
 
-    def _fix_encoding(self, text):
-        """修复错误编码的文本"""
-        if not text or not isinstance(text, str):
-            return text
-        try:
-            # 尝试修复 Latin-1 误编码为 UTF-8 的情况
-            return text.encode('latin-1').decode('utf-8')
-        except (UnicodeDecodeError, UnicodeEncodeError):
-            # 如果修复失败，返回原文本
-            return text
-
-    # 修改后的 save_session 方法
-    def save_session(self, userid, messages, aimessages, apply_id, end_user_id):
+    def save_session_write(self, userid: str, messages: str) -> str:
         """
         写入一条会话数据，返回 session_id
-        优化版本：确保写入时间不超过1秒
+
+        Args:
+            userid: 用户ID
+            messages: 用户消息
+
+        Returns:
+            str: 新生成的 session_id
         """
         try:
-            session_id = str(uuid.uuid4())  # 为每次会话生成新的 ID
-            starttime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-            key = f"session:{session_id}"  # 使用新生成的 session_id 作为 key
+            messages = serialize_messages(messages)
+            session_id = str(uuid.uuid4())
+            key = generate_session_key(session_id, key_type="write")
 
-            # 使用 pipeline 批量写入，减少网络往返
             pipe = self.r.pipeline()
+            pipe.hset(key, mapping={
+                "id": self.uudi,
+                "sessionid": userid,
+                "messages": messages,
+                "starttime": get_current_timestamp()
+            })
+            result = pipe.execute()
 
-            # 直接写入数据，decode_responses=True 已经处理了编码
+            logger.debug(f"[save_session_write] 保存结果: {result[0]}, session_id: {session_id}")
+            return session_id
+        except Exception as e:
+            logger.error(f"[save_session_write] 保存会话失败: {e}")
+            raise e
+
+    def get_session_by_userid(self, userid: str) -> Union[List[Dict[str, str]], bool]:
+        """
+        通过 save_session_write 的 userid 获取 sessionid 和 messages
+        
+        Args:
+            userid: 用户ID (对应 sessionid 字段)
+            
+        Returns:
+            List[Dict] 或 False: 如果找到数据返回 [{"sessionid": "...", "messages": "..."}, ...]，否则返回 False
+        """
+        try:
+            # 只查询 write 类型的 key
+            keys = self.r.keys('session:write:*')
+            if not keys:
+                return False
+
+            # 批量获取数据
+            pipe = self.r.pipeline()
+            for key in keys:
+                pipe.hgetall(key)
+            all_data = pipe.execute()
+
+            # 筛选符合 userid 的数据
+            results = []
+            for key, data in zip(keys, all_data):
+                if not data:
+                    continue
+
+                # 从 write 类型读取，匹配 sessionid 字段
+                if data.get('sessionid') == userid:
+                    # 从 key 中提取 session_id: session:write:{session_id}
+                    session_id = key.split(':')[-1]
+                    results.append({
+                        "sessionid": session_id,
+                        "messages": fix_encoding(data.get('messages', ''))
+                    })
+
+            if not results:
+                return False
+
+            logger.debug(f"[get_session_by_userid] userid={userid}, 找到 {len(results)} 条数据")
+            return results
+        except Exception as e:
+            logger.error(f"[get_session_by_userid] 查询失败: {e}")
+            return False
+
+    def get_all_sessions_by_end_user_id(self, end_user_id: str) -> Union[List[Dict[str, Any]], bool]:
+        """
+        通过 end_user_id 获取所有 write 类型的会话数据
+        
+        Args:
+            end_user_id: 终端用户ID (对应 sessionid 字段)
+            
+        Returns:
+            List[Dict] 或 False: 如果找到数据返回完整的会话信息列表，否则返回 False
+            
+        返回格式:
+        [
+            {
+                "session_id": "uuid",
+                "id": "...",
+                "sessionid": "end_user_id",
+                "messages": "...",
+                "starttime": "timestamp"
+            },
+            ...
+        ]
+        """
+        try:
+            # 只查询 write 类型的 key
+            keys = self.r.keys('session:write:*')
+            if not keys:
+                logger.debug(f"[get_all_sessions_by_end_user_id] 没有找到任何 write 类型的会话")
+                return False
+
+            # 批量获取数据
+            pipe = self.r.pipeline()
+            for key in keys:
+                pipe.hgetall(key)
+            all_data = pipe.execute()
+
+            # 筛选符合 end_user_id 的数据
+            results = []
+            for key, data in zip(keys, all_data):
+                if not data:
+                    continue
+
+                # 从 write 类型读取，匹配 sessionid 字段
+                if data.get('sessionid') == end_user_id:
+                    # 从 key 中提取 session_id: session:write:{session_id}
+                    session_id = key.split(':')[-1]
+
+                    # 构建完整的会话信息
+                    session_info = {
+                        "session_id": session_id,
+                        "id": data.get('id', ''),
+                        "sessionid": data.get('sessionid', ''),
+                        "messages": fix_encoding(data.get('messages', '')),
+                        "starttime": data.get('starttime', '')
+                    }
+                    results.append(session_info)
+
+            if not results:
+                logger.debug(f"[get_all_sessions_by_end_user_id] end_user_id={end_user_id}, 没有找到数据")
+                return False
+
+            # 按时间排序（最新的在前）
+            results.sort(key=lambda x: x.get('starttime', ''), reverse=True)
+
+            logger.debug(f"[get_all_sessions_by_end_user_id] end_user_id={end_user_id}, 找到 {len(results)} 条数据")
+            return results
+        except Exception as e:
+            logger.error(f"[get_all_sessions_by_end_user_id] 查询失败: {e}", exc_info=True)
+            return False
+
+    def find_user_recent_sessions(self, userid: str,
+                                  minutes: int = 5) -> List[Dict[str, str]]:
+        """
+        根据 userid 从 save_session_write 写入的数据中查询最近 N 分钟内的会话数据
+        
+        Args:
+            userid: 用户ID (对应 sessionid 字段)
+            minutes: 查询最近几分钟的数据，默认5分钟
+            
+        Returns:
+            List[Dict]: 会话列表 [{"Query": "...", "Answer": "..."}, ...]
+        """
+        import time
+        start_time = time.time()
+
+        # 只查询 write 类型的 key
+        keys = self.r.keys('session:write:*')
+        if not keys:
+            logger.debug(f"[find_user_recent_sessions] 查询耗时: {time.time() - start_time:.3f}秒, 结果数: 0")
+            return []
+
+        # 批量获取数据
+        pipe = self.r.pipeline()
+        for key in keys:
+            pipe.hgetall(key)
+        all_data = pipe.execute()
+
+        # 筛选符合 userid 的数据
+        matched_items = []
+        for data in all_data:
+            if not data:
+                continue
+
+            # 从 write 类型读取，匹配 sessionid 字段
+            if data.get('sessionid') == userid and data.get('starttime'):
+                # write 类型没有 aimessages，所以 Answer 为空
+                matched_items.append({
+                    "Query": fix_encoding(data.get('messages', '')),
+                    "Answer": "",
+                    "starttime": data.get('starttime', '')
+                })
+
+        # 根据时间范围过滤
+        filtered_items = filter_by_time_range(matched_items, minutes)
+        # 排序并移除时间字段
+        result_items = sort_and_limit_results(filtered_items)
+
+        elapsed_time = time.time() - start_time
+        logger.debug(f"[find_user_recent_sessions] userid={userid}, minutes={minutes}, "
+              f"查询耗时: {elapsed_time:.3f}秒, 结果数: {len(result_items)}")
+
+        return result_items
+
+    def delete_all_write_sessions(self) -> int:
+        """
+        删除所有 write 类型的会话
+        
+        Returns:
+            int: 删除的数量
+        """
+        keys = self.r.keys('session:write:*')
+        if keys:
+            return self.r.delete(*keys)
+        return 0
+
+
+class RedisCountStore:
+    """Redis Count 类型存储类，用于管理访问次数统计相关的数据"""
+
+    def __init__(self, host='localhost', port=6379, db=0, password=None, session_id=''):
+        """
+        初始化 Redis 连接
+        
+        Args:
+            host: Redis 主机地址
+            port: Redis 端口
+            db: Redis 数据库编号
+            password: Redis 密码
+            session_id: 会话ID
+        """
+        self.r = redis.Redis(
+            host=host,
+            port=port,
+            db=db,
+            password=password,
+            decode_responses=True,
+            encoding='utf-8'
+        )
+        self.uuid = session_id
+
+    def save_sessions_count(self, end_user_id: str, count: int, messages: Any) -> str:
+        """
+        保存用户访问次数统计
+        
+        Args:
+            end_user_id: 终端用户ID
+            count: 访问次数
+            messages: 消息内容
+            
+        Returns:
+            str: 新生成的 session_id
+        """
+        session_id = str(uuid.uuid4())
+        key = generate_session_key(session_id, key_type="count")
+        index_key = f'session:count:index:{end_user_id}'  # 索引键
+
+        pipe = self.r.pipeline()
+        pipe.hset(key, mapping={
+            "id": self.uuid,
+            "end_user_id": end_user_id,
+            "count": int(count),
+            "messages": serialize_messages(messages),
+            "starttime": get_current_timestamp()
+        })
+        pipe.expire(key, 30 * 24 * 60 * 60)  # 30天过期
+
+        # 创建索引：end_user_id -> session_id 映射
+        pipe.set(index_key, session_id, ex=30 * 24 * 60 * 60)
+
+        result = pipe.execute()
+
+        logger.debug(f"[save_sessions_count] 保存结果: {result}, session_id: {session_id}")
+        return session_id
+
+    def get_sessions_count(self, end_user_id: str) -> tuple[int, list[dict]] | bool:
+        """
+        通过 end_user_id 查询访问次数统计
+        
+        Args:
+            end_user_id: 终端用户ID
+            
+        Returns:
+            list 或 False: 如果找到返回 [count, messages]，否则返回 False
+        """
+        try:
+            # 使用索引键快速查找
+            index_key = f'session:count:index:{end_user_id}'
+
+            # 检查索引键类型，避免 WRONGTYPE 错误
+            try:
+                key_type = self.r.type(index_key)
+                if key_type != 'string' and key_type != 'none':
+                    self.r.delete(index_key)
+                    return False
+            except Exception as type_error:
+                logger.error(f"[get_sessions_count] 检查键类型失败: {type_error}")
+
+            session_id = self.r.get(index_key)
+
+            if not session_id:
+                return False
+
+            # 直接获取数据
+            key = generate_session_key(session_id, key_type="count")
+            data = self.r.hgetall(key)
+
+            if not data:
+                # 索引存在但数据不存在，清理索引
+                self.r.delete(index_key)
+                return False
+
+            count = data.get('count')
+            messages_str = data.get('messages')
+
+            if count is not None:
+                messages: list[dict] = deserialize_messages(messages_str)
+                return int(count), messages
+
+            return False
+        except Exception as e:
+            logger.error(f"[get_sessions_count] 查询失败: {e}")
+            return False
+
+    def update_sessions_count(
+            self,
+            end_user_id: str,
+            new_count: int,
+            messages: Any
+    ) -> bool:
+        """
+        通过 end_user_id 修改访问次数统计（优化版：使用索引）
+        
+        Args:
+            end_user_id: 终端用户ID
+            new_count: 新的 count 值
+            messages: 消息内容
+            
+        Returns:
+            bool: 更新成功返回 True，未找到记录返回 False
+        """
+        try:
+            # 使用索引键快速查找
+            index_key = f'session:count:index:{end_user_id}'
+
+            # 检查索引键类型，避免 WRONGTYPE 错误
+            try:
+                key_type = self.r.type(index_key)
+                if key_type != 'string' and key_type != 'none':
+                    # 索引键类型错误，删除并返回 False
+                    logger.warning(f"[update_sessions_count] 索引键类型错误: {key_type}，删除索引")
+                    self.r.delete(index_key)
+                    logger.debug(f"[update_sessions_count] 未找到记录: end_user_id={end_user_id}")
+                    return False
+            except Exception as type_error:
+                logger.error(f"[update_sessions_count] 检查键类型失败: {type_error}")
+
+            session_id = self.r.get(index_key)
+
+            if not session_id:
+                logger.debug(f"[update_sessions_count] 未找到记录: end_user_id={end_user_id}")
+                return False
+
+            # 直接更新数据
+            key = generate_session_key(session_id, key_type="count")
+            messages_str = serialize_messages(messages)
+
+            pipe = self.r.pipeline()
+            pipe.hset(key, 'count', str(new_count))
+            pipe.hset(key, 'messages', messages_str)
+            result = pipe.execute()
+
+            logger.debug(f"[update_sessions_count] 更新成功: end_user_id={end_user_id}, new_count={new_count}, key={key}")
+            return True
+
+        except Exception as e:
+            logger.debug(f"[update_sessions_count] 更新失败: {e}")
+            return False
+
+    def delete_all_count_sessions(self) -> int:
+        """
+        删除所有 count 类型的会话
+        
+        Returns:
+            int: 删除的数量
+        """
+        keys = self.r.keys('session:count:*')
+        if keys:
+            return self.r.delete(*keys)
+        return 0
+
+
+class RedisSessionStore:
+    """Redis 会话存储类，用于管理会话数据"""
+
+    def __init__(self, host='localhost', port=6379, db=0, password=None, session_id=''):
+        """
+        初始化 Redis 连接
+        
+        Args:
+            host: Redis 主机地址
+            port: Redis 端口
+            db: Redis 数据库编号
+            password: Redis 密码
+            session_id: 会话ID
+        """
+        self.r = redis.Redis(
+            host=host,
+            port=port,
+            db=db,
+            password=password,
+            decode_responses=True,
+            encoding='utf-8'
+        )
+        self.uudi = session_id
+
+    # ==================== 写入操作 ====================
+
+    def save_session(self, userid: str, messages: str, aimessages: str,
+                     apply_id: str, end_user_id: str) -> str:
+        """
+        写入一条会话数据，返回 session_id
+        
+        Args:
+            userid: 用户ID
+            messages: 用户消息
+            aimessages: AI回复消息
+            apply_id: 应用ID
+            end_user_id: 终端用户ID
+            
+        Returns:
+            str: 新生成的 session_id
+        """
+        try:
+            session_id = str(uuid.uuid4())
+            key = generate_session_key(session_id, key_type="read")
+
+            pipe = self.r.pipeline()
             pipe.hset(key, mapping={
                 "id": self.uudi,
                 "sessionid": userid,
@@ -49,177 +482,195 @@ class RedisSessionStore:
                 "end_user_id": end_user_id,
                 "messages": messages,
                 "aimessages": aimessages,
-                "starttime": starttime
+                "starttime": get_current_timestamp()
             })
-
-            # 可选：设置过期时间（例如30天），避免数据无限增长
-            # pipe.expire(key, 30 * 24 * 60 * 60)
-
-            # 执行批量操作
             result = pipe.execute()
 
-            print(f"保存结果: {result[0]}, session_id: {session_id}")
-            return session_id  # 返回新生成的 session_id
+            logger.debug(f"[save_session] 保存结果: {result[0]}, session_id: {session_id}")
+            return session_id
         except Exception as e:
-            print(f"保存会话失败: {e}")
+            logger.error(f"[save_session] 保存会话失败: {e}")
             raise e
 
-    def save_sessions_batch(self, sessions_data):
-        """
-        批量写入多条会话数据，返回 session_id 列表
-        sessions_data: list of dict, 每个 dict 包含 userid, messages, aimessages, apply_id, end_user_id
-        优化版本：批量操作，大幅提升性能
-        """
-        try:
-            session_ids = []
-            pipe = self.r.pipeline()
+    # ==================== 读取操作 ====================
 
-            for session in sessions_data:
-                session_id = str(uuid.uuid4())
-                starttime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-                key = f"session:{session_id}"
-
-                pipe.hset(key, mapping={
-                    "id": self.uudi,
-                    "sessionid": session.get('userid'),
-                    "apply_id": session.get('apply_id'),
-                    "end_user_id": session.get('end_user_id'),
-                    "messages": session.get('messages'),
-                    "aimessages": session.get('aimessages'),
-                    "starttime": starttime
-                })
-
-                session_ids.append(session_id)
-
-            # 一次性执行所有写入操作
-            results = pipe.execute()
-            print(f"批量保存完成: {len(session_ids)} 条记录")
-            return session_ids
-        except Exception as e:
-            print(f"批量保存会话失败: {e}")
-            raise e
-
-    # ---------------- 读取 ----------------
-    def get_session(self, session_id):
+    def get_session(self, session_id: str) -> Optional[Dict[str, Any]]:
         """
         读取一条会话数据
+        
+        Args:
+            session_id: 会话ID
+            
+        Returns:
+            Dict 或 None: 会话数据
         """
-        key = f"session:{session_id}"
+        key = generate_session_key(session_id)
         data = self.r.hgetall(key)
         return data if data else None
 
-    def get_session_apply_group(self, sessionid, apply_id, end_user_id):
+    def get_all_sessions(self) -> Dict[str, Dict[str, Any]]:
         """
-        根据 sessionid、apply_id 和 end_user_id 三个条件查询会话数据
-        """
-        result_items = []
-
-        # 遍历所有会话数据
-        for key in self.r.keys('session:*'):
-            data = self.r.hgetall(key)
-
-            if not data:
-                continue
-
-            # 检查三个条件是否都匹配
-            if (data.get('sessionid') == sessionid and
-                    data.get('apply_id') == apply_id and
-                    data.get('end_user_id') == end_user_id):
-                result_items.append(data)
-
-        return result_items
-
-    def get_all_sessions(self):
-        """
-        获取所有会话数据
+        获取所有会话数据（不包括 count 和 write 类型）
+        
+        Returns:
+            Dict: 所有会话数据，key 为 session_id
         """
         sessions = {}
         for key in self.r.keys('session:*'):
-            sid = key.split(':')[1]
-            sessions[sid] = self.get_session(sid)
+            # 排除 count 和 write 类型的 key
+            if ':count:' not in key and ':write:' not in key:
+                sid = key.split(':')[1]
+                sessions[sid] = self.get_session(sid)
         return sessions
 
-    # ---------------- 更新 ----------------
-    def update_session(self, session_id, field, value):
+    def find_user_apply_group(self, sessionid: str, apply_id: str,
+                              end_user_id: str) -> List[Dict[str, str]]:
         """
-        更新单个字段
-        优化版本：使用 pipeline 减少网络往返
-        """
-        key = f"session:{session_id}"
-        pipe = self.r.pipeline()
-        pipe.exists(key)
-        pipe.hset(key, field, value)
-        results = pipe.execute()
-        return bool(results[0])  # 返回 key 是否存在
-
-    # ---------------- 删除 ----------------
-    def delete_session(self, session_id):
-        """
-        删除单条会话
-        """
-        key = f"session:{session_id}"
-        return self.r.delete(key)
-
-    def delete_all_sessions(self):
-        """
-        删除所有会话
-        """
-        keys = self.r.keys('session:*')
-        if keys:
-            return self.r.delete(*keys)
-        return 0
-
-    def delete_duplicate_sessions(self):
-        """
-        删除重复会话数据，条件：
-        "sessionid"、"user_id"、"end_user_id"、"messages"、"aimessages" 五个字段都相同的只保留一个，其他删除
-        优化版本：使用 pipeline 批量操作，确保在1秒内完成
+        根据 sessionid、apply_id 和 end_user_id 查询会话数据，返回最新的6条
+        
+        Args:
+            sessionid: 会话ID（支持模糊匹配）
+            apply_id: 应用ID
+            end_user_id: 终端用户ID
+            
+        Returns:
+            List[Dict]: 会话列表 [{"Query": "...", "Answer": "..."}, ...]
         """
         import time
         start_time = time.time()
 
-        # 第一步：使用 pipeline 批量获取所有 key
         keys = self.r.keys('session:*')
-
         if not keys:
-            print("[delete_duplicate_sessions] 没有会话数据")
-            return 0
+            logger.debug(f"[find_user_apply_group] 查询耗时: {time.time() - start_time:.3f}秒, 结果数: 0")
+            return []
 
-        # 第二步：使用 pipeline 批量获取所有数据
+        # 批量获取数据
         pipe = self.r.pipeline()
         for key in keys:
-            pipe.hgetall(key)
+            # 排除 count 和 write 类型
+            if ':count:' not in key and ':write:' not in key:
+                pipe.hgetall(key)
         all_data = pipe.execute()
 
-        # 第三步：在内存中识别重复数据
-        seen = {}  # 用字典记录：identifier -> key（保留第一个出现的 key）
-        keys_to_delete = []  # 需要删除的 key 列表
-
-        for key, data in zip(keys, all_data, strict=False):
+        # 筛选符合条件的数据
+        matched_items = []
+        for data in all_data:
             if not data:
                 continue
 
-            # 获取五个字段的值
-            sessionid = data.get('sessionid', '')
-            user_id = data.get('id', '')
-            end_user_id = data.get('end_user_id', '')
-            messages = data.get('messages', '')
-            aimessages = data.get('aimessages', '')
+            if (data.get('apply_id') == apply_id and
+                    data.get('end_user_id') == end_user_id):
+                # 支持模糊匹配或完全匹配 sessionid
+                if sessionid in data.get('sessionid', '') or data.get('sessionid') == sessionid:
+                    matched_items.append(format_session_data(data, include_time=True))
+
+        # 排序、限制数量并移除时间字段
+        result_items = sort_and_limit_results(matched_items, limit=6)
+
+        elapsed_time = time.time() - start_time
+        logger.debug(f"[find_user_apply_group] 查询耗时: {elapsed_time:.3f}秒, 结果数: {len(result_items)}")
+
+        return result_items
+
+    # ==================== 更新操作 ====================
+
+    def update_session(self, session_id: str, field: str, value: Any) -> bool:
+        """
+        更新单个字段
+        
+        Args:
+            session_id: 会话ID
+            field: 字段名
+            value: 字段值
+            
+        Returns:
+            bool: 是否更新成功
+        """
+        key = generate_session_key(session_id)
+        pipe = self.r.pipeline()
+        pipe.exists(key)
+        pipe.hset(key, field, value)
+        results = pipe.execute()
+        return bool(results[0])
+
+    # ==================== 删除操作 ====================
+
+    def delete_session(self, session_id: str) -> int:
+        """
+        删除单条会话
+        
+        Args:
+            session_id: 会话ID
+            
+        Returns:
+            int: 删除的数量
+        """
+        key = generate_session_key(session_id)
+        return self.r.delete(key)
+
+    def delete_all_sessions(self) -> int:
+        """
+        删除所有会话（不包括 count 和 write 类型）
+        
+        Returns:
+            int: 删除的数量
+        """
+        keys = self.r.keys('session:*')
+        # 过滤掉 count 和 write 类型
+        keys_to_delete = [k for k in keys if ':count:' not in k and ':write:' not in k]
+        if keys_to_delete:
+            return self.r.delete(*keys_to_delete)
+        return 0
+
+    def delete_duplicate_sessions(self) -> int:
+        """
+        删除重复会话数据（不包括 count 和 write 类型）
+        条件：sessionid、user_id、end_user_id、messages、aimessages 五个字段都相同的只保留一个
+        
+        Returns:
+            int: 删除的数量
+        """
+        import time
+        start_time = time.time()
+
+        keys = self.r.keys('session:*')
+        if not keys:
+            logger.debug("[delete_duplicate_sessions] 没有会话数据")
+            return 0
+
+        # 批量获取所有数据
+        pipe = self.r.pipeline()
+        for key in keys:
+            # 排除 count 和 write 类型
+            if ':count:' not in key and ':write:' not in key:
+                pipe.hgetall(key)
+        all_data = pipe.execute()
+
+        # 识别重复数据
+        seen = {}
+        keys_to_delete = []
+
+        for key, data in zip([k for k in keys if ':count:' not in k and ':write:' not in k], all_data, strict=False):
+            if not data:
+                continue
 
             # 用五元组作为唯一标识
-            identifier = (sessionid, user_id, end_user_id, messages, aimessages)
+            identifier = (
+                data.get('sessionid', ''),
+                data.get('id', ''),
+                data.get('end_user_id', ''),
+                data.get('messages', ''),
+                data.get('aimessages', '')
+            )
 
             if identifier in seen:
-                # 重复，标记为待删除
                 keys_to_delete.append(key)
             else:
-                # 第一次出现，记录
                 seen[identifier] = key
 
-        # 第四步：使用 pipeline 批量删除重复的 key
+        # 批量删除重复的 key
         deleted_count = 0
         if keys_to_delete:
-            # 分批删除，避免单次操作过大
             batch_size = 1000
             for i in range(0, len(keys_to_delete), batch_size):
                 batch = keys_to_delete[i:i + batch_size]
@@ -230,82 +681,31 @@ class RedisSessionStore:
                 deleted_count += len(batch)
 
         elapsed_time = time.time() - start_time
-        print(f"[delete_duplicate_sessions] 删除重复会话数量: {deleted_count}, 耗时: {elapsed_time:.3f}秒")
+        logger.debug(f"[delete_duplicate_sessions] 删除重复会话数量: {deleted_count}, 耗时: {elapsed_time:.3f}秒")
         return deleted_count
 
-    def find_user_session(self, sessionid):
-        user_id = sessionid
-
-        result_items = []
-        for key, values in store.get_all_sessions().items():
-            history = {}
-            if user_id == str(values['sessionid']):
-                history["Query"] = values['messages']
-                history["Answer"] = values['aimessages']
-                result_items.append(history)
-
-        if len(result_items) <= 1:
-            result_items = []
-        return (result_items)
-
-    def find_user_apply_group(self, sessionid, apply_id, end_user_id):
-        """
-        根据 sessionid、apply_id 和 end_user_id 三个条件查询会话数据，返回最新的6条
-        """
-        import time
-        start_time = time.time()
-        # 使用 pipeline 批量获取数据，提高性能
-        keys = self.r.keys('session:*')
-
-        if not keys:
-            print(f"查询耗时: {time.time() - start_time:.3f}秒, 结果数: 0")
-            return []
-
-        # 使用 pipeline 批量获取所有 hash 数据
-        pipe = self.r.pipeline()
-        for key in keys:
-            pipe.hgetall(key)
-        all_data = pipe.execute()
-
-        # 解析并筛选符合条件的数据
-        matched_items = []
-        for data in all_data:
-            if not data:
-                continue
-
-            # 检查是否符合三个条件
-
-            if (data.get('apply_id') == apply_id and
-                    data.get('end_user_id') == end_user_id):
-                # 支持模糊匹配 sessionid 或者完全匹配
-                if sessionid in data.get('sessionid', '') or data.get('sessionid') == sessionid:
-                    matched_items.append({
-                        "Query": self._fix_encoding(data.get('messages')),
-                        "Answer": self._fix_encoding(data.get('aimessages')),
-                        "starttime": data.get('starttime', '')
-                    })
-        # 按时间降序排序（最新的在前）
-        matched_items.sort(key=lambda x: x.get('starttime', ''), reverse=True)
-        # 只保留最新的6条
-        result_items = matched_items[:6]
-        # # 移除 starttime 字段
-        for item in result_items:
-            item.pop('starttime', None)
-
-        # 如果结果少于等于1条，返回空列表
-        if len(result_items) <= 1:
-            result_items = []
-
-        elapsed_time = time.time() - start_time
-        print(f"查询耗时: {elapsed_time:.3f}秒, 结果数: {len(result_items)}")
-
-        return result_items
-
 
+# 全局实例
 store = RedisSessionStore(
     host=settings.REDIS_HOST,
     port=settings.REDIS_PORT,
     db=settings.REDIS_DB,
     password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None,
     session_id=str(uuid.uuid4())
-)
+)
+
+write_store = RedisWriteStore(
+    host=settings.REDIS_HOST,
+    port=settings.REDIS_PORT,
+    db=settings.REDIS_DB,
+    password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None,
+    session_id=str(uuid.uuid4())
+)
+
+count_store = RedisCountStore(
+    host=settings.REDIS_HOST,
+    port=settings.REDIS_PORT,
+    db=settings.REDIS_DB,
+    password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None,
+    session_id=str(uuid.uuid4())
+)

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

@@ -4,15 +4,20 @@ Write Tools for Memory Knowledge Extraction Pipeline
 This module provides the main write function for executing the knowledge extraction
 pipeline. Only MemoryConfig is needed - clients are constructed internally.
 """
+import asyncio
 import time
+import uuid
 from datetime import datetime
+from typing import List, Optional
 
 from dotenv import load_dotenv
 
 from app.core.logging_config import get_agent_logger
 from app.core.memory.agent.utils.get_dialogs import get_chunked_dialogs
+from app.core.memory.storage_services.extraction_engine.deduplication.deduped_and_disamb import _USER_PLACEHOLDER_NAMES
 from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import ExtractionOrchestrator
-from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import memory_summary_generation
+from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import \
+    memory_summary_generation
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.core.memory.utils.log.logging_utils import log_time
 from app.db import get_db_context
@@ -22,29 +27,30 @@ from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_config_schema import MemoryConfig
 
-
 load_dotenv()
 
 logger = get_agent_logger(__name__)
 
 
 async def write(
-    end_user_id: str,
-    memory_config: MemoryConfig,
-    messages: list,
-    ref_id: str = "wyl20251027",
+        end_user_id: str,
+        memory_config: MemoryConfig,
+        messages: list,
+        ref_id: str = "",
+        language: str = "zh",
 ) -> None:
     """
     Execute the complete knowledge extraction pipeline.
 
     Args:
-        user_id: User identifier
-        apply_id: Application identifier
         end_user_id: Group identifier
         memory_config: MemoryConfig object containing all configuration
         messages: Structured message list [{"role": "user", "content": "..."}, ...]
-        ref_id: Reference ID, defaults to "wyl20251027"
+        ref_id: Reference ID, defaults to ""
+        language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
     """
+    if not ref_id:
+        ref_id = uuid.uuid4().hex
     # Extract config values
     embedding_model_id = str(memory_config.embedding_model_id)
     chunker_strategy = memory_config.chunker_strategy
@@ -93,12 +99,39 @@ async def write(
     from app.core.memory.utils.config.config_utils import get_pipeline_config
     pipeline_config = get_pipeline_config(memory_config)
 
+    # Fetch ontology types if scene_id is configured
+    ontology_types = None
+    if memory_config.scene_id:
+        try:
+            from app.core.memory.ontology_services.ontology_type_loader import load_ontology_types_for_scene
+
+            with get_db_context() as db:
+                ontology_types = load_ontology_types_for_scene(
+                    scene_id=memory_config.scene_id,
+                    workspace_id=memory_config.workspace_id,
+                    db=db
+                )
+
+                if ontology_types:
+                    logger.info(
+                        f"Loaded {len(ontology_types.types)} ontology types for scene_id: {memory_config.scene_id}"
+                    )
+                else:
+                    logger.info(f"No ontology classes found for scene_id: {memory_config.scene_id}")
+        except Exception as e:
+            logger.warning(
+                f"Failed to fetch ontology types for scene_id {memory_config.scene_id}: {e}",
+                exc_info=True
+            )
+
     orchestrator = ExtractionOrchestrator(
         llm_client=llm_client,
         embedder_client=embedder_client,
         connector=neo4j_connector,
         config=pipeline_config,
         embedding_id=embedding_model_id,
+        language=language,
+        ontology_types=ontology_types,
     )
 
     # Run the complete extraction pipeline
@@ -107,9 +140,11 @@ async def write(
         all_chunk_nodes,
         all_statement_nodes,
         all_entity_nodes,
+        all_perceptual_nodes,
         all_statement_chunk_edges,
         all_statement_entity_edges,
         all_entity_entity_edges,
+        all_perceptual_edges,
         all_dedup_details,
     ) = await orchestrator.run(chunked_dialogs, is_pilot_run=False)
 
@@ -117,29 +152,117 @@ async def write(
 
     # Step 3: Save all data to Neo4j database
     step_start = time.time()
-    from app.repositories.neo4j.create_indexes import create_fulltext_indexes
+
+    # Neo4j 写入前：清洗用户/AI助手实体之间的别名交叉污染
+    # 从 Neo4j 查询已有的 AI 助手别名，与本轮实体中的 AI 助手别名合并，
+    # 确保用户实体的 aliases 不包含 AI 助手的名字
     try:
-        await create_fulltext_indexes()
+        from app.core.memory.storage_services.extraction_engine.deduplication.deduped_and_disamb import (
+            clean_cross_role_aliases,
+            fetch_neo4j_assistant_aliases,
+        )
+        neo4j_assistant_aliases = set()
+        if all_entity_nodes:
+            _eu_id = all_entity_nodes[0].end_user_id
+            if _eu_id:
+                neo4j_assistant_aliases = await fetch_neo4j_assistant_aliases(neo4j_connector, _eu_id)
+        clean_cross_role_aliases(all_entity_nodes, external_assistant_aliases=neo4j_assistant_aliases)
+        logger.info(f"Neo4j 写入前别名清洗完成，AI助手别名排除集大小: {len(neo4j_assistant_aliases)}")
     except Exception as e:
-        logger.error(f"Error creating indexes: {e}", exc_info=True)
+        logger.warning(f"Neo4j 写入前别名清洗失败（不影响主流程）: {e}")
+
+    # 添加死锁重试机制
+    max_retries = 3
+    retry_delay = 1  # 秒
+
+    for attempt in range(max_retries):
+        try:
+            success = await save_dialog_and_statements_to_neo4j(
+                dialogue_nodes=all_dialogue_nodes,
+                chunk_nodes=all_chunk_nodes,
+                statement_nodes=all_statement_nodes,
+                entity_nodes=all_entity_nodes,
+                perceptual_nodes=all_perceptual_nodes,
+                statement_chunk_edges=all_statement_chunk_edges,
+                statement_entity_edges=all_statement_entity_edges,
+                entity_edges=all_entity_entity_edges,
+                perceptual_edges=all_perceptual_edges,
+                connector=neo4j_connector,
+            )
+            if success:
+                logger.info("Successfully saved all data to Neo4j")
+                
+                if all_entity_nodes:
+                    end_user_id = all_entity_nodes[0].end_user_id
+
+                    # Neo4j 写入完成后，用 PgSQL 权威 aliases 覆盖 Neo4j 用户实体
+                    try:
+                        from app.repositories.end_user_info_repository import EndUserInfoRepository
+                        if end_user_id:
+                            with get_db_context() as db_session:
+                                info = EndUserInfoRepository(db_session).get_by_end_user_id(uuid.UUID(end_user_id))
+                                pg_aliases = info.aliases if info and info.aliases else []
+                            if info is not None:
+                                # 将 Python 侧占位名集合作为参数传入，避免 Cypher 硬编码
+                                placeholder_names = list(_USER_PLACEHOLDER_NAMES)
+                                await neo4j_connector.execute_query(
+                                    """
+                                    MATCH (e:ExtractedEntity)
+                                    WHERE e.end_user_id = $end_user_id AND toLower(e.name) IN $placeholder_names
+                                    SET e.aliases = $aliases
+                                    """,
+                                    end_user_id=end_user_id, aliases=pg_aliases,
+                                    placeholder_names=placeholder_names,
+                                )
+                                logger.info(f"[AliasSync] Neo4j 用户实体 aliases 已用 PgSQL 权威源覆盖: {pg_aliases}")
+                    except Exception as sync_err:
+                        logger.warning(f"[AliasSync] PgSQL→Neo4j aliases 同步失败（不影响主流程）: {sync_err}")
+
+                    # 使用 Celery 异步任务触发聚类（不阻塞主流程）
+                    try:
+                        from app.tasks import run_incremental_clustering
+                        
+                        new_entity_ids = [e.id for e in all_entity_nodes]
+                        task = run_incremental_clustering.apply_async(
+                            kwargs={
+                                "end_user_id": end_user_id,
+                                "new_entity_ids": new_entity_ids,
+                                "llm_model_id": str(memory_config.llm_model_id) if memory_config.llm_model_id else None,
+                                "embedding_model_id": str(memory_config.embedding_model_id) if memory_config.embedding_model_id else None,
+                            },
+                            priority=3,
+                        )
+                        logger.info(
+                            f"[Clustering] 增量聚类任务已提交到 Celery - "
+                            f"task_id={task.id}, end_user_id={end_user_id}, entity_count={len(new_entity_ids)}"
+                        )
+                    except Exception as e:
+                        logger.error(f"[Clustering] 提交聚类任务失败（不影响主流程）: {e}", exc_info=True)
+                
+                break
+            else:
+                logger.warning("Failed to save some data to Neo4j")
+                if attempt < max_retries - 1:
+                    logger.info(f"Retrying... (attempt {attempt + 2}/{max_retries})")
+                    await asyncio.sleep(retry_delay * (attempt + 1))  # 指数退避
+        except Exception as e:
+            error_msg = str(e)
+            # 检查是否是死锁错误
+            if "DeadlockDetected" in error_msg or "deadlock" in error_msg.lower():
+                if attempt < max_retries - 1:
+                    logger.warning(f"Deadlock detected, retrying... (attempt {attempt + 2}/{max_retries})")
+                    await asyncio.sleep(retry_delay * (attempt + 1))  # 指数退避
+                else:
+                    logger.error(f"Failed after {max_retries} attempts due to deadlock: {e}")
+                    raise
+            else:
+                # 非死锁错误，直接抛出
+                raise
 
     try:
-        success = await save_dialog_and_statements_to_neo4j(
-            dialogue_nodes=all_dialogue_nodes,
-            chunk_nodes=all_chunk_nodes,
-            statement_nodes=all_statement_nodes,
-            entity_nodes=all_entity_nodes,
-            statement_chunk_edges=all_statement_chunk_edges,
-            statement_entity_edges=all_statement_entity_edges,
-            entity_edges=all_entity_entity_edges,
-            connector=neo4j_connector
-        )
-        if success:
-            logger.info("Successfully saved all data to Neo4j")
-        else:
-            logger.warning("Failed to save some data to Neo4j")
-    finally:
         await neo4j_connector.close()
+    except Exception as e:
+        logger.error(f"Error closing Neo4j connector: {e}")
 
     log_time("Neo4j Database Save", time.time() - step_start, log_file)
 
@@ -147,11 +270,10 @@ async def write(
     step_start = time.time()
     try:
         summaries = await memory_summary_generation(
-            chunked_dialogs, llm_client=llm_client, embedder_client=embedder_client
+            chunked_dialogs, llm_client=llm_client, embedder_client=embedder_client, language=language
         )
-
+        ms_connector = Neo4jConnector()
         try:
-            ms_connector = Neo4jConnector()
             await add_memory_summary_nodes(summaries, ms_connector)
             await add_memory_summary_statement_edges(summaries, ms_connector)
         finally:
@@ -172,5 +294,40 @@ async def write(
     with open(log_file, "a", encoding="utf-8") as f:
         f.write(f"=== Pipeline Run Completed: {timestamp} ===\n\n")
 
+    # 将提取统计写入 Redis，按 workspace_id 存储
+    try:
+        from app.cache.memory.activity_stats_cache import ActivityStatsCache
+
+        stats_to_cache = {
+            "chunk_count": len(all_chunk_nodes) if all_chunk_nodes else 0,
+            "statements_count": len(all_statement_nodes) if all_statement_nodes else 0,
+            "triplet_entities_count": len(all_entity_nodes) if all_entity_nodes else 0,
+            "triplet_relations_count": len(all_entity_entity_edges) if all_entity_entity_edges else 0,
+            "temporal_count": 0,
+        }
+        await ActivityStatsCache.set_activity_stats(
+            workspace_id=str(memory_config.workspace_id),
+            stats=stats_to_cache,
+        )
+        logger.info(f"[WRITE] 活动统计已写入 Redis: workspace_id={memory_config.workspace_id}")
+    except Exception as cache_err:
+        logger.warning(f"[WRITE] 写入活动统计缓存失败（不影响主流程）: {cache_err}", exc_info=True)
+
+    # Close LLM/Embedder underlying httpx clients to prevent
+    # 'RuntimeError: Event loop is closed' during garbage collection
+    for client_obj in (llm_client, embedder_client):
+        try:
+            underlying = getattr(client_obj, 'client', None) or getattr(client_obj, 'model', None)
+            if underlying is None:
+                continue
+            # Unwrap RedBearLLM / RedBearEmbeddings to get the LangChain model
+            inner = getattr(underlying, '_model', underlying)
+            # LangChain OpenAI models expose async_client (httpx.AsyncClient)
+            http_client = getattr(inner, 'async_client', None)
+            if http_client is not None and hasattr(http_client, 'aclose'):
+                await http_client.aclose()
+        except Exception:
+            pass
+
     logger.info("=== Pipeline Complete ===")
     logger.info(f"Total execution time: {total_time:.2f} seconds")

api/app/core/memory/analytics/hot_memory_tags.py

@@ -1,9 +1,12 @@
 import asyncio
 import json
+import logging
 import os
 from typing import List, Tuple
 
 from app.core.config import settings
+
+logger = logging.getLogger(__name__)
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.db import get_db_context
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
@@ -16,6 +19,10 @@ class FilteredTags(BaseModel):
     """用于接收LLM筛选后的核心标签列表的模型。"""
     meaningful_tags: List[str] = Field(..., description="从原始列表中筛选出的具有核心代表意义的名词列表。")
 
+class InterestTags(BaseModel):
+    """用于接收LLM筛选后的兴趣活动标签列表的模型。"""
+    interest_tags: List[str] = Field(..., description="从原始列表中筛选出的代表用户兴趣活动的标签列表。")
+
 async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]:
     """
     使用LLM筛选标签列表，仅保留具有代表性的核心名词。
@@ -39,16 +46,20 @@ async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]:
             
             connected_config = get_end_user_connected_config(end_user_id, db)
             config_id = connected_config.get("memory_config_id")
+            workspace_id = connected_config.get("workspace_id")
             
-            if not config_id:
+            if not config_id and not workspace_id:
                 raise ValueError(
                     f"No memory_config_id found for end_user_id: {end_user_id}. "
                     "Please ensure the user has a valid memory configuration."
                 )
             
-            # Use the config_id to get the proper LLM client
+            # Use the config_id to get the proper LLM client with workspace fallback
             config_service = MemoryConfigService(db)
-            memory_config = config_service.load_memory_config(config_id)
+            memory_config = config_service.load_memory_config(
+                config_id=config_id,
+                workspace_id=workspace_id
+            )
             
             if not memory_config.llm_model_id:
                 raise ValueError(
@@ -81,10 +92,74 @@ async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]:
         return structured_response.meaningful_tags
 
     except Exception as e:
-        print(f"LLM筛选过程中发生错误: {e}")
+        logger.error(f"LLM筛选过程中发生错误: {e}", exc_info=True)
         # 在LLM失败时返回原始标签，确保流程继续
         return tags
 
+async def filter_interests_with_llm(tags: List[str], end_user_id: str, language: str = "zh") -> List[str]:
+    """
+    使用LLM从标签列表中筛选出代表用户兴趣活动的标签。
+    
+    与 filter_tags_with_llm 不同，此函数专注于识别"活动/行为"类兴趣，
+    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。
+    
+    Args:
+        tags: 原始标签列表
+        end_user_id: 用户ID，用于获取LLM配置
+        
+    Returns:
+        筛选后的兴趣活动标签列表
+    """
+    try:
+        with get_db_context() as db:
+            from app.services.memory_agent_service import (
+                get_end_user_connected_config,
+            )
+            connected_config = get_end_user_connected_config(end_user_id, db)
+            config_id = connected_config.get("memory_config_id")
+            workspace_id = connected_config.get("workspace_id")
+
+            if not config_id and not workspace_id:
+                raise ValueError(
+                    f"No memory_config_id found for end_user_id: {end_user_id}."
+                )
+
+            config_service = MemoryConfigService(db)
+            memory_config = config_service.load_memory_config(
+                config_id=config_id,
+                workspace_id=workspace_id
+            )
+
+            if not memory_config.llm_model_id:
+                raise ValueError(
+                    f"No llm_model_id found in memory config {config_id}."
+                )
+
+            factory = MemoryClientFactory(db)
+            llm_client = factory.get_llm_client(memory_config.llm_model_id)
+
+        tag_list_str = ", ".join(tags)
+        from app.core.memory.utils.prompt.prompt_utils import render_interest_filter_prompt
+        rendered_prompt = render_interest_filter_prompt(tag_list_str, language=language)
+        messages = [
+            {
+                "role": "user",
+                "content": rendered_prompt
+            }
+        ]
+
+        structured_response = await llm_client.response_structured(
+            messages=messages,
+            response_model=InterestTags
+        )
+
+        return structured_response.interest_tags
+
+    except Exception as e:
+        logger.error(f"兴趣标签LLM筛选过程中发生错误: {e}", exc_info=True)
+        return tags
+
+
 async def get_raw_tags_from_db(
     connector: Neo4jConnector,
     end_user_id: str,
@@ -135,14 +210,14 @@ async def get_raw_tags_from_db(
     
     return [(record["name"], record["frequency"]) for record in results]
 
-async def get_hot_memory_tags(end_user_id: str, limit: int = 40, by_user: bool = False) -> List[Tuple[str, int]]:
+async def get_hot_memory_tags(end_user_id: str, limit: int = 10, by_user: bool = False) -> List[Tuple[str, int]]:
     """
     获取原始标签，然后使用LLM进行筛选，返回最终的热门标签列表。
-    查询更多的标签(limit=40)给LLM提供更丰富的上下文进行筛选。
+    查询更多的标签(40条)给LLM提供更丰富的上下文进行筛选，但最终返回数量由limit参数控制。
 
     Args:
         end_user_id: 必需参数。如果by_user=False，则为end_user_id；如果by_user=True，则为user_id
-        limit: 返回的标签数量限制
+        limit: 最终返回的标签数量限制（默认10）
         by_user: 是否按user_id查询（默认False，按end_user_id查询）
         
     Raises:
@@ -157,8 +232,9 @@ async def get_hot_memory_tags(end_user_id: str, limit: int = 40, by_user: bool =
     # 使用项目的Neo4jConnector
     connector = Neo4jConnector()
     try:
-        # 1. 从数据库获取原始排名靠前的标签
-        raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, limit, by_user=by_user)
+        # 1. 从数据库获取原始排名靠前的标签（查询40条给LLM提供更丰富的上下文）
+        query_limit = 40
+        raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, query_limit, by_user=by_user)
         if not raw_tags_with_freq:
             return []
 
@@ -173,7 +249,61 @@ async def get_hot_memory_tags(end_user_id: str, limit: int = 40, by_user: bool =
             if tag in meaningful_tag_names:
                 final_tags.append((tag, freq))
 
-        return final_tags
+        # 4. 限制返回的标签数量
+        return final_tags[:limit]
     finally:
         # 确保关闭连接
         await connector.close()
+
+async def get_interest_distribution(end_user_id: str, limit: int = 10, by_user: bool = False, language: str = "zh") -> List[Tuple[str, int]]:
+    """
+    获取用户的兴趣分布标签。
+    
+    与 get_hot_memory_tags 不同，此函数使用专门针对"活动/行为"的LLM prompt，
+    过滤掉纯物品、工具、地点等，只保留能代表用户兴趣爱好的活动类标签。
+
+    Args:
+        end_user_id: 必需参数。如果by_user=False，则为end_user_id；如果by_user=True，则为user_id
+        limit: 最终返回的标签数量限制（默认10）
+        by_user: 是否按user_id查询（默认False，按end_user_id查询）
+
+    Raises:
+        ValueError: 如果end_user_id未提供或为空
+    """
+    if not end_user_id or not end_user_id.strip():
+        raise ValueError(
+            "end_user_id is required. Please provide a valid end_user_id or user_id."
+        )
+
+    connector = Neo4jConnector()
+    try:
+        # 查询更多原始标签，给LLM提供充足上下文
+        query_limit = 40
+        raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, query_limit, by_user=by_user)
+        if not raw_tags_with_freq:
+            return []
+
+        raw_tag_names = [tag for tag, freq in raw_tags_with_freq]
+        raw_freq_map = {tag: freq for tag, freq in raw_tags_with_freq}
+
+        # 使用兴趣活动专用prompt进行筛选（支持语义推断出新标签）
+        interest_tag_names = await filter_interests_with_llm(raw_tag_names, end_user_id, language=language)
+
+        # 构建最终标签列表：
+        # - 原始标签中存在的，保留原始频率
+        # - LLM推断出的新标签（不在原始列表中），赋予默认频率1
+        final_tags = []
+        seen = set()
+        for tag in interest_tag_names:
+            if tag in seen:
+                continue
+            seen.add(tag)
+            freq = raw_freq_map.get(tag, 1)
+            final_tags.append((tag, freq))
+
+        # 按频率降序排列
+        final_tags.sort(key=lambda x: x[1], reverse=True)
+
+        return final_tags[:limit]
+    finally:
+        await connector.close()