Release/v0.2.3 (#355)

* feat(web): add PageEmpty component

* feat(web): add PageTabs component

* feat(web): add PageEmpty component

* feat(web): add PageTabs component

* feat(prompt): add history tracking for prompt releases

* feat(web): add prompt menu

* refactor: The PageScrollList component supports two generic parameters

* feat(web): BodyWrapper compoent update PageLoading

* feat(web): add Ontology menu

* feat(web): memory management add scene

* feat(tasks): add celery task configuration for periodic jobs

- Add ignore_result=True to prevent storing results for periodic tasks
- Set max_retries=0 to skip failed periodic tasks without retry attempts
- Configure acks_late=False for immediate acknowledgment in beat tasks
- Add time_limit and soft_time_limit to regenerate_memory_cache task (3600s/3300s)
- Add time_limit and soft_time_limit to workspace_reflection_task (300s/240s)
- Add time_limit and soft_time_limit to run_forgetting_cycle_task (7200s/7000s)
- Improve task reliability and resource management for scheduled jobs

* feat(sandbox): add Node.js code execution support to sandbox

* Release/v0.2.2 (#260)

* [modify] migration script

* [add] migration script

* fix(web): change form message

* fix(web): the memoryContent field is compatible with numbers and strings

* feat(web): code node hidden

* fix(model):
1. create a basic model to check if the name and provider are duplicated.
2. The result shows error models because the provider created API Keys for all matching models.

---------

Co-authored-by: Mark <zhuwenhui5566@163.com>
Co-authored-by: zhaoying <yzhao96@best-inc.com>
Co-authored-by: yingzhao <zhaoyingyz@126.com>
Co-authored-by: Timebomb2018 <18868801967@163.com>

* Feature/ontology class clean (#249)

* [add] Complete ontology engineering feature implementation

* [add] Add ontology feature integration and validation utilities

* [add] Add OWL validator and validation utilities

* [fix] Add missing render_ontology_extraction_prompt function

* [fix]Add dependencies, fix functionality

* [add] migration script

* feat(celery): add dedicated periodic tasks worker and queue (#261)

* fix(web): conflict resolve

* Fix/v022 bug (#263)

* [fix]Fix the issue of inconsistent language in explicit and episodic memory.

* [fix]Fix the issue of inconsistent language in explicit and episodic memory.

* [add]Add scene_id

* [fix]Based on the AI review to fix the code

* Fix/develop memory reflex (#265)

* 遗漏的历史映射

* 遗漏的历史映射

* 反思后台报错处理

* [add] migration script

* fix: chat conversation_id add node_start

* feat(web): show code node

* fix(web): Restructure the CustomSelect component, repair the interface that is called multiple times when the form is updated

* feat(web): RadioGroupCard support block mode

* feat(web): create space add icon

* feat(app and model): token consumption statistics

* Add/develop memory (#264)

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 新增长期记忆功能

* 新增长期记忆功能

* 新增长期记忆功能

* 知识库检索多余字段

* 长期

* feat(app and model): token consumption statistics of the cluster

* memory_BUG_fix

* fix(web): prompt history remove pageLoading

* fix(prompt): remove hard-coded import of prompt file paths (#279)

* Fix/develop memory bug (#274)

* 遗漏的历史映射

* 遗漏的历史映射

* fix_timeline_memories

* fix(web): update retrieve_type key

* Fix/develop memory bug (#276)

* 遗漏的历史映射

* 遗漏的历史映射

* fix_timeline_memories

* fix_timeline_memories

* write_gragp/bug_fix

* write_gragp/bug_fix

* write_gragp/bug_fix

* chore(celery): disable periodic task scheduling

* fix(prompt): remove hard-coded import of prompt file paths

---------

Co-authored-by: lixinyue11 <94037597+lixinyue11@users.noreply.github.com>
Co-authored-by: zhaoying <yzhao96@best-inc.com>
Co-authored-by: yingzhao <zhaoyingyz@126.com>
Co-authored-by: Ke Sun <kesun5@illinois.edu>

* fix(web): remove delete confirm content

* refactor(workflow): relocate template directory into workflow

* feat(memory): add long-term storage task routing and batching

* fix(web): PageScrollList loading update

* fix(web): PageScrollList loading update

* Ontology v1 bug (#291)

* [changes]Add 'id' as the secondary sorting key, and 'scene_id' now returns a UUID object

* [fix]Fix the "end_user" return to be sorted by update time.

* [fix]Set the default values of the memory configuration model based on the spatial model.

* [fix]Remove the entity extraction check combination model, read the configuration list, and add the return of scene_id

* [fix]Fix the "end_user" return to be sorted by update time.

* [fix]

* fix(memory): add Redis session validation

- Add macOS fork() safety configuration in celery_app.py to prevent initialization issues
- Add null/False checks for Redis session queries in term_memory_save to handle missing sessions gracefully
- Add null/False checks in memory_long_term_storage to prevent processing empty Redis results
- Add null/False checks in aggregate_judgment before format_parsing to avoid errors on missing data
- Initialize redis_messages variable in window_dialogue for consistency
- Add debug logging when no existing session found in Redis for better troubleshooting
- Add TODO comments for magic numbers (scope=6, time=5) to be extracted as constants
- Improve error handling when Redis returns False or empty results instead of crashing

* fix(web): PageScrollList style update

* fix(workflow): fix argument passing in code execution nodes

* fix(web): prompt add disabled

* fix(web): space icon required

* feat(app): modify the key of the token

* fix(fix the key of the app's token):

* fix(workflow): switch code input encoding to base64+URL encoding

* [add]The main project adds multi-API Key load balancing.

* [changes]Attribute security access, secure numerical conversion, unified use of local variables

* fix(web): save add session update

* fix(web): language editor support paste

* [changes]Active status filtering logic, API Key selection strategy

* memory_BUG

* memory_BUG_long_term

* [changes]

* memory_BUG_long_term

* memory_BUG_long_term

* Fix/release memory bug (#306)

* memory_BUG_fix

* memory_BUG

* memory_BUG_long_term

* memory_BUG_long_term

* memory_BUG_long_term

* knowledge_retrieval/bug/fix

* knowledge_retrieval/bug/fix

* knowledge_retrieval/bug/fix

* [fix]1.The "read_all_config" interface returns "scene_name";2.Memory configuration for lightweight query ontology scenarios

* fix(web): replace code editor

* [changes]Modify the description of the time for the recent event

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

* feat(web): update memory config ontology api

* fix(web): ui update

* knowledge_retrieval/bug/fix

* knowledge_retrieval/bug/fix

* knowledge_retrieval/bug/fix

* feat(workflow): add token usage statistics for question classifier and parameter extraction

* feat(web): move prompt menu

* Multiple independent transactions - single transaction

* Multiple independent transactions - single transaction

* Multiple independent transactions - single transaction

* Multiple independent transactions - single transaction

* Write Missing None (#321)

* Write Missing None

* Write Missing None

* Write Missing None

* Apply suggestion from @sourcery-ai[bot]

Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>

* Write Missing None

---------

Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>

* Fix/release memory bug (#324)

* Write Missing None

* Write Missing None

* Write Missing None

* Apply suggestion from @sourcery-ai[bot]

Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>

* Write Missing None

* redis update

* redis update

* redis update

* redis update

---------

Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>

* Fix/writer memory bug (#326)

* [fix]Fix the bug

* [fix]Fix the bug

* [fix]Correct the direction indication.

* fix(web): markdown table ui update

* Fix/release memory bug (#332)

* Write Missing None

* Write Missing None

* Write Missing None

* Apply suggestion from @sourcery-ai[bot]

Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>

* Write Missing None

* redis update

* redis update

* redis update

* redis update

* writer_dup_bug/fix

---------

Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>

* Fix/fact summary (#333)

* [fix]Disable the contents related to fact_summary

* [fix]Disable the contents related to fact_summary

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

* Fix/release memory bug (#335)

* Write Missing None

* Write Missing None

* Write Missing None

* Apply suggestion from @sourcery-ai[bot]

Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>

* Write Missing None

* redis update

* redis update

* redis update

* redis update

* writer_dup_bug/fix

* writer_graph_bug/fix

* writer_graph_bug/fix

---------

Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>

* Revert "feat(web): move prompt menu"

This reverts commit 9e6e8f50f8.

* fix(web): ui update

* fix(web): update text

* fix(web): ui update

* fix(model): change the "vl" model type of dashscope to "chat"

* fix(model): change the "vl" model type of dashscope to "chat"

---------

Co-authored-by: zhaoying <yzhao96@best-inc.com>
Co-authored-by: Eternity <1533512157@qq.com>
Co-authored-by: Mark <zhuwenhui5566@163.com>
Co-authored-by: yingzhao <zhaoyingyz@126.com>
Co-authored-by: Timebomb2018 <18868801967@163.com>
Co-authored-by: 乐力齐 <162269739+lanceyq@users.noreply.github.com>
Co-authored-by: lixinyue11 <94037597+lixinyue11@users.noreply.github.com>
Co-authored-by: lixinyue <2569494688@qq.com>
Co-authored-by: Eternity <61316157+myhMARS@users.noreply.github.com>
Co-authored-by: lanceyq <1982376970@qq.com>
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>

api/app/celery_app.py

@@ -3,9 +3,14 @@ import platform
 from datetime import timedelta
 from urllib.parse import quote
 
-from app.core.config import settings
 from celery import Celery
 
+from app.core.config import settings
+
+# macOS fork() safety - must be set before any Celery initialization
+if platform.system() == 'Darwin':
+    os.environ.setdefault('OBJC_DISABLE_INITIALIZE_FORK_SAFETY', 'YES')
+
 # 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）
@@ -63,15 +68,20 @@ celery_app.conf.update(
         'app.core.memory.agent.read_message': {'queue': 'memory_tasks'},
         'app.core.memory.agent.write_message': {'queue': 'memory_tasks'},
         
+        # Long-term storage tasks → memory_tasks queue (batched write strategies)
+        'app.core.memory.agent.long_term_storage.window': {'queue': 'memory_tasks'},
+        'app.core.memory.agent.long_term_storage.time': {'queue': 'memory_tasks'},
+        'app.core.memory.agent.long_term_storage.aggregate': {'queue': 'memory_tasks'},
+        
         # Document tasks → document_tasks queue (prefork worker)
         'app.core.rag.tasks.parse_document': {'queue': 'document_tasks'},
         'app.core.rag.tasks.build_graphrag_for_kb': {'queue': 'document_tasks'},
         
-        # Beat/periodic tasks → document_tasks queue (prefork worker)
+        # Beat/periodic tasks → periodic_tasks queue (dedicated periodic worker)
-        'app.tasks.workspace_reflection_task': {'queue': 'document_tasks'},
+        'app.tasks.workspace_reflection_task': {'queue': 'periodic_tasks'},
-        'app.tasks.regenerate_memory_cache': {'queue': 'document_tasks'},
+        'app.tasks.regenerate_memory_cache': {'queue': 'periodic_tasks'},
-        'app.tasks.run_forgetting_cycle_task': {'queue': 'document_tasks'},
+        'app.tasks.run_forgetting_cycle_task': {'queue': 'periodic_tasks'},
-        'app.controllers.memory_storage_controller.search_all': {'queue': 'document_tasks'},
+        'app.controllers.memory_storage_controller.search_all': {'queue': 'periodic_tasks'},
     },
 )
 
@@ -79,40 +89,40 @@ 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 = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS)
-memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS)
+# 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
+# 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
+# forgetting_cycle_schedule = timedelta(hours=24)  # 每24小时运行一次遗忘周期
 
 # 构建定时任务配置
-beat_schedule_config = {
+# beat_schedule_config = {
-    "run-workspace-reflection": {
+#     "run-workspace-reflection": {
-        "task": "app.tasks.workspace_reflection_task",
+#         "task": "app.tasks.workspace_reflection_task",
-        "schedule": workspace_reflection_schedule,
+#         "schedule": workspace_reflection_schedule,
-        "args": (),
+#         "args": (),
-    },
+#     },
-    "regenerate-memory-cache": {
+#     "regenerate-memory-cache": {
-        "task": "app.tasks.regenerate_memory_cache",
+#         "task": "app.tasks.regenerate_memory_cache",
-        "schedule": memory_cache_regeneration_schedule,
+#         "schedule": memory_cache_regeneration_schedule,
-        "args": (),
+#         "args": (),
-    },
+#     },
-    "run-forgetting-cycle": {
+#     "run-forgetting-cycle": {
-        "task": "app.tasks.run_forgetting_cycle_task",
+#         "task": "app.tasks.run_forgetting_cycle_task",
-        "schedule": forgetting_cycle_schedule,
+#         "schedule": forgetting_cycle_schedule,
-        "kwargs": {
+#         "kwargs": {
-            "config_id": None,  # 使用默认配置，可以通过环境变量配置
+#             "config_id": None,  # 使用默认配置，可以通过环境变量配置
-        },
+#         },
-    },
+#     },
-}
+# }
 
 # 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:
+# if settings.DEFAULT_WORKSPACE_ID:
-    beat_schedule_config["write-total-memory"] = {
+#     beat_schedule_config["write-total-memory"] = {
-        "task": "app.controllers.memory_storage_controller.search_all",
+#         "task": "app.controllers.memory_storage_controller.search_all",
-        "schedule": memory_increment_schedule,
+#         "schedule": memory_increment_schedule,
-        "kwargs": {
+#         "kwargs": {
-            "workspace_id": settings.DEFAULT_WORKSPACE_ID,
+#             "workspace_id": settings.DEFAULT_WORKSPACE_ID,
-        },
+#         },
-    }
+#     }
 
-celery_app.conf.beat_schedule = beat_schedule_config
+# celery_app.conf.beat_schedule = beat_schedule_config

api/app/controllers/__init__.py

@@ -45,6 +45,7 @@ from . import (
     home_page_controller,
     memory_perceptual_controller,
     memory_working_controller,
+    ontology_controller,
 )
 
 # 创建管理端 API 路由器
@@ -90,5 +91,6 @@ 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)
 
 __all__ = ["manager_router"]

api/app/controllers/emotion_config_controller.py

@@ -7,10 +7,11 @@ Routes:
     GET /memory/config/emotion - 获取情绪引擎配置
     POST /memory/config/emotion - 更新情绪引擎配置
 """
+import uuid
 
 from fastapi import APIRouter, Depends, Query, HTTPException, status
 from pydantic import BaseModel, Field
-from typing import Optional
+from typing import Optional, Union
 from sqlalchemy.orm import Session
 from uuid import UUID
 
@@ -21,6 +22,7 @@ from app.schemas.response_schema import ApiResponse
 from app.services.emotion_config_service import EmotionConfigService
 from app.core.logging_config import get_api_logger
 from app.db import get_db
+from app.utils.config_utils import resolve_config_id
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -37,7 +39,7 @@ class EmotionConfigQuery(BaseModel):
 
 class EmotionConfigUpdate(BaseModel):
     """情绪配置更新请求模型"""
-    config_id: UUID = Field(..., description="配置ID")
+    config_id: Union[uuid.UUID, int, str]= Field(..., description="配置ID")
     emotion_enabled: bool = Field(..., description="是否启用情绪提取")
     emotion_model_id: Optional[str] = Field(None, description="情绪分析专用模型ID")
     emotion_extract_keywords: bool = Field(..., description="是否提取情绪关键词")
@@ -46,7 +48,7 @@ class EmotionConfigUpdate(BaseModel):
 
 @router.get("/read_config", response_model=ApiResponse)
 def get_emotion_config(
-    config_id: UUID = Query(..., description="配置ID"),
+    config_id: UUID|int = Query(..., description="配置ID"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
@@ -79,7 +81,7 @@ def get_emotion_config(
             f"用户 {current_user.username} 请求获取情绪配置",
             extra={"config_id": config_id}
         )
-        
+        config_id=resolve_config_id(config_id, db)
         # 初始化服务
         config_service = EmotionConfigService(db)
         
@@ -158,6 +160,7 @@ def update_emotion_config(
             }
         }
     """
+    config.config_id=resolve_config_id(config.config_id, db)
     try:
         api_logger.info(
             f"用户 {current_user.username} 请求更新情绪配置",

api/app/controllers/memory_forget_controller.py

@@ -34,7 +34,7 @@ from app.schemas.memory_storage_schema import (
 )
 from app.schemas.response_schema import ApiResponse
 from app.services.memory_forget_service import MemoryForgetService
-
+from app.utils.config_utils import resolve_config_id
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -84,7 +84,8 @@ async def trigger_forgetting_cycle(
         
         connected_config = get_end_user_connected_config(end_user_id, db)
         config_id = connected_config.get("memory_config_id")
-        
+        config_id = resolve_config_id((config_id), db)
+
         if config_id is None:
             api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置")
             return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None")
@@ -129,7 +130,7 @@ async def trigger_forgetting_cycle(
 
 @router.get("/read_config", response_model=ApiResponse)
 async def read_forgetting_config(
-    config_id: UUID,
+    config_id: UUID|int,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db)
 ):
@@ -158,6 +159,7 @@ async def read_forgetting_config(
     )
     
     try:
+        config_id=resolve_config_id(config_id, db)
         # 调用服务层读取配置
         config = forget_service.read_forgetting_config(db=db, config_id=config_id)
         
@@ -195,6 +197,8 @@ async def update_forgetting_config(
         ApiResponse: 包含更新结果的响应
     """
     workspace_id = current_user.current_workspace_id
+    payload.config_id=resolve_config_id((payload.config_id), db)
+
     
     # 检查用户是否已选择工作空间
     if workspace_id is None:
@@ -255,12 +259,10 @@ async def get_forgetting_stats(
         ApiResponse: 包含统计信息的响应
     """
     workspace_id = current_user.current_workspace_id
-    
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘引擎统计但未选择工作空间")
         return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
-    
     # 如果提供了 end_user_id，通过它获取 config_id
     config_id = None
     if end_user_id:
@@ -269,6 +271,7 @@ async def get_forgetting_stats(
             
             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")
@@ -325,7 +328,7 @@ async def get_forgetting_curve(
         ApiResponse: 包含遗忘曲线数据的响应
     """
     workspace_id = current_user.current_workspace_id
-    
+    request.config_id = resolve_config_id((request.config_id), db)
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘曲线但未选择工作空间")

api/app/controllers/memory_reflection_controller.py

@@ -25,6 +25,8 @@ from fastapi import APIRouter, Depends, HTTPException, status,Header
 from sqlalchemy import text
 from sqlalchemy.orm import Session
 
+from app.utils.config_utils import resolve_config_id
+
 load_dotenv()
 api_logger = get_api_logger()
 
@@ -43,12 +45,12 @@ async def save_reflection_config(
     """Save reflection configuration to data_comfig table"""
     try:
         config_id = request.config_id
+        config_id = resolve_config_id(config_id, db)
         if not config_id:
             raise HTTPException(
                 status_code=status.HTTP_400_BAD_REQUEST,
                 detail="缺少必需参数: config_id"
             )
-
         api_logger.info(f"用户 {current_user.username} 保存反思配置，config_id: {config_id}")
 
         memory_config = MemoryConfigRepository.update_reflection_config(
@@ -99,7 +101,7 @@ 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"""
+    """启动工作空间中所有匹配应用的反思功能"""
     workspace_id = current_user.current_workspace_id
     reflection_service = MemoryReflectionService(db)
 
@@ -108,42 +110,55 @@ async def start_workspace_reflection(
 
         service = WorkspaceAppService(db)
         result = service.get_workspace_apps_detailed(workspace_id)
-        
         reflection_results = []
-        
         for data in result['apps_detailed_info']:
-            if data['memory_configs'] == []: 
+            # 跳过没有配置的应用
+            if not data['memory_configs']:
+                api_logger.debug(f"应用 {data['id']} 没有memory_configs，跳过")
                 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):
+            # 为每个配置和用户组合执行反思
-                # 安全地转换为整数，处理空字符串和None的情况
+            for config in memory_configs:
-                print(base['config'])
+                config_id_str = str(config['config_id'])
-                try:
+
-                    base_config = int(base['config']) if base['config'] else 0
+                # 找到匹配此配置的所有release
-                    config_id = int(config['config_id']) if config['config_id'] else 0
+                matching_releases = [r for r in releases if str(r['config']) == config_id_str]
-                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')}")
+                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']:
+                # 为每个用户执行反思
-                    # 调用反思服务
+                for user in end_users:
-                    api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config['config_id']}")
+                    api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config_id_str}")
-                    
+
-                    reflection_result = await reflection_service.start_text_reflection(
+                    try:
-                        config_data=config,
+                        reflection_result = await reflection_service.start_text_reflection(
-                        end_user_id=user['id']
+                            config_data=config,
-                    )
+                            end_user_id=user['id']
-                    
+                        )
-                    reflection_results.append({
+
-                        "app_id": base['app_id'],
+                        reflection_results.append({
-                        "config_id": config['config_id'],
+                            "app_id": data['id'],
-                        "end_user_id": user['id'],
+                            "config_id": config_id_str,
-                        "reflection_result": reflection_result
+                            "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")
 
@@ -157,17 +172,20 @@ async def start_workspace_reflection(
 
 @router.get("/reflection/configs")
 async def start_reflection_configs(
-        config_id: uuid.UUID,
+        config_id: uuid.UUID|int,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db),
 ) -> dict:
     """通过config_id查询memory_config表中的反思配置信息"""
+    config_id = resolve_config_id(config_id, db)
     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
         reflection_config = {
-            "config_id": result.config_id,
+            "config_id": memory_config_id,
             "reflection_enabled": result.enable_self_reflexion,
             "reflection_period_in_hours": result.iteration_period,
             "reflexion_range": result.reflexion_range,
@@ -192,7 +210,7 @@ async def start_reflection_configs(
 
 @router.get("/reflection/run")
 async def reflection_run(
-    config_id: UUID,
+    config_id: UUID|int,
     language_type: str = Header(default="zh", alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
@@ -200,7 +218,7 @@ async def reflection_run(
     """Activate the reflection function for all matching applications in the workspace"""
 
     api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
-
+    config_id = resolve_config_id(config_id, db)
     # 使用MemoryConfigRepository查询反思配置
     result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id)
     if not result:

api/app/controllers/memory_storage_controller.py

@@ -35,6 +35,8 @@ from fastapi import APIRouter, Depends
 from fastapi.responses import StreamingResponse
 from sqlalchemy.orm import Session
 
+from app.utils.config_utils import resolve_config_id
+
 # Get API logger
 api_logger = get_api_logger()
 
@@ -141,7 +143,6 @@ def create_config(
     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} 尝试创建配置但未选择工作空间")
@@ -161,12 +162,12 @@ def create_config(
 
 @router.delete("/delete_config", response_model=ApiResponse)  # 删除数据库中的内容（按配置名称）
 def delete_config(
-    config_id: UUID,
+    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} 尝试删除配置但未选择工作空间")
@@ -188,12 +189,17 @@ def update_config(
     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)
@@ -211,7 +217,7 @@ def update_config_extracted(
     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")
@@ -233,12 +239,12 @@ def update_config_extracted(
 
 @router.get("/read_config_extracted", response_model=ApiResponse) # 通过查询参数读取某条配置（固定路径） 没有意义的话就删除
 def read_config_extracted(
-    config_id: UUID,
+    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")
@@ -286,6 +292,7 @@ async def pilot_run(
         f"Pilot run requested: config_id={payload.config_id}, "
         f"dialogue_text_length={len(payload.dialogue_text)}"
     )
+    payload.config_id = resolve_config_id(payload.config_id, db)
     svc = DataConfigService(db)
     return StreamingResponse(
         svc.pilot_run_stream(payload),

api/app/controllers/model_controller.py

@@ -7,7 +7,7 @@ from app.core.error_codes import BizCode
 from app.core.exceptions import BusinessException
 from app.db import get_db
 from app.dependencies import get_current_user
-from app.models.models_model import ModelProvider, ModelType
+from app.models.models_model import ModelProvider, ModelType, LoadBalanceStrategy
 from app.models.user_model import User
 from app.repositories.model_repository import ModelConfigRepository
 from app.schemas import model_schema
@@ -31,7 +31,12 @@ def get_model_types():
 
 @router.get("/provider", response_model=ApiResponse)
 def get_model_providers():
-    return success(msg="获取模型提供商成功", data=list(ModelProvider))
+    providers = [p for p in ModelProvider if p != ModelProvider.COMPOSITE]
+    return success(msg="获取模型提供商成功", data=providers)
+
+@router.get("/strategy", response_model=ApiResponse)
+def get_model_strategies():
+    return success(msg="获取模型策略成功", data=list(LoadBalanceStrategy))
 
 
 @router.get("", response_model=ApiResponse)
@@ -91,7 +96,7 @@ def get_model_list(
 
 
 @router.get("/new", response_model=ApiResponse)
-def get_model_list(
+def get_model_list_new(
     type: Optional[list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"),
     provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于ModelConfig)"),
     is_active: Optional[bool] = Query(None, description="激活状态筛选"),
@@ -147,7 +152,7 @@ def get_model_plaza_list(
     type: Optional[ModelType] = Query(None, description="模型类型"),
     provider: Optional[ModelProvider] = Query(None, description="供应商"),
     is_official: Optional[bool] = Query(None, description="是否官方模型"),
-    is_deprecated: Optional[bool] = Query(False, description="是否弃用"),
+    is_deprecated: Optional[bool] = Query(None, description="是否弃用"),
     search: Optional[str] = Query(None, description="搜索关键词"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user)
@@ -198,6 +203,10 @@ def update_model_base(
 ):
     """更新基础模型"""
     
+    # 不允许更改type类型
+    if data.type is not None or data.provider is not None:
+        raise BusinessException("不允许更改模型类型和供应商", BizCode.INVALID_PARAMETER)
+    
     result = ModelBaseService.update_model_base(db=db, model_base_id=model_base_id, data=data)
     return success(data=model_schema.ModelBase.model_validate(result), msg="基础模型更新成功")
 
@@ -318,6 +327,8 @@ async def update_composite_model(
     api_logger.info(f"更新组合模型请求: model_id={model_id}, 用户: {current_user.username}")
     
     try:
+        if model_data.type is not None:
+            raise BusinessException("不允许更改模型类型和供应商", 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})")
         
@@ -457,11 +468,13 @@ async def create_model_api_key_by_provider(
             priority=api_key_data.priority,
             model_config_ids=model_config_ids
         )
-        created_keys = await ModelApiKeyService.create_api_key_by_provider(db=db, data=create_data)
+        created_keys, failed_models = await ModelApiKeyService.create_api_key_by_provider(db=db, data=create_data)
         
         api_logger.info(f"API Key创建成功: 关联{len(created_keys)}个模型")
-        result_list = [model_schema.ModelApiKey.model_validate(key) for key in created_keys]
+        # result_list = [model_schema.ModelApiKey.model_validate(key) for key in created_keys]
-        return success(data=result_list, msg=f"成功为 {len(created_keys)} 个模型创建API Key")
+        result = "API Key已存在" if len(created_keys) == 0 and len(failed_models) == 0 else \
+            f"成功为 {len(created_keys)} 个模型创建API Key, 失败模型列表{failed_models}"
+        return success(data=result, msg=f"成功为 {len(created_keys)} 个模型创建API Key")
     except Exception as e:
         api_logger.error(f"创建API Key失败: {str(e)}")
         raise

api/app/controllers/ontology_secondary_routes.py

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

api/app/controllers/prompt_optimizer_controller.py

@@ -1,5 +1,5 @@
-import 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 (
-from app.schemas.prompt_optimizer_schema import PromptOptMessage, PromptOptModelSet, CreateSessionResponse, \
+    PromptOptMessage,
-    OptimizePromptResponse, SessionHistoryResponse, SessionMessage
+    CreateSessionResponse,
+    SessionHistoryResponse,
+    SessionMessage,
+    PromptSaveRequest
+)
 from app.schemas.response_schema import ApiResponse
 from app.services.prompt_optimizer_service import PromptOptimizerService
 
@@ -135,3 +139,109 @@ 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/service/app_api_controller.py

@@ -235,11 +235,11 @@ async def chat(
 
                     message=payload.message,
                     conversation_id=conversation.id,  # 使用已创建的会话 ID
-                    user_id=new_end_user.id,  # 转换为字符串
+                    user_id=end_user_id,  # 转换为字符串
                     variables=payload.variables,
                     config=config,
-                    web_search=payload.web_search,
+                    web_search=web_search,
-                    memory=payload.memory,
+                    memory=memory,
                     storage_type=storage_type,
                     user_rag_memory_id=user_rag_memory_id,
                     app_id=app.id,
@@ -268,11 +268,11 @@ async def chat(
 
             message=payload.message,
             conversation_id=conversation.id,  # 使用已创建的会话 ID
-            user_id=new_end_user.id,  # 转换为字符串
+            user_id=end_user_id,  # 转换为字符串
             variables=payload.variables,
             config=config,
-            web_search=payload.web_search,
+            web_search=web_search,
-            memory=payload.memory,
+            memory=memory,
             storage_type=storage_type,
             user_rag_memory_id=user_rag_memory_id,
             app_id=app.id,

api/app/core/agent/langchain_agent.py

@@ -7,27 +7,21 @@ LangChain Agent 封装
 - 支持流式输出
 - 使用 RedBearLLM 支持多提供商
 """
-import os
+
 import time
 from typing import Any, AsyncGenerator, Dict, List, Optional, Sequence
 
-
+from app.core.memory.agent.langgraph_graph.write_graph import  write_long_term
 from app.db import get_db
 from app.core.logging_config import get_business_logger
-from app.core.memory.agent.utils.redis_tool import store
 from app.core.models import RedBearLLM, RedBearModelConfig
 from app.models.models_model import ModelType
-from app.repositories.memory_short_repository import LongTermMemoryRepository
 from app.services.memory_agent_service import (
     get_end_user_connected_config,
 )
-from app.services.memory_konwledges_server import write_rag
-from app.services.task_service import get_task_memory_write_result
-from app.tasks import write_message_task
 from langchain.agents import create_agent
 from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
 from langchain_core.tools import BaseTool
-
 logger = get_business_logger()
 
 
@@ -104,7 +98,7 @@ class LangChainAgent:
                 "streaming": streaming,
                 "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)
             }
         )
 
@@ -143,100 +137,7 @@ class LangChainAgent:
             user_content = f"参考信息：\n{context}\n\n用户问题：\n{user_content}"
 
         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):
-        """
-        写入记忆（支持结构化消息）
-
-        Args:
-            storage_type: 存储类型 (neo4j/rag)
-            end_user_id: 终端用户ID
-            user_message: 用户消息内容
-            ai_message: AI 回复内容
-            user_rag_memory_id: RAG 记忆ID
-            actual_end_user_id: 实际用户ID
-            actual_config_id: 配置ID
-
-        逻辑说明：
-        - RAG 模式：组合 user_message 和 ai_message 为字符串格式，保持原有逻辑不变
-        - Neo4j 模式：使用结构化消息列表
-          1. 如果 user_message 和 ai_message 都不为空：创建配对消息 [user, assistant]
-          2. 如果只有 user_message：创建单条用户消息 [user]（用于历史记忆场景）
-          3. 每条消息会被转换为独立的 Chunk，保留 speaker 字段
-        """
-        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 = []
-
-            # 始终添加用户消息（如果不为空）
-            if user_message:
-                structured_messages.append({"role": "user", "content": user_message})
-
-            # 只有当 AI 回复不为空时才添加 assistant 消息
-            if ai_message:
-                structured_messages.append({"role": "assistant", "content": ai_message})
-
-            # 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
-
-            # 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}')
 
     async def chat(
             self,
@@ -281,30 +182,6 @@ class LangChainAgent:
         actual_end_user_id = end_user_id if end_user_id is not None else "unknown"
         logger.info(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}')
         print(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}')
-# # 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)
@@ -325,17 +202,17 @@ class LangChainAgent:
             # 获取最后的 AI 消息
             output_messages = result.get("messages", [])
             content = ""
+            total_tokens = 0
             for msg in reversed(output_messages):
                 if isinstance(msg, AIMessage):
                     content = msg.content
+                    response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
+                    total_tokens = response_meta.get("token_usage", {}).get("total_tokens", 0) if response_meta else 0
                     break
 
             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 write_long_term(storage_type, end_user_id, message_chat, content, user_rag_memory_id, actual_config_id)
-                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,
@@ -343,7 +220,7 @@ class LangChainAgent:
                 "usage": {
                     "prompt_tokens": 0,
                     "completion_tokens": 0,
-                    "total_tokens": 0
+                    "total_tokens": total_tokens
                 }
             }
 
@@ -403,25 +280,7 @@ class LangChainAgent:
                     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:
@@ -437,7 +296,7 @@ class LangChainAgent:
 
             # 统一使用 agent 的 astream_events 实现流式输出
             logger.debug("使用 Agent astream_events 实现流式输出")
-            full_content=''
+            full_content = ''
             try:
                 async for event in self.agent.astream_events(
                     {"messages": messages},
@@ -474,12 +333,17 @@ class LangChainAgent:
                         logger.debug(f"工具调用结束: {event.get('name')}")
                 
                 logger.debug(f"Agent 流式完成，共 {chunk_count} 个事件")
+                # 统计token消耗
+                output_messages = event.get("data", {}).get("output", {}).get("messages", [])
+                for msg in reversed(output_messages):
+                    if isinstance(msg, AIMessage):
+                        response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
+                        total_tokens = response_meta.get("token_usage", {}).get("total_tokens",
+                                                                                0) if response_meta else 0
+                        yield total_tokens
+                        break
                 if memory_flag:
-                    # 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 write_long_term(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id, actual_config_id)
-                    await self.write(storage_type, end_user_id, 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)
-                
             except Exception as e:
                 logger.error(f"Agent astream_events 失败: {str(e)}", exc_info=True)
                 raise

api/app/core/config.py

@@ -157,6 +157,11 @@ class Settings:
         if origin.strip()
     ]
 
+    # Language Configuration
+    # Supported values: "zh" (Chinese), "en" (English)
+    # This controls the language used for memory summary titles and other generated content
+    DEFAULT_LANGUAGE: str = os.getenv("DEFAULT_LANGUAGE", "zh")
+
     # Logging settings
     LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
     LOG_FORMAT: str = os.getenv("LOG_FORMAT", "%(asctime)s - %(name)s - %(levelname)s - %(message)s")

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

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

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

@@ -186,10 +186,11 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
             清理后的数据
         """
         # 需要过滤的字段列表
+        # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
         fields_to_remove = {
             'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids', 
             'expired_at', 'created_at', 'chunk_id', 'id', 'apply_id', 
-            'user_id', 'statement_ids', 'updated_at',"chunk_ids","fact_summary"
+            'user_id', 'statement_ids', 'updated_at',"chunk_ids"  ,"fact_summary"
         }
         
         if isinstance(data, dict):

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

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

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

@@ -1,22 +1,20 @@
 
 import asyncio
+import json
 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, get_db_context
-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.schemas.memory_agent_schema import AgentMemory_Long_Term
 from app.services.memory_config_service import MemoryConfigService
 
+
 warnings.filterwarnings("ignore", category=RuntimeWarning)
 logger = get_agent_logger(__name__)
 
@@ -34,14 +32,6 @@ async def make_write_graph():
         end_user_id: Group identifier
         memory_config: MemoryConfig object containing all configuration
     """
-    # 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")
@@ -51,43 +41,63 @@ async def make_write_graph():
 
     yield graph
 
-
+async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[],memory_config:str='',end_user_id:str='',scope:int=6):
-async def main():
+    from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue,aggregate_judgment
-    """主函数 - 运行工作流"""
+    from app.core.memory.agent.utils.redis_tool import write_store
-    message = "今天周一"
+    write_store.save_session_write(end_user_id,  (langchain_messages))
-    end_user_id = 'new_2025test1103'  # 组ID
-
-
     # 获取数据库会话
-    db_session = next(get_db())
+    with get_db_context() as db_session:
-    config_service = MemoryConfigService(db_session)
+        config_service = MemoryConfigService(db_session)
-    memory_config = config_service.load_memory_config(
+        memory_config = config_service.load_memory_config(
-        config_id=17,  # 改为整数
+            config_id=memory_config,  # 改为整数
-        service_name="MemoryAgentService"
+            service_name="MemoryAgentService"
-    )
+        )
-    try:
+        if long_term_type=='chunk':
-        async with make_write_graph() as graph:
+            '''方案一:对话窗口6轮对话'''
-            config = {"configurable": {"thread_id": end_user_id}}
+            await window_dialogue(end_user_id,langchain_messages,memory_config,scope)
-            # 初始状态 - 包含所有必要字段
+        if long_term_type=='time':
-            initial_state = {"messages": [HumanMessage(content=message)],  "end_user_id": end_user_id, "memory_config": memory_config}
+            """时间"""
-
+            await memory_long_term_storage(end_user_id, memory_config,5)
-            # 获取节点更新信息
+        if  long_term_type=='aggregate':
-            async for update_event in graph.astream(
+            """方案三：聚合判断"""
-                    initial_state,
+            await aggregate_judgment(end_user_id, langchain_messages, memory_config)
-                    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()
 
 
-if __name__ == "__main__":
+
-    import asyncio
+async def write_long_term(storage_type,end_user_id,message_chat,aimessages,user_rag_memory_id,actual_config_id):
-    asyncio.run(main())
+    from app.core.memory.agent.langgraph_graph.routing.write_router import write_rag_agent
+    from app.core.memory.agent.langgraph_graph.routing.write_router import term_memory_save
+    from app.core.memory.agent.langgraph_graph.tools.write_tool import  agent_chat_messages
+    if storage_type == AgentMemory_Long_Term.STORAGE_RAG:
+        await write_rag_agent(end_user_id, message_chat, aimessages, user_rag_memory_id)
+    else:
+        # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
+        CHUNK = AgentMemory_Long_Term.STRATEGY_CHUNK
+        SCOPE = AgentMemory_Long_Term.DEFAULT_SCOPE
+        long_term_messages = await agent_chat_messages(message_chat, aimessages)
+        await long_term_storage(long_term_type=CHUNK, langchain_messages=long_term_messages,
+                                memory_config=actual_config_id, end_user_id=end_user_id, scope=SCOPE)
+        await term_memory_save(long_term_messages, actual_config_id, end_user_id, CHUNK, scope=SCOPE)
+
+# async def main():
+#     """主函数 - 运行工作流"""
+#     langchain_messages = [
+#     {
+#       "role": "user",
+#       "content": "今天周五去爬山"
+#     },
+#     {
+#       "role": "assistant",
+#       "content": "好耶"
+#     }
+#
+#   ]
+#     end_user_id = '837fee1b-04a2-48ee-94d7-211488908940'  # 组ID
+#     memory_config="08ed205c-0f05-49c3-8e0c-a580d28f5fd4"
+#     await long_term_storage(long_term_type="chunk",langchain_messages=langchain_messages,memory_config=memory_config,end_user_id=end_user_id,scope=2)
+#
+#
+#
+# if __name__ == "__main__":
+#     import asyncio
+#     asyncio.run(main())

api/app/core/memory/agent/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/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 +1,36 @@
 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.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
+)
 
 
-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 +41,437 @@ class RedisSessionStore:
         )
         self.uudi = session_id
 
-    def _fix_encoding(self, text):
+    def save_session_write(self, userid: str, messages: str) -> str:
-        """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):
         """
         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
+            messages = serialize_messages(messages)
-            starttime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            session_id = str(uuid.uuid4())
-            key = f"session:{session_id}"  # 使用新生成的 session_id 作为 key
+            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
+            print(f"[save_session_write] 保存结果: {result[0]}, session_id: {session_id}")
+            return session_id
+        except Exception as e:
+            print(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
+            
+            print(f"[get_session_by_userid] userid={userid}, 找到 {len(results)} 条数据")
+            return results
+        except Exception as e:
+            print(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:
+                print(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:
+                print(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)
+            
+            print(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:
+            print(f"[get_all_sessions_by_end_user_id] 查询失败: {e}")
+            import traceback
+            traceback.print_exc()
+            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:
+            print(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, limit=None)
+        print(result_items)
+
+        elapsed_time = time.time() - start_time
+        print(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.uudi = 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.uudi,
+            "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()
+        
+        print(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) -> Union[List[Any], 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:
+                print(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 = deserialize_messages(messages_str)
+                return [int(count), messages]
+            
+            return False
+        except Exception as e:
+            print(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
+                    print(f"[update_sessions_count] 索引键类型错误: {key_type}，删除索引")
+                    self.r.delete(index_key)
+                    print(f"[update_sessions_count] 未找到记录: end_user_id={end_user_id}")
+                    return False
+            except Exception as type_error:
+                print(f"[update_sessions_count] 检查键类型失败: {type_error}")
+            
+            session_id = self.r.get(index_key)
+            
+            if not session_id:
+                print(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', int(new_count))
+            pipe.hset(key, 'messages', messages_str)
+            result = pipe.execute()
+            
+            print(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:
+            print(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 +479,195 @@ 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}")
+            print(f"[save_session] 保存结果: {result[0]}, session_id: {session_id}")
-            return session_id  # 返回新生成的 session_id
+            return session_id
         except Exception as e:
-            print(f"保存会话失败: {e}")
+            print(f"[save_session] 保存会话失败: {e}")
             raise e
 
-    def save_sessions_batch(self, sessions_data):
+    # ==================== 读取操作 ====================
-        """
+    
-        批量写入多条会话数据，返回 session_id 列表
+    def get_session(self, session_id: str) -> Optional[Dict[str, Any]]:
-        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()
-
-            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):
         """
         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
+        获取所有会话数据（不包括 count 和 write 类型）
-        """
+        
-        result_items = []
+        Returns:
-
+            Dict: 所有会话数据，key 为 session_id
-        # 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
         """
         sessions = {}
         for key in self.r.keys('session:*'):
-            sid = key.split(':')[1]
+            # 排除 count 和 write 类型的 key
-            sessions[sid] = self.get_session(sid)
+            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 find_user_apply_group(self, sessionid: str, apply_id: str, 
-    def update_session(self, session_id, field, value):
+                             end_user_id: str) -> List[Dict[str, str]]:
+        """
+        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()
+        
+        keys = self.r.keys('session:*')
+        if not keys:
+            print(f"[find_user_apply_group] 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:
+            # 排除 count 和 write 类型
+            if ':count:' not in key and ':write:' not in key:
+                pipe.hgetall(key)
+        all_data = pipe.execute()
+
+        # 筛选符合条件的数据
+        matched_items = []
+        for data in all_data:
+            if not data:
+                continue
+
+            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
+        print(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
-        优化版本：使用 pipeline 减少网络往返
+        
+        Args:
+            session_id: 会话ID
+            field: 字段名
+            value: 字段值
+            
+        Returns:
+            bool: 是否更新成功
         """
-        key = f"session:{session_id}"
+        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])  # 返回 key 是否存在
+        return bool(results[0])
 
-    # ---------------- 删除 ----------------
+    # ==================== 删除操作 ====================
-    def delete_session(self, session_id):
+    
+    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 = f"session:{session_id}"
+        key = generate_session_key(session_id)
         return self.r.delete(key)
 
-    def delete_all_sessions(self):
+    def delete_all_sessions(self) -> int:
         """
-        删除所有会话
+        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:*')
-        if keys:
+        # 过滤掉 count 和 write 类型
-            return self.r.delete(*keys)
+        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):
+    def delete_duplicate_sessions(self) -> int:
         """
-        删除重复会话数据，条件：
+        删除重复会话数据（不包括 count 和 write 类型）
-        "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
+        条件：sessionid、user_id、end_user_id、messages、aimessages 五个字段都相同的只保留一个
-        优化版本：使用 pipeline 批量操作，确保在1秒内完成
+        
+        Returns:
+            int: 删除的数量
         """
         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
 
-        # 第二步：使用 pipeline 批量获取所有数据
+        # 批量获取所有数据
         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
+        # 识别重复数据
-        seen = {}  # 用字典记录：identifier -> key（保留第一个出现的 key）
+        seen = {}
-        keys_to_delete = []  # 需要删除的 key 列表
+        keys_to_delete = []
 
-        for key, data in zip(keys, all_data, strict=False):
+        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
 
-            # 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', '')
-
             # 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]
@@ -233,79 +681,28 @@ class RedisSessionStore:
         print(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

@@ -4,6 +4,7 @@ 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
 from datetime import datetime
 
@@ -123,23 +124,48 @@ async def write(
     except Exception as e:
         logger.error(f"Error creating indexes: {e}", exc_info=True)
 
+    # 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,
+                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")
+                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)
 

api/app/core/memory/models/__init__.py

@@ -58,6 +58,12 @@ from app.core.memory.models.triplet_models import (
     TripletExtractionResponse,
 )
 
+# Ontology models
+from app.core.memory.models.ontology_models import (
+    OntologyClass,
+    OntologyExtractionResponse,
+)
+
 # Variable configuration models
 from app.core.memory.models.variate_config import (
     StatementExtractionConfig,
@@ -105,6 +111,9 @@ __all__ = [
     "Entity",
     "Triplet",
     "TripletExtractionResponse",
+    # Ontology models
+    "OntologyClass",
+    "OntologyExtractionResponse",
     # Variable configuration
     "StatementExtractionConfig",
     "ForgettingEngineConfig",

api/app/core/memory/models/graph_models.py

@@ -413,7 +413,8 @@ class ExtractedEntityNode(Node):
         description="Entity aliases - alternative names for this entity"
     )
     name_embedding: Optional[List[float]] = Field(default_factory=list, description="Name embedding vector")
-    fact_summary: str = Field(default="", description="Summary of the fact about this entity")
+    # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+    # fact_summary: str = Field(default="", description="Summary of the fact about this entity")
     connect_strength: str = Field(..., description="Strong VS Weak about this entity")
     config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this entity (integer or string)")
     

api/app/core/memory/models/ontology_models.py

@@ -0,0 +1,135 @@
+"""Models for ontology classes and extraction responses.
+
+This module contains Pydantic models for representing extracted ontology classes
+from scenario descriptions, following OWL ontology engineering standards.
+
+Classes:
+    OntologyClass: Represents an extracted ontology class
+    OntologyExtractionResponse: Response model containing extracted ontology classes
+"""
+
+from typing import List, Optional
+from uuid import uuid4
+
+from pydantic import BaseModel, ConfigDict, Field, field_validator
+
+
+class OntologyClass(BaseModel):
+    """Represents an extracted ontology class from scenario description.
+
+    An ontology class represents an abstract category or concept in a domain,
+    following OWL ontology engineering standards and naming conventions.
+
+    Attributes:
+        id: Unique string identifier for the ontology class
+        name: Name of the class in PascalCase format (e.g., 'MedicalProcedure')
+        name_chinese: Chinese translation of the class name (e.g., '医疗程序')
+        description: Textual description of the class
+        examples: List of concrete instance examples of this class
+        parent_class: Optional name of the parent class in the hierarchy
+        entity_type: Type/category of the entity (e.g., 'Person', 'Organization', 'Concept')
+        domain: Domain this class belongs to (e.g., 'Healthcare', 'Education')
+
+    Config:
+        extra: Ignore extra fields from LLM output
+    """
+    model_config = ConfigDict(extra='ignore')
+    
+    id: str = Field(
+        default_factory=lambda: uuid4().hex,
+        description="Unique identifier for the ontology class"
+    )
+    name: str = Field(
+        ...,
+        description="Name of the class in PascalCase format"
+    )
+    name_chinese: Optional[str] = Field(
+        None,
+        description="Chinese translation of the class name"
+    )
+    description: str = Field(
+        ...,
+        description="Description of the class"
+    )
+    examples: List[str] = Field(
+        default_factory=list,
+        description="List of concrete instance examples"
+    )
+    parent_class: Optional[str] = Field(
+        None,
+        description="Name of the parent class in the hierarchy"
+    )
+    entity_type: str = Field(
+        ...,
+        description="Type/category of the entity"
+    )
+    domain: str = Field(
+        ...,
+        description="Domain this class belongs to"
+    )
+
+    @field_validator('name')
+    @classmethod
+    def validate_pascal_case(cls, v: str) -> str:
+        """Validate that the class name follows PascalCase convention.
+
+        PascalCase rules:
+        - Must start with an uppercase letter
+        - Cannot contain spaces
+        - Should not contain special characters except underscores
+
+        Args:
+            v: The class name to validate
+
+        Returns:
+            The validated class name
+
+        Raises:
+            ValueError: If the name doesn't follow PascalCase convention
+        """
+        if not v:
+            raise ValueError("Class name cannot be empty")
+        
+        if not v[0].isupper():
+            raise ValueError(
+                f"Class name '{v}' must start with an uppercase letter (PascalCase)"
+            )
+        
+        if ' ' in v:
+            raise ValueError(
+                f"Class name '{v}' cannot contain spaces (PascalCase)"
+            )
+        
+        # Check for invalid characters (allow alphanumeric and underscore only)
+        if not all(c.isalnum() or c == '_' for c in v):
+            raise ValueError(
+                f"Class name '{v}' contains invalid characters. "
+                "Only alphanumeric characters and underscores are allowed"
+            )
+        
+        return v
+
+
+class OntologyExtractionResponse(BaseModel):
+    """Response model for ontology extraction from LLM.
+
+    This model represents the structured output from the LLM when
+    extracting ontology classes from scenario descriptions.
+
+    Attributes:
+        classes: List of extracted ontology classes
+        domain: Domain/field the scenario belongs to
+
+    Config:
+        extra: Ignore extra fields from LLM output
+    """
+    model_config = ConfigDict(extra='ignore')
+    
+    classes: List[OntologyClass] = Field(
+        default_factory=list,
+        description="List of extracted ontology classes"
+    )
+    domain: str = Field(
+        ...,
+        description="Domain/field the scenario belongs to"
+    )

api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py

@@ -134,42 +134,45 @@ def _merge_attribute(canonical: ExtractedEntityNode, ent: ExtractedEntityNode):
         if len(desc_b) > len(desc_a):
             canonical.description = desc_b
         # 合并事实摘要：统一保留一个“实体: name”行，来源行去重保序
-        fact_a = getattr(canonical, "fact_summary", "") or ""
+        # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
-        fact_b = getattr(ent, "fact_summary", "") or ""
+        # fact_a = getattr(canonical, "fact_summary", "") or ""
-        def _extract_sources(txt: str) -> List[str]:
+        # fact_b = getattr(ent, "fact_summary", "") or ""
-            sources: List[str] = []
+        # def _extract_sources(txt: str) -> List[str]:
-            if not txt:
+            # sources: List[str] = []
-                return sources
+            # if not txt:
-            for line in str(txt).splitlines():
+                # return sources
-                ln = line.strip()
+            # for line in str(txt).splitlines():
+                # ln = line.strip()
                 # 支持“来源:”或“来源：”前缀
-                m = re.match(r"^来源[:：]\s*(.+)$", ln)
+                # m = re.match(r"^来源[:：]\s*(.+)$", ln)
-                if m:
+                # if m:
-                    content = m.group(1).strip()
+                    # content = m.group(1).strip()
-                    if content:
+                    # if content:
-                        sources.append(content)
+                        # sources.append(content)
             # 如果不存在“来源”前缀，则将整体文本视为一个来源片段，避免信息丢失
-            if not sources and txt.strip():
+            # if not sources and txt.strip():
-                sources.append(txt.strip())
+                # sources.append(txt.strip())
-            return sources
+            # return sources
         try:
-            src_a = _extract_sources(fact_a)
+            #     src_a = _extract_sources(fact_a)
-            src_b = _extract_sources(fact_b)
+            #     src_b = _extract_sources(fact_b)
-            seen = set()
+            #     seen = set()
-            merged_sources: List[str] = []
+            #     merged_sources: List[str] = []
-            for s in src_a + src_b:
+            #     for s in src_a + src_b:
-                if s and s not in seen:
+            #         if s and s not in seen:
-                    seen.add(s)
+            #             seen.add(s)
-                    merged_sources.append(s)
+            #             merged_sources.append(s)
-            if merged_sources:
+            #     if merged_sources:
-                name_line = f"实体: {getattr(canonical, 'name', '')}".strip()
+            #         name_line = f"实体: {getattr(canonical, 'name', '')}".strip()
-                canonical.fact_summary = "\n".join([name_line] + [f"来源: {s}" for s in merged_sources])
+            #         canonical.fact_summary = "\n".join([name_line] + [f"来源: {s}" for s in merged_sources])
-            elif fact_b and not fact_a:
+            #     elif fact_b and not fact_a:
-                canonical.fact_summary = fact_b
+            #         canonical.fact_summary = fact_b
+            pass
         except Exception:
             # 兜底：若解析失败，保留较长文本
-            if len(fact_b) > len(fact_a):
+            # if len(fact_b) > len(fact_a):
-                canonical.fact_summary = fact_b
+            #     canonical.fact_summary = fact_b
+            pass
     except Exception:
         pass
 

api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py

@@ -145,10 +145,13 @@ def _choose_canonical(a: ExtractedEntityNode, b: ExtractedEntityNode) -> int: #
      # 2. 第二优先级：按“描述+事实摘要”的总长度排序（内容越长，信息越完整）
     desc_a = (getattr(a, "description", "") or "")
     desc_b = (getattr(b, "description", "") or "")
-    fact_a = (getattr(a, "fact_summary", "") or "")
+    # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
-    fact_b = (getattr(b, "fact_summary", "") or "")
+    # fact_a = (getattr(a, "fact_summary", "") or "")
-    score_a = len(desc_a) + len(fact_a)
+    # fact_b = (getattr(b, "fact_summary", "") or "")
-    score_b = len(desc_b) + len(fact_b)
+    # score_a = len(desc_a) + len(fact_a)
+    # score_b = len(desc_b) + len(fact_b)
+    score_a = len(desc_a)
+    score_b = len(desc_b)
     if score_a != score_b:
         return 0 if score_a >= score_b else 1
     return 0
@@ -189,7 +192,8 @@ async def _judge_pair(
         "entity_type": getattr(a, "entity_type", None),
         "description": getattr(a, "description", None),
         "aliases": getattr(a, "aliases", None) or [],
-        "fact_summary": getattr(a, "fact_summary", None),
+        # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+        # "fact_summary": getattr(a, "fact_summary", None),
         "connect_strength": getattr(a, "connect_strength", None),
     }
     entity_b = {
@@ -197,7 +201,8 @@ async def _judge_pair(
         "entity_type": getattr(b, "entity_type", None),
         "description": getattr(b, "description", None),
         "aliases": getattr(b, "aliases", None) or [],
-        "fact_summary": getattr(b, "fact_summary", None),
+        # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+        # "fact_summary": getattr(b, "fact_summary", None),
         "connect_strength": getattr(b, "connect_strength", None),
     }
  # 5. 渲染LLM提示词（用工具函数填充模板，包含实体信息、上下文、输出格式）
@@ -248,7 +253,8 @@ async def _judge_pair_disamb(
         "entity_type": getattr(a, "entity_type", None),
         "description": getattr(a, "description", None),
         "aliases": getattr(a, "aliases", None) or [],
-        "fact_summary": getattr(a, "fact_summary", None),
+        # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+        # "fact_summary": getattr(a, "fact_summary", None),
         "connect_strength": getattr(a, "connect_strength", None),
     }
     entity_b = {
@@ -256,7 +262,8 @@ async def _judge_pair_disamb(
         "entity_type": getattr(b, "entity_type", None),
         "description": getattr(b, "description", None),
         "aliases": getattr(b, "aliases", None) or [],
-        "fact_summary": getattr(b, "fact_summary", None),
+        # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+        # "fact_summary": getattr(b, "fact_summary", None),
         "connect_strength": getattr(b, "connect_strength", None),
     }
     prompt = render_entity_dedup_prompt(

api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py

@@ -72,7 +72,8 @@ def _row_to_entity(row: Dict[str, Any]) -> ExtractedEntityNode:
         description=row.get("description") or "",
         aliases=row.get("aliases") or [],
         name_embedding=row.get("name_embedding") or [],
-        fact_summary=row.get("fact_summary") or "",
+        # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+        # fact_summary=row.get("fact_summary") or "",
         connect_strength=row.get("connect_strength") or "",
     )
 

api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py

@@ -1085,7 +1085,8 @@ class ExtractionOrchestrator:
                                     entity_type=getattr(entity, 'type', 'unknown'),  # 使用 type 而不是 entity_type
                                     description=getattr(entity, 'description', ''),  # 添加必需的 description 字段
                                     example=getattr(entity, 'example', ''),  # 新增：传递示例字段
-                                    fact_summary=getattr(entity, 'fact_summary', ''),  # 添加必需的 fact_summary 字段
+                                    # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+                                    # fact_summary=getattr(entity, 'fact_summary', ''),  # 添加必需的 fact_summary 字段
                                     connect_strength=entity_connect_strength if entity_connect_strength is not None else 'Strong',  # 添加必需的 connect_strength 字段
                                     aliases=getattr(entity, 'aliases', []) or [],  # 传递从三元组提取阶段获取的aliases
                                     name_embedding=getattr(entity, 'name_embedding', None),

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/__init__.py

@@ -8,4 +8,5 @@
 - TemporalExtractor: 时间信息提取
 - EmbeddingGenerator: 嵌入向量生成
 - MemorySummaryGenerator: 记忆摘要生成
+- OntologyExtractor: 本体类提取
 """

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py

@@ -14,6 +14,34 @@ from pydantic import Field
 
 logger = get_memory_logger(__name__)
 
+# 支持的语言列表和默认回退值
+SUPPORTED_LANGUAGES = {"zh", "en"}
+FALLBACK_LANGUAGE = "en"
+
+
+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
+        
+    Returns:
+        有效的语言代码（"zh" 或 "en"）
+    """
+    if language is None:
+        return FALLBACK_LANGUAGE
+    
+    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 '{FALLBACK_LANGUAGE}'。"
+        f"支持的语言: {SUPPORTED_LANGUAGES}"
+    )
+    return FALLBACK_LANGUAGE
+
 
 class MemorySummaryResponse(RobustLLMResponse):
     """Structured response for summary generation per chunk.
@@ -31,7 +59,8 @@ class MemorySummaryResponse(RobustLLMResponse):
 
 async def generate_title_and_type_for_summary(
     content: str,
-    llm_client
+    llm_client,
+    language: str = None
 ) -> Tuple[str, str]:
     """
     为MemorySummary生成标题和类型
@@ -41,11 +70,18 @@ async def generate_title_and_type_for_summary(
     Args:
         content: Summary的内容文本
         llm_client: LLM客户端实例
+        language: 生成标题使用的语言 ("zh" 中文, "en" 英文)，如果为None则从配置读取
         
     Returns:
         (标题, 类型)元组
     """
     from app.core.memory.utils.prompt.prompt_utils import render_episodic_title_and_type_prompt
+    from app.core.config import settings
+    
+    # 如果没有指定语言，从配置中读取，并校验有效性
+    if language is None:
+        language = settings.DEFAULT_LANGUAGE
+    language = validate_language(language)
     
     # 定义有效的类型集合
     VALID_TYPES = {
@@ -57,13 +93,19 @@ async def generate_title_and_type_for_summary(
     }
     DEFAULT_TYPE = "conversation"  # 默认类型
     
+    # 根据语言设置默认标题
+    DEFAULT_TITLE = "空内容" if language == "zh" else "Empty Content"
+    PARSE_ERROR_TITLE = "解析失败" if language == "zh" else "Parse Failed"
+    ERROR_TITLE = "错误" if language == "zh" else "Error"
+    UNKNOWN_TITLE = "未知标题" if language == "zh" else "Unknown Title"
+    
     try:
         if not content:
-            logger.warning("content为空，无法生成标题和类型")
+            logger.warning(f"content为空，无法生成标题和类型 (language={language})")
-            return ("空内容", DEFAULT_TYPE)
+            return (DEFAULT_TITLE, DEFAULT_TYPE)
         
-        # 1. 渲染Jinja2提示词模板
+        # 1. 渲染Jinja2提示词模板，传递语言参数
-        prompt = await render_episodic_title_and_type_prompt(content)
+        prompt = await render_episodic_title_and_type_prompt(content, language=language)
         
         # 2. 调用LLM生成标题和类型
         messages = [
@@ -102,7 +144,7 @@ async def generate_title_and_type_for_summary(
             json_str = json_str.strip()
             
             result_data = json.loads(json_str)
-            title = result_data.get("title", "未知标题")
+            title = result_data.get("title", UNKNOWN_TITLE)
             episodic_type_raw = result_data.get("type", DEFAULT_TYPE)
             
             # 5. 校验和归一化类型
@@ -130,16 +172,16 @@ async def generate_title_and_type_for_summary(
                     f"已归一化为 '{episodic_type}'"
                 )
             
-            logger.info(f"成功生成标题和类型: title={title}, type={episodic_type}")
+            logger.info(f"成功生成标题和类型 (language={language}): title={title}, type={episodic_type}")
             return (title, episodic_type)
             
         except json.JSONDecodeError:
-            logger.error(f"无法解析LLM响应为JSON: {full_response}")
+            logger.error(f"无法解析LLM响应为JSON (language={language}): {full_response}")
-            return ("解析失败", DEFAULT_TYPE)
+            return (PARSE_ERROR_TITLE, DEFAULT_TYPE)
         
     except Exception as e:
-        logger.error(f"生成标题和类型时出错: {str(e)}", exc_info=True)
+        logger.error(f"生成标题和类型时出错 (language={language}): {str(e)}", exc_info=True)
-        return ("错误", DEFAULT_TYPE)
+        return (ERROR_TITLE, DEFAULT_TYPE)
 
 async def _process_chunk_summary(
     dialog: DialogData,
@@ -153,11 +195,16 @@ async def _process_chunk_summary(
         return None
 
     try:
+        # 从配置中获取语言设置（只获取一次，复用），并校验有效性
+        from app.core.config import settings
+        language = validate_language(settings.DEFAULT_LANGUAGE)
+        
         # Render prompt via Jinja2 for a single chunk
         prompt_content = await render_memory_summary_prompt(
             chunk_texts=chunk.content,
             json_schema=MemorySummaryResponse.model_json_schema(),
             max_words=200,
+            language=language,
         )
 
         messages = [
@@ -178,9 +225,10 @@ async def _process_chunk_summary(
         try:
             title, episodic_type = await generate_title_and_type_for_summary(
                 content=summary_text,
-                llm_client=llm_client
+                llm_client=llm_client,
+                language=language
             )
-            logger.info(f"Generated title and type for MemorySummary: title={title}, type={episodic_type}")
+            logger.info(f"Generated title and type for MemorySummary (language={language}): title={title}, type={episodic_type}")
         except Exception as e:
             logger.warning(f"Failed to generate title and type for chunk {chunk.id}: {e}")
             # Continue without title and type

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/ontology_extraction.py

@@ -0,0 +1,482 @@
+"""Ontology class extraction from scenario descriptions using LLM.
+
+This module provides the OntologyExtractor class for extracting ontology classes
+from natural language scenario descriptions. It uses LLM-driven extraction combined
+with two-layer validation (string validation + OWL semantic validation).
+
+Classes:
+    OntologyExtractor: Extracts ontology classes from scenario descriptions
+"""
+
+import asyncio
+import logging
+import time
+from typing import List, Optional
+
+from app.core.memory.llm_tools.openai_client import OpenAIClient
+from app.core.memory.models.ontology_models import (
+    OntologyClass,
+    OntologyExtractionResponse,
+)
+from app.core.memory.utils.validation.ontology_validator import OntologyValidator
+from app.core.memory.utils.validation.owl_validator import OWLValidator
+from app.core.memory.utils.prompt.prompt_utils import render_ontology_extraction_prompt
+
+
+logger = logging.getLogger(__name__)
+
+
+class OntologyExtractor:
+    """Extractor for ontology classes from scenario descriptions.
+    
+    This extractor uses LLM to identify abstract classes and concepts from
+    natural language scenario descriptions, following OWL ontology engineering
+    standards. It performs two-layer validation:
+    1. String validation (naming conventions, reserved words, duplicates)
+    2. OWL semantic validation (consistency checking, circular inheritance)
+    
+    Attributes:
+        llm_client: OpenAI client for LLM calls
+        validator: String validator for class names and descriptions
+        owl_validator: OWL validator for semantic validation
+    """
+    
+    def __init__(self, llm_client: OpenAIClient):
+        """Initialize the OntologyExtractor.
+        
+        Args:
+            llm_client: OpenAIClient instance for LLM processing
+        """
+        self.llm_client = llm_client
+        self.validator = OntologyValidator()
+        self.owl_validator = OWLValidator()
+        
+        logger.info("OntologyExtractor initialized")
+    
+    async def extract_ontology_classes(
+        self,
+        scenario: str,
+        domain: Optional[str] = None,
+        max_classes: int = 15,
+        min_classes: int = 5,
+        enable_owl_validation: bool = True,
+        llm_temperature: float = 0.3,
+        llm_max_tokens: int = 2000,
+        max_description_length: int = 500,
+        timeout: Optional[float] = None,
+    ) -> OntologyExtractionResponse:
+        """Extract ontology classes from a scenario description.
+        
+        This is the main extraction method that orchestrates the entire process:
+        1. Call LLM to extract ontology classes
+        2. Perform first-layer validation (string validation and cleaning)
+        3. Perform second-layer validation (OWL semantic validation)
+        4. Filter invalid classes based on validation errors
+        5. Return validated ontology classes
+        
+        Args:
+            scenario: Natural language scenario description
+            domain: Optional domain hint (e.g., "Healthcare", "Education")
+            max_classes: Maximum number of classes to extract (default: 15)
+            min_classes: Minimum number of classes to extract (default: 5)
+            enable_owl_validation: Whether to enable OWL validation (default: True)
+            llm_temperature: LLM temperature parameter (default: 0.3)
+            llm_max_tokens: LLM max tokens parameter (default: 2000)
+            max_description_length: Maximum description length (default: 500)
+            timeout: Optional timeout in seconds for LLM call (default: None, no timeout)
+            
+        Returns:
+            OntologyExtractionResponse containing validated ontology classes
+            
+        Raises:
+            ValueError: If scenario is empty or invalid
+            asyncio.TimeoutError: If extraction times out
+            
+        Examples:
+            >>> extractor = OntologyExtractor(llm_client)
+            >>> response = await extractor.extract_ontology_classes(
+            ...     scenario="A hospital manages patient records...",
+            ...     domain="Healthcare",
+            ...     max_classes=10,
+            ...     timeout=30.0
+            ... )
+            >>> len(response.classes)
+            7
+        """
+        # Start timing
+        start_time = time.time()
+        
+        # Validate input
+        if not scenario or not scenario.strip():
+            logger.error("Scenario description is empty")
+            raise ValueError("Scenario description cannot be empty")
+        
+        scenario = scenario.strip()
+        
+        logger.info(
+            f"Starting ontology extraction - scenario_length={len(scenario)}, "
+            f"domain={domain}, max_classes={max_classes}, min_classes={min_classes}, "
+            f"timeout={timeout}"
+        )
+        
+        try:
+            # Step 1: Call LLM for extraction with timeout
+            logger.info("Step 1: Calling LLM for ontology extraction")
+            llm_start_time = time.time()
+            
+            if timeout is not None:
+                # Wrap LLM call with timeout
+                try:
+                    response = await asyncio.wait_for(
+                        self._call_llm_for_extraction(
+                            scenario=scenario,
+                            domain=domain,
+                            max_classes=max_classes,
+                            llm_temperature=llm_temperature,
+                            llm_max_tokens=llm_max_tokens,
+                        ),
+                        timeout=timeout
+                    )
+                except asyncio.TimeoutError:
+                    llm_duration = time.time() - llm_start_time
+                    logger.error(
+                        f"LLM extraction timed out after {timeout} seconds "
+                        f"(actual duration: {llm_duration:.2f}s)"
+                    )
+                    # Return empty response on timeout
+                    return OntologyExtractionResponse(
+                        classes=[],
+                        domain=domain or "Unknown",
+                    )
+            else:
+                # No timeout specified, call directly
+                response = await self._call_llm_for_extraction(
+                    scenario=scenario,
+                    domain=domain,
+                    max_classes=max_classes,
+                    llm_temperature=llm_temperature,
+                    llm_max_tokens=llm_max_tokens,
+                )
+            
+            llm_duration = time.time() - llm_start_time
+            logger.info(
+                f"LLM returned {len(response.classes)} classes in {llm_duration:.2f}s"
+            )
+            
+            # Step 2: First-layer validation (string validation and cleaning)
+            logger.info("Step 2: Performing first-layer validation (string validation)")
+            validation_start_time = time.time()
+            
+            response = self._validate_and_clean(
+                response=response,
+                max_description_length=max_description_length,
+            )
+            
+            validation_duration = time.time() - validation_start_time
+            logger.info(
+                f"After first-layer validation: {len(response.classes)} classes remain "
+                f"(validation took {validation_duration:.2f}s)"
+            )
+            
+            # Check if we have enough classes after first-layer validation
+            if len(response.classes) < min_classes:
+                logger.warning(
+                    f"Only {len(response.classes)} classes remain after validation, "
+                    f"which is below minimum of {min_classes}"
+                )
+            
+            # Step 3: Second-layer validation (OWL semantic validation)
+            if enable_owl_validation and response.classes:
+                logger.info("Step 3: Performing second-layer validation (OWL validation)")
+                owl_start_time = time.time()
+                
+                is_valid, errors, world = self.owl_validator.validate_ontology_classes(
+                    classes=response.classes,
+                )
+                
+                owl_duration = time.time() - owl_start_time
+                
+                if not is_valid:
+                    logger.warning(
+                        f"OWL validation found {len(errors)} issues in {owl_duration:.2f}s: {errors}"
+                    )
+                    
+                    # Filter invalid classes based on errors
+                    response = self._filter_invalid_classes(
+                        response=response,
+                        errors=errors,
+                    )
+                    
+                    logger.info(
+                        f"After second-layer validation: {len(response.classes)} classes remain"
+                    )
+                else:
+                    logger.info(f"OWL validation passed successfully in {owl_duration:.2f}s")
+            else:
+                if not enable_owl_validation:
+                    logger.info("Step 3: OWL validation disabled, skipping")
+                else:
+                    logger.info("Step 3: No classes to validate, skipping OWL validation")
+            
+            # Calculate total duration
+            total_duration = time.time() - start_time
+            
+            # Log extraction statistics
+            logger.info(
+                f"Ontology extraction completed - "
+                f"final_class_count={len(response.classes)}, "
+                f"domain={response.domain}, "
+                f"total_duration={total_duration:.2f}s, "
+                f"llm_duration={llm_duration:.2f}s"
+            )
+            
+            return response
+            
+        except asyncio.TimeoutError:
+            # Re-raise timeout errors
+            total_duration = time.time() - start_time
+            logger.error(
+                f"Ontology extraction timed out after {timeout} seconds "
+                f"(total duration: {total_duration:.2f}s)",
+                exc_info=True
+            )
+            raise
+        except Exception as e:
+            total_duration = time.time() - start_time
+            logger.error(
+                f"Ontology extraction failed after {total_duration:.2f}s: {str(e)}",
+                exc_info=True
+            )
+            # Return empty response on failure
+            return OntologyExtractionResponse(
+                classes=[],
+                domain=domain or "Unknown",
+            )
+    
+    async def _call_llm_for_extraction(
+        self,
+        scenario: str,
+        domain: Optional[str],
+        max_classes: int,
+        llm_temperature: float,
+        llm_max_tokens: int,
+    ) -> OntologyExtractionResponse:
+        """Call LLM to extract ontology classes from scenario.
+        
+        This method renders the extraction prompt using the Jinja2 template
+        and calls the LLM with structured output to get ontology classes.
+        
+        Args:
+            scenario: Scenario description text
+            domain: Optional domain hint
+            max_classes: Maximum number of classes to extract
+            llm_temperature: LLM temperature parameter
+            llm_max_tokens: LLM max tokens parameter
+            
+        Returns:
+            OntologyExtractionResponse from LLM
+            
+        Raises:
+            Exception: If LLM call fails
+        """
+        try:
+            # Render prompt using template
+            prompt_content = await render_ontology_extraction_prompt(
+                scenario=scenario,
+                domain=domain,
+                max_classes=max_classes,
+                json_schema=OntologyExtractionResponse.model_json_schema(),
+            )
+            
+            logger.debug(f"Rendered prompt length: {len(prompt_content)}")
+            
+            # Create messages for LLM
+            messages = [
+                {
+                    "role": "system",
+                    "content": (
+                        "You are an expert ontology engineer specializing in knowledge "
+                        "representation and OWL standards. Extract ontology classes from "
+                        "scenario descriptions following the provided instructions. "
+                        "Return valid JSON conforming to the schema."
+                    ),
+                },
+                {
+                    "role": "user",
+                    "content": prompt_content,
+                },
+            ]
+            
+            # Call LLM with structured output
+            logger.debug(
+                f"Calling LLM with temperature={llm_temperature}, "
+                f"max_tokens={llm_max_tokens}"
+            )
+            
+            response = await self.llm_client.response_structured(
+                messages=messages,
+                response_model=OntologyExtractionResponse,
+            )
+            
+            logger.info(
+                f"LLM extraction successful - extracted {len(response.classes)} classes"
+            )
+            
+            return response
+            
+        except Exception as e:
+            logger.error(
+                f"LLM extraction failed: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def _validate_and_clean(
+        self,
+        response: OntologyExtractionResponse,
+        max_description_length: int,
+    ) -> OntologyExtractionResponse:
+        """Perform first-layer validation: string validation and cleaning.
+        
+        This method validates and cleans the extracted ontology classes:
+        1. Validate class names (PascalCase, no reserved words)
+        2. Sanitize invalid class names
+        3. Truncate long descriptions
+        4. Remove duplicate classes
+        
+        Args:
+            response: OntologyExtractionResponse from LLM
+            max_description_length: Maximum description length
+            
+        Returns:
+            Cleaned OntologyExtractionResponse
+        """
+        if not response.classes:
+            logger.debug("No classes to validate")
+            return response
+        
+        logger.debug(f"Validating {len(response.classes)} classes")
+        
+        validated_classes = []
+        
+        for ontology_class in response.classes:
+            # Validate class name
+            is_valid, error_msg = self.validator.validate_class_name(
+                ontology_class.name
+            )
+            
+            if not is_valid:
+                logger.warning(
+                    f"Invalid class name '{ontology_class.name}': {error_msg}"
+                )
+                
+                # Attempt to sanitize
+                sanitized_name = self.validator.sanitize_class_name(
+                    ontology_class.name
+                )
+                
+                logger.info(
+                    f"Sanitized class name: '{ontology_class.name}' -> '{sanitized_name}'"
+                )
+                
+                # Update class name
+                ontology_class.name = sanitized_name
+                
+                # Re-validate sanitized name
+                is_valid, error_msg = self.validator.validate_class_name(
+                    sanitized_name
+                )
+                
+                if not is_valid:
+                    logger.error(
+                        f"Failed to sanitize class name '{ontology_class.name}': {error_msg}. "
+                        "Skipping this class."
+                    )
+                    continue
+            
+            # Truncate description if too long
+            if ontology_class.description:
+                original_length = len(ontology_class.description)
+                ontology_class.description = self.validator.truncate_description(
+                    ontology_class.description,
+                    max_length=max_description_length,
+                )
+                
+                if len(ontology_class.description) < original_length:
+                    logger.debug(
+                        f"Truncated description for '{ontology_class.name}': "
+                        f"{original_length} -> {len(ontology_class.description)} chars"
+                    )
+            
+            validated_classes.append(ontology_class)
+        
+        # Remove duplicates (case-insensitive)
+        original_count = len(validated_classes)
+        validated_classes = self.validator.remove_duplicates(validated_classes)
+        
+        if len(validated_classes) < original_count:
+            logger.info(
+                f"Removed {original_count - len(validated_classes)} duplicate classes"
+            )
+        
+        # Return cleaned response
+        return OntologyExtractionResponse(
+            classes=validated_classes,
+            domain=response.domain,
+        )
+    
+    def _filter_invalid_classes(
+        self,
+        response: OntologyExtractionResponse,
+        errors: List[str],
+    ) -> OntologyExtractionResponse:
+        """Filter invalid classes based on OWL validation errors.
+        
+        This method analyzes OWL validation errors and removes classes
+        that caused validation failures (e.g., circular inheritance,
+        inconsistencies).
+        
+        Args:
+            response: OntologyExtractionResponse to filter
+            errors: List of error messages from OWL validation
+            
+        Returns:
+            Filtered OntologyExtractionResponse
+        """
+        if not errors:
+            return response
+        
+        logger.debug(f"Filtering classes based on {len(errors)} OWL validation errors")
+        
+        # Extract class names mentioned in errors
+        invalid_class_names = set()
+        
+        for error in errors:
+            # Look for class names in error messages
+            for ontology_class in response.classes:
+                if ontology_class.name in error:
+                    invalid_class_names.add(ontology_class.name)
+                    logger.debug(
+                        f"Class '{ontology_class.name}' marked as invalid due to error: {error}"
+                    )
+        
+        # Filter out invalid classes
+        if invalid_class_names:
+            original_count = len(response.classes)
+            
+            filtered_classes = [
+                c for c in response.classes
+                if c.name not in invalid_class_names
+            ]
+            
+            logger.info(
+                f"Filtered out {original_count - len(filtered_classes)} invalid classes: "
+                f"{invalid_class_names}"
+            )
+            
+            return OntologyExtractionResponse(
+                classes=filtered_classes,
+                domain=response.domain,
+            )
+        
+        return response

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py

@@ -25,6 +25,15 @@ class TripletExtractor:
         """
         self.llm_client = llm_client
 
+    def _get_language(self) -> str:
+        """Get the configured language for entity descriptions
+        
+        Returns:
+            Language code ("zh" or "en")
+        """
+        from app.core.config import settings
+        return settings.DEFAULT_LANGUAGE
+
     async def _extract_triplets(self, statement: Statement, chunk_content: str) -> TripletExtractionResponse:
         """Process a single statement and return extracted triplets and entities"""
         # Render the prompt using helper function
@@ -40,7 +49,8 @@ class TripletExtractor:
             statement=statement.statement,
             chunk_content=chunk_content,
             json_schema=TripletExtractionResponse.model_json_schema(),
-            predicate_instructions=PREDICATE_DEFINITIONS
+            predicate_instructions=PREDICATE_DEFINITIONS,
+            language=self._get_language()
         )
 
         # Create messages for LLM

api/app/core/memory/utils/alias_utils.py

@@ -296,7 +296,9 @@ def resolve_alias_cycles(entities: List[Any], cycles: Dict[str, Set[str]]) -> Li
             key=lambda eid: (
                 _strength_rank(eid),
                 len(getattr(entity_by_id.get(eid), 'description', '') or ''),
-                len(getattr(entity_by_id.get(eid), 'fact_summary', '') or '')
+                # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+                # len(getattr(entity_by_id.get(eid), 'fact_summary', '') or '')
+                0  # 临时占位
             ),
             reverse=True
         )

api/app/core/memory/utils/prompt/prompt_utils.py

@@ -177,7 +177,7 @@ def render_entity_dedup_prompt(
 
 #     Args:
 #         entity_a: Dict of entity A attributes
-async def render_triplet_extraction_prompt(statement: str, chunk_content: str, json_schema: dict, predicate_instructions: dict = None) -> str:
+async def render_triplet_extraction_prompt(statement: str, chunk_content: str, json_schema: dict, predicate_instructions: dict = None, language: str = "zh") -> str:
     """
     Renders the triplet extraction prompt using the extract_triplet.jinja2 template.
 
@@ -186,6 +186,7 @@ async def render_triplet_extraction_prompt(statement: str, chunk_content: str, j
         chunk_content: The content of the chunk to process
         json_schema: JSON schema for the expected output format
         predicate_instructions: Optional predicate instructions
+        language: The language to use for entity descriptions ("zh" for Chinese, "en" for English)
 
     Returns:
         Rendered prompt content as string
@@ -195,7 +196,8 @@ async def render_triplet_extraction_prompt(statement: str, chunk_content: str, j
         statement=statement,
         chunk_content=chunk_content,
         json_schema=json_schema,
-        predicate_instructions=predicate_instructions
+        predicate_instructions=predicate_instructions,
+        language=language
     )
     # 记录渲染结果到提示日志（与示例日志结构一致）
     log_prompt_rendering('triplet extraction', rendered_prompt)
@@ -204,7 +206,8 @@ async def render_triplet_extraction_prompt(statement: str, chunk_content: str, j
         'statement': 'str',
         'chunk_content': 'str',
         'json_schema': 'TripletExtractionResponse.schema',
-        'predicate_instructions': 'PREDICATE_DEFINITIONS'
+        'predicate_instructions': 'PREDICATE_DEFINITIONS',
+        'language': language
     })
 
     return rendered_prompt
@@ -213,6 +216,7 @@ async def render_memory_summary_prompt(
     chunk_texts: str,
     json_schema: dict,
     max_words: int = 200,
+    language: str = "zh",
 ) -> str:
     """
     Renders the memory summary prompt using the memory_summary.jinja2 template.
@@ -221,6 +225,7 @@ async def render_memory_summary_prompt(
         chunk_texts: Concatenated text of conversation chunks
         json_schema: JSON schema for the expected output format
         max_words: Maximum words for the summary
+        language: The language to use for summary generation ("zh" for Chinese, "en" for English)
 
     Returns:
         Rendered prompt content as string.
@@ -230,12 +235,14 @@ async def render_memory_summary_prompt(
         chunk_texts=chunk_texts,
         json_schema=json_schema,
         max_words=max_words,
+        language=language,
     )
     log_prompt_rendering('memory summary', rendered_prompt)
     log_template_rendering('memory_summary.jinja2', {
         'chunk_texts_len': len(chunk_texts or ""),
         'max_words': max_words,
-        'json_schema': 'MemorySummaryResponse.schema'
+        'json_schema': 'MemorySummaryResponse.schema',
+        'language': language
     })
     return rendered_prompt
 
@@ -388,24 +395,65 @@ async def render_memory_insight_prompt(
     return rendered_prompt
 
 
-async def render_episodic_title_and_type_prompt(content: str) -> str:
+async def render_episodic_title_and_type_prompt(content: str, language: str = "zh") -> str:
     """
     Renders the episodic title and type classification prompt using the episodic_type_classification.jinja2 template.
 
     Args:
         content: The content of the episodic memory summary to analyze
+        language: The language to use for title generation ("zh" for Chinese, "en" for English)
 
     Returns:
         Rendered prompt content as string
     """
     template = prompt_env.get_template("episodic_type_classification.jinja2")
-    rendered_prompt = template.render(content=content)
+    rendered_prompt = template.render(content=content, language=language)
     
     # 记录渲染结果到提示日志
     log_prompt_rendering('episodic title and type classification', rendered_prompt)
     # 可选：记录模板渲染信息
     log_template_rendering('episodic_type_classification.jinja2', {
-        'content_len': len(content) if content else 0
+        'content_len': len(content) if content else 0,
+        'language': language
+    })
+    
+    return rendered_prompt
+
+
+async def render_ontology_extraction_prompt(
+    scenario: str,
+    domain: str | None = None,
+    max_classes: int = 15,
+    json_schema: dict | None = None
+) -> str:
+    """
+    Renders the ontology extraction prompt using the extract_ontology.jinja2 template.
+
+    Args:
+        scenario: The scenario description text to extract ontology classes from
+        domain: Optional domain hint for the scenario (e.g., "Healthcare", "Education")
+        max_classes: Maximum number of classes to extract (default: 15)
+        json_schema: JSON schema for the expected output format
+
+    Returns:
+        Rendered prompt content as string
+    """
+    template = prompt_env.get_template("extract_ontology.jinja2")
+    rendered_prompt = template.render(
+        scenario=scenario,
+        domain=domain,
+        max_classes=max_classes,
+        json_schema=json_schema
+    )
+    
+    # 记录渲染结果到提示日志
+    log_prompt_rendering('ontology extraction', rendered_prompt)
+    # 可选：记录模板渲染信息
+    log_template_rendering('extract_ontology.jinja2', {
+        'scenario_len': len(scenario) if scenario else 0,
+        'domain': domain,
+        'max_classes': max_classes,
+        'json_schema': 'OntologyExtractionResponse.schema'
     })
     
     return rendered_prompt

api/app/core/memory/utils/prompt/prompts/entity_dedup.jinja2

@@ -9,7 +9,8 @@
 - 类型: "{{ entity_a.entity_type | default('') }}"
 - 描述: "{{ entity_a.description | default('') }}"
 - 别名: {{ entity_a.aliases | default([]) }}
-- 摘要: "{{ entity_a.fact_summary | default('') }}"
+{# TODO: fact_summary 功能暂时禁用，待后续开发完善后启用 #}
+{# - 摘要: "{{ entity_a.fact_summary | default('') }}" #}
 - 连接强弱: "{{ entity_a.connect_strength | default('') }}"
 
 实体B:
@@ -17,7 +18,8 @@
 - 类型: "{{ entity_b.entity_type | default('') }}"
 - 描述: "{{ entity_b.description | default('') }}"
 - 别名: {{ entity_b.aliases | default([]) }}
-- 摘要: "{{ entity_b.fact_summary | default('') }}"
+{# TODO: fact_summary 功能暂时禁用，待后续开发完善后启用 #}
+{# - 摘要: "{{ entity_b.fact_summary | default('') }}" #}
 - 连接强弱: "{{ entity_b.connect_strength | default('') }}"
 
 上下文：

api/app/core/memory/utils/prompt/prompts/episodic_type_classification.jinja2

@@ -1,8 +1,19 @@
 === Task ===
 Generate a concise title and classify the episodic memory into the most appropriate category.
 
+{% if language == "zh" %}
+**重要：请使用中文生成标题和分类。**
+{% else %}
+**Important: Please generate the title and classification in English.**
+{% endif %}
+
 === Requirements ===
 - Extract a clear, concise title (10-20 characters) that captures the core content
+{% if language == "zh" %}
+- 标题必须使用中文
+{% else %}
+- Title must be in English
+{% endif %}
 - Classify into exactly one category based on the primary theme
 - Be specific and avoid ambiguity
 - Output must be valid JSON conforming to the schema below

api/app/core/memory/utils/prompt/prompts/extract_ontology.jinja2

@@ -0,0 +1,210 @@
+===Task===
+Extract ontology classes from the given scenario description following ontology engineering standards.
+
+===Role===
+You are a professional ontology engineer with expertise in knowledge representation and OWL (Web Ontology Language) standards. Your task is to identify abstract classes and concepts from scenario descriptions, not concrete instances.
+
+===Scenario Description===
+{{ scenario }}
+
+{% if domain -%}
+===Domain Hint===
+This scenario belongs to the **{{ domain }}** domain. Consider domain-specific concepts and terminology when extracting classes.
+{%- endif %}
+
+===Extraction Rules===
+
+**1. Abstract Classes, Not Instances:**
+- Extract abstract categories and concepts (e.g., "MedicalProcedure", "Patient", "Diagnosis")
+- Do NOT extract concrete instances (e.g., "John Smith", "Room 301", "2024-01-15")
+- Think in terms of "types of things" rather than "specific things"
+
+**2. Naming Convention (PascalCase):**
+- Use PascalCase format for the "name" field: start with uppercase letter, capitalize each word, no spaces
+- Examples: "MedicalProcedure", "HealthcareProvider", "DiagnosticTest"
+- Avoid: "medical procedure", "healthcare_provider", "diagnostic-test"
+- Use clear, descriptive names in English
+- Avoid abbreviations unless they are standard in the domain (e.g., "API", "DNA")
+- Provide Chinese translation in the "name_chinese" field (e.g., "医疗程序", "医疗服务提供者", "诊断测试")
+
+**3. Domain Relevance:**
+- Focus on classes that are central to the scenario's domain
+- Prioritize classes that represent key concepts, entities, or relationships
+- Avoid overly generic classes (e.g., "Thing", "Object") unless they have specific domain meaning
+
+**4. Class Quantity:**
+- Extract between 5 and {{ max_classes }} classes
+- Aim for a balanced set covering the main concepts in the scenario
+- Quality over quantity: prefer well-defined classes over exhaustive lists
+
+**5. Clear Descriptions:**
+- Provide concise, informative descriptions in Chinese (max 500 characters)
+- Describe what the class represents, not specific instances
+- Use clear, natural Chinese language that explains the class's role in the domain
+
+**6. Concrete Examples:**
+- Provide 2-5 concrete instance examples in Chinese for each class
+- Examples should be specific, realistic instances of the class
+- Examples help clarify the class's scope and meaning
+- Use natural Chinese language for examples
+- Example format: ["示例1", "示例2", "示例3"]
+
+**7. Class Hierarchy:**
+- Identify parent-child relationships where applicable
+- Use the parent_class field to specify inheritance
+- Parent class must be one of the extracted classes or a standard OWL class
+- Leave parent_class as null for top-level classes
+
+**8. Entity Types:**
+- Classify each class with an appropriate entity_type
+- Common types: "Person", "Organization", "Location", "Event", "Concept", "Process", "Object", "Role"
+- Choose the most specific type that applies
+
+**9. OWL Reserved Words:**
+- Do NOT use OWL reserved words as class names
+- Reserved words include: "Thing", "Nothing", "Class", "Property", "ObjectProperty", "DatatypeProperty", "AnnotationProperty", "Ontology", "Individual", "Literal"
+- If a reserved word is needed, add a domain-specific prefix (e.g., "MedicalClass" instead of "Class")
+
+**10. Language Consistency:**
+- Extract all class names in English (PascalCase format) for the "name" field
+- Provide Chinese translation for class names in the "name_chinese" field
+- Descriptions MUST be in Chinese (中文)
+- Examples MUST be in Chinese (中文)
+- Use clear, natural Chinese language for descriptions and examples
+
+===Examples===
+
+**Example 1 (Healthcare Domain):**
+Scenario: "A hospital manages patient records, schedules appointments, and coordinates medical procedures. Doctors diagnose conditions and prescribe treatments."
+
+Output:
+{
+  "classes": [
+    {
+      "name": "Patient",
+      "name_chinese": "患者",
+      "description": "在医疗机构接受医疗护理或治疗的人",
+      "examples": ["张三", "李四", "患有糖尿病的老年患者"],
+      "parent_class": null,
+      "entity_type": "Person",
+      "domain": "Healthcare"
+    },
+    {
+      "name": "MedicalProcedure",
+      "name_chinese": "医疗程序",
+      "description": "为医疗诊断或治疗而执行的系统性操作流程",
+      "examples": ["手术", "血液检查", "X光检查", "疫苗接种"],
+      "parent_class": null,
+      "entity_type": "Process",
+      "domain": "Healthcare"
+    },
+    {
+      "name": "Diagnosis",
+      "name_chinese": "诊断",
+      "description": "基于症状和检查结果对疾病或状况的识别",
+      "examples": ["糖尿病诊断", "癌症诊断", "流感诊断"],
+      "parent_class": null,
+      "entity_type": "Concept",
+      "domain": "Healthcare"
+    },
+    {
+      "name": "Doctor",
+      "name_chinese": "医生",
+      "description": "诊断和治疗患者的持证医疗专业人员",
+      "examples": ["全科医生", "外科医生", "心脏病专家"],
+      "parent_class": null,
+      "entity_type": "Role",
+      "domain": "Healthcare"
+    },
+    {
+      "name": "Treatment",
+      "name_chinese": "治疗",
+      "description": "为治愈或管理疾病状况而提供的医疗护理或疗法",
+      "examples": ["药物治疗", "物理治疗", "化疗", "手术治疗"],
+      "parent_class": null,
+      "entity_type": "Process",
+      "domain": "Healthcare"
+    }
+  ],
+  "domain": "Healthcare",
+  "namespace": "http://example.org/healthcare#"
+}
+
+**Example 2 (Education Domain):**
+Scenario: "A university offers courses taught by professors. Students enroll in programs, attend lectures, and complete assignments to earn degrees."
+
+Output:
+{
+  "classes": [
+    {
+      "name": "Student",
+      "name_chinese": "学生",
+      "description": "在教育机构注册学习的人",
+      "examples": ["本科生", "研究生", "在职学生"],
+      "parent_class": null,
+      "entity_type": "Role",
+      "domain": "Education"
+    },
+    {
+      "name": "Course",
+      "name_chinese": "课程",
+      "description": "涵盖特定学科或主题的结构化教育课程",
+      "examples": ["计算机科学导论", "微积分I", "世界历史"],
+      "parent_class": null,
+      "entity_type": "Concept",
+      "domain": "Education"
+    },
+    {
+      "name": "Professor",
+      "name_chinese": "教授",
+      "description": "教授课程并进行研究的学术教师",
+      "examples": ["助理教授", "副教授", "正教授"],
+      "parent_class": null,
+      "entity_type": "Role",
+      "domain": "Education"
+    },
+    {
+      "name": "AcademicProgram",
+      "name_chinese": "学术项目",
+      "description": "通向学位或证书的结构化课程体系",
+      "examples": ["理学学士", "文学硕士", "博士项目"],
+      "parent_class": null,
+      "entity_type": "Concept",
+      "domain": "Education"
+    },
+    {
+      "name": "Assignment",
+      "name_chinese": "作业",
+      "description": "分配给学生以评估学习成果的任务或项目",
+      "examples": ["论文", "习题集", "研究报告", "实验报告"],
+      "parent_class": null,
+      "entity_type": "Object",
+      "domain": "Education"
+    },
+    {
+      "name": "Lecture",
+      "name_chinese": "讲座",
+      "description": "由教师进行的教育性演讲或讲座",
+      "examples": ["入门讲座", "客座讲座", "在线讲座"],
+      "parent_class": null,
+      "entity_type": "Event",
+      "domain": "Education"
+    }
+  ],
+  "domain": "Education",
+  "namespace": "http://example.org/education#"
+}
+
+===Output Format===
+
+**JSON Requirements:**
+- Use only ASCII double quotes (") for JSON structure
+- Never use Chinese quotation marks ("") or Unicode quotes
+- Escape quotation marks in text with backslashes (\")
+- Ensure proper string closure and comma separation
+- No line breaks within JSON string values
+- All class names must be in PascalCase format
+- All class names must be unique (case-insensitive)
+- Extract between 5 and {{ max_classes }} classes
+
+{{ json_schema }}

api/app/core/memory/utils/prompt/prompts/extract_triplet.jinja2

@@ -5,6 +5,12 @@
 ===Task===
 Extract entities and knowledge triplets from the given statement.
 
+{% if language == "zh" %}
+**重要：请使用中文生成实体描述（description）和示例（example）。**
+{% else %}
+**Important: Please generate entity descriptions and examples in English.**
+{% endif %}
+
 ===Inputs===
 **Chunk Content:** "{{ chunk_content }}"
 **Statement:** "{{ statement }}"
@@ -13,6 +19,13 @@ Extract entities and knowledge triplets from the given statement.
 
 **Entity Extraction:**
 - Extract entities with their types, context-independent descriptions, **concise examples**, aliases, and semantic memory classification
+{% if language == "zh" %}
+- **实体描述（description）必须使用中文**
+- **示例（example）必须使用中文**
+{% else %}
+- **Entity descriptions must be in English**
+- **Examples must be in English**
+{% endif %}
 - **Semantic Memory Classification (is_explicit_memory):**
   * Set to `true` if the entity represents **explicit/semantic memory**:
     - **Concepts:** "Machine Learning", "Photosynthesis", "Democracy", "人工智能", "光合作用", "民主"
@@ -334,9 +347,11 @@ Output:
 - Escape quotation marks in text with backslashes (\")
 - Ensure proper string closure and comma separation
 - No line breaks within JSON string values
-- The output language should ALWAYS match the input language
+{% if language == "zh" %}
-- If input is in English, extract statements in English
+- **语言要求：实体描述（description）和示例（example）必须使用中文**
-- If input is in Chinese, extract statements in Chinese
+{% else %}
+- **Language Requirement: Entity descriptions and examples must be in English**
+{% endif %}
 - Preserve the original language and do not translate
 
 {{ json_schema }}

api/app/core/memory/utils/prompt/prompts/memory_summary.jinja2

@@ -5,10 +5,21 @@
 === Task ===
 Summarize the provided conversation chunks into a concise Memory summary.
 
+{% if language == "zh" %}
+**重要：请使用中文生成摘要内容。**
+{% else %}
+**Important: Please generate the summary content in English.**
+{% endif %}
+
 === Requirements ===
 - Focus on factual statements, user preferences, relationships, and salient temporal context.
 - Avoid repetition and filler; be specific.
 - Keep it under {{ max_words or 200 }} words.
+{% if language == "zh" %}
+- 摘要内容必须使用中文
+{% else %}
+- Summary content must be in English
+{% endif %}
 - Output must be valid JSON conforming to the schema below.
 
 === Input ===
@@ -24,6 +35,11 @@ Summarize the provided conversation chunks into a concise Memory summary.
 4. Do not include line breaks within JSON string values
 5. Example of proper escaping: "statement": "张曼婷说：\"我很喜欢这本书。\""
 
-The output language should always be the same as the input language.
+{% if language == "zh" %}
+**语言要求：输出内容必须使用中文。**
+{% else %}
+**Language Requirement: The output content must be in English.**
+{% endif %}
+
 Return only a list of extracted labelled statements in the JSON ARRAY of objects that match the schema below:
 {{ json_schema }}

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

@@ -0,0 +1,10 @@
+"""Validation utilities for ontology extraction.
+
+This module provides validation classes for ontology class names,
+descriptions, and OWL compliance checking.
+"""
+
+from .ontology_validator import OntologyValidator
+from .owl_validator import OWLValidator
+
+__all__ = ['OntologyValidator', 'OWLValidator']

api/app/core/memory/utils/validation/ontology_validator.py

@@ -0,0 +1,268 @@
+"""String validation for ontology class names and descriptions.
+
+This module provides the OntologyValidator class for validating and sanitizing
+ontology class names according to OWL standards and naming conventions.
+
+Classes:
+    OntologyValidator: Validates class names, removes duplicates, and truncates descriptions
+"""
+
+import logging
+import re
+from typing import List, Tuple
+
+from app.core.memory.models.ontology_models import OntologyClass
+
+
+logger = logging.getLogger(__name__)
+
+
+class OntologyValidator:
+    """Validator for ontology class names and descriptions.
+    
+    This validator performs string-level validation including:
+    - PascalCase naming convention validation
+    - OWL reserved word checking
+    - Duplicate class name removal
+    - Description length truncation
+    
+    Attributes:
+        OWL_RESERVED_WORDS: Set of OWL reserved words that cannot be used as class names
+    """
+    
+    # OWL reserved words that cannot be used as class names
+    OWL_RESERVED_WORDS = {
+        'Thing', 'Nothing', 'Class', 'Property',
+        'ObjectProperty', 'DatatypeProperty', 'FunctionalProperty',
+        'InverseFunctionalProperty', 'TransitiveProperty', 'SymmetricProperty',
+        'AsymmetricProperty', 'ReflexiveProperty', 'IrreflexiveProperty',
+        'Restriction', 'Ontology', 'Individual', 'NamedIndividual',
+        'Annotation', 'AnnotationProperty', 'Axiom',
+        'AllDifferent', 'AllDisjointClasses', 'AllDisjointProperties',
+        'Datatype', 'DataRange', 'Literal',
+        'DeprecatedClass', 'DeprecatedProperty',
+        'Imports', 'IncompatibleWith', 'PriorVersion', 'VersionInfo',
+        'BackwardCompatibleWith', 'OntologyProperty',
+    }
+    
+    def validate_class_name(self, name: str) -> Tuple[bool, str]:
+        """Validate that a class name follows OWL naming conventions.
+        
+        Validation rules:
+        1. Must not be empty
+        2. Must start with an uppercase letter (PascalCase)
+        3. Cannot contain spaces
+        4. Can only contain alphanumeric characters and underscores
+        5. Cannot be an OWL reserved word
+        
+        Args:
+            name: The class name to validate
+            
+        Returns:
+            Tuple of (is_valid, error_message)
+            - is_valid: True if the name is valid, False otherwise
+            - error_message: Empty string if valid, error description if invalid
+            
+        Examples:
+            >>> validator = OntologyValidator()
+            >>> validator.validate_class_name("MedicalProcedure")
+            (True, "")
+            >>> validator.validate_class_name("medical procedure")
+            (False, "Class name 'medical procedure' cannot contain spaces")
+            >>> validator.validate_class_name("Thing")
+            (False, "Class name 'Thing' is an OWL reserved word")
+        """
+        logger.debug(f"Validating class name: '{name}'")
+        
+        # Check if empty
+        if not name or not name.strip():
+            error_msg = "Class name cannot be empty"
+            logger.warning(f"Validation failed: {error_msg}")
+            return False, error_msg
+        
+        name = name.strip()
+        
+        # Check if it's an OWL reserved word
+        if name in self.OWL_RESERVED_WORDS:
+            error_msg = f"Class name '{name}' is an OWL reserved word"
+            logger.warning(f"Validation failed: {error_msg}")
+            return False, error_msg
+        
+        # Check if starts with uppercase letter
+        if not name[0].isupper():
+            error_msg = f"Class name '{name}' must start with an uppercase letter (PascalCase)"
+            logger.warning(f"Validation failed: {error_msg}")
+            return False, error_msg
+        
+        # Check for spaces
+        if ' ' in name:
+            error_msg = f"Class name '{name}' cannot contain spaces"
+            logger.warning(f"Validation failed: {error_msg}")
+            return False, error_msg
+        
+        # Check for invalid characters (only alphanumeric and underscore allowed)
+        if not re.match(r'^[A-Za-z0-9_]+$', name):
+            error_msg = f"Class name '{name}' contains invalid characters. Only alphanumeric characters and underscores are allowed"
+            logger.warning(f"Validation failed: {error_msg}")
+            return False, error_msg
+        
+        logger.debug(f"Class name '{name}' is valid")
+        return True, ""
+    
+    def sanitize_class_name(self, name: str) -> str:
+        """Attempt to sanitize an invalid class name into a valid format.
+        
+        Sanitization steps:
+        1. Strip whitespace
+        2. Remove invalid characters
+        3. Replace spaces with empty string (PascalCase)
+        4. Capitalize first letter of each word
+        5. If result is empty or starts with number, prefix with 'Class'
+        
+        Args:
+            name: The class name to sanitize
+            
+        Returns:
+            Sanitized class name that should pass validation
+            
+        Examples:
+            >>> validator = OntologyValidator()
+            >>> validator.sanitize_class_name("medical procedure")
+            'MedicalProcedure'
+            >>> validator.sanitize_class_name("patient-record")
+            'PatientRecord'
+            >>> validator.sanitize_class_name("123invalid")
+            'Class123Invalid'
+        """
+        logger.debug(f"Sanitizing class name: '{name}'")
+        
+        if not name or not name.strip():
+            logger.warning("Empty class name provided for sanitization, returning 'UnnamedClass'")
+            return "UnnamedClass"
+        
+        # Strip whitespace
+        name = name.strip()
+        original_name = name
+        
+        # Split on spaces, hyphens, and underscores, then capitalize each word
+        words = re.split(r'[\s\-_]+', name)
+        
+        # Capitalize first letter of each word and keep rest as is
+        sanitized_words = []
+        for word in words:
+            if word:
+                # Remove non-alphanumeric characters except underscore
+                clean_word = re.sub(r'[^A-Za-z0-9_]', '', word)
+                if clean_word:
+                    # Capitalize first letter
+                    sanitized_words.append(clean_word[0].upper() + clean_word[1:])
+        
+        # Join words
+        sanitized = ''.join(sanitized_words)
+        
+        # If empty or starts with number, prefix with 'Class'
+        if not sanitized or sanitized[0].isdigit():
+            sanitized = 'Class' + sanitized
+            logger.info(f"Prefixed class name with 'Class': '{original_name}' -> '{sanitized}'")
+        
+        # If it's a reserved word, append 'Class' suffix
+        if sanitized in self.OWL_RESERVED_WORDS:
+            sanitized = sanitized + 'Class'
+            logger.info(f"Appended 'Class' suffix to reserved word: '{original_name}' -> '{sanitized}'")
+        
+        logger.info(f"Sanitized class name: '{original_name}' -> '{sanitized}'")
+        return sanitized
+    
+    def remove_duplicates(self, classes: List[OntologyClass]) -> List[OntologyClass]:
+        """Remove duplicate ontology classes based on case-insensitive name comparison.
+        
+        When duplicates are found, keeps the first occurrence and discards subsequent ones.
+        Comparison is case-insensitive to catch variations like 'Patient' and 'patient'.
+        
+        Args:
+            classes: List of OntologyClass objects
+            
+        Returns:
+            List of OntologyClass objects with duplicates removed
+            
+        Examples:
+            >>> validator = OntologyValidator()
+            >>> classes = [
+            ...     OntologyClass(name="Patient", description="A patient", entity_type="Person", domain="Healthcare"),
+            ...     OntologyClass(name="patient", description="Another patient", entity_type="Person", domain="Healthcare"),
+            ...     OntologyClass(name="Doctor", description="A doctor", entity_type="Person", domain="Healthcare"),
+            ... ]
+            >>> unique = validator.remove_duplicates(classes)
+            >>> len(unique)
+            2
+            >>> [c.name for c in unique]
+            ['Patient', 'Doctor']
+        """
+        if not classes:
+            logger.debug("No classes to check for duplicates")
+            return classes
+        
+        logger.debug(f"Checking {len(classes)} classes for duplicates")
+        
+        seen_names = set()
+        unique_classes = []
+        duplicates_found = []
+        
+        for ontology_class in classes:
+            # Use lowercase for comparison
+            name_lower = ontology_class.name.lower()
+            
+            if name_lower not in seen_names:
+                seen_names.add(name_lower)
+                unique_classes.append(ontology_class)
+            else:
+                duplicates_found.append(ontology_class.name)
+                logger.debug(f"Duplicate class found and removed: '{ontology_class.name}'")
+        
+        if duplicates_found:
+            logger.info(
+                f"Removed {len(duplicates_found)} duplicate classes: {duplicates_found}"
+            )
+        else:
+            logger.debug("No duplicate classes found")
+        
+        return unique_classes
+    
+    def truncate_description(self, description: str, max_length: int = 500) -> str:
+        """Truncate a description to a maximum length.
+        
+        If the description exceeds max_length, it will be truncated and
+        an ellipsis (...) will be appended to indicate truncation.
+        
+        Args:
+            description: The description text to truncate
+            max_length: Maximum allowed length (default: 500)
+            
+        Returns:
+            Truncated description string
+            
+        Examples:
+            >>> validator = OntologyValidator()
+            >>> long_desc = "A" * 600
+            >>> truncated = validator.truncate_description(long_desc, max_length=500)
+            >>> len(truncated)
+            500
+            >>> truncated.endswith("...")
+            True
+        """
+        if not description:
+            return ""
+        
+        if len(description) <= max_length:
+            return description
+        
+        # Truncate and add ellipsis
+        # Reserve 3 characters for "..."
+        truncate_at = max_length - 3
+        truncated = description[:truncate_at] + "..."
+        
+        logger.debug(
+            f"Truncated description from {len(description)} to {len(truncated)} characters"
+        )
+        
+        return truncated

api/app/core/memory/utils/validation/owl_validator.py

@@ -0,0 +1,585 @@
+"""OWL semantic validation for ontology classes using Owlready2.
+
+This module provides the OWLValidator class for validating ontology classes
+against OWL standards using the Owlready2 library. It performs semantic
+validation including consistency checking, circular inheritance detection,
+and OWL file export.
+
+Classes:
+    OWLValidator: Validates ontology classes using OWL reasoning and exports to OWL formats
+"""
+
+import logging
+from typing import List, Optional, Tuple
+
+from owlready2 import (
+    World,
+    Thing,
+    get_ontology,
+    sync_reasoner_pellet,
+    OwlReadyInconsistentOntologyError,
+)
+
+from app.core.memory.models.ontology_models import OntologyClass
+logger = logging.getLogger(__name__)
+
+
+class OWLValidator:
+    """Validator for OWL semantic validation of ontology classes.
+    
+    This validator performs semantic-level validation using Owlready2 including:
+    - Creating OWL classes from ontology class definitions
+    - Running consistency checking with Pellet reasoner
+    - Detecting circular inheritance
+    - Validating Protégé compatibility
+    - Exporting ontologies to various OWL formats (RDF/XML, Turtle, N-Triples)
+    
+    Attributes:
+        base_namespace: Base URI for the ontology namespace
+    """
+    
+    def __init__(self, base_namespace: str = "http://example.org/ontology#"):
+        """Initialize the OWL validator.
+        
+        Args:
+            base_namespace: Base URI for the ontology namespace (default: http://example.org/ontology#)
+        """
+        self.base_namespace = base_namespace
+    
+    def validate_ontology_classes(
+        self,
+        classes: List[OntologyClass],
+    ) -> Tuple[bool, List[str], Optional[World]]:
+        """Validate extracted ontology classes against OWL standards.
+        
+        This method creates an OWL ontology from the provided classes using Owlready2,
+        runs consistency checking with the Pellet reasoner, and detects common issues
+        like circular inheritance.
+        
+        Args:
+            classes: List of OntologyClass objects to validate
+            
+        Returns:
+            Tuple of (is_valid, error_messages, world):
+            - is_valid: True if ontology is valid and consistent, False otherwise
+            - error_messages: List of error/warning messages
+            - world: Owlready2 World object containing the ontology (None if validation failed)
+            
+        Examples:
+            >>> validator = OWLValidator()
+            >>> classes = [
+            ...     OntologyClass(name="Patient", description="A patient", entity_type="Person", domain="Healthcare"),
+            ...     OntologyClass(name="Doctor", description="A doctor", entity_type="Person", domain="Healthcare"),
+            ... ]
+            >>> is_valid, errors, world = validator.validate_ontology_classes(classes)
+            >>> is_valid
+            True
+            >>> len(errors)
+            0
+        """
+        if not classes:
+            return False, ["No classes provided for validation"], None
+        
+        errors = []
+        
+        try:
+            # Create a new world (isolated ontology environment)
+            world = World()
+            
+            # Use a proper ontology IRI
+            # Owlready2 expects the IRI to end with .owl or similar
+            onto_iri = self.base_namespace.rstrip('#/')
+            if not onto_iri.endswith('.owl'):
+                onto_iri = onto_iri + '.owl'
+            
+            # Create ontology
+            onto = world.get_ontology(onto_iri)
+            
+            with onto:
+                # Dictionary to store created OWL classes for parent reference
+                owl_classes = {}
+                
+                # First pass: Create all classes without parent relationships
+                for ontology_class in classes:
+                    try:
+                        # Create OWL class dynamically using type() with Thing as base
+                        # The key is to NOT set namespace in the dict, let Owlready2 handle it
+                        owl_class = type(
+                            ontology_class.name,  # Class name
+                            (Thing,),              # Base classes
+                            {}                     # Class dict (empty, let Owlready2 manage)
+                        )
+                        
+                        # Add label (rdfs:label) - include both English and Chinese names
+                        labels = [ontology_class.name]
+                        if ontology_class.name_chinese:
+                            labels.append(ontology_class.name_chinese)
+                        owl_class.label = labels
+                        
+                        # Add comment (rdfs:comment) with description
+                        if ontology_class.description:
+                            owl_class.comment = [ontology_class.description]
+                        
+                        # Store for parent relationship setup
+                        owl_classes[ontology_class.name] = owl_class
+                        
+                        logger.debug(
+                            f"Created OWL class: {ontology_class.name} "
+                            f"(Chinese: {ontology_class.name_chinese}) "
+                            f"IRI: {owl_class.iri if hasattr(owl_class, 'iri') else 'N/A'}"
+                        )
+                        
+                    except Exception as e:
+                        error_msg = f"Failed to create OWL class '{ontology_class.name}': {str(e)}"
+                        errors.append(error_msg)
+                        logger.error(error_msg, exc_info=True)
+                
+                # Second pass: Set up parent relationships
+                for ontology_class in classes:
+                    if ontology_class.parent_class and ontology_class.name in owl_classes:
+                        parent_name = ontology_class.parent_class
+                        
+                        # Check if parent exists
+                        if parent_name in owl_classes:
+                            try:
+                                child_class = owl_classes[ontology_class.name]
+                                parent_class = owl_classes[parent_name]
+                                
+                                # Set parent by modifying is_a
+                                child_class.is_a = [parent_class]
+                                
+                                logger.debug(
+                                    f"Set parent relationship: {ontology_class.name} -> {parent_name}"
+                                )
+                                
+                            except Exception as e:
+                                error_msg = (
+                                    f"Failed to set parent relationship "
+                                    f"'{ontology_class.name}' -> '{parent_name}': {str(e)}"
+                                )
+                                errors.append(error_msg)
+                                logger.warning(error_msg)
+                        else:
+                            warning_msg = (
+                                f"Parent class '{parent_name}' not found for '{ontology_class.name}'"
+                            )
+                            errors.append(warning_msg)
+                            logger.warning(warning_msg)
+                
+                # Check for circular inheritance
+                for class_name, owl_class in owl_classes.items():
+                    if self._has_circular_inheritance(owl_class):
+                        error_msg = f"Circular inheritance detected for class '{class_name}'"
+                        errors.append(error_msg)
+                        logger.error(error_msg)
+            
+            # Run consistency checking with Pellet reasoner
+            try:
+                logger.info("Running Pellet reasoner for consistency checking...")
+                sync_reasoner_pellet(world, infer_property_values=True, infer_data_property_values=True)
+                logger.info("Consistency check passed")
+                
+            except OwlReadyInconsistentOntologyError as e:
+                error_msg = f"Ontology is inconsistent: {str(e)}"
+                errors.append(error_msg)
+                logger.error(error_msg)
+                return False, errors, world
+                
+            except Exception as e:
+                # Reasoner errors are often due to Java not being installed or configured
+                # Log as warning but don't fail validation - ontology structure is still valid
+                warning_msg = f"Reasoner check skipped: {str(e)}"
+                if str(e).strip():  # Only log if there's an actual error message
+                    logger.warning(warning_msg)
+                else:
+                    logger.warning("Reasoner check skipped: Java may not be installed or configured")
+                # Continue - ontology structure is valid even without reasoner check
+            
+            # If we have errors (excluding warnings), validation failed
+            is_valid = len(errors) == 0
+            
+            return is_valid, errors, world
+            
+        except Exception as e:
+            error_msg = f"OWL validation failed: {str(e)}"
+            errors.append(error_msg)
+            logger.error(error_msg, exc_info=True)
+            return False, errors, None
+    
+    def _has_circular_inheritance(self, owl_class) -> bool:
+        """Check if an OWL class has circular inheritance.
+        
+        Circular inheritance occurs when a class inherits from itself through
+        a chain of parent relationships (e.g., A -> B -> C -> A).
+        
+        Args:
+            owl_class: Owlready2 class object to check
+            
+        Returns:
+            True if circular inheritance is detected, False otherwise
+        """
+        visited = set()
+        current = owl_class
+        
+        while current:
+            # Get class IRI or name as identifier
+            class_id = str(current.iri) if hasattr(current, 'iri') else str(current)
+            
+            if class_id in visited:
+                # Found a cycle
+                return True
+            
+            visited.add(class_id)
+            
+            # Get parent classes (is_a relationship)
+            parents = getattr(current, 'is_a', [])
+            
+            # Filter out Thing and other base classes
+            parent_classes = [p for p in parents if p != Thing and hasattr(p, 'is_a')]
+            
+            if not parent_classes:
+                # No more parents, no cycle
+                break
+            
+            # Check first parent (in single inheritance)
+            current = parent_classes[0] if parent_classes else None
+        
+        return False
+    
+    def export_to_owl(
+        self,
+        world: World,
+        output_path: Optional[str] = None,
+        format: str = "rdfxml",
+        classes: Optional[List] = None
+    ) -> str:
+        """Export ontology to OWL file in specified format.
+        
+        Supported formats:
+        - rdfxml: RDF/XML format (default, most compatible)
+        - turtle: Turtle format (more readable)
+        - ntriples: N-Triples format (simplest)
+        - json: JSON format (simplified, human-readable)
+        
+        Args:
+            world: Owlready2 World object containing the ontology
+            output_path: Optional file path to save the ontology (if None, returns string)
+            format: Export format - "rdfxml", "turtle", "ntriples", or "json" (default: "rdfxml")
+            classes: Optional list of OntologyClass objects (required for json format)
+            
+        Returns:
+            String representation of the exported ontology
+            
+        Raises:
+            ValueError: If format is not supported
+            RuntimeError: If export fails
+            
+        Examples:
+            >>> validator = OWLValidator()
+            >>> is_valid, errors, world = validator.validate_ontology_classes(classes)
+            >>> owl_content = validator.export_to_owl(world, "ontology.owl", format="rdfxml")
+        """
+        # Validate format
+        valid_formats = ["rdfxml", "turtle", "ntriples", "json"]
+        if format not in valid_formats:
+            raise ValueError(
+                f"Unsupported format '{format}'. Must be one of: {', '.join(valid_formats)}"
+            )
+        
+        # JSON format doesn't need OWL processing
+        if format == "json":
+            if not classes:
+                raise ValueError("Classes list is required for JSON format export")
+            return self._export_to_json(classes)
+        
+        # For OWL formats, world is required
+        if not world:
+            raise ValueError("World object is None. Cannot export ontology.")
+        
+        # Note: Owlready2 has issues with turtle format export
+        # We'll handle it specially by converting from rdfxml
+        use_conversion = (format == "turtle")
+        
+        try:
+            # Get all ontologies in the world
+            ontologies = list(world.ontologies.values())
+            
+            if not ontologies:
+                raise RuntimeError("No ontologies found in world")
+            
+            # Find the ontology with classes (skip anonymous/empty ontologies)
+            onto = None
+            for ont in ontologies:
+                classes_count = len(list(ont.classes()))
+                logger.debug(f"Checking ontology {ont.base_iri}: {classes_count} classes")
+                if classes_count > 0:
+                    onto = ont
+                    break
+            
+            # If no ontology with classes found, use the last non-anonymous one
+            if onto is None:
+                for ont in reversed(ontologies):
+                    if ont.base_iri != "http://anonymous/":
+                        onto = ont
+                        break
+            
+            # If still no ontology, use the first one
+            if onto is None:
+                onto = ontologies[0]
+            
+            # Log ontology contents for debugging
+            logger.info(f"Ontology IRI: {onto.base_iri}")
+            logger.info(f"Ontology contains {len(list(onto.classes()))} classes")
+            
+            # List all classes in the ontology
+            all_classes = list(onto.classes())
+            for cls in all_classes:
+                logger.info(f"Class in ontology: {cls.name} (IRI: {cls.iri})")
+                if hasattr(cls, 'label'):
+                    logger.debug(f"  Labels: {cls.label}")
+                if hasattr(cls, 'comment'):
+                    logger.debug(f"  Comments: {cls.comment}")
+            
+            if len(all_classes) == 0:
+                logger.warning("No classes found in ontology! This may indicate a problem with class creation.")
+            
+            if output_path:
+                # Save to file
+                export_format = "rdfxml" if use_conversion else format
+                logger.info(f"Exporting ontology to {output_path} in {export_format} format")
+                onto.save(file=output_path, format=export_format)
+                
+                # Read back the file content to return
+                with open(output_path, 'r', encoding='utf-8') as f:
+                    content = f.read()
+                
+                # Convert to turtle if needed
+                if use_conversion:
+                    content = self._convert_to_turtle(content)
+                
+                logger.info(f"Successfully exported ontology to {output_path}")
+                
+                # Format the content for better readability
+                content = self._format_owl_content(content, format)
+                
+                return content
+            else:
+                # Export to string (save to temporary location and read)
+                import tempfile
+                import os
+                
+                with tempfile.NamedTemporaryFile(mode='w', suffix='.owl', delete=False) as tmp:
+                    tmp_path = tmp.name
+                
+                try:
+                    export_format = "rdfxml" if use_conversion else format
+                    onto.save(file=tmp_path, format=export_format)
+                    
+                    with open(tmp_path, 'r', encoding='utf-8') as f:
+                        content = f.read()
+                    
+                    # Convert to turtle if needed
+                    if use_conversion:
+                        content = self._convert_to_turtle(content)
+                    
+                    # Format the content for better readability
+                    content = self._format_owl_content(content, format)
+                    
+                    return content
+                    
+                finally:
+                    # Clean up temporary file
+                    if os.path.exists(tmp_path):
+                        os.remove(tmp_path)
+                        
+        except Exception as e:
+            error_msg = f"Failed to export ontology: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            raise RuntimeError(error_msg) from e
+    
+    def _export_to_json(self, classes: List) -> str:
+        """Export ontology classes to simplified JSON format.
+        
+        This format is more compact and easier to parse than OWL XML.
+        
+        Args:
+            classes: List of OntologyClass objects
+            
+        Returns:
+            JSON string representation (compact format)
+        """
+        import json
+        
+        result = {
+            "ontology": {
+                "namespace": self.base_namespace,
+                "classes": []
+            }
+        }
+        
+        for cls in classes:
+            class_data = {
+                "name": cls.name,
+                "name_chinese": cls.name_chinese,
+                "description": cls.description,
+                "entity_type": cls.entity_type,
+                "domain": cls.domain,
+                "parent_class": cls.parent_class,
+                "examples": cls.examples if hasattr(cls, 'examples') else []
+            }
+            result["ontology"]["classes"].append(class_data)
+        
+        # 使用紧凑格式：无缩进，使用分隔符减少空格
+        return json.dumps(result, ensure_ascii=False, separators=(',', ':'))
+    
+    def _convert_to_turtle(self, rdfxml_content: str) -> str:
+        """Convert RDF/XML content to Turtle format using rdflib.
+        
+        Args:
+            rdfxml_content: RDF/XML format content
+            
+        Returns:
+            Turtle format content
+        """
+        try:
+            from rdflib import Graph
+            
+            # Parse RDF/XML
+            g = Graph()
+            g.parse(data=rdfxml_content, format="xml")
+            
+            # Serialize to Turtle
+            turtle_content = g.serialize(format="turtle")
+            
+            # Handle bytes vs string
+            if isinstance(turtle_content, bytes):
+                turtle_content = turtle_content.decode('utf-8')
+            
+            return turtle_content
+            
+        except ImportError:
+            logger.warning(
+                "rdflib is not installed. Cannot convert to Turtle format. "
+                "Install with: pip install rdflib"
+            )
+            return rdfxml_content
+        except Exception as e:
+            logger.error(f"Failed to convert to Turtle format: {e}")
+            return rdfxml_content
+    
+    def _format_owl_content(self, content: str, format: str) -> str:
+        """Format OWL content for better readability.
+        
+        Args:
+            content: Raw OWL content string
+            format: Format type (rdfxml, turtle, ntriples)
+            
+        Returns:
+            Formatted OWL content string
+        """
+        if format == "rdfxml":
+            # Format XML with proper indentation
+            try:
+                import xml.dom.minidom as minidom
+                dom = minidom.parseString(content)
+                # Pretty print with 2-space indentation
+                formatted = dom.toprettyxml(indent="  ", encoding="utf-8").decode("utf-8")
+                
+                # Remove extra blank lines
+                lines = []
+                prev_blank = False
+                for line in formatted.split('\n'):
+                    is_blank = not line.strip()
+                    if not (is_blank and prev_blank):  # Skip consecutive blank lines
+                        lines.append(line)
+                    prev_blank = is_blank
+                
+                formatted = '\n'.join(lines)
+                
+                return formatted
+            except Exception as e:
+                logger.warning(f"Failed to format XML content: {e}")
+                return content
+        
+        elif format == "turtle":
+            # Turtle format is already relatively readable
+            # Just ensure consistent line endings and not empty
+            if not content or content.strip() == "":
+                logger.warning("Turtle content is empty, this may indicate an export issue")
+            return content.strip() + '\n' if content.strip() else content
+        
+        elif format == "ntriples":
+            # N-Triples format is line-based, ensure proper line endings
+            return content.strip() + '\n' if content.strip() else content
+        
+        return content
+    
+    def validate_with_protege_compatibility(
+        self,
+        classes: List[OntologyClass]
+    ) -> Tuple[bool, List[str]]:
+        """Validate that ontology classes are compatible with Protégé editor.
+        
+        Protégé compatibility checks:
+        - Class names are valid OWL identifiers
+        - No special characters that Protégé cannot handle
+        - Namespace is properly formatted
+        - Labels and comments are properly encoded
+        
+        Args:
+            classes: List of OntologyClass objects to validate
+            
+        Returns:
+            Tuple of (is_compatible, warnings):
+            - is_compatible: True if compatible with Protégé, False otherwise
+            - warnings: List of compatibility warning messages
+            
+        Examples:
+            >>> validator = OWLValidator()
+            >>> classes = [OntologyClass(name="Patient", description="A patient", entity_type="Person", domain="Healthcare")]
+            >>> is_compatible, warnings = validator.validate_with_protege_compatibility(classes)
+            >>> is_compatible
+            True
+        """
+        warnings = []
+        
+        # Check namespace format
+        if not self.base_namespace.startswith(('http://', 'https://')):
+            warnings.append(
+                f"Namespace '{self.base_namespace}' should start with http:// or https:// "
+                "for Protégé compatibility"
+            )
+        
+        if not self.base_namespace.endswith(('#', '/')):
+            warnings.append(
+                f"Namespace '{self.base_namespace}' should end with # or / "
+                "for Protégé compatibility"
+            )
+        
+        # Check each class
+        for ontology_class in classes:
+            # Check for special characters that might cause issues
+            if any(char in ontology_class.name for char in ['<', '>', '"', '{', '}', '|', '^', '`']):
+                warnings.append(
+                    f"Class name '{ontology_class.name}' contains special characters "
+                    "that may cause issues in Protégé"
+                )
+            
+            # Check description length (Protégé can handle long descriptions but may display poorly)
+            if ontology_class.description and len(ontology_class.description) > 1000:
+                warnings.append(
+                    f"Class '{ontology_class.name}' has a very long description ({len(ontology_class.description)} chars) "
+                    "which may display poorly in Protégé"
+                )
+            
+            # Check for non-ASCII characters (Protégé supports them but encoding issues may occur)
+            if not ontology_class.name.isascii():
+                warnings.append(
+                    f"Class name '{ontology_class.name}' contains non-ASCII characters "
+                    "which may cause encoding issues in some Protégé versions"
+                )
+        
+        # If no warnings, it's compatible
+        is_compatible = len(warnings) == 0
+        
+        return is_compatible, warnings

api/app/core/models/scripts/__init__.py

@@ -0,0 +1 @@
+"""模型配置脚本模块"""

api/app/core/models/scripts/bedrock_models.yaml

@@ -0,0 +1,174 @@
+provider: bedrock
+enabled: true
+models:
+- name: ai21
+  type: llm
+  provider: bedrock
+  description: AI21 Labs大语言模型，completion生成模式，256000上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  logo: bedrock
+- name: amazon nova
+  type: llm
+  provider: bedrock
+  description: Amazon Nova大语言模型，支持智能体思考、工具调用、流式工具调用、视觉能力，300000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - vision
+  logo: bedrock
+- name: anthropic claude
+  type: llm
+  provider: bedrock
+  description: Anthropic Claude大语言模型，支持智能体思考、视觉能力、工具调用、流式工具调用、文档处理，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - document
+  logo: bedrock
+- name: cohere
+  type: llm
+  provider: bedrock
+  description: Cohere大语言模型，支持智能体思考、工具调用、流式工具调用，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  logo: bedrock
+- name: deepseek
+  type: llm
+  provider: bedrock
+  description: DeepSeek大语言模型，支持智能体思考、视觉能力、工具调用、流式工具调用，32768上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  logo: bedrock
+- name: meta
+  type: llm
+  provider: bedrock
+  description: Meta Llama大语言模型，支持智能体思考、工具调用，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  logo: bedrock
+- name: mistral
+  type: llm
+  provider: bedrock
+  description: Mistral AI大语言模型，支持智能体思考、工具调用，32000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  logo: bedrock
+- name: openai
+  type: llm
+  provider: bedrock
+  description: OpenAI大语言模型，支持智能体思考、工具调用、流式工具调用，32768上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  logo: bedrock
+- name: qwen
+  type: llm
+  provider: bedrock
+  description: Qwen大语言模型，支持智能体思考、工具调用、流式工具调用，32768上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  logo: bedrock
+- name: amazon.rerank-v1:0
+  type: rerank
+  provider: bedrock
+  description: amazon.rerank-v1:0重排序模型，5120上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 重排序模型
+  logo: bedrock
+- name: cohere.rerank-v3-5:0
+  type: rerank
+  provider: bedrock
+  description: cohere.rerank-v3-5:0重排序模型，5120上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 重排序模型
+  logo: bedrock
+- name: amazon.nova-2-multimodal-embeddings-v1:0
+  type: embedding
+  provider: bedrock
+  description: amazon.nova-2-multimodal-embeddings-v1:0文本嵌入模型，支持视觉能力，8192上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本嵌入模型
+  - vision
+  logo: bedrock
+- name: amazon.titan-embed-text-v1
+  type: embedding
+  provider: bedrock
+  description: amazon.titan-embed-text-v1文本嵌入模型，8192上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本嵌入模型
+  logo: bedrock
+- name: amazon.titan-embed-text-v2:0
+  type: embedding
+  provider: bedrock
+  description: amazon.titan-embed-text-v2:0文本嵌入模型，8192上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本嵌入模型
+  logo: bedrock
+- name: cohere.embed-english-v3
+  type: embedding
+  provider: bedrock
+  description: Cohere Embed 3 English文本嵌入模型，512上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本嵌入模型
+  logo: bedrock
+- name: cohere.embed-multilingual-v3
+  type: embedding
+  provider: bedrock
+  description: Cohere Embed 3 Multilingual文本嵌入模型，512上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本嵌入模型
+  logo: bedrock

api/app/core/models/scripts/dashscope_models.yaml

@@ -0,0 +1,820 @@
+provider: dashscope
+enabled: true
+models:
+- name: deepseek-r1-distill-qwen-14b
+  type: llm
+  provider: dashscope
+  description: DeepSeek-R1-Distill-Qwen-14B大语言模型，支持智能体思考，32000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-r1-distill-qwen-32b
+  type: llm
+  provider: dashscope
+  description: DeepSeek-R1-Distill-Qwen-32B大语言模型，支持智能体思考，32000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-r1
+  type: llm
+  provider: dashscope
+  description: DeepSeek-R1大语言模型，支持智能体思考，131072超大上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-v3.1
+  type: llm
+  provider: dashscope
+  description: DeepSeek-V3.1大语言模型，支持智能体思考，131072超大上下文窗口，对话模式，支持丰富生成参数调节
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-v3.2-exp
+  type: llm
+  provider: dashscope
+  description: DeepSeek-V3.2-exp实验版大语言模型，支持智能体思考，131072超大上下文窗口，对话模式，支持丰富生成参数调节
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-v3.2
+  type: llm
+  provider: dashscope
+  description: DeepSeek-V3.2大语言模型，支持智能体思考，131072超大上下文窗口，对话模式，支持丰富生成参数调节
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-v3
+  type: llm
+  provider: dashscope
+  description: DeepSeek-V3大语言模型，支持智能体思考，64000上下文窗口，对话模式，支持文本与JSON格式输出
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: farui-plus
+  type: llm
+  provider: dashscope
+  description: farui-plus大语言模型，支持多工具调用、智能体思考、流式工具调用，12288上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: glm-4.7
+  type: llm
+  provider: dashscope
+  description: GLM-4.7大语言模型，支持多工具调用、智能体思考、流式工具调用，202752超大上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qvq-max-latest
+  type: llm
+  provider: dashscope
+  description: qvq-max-latest大语言模型，支持视觉、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - vision
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qvq-max
+  type: llm
+  provider: dashscope
+  description: qvq-max大语言模型，支持视觉、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - vision
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-coder-turbo-0919
+  type: llm
+  provider: dashscope
+  description: qwen-coder-turbo-0919代码专用大语言模型，支持智能体思考，131072上下文窗口，对话模式，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - 代码模型
+  - agent-thought
+  logo: dashscope
+- name: qwen-max-latest
+  type: llm
+  provider: dashscope
+  description: qwen-max-latest大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-max-longcontext
+  type: llm
+  provider: dashscope
+  description: qwen-max-longcontext长上下文大语言模型，支持多工具调用、智能体思考、流式工具调用，32000上下文窗口，对话模式，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-max
+  type: llm
+  provider: dashscope
+  description: qwen-max大语言模型，支持多工具调用、智能体思考、流式工具调用，32768上下文窗口，对话模式，支持联网搜索
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-mt-plus
+  type: llm
+  provider: dashscope
+  description: qwen-mt-plus多语言翻译大语言模型，支持智能体思考，16384上下文窗口，对话模式，支持多语种互译与领域翻译适配
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 翻译模型
+  - agent-thought
+  logo: dashscope
+- name: qwen-mt-turbo
+  type: llm
+  provider: dashscope
+  description: qwen-mt-turbo轻量化多语言翻译大语言模型，支持智能体思考，16384上下文窗口，对话模式，支持多语种互译与领域翻译适配
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 翻译模型
+  - agent-thought
+  logo: dashscope
+- name: qwen-plus-0112
+  type: llm
+  provider: dashscope
+  description: qwen-plus-0112大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-0125
+  type: llm
+  provider: dashscope
+  description: qwen-plus-0125大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-0723
+  type: llm
+  provider: dashscope
+  description: qwen-plus-0723大语言模型，支持多工具调用、智能体思考、流式工具调用，32000上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-0806
+  type: llm
+  provider: dashscope
+  description: qwen-plus-0806大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-0919
+  type: llm
+  provider: dashscope
+  description: qwen-plus-0919大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-1125
+  type: llm
+  provider: dashscope
+  description: qwen-plus-1125大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-1127
+  type: llm
+  provider: dashscope
+  description: qwen-plus-1127大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-1220
+  type: llm
+  provider: dashscope
+  description: qwen-plus-1220大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-vl-max
+  type: chat
+  provider: dashscope
+  description: qwen-vl-max多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen-vl-plus-0809
+  type: chat
+  provider: dashscope
+  description: qwen-vl-plus-0809多模态大模型，支持视觉理解、智能体思考、视频理解，32768上下文窗口，对话模式，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen-vl-plus-2025-01-02
+  type: chat
+  provider: dashscope
+  description: qwen-vl-plus-2025-01-02多模态大模型，支持视觉理解、智能体思考、视频理解，32768上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen-vl-plus-2025-01-25
+  type: chat
+  provider: dashscope
+  description: qwen-vl-plus-2025-01-25多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen-vl-plus-latest
+  type: chat
+  provider: dashscope
+  description: qwen-vl-plus-latest多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen-vl-plus
+  type: chat
+  provider: dashscope
+  description: qwen-vl-plus多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen2.5-0.5b-instruct
+  type: llm
+  provider: dashscope
+  description: qwen2.5-0.5b-instruct大语言模型，支持多工具调用、智能体思考、流式工具调用，32768上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-14b
+  type: llm
+  provider: dashscope
+  description: qwen3-14b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-235b-a22b-instruct-2507
+  type: llm
+  provider: dashscope
+  description: qwen3-235b-a22b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-235b-a22b-thinking-2507
+  type: llm
+  provider: dashscope
+  description: qwen3-235b-a22b-thinking-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-235b-a22b
+  type: llm
+  provider: dashscope
+  description: qwen3-235b-a22b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-30b-a3b-instruct-2507
+  type: llm
+  provider: dashscope
+  description: qwen3-30b-a3b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-30b-a3b
+  type: llm
+  provider: dashscope
+  description: qwen3-30b-a3b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-32b
+  type: llm
+  provider: dashscope
+  description: qwen3-32b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-4b
+  type: llm
+  provider: dashscope
+  description: qwen3-4b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-8b
+  type: llm
+  provider: dashscope
+  description: qwen3-8b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-coder-30b-a3b-instruct
+  type: llm
+  provider: dashscope
+  description: qwen3-coder-30b-a3b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 代码模型
+  - agent-thought
+  logo: dashscope
+- name: qwen3-coder-480b-a35b-instruct
+  type: llm
+  provider: dashscope
+  description: qwen3-coder-480b-a35b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 代码模型
+  - agent-thought
+  logo: dashscope
+- name: qwen3-coder-plus-2025-09-23
+  type: llm
+  provider: dashscope
+  description: qwen3-coder-plus-2025-09-23大语言模型，支持智能体思考，1000000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 代码模型
+  - agent-thought
+  logo: dashscope
+- name: qwen3-coder-plus
+  type: llm
+  provider: dashscope
+  description: qwen3-coder-plus大语言模型，支持智能体思考，1000000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 代码模型
+  - agent-thought
+  logo: dashscope
+- name: qwen3-max-2025-09-23
+  type: llm
+  provider: dashscope
+  description: qwen3-max-2025-09-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - 联网搜索
+  logo: dashscope
+- name: qwen3-max-2026-01-23
+  type: llm
+  provider: dashscope
+  description: qwen3-max-2026-01-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - 联网搜索
+  logo: dashscope
+- name: qwen3-max-preview
+  type: llm
+  provider: dashscope
+  description: qwen3-max-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-max
+  type: llm
+  provider: dashscope
+  description: qwen3-max大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - 联网搜索
+  logo: dashscope
+- name: qwen3-next-80b-a3b-instruct
+  type: llm
+  provider: dashscope
+  description: qwen3-next-80b-a3b-instruct大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-next-80b-a3b-thinking
+  type: llm
+  provider: dashscope
+  description: qwen3-next-80b-a3b-thinking大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-omni-flash-2025-12-01
+  type: llm
+  provider: dashscope
+  description: qwen3-omni-flash-2025-12-01多模态大语言模型，支持视觉、智能体思考、视频、音频能力，65536上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  - audio
+  logo: dashscope
+- name: qwen3-vl-235b-a22b-instruct
+  type: chat
+  provider: dashscope
+  description: qwen3-vl-235b-a22b-instruct多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - video
+  logo: dashscope
+- name: qwen3-vl-235b-a22b-thinking
+  type: chat
+  provider: dashscope
+  description: qwen3-vl-235b-a22b-thinking多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - video
+  logo: dashscope
+- name: qwen3-vl-30b-a3b-instruct
+  type: chat
+  provider: dashscope
+  description: qwen3-vl-30b-a3b-instruct多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - video
+  logo: dashscope
+- name: qwen3-vl-30b-a3b-thinking
+  type: chat
+  provider: dashscope
+  description: qwen3-vl-30b-a3b-thinking多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - video
+  logo: dashscope
+- name: qwen3-vl-flash
+  type: chat
+  provider: dashscope
+  description: qwen3-vl-flash多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - video
+  logo: dashscope
+- name: qwen3-vl-plus-2025-09-23
+  type: chat
+  provider: dashscope
+  description: qwen3-vl-plus-2025-09-23多模态大语言模型，支持视觉、智能体思考、视频能力，262144上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen3-vl-plus
+  type: chat
+  provider: dashscope
+  description: qwen3-vl-plus多模态大语言模型，支持视觉、智能体思考、视频能力，262144上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwq-32b
+  type: llm
+  provider: dashscope
+  description: qwq-32b大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwq-plus-0305
+  type: llm
+  provider: dashscope
+  description: qwq-plus-0305大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwq-plus
+  type: llm
+  provider: dashscope
+  description: qwq-plus大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: gte-rerank-v2
+  type: rerank
+  provider: dashscope
+  description: gte-rerank-v2重排序模型，4000上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 重排序模型
+  logo: dashscope
+- name: gte-rerank
+  type: rerank
+  provider: dashscope
+  description: gte-rerank重排序模型，4000上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 重排序模型
+  logo: dashscope
+- name: multimodal-embedding-v1
+  type: embedding
+  provider: dashscope
+  description: multimodal-embedding-v1多模态嵌入模型，支持视觉能力，8192上下文窗口，最大分块数10
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 嵌入模型
+  - 多模态模型
+  - vision
+  logo: dashscope
+- name: text-embedding-v1
+  type: embedding
+  provider: dashscope
+  description: text-embedding-v1文本嵌入模型，2048上下文窗口，最大分块数25
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 嵌入模型
+  - 文本嵌入
+  logo: dashscope
+- name: text-embedding-v2
+  type: embedding
+  provider: dashscope
+  description: text-embedding-v2文本嵌入模型，2048上下文窗口，最大分块数25
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 嵌入模型
+  - 文本嵌入
+  logo: dashscope
+- name: text-embedding-v3
+  type: embedding
+  provider: dashscope
+  description: text-embedding-v3文本嵌入模型，8192上下文窗口，最大分块数10
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 嵌入模型
+  - 文本嵌入
+  logo: dashscope
+- name: text-embedding-v4
+  type: embedding
+  provider: dashscope
+  description: text-embedding-v4文本嵌入模型，8192上下文窗口，最大分块数10
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 嵌入模型
+  - 文本嵌入
+  logo: dashscope

api/app/core/models/scripts/loader.py

@@ -0,0 +1,143 @@
+"""模型配置加载器 - 用于将预定义模型批量导入到数据库"""
+
+import os
+from pathlib import Path
+from typing import Callable
+
+import yaml
+from sqlalchemy.orm import Session
+from app.models.models_model import ModelBase, ModelProvider
+
+
+def _load_yaml_config(provider: ModelProvider) -> list[dict]:
+    """从YAML文件加载指定供应商的模型配置"""
+    config_dir = Path(__file__).parent
+    config_file = config_dir / f"{provider.value}_models.yaml"
+    
+    if not config_file.exists():
+        return []
+    
+    with open(config_file, 'r', encoding='utf-8') as f:
+        data = yaml.safe_load(f)
+        
+        # 检查是否需要加载（默认为 true）
+        if not data.get('enabled', True):
+            return []
+        
+        return data.get('models', [])
+
+
+def _disable_yaml_config(provider: ModelProvider) -> None:
+    """将YAML文件的enabled标志设置为false"""
+    config_dir = Path(__file__).parent
+    config_file = config_dir / f"{provider.value}_models.yaml"
+    
+    if not config_file.exists():
+        return
+    
+    with open(config_file, 'r', encoding='utf-8') as f:
+        data = yaml.safe_load(f)
+    
+    data['enabled'] = False
+    
+    with open(config_file, 'w', encoding='utf-8') as f:
+        yaml.dump(data, f, allow_unicode=True, sort_keys=False)
+
+
+def load_models(db: Session, providers: list[str] = None, silent: bool = False) -> dict:
+    """
+    加载模型配置到数据库
+    
+    Args:
+        db: 数据库会话
+        providers: 要加载的供应商列表，None表示加载所有
+        silent: 是否静默模式（不输出详细日志）
+        
+    Returns:
+        dict: 加载结果统计 {"success": int, "skipped": int, "failed": int}
+    """
+    result = {"success": 0, "skipped": 0, "failed": 0}
+    
+    # 确定要加载的供应商
+    if providers:
+        target_providers = [ModelProvider(p) if isinstance(p, str) else p for p in providers]
+    else:
+        target_providers = [p for p in ModelProvider if p != ModelProvider.COMPOSITE]
+    
+    for provider in target_providers:
+        # 从YAML文件加载模型配置
+        models = _load_yaml_config(provider)
+        
+        if not models:
+            if not silent:
+                print(f"警告: 供应商 '{provider.value}' 暂无预定义模型")
+            continue
+            
+        if not silent:
+            print(f"\n正在加载 {provider.value} 的 {len(models)} 个模型...")
+        
+        # provider_success = 0
+        for model_data in models:
+            try:
+                # 检查模型是否已存在
+                existing = db.query(ModelBase).filter(
+                    ModelBase.name == model_data["name"],
+                    ModelBase.provider == model_data["provider"]
+                ).first()
+                
+                if existing:
+                    # 更新现有模型配置
+                    for key, value in model_data.items():
+                        setattr(existing, key, value)
+                    db.commit()
+                    if not silent:
+                        print(f"更新成功: {model_data['name']}")
+                    result["success"] += 1
+                    # provider_success += 1
+                else:
+                    # 创建新模型
+                    model = ModelBase(**model_data)
+                    db.add(model)
+                    db.commit()
+                    if not silent:
+                        print(f"添加成功: {model_data['name']}")
+                    result["success"] += 1
+                    # provider_success += 1
+                
+            except Exception as e:
+                db.rollback()
+                if not silent:
+                    print(f"添加失败: {model_data['name']} - {str(e)}")
+                result["failed"] += 1
+        
+        # 如果该供应商的模型全部加载成功，将enabled设置为false
+        # if provider_success == len(models):
+        _disable_yaml_config(provider)
+    
+    return result
+
+
+def load_models_by_provider(db: Session, provider: str) -> dict:
+    """
+    加载指定供应商的模型配置
+    
+    Args:
+        db: 数据库会话
+        provider: 供应商名称（字符串或ModelProvider枚举）
+        
+    Returns:
+        dict: 加载结果统计
+    """
+    provider_enum = ModelProvider(provider) if isinstance(provider, str) else provider
+    return load_models(db, providers=[provider_enum])
+
+
+def get_available_providers() -> list[Callable[[], str]]:
+    """获取所有可用的供应商列表（从ModelProvider枚举获取，排除COMPOSITE）"""
+    return [p.value for p in ModelProvider if p != ModelProvider.COMPOSITE]
+
+
+def get_models_by_provider(provider: str) -> list[dict]:
+    """获取指定供应商的模型配置列表"""
+    provider_enum = ModelProvider(provider) if isinstance(provider, str) else provider
+    return _load_yaml_config(provider_enum)

api/app/core/models/scripts/openai_models.yaml

@@ -0,0 +1,294 @@
+provider: openai
+enabled: true
+models:
+- name: chatgpt-4o-latest
+  type: llm
+  provider: openai
+  description: chatgpt-4o-latest大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  logo: openai
+- name: gpt-3.5-turbo-0125
+  type: llm
+  provider: openai
+  description: gpt-3.5-turbo-0125大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-3.5-turbo-1106
+  type: llm
+  provider: openai
+  description: gpt-3.5-turbo-1106大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-3.5-turbo-16k
+  type: llm
+  provider: openai
+  description: gpt-3.5-turbo-16k大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-3.5-turbo-instruct
+  type: llm
+  provider: openai
+  description: gpt-3.5-turbo-instruct大语言模型，4096上下文窗口，文本补全模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  logo: openai
+- name: gpt-3.5-turbo
+  type: llm
+  provider: openai
+  description: gpt-3.5-turbo大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-4-0125-preview
+  type: llm
+  provider: openai
+  description: gpt-4-0125-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-4-1106-preview
+  type: llm
+  provider: openai
+  description: gpt-4-1106-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-4-turbo-2024-04-09
+  type: llm
+  provider: openai
+  description: gpt-4-turbo-2024-04-09大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  logo: openai
+- name: gpt-4-turbo-preview
+  type: llm
+  provider: openai
+  description: gpt-4-turbo-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-4-turbo
+  type: llm
+  provider: openai
+  description: gpt-4-turbo大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  logo: openai
+- name: o1-preview
+  type: llm
+  provider: openai
+  description: o1-preview大语言模型，支持智能体思考，128000上下文窗口，对话模式，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: openai
+- name: o1
+  type: llm
+  provider: openai
+  description: o1大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - structured-output
+  logo: openai
+- name: o3-2025-04-16
+  type: llm
+  provider: openai
+  description: o3-2025-04-16大语言模型，支持智能体思考、工具调用、视觉能力、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - vision
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: o3-mini-2025-01-31
+  type: llm
+  provider: openai
+  description: o3-mini-2025-01-31大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: o3-mini
+  type: llm
+  provider: openai
+  description: o3-mini大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: o3-pro-2025-06-10
+  type: llm
+  provider: openai
+  description: o3-pro-2025-06-10大语言模型，支持智能体思考、工具调用、视觉能力、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - vision
+  - structured-output
+  logo: openai
+- name: o3-pro
+  type: llm
+  provider: openai
+  description: o3-pro大语言模型，支持智能体思考、工具调用、视觉能力、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - vision
+  - structured-output
+  logo: openai
+- name: o3
+  type: llm
+  provider: openai
+  description: o3大语言模型，支持智能体思考、视觉能力、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: o4-mini-2025-04-16
+  type: llm
+  provider: openai
+  description: o4-mini-2025-04-16大语言模型，支持智能体思考、工具调用、视觉能力、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - vision
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: o4-mini
+  type: llm
+  provider: openai
+  description: o4-mini大语言模型，支持智能体思考、工具调用、视觉能力、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - vision
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: text-embedding-3-large
+  type: embedding
+  provider: openai
+  description: text-embedding-3-large文本向量模型，8191上下文窗口，最大分块数32
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本向量模型
+  logo: openai
+- name: text-embedding-3-small
+  type: embedding
+  provider: openai
+  description: text-embedding-3-small文本向量模型，8191上下文窗口，最大分块数32
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本向量模型
+  logo: openai
+- name: text-embedding-ada-002
+  type: embedding
+  provider: openai
+  description: text-embedding-ada-002文本向量模型，8097上下文窗口，最大分块数32
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本向量模型
+  logo: openai

api/app/core/rag/nlp/search.py

@@ -28,7 +28,9 @@ from app.core.rag.common.float_utils import get_float
 from app.core.rag.common.constants import PAGERANK_FLD, TAG_FLD
 from app.core.rag.llm.chat_model import Base
 from app.core.rag.llm.embedding_model import OpenAIEmbed
+import logging
 
+logger = logging.getLogger(__name__)
 
 def knowledge_retrieval(
         query: str,
@@ -62,7 +64,15 @@ def knowledge_retrieval(
         merge_strategy = config.get("merge_strategy", "weight")
         reranker_id = config.get("reranker_id")
         reranker_top_k = config.get("reranker_top_k", 1024)
-        use_graph = config.get("use_graph", "false").lower() == "true"
+        # use_graph = config.get("use_graph", "false").lower() == "true"
+
+        use_graph_value = config.get("use_graph", False)
+        if isinstance(use_graph_value, bool):
+            use_graph = use_graph_value
+        elif isinstance(use_graph_value, str):
+            use_graph = use_graph_value.lower() in ("true", "1", "yes")
+        else:
+            use_graph = False
 
         file_names_filter = []
         if user_ids:
@@ -159,13 +169,29 @@ def knowledge_retrieval(
 
         # Use the specified reranker for re-ranking
         if reranker_id:
-            return rerank(db=db, reranker_id=reranker_id, query=query, docs=all_results, top_k=reranker_top_k)
+            try:
-        # use graph
+                return rerank(db=db, reranker_id=reranker_id, query=query, docs=all_results, top_k=reranker_top_k)
+            except Exception as rerank_error:
+                # If reranker fails, log warning and continue with original results
+                logger.warning(
+                    "Reranker failed, falling back to original results",
+                    extra={
+                        "reranker_id": reranker_id,
+                        "query": query,
+                        "doc_count": len(all_results),
+                        "error": str(rerank_error),
+                    },
+                )
+
         if use_graph:
-            from app.core.rag.common.settings import kg_retriever
+            try:
-            doc = kg_retriever.retrieval(question=query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
+                from app.core.rag.common.settings import kg_retriever
-            if doc:
+                doc = kg_retriever.retrieval(question=query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
-                all_results.insert(0, doc)
+                if doc:
+                    all_results.insert(0, doc)
+            except Exception as graph_error:
+                print(f"Failed to retrieve from knowledge graph: {str(graph_error)}")
+        
         return all_results
 
     except Exception as e:

api/app/core/workflow/executor.py

@@ -16,7 +16,6 @@ from app.core.workflow.graph_builder import GraphBuilder, StreamOutputConfig
 from app.core.workflow.nodes import WorkflowState
 from app.core.workflow.nodes.base_config import VariableType
 from app.core.workflow.nodes.enums import NodeType
-from app.core.workflow.template_renderer import render_template
 
 logger = logging.getLogger(__name__)
 
@@ -157,12 +156,137 @@ class WorkflowExecutor:
             "error": result.get("error"),
         }
 
-    def _update_end_activate(self, node_id):
+    def _update_scope_activate(self, scope, status=None):
+        """
+        Update the activation state of all End nodes based on a completed scope (node or variable).
+
+        Iterates over all End nodes in `self.end_outputs` and calls
+        `update_activate` on each, which may:
+          - Activate variable segments that depend on the completed node/scope.
+          - Activate the entire End node output if all control conditions are met.
+
+        If any End node becomes active and `self.activate_end` is not yet set,
+        this node will be marked as the currently active End node.
+
+        Args:
+            scope (str): The node ID or scope that has completed execution.
+            status (str | None): Optional status of the node (used for branch/control nodes).
+        """
         for node in self.end_outputs.keys():
-            self.end_outputs[node].update_activate(node_id)
+            self.end_outputs[node].update_activate(scope, status)
             if self.end_outputs[node].activate and self.activate_end is None:
                 self.activate_end = node
 
+    def _update_stream_output_status(self, activate, data):
+        """
+        Update the stream output state of End nodes based on workflow state updates.
+
+        This method checks which nodes/scopes are activated and propagates
+        activation to End nodes accordingly.
+
+        Args:
+            activate (dict): Mapping of node_id -> bool indicating which nodes/scopes are activated.
+            data (dict): Mapping of node_id -> node runtime data, including outputs.
+
+        Behavior:
+            For each node in `data`:
+            1. If the node is activated (`activate[node_id]` is True),
+               retrieve its output status from `runtime_vars`.
+            2. Call `_update_scope_activate` to propagate the activation
+               to all relevant End nodes and update `self.activate_end`.
+        """
+        for node_id in data.keys():
+            if activate.get(node_id):
+                node_output_status = (
+                    data[node_id]
+                    .get('runtime_vars', {})
+                    .get(node_id)
+                    .get("output")
+                )
+                self._update_scope_activate(node_id, status=node_output_status)
+
+    async def _emit_active_chunks(
+            self,
+            node_outputs: dict,
+            variables: dict,
+            force=False
+    ):
+        """
+        Process and yield all currently active output segments for the currently active End node.
+
+        This method handles stream-mode output for an End node by iterating through its output segments
+        (`OutputContent`). Only segments marked as active (`activate=True`) are processed, unless
+        `force=True`, which allows all segments to be processed regardless of their activation state.
+
+        Behavior:
+        1. Iterates from the current `cursor` position to the end of the outputs list.
+        2. For each segment:
+           - If the segment is literal text (`is_variable=False`), append it directly.
+           - If the segment is a variable (`is_variable=True`), evaluate it using
+             `evaluate_expression` with the given `node_outputs` and `variables`,
+             then transform the result with `_trans_output_string`.
+        3. Yield a stream event of type "message" containing the processed chunk.
+        4. Move the `cursor` forward after processing each segment.
+        5. When all segments have been processed, remove this End node from `end_outputs`
+           and reset `activate_end` to None.
+
+        Args:
+            node_outputs (dict): Current runtime node outputs, used for variable evaluation.
+            variables (dict): Current runtime variables, used for variable evaluation.
+            force (bool, default=False): If True, process segments even if `activate=False`.
+
+        Yields:
+            dict: A stream event of type "message" containing the processed chunk.
+
+        Notes:
+            - Segments that fail evaluation (ValueError) are skipped with a warning logged.
+            - This method only processes the currently active End node (`self.activate_end`).
+            - Use `force=True` for final emission regardless of activation state.
+        """
+
+        end_info = self.end_outputs[self.activate_end]
+
+        while end_info.cursor < len(end_info.outputs):
+            final_chunk = ''
+            current_segment = end_info.outputs[end_info.cursor]
+
+            if not current_segment.activate and not force:
+                # Stop processing until this segment becomes active
+                break
+
+            # Literal segment
+            if not current_segment.is_variable:
+                final_chunk += current_segment.literal
+            else:
+                # Variable segment: evaluate and transform
+                try:
+                    chunk = evaluate_expression(
+                        current_segment.literal,
+                        variables=variables,
+                        node_outputs=node_outputs
+                    )
+                    chunk = self._trans_output_string(chunk)
+                    final_chunk += chunk
+                except ValueError:
+                    # Log failed evaluation but continue streaming
+                    logger.warning(f"[STREAM] Failed to evaluate segment: {current_segment.literal}")
+
+            if final_chunk:
+                yield {
+                    "event": "message",
+                    "data": {
+                        "chunk": final_chunk
+                    }
+                }
+
+            # Advance cursor after processing
+            end_info.cursor += 1
+
+        # Remove End node from active tracking if all segments have been processed
+        if end_info.cursor >= len(end_info.outputs):
+            self.end_outputs.pop(self.activate_end)
+            self.activate_end = None
+
     @staticmethod
     def _trans_output_string(content):
         if isinstance(content, str):
@@ -218,14 +342,8 @@ class WorkflowExecutor:
 
             result = await graph.ainvoke(initial_state, config=self.checkpoint_config)
             full_content = ''
-            for end_info in self.end_outputs.values():
+            for end_id in self.end_outputs.keys():
-                output_template = "".join([output.literal for output in end_info.outputs])
+                full_content += result.get('runtime_vars', {}).get(end_id, {}).get('output', '')
-                full_content += render_template(
-                    output_template,
-                    result.get("variables", {}),
-                    result.get("runtime_vars", {}),
-                    strict=False
-                )
             result["messages"].extend(
                 [
                     {
@@ -306,7 +424,7 @@ class WorkflowExecutor:
         try:
             chunk_count = 0
             full_content = ''
-
+            self._update_scope_activate("sys")
             async for event in graph.astream(
                     initial_state,
                     stream_mode=["updates", "debug", "custom"],  # Use updates + debug + custom mode
@@ -333,9 +451,12 @@ class WorkflowExecutor:
                             if not end_info or end_info.cursor >= len(end_info.outputs):
                                 continue
                             current_output = end_info.outputs[end_info.cursor]
-                            if current_output.is_variable and current_output.depends_on_node(node_id):
+                            if current_output.is_variable and current_output.depends_on_scope(node_id):
                                 if data.get("done"):
                                     end_info.cursor += 1
+                                    if end_info.cursor >= len(end_info.outputs):
+                                        self.end_outputs.pop(self.activate_end)
+                                        self.activate_end = None
                                 else:
                                     full_content += data.get("chunk")
                                     yield {
@@ -415,91 +536,53 @@ class WorkflowExecutor:
 
                 elif mode == "updates":
                     # Handle state updates - store final state
-                    for node_id in data.keys():
+                    state = graph.get_state(config=self.checkpoint_config).values
-                        self._update_end_activate(node_id)
+                    node_outputs = state.get("runtime_vars", {})
-                    wait = False
+                    variables = state.get("variables", {})
-                    state = graph.get_state(config=self.checkpoint_config)
+                    activate = state.get("activate", {})
-                    node_outputs = state.values.get("runtime_vars", {})
+                    for _, node_data in data.items():
-                    for _ in data.keys():
+                        node_outputs |= node_data.get("runtime_vars", {})
-                        node_outputs = node_outputs | data.get(_).get("runtime_vars", {})
+                        variables |= node_data.get("variables", {})
 
+                    self._update_stream_output_status(activate, data)
+                    wait = False
                     while self.activate_end and not wait:
-                        message = ''
+                        async for msg_event in self._emit_active_chunks(
-                        logger.info(self.activate_end)
+                                node_outputs=node_outputs,
-                        end_info = self.end_outputs[self.activate_end]
+                                variables=variables
-                        content = end_info.outputs[end_info.cursor]
+                        ):
-                        while content.activate:
+                            full_content += msg_event["data"]['chunk']
-                            if not content.is_variable:
+                            yield msg_event
-                                full_content += content.literal
+
-                                message += content.literal
+                        if self.activate_end:
-                            else:
-                                try:
-                                    chunk = evaluate_expression(
-                                        content.literal,
-                                        variables={},
-                                        node_outputs=node_outputs
-                                    )
-                                    chunk = self._trans_output_string(chunk)
-                                    message += chunk
-                                    full_content += chunk
-                                except ValueError:
-                                    pass
-                            end_info.cursor += 1
-                            if end_info.cursor == len(end_info.outputs):
-                                break
-                            content = end_info.outputs[end_info.cursor]
-                        if end_info.cursor != len(end_info.outputs):
                             wait = True
                         else:
-                            self.end_outputs.pop(self.activate_end)
+                            self._update_stream_output_status(activate, data)
-                            self.activate_end = None
-                            for node_id in data.keys():
-                                self._update_end_activate(node_id)
-                        if message:
-                            yield {
-                                "event": "message",
-                                "data": {
-                                    "chunk": message
-                                }
-                            }
 
                     logger.debug(f"[UPDATES] 收到 state 更新 from {list(data.keys())} "
                                  f"- execution_id: {self.execution_id}")
 
             result = graph.get_state(self.checkpoint_config).values
-            while self.activate_end:
+            node_outputs = result.get("runtime_vars", {})
-                message = ''
+            variables = result.get("variables", {})
-                end_info = self.end_outputs[self.activate_end]
+            self.end_outputs = {
-                content = end_info.outputs[end_info.cursor]
+                node_id: node_info
-                if not content.is_variable:
+                for node_id, node_info in self.end_outputs.items()
-                    message += content.literal
+                if node_info.activate
-                else:
+            }
-                    node_outputs = result.get("runtime_vars", {})
+
-                    variables = result.get("variables", {})
+            if self.end_outputs or self.activate_end:
-                    try:
+                while self.activate_end:
-                        chunk = evaluate_expression(
+                    async for msg_event in self._emit_active_chunks(
-                            content.literal,
+                            node_outputs=node_outputs,
                             variables=variables,
-                            node_outputs=node_outputs
+                            force=True
-                        )
+                    ):
-                        chunk = self._trans_output_string(chunk)
+                        full_content += msg_event["data"]['chunk']
-                        message += chunk
+                        yield msg_event
-                        full_content += chunk
+
-                    except ValueError:
+                    if not self.activate_end and self.end_outputs:
-                        pass
-                end_info.cursor += 1
-                if end_info.cursor == len(end_info.outputs):
-                    self.end_outputs.pop(self.activate_end)
-                    self.activate_end = None
-                    if self.end_outputs:
                         self.activate_end = list(self.end_outputs.keys())[0]
-                if message:
-                    yield {
-                        "event": "message",
-                        "data": {
-                            "chunk": message
-                        }
-                    }
 
             # 计算耗时
             end_time = datetime.datetime.now()

api/app/core/workflow/graph_builder.py

@@ -53,114 +53,110 @@ class OutputContent(BaseModel):
         )
     )
 
-    def depends_on_node(self, node_id: str) -> bool:
+    def depends_on_scope(self, scope: str) -> bool:
         """
-        Check if this output segment depends on a specific node's variable.
+        Check if this segment depends on a given scope.
-
-        This method examines the `literal` of the output segment to see if it
-        contains a variable placeholder referencing the given node in the form:
-
-            {{ node_id.field_name }}
-
-        It uses a regular expression to match the exact node ID, avoiding
-        false positives from substring matches (e.g., 'node1' should not match 'node10').
 
         Args:
-            node_id (str): The ID of the node to check for in this segment's variable placeholders.
+            scope (str): Node ID or special variable prefix (e.g., "sys").
 
         Returns:
-            bool:
+            bool: True if this segment references the given scope.
-                - True if the segment contains a variable referencing the given node.
-                - False otherwise.
-
-        Example:
-            literal = "{{node1.name}}"
-
-            depends_on_node("node1") -> True
-            depends_on_node("node2") -> False
-
-        Usage:
-            This method is primarily used in stream mode to determine whether
-            a particular variable output segment should be activated when a
-            specific upstream node completes execution.
         """
-        variable_pattern = rf"\{{\{{\s*{re.escape(node_id)}\.[a-zA-Z0-9_]+\s*\}}\}}"
+        pattern = rf"\{{\{{\s*{re.escape(scope)}\.[a-zA-Z0-9_]+\s*\}}\}}"
-        pattern = re.compile(variable_pattern)
+        return bool(re.search(pattern, self.literal))
-        match = pattern.search(self.literal)
-        if match:
-            return True
-        return False
 
 
 class StreamOutputConfig(BaseModel):
     """
     Streaming output configuration for an End node.
 
-    This structure controls:
+    This configuration describes how the End node output behaves in streaming mode,
-    - whether the End node output is globally active
+    including:
-    - which upstream branch nodes are responsible for activation
+    - whether output emission is globally activated
-    - how each output segment behaves in streaming mode
+    - which upstream branch/control nodes gate the activation
+    - how each parsed output segment is streamed and activated
     """
 
     activate: bool = Field(
         ...,
         description=(
-            "Global activation state of the End node output.\n"
+            "Global activation flag for the End node output.\n"
-            "If False, no output should be emitted until all control nodes are resolved."
+            "When False, output segments should not be emitted even if available.\n"
+            "This flag typically becomes True once required control branch conditions "
+            "are satisfied."
         )
     )
 
-    control_nodes: list[str] = Field(
+    control_nodes: dict[str, str] = Field(
         ...,
         description=(
-            "List of upstream branch node IDs that control this End node.\n"
+            "Control branch conditions for this End node output.\n"
-            "Each node must signal completion before output becomes active."
+            "Mapping of `branch_node_id -> expected_branch_label`.\n"
+            "The End node output becomes globally active when a controlling branch node "
+            "reports a matching completion status."
         )
     )
 
     outputs: list[OutputContent] = Field(
         ...,
-        description="Ordered list of output segments parsed from the output template."
+        description=(
+            "Ordered list of output segments parsed from the output template.\n"
+            "Each segment represents either a literal text block or a variable placeholder "
+            "that may be activated independently."
+        )
     )
 
     cursor: int = Field(
         ...,
         description=(
             "Streaming cursor index.\n"
-            "Indicates how many output segments have already been emitted."
+            "Indicates the next output segment index to be emitted.\n"
+            "Segments with index < cursor are considered already streamed."
         )
     )
 
-    def update_activate(self, node_id):
+    def update_activate(self, scope: str, status=None):
         """
-        Update activation state based on an upstream node completion.
+        Update streaming activation state based on an upstream node or special variable.
 
-        This method is typically called when a branch/control node finishes execution.
+        Args:
+            scope (str):
+                Identifier of the completed upstream entity.
+                - If a control branch node, it should match a key in `control_nodes`.
+                - If a variable placeholder (e.g., "sys.xxx"), it may appear in output segments.
+            status (optional):
+                Completion status of the control branch node.
+                Required when `scope` refers to a control node.
 
         Behavior:
-        1. If the node is a control node:
+        1. Control branch nodes:
-           - Remove it from `control_nodes`
+           - If `scope` matches a key in `control_nodes` and `status` matches the expected
-           - If all control nodes are resolved, activate the entire output
+             branch label, the End node output becomes globally active (`activate = True`).
 
-        2. Activate variable output segments that depend on this node:
+        2. Variable output segments:
-           - If an output segment is a variable
+           - For each segment that is a variable (`is_variable=True`):
-           - And its literal references the completed node_id
+               - If the segment literal references `scope`, mark the segment as active.
-           - Mark that segment as active
+               - This applies both to regular node variables (e.g., "node_id.field")
+                 and special system variables (e.g., "sys.xxx").
+
+        Notes:
+        - This method does not emit output or advance the streaming cursor.
+        - It only updates activation flags based on upstream events or special variables.
         """
 
         # Case 1: resolve control branch dependency
-        if node_id in self.control_nodes:
+        if scope in self.control_nodes.keys():
-            self.control_nodes.remove(node_id)
+            if status is None:
-
+                raise RuntimeError("[Stream Output] Control node activation status not provided")
-            # All branch constraints resolved → enable output
+            if status == self.control_nodes[scope]:
-            if not self.control_nodes:
                 self.activate = True
 
         # Case 2: activate variable segments related to this node
         for i in range(len(self.outputs)):
             if (
                     self.outputs[i].is_variable
-                    and self.outputs[i].depends_on_node(node_id)
+                    and self.outputs[i].depends_on_scope(scope)
             ):
                 self.outputs[i].activate = True
 
@@ -184,11 +180,11 @@ class GraphBuilder:
         self._find_upstream_branch_node = lru_cache(
             maxsize=len(self.nodes) * 2
         )(self._find_upstream_branch_node)
-        self._analyze_end_node_output()
 
         self.graph = StateGraph(WorkflowState)
         self.add_nodes()
         self.add_edges()
+        self._analyze_end_node_output()
         # EDGES MUST BE ADDED AFTER NODES ARE ADDED.
 
     @property
@@ -216,30 +212,53 @@ class GraphBuilder:
         except KeyError:
             raise RuntimeError(f"Node not found: Id={node_id}")
 
-    def _find_upstream_branch_node(self, target_node: str) -> tuple[bool, tuple[str]]:
+    def _find_upstream_branch_node(self, target_node: str) -> tuple[bool, tuple[tuple[str, str]]]:
-        """Find upstream branch nodes for a given target node in the workflow graph.
+        """
+        Recursively find all upstream branch (control) nodes that influence the execution
+        of the given target node.
 
-        This method identifies all upstream control (branch) nodes that can affect
+        This method walks upstream along the workflow graph starting from `target_node`.
-        the execution of `target_node`. If `target_node` is reachable from a start
+        It distinguishes between:
-        node (i.e., a node with no upstream nodes), the method returns an empty tuple.
+          - branch nodes (node types listed in `BRANCH_NODES`)
+          - non-branch nodes (ordinary processing nodes)
 
-        The function distinguishes between branch nodes (defined in `BRANCH_NODES`)
+        Traversal rules:
-        and non-branch nodes, recursively traversing upstream through non-branch
+        1. For each immediate upstream node:
-        nodes. If any non-branch upstream path does not lead to a branch node,
+           - If it is a branch node, it is recorded as an affecting control node.
-        the result will indicate that no valid upstream branch node exists.
+           - If it is a non-branch node, the traversal continues recursively upstream.
+        2. If ANY upstream path reaches a START / CYCLE_START node without encountering
+           a branch node, the traversal is considered invalid:
+           - `has_branch` will be False
+           - no branch nodes are returned.
+        3. Only when ALL upstream non-branch paths eventually lead to at least one
+           branch node will `has_branch` be True.
+
+        Special case:
+        - If `target_node` has no upstream nodes AND its type is START or CYCLE_START,
+          it is considered directly reachable from the workflow entry, and therefore
+          has no controlling branch nodes.
 
         Args:
-            target_node (str): The identifier of the target node.
+            target_node (str):
+                The identifier of the node whose upstream control branches
+                are to be resolved.
 
         Returns:
-            tuple[bool, tuple[str]]:
+            tuple[bool, tuple[tuple[str, str]]]:
-                - has_branch (bool): True if all upstream non-branch paths lead to at least
+                - has_branch (bool):
-                  one branch node; False if any path reaches a start node without a branch.
+                    True if every upstream path from `target_node` encounters
-                - branch_nodes (tuple[str]): A deduplicated tuple of upstream branch node IDs
+                    at least one branch node.
-                  affecting `target_node`. Returns an empty tuple if `has_branch` is False.
+                    False if any path reaches a start node without a branch.
+                - branch_nodes (tuple[tuple[str, str]]):
+                    A deduplicated tuple of `(branch_node_id, branch_label)` pairs
+                    representing all branch nodes that can influence `target_node`.
+                    Returns an empty tuple if `has_branch` is False.
         """
         source_nodes = [
-            edge.get("source")
+            {
+                "id": edge.get("source"),
+                "branch": edge.get("label")
+            }
             for edge in self.edges
             if edge.get("target") == target_node
         ]
@@ -249,11 +268,13 @@ class GraphBuilder:
         branch_nodes = []
         non_branch_nodes = []
 
-        for node_id in source_nodes:
+        for node_info in source_nodes:
-            if self.get_node_type(node_id) in BRANCH_NODES:
+            if self.get_node_type(node_info["id"]) in BRANCH_NODES:
-                branch_nodes.append(node_id)
+                branch_nodes.append(
+                    (node_info["id"], node_info["branch"])
+                )
             else:
-                non_branch_nodes.append(node_id)
+                non_branch_nodes.append(node_info["id"])
 
         has_branch = True
         for node_id in non_branch_nodes:
@@ -334,7 +355,7 @@ class GraphBuilder:
                     activate=not has_branch,
 
                     # Branch nodes that control activation of this End node
-                    control_nodes=list(control_nodes),
+                    control_nodes=dict(control_nodes),
 
                     # Convert output segments into OutputContent objects
                     outputs=list(
@@ -362,7 +383,7 @@ class GraphBuilder:
             else:
                 self.end_node_map[end_node_id] = StreamOutputConfig(
                     activate=True,
-                    control_nodes=[],
+                    control_nodes={},
                     outputs=list(
                         [
                             OutputContent(

api/app/core/workflow/nodes/code/config.py

@@ -44,7 +44,7 @@ class CodeNodeConfig(BaseNodeConfig):
         description="code content"
     )
 
-    language: Literal['python3', 'nodejs'] = Field(
+    language: Literal['python3', 'javascript'] = Field(
         ...,
         description="language"
     )

api/app/core/workflow/nodes/code/node.py

@@ -2,6 +2,7 @@ import base64
 import json
 import logging
 import re
+import urllib.parse
 from string import Template
 from textwrap import dedent
 from typing import Any
@@ -14,7 +15,7 @@ from app.core.workflow.nodes.code.config import CodeNodeConfig
 
 logger = logging.getLogger(__name__)
 
-SCRIPT_TEMPLATE = Template(dedent("""
+PYTHON_SCRIPT_TEMPLATE = Template(dedent("""
 $code
 
 import json
@@ -32,6 +33,20 @@ result = "<<RESULT>>" + output_json + "<<RESULT>>"
 print(result)
 """))
 
+NODEJS_SCRIPT_TEMPLATE = Template(dedent("""
+$code
+// decode and prepare input object
+var inputs_obj = JSON.parse(Buffer.from('$inputs_variable', 'base64').toString('utf-8'))
+
+// execute main function
+var output_obj = main(inputs_obj)
+
+// convert output to json and print
+var output_json = JSON.stringify(output_obj)
+var result = `<<RESULT>>$${output_json}<<RESULT>>`
+console.log(result)
+"""))
+
 
 class CodeNode(BaseNode):
     def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
@@ -83,18 +98,27 @@ class CodeNode(BaseNode):
         input_variable_dict = {}
         for input_variable in self.typed_config.input_variables:
             input_variable_dict[input_variable.name] = self.get_variable(input_variable.variable, state)
+
         code = base64.b64decode(
             self.typed_config.code
         ).decode("utf-8")
+        code = urllib.parse.unquote(code, encoding='utf-8')
 
         input_variable_dict = base64.b64encode(
             json.dumps(input_variable_dict).encode("utf-8")
         ).decode("utf-8")
-
+        if self.typed_config.language == "python3":
-        final_script = SCRIPT_TEMPLATE.substitute(
+            final_script = PYTHON_SCRIPT_TEMPLATE.substitute(
-            code=code,
+                code=code,
-            inputs_variable=input_variable_dict,
+                inputs_variable=input_variable_dict,
-        )
+            )
+        elif self.typed_config.language == 'javascript':
+            final_script = NODEJS_SCRIPT_TEMPLATE.substitute(
+                code=code,
+                inputs_variable=input_variable_dict,
+            )
+        else:
+            raise ValueError(f"Unsupported language: {self.typed_config.language}")
 
         async with httpx.AsyncClient() as client:
             response = await client.post(

api/app/core/workflow/nodes/memory/config.py

@@ -25,6 +25,6 @@ class MemoryWriteNodeConfig(BaseNodeConfig):
         ...
     )
 
-    config_id: UUID = Field(
+    config_id: UUID | int = Field(
         ...
     )

api/app/core/workflow/nodes/memory/node.py

@@ -36,9 +36,10 @@ class MemoryReadNode(BaseNode):
 class MemoryWriteNode(BaseNode):
     def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
         super().__init__(node_config, workflow_config)
-        self.typed_config = MemoryWriteNodeConfig(**self.config)
+        self.typed_config: MemoryWriteNodeConfig | None = None
 
     async def execute(self, state: WorkflowState) -> Any:
+        self.typed_config = MemoryWriteNodeConfig(**self.config)
         end_user_id = self.get_variable("sys.user_id", state)
 
         if not end_user_id:

api/app/core/workflow/nodes/parameter_extractor/node.py

@@ -23,6 +23,18 @@ class ParameterExtractorNode(BaseNode):
     def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
         super().__init__(node_config, workflow_config)
         self.typed_config: ParameterExtractorNodeConfig | None = None
+        self.response_metadata = {}
+
+    def _extract_token_usage(self, business_result: Any) -> dict[str, int] | None:
+        if self.response_metadata:
+            usage = self.response_metadata.get('token_usage')
+            if usage:
+                return {
+                    "prompt_tokens": usage.get('prompt_tokens', 0),
+                    "completion_tokens": usage.get('completion_tokens', 0),
+                    "total_tokens": usage.get('total_tokens', 0)
+                }
+        return None
 
     @staticmethod
     def _get_prompt():
@@ -171,6 +183,7 @@ class ParameterExtractorNode(BaseNode):
             ])
 
         model_resp = await llm.ainvoke(messages)
+        self.response_metadata = model_resp.response_metadata
         result = json_repair.repair_json(model_resp.content, return_objects=True)
         logger.info(f"node: {self.node_id} get params:{result}")
 

api/app/core/workflow/nodes/question_classifier/node.py

@@ -23,6 +23,18 @@ class QuestionClassifierNode(BaseNode):
         super().__init__(node_config, workflow_config)
         self.typed_config: QuestionClassifierNodeConfig | None = None
         self.category_to_case_map = {}
+        self.response_metadata = {}
+
+    def _extract_token_usage(self, business_result: Any) -> dict[str, int] | None:
+        if self.response_metadata:
+            usage = self.response_metadata.get('token_usage')
+            if usage:
+                return {
+                    "prompt_tokens": usage.get('prompt_tokens', 0),
+                    "completion_tokens": usage.get('completion_tokens', 0),
+                    "total_tokens": usage.get('total_tokens', 0)
+                }
+        return None
 
     def _get_llm_instance(self) -> RedBearLLM:
         """获取LLM实例"""
@@ -112,6 +124,7 @@ class QuestionClassifierNode(BaseNode):
 
             response = await llm.ainvoke(messages)
             result = response.content.strip()
+            self.response_metadata = response.response_metadata
 
             if result in category_names:
                 category = result

api/app/core/workflow/template_loader.py

@@ -4,16 +4,19 @@
 从文件系统加载预定义的工作流模板
 """
 
+import os
 from pathlib import Path
 from typing import Optional
 
 import yaml
 
+TEMPLATE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'templates')
+
 
 class TemplateLoader:
     """工作流模板加载器"""
-    
+
-    def __init__(self, templates_dir: str = "app/templates/workflows"):
+    def __init__(self, templates_dir: str = TEMPLATE_DIR):
         """初始化模板加载器
         
         Args:
@@ -22,7 +25,7 @@ class TemplateLoader:
         self.templates_dir = Path(templates_dir)
         if not self.templates_dir.exists():
             raise ValueError(f"模板目录不存在: {templates_dir}")
-    
+
     def list_templates(self) -> list[dict]:
         """列出所有可用的模板
         
@@ -30,22 +33,22 @@ class TemplateLoader:
             模板列表，每个模板包含 id, name, description 等信息
         """
         templates = []
-        
+
         # 遍历模板目录
         for template_dir in self.templates_dir.iterdir():
             if not template_dir.is_dir():
                 continue
-            
+
             # 检查是否有 template.yml 文件
             template_file = template_dir / "template.yml"
             if not template_file.exists():
                 continue
-            
+
             try:
                 # 读取模板配置
                 with open(template_file, 'r', encoding='utf-8') as f:
                     template_data = yaml.safe_load(f)
-                
+
                 # 提取模板信息
                 templates.append({
                     "id": template_dir.name,
@@ -59,9 +62,9 @@ class TemplateLoader:
             except Exception as e:
                 print(f"加载模板 {template_dir.name} 失败: {e}")
                 continue
-        
+
         return templates
-    
+
     def load_template(self, template_id: str) -> Optional[dict]:
         """加载指定的模板
         
@@ -73,14 +76,14 @@ class TemplateLoader:
         """
         template_dir = self.templates_dir / template_id
         template_file = template_dir / "template.yml"
-        
+
         if not template_file.exists():
             return None
-        
+
         try:
             with open(template_file, 'r', encoding='utf-8') as f:
                 template_data = yaml.safe_load(f)
-            
+
             # 返回工作流配置部分
             return {
                 "name": template_data.get("name", template_id),
@@ -94,7 +97,7 @@ class TemplateLoader:
         except Exception as e:
             print(f"加载模板 {template_id} 失败: {e}")
             return None
-    
+
     def get_template_readme(self, template_id: str) -> Optional[str]:
         """获取模板的 README 文档
         
@@ -106,10 +109,10 @@ class TemplateLoader:
         """
         template_dir = self.templates_dir / template_id
         readme_file = template_dir / "README.md"
-        
+
         if not readme_file.exists():
             return None
-        
+
         try:
             with open(readme_file, 'r', encoding='utf-8') as f:
                 return f.read()

api/app/main.py

@@ -16,6 +16,8 @@ from app.core.error_codes import BizCode, HTTP_MAPPING
 from app.core.exceptions import BusinessException
 from app.core.logging_config import LoggingConfig, get_logger
 from app.core.response_utils import fail
+from app.core.models.scripts.loader import load_models
+from app.db import get_db_context
 
 # Initialize logging system
 LoggingConfig.setup_logging()
@@ -47,6 +49,15 @@ async def lifespan(app: FastAPI):
     else:
         logger.info("自动数据库升级已禁用 (DB_AUTO_UPGRADE=false)")
 
+    # 加载预定义模型
+    logger.info("开始加载预定义模型...")
+    try:
+        with get_db_context() as db:
+            result = load_models(db, silent=True)
+            logger.info(f"预定义模型加载完成: 成功{result['success']}个, 跳过{result['skipped']}个, 失败{result['failed']}个")
+    except Exception as e:
+        logger.warning(f"加载预定义模型时出错: {str(e)}")
+
     logger.info("应用程序启动完成")
     yield
     # 应用关闭事件

api/app/models/__init__.py

@@ -28,6 +28,10 @@ from .tool_model import (
     ToolExecution, ToolType, ToolStatus, AuthType, ExecutionStatus
 )
 from .memory_perceptual_model import MemoryPerceptualModel
+from .ontology_scene import OntologyScene
+from .ontology_class import OntologyClass
+from .ontology_scene import OntologyScene
+from .ontology_class import OntologyClass
 
 __all__ = [
     "Tenants",

api/app/models/memory_config_model.py

@@ -20,6 +20,9 @@ class MemoryConfig(Base):
     end_user_id = Column(String, nullable=True, comment="组ID")
     user_id = Column(String, nullable=True, comment="用户ID")
     apply_id = Column(String, nullable=True, comment="应用ID")
+    
+    # 本体场景关联
+    scene_id = Column(UUID(as_uuid=True), nullable=True, comment="本体场景ID，关联ontology_scene表")
 
     # 模型选择（从workspace继承）
     llm_id = Column(String, nullable=True, comment="LLM模型配置ID")

api/app/models/models_model.py

@@ -5,6 +5,7 @@ from enum import StrEnum
 from sqlalchemy import Column, String, Boolean, DateTime, Text, ForeignKey, Enum as SQLEnum, UniqueConstraint, Integer, ARRAY, Table
 from sqlalchemy.dialects.postgresql import UUID, JSON
 from sqlalchemy.orm import relationship
+from sqlalchemy.sql import func
 from app.db import Base
 
 
@@ -23,6 +24,8 @@ class ModelType(StrEnum):
     CHAT = "chat"
     EMBEDDING = "embedding"
     RERANK = "rerank"
+    # TTS = "tts"
+    # SPEECH2TEXT = "speech2text"
     # IMAGE = "image"
     # AUDIO = "audio"
     # VISION = "vision"
@@ -48,8 +51,7 @@ class ModelProvider(StrEnum):
 class LoadBalanceStrategy(StrEnum):
     """API Key负载均衡策略枚举"""
     ROUND_ROBIN = "round_robin"  # 轮询
-    WEIGHTED_ROUND_ROBIN = "weighted_round_robin"  # 加权轮询
+    NONE = "none"  # 无
-    RANDOM = "random"  # 随机
 
 
 # 多对多关联表
@@ -90,7 +92,8 @@ class ModelConfig(BaseModel):
     
     # 状态管理
     is_public = Column(Boolean, default=False, nullable=False, comment="是否公开")
-    load_balance_strategy = Column(String, nullable=True, comment="负载均衡策略")
+    load_balance_strategy = Column(String, nullable=True, comment="负载均衡策略", default=LoadBalanceStrategy.NONE,
+                                   server_default=LoadBalanceStrategy.NONE)
     
     # 关联关系
     model_base = relationship("ModelBase", back_populates="configs")
@@ -151,6 +154,7 @@ class ModelBase(Base):
     is_official = Column(Boolean, default=True, comment="是否供应商官方模型（区分自定义）")
     tags = Column(ARRAY(String), default=list, nullable=False, comment="模型标签（如['聊天', '创作']）")
     add_count = Column(Integer, default=0, nullable=False, comment="模型被用户添加的次数")
+    created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间", server_default=func.now())
 
     # 关联关系
     configs = relationship("ModelConfig", back_populates="model_base", cascade="all, delete-orphan")

api/app/models/ontology_class.py

@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+"""本体类型模型
+
+本模块定义本体类型的数据模型。
+
+Classes:
+    OntologyClass: 本体类型表模型
+"""
+
+import datetime
+import uuid
+from sqlalchemy import Column, String, DateTime, Text, ForeignKey
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import relationship
+from app.db import Base
+
+
+class OntologyClass(Base):
+    """本体类型表 - 用于存储某个场景提取出来的本体类型信息"""
+    __tablename__ = "ontology_class"
+
+    # 主键
+    class_id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True, comment="类型ID")
+
+    # 类型信息
+    class_name = Column(String(200), nullable=False, comment="类型名称")
+    class_description = Column(Text, nullable=True, comment="类型描述")
+
+    # 外键：关联到本体场景
+    scene_id = Column(UUID(as_uuid=True), ForeignKey("ontology_scene.scene_id", ondelete="CASCADE"), nullable=False, index=True, comment="所属场景ID")
+
+    # 时间戳
+    created_at = Column(DateTime, default=datetime.datetime.now, nullable=False, comment="创建时间")
+    updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, nullable=False, comment="更新时间")
+
+    # 关系：类型属于某个场景
+    scene = relationship("OntologyScene", back_populates="classes")
+
+    def __repr__(self):
+        return f"<OntologyClass(id={self.class_id}, name={self.class_name}, scene_id={self.scene_id})>"

api/app/models/ontology_scene.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+"""本体场景模型
+
+本模块定义本体场景的数据模型。
+
+Classes:
+    OntologyScene: 本体场景表模型
+"""
+
+import datetime
+import uuid
+from sqlalchemy import Column, String, DateTime, Integer, Text, ForeignKey, UniqueConstraint
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import relationship
+from app.db import Base
+
+
+class OntologyScene(Base):
+    """本体场景表 - 用于存储本体场景下不同的类型信息"""
+    __tablename__ = "ontology_scene"
+    __table_args__ = (
+        UniqueConstraint('workspace_id', 'scene_name', name='uq_workspace_scene_name'),
+    )
+
+    # 主键
+    scene_id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True, comment="场景ID")
+
+    # 场景信息
+    scene_name = Column(String(200), nullable=False, comment="场景名称")
+    scene_description = Column(Text, nullable=True, comment="场景描述")
+
+    # 外键：关联到工作空间
+    workspace_id = Column(UUID(as_uuid=True), ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False, index=True, comment="所属工作空间ID")
+
+    # 时间戳
+    created_at = Column(DateTime, default=datetime.datetime.now, nullable=False, comment="创建时间")
+    updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, nullable=False, comment="更新时间")
+
+    # 关系：一个场景可以有多个类型
+    classes = relationship("OntologyClass", back_populates="scene", cascade="all, delete-orphan")
+
+    def __repr__(self):
+        return f"<OntologyScene(id={self.scene_id}, name={self.scene_name})>"

api/app/models/prompt_optimizer_model.py

@@ -2,7 +2,7 @@ import datetime
 import uuid
 from enum import StrEnum
 
-from sqlalchemy import Column, ForeignKey, Text, DateTime, String, Index
+from sqlalchemy import Column, ForeignKey, Text, DateTime, String, Index, Boolean
 from sqlalchemy.dialects.postgresql import UUID
 
 from app.db import Base
@@ -121,10 +121,33 @@ class PromptOptimizerSessionHistory(Base):
     id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True)
     tenant_id = Column(UUID(as_uuid=True), ForeignKey("tenants.id"), nullable=False, comment="Tenant ID")
     # app_id = Column(UUID(as_uuid=True), ForeignKey("apps.id"), nullable=False, comment="Application ID")
-    session_id = Column(UUID(as_uuid=True), ForeignKey("prompt_opt_session_list.id"),nullable=False, comment="Session ID")
+    session_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("prompt_opt_session_list.id"),
+        nullable=False,
+        comment="Session ID"
+    )
     user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False, comment="User ID")
     role = Column(String, nullable=False, comment="Message Role")
     content = Column(Text, nullable=False, comment="Message Content")
     # prompt = Column(Text, nullable=False, comment="Prompt")
 
     created_at = Column(DateTime, default=datetime.datetime.now, comment="Creation Time", index=True)
+
+
+class PromptHistory(Base):
+    __tablename__ = "prompt_history"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True)
+    tenant_id = Column(UUID(as_uuid=True), ForeignKey("tenants.id"), nullable=False, comment="Tenant ID")
+
+    session_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("prompt_opt_session_list.id"),
+        nullable=False,
+        comment="Session ID"
+    )
+    title = Column(String, nullable=False, comment="Title")
+    prompt = Column(Text, nullable=False, comment="Prompt")
+    created_at = Column(DateTime, default=datetime.datetime.now, comment="Creation Time", index=True)
+    is_delete = Column(Boolean, default=False, comment="Delete")

api/app/repositories/memory_config_repository.py

@@ -24,12 +24,16 @@ from app.schemas.memory_storage_schema import (
 from sqlalchemy import desc, select
 from sqlalchemy.orm import Session
 
+from app.utils.config_utils import resolve_config_id
+
 # 获取数据库专用日志器
 db_logger = get_db_logger()
 # 获取配置专用日志器
 config_logger = get_config_logger()
 
 TABLE_NAME = "memory_config"
+
+
 class MemoryConfigRepository:
     """记忆配置Repository
 
@@ -82,7 +86,8 @@ class MemoryConfigRepository:
         n.description AS description, 
         n.entity_type AS entity_type, 
         n.name AS name,
-        COALESCE(n.fact_summary, '') AS fact_summary,
+        // TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+        // COALESCE(n.fact_summary, '') AS fact_summary,
         n.end_user_id AS end_user_id,
         n.apply_id AS apply_id,
         n.user_id AS user_id,
@@ -152,7 +157,7 @@ class MemoryConfigRepository:
         return memory_config_obj
 
     @staticmethod
-    def query_reflection_config_by_id(db: Session, config_id: uuid.UUID) -> MemoryConfig:
+    def query_reflection_config_by_id(db: Session, config_id: uuid.UUID|int|str) -> MemoryConfig:
         """构建反思配置查询语句，通过config_id查询反思配置（SQLAlchemy text() 命名参数）
 
         Args:
@@ -187,7 +192,6 @@ class MemoryConfigRepository:
             raise RuntimeError("reflection config not found")
         return memory_config
 
-
     @staticmethod
     def build_select_all(workspace_id: uuid.UUID) -> Tuple[str, Dict]:
         """构建查询所有配置的语句（SQLAlchemy text() 命名参数）
@@ -227,9 +231,12 @@ class MemoryConfigRepository:
                 config_name=params.config_name,
                 config_desc=params.config_desc,
                 workspace_id=params.workspace_id,
+                scene_id=params.scene_id,
                 llm_id=params.llm_id,
                 embedding_id=params.embedding_id,
                 rerank_id=params.rerank_id,
+                reflection_model_id=params.reflection_model_id,
+                emotion_model_id=params.emotion_model_id,
             )
             db.add(db_config)
             db.flush()  # 获取自增ID但不提交事务
@@ -272,6 +279,9 @@ class MemoryConfigRepository:
             if update.config_desc is not None:
                 db_config.config_desc = update.config_desc
                 has_update = True
+            if update.scene_id is not None:
+                db_config.scene_id = update.scene_id
+                has_update = True
 
             if not has_update:
                 raise ValueError("No fields to update")
@@ -287,7 +297,6 @@ class MemoryConfigRepository:
             db_logger.error(f"更新记忆配置失败: config_id={update.config_id} - {str(e)}")
             raise
 
-
     @staticmethod
     def update_extracted(db: Session, update: ConfigUpdateExtracted) -> Optional[MemoryConfig]:
         """更新记忆萃取引擎配置
@@ -410,7 +419,7 @@ class MemoryConfigRepository:
             raise
 
     @staticmethod
-    def get_extracted_config(db: Session, config_id: UUID) -> Optional[Dict]:
+    def get_extracted_config(db: Session, config_id: UUID | int) -> Optional[Dict]:
         """获取萃取配置，通过主键查询某条配置
 
         Args:
@@ -420,8 +429,8 @@ class MemoryConfigRepository:
         Returns:
             Optional[Dict]: 萃取配置字典，不存在则返回None
         """
+        config_id = resolve_config_id(config_id, db)
         db_logger.debug(f"查询萃取配置: config_id={config_id}")
-
         try:
             db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first()
             if not db_config:
@@ -514,26 +523,27 @@ class MemoryConfigRepository:
         except Exception as e:
             db_logger.error(f"根据ID查询记忆配置失败: config_id={config_id} - {str(e)}")
             raise
+
     @staticmethod
-    def get_config_with_workspace(db: Session, config_id: uuid.UUID) -> Optional[tuple]:
+    def get_config_with_workspace(db: Session, config_id: uuid.UUID | int | str) -> Optional[tuple]:
         """Get memory config and its associated workspace information
-        
+
         Args:
             db: Database session
             config_id: Configuration ID
-            
+
         Returns:
             Optional[tuple]: (MemoryConfig, Workspace) tuple, None if not found
-            
         Raises:
             ValueError: Raised when config exists but workspace doesn't
         """
         import time
 
         from app.models.workspace_model import Workspace
-        
+
         start_time = time.time()
-        
+        config_id = resolve_config_id(config_id, db)
+
         # Log configuration loading start
         config_logger.info(
             "Loading configuration with workspace",
@@ -542,17 +552,16 @@ class MemoryConfigRepository:
                 "config_id": config_id
             }
         )
-        
+
         db_logger.debug(f"Querying memory config and workspace: config_id={config_id}")
-        
+
         try:
             # Use join query to get both config and workspace
             result = db.query(MemoryConfig, Workspace).join(
                 Workspace, MemoryConfig.workspace_id == Workspace.id
             ).filter(MemoryConfig.config_id == config_id).first()
-            
             elapsed_ms = (time.time() - start_time) * 1000
-            
+
             if not result:
                 # Check if config exists but workspace is missing
                 config_only = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first()
@@ -581,9 +590,11 @@ class MemoryConfigRepository:
                                 "elapsed_ms": elapsed_ms
                             }
                         )
-                        db_logger.error(f"Memory config {config_id} references non-existent workspace {config_only.workspace_id}")
+                        db_logger.error(
-                        raise ValueError(f"Workspace {config_only.workspace_id} not found for configuration {config_id}")
+                            f"Memory config {config_id} references non-existent workspace {config_only.workspace_id}")
-                
+                        raise ValueError(
+                            f"Workspace {config_only.workspace_id} not found for configuration {config_id}")
+
                 config_logger.debug(
                     "Configuration not found",
                     extra={
@@ -595,9 +606,9 @@ class MemoryConfigRepository:
                 )
                 db_logger.debug(f"Memory config not found: config_id={config_id}")
                 return None
-            
+
             config, workspace = result
-            
+
             # Log successful configuration loading
             config_logger.info(
                 "Configuration with workspace loaded successfully",
@@ -612,16 +623,17 @@ class MemoryConfigRepository:
                     "elapsed_ms": elapsed_ms
                 }
             )
-            
+
-            db_logger.debug(f"Memory config and workspace query successful: config={config.config_name}, workspace={workspace.name}")
+            db_logger.debug(
+                f"Memory config and workspace query successful: config={config.config_name}, workspace={workspace.name}")
             return (config, workspace)
-            
+
         except ValueError:
             # Re-raise known business exceptions
             raise
         except Exception as e:
             elapsed_ms = (time.time() - start_time) * 1000
-            
+
             config_logger.error(
                 "Failed to load configuration with workspace",
                 extra={
@@ -634,32 +646,36 @@ class MemoryConfigRepository:
                 },
                 exc_info=True
             )
-            
             db_logger.error(f"Failed to query memory config and workspace: config_id={config_id} - {str(e)}")
             raise
+
     @staticmethod
-    def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[MemoryConfig]:
+    def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[Tuple[MemoryConfig, Optional[str]]]:
-        """获取所有配置参数
+        """获取所有配置参数，包含关联的场景名称
 
         Args:
             db: 数据库会话
             workspace_id: 工作空间ID，用于过滤查询结果
 
         Returns:
-            List[MemoryConfig]: 配置列表
+            List[Tuple[MemoryConfig, Optional[str]]]: 配置列表，每项为 (配置对象, 场景名称)
         """
+        from app.models.ontology_scene import OntologyScene
+        
         db_logger.debug(f"查询所有配置: workspace_id={workspace_id}")
 
         try:
-            query = db.query(MemoryConfig)
+            query = db.query(MemoryConfig, OntologyScene.scene_name).outerjoin(
+                OntologyScene, MemoryConfig.scene_id == OntologyScene.scene_id
+            )
 
             if workspace_id:
                 query = query.filter(MemoryConfig.workspace_id == workspace_id)
 
-            configs = query.order_by(desc(MemoryConfig.updated_at)).all()
+            results = query.order_by(desc(MemoryConfig.updated_at)).all()
 
-            db_logger.debug(f"配置列表查询成功: 数量={len(configs)}")
+            db_logger.debug(f"配置列表查询成功: 数量={len(results)}")
-            return configs
+            return results
 
         except Exception as e:
             db_logger.error(f"查询所有配置失败: workspace_id={workspace_id} - {str(e)}")

api/app/repositories/model_repository.py

@@ -165,7 +165,7 @@ class ModelConfigRepository:
             total = base_query.count()
 
             # 分页查询
-            models = base_query.order_by(desc(ModelConfig.updated_at)).offset(
+            models = base_query.order_by(desc(ModelConfig.created_at)).offset(
                 (query.page - 1) * query.pagesize
             ).limit(query.pagesize).all()
 
@@ -234,7 +234,7 @@ class ModelConfigRepository:
             # 获取总数
             total = base_query.count()
 
-            query_results = base_query.order_by(desc(ModelConfig.updated_at)).all()
+            query_results = base_query.order_by(desc(ModelConfig.created_at)).all()
 
             provider_groups: Dict[str, List[ModelConfig]] = {}
             for model_config in query_results:
@@ -433,6 +433,7 @@ class ModelConfigRepository:
                         ModelConfig.is_public
                     ),
                     ModelBase.provider == provider,
+                    ModelConfig.is_active,
                     ~ModelConfig.is_composite
                 )
             ).distinct().all()
@@ -621,7 +622,7 @@ class ModelBaseRepository:
         if filters:
             q = q.filter(and_(*filters))
         
-        return q.order_by(ModelBase.add_count.desc()).all()
+        return q.order_by(ModelBase.add_count.desc(), ModelBase.created_at.desc()).all()
 
     @staticmethod
     def create(db: Session, data: dict) -> 'ModelBase':
@@ -629,6 +630,13 @@ class ModelBaseRepository:
         db.add(model_base)
         return model_base
 
+    @staticmethod
+    def get_by_name_and_provider(db: Session, name: str, provider: str) -> Optional['ModelBase']:
+        return db.query(ModelBase).filter(
+            ModelBase.name == name,
+            ModelBase.provider == provider
+        ).first()
+
     @staticmethod
     def update(db: Session, model_base_id: uuid.UUID, data: dict) -> Optional['ModelBase']:
         model_base = db.query(ModelBase).filter(ModelBase.id == model_base_id).first()
@@ -636,6 +644,17 @@ class ModelBaseRepository:
             return None
         for key, value in data.items():
             setattr(model_base, key, value)
+        
+        # 同步更新绑定的非组合模型配置
+        if any(k in data for k in ['name', 'description', 'logo']):
+            db.query(ModelConfig).filter(
+                ModelConfig.model_id == model_base_id,
+                ModelConfig.is_composite == False
+            ).update({
+                k: v for k, v in data.items() 
+                if k in ['name', 'description', 'logo']
+            }, synchronize_session=False)
+        
         return model_base
 
     @staticmethod

api/app/repositories/neo4j/add_edges.py

@@ -79,7 +79,8 @@ async def add_memory_summary_statement_edges(summaries: List[MemorySummaryNode],
     try:
         edges: List[dict] = []
         for s in summaries:
-            for chunk_id in getattr(s, "chunk_ids", []) or []:
+            chunk_ids = getattr(s, "chunk_ids", []) or []
+            for chunk_id in chunk_ids:
                 edges.append({
                     "summary_id": s.id,
                     "chunk_id": chunk_id,
@@ -91,12 +92,11 @@ async def add_memory_summary_statement_edges(summaries: List[MemorySummaryNode],
 
         if not edges:
             return []
-
         result = await connector.execute_query(
             MEMORY_SUMMARY_STATEMENT_EDGE_SAVE,
             edges=edges
         )
         created = [record.get("uuid") for record in result] if result else []
         return created
-    except Exception:
+    except Exception as e:
         return None

api/app/repositories/neo4j/add_nodes.py

@@ -217,8 +217,10 @@ async def add_memory_summary_nodes(summaries: List[MemorySummaryNode], connector
             summaries=flattened
         )
         created_ids = [record.get("uuid") for record in result]
+        print(f"Successfully saved {len(created_ids)} MemorySummary nodes to Neo4j")
         return created_ids
-    except Exception:
+    except Exception as e:
+        print(f"Failed to save MemorySummary nodes to Neo4j: {e}")
         return None
 
 

api/app/repositories/neo4j/cypher_queries.py

@@ -101,10 +101,11 @@ SET e.name = CASE WHEN entity.name IS NOT NULL AND entity.name <> '' THEN entity
     e.name_embedding = CASE
         WHEN entity.name_embedding IS NOT NULL AND size(entity.name_embedding) > 0 THEN entity.name_embedding
         ELSE e.name_embedding END,
-    e.fact_summary = CASE
+    // TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
-        WHEN entity.fact_summary IS NOT NULL AND entity.fact_summary <> ''
+    // e.fact_summary = CASE
-         AND (e.fact_summary IS NULL OR size(e.fact_summary) = 0 OR size(entity.fact_summary) > size(e.fact_summary))
+    //     WHEN entity.fact_summary IS NOT NULL AND entity.fact_summary <> ''
-        THEN entity.fact_summary ELSE e.fact_summary END,
+    //      AND (e.fact_summary IS NULL OR size(e.fact_summary) = 0 OR size(entity.fact_summary) > size(e.fact_summary))
+    //     THEN entity.fact_summary ELSE e.fact_summary END,
     e.connect_strength = CASE
         WHEN entity.connect_strength IS NULL OR entity.connect_strength = '' THEN e.connect_strength
         ELSE CASE
@@ -321,7 +322,8 @@ RETURN e.id AS id,
        e.description AS description,
        e.aliases AS aliases,
        e.name_embedding AS name_embedding,
-       COALESCE(e.fact_summary, '') AS fact_summary,
+       // TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+       // COALESCE(e.fact_summary, '') AS fact_summary,
        e.connect_strength AS connect_strength,
        collect(DISTINCT s.id) AS statement_ids,
        collect(DISTINCT c.id) AS chunk_ids,
@@ -877,7 +879,8 @@ RETURN
     CASE
       WHEN ms:ExtractedEntity THEN {
         text: ms.name,
-        created_at: ms.created_at
+        created_at: ms.created_at,
+        type: "情景记忆" 
       }
     END
   ) AS ExtractedEntity,
@@ -887,7 +890,8 @@ RETURN
     CASE
       WHEN n:MemorySummary THEN {
         text: n.content,
-        created_at: n.created_at
+        created_at: n.created_at,
+        type: "长期沉淀" 
       }
     END
   ) AS MemorySummary,
@@ -895,7 +899,8 @@ RETURN
   collect(
     DISTINCT {
       text: e.statement,
-      created_at: e.created_at
+      created_at: e.created_at,
+      type: "情绪记忆" 
     }
   ) AS statement;
 """
@@ -999,3 +1004,58 @@ RETURN DISTINCT
  x.statement as statement,x.created_at as created_at
 """
 
+Graph_Node_query = """
+            MATCH (n:MemorySummary)
+                WHERE n.end_user_id = $end_user_id
+                RETURN
+                  elementId(n) AS id,
+                  labels(n) AS labels,
+                  properties(n) AS properties,
+                  0 AS priority
+                LIMIT $limit
+                
+                UNION ALL
+
+                    MATCH (n:Dialogue)
+                    WHERE n.end_user_id =  $end_user_id
+                    RETURN
+                      elementId(n) AS id,
+                      labels(n) AS labels,
+                      properties(n) AS properties,
+                      1 AS priority
+                    LIMIT 1
+
+                UNION ALL
+
+                MATCH (n:Statement)
+                WHERE n.end_user_id =  $end_user_id
+                RETURN
+                  elementId(n) AS id,
+                  labels(n) AS labels,
+                  properties(n) AS properties,
+                  1 AS priority
+                LIMIT $limit
+
+                UNION ALL
+
+                MATCH (n:ExtractedEntity)
+                WHERE n.end_user_id =  $end_user_id
+                RETURN
+                  elementId(n) AS id,
+                  labels(n) AS labels,
+                  properties(n) AS properties,
+                  2 AS priority
+                LIMIT $limit
+
+                UNION ALL
+
+                MATCH (n:Chunk)
+                WHERE n.end_user_id =  $end_user_id
+                RETURN
+                  elementId(n) AS id,
+                  labels(n) AS labels,
+                  properties(n) AS properties,
+                  3 AS priority
+                LIMIT $limit
+
+            """

api/app/repositories/neo4j/graph_saver.py

@@ -21,7 +21,8 @@ from app.core.memory.models.graph_models import (
     ExtractedEntityNode,
     EntityEntityEdge,
 )
-
+import logging
+logger = logging.getLogger(__name__)
 async def save_entities_and_relationships(
     entity_nodes: List[ExtractedEntityNode],
     entity_entity_edges: List[EntityEntityEdge],
@@ -41,8 +42,8 @@ async def save_entities_and_relationships(
             'statement': edge.statement,
             'valid_at': edge.valid_at.isoformat() if edge.valid_at else None,
             'invalid_at': edge.invalid_at.isoformat() if edge.invalid_at else None,
-            'created_at': edge.created_at.isoformat(),
+            'created_at': edge.created_at.isoformat() if edge.created_at else None,
-            'expired_at': edge.expired_at.isoformat(),
+            'expired_at': edge.expired_at.isoformat() if edge.expired_at else None,
             'run_id': edge.run_id,
             'end_user_id': edge.end_user_id,
         }
@@ -147,14 +148,14 @@ async def save_statement_entity_edges(
 
 
 async def save_dialog_and_statements_to_neo4j(
-    dialogue_nodes: List[DialogueNode],
+        dialogue_nodes: List[DialogueNode],
-    chunk_nodes: List[ChunkNode],
+        chunk_nodes: List[ChunkNode],
-    statement_nodes: List[StatementNode],
+        statement_nodes: List[StatementNode],
-    entity_nodes: List[ExtractedEntityNode],
+        entity_nodes: List[ExtractedEntityNode],
-    entity_edges: List[EntityEntityEdge],
+        entity_edges: List[EntityEntityEdge],
-    statement_chunk_edges: List[StatementChunkEdge],
+        statement_chunk_edges: List[StatementChunkEdge],
-    statement_entity_edges: List[StatementEntityEdge],
+        statement_entity_edges: List[StatementEntityEdge],
-    connector: Neo4jConnector
+        connector: Neo4jConnector
 ) -> bool:
     """Save dialogue nodes, chunk nodes, statement nodes, entities, and all relationships to Neo4j using graph models.
 
@@ -171,40 +172,127 @@ async def save_dialog_and_statements_to_neo4j(
     Returns:
         bool: True if successful, False otherwise
     """
-    try:
+
-        # Save all dialogue nodes in batch
+    # 定义事务函数，将所有写操作放在一个事务中
-        dialogue_uuids = await add_dialogue_nodes(dialogue_nodes, connector)
+    async def _save_all_in_transaction(tx):
-        if dialogue_uuids:
+        """在单个事务中执行所有保存操作，避免死锁"""
+        results = {}
+
+        # 1. Save all dialogue nodes in batch
+        if dialogue_nodes:
+            from app.repositories.neo4j.cypher_queries import DIALOGUE_NODE_SAVE
+            dialogue_data = [node.model_dump() for node in dialogue_nodes]
+            result = await tx.run(DIALOGUE_NODE_SAVE, dialogues=dialogue_data)
+            dialogue_uuids = [record["uuid"] async for record in result]
+            results['dialogues'] = dialogue_uuids
             print(f"Dialogues saved to Neo4j with UUIDs: {dialogue_uuids}")
-        else:
-            print("Failed to save dialogues to Neo4j")
-            return False
 
-        # Save all chunk nodes in batch
+        # 2. Save all chunk nodes in batch
-        await save_chunk_nodes(chunk_nodes, connector)
+        if chunk_nodes:
+            from app.repositories.neo4j.cypher_queries import CHUNK_NODE_SAVE
+            chunk_data = [node.model_dump() for node in chunk_nodes]
+            result = await tx.run(CHUNK_NODE_SAVE, chunks=chunk_data)
+            chunk_uuids = [record["uuid"] async for record in result]
+            results['chunks'] = chunk_uuids
+            logger.info(f"Successfully saved {len(chunk_uuids)} chunk nodes to Neo4j")
 
-        # Save all statement nodes in batch
+        # 3. Save all statement nodes in batch
         if statement_nodes:
-            statement_uuids = await add_statement_nodes(statement_nodes, connector)
+            from app.repositories.neo4j.cypher_queries import STATEMENT_NODE_SAVE
-            if statement_uuids:
+            statement_data = [node.model_dump() for node in statement_nodes]
-                print(f"Successfully saved {len(statement_uuids)} statement nodes to Neo4j")
+            result = await tx.run(STATEMENT_NODE_SAVE, statements=statement_data)
-            else:
+            statement_uuids = [record["uuid"] async for record in result]
-                print("Failed to save statement nodes to Neo4j")
+            results['statements'] = statement_uuids
-                return False
+            logger.info(f"Successfully saved {len(statement_uuids)} statement nodes to Neo4j")
-        else:
-            print("No statement nodes to save")
 
-        # Save entities and relationships
+        # 4. Save entities
-        await save_entities_and_relationships(entity_nodes, entity_edges, connector)
+        if entity_nodes:
-        print("Successfully saved entities and relationships to Neo4j")
+            from app.repositories.neo4j.cypher_queries import EXTRACTED_ENTITY_NODE_SAVE
+            entity_data = [entity.model_dump() for entity in entity_nodes]
+            result = await tx.run(EXTRACTED_ENTITY_NODE_SAVE, entities=entity_data)
+            entity_uuids = [record["uuid"] async for record in result]
+            results['entities'] = entity_uuids
+            logger.info(f"Successfully saved {len(entity_uuids)} entity nodes to Neo4j")
 
-        # Save new edges
+        # 5. Create entity relationships
-        await save_statement_chunk_edges(statement_chunk_edges, connector)
+        if entity_edges:
-        await save_statement_entity_edges(statement_entity_edges, connector)
+            from app.repositories.neo4j.cypher_queries import ENTITY_RELATIONSHIP_SAVE
+            relationship_data = []
+            for edge in entity_edges:
+                relationship_data.append({
+                    'source_id': edge.source,
+                    'target_id': edge.target,
+                    'predicate': edge.relation_type,
+                    'statement_id': edge.source_statement_id,
+                    'value': edge.relation_value,
+                    'statement': edge.statement,
+                    'valid_at': edge.valid_at.isoformat() if edge.valid_at else None,
+                    'invalid_at': edge.invalid_at.isoformat() if edge.invalid_at else None,
+                    'created_at': edge.created_at.isoformat() if edge.created_at else None,
+                    'expired_at': edge.expired_at.isoformat() if edge.expired_at else None,
+                    'run_id': edge.run_id,
+                    'end_user_id': edge.end_user_id,
+                })
+            result = await tx.run(ENTITY_RELATIONSHIP_SAVE, relationships=relationship_data)
+            rel_uuids = [record["uuid"] async for record in result]
+            results['entity_relationships'] = rel_uuids
+            logger.info(f"Successfully saved {len(rel_uuids)} entity relationships to Neo4j")
 
+        # 6. Save statement-chunk edges
+        if statement_chunk_edges:
+            from app.repositories.neo4j.cypher_queries import CHUNK_STATEMENT_EDGE_SAVE
+            sc_edge_data = []
+            for edge in statement_chunk_edges:
+                sc_edge_data.append({
+                    "id": edge.id,
+                    "source": edge.source,
+                    "target": edge.target,
+                    "created_at": edge.created_at.isoformat() if edge.created_at else None,
+                    "expired_at": edge.expired_at.isoformat() if edge.expired_at else None,
+                    "run_id": edge.run_id,
+                    "end_user_id": edge.end_user_id,
+                })
+            result = await tx.run(CHUNK_STATEMENT_EDGE_SAVE, chunk_statement_edges=sc_edge_data)
+            sc_uuids = [record["uuid"] async for record in result]
+            results['statement_chunk_edges'] = sc_uuids
+            logger.info(f"Successfully saved {len(sc_uuids)} statement-chunk edges to Neo4j")
+
+        # 7. Save statement-entity edges
+        if statement_entity_edges:
+            from app.repositories.neo4j.cypher_queries import STATEMENT_ENTITY_EDGE_SAVE
+            se_edge_data = []
+            for edge in statement_entity_edges:
+                se_edge_data.append({
+                    "source": edge.source,
+                    "target": edge.target,
+                    "created_at": edge.created_at.isoformat() if edge.created_at else None,
+                    "expired_at": edge.expired_at.isoformat() if edge.expired_at else None,
+                    "run_id": edge.run_id,
+                    "end_user_id": edge.end_user_id,
+                    "connect_strength": getattr(edge, "connect_strength", "strong"),
+                })
+            result = await tx.run(STATEMENT_ENTITY_EDGE_SAVE, relationships=se_edge_data)
+            se_uuids = [record["uuid"] async for record in result]
+            results['statement_entity_edges'] = se_uuids
+            logger.info(f"Successfully saved {len(se_uuids)} statement-entity edges to Neo4j")
+
+        return results
+
+    try:
+        # 使用显式写事务执行所有操作，避免死锁
+        results = await connector.execute_write_transaction(_save_all_in_transaction)
+        summary = {
+            key: len(value)
+            for key, value in results.items()
+            if isinstance(value, (list, tuple, set))
+        }
+        logger.info("Transaction completed. Summary: %s", summary)
+        logger.debug("Full transaction results: %r", results)
         return True
 
     except Exception as e:
+        logger.error(f"Neo4j integration error: {e}", exc_info=True)
         print(f"Neo4j integration error: {e}")
         print("Continuing without database storage...")
         return False
+

api/app/repositories/ontology_class_repository.py

@@ -0,0 +1,404 @@
+# -*- coding: utf-8 -*-
+"""本体类型Repository层
+
+本模块提供本体类型的数据访问层实现。
+
+Classes:
+    OntologyClassRepository: 本体类型数据访问类
+"""
+
+import logging
+from typing import List, Optional
+from uuid import UUID
+
+from sqlalchemy.orm import Session, joinedload
+
+from app.core.logging_config import get_db_logger
+from app.models.ontology_class import OntologyClass
+from app.models.ontology_scene import OntologyScene
+
+
+logger = get_db_logger()
+
+
+class OntologyClassRepository:
+    """本体类型Repository
+    
+    提供本体类型的CRUD操作和权限检查。
+    
+    Attributes:
+        db: SQLAlchemy数据库会话
+    """
+    
+    def __init__(self, db: Session):
+        """初始化Repository
+        
+        Args:
+            db: SQLAlchemy数据库会话
+        """
+        self.db = db
+    
+    def create(self, class_data: dict, scene_id: UUID) -> OntologyClass:
+        """创建本体类型
+        
+        Args:
+            class_data: 类型数据字典，包含class_name和class_description
+            scene_id: 所属场景ID
+            
+        Returns:
+            OntologyClass: 创建的类型对象
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> ontology_class = repo.create(
+            ...     {"class_name": "患者", "class_description": "描述"},
+            ...     scene_id
+            ... )
+        """
+        try:
+            logger.info(
+                f"Creating ontology class - "
+                f"name={class_data.get('class_name')}, "
+                f"scene_id={scene_id}"
+            )
+            
+            ontology_class = OntologyClass(
+                class_name=class_data.get("class_name"),
+                class_description=class_data.get("class_description"),
+                scene_id=scene_id
+            )
+            
+            self.db.add(ontology_class)
+            self.db.flush()  # 获取ID但不提交
+            
+            logger.info(
+                f"Ontology class created successfully - "
+                f"class_id={ontology_class.class_id}"
+            )
+            
+            return ontology_class
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to create ontology class: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_id(self, class_id: UUID) -> Optional[OntologyClass]:
+        """根据ID获取类型
+        
+        Args:
+            class_id: 类型ID
+            
+        Returns:
+            Optional[OntologyClass]: 类型对象，不存在则返回None
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> ontology_class = repo.get_by_id(class_id)
+        """
+        try:
+            logger.debug(f"Getting ontology class by ID: {class_id}")
+            
+            ontology_class = self.db.query(OntologyClass).filter(
+                OntologyClass.class_id == class_id
+            ).first()
+            
+            if ontology_class:
+                logger.debug(f"Ontology class found: {class_id}")
+            else:
+                logger.debug(f"Ontology class not found: {class_id}")
+            
+            return ontology_class
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology class by ID: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_name(self, class_name: str, scene_id: UUID) -> Optional[OntologyClass]:
+        """根据类型名称和场景ID获取类型（精确匹配）
+        
+        Args:
+            class_name: 类型名称
+            scene_id: 场景ID
+            
+        Returns:
+            Optional[OntologyClass]: 类型对象，不存在则返回None
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> ontology_class = repo.get_by_name("患者", scene_id)
+        """
+        try:
+            logger.debug(f"Getting ontology class by name: {class_name}, scene_id: {scene_id}")
+            
+            ontology_class = self.db.query(OntologyClass).filter(
+                OntologyClass.class_name == class_name,
+                OntologyClass.scene_id == scene_id
+            ).first()
+            
+            if ontology_class:
+                logger.debug(f"Ontology class found: {class_name}")
+            else:
+                logger.debug(f"Ontology class not found: {class_name}")
+            
+            return ontology_class
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology class by name: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def search_by_name(self, keyword: str, scene_id: UUID) -> List[OntologyClass]:
+        """根据关键词模糊搜索类型
+        
+        使用 LIKE 进行模糊匹配，支持中文和英文。
+        
+        Args:
+            keyword: 搜索关键词
+            scene_id: 场景ID
+            
+        Returns:
+            List[OntologyClass]: 匹配的类型列表
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> classes = repo.search_by_name("患者", scene_id)
+        """
+        try:
+            logger.debug(
+                f"Searching ontology classes by keyword - "
+                f"keyword={keyword}, scene_id={scene_id}"
+            )
+            
+            # 使用 ilike 进行不区分大小写的模糊匹配
+            classes = self.db.query(OntologyClass).filter(
+                OntologyClass.class_name.ilike(f"%{keyword}%"),
+                OntologyClass.scene_id == scene_id
+            ).order_by(
+                OntologyClass.created_at.desc()
+            ).all()
+            
+            logger.info(
+                f"Found {len(classes)} ontology classes matching keyword '{keyword}' "
+                f"in scene {scene_id}"
+            )
+            
+            return classes
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to search ontology classes by keyword: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_scene(self, scene_id: UUID) -> List[OntologyClass]:
+        """获取场景下的所有类型
+        
+        按创建时间倒序排列。
+        
+        Args:
+            scene_id: 场景ID
+            
+        Returns:
+            List[OntologyClass]: 类型列表
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> classes = repo.get_by_scene(scene_id)
+        """
+        try:
+            logger.debug(f"Getting ontology classes by scene: {scene_id}")
+            
+            classes = self.db.query(OntologyClass).filter(
+                OntologyClass.scene_id == scene_id
+            ).order_by(
+                OntologyClass.created_at.desc()
+            ).all()
+            
+            logger.info(
+                f"Found {len(classes)} ontology classes in scene {scene_id}"
+            )
+            
+            return classes
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology classes by scene: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def update(self, class_id: UUID, update_data: dict) -> Optional[OntologyClass]:
+        """更新类型信息
+        
+        Args:
+            class_id: 类型ID
+            update_data: 更新数据字典
+            
+        Returns:
+            Optional[OntologyClass]: 更新后的类型对象，不存在则返回None
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> ontology_class = repo.update(
+            ...     class_id,
+            ...     {"class_name": "新名称"}
+            ... )
+        """
+        try:
+            logger.info(f"Updating ontology class: {class_id}")
+            
+            ontology_class = self.get_by_id(class_id)
+            if not ontology_class:
+                logger.warning(f"Ontology class not found for update: {class_id}")
+                return None
+            
+            # 更新字段
+            if "class_name" in update_data and update_data["class_name"] is not None:
+                ontology_class.class_name = update_data["class_name"]
+            
+            if "class_description" in update_data:
+                ontology_class.class_description = update_data["class_description"]
+            
+            self.db.flush()
+            
+            logger.info(f"Ontology class updated successfully: {class_id}")
+            
+            return ontology_class
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to update ontology class: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def delete(self, class_id: UUID) -> bool:
+        """删除类型
+        
+        Args:
+            class_id: 类型ID
+            
+        Returns:
+            bool: 删除成功返回True，类型不存在返回False
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> success = repo.delete(class_id)
+        """
+        try:
+            logger.info(f"Deleting ontology class: {class_id}")
+            
+            ontology_class = self.get_by_id(class_id)
+            if not ontology_class:
+                logger.warning(f"Ontology class not found for delete: {class_id}")
+                return False
+            
+            self.db.delete(ontology_class)
+            self.db.flush()
+            
+            logger.info(f"Ontology class deleted successfully: {class_id}")
+            
+            return True
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to delete ontology class: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def check_ownership(self, class_id: UUID, workspace_id: UUID) -> bool:
+        """检查类型是否属于指定工作空间（通过场景关联）
+        
+        Args:
+            class_id: 类型ID
+            workspace_id: 工作空间ID
+            
+        Returns:
+            bool: 属于返回True，否则返回False
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> is_owner = repo.check_ownership(class_id, workspace_id)
+        """
+        try:
+            logger.debug(
+                f"Checking class ownership - "
+                f"class_id={class_id}, workspace_id={workspace_id}"
+            )
+            
+            count = self.db.query(OntologyClass).join(
+                OntologyScene,
+                OntologyClass.scene_id == OntologyScene.scene_id
+            ).filter(
+                OntologyClass.class_id == class_id,
+                OntologyScene.workspace_id == workspace_id
+            ).count()
+            
+            is_owner = count > 0
+            
+            logger.debug(
+                f"Class ownership check result: {is_owner} - "
+                f"class_id={class_id}"
+            )
+            
+            return is_owner
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to check class ownership: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_scene_id_by_class(self, class_id: UUID) -> Optional[UUID]:
+        """根据类型ID获取所属场景ID
+        
+        Args:
+            class_id: 类型ID
+            
+        Returns:
+            Optional[UUID]: 场景ID，类型不存在则返回None
+            
+        Examples:
+            >>> repo = OntologyClassRepository(db)
+            >>> scene_id = repo.get_scene_id_by_class(class_id)
+        """
+        try:
+            logger.debug(f"Getting scene ID by class: {class_id}")
+            
+            ontology_class = self.get_by_id(class_id)
+            if not ontology_class:
+                logger.debug(f"Class not found: {class_id}")
+                return None
+            
+            logger.debug(
+                f"Found scene ID: {ontology_class.scene_id} for class: {class_id}"
+            )
+            
+            return ontology_class.scene_id
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get scene ID by class: {str(e)}",
+                exc_info=True
+            )
+            raise

api/app/repositories/ontology_scene_repository.py

@@ -0,0 +1,439 @@
+# -*- coding: utf-8 -*-
+"""本体场景Repository层
+
+本模块提供本体场景的数据访问层实现。
+
+Classes:
+    OntologySceneRepository: 本体场景数据访问类
+"""
+
+import logging
+from typing import List, Optional
+from uuid import UUID
+
+from sqlalchemy.orm import Session, joinedload
+
+from app.core.logging_config import get_db_logger
+from app.models.ontology_scene import OntologyScene
+
+
+logger = get_db_logger()
+
+
+class OntologySceneRepository:
+    """本体场景Repository
+    
+    提供本体场景的CRUD操作和权限检查。
+    
+    Attributes:
+        db: SQLAlchemy数据库会话
+    """
+    
+    def __init__(self, db: Session):
+        """初始化Repository
+        
+        Args:
+            db: SQLAlchemy数据库会话
+        """
+        self.db = db
+    
+    def create(self, scene_data: dict, workspace_id: UUID) -> OntologyScene:
+        """创建本体场景
+        
+        Args:
+            scene_data: 场景数据字典，包含scene_name和scene_description
+            workspace_id: 所属工作空间ID
+            
+        Returns:
+            OntologyScene: 创建的场景对象
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scene = repo.create(
+            ...     {"scene_name": "医疗场景", "scene_description": "描述"},
+            ...     workspace_id
+            ... )
+        """
+        try:
+            logger.info(
+                f"Creating ontology scene - "
+                f"name={scene_data.get('scene_name')}, "
+                f"workspace_id={workspace_id}"
+            )
+            
+            scene = OntologyScene(
+                scene_name=scene_data.get("scene_name"),
+                scene_description=scene_data.get("scene_description"),
+                workspace_id=workspace_id
+            )
+            
+            self.db.add(scene)
+            self.db.flush()  # 获取ID但不提交
+            
+            logger.info(
+                f"Ontology scene created successfully - "
+                f"scene_id={scene.scene_id}"
+            )
+            
+            return scene
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to create ontology scene: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_id(self, scene_id: UUID) -> Optional[OntologyScene]:
+        """根据ID获取场景
+        
+        Args:
+            scene_id: 场景ID
+            
+        Returns:
+            Optional[OntologyScene]: 场景对象，不存在则返回None
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scene = repo.get_by_id(scene_id)
+        """
+        try:
+            logger.debug(f"Getting ontology scene by ID: {scene_id}")
+            
+            scene = self.db.query(OntologyScene).filter(
+                OntologyScene.scene_id == scene_id
+            ).first()
+            
+            if scene:
+                logger.debug(f"Ontology scene found: {scene_id}")
+            else:
+                logger.debug(f"Ontology scene not found: {scene_id}")
+            
+            return scene
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology scene by ID: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_name(self, scene_name: str, workspace_id: UUID) -> Optional[OntologyScene]:
+        """根据场景名称和工作空间ID获取场景（精确匹配）
+        
+        Args:
+            scene_name: 场景名称
+            workspace_id: 工作空间ID
+            
+        Returns:
+            Optional[OntologyScene]: 场景对象，不存在则返回None
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scene = repo.get_by_name("医疗场景", workspace_id)
+        """
+        try:
+            logger.debug(
+                f"Getting ontology scene by name - "
+                f"scene_name={scene_name}, workspace_id={workspace_id}"
+            )
+            
+            scene = self.db.query(OntologyScene).options(
+                joinedload(OntologyScene.classes)
+            ).filter(
+                OntologyScene.scene_name == scene_name,
+                OntologyScene.workspace_id == workspace_id
+            ).first()
+            
+            if scene:
+                logger.debug(f"Ontology scene found: {scene_name}")
+            else:
+                logger.debug(f"Ontology scene not found: {scene_name}")
+            
+            return scene
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology scene by name: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def search_by_name(self, keyword: str, workspace_id: UUID) -> List[OntologyScene]:
+        """根据关键词模糊搜索场景
+        
+        使用 LIKE 进行模糊匹配，支持中文和英文。
+        
+        Args:
+            keyword: 搜索关键词
+            workspace_id: 工作空间ID
+            
+        Returns:
+            List[OntologyScene]: 匹配的场景列表
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scenes = repo.search_by_name("医疗", workspace_id)
+        """
+        try:
+            logger.debug(
+                f"Searching ontology scenes by keyword - "
+                f"keyword={keyword}, workspace_id={workspace_id}"
+            )
+            
+            # 使用 ilike 进行不区分大小写的模糊匹配
+            scenes = self.db.query(OntologyScene).options(
+                joinedload(OntologyScene.classes)
+            ).filter(
+                OntologyScene.scene_name.ilike(f"%{keyword}%"),
+                OntologyScene.workspace_id == workspace_id
+            ).order_by(
+                OntologyScene.updated_at.desc()
+            ).all()
+            
+            logger.info(
+                f"Found {len(scenes)} ontology scenes matching keyword '{keyword}' "
+                f"in workspace {workspace_id}"
+            )
+            
+            return scenes
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to search ontology scenes by keyword: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def get_by_workspace(self, workspace_id: UUID, page: Optional[int] = None, page_size: Optional[int] = None) -> tuple:
+        """获取工作空间下的所有场景（支持分页）
+        
+        使用joinedload预加载classes关系以统计数量。
+        
+        Args:
+            workspace_id: 工作空间ID
+            page: 页码（可选，从1开始）
+            page_size: 每页数量（可选）
+            
+        Returns:
+            tuple: (场景列表, 总数量)
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scenes, total = repo.get_by_workspace(workspace_id)
+            >>> scenes, total = repo.get_by_workspace(workspace_id, page=1, page_size=10)
+        """
+        try:
+            logger.debug(f"Getting ontology scenes by workspace: {workspace_id}, page={page}, page_size={page_size}")
+            
+            # 构建基础查询
+            query = self.db.query(OntologyScene).options(
+                joinedload(OntologyScene.classes)
+            ).filter(
+                OntologyScene.workspace_id == workspace_id
+            ).order_by(
+                OntologyScene.updated_at.desc()
+            )
+            
+            # 获取总数
+            total = query.count()
+            
+            # 如果提供了分页参数，应用分页
+            if page is not None and page_size is not None:
+                offset = (page - 1) * page_size
+                query = query.offset(offset).limit(page_size)
+                logger.debug(f"Applying pagination: offset={offset}, limit={page_size}")
+            
+            scenes = query.all()
+            
+            logger.info(
+                f"Found {len(scenes)} ontology scenes (total: {total}) in workspace {workspace_id}"
+            )
+            
+            return scenes, total
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get ontology scenes by workspace: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def update(self, scene_id: UUID, update_data: dict) -> Optional[OntologyScene]:
+        """更新场景信息
+        
+        Args:
+            scene_id: 场景ID
+            update_data: 更新数据字典
+            
+        Returns:
+            Optional[OntologyScene]: 更新后的场景对象，不存在则返回None
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scene = repo.update(
+            ...     scene_id,
+            ...     {"scene_name": "新名称"}
+            ... )
+        """
+        try:
+            logger.info(f"Updating ontology scene: {scene_id}")
+            
+            scene = self.get_by_id(scene_id)
+            if not scene:
+                logger.warning(f"Ontology scene not found for update: {scene_id}")
+                return None
+            
+            # 更新字段
+            if "scene_name" in update_data and update_data["scene_name"] is not None:
+                scene.scene_name = update_data["scene_name"]
+            
+            if "scene_description" in update_data:
+                scene.scene_description = update_data["scene_description"]
+            
+            self.db.flush()
+            
+            logger.info(f"Ontology scene updated successfully: {scene_id}")
+            
+            return scene
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to update ontology scene: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def delete(self, scene_id: UUID) -> bool:
+        """删除场景（级联删除类型）
+        
+        依赖数据库级联删除配置（ondelete="CASCADE"）。
+        
+        Args:
+            scene_id: 场景ID
+            
+        Returns:
+            bool: 删除成功返回True，场景不存在返回False
+            
+        Raises:
+            Exception: 数据库操作失败
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> success = repo.delete(scene_id)
+        """
+        try:
+            logger.info(f"Deleting ontology scene: {scene_id}")
+            
+            scene = self.get_by_id(scene_id)
+            if not scene:
+                logger.warning(f"Ontology scene not found for delete: {scene_id}")
+                return False
+            
+            self.db.delete(scene)
+            self.db.flush()
+            
+            logger.info(
+                f"Ontology scene deleted successfully (cascade): {scene_id}"
+            )
+            
+            return True
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to delete ontology scene: {str(e)}",
+                exc_info=True
+            )
+            raise
+    
+    def check_ownership(self, scene_id: UUID, workspace_id: UUID) -> bool:
+        """检查场景是否属于指定工作空间
+        
+        Args:
+            scene_id: 场景ID
+            workspace_id: 工作空间ID
+            
+        Returns:
+            bool: 属于返回True，否则返回False
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> is_owner = repo.check_ownership(scene_id, workspace_id)
+        """
+        try:
+            logger.debug(
+                f"Checking scene ownership - "
+                f"scene_id={scene_id}, workspace_id={workspace_id}"
+            )
+            
+            count = self.db.query(OntologyScene).filter(
+                OntologyScene.scene_id == scene_id,
+                OntologyScene.workspace_id == workspace_id
+            ).count()
+            
+            is_owner = count > 0
+            
+            logger.debug(
+                f"Scene ownership check result: {is_owner} - "
+                f"scene_id={scene_id}"
+            )
+            
+            return is_owner
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to check scene ownership: {str(e)}",
+                exc_info=True
+            )
+            raise
+
+    def get_simple_list(self, workspace_id: UUID) -> List[dict]:
+        """获取场景简单列表（仅包含scene_id和scene_name，用于下拉选择）
+        
+        这是一个轻量级查询，不加载关联的classes，响应速度快。
+        
+        Args:
+            workspace_id: 工作空间ID
+            
+        Returns:
+            List[dict]: 场景简单列表，每项包含scene_id和scene_name
+            
+        Examples:
+            >>> repo = OntologySceneRepository(db)
+            >>> scenes = repo.get_simple_list(workspace_id)
+            >>> # [{"scene_id": "xxx", "scene_name": "场景1"}, ...]
+        """
+        try:
+            logger.debug(f"Getting simple scene list for workspace: {workspace_id}")
+            
+            # 只查询需要的字段，不加载关联数据
+            results = self.db.query(
+                OntologyScene.scene_id,
+                OntologyScene.scene_name
+            ).filter(
+                OntologyScene.workspace_id == workspace_id
+            ).order_by(
+                OntologyScene.updated_at.desc()
+            ).all()
+            
+            scenes = [
+                {"scene_id": str(r.scene_id), "scene_name": r.scene_name}
+                for r in results
+            ]
+            
+            logger.info(f"Found {len(scenes)} scenes (simple list) in workspace {workspace_id}")
+            
+            return scenes
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to get simple scene list: {str(e)}",
+                exc_info=True
+            )
+            raise

api/app/repositories/prompt_optimizer_repository.py

@@ -4,7 +4,10 @@ from sqlalchemy.orm import Session
 
 from app.core.logging_config import get_db_logger
 from app.models.prompt_optimizer_model import (
-    PromptOptimizerSession, PromptOptimizerSessionHistory, RoleType
+    PromptOptimizerSession,
+    PromptOptimizerSessionHistory,
+    RoleType,
+    PromptHistory
 )
 
 db_logger = get_db_logger()
@@ -16,6 +19,12 @@ class PromptOptimizerSessionRepository:
     def __init__(self, db: Session):
         self.db = db
 
+    def get_session_by_id(self, session_id: uuid.UUID) -> PromptOptimizerSession | None:
+        session = self.db.query(PromptOptimizerSession).filter(
+            PromptOptimizerSession.id == session_id,
+        ).first()
+        return session
+
     def create_session(
             self,
             tenant_id: uuid.UUID,
@@ -38,12 +47,9 @@ class PromptOptimizerSessionRepository:
                 user_id=user_id,
             )
             self.db.add(session)
-            self.db.commit()
-            self.db.refresh(session)
-            db_logger.debug(f"Prompt optimization session created: ID:{session.id}")
             return session
         except Exception as e:
-            db_logger.error(f"Error creating prompt optimization session: user_id={user_id} - {str(e)}")
+            db_logger.error(f"Error creating prompt optimization session: - {str(e)}")
             raise
 
     def get_session_history(
@@ -71,10 +77,10 @@ class PromptOptimizerSessionRepository:
                 PromptOptimizerSession.id == session_id,
                 PromptOptimizerSession.user_id == user_id
             ).first()
-            
+
             if not session:
                 return []
-            
+
             history = self.db.query(PromptOptimizerSessionHistory).filter(
                 PromptOptimizerSessionHistory.session_id == session.id,
                 PromptOptimizerSessionHistory.user_id == user_id
@@ -104,11 +110,11 @@ class PromptOptimizerSessionRepository:
                 PromptOptimizerSession.user_id == user_id,
                 PromptOptimizerSession.tenant_id == tenant_id
             ).first()
-            
+
             if not session:
                 db_logger.error(f"Session {session_id} not found for user {user_id}")
                 raise ValueError(f"Session {session_id} not found for user {user_id}")
-            
+
             message = PromptOptimizerSessionHistory(
                 tenant_id=tenant_id,
                 session_id=session.id,
@@ -117,8 +123,199 @@ class PromptOptimizerSessionRepository:
                 content=content,
             )
             self.db.add(message)
-            self.db.commit()
+
             return message
         except Exception as e:
             db_logger.error(f"Error creating prompt optimization session history: session_id={session_id} - {str(e)}")
             raise
+
+    def get_first_user_message(self, session_id: uuid.UUID) -> str | None:
+        """
+        Get the first user message from a session.
+
+        Args:
+            session_id (uuid.UUID): The session ID.
+
+        Returns:
+            str | None: The content of the first user message, or None if not found.
+        """
+        try:
+            message = self.db.query(PromptOptimizerSessionHistory).filter(
+                PromptOptimizerSessionHistory.session_id == session_id,
+                PromptOptimizerSessionHistory.role == RoleType.USER.value
+            ).order_by(
+                PromptOptimizerSessionHistory.created_at.asc()
+            ).first()
+            
+            return message.content if message else None
+        except Exception as e:
+            db_logger.error(f"Error getting first user message: session_id={session_id} - {str(e)}")
+            raise
+
+
+class PromptReleaseRepository:
+    def __init__(self, db: Session):
+        self.db = db
+
+    def get_prompt_by_session_id(self, session_id: uuid.UUID) -> PromptHistory | None:
+        prompt_obj = self.db.query(PromptHistory).filter(
+            PromptHistory.session_id == session_id,
+            PromptHistory.is_delete.is_(False)
+        ).first()
+        return prompt_obj
+
+    def create_prompt_release(
+            self,
+            tenant_id: uuid.UUID,
+            title: str,
+            session_id: uuid.UUID,
+            prompt: str,
+    ) -> PromptHistory:
+        try:
+            prompt_obj = PromptHistory(
+                tenant_id=tenant_id,
+                title=title,
+                session_id=session_id,
+                prompt=prompt,
+            )
+            self.db.add(prompt_obj)
+            return prompt_obj
+        except Exception as e:
+            db_logger.error(f"Error creating prompt release: session_id={session_id} - {str(e)}")
+            raise
+
+    def soft_delete_prompt(self, prompt_obj: PromptHistory) -> None:
+        """
+        Soft delete a prompt release by setting is_delete flag to True.
+
+        Args:
+            prompt_obj (PromptHistory): The prompt release object to delete.
+        """
+        try:
+            prompt_obj.is_delete = True
+            db_logger.debug(f"Soft deleted prompt release: id={prompt_obj.id}, session_id={prompt_obj.session_id}")
+        except Exception as e:
+            db_logger.error(f"Error soft deleting prompt release: id={prompt_obj.id} - {str(e)}")
+            raise
+
+    def get_prompt_by_id(self, prompt_id: uuid.UUID) -> PromptHistory | None:
+        """
+        Get a prompt release by its ID.
+
+        Args:
+            prompt_id (uuid.UUID): The prompt release ID.
+
+        Returns:
+            PromptHistory | None: The prompt release object or None if not found.
+        """
+        try:
+            prompt_obj = self.db.query(PromptHistory).filter(
+                PromptHistory.id == prompt_id
+            ).first()
+            return prompt_obj
+        except Exception as e:
+            db_logger.error(f"Error getting prompt release by id: id={prompt_id} - {str(e)}")
+            raise
+
+    def count_prompts(self, tenant_id: uuid.UUID) -> int:
+        """
+        Count total number of non-deleted prompts for a tenant.
+
+        Args:
+            tenant_id (uuid.UUID): The tenant ID.
+
+        Returns:
+            int: Total count of prompts.
+        """
+        try:
+            count = self.db.query(PromptHistory).filter(
+                PromptHistory.tenant_id == tenant_id,
+                PromptHistory.is_delete.is_(False)
+            ).count()
+            return count
+        except Exception as e:
+            db_logger.error(f"Error counting prompts: tenant_id={tenant_id} - {str(e)}")
+            raise
+
+    def get_prompts_paginated(
+            self,
+            tenant_id: uuid.UUID,
+            offset: int,
+            limit: int
+    ) -> list[PromptHistory]:
+        """
+        Get paginated list of prompt releases for a tenant.
+
+        Args:
+            tenant_id (uuid.UUID): The tenant ID.
+            offset (int): Number of records to skip.
+            limit (int): Maximum number of records to return.
+
+        Returns:
+            list[PromptHistory]: List of prompt releases.
+        """
+        try:
+            prompts = self.db.query(PromptHistory).filter(
+                PromptHistory.tenant_id == tenant_id,
+                PromptHistory.is_delete.is_(False)
+            ).order_by(
+                PromptHistory.created_at.desc()
+            ).offset(offset).limit(limit).all()
+            return prompts
+        except Exception as e:
+            db_logger.error(f"Error getting paginated prompts: tenant_id={tenant_id} - {str(e)}")
+            raise
+
+    def count_prompts_by_keyword(self, tenant_id: uuid.UUID, keyword: str) -> int:
+        """
+        Count total number of non-deleted prompts matching keyword for a tenant.
+
+        Args:
+            tenant_id (uuid.UUID): The tenant ID.
+            keyword (str): Search keyword for title.
+
+        Returns:
+            int: Total count of matching prompts.
+        """
+        try:
+            count = self.db.query(PromptHistory).filter(
+                PromptHistory.tenant_id == tenant_id,
+                PromptHistory.is_delete.is_(False),
+                PromptHistory.title.ilike(f"%{keyword}%")
+            ).count()
+            return count
+        except Exception as e:
+            db_logger.error(f"Error counting prompts by keyword: tenant_id={tenant_id}, keyword={keyword} - {str(e)}")
+            raise
+
+    def search_prompts_paginated(
+            self,
+            tenant_id: uuid.UUID,
+            keyword: str,
+            offset: int,
+            limit: int
+    ) -> list[PromptHistory]:
+        """
+        Search prompt releases by keyword in title with pagination.
+
+        Args:
+            tenant_id (uuid.UUID): The tenant ID.
+            keyword (str): Search keyword for title.
+            offset (int): Number of records to skip.
+            limit (int): Maximum number of records to return.
+
+        Returns:
+            list[PromptHistory]: List of matching prompt releases.
+        """
+        try:
+            prompts = self.db.query(PromptHistory).filter(
+                PromptHistory.tenant_id == tenant_id,
+                PromptHistory.is_delete.is_(False),
+                PromptHistory.title.ilike(f"%{keyword}%")
+            ).order_by(
+                PromptHistory.created_at.desc()
+            ).offset(offset).limit(limit).all()
+            return prompts
+        except Exception as e:
+            db_logger.error(f"Error searching prompts: tenant_id={tenant_id}, keyword={keyword} - {str(e)}")
+            raise

api/app/schemas/app_schema.py

@@ -12,8 +12,8 @@ class KnowledgeBaseConfig(BaseModel):
     kb_id: str = Field(..., description="知识库ID")
     top_k: int = Field(default=3, ge=1, le=20, description="检索返回的文档数量")
     similarity_threshold: float = Field(default=0.7, ge=0.0, le=1.0, description="相似度阈值")
-    strategy: str = Field(default="hybrid", description="检索策略: hybrid | bm25 | dense")
+    # strategy: str = Field(default="hybrid", description="检索策略: hybrid | bm25 | dense")
-    weight: float = Field(default=1.0, ge=0.0, le=1.0, description="知识库权重（用于多知识库融合）")
+    # weight: float = Field(default=1.0, ge=0.0, le=1.0, description="知识库权重（用于多知识库融合）")
     vector_similarity_weight: float = Field(default=0.5, ge=0.0, le=1.0, description="向量相似度权重")
     retrieve_type: str = Field(default="hybrid", description="检索方式participle｜ semantic｜hybrid")
 

api/app/schemas/memory_agent_schema.py

@@ -1,3 +1,4 @@
+from abc import ABC
 from typing import Optional
 
 from pydantic import BaseModel
@@ -14,4 +15,15 @@ class UserInput(BaseModel):
 class Write_UserInput(BaseModel):
     messages: list[dict]
     end_user_id: str
-    config_id: Optional[str] = None
+    config_id: Optional[str] = None
+
+class AgentMemory_Long_Term(ABC):
+    """长期记忆配置常量"""
+    STORAGE_NEO4J = "neo4j"
+    STORAGE_RAG = "rag"
+    STRATEGY_AGGREGATE = "aggregate"
+    STRATEGY_CHUNK = "chunk"
+    STRATEGY_TIME = "time"
+    DEFAULT_SCOPE = 6
+
+

api/app/schemas/memory_reflection_schemas.py

@@ -1,5 +1,7 @@
+import uuid
+
 from pydantic import BaseModel, Field
-from typing import Optional
+from typing import Optional, Union
 from uuid import UUID
 from enum import Enum
 
@@ -10,7 +12,7 @@ class OptimizationStrategy(str, Enum):
     ACCURACY_FIRST = "accuracy_first"
     BALANCED = "balanced"
 class Memory_Reflection(BaseModel):
-    config_id: Optional[UUID] = None
+    config_id:  Union[uuid.UUID, int, str]  = None
     reflection_enabled: bool
     reflection_period_in_hours: str
     reflexion_range: Optional[str] = "partial"

api/app/schemas/memory_storage_schema.py

@@ -147,7 +147,7 @@ class ReflexionResultSchema(BaseModel):
 # Composite key identifying a config row
 class ConfigKey(BaseModel):  # 配置参数键模型
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
-    config_id: uuid.UUID = Field("config_id", description="配置唯一标识（UUID）")
+    config_id:Union[uuid.UUID, int, str] = Field(..., description="配置唯一标识（UUID或int)")
     user_id: str = Field("user_id", description="用户标识（字符串）")
     apply_id: str = Field("apply_id", description="应用或场景标识（字符串）")
 
@@ -229,26 +229,32 @@ class ConfigParamsCreate(BaseModel):  # 创建配置参数模型（仅 body，
     config_desc: str = Field("配置描述", description="配置描述（字符串）")
     workspace_id: Optional[uuid.UUID] = Field(None, description="工作空间ID（UUID）")
     
+    # 本体场景关联（可选）
+    scene_id: Optional[uuid.UUID] = Field(None, description="本体场景ID（UUID），关联ontology_scene表")
+    
     # 模型配置字段（可选，用于手动指定或自动填充）
     llm_id: Optional[str] = Field(None, description="LLM模型配置ID")
     embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID")
     rerank_id: Optional[str] = Field(None, description="重排序模型配置ID")
+    reflection_model_id: Optional[str] = Field(None, description="反思模型ID，默认与llm_id一致")
+    emotion_model_id: Optional[str] = Field(None, description="情绪分析模型ID，默认与llm_id一致")
 
 
 class ConfigParamsDelete(BaseModel):  # 删除配置参数模型（请求体）
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
     # config_name: str = Field("配置名称", description="配置名称（字符串）")
-    config_id: uuid.UUID = Field("配置ID", description="配置ID（UUID）")
+    config_id:Union[uuid.UUID, int, str]  = Field(..., description="配置ID（支持UUID、整数或字符串）")
 
 
 class ConfigUpdate(BaseModel):  # 更新记忆萃取引擎配置参数时使用的模型
-    config_id: Optional[uuid.UUID] = None
+    config_id: Union[uuid.UUID, int, str] = None
-    config_name: str = Field("配置名称", description="配置名称（字符串）")
+    config_name: Optional[str] = Field(None, description="配置名称（字符串）")
-    config_desc: str = Field("配置描述", description="配置描述（字符串）")
+    config_desc: Optional[str] = Field(None, description="配置描述（字符串）")
+    scene_id: Optional[uuid.UUID] = Field(None, description="本体场景ID")
 
 
 class ConfigUpdateExtracted(BaseModel):  # 更新记忆萃取引擎配置参数时使用的模型
-    config_id: Optional[uuid.UUID] = None
+    config_id:Union[uuid.UUID, int, str]  = None
     llm_id: Optional[str] = Field(None, description="LLM模型配置ID")
     embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID")
     rerank_id: Optional[str] = Field(None, description="重排序模型配置ID")
@@ -315,14 +321,14 @@ class ConfigUpdateExtracted(BaseModel):  # 更新记忆萃取引擎配置参数
 
 class ConfigUpdateForget(BaseModel):  # 更新遗忘引擎配置参数时使用的模型
     # 遗忘引擎配置参数更新模型
-    config_id: Optional[uuid.UUID] = None
+    config_id:Union[uuid.UUID, int, str]  = None
     lambda_time: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="最低保持度，0-1 小数；默认 0.5")
     lambda_mem: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="遗忘率，0-1 小数；默认 0.5")
     offset: Optional[float] = Field(0.0, ge=0.0, le=1.0, description="偏移度，0-1 小数；默认 0.0")
 
 
 class ConfigPilotRun(BaseModel):  # 试运行触发请求模型
-    config_id: uuid.UUID = Field(..., description="配置ID（唯一）")
+    config_id:Union[uuid.UUID, int, str] = Field(..., description="配置ID（唯一，支持UUID、整数或字符串）")
     dialogue_text: str = Field(..., description="前端传入的对话文本，格式如 '用户: ...\nAI: ...' 可多行，试运行必填")
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
 
@@ -330,7 +336,7 @@ class ConfigPilotRun(BaseModel):  # 试运行触发请求模型
 class ConfigFilter(BaseModel):  # 查询配置参数时使用的模型
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
 
-    config_id: Optional[uuid.UUID] = None
+    config_id: Union[uuid.UUID, int, str] = None
     user_id: Optional[str] = None
     apply_id: Optional[str] = None
 
@@ -406,7 +412,7 @@ class ForgettingConfigResponse(BaseModel):
     """遗忘引擎配置响应模型"""
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
     
-    config_id: uuid.UUID = Field(..., description="配置ID")
+    config_id: Union[uuid.UUID, int, str] = Field(..., description="配置ID（支持UUID、整数或字符串）")
     decay_constant: float = Field(..., description="衰减常数 d")
     lambda_time: float = Field(..., description="时间衰减参数")
     lambda_mem: float = Field(..., description="记忆衰减参数")
@@ -423,8 +429,8 @@ class ForgettingConfigResponse(BaseModel):
 class ForgettingConfigUpdateRequest(BaseModel):
     """遗忘引擎配置更新请求模型"""
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
-    
+
-    config_id: uuid.UUID = Field(..., description="配置ID")
+    config_id: Union[uuid.UUID, int,str] = Field(..., description="配置唯一标识（UUID或int)")
     decay_constant: Optional[float] = Field(None, ge=0.0, le=1.0, description="衰减常数 d")
     lambda_time: Optional[float] = Field(None, ge=0.0, le=1.0, description="时间衰减参数")
     lambda_mem: Optional[float] = Field(None, ge=0.0, le=1.0, description="记忆衰减参数")
@@ -499,7 +505,7 @@ class ForgettingCurveRequest(BaseModel):
     
     importance_score: float = Field(0.5, ge=0.0, le=1.0, description="重要性分数（0-1）")
     days: int = Field(60, ge=1, le=365, description="模拟天数（默认60天）")
-    config_id: Optional[uuid.UUID] = Field(None, description="配置ID（可选，如果为None则使用默认配置）")
+    config_id: Union[uuid.UUID, int, str] = Field(..., description="配置唯一标识（UUID或int)")
 
 
 class ForgettingCurveResponse(BaseModel):

api/app/schemas/model_schema.py

@@ -3,14 +3,12 @@ from typing import Optional, List, Dict, Any
 import datetime
 import uuid
 
-from app.models.models_model import ModelProvider, ModelType
+from app.models.models_model import ModelProvider, ModelType, LoadBalanceStrategy
 from app.core.logging_config import get_business_logger
 
 schema_logger = get_business_logger()
 
 
-
-
 # ModelConfig Schemas
 class ModelConfigBase(BaseModel):
     """模型配置基础Schema"""
@@ -22,6 +20,7 @@ class ModelConfigBase(BaseModel):
     config: Optional[Dict[str, Any]] = Field({}, description="模型配置参数")
     is_active: bool = Field(True, description="是否激活")
     is_public: bool = Field(False, description="是否公开")
+    load_balance_strategy: Optional[str] = Field(LoadBalanceStrategy.NONE.value, description="负载均衡策略")
 
 
 class ApiKeyCreateNested(BaseModel):
@@ -44,13 +43,14 @@ class ModelConfigCreate(ModelConfigBase):
 class CompositeModelCreate(BaseModel):
     """创建组合模型Schema"""
     name: str = Field(..., description="组合模型名称", max_length=255)
-    type: ModelType = Field(..., description="模型类型")
+    type: Optional[ModelType] = Field(None, description="模型类型")
     logo: Optional[str] = Field(None, description="模型logo图片URL", max_length=255)
     description: Optional[str] = Field(None, description="模型描述")
     config: Optional[Dict[str, Any]] = Field({}, description="模型配置参数")
     is_active: bool = Field(True, description="是否激活")
     is_public: bool = Field(False, description="是否公开")
     api_key_ids: List[uuid.UUID] = Field(..., description="绑定的API Key ID列表")
+    load_balance_strategy: Optional[str] = Field(default=LoadBalanceStrategy.NONE.value, description="负载均衡策略")
 
 
 class ModelConfigUpdate(BaseModel):

api/app/schemas/ontology_schemas.py

@@ -0,0 +1,461 @@
+"""本体提取API的请求和响应模型
+
+本模块定义了本体提取系统的所有API请求和响应的Pydantic模型。
+
+Classes:
+    ExtractionRequest: 本体提取请求模型
+    ExtractionResponse: 本体提取响应模型
+    ExportRequest: OWL文件导出请求模型
+    ExportResponse: OWL文件导出响应模型
+    OntologyResultResponse: 本体提取结果响应模型(带毫秒时间戳)
+    SceneCreateRequest: 场景创建请求模型
+    SceneUpdateRequest: 场景更新请求模型
+    SceneResponse: 场景响应模型
+    SceneListResponse: 场景列表响应模型
+    ClassCreateRequest: 类型创建请求模型
+    ClassUpdateRequest: 类型更新请求模型
+    ClassResponse: 类型响应模型
+    ClassListResponse: 类型列表响应模型
+"""
+
+from typing import List, Optional
+import datetime
+from uuid import UUID
+
+from pydantic import BaseModel, Field, field_serializer, ConfigDict
+
+from app.core.memory.models.ontology_models import OntologyClass
+
+
+class ExtractionRequest(BaseModel):
+    """本体提取请求模型
+    
+    用于POST /api/ontology/extract端点的请求体。
+    
+    Attributes:
+        scenario: 场景描述文本,不能为空
+        domain: 可选的领域提示(如Healthcare, Education等)
+        llm_id: LLM模型ID,必须提供
+        scene_id: 场景ID,必须提供,用于将提取的类保存到指定场景
+    
+    Examples:
+        >>> request = ExtractionRequest(
+        ...     scenario="医院管理患者记录...",
+        ...     domain="Healthcare",
+        ...     llm_id="550e8400-e29b-41d4-a716-446655440000",
+        ...     scene_id="660e8400-e29b-41d4-a716-446655440000"
+        ... )
+    """
+    scenario: str = Field(..., description="场景描述文本", min_length=1)
+    domain: Optional[str] = Field(None, description="可选的领域提示")
+    llm_id: str = Field(..., description="LLM模型ID")
+    scene_id: UUID = Field(..., description="场景ID,用于将提取的类保存到指定场景")
+
+
+class ExtractionResponse(BaseModel):
+    """本体提取响应模型
+    
+    用于POST /api/ontology/extract端点的响应体。
+    
+    Attributes:
+        classes: 提取的本体类列表
+        domain: 识别的领域
+        extracted_count: 提取的类数量
+    
+    Examples:
+        >>> response = ExtractionResponse(
+        ...     classes=[...],
+        ...     domain="Healthcare",
+        ...     extracted_count=7
+        ... )
+    """
+    classes: List[OntologyClass] = Field(default_factory=list, description="提取的本体类列表")
+    domain: str = Field(..., description="识别的领域")
+    extracted_count: int = Field(..., description="提取的类数量")
+
+
+class ExportRequest(BaseModel):
+    """OWL文件导出请求模型
+    
+    用于POST /api/ontology/export端点的请求体。
+    
+    Attributes:
+        classes: 要导出的本体类列表
+        format: 导出格式,可选值: rdfxml, turtle, ntriples, json
+        include_metadata: 是否包含完整的OWL元数据(命名空间等),默认True
+    
+    Examples:
+        >>> request = ExportRequest(
+        ...     classes=[...],
+        ...     format="rdfxml",
+        ...     include_metadata=True
+        ... )
+    """
+    classes: List[OntologyClass] = Field(..., description="要导出的本体类列表", min_length=1)
+    format: str = Field("rdfxml", description="导出格式: rdfxml, turtle, ntriples, json")
+    include_metadata: bool = Field(True, description="是否包含完整的OWL元数据")
+
+
+class ExportResponse(BaseModel):
+    """OWL文件导出响应模型
+    
+    用于POST /api/ontology/export端点的响应体。
+    
+    Attributes:
+        owl_content: OWL文件内容
+        format: 导出格式
+        classes_count: 导出的类数量
+    
+    Examples:
+        >>> response = ExportResponse(
+        ...     owl_content="<?xml version='1.0'?>...",
+        ...     format="rdfxml",
+        ...     classes_count=7
+        ... )
+    """
+    owl_content: str = Field(..., description="OWL文件内容")
+    format: str = Field(..., description="导出格式")
+    classes_count: int = Field(..., description="导出的类数量")
+
+
+class OntologyResultResponse(BaseModel):
+    """本体提取结果响应模型
+    
+    用于返回数据库中存储的提取结果,时间戳为毫秒级。
+    
+    Attributes:
+        id: 结果ID (UUID)
+        scenario: 场景描述文本
+        domain: 领域
+        classes_json: 提取的本体类数据(JSON格式)
+        extracted_count: 提取的类数量
+        user_id: 用户ID
+        created_at: 创建时间(毫秒时间戳)
+    
+    Examples:
+        >>> response = OntologyResultResponse(
+        ...     id=uuid.uuid4(),
+        ...     scenario="医院管理患者记录...",
+        ...     domain="Healthcare",
+        ...     classes_json={"classes": [...]},
+        ...     extracted_count=7,
+        ...     user_id=123,
+        ...     created_at=datetime.now()
+        ... )
+    """
+    id: UUID = Field(..., description="结果ID")
+    scenario: str = Field(..., description="场景描述文本")
+    domain: Optional[str] = Field(None, description="领域")
+    classes_json: dict = Field(..., description="提取的本体类数据(JSON格式)")
+    extracted_count: int = Field(..., description="提取的类数量")
+    user_id: Optional[int] = Field(None, description="用户ID")
+    created_at: datetime.datetime = Field(..., description="创建时间")
+    
+    @field_serializer("created_at", when_used="json")
+    def _serialize_created_at(self, dt: datetime.datetime):
+        """将创建时间序列化为毫秒时间戳"""
+        return int(dt.timestamp() * 1000) if dt else None
+    
+    class Config:
+        from_attributes = True
+
+
+
+# ==================== 本体场景相关 Schema ====================
+
+class SceneCreateRequest(BaseModel):
+    """场景创建请求模型
+    
+    用于创建新的本体场景。
+    
+    Attributes:
+        scene_name: 场景名称，必填，1-200字符
+        scene_description: 场景描述，可选
+    
+    Examples:
+        >>> request = SceneCreateRequest(
+        ...     scene_name="医疗场景",
+        ...     scene_description="用于医疗领域的本体建模"
+        ... )
+    """
+    scene_name: str = Field(..., min_length=1, max_length=200, description="场景名称")
+    scene_description: Optional[str] = Field(None, description="场景描述")
+
+
+class SceneUpdateRequest(BaseModel):
+    """场景更新请求模型
+    
+    用于更新已有本体场景信息。
+    
+    Attributes:
+        scene_name: 场景名称，可选，1-200字符
+        scene_description: 场景描述，可选
+    
+    Examples:
+        >>> request = SceneUpdateRequest(
+        ...     scene_name="更新后的场景名称",
+        ...     scene_description="更新后的描述"
+        ... )
+    """
+    scene_name: Optional[str] = Field(None, min_length=1, max_length=200, description="场景名称")
+    scene_description: Optional[str] = Field(None, description="场景描述")
+
+
+class SceneResponse(BaseModel):
+    """场景响应模型
+    
+    用于返回本体场景信息。
+    
+    Attributes:
+        scene_id: 场景ID
+        scene_name: 场景名称
+        scene_description: 场景描述
+        type_num: 类型数量
+        workspace_id: 所属工作空间ID
+        created_at: 创建时间（毫秒时间戳）
+        updated_at: 更新时间（毫秒时间戳）
+        classes_count: 类型数量
+    
+    Examples:
+        >>> response = SceneResponse(
+        ...     scene_id=uuid.uuid4(),
+        ...     scene_name="医疗场景",
+        ...     scene_description="用于医疗领域的本体建模",
+        ...     type_num=0,
+        ...     workspace_id=uuid.uuid4(),
+        ...     created_at=datetime.now(),
+        ...     updated_at=datetime.now(),
+        ...     classes_count=5
+        ... )
+    """
+    scene_id: UUID = Field(..., description="场景ID")
+    scene_name: str = Field(..., description="场景名称")
+    scene_description: Optional[str] = Field(None, description="场景描述")
+    type_num: int = Field(..., description="类型数量")
+    entity_type: Optional[List[str]] = Field(None, description="实体类型列表（最多3个class_name）")
+    workspace_id: UUID = Field(..., description="所属工作空间ID")
+    created_at: datetime.datetime = Field(..., description="创建时间（毫秒时间戳）")
+    updated_at: datetime.datetime = Field(..., description="更新时间（毫秒时间戳）")
+    classes_count: int = Field(0, description="类型数量")
+    
+    @field_serializer("created_at", when_used="json")
+    def _serialize_created_at(self, dt: datetime.datetime):
+        """将创建时间序列化为毫秒时间戳"""
+        return int(dt.timestamp() * 1000) if dt else None
+    
+    @field_serializer("updated_at", when_used="json")
+    def _serialize_updated_at(self, dt: datetime.datetime):
+        """将更新时间序列化为毫秒时间戳"""
+        return int(dt.timestamp() * 1000) if dt else None
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+class PaginationInfo(BaseModel):
+    """分页信息模型
+    
+    Attributes:
+        page: 当前页码
+        pagesize: 每页数量
+        total: 总数量
+        hasnext: 是否有下一页
+    """
+    page: int = Field(..., description="当前页码")
+    pagesize: int = Field(..., description="每页数量")
+    total: int = Field(..., description="总数量")
+    hasnext: bool = Field(..., description="是否有下一页")
+
+
+class SceneListResponse(BaseModel):
+    """场景列表响应模型（支持分页）
+    
+    用于返回本体场景列表。
+    
+    Attributes:
+        items: 场景列表
+        page: 分页信息（可选，分页时返回）
+    
+    Examples:
+        >>> # 不分页
+        >>> response = SceneListResponse(
+        ...     items=[scene1, scene2]
+        ... )
+        >>> # 分页
+        >>> response = SceneListResponse(
+        ...     items=[scene1, scene2, ...],
+        ...     page=PaginationInfo(page=1, pagesize=100, total=150, hasnext=True)
+        ... )
+    """
+    items: List[SceneResponse] = Field(..., description="场景列表")
+    page: Optional[PaginationInfo] = Field(None, description="分页信息")
+
+
+# ==================== 本体类型相关 Schema ====================
+
+class ClassItem(BaseModel):
+    """单个类型信息模型
+    
+    Attributes:
+        class_name: 类型名称，必填，1-200字符
+        class_description: 类型描述，可选
+    
+    Examples:
+        >>> item = ClassItem(
+        ...     class_name="患者",
+        ...     class_description="医院患者信息"
+        ... )
+    """
+    class_name: str = Field(..., min_length=1, max_length=200, description="类型名称")
+    class_description: Optional[str] = Field(None, description="类型描述")
+
+
+class ClassCreateRequest(BaseModel):
+    """类型创建请求模型（统一使用列表形式）
+    
+    通过列表中元素数量决定创建模式：
+    - 列表包含 1 个元素：单个创建
+    - 列表包含多个元素：批量创建
+    
+    Attributes:
+        scene_id: 所属场景ID，必填
+        classes: 类型列表，必填，至少包含 1 个元素
+    
+    Examples:
+        # 单个创建（列表中 1 个元素）
+        >>> request = ClassCreateRequest(
+        ...     scene_id=uuid.uuid4(),
+        ...     classes=[
+        ...         ClassItem(class_name="患者", class_description="医院患者信息")
+        ...     ]
+        ... )
+        
+        # 批量创建（列表中多个元素）
+        >>> request = ClassCreateRequest(
+        ...     scene_id=uuid.uuid4(),
+        ...     classes=[
+        ...         ClassItem(class_name="患者", class_description="医院患者信息"),
+        ...         ClassItem(class_name="医生", class_description="医院医生信息"),
+        ...         ClassItem(class_name="药品", class_description="医院药品信息")
+        ...     ]
+        ... )
+    """
+    scene_id: UUID = Field(..., description="所属场景ID")
+    classes: List[ClassItem] = Field(..., min_length=1, description="类型列表，至少包含 1 个元素")
+
+
+class ClassUpdateRequest(BaseModel):
+    """类型更新请求模型
+    
+    用于更新已有本体类型信息。
+    
+    Attributes:
+        class_name: 类型名称，可选，1-200字符
+        class_description: 类型描述，可选
+    
+    Examples:
+        >>> request = ClassUpdateRequest(
+        ...     class_name="更新后的类型名称",
+        ...     class_description="更新后的描述"
+        ... )
+    """
+    class_name: Optional[str] = Field(None, min_length=1, max_length=200, description="类型名称")
+    class_description: Optional[str] = Field(None, description="类型描述")
+
+
+class ClassResponse(BaseModel):
+    """类型响应模型
+    
+    用于返回本体类型信息。
+    
+    Attributes:
+        class_id: 类型ID
+        class_name: 类型名称
+        class_description: 类型描述
+        scene_id: 所属场景ID
+        created_at: 创建时间（毫秒时间戳）
+        updated_at: 更新时间（毫秒时间戳）
+    
+    Examples:
+        >>> response = ClassResponse(
+        ...     class_id=uuid.uuid4(),
+        ...     class_name="患者",
+        ...     class_description="医院患者信息",
+        ...     scene_id=uuid.uuid4(),
+        ...     created_at=datetime.now(),
+        ...     updated_at=datetime.now()
+        ... )
+    """
+    class_id: UUID = Field(..., description="类型ID")
+    class_name: str = Field(..., description="类型名称")
+    class_description: Optional[str] = Field(None, description="类型描述")
+    scene_id: UUID = Field(..., description="所属场景ID")
+    created_at: datetime.datetime = Field(..., description="创建时间（毫秒时间戳）")
+    updated_at: datetime.datetime = Field(..., description="更新时间（毫秒时间戳）")
+    
+    @field_serializer("created_at", when_used="json")
+    def _serialize_created_at(self, dt: datetime.datetime):
+        """将创建时间序列化为毫秒时间戳"""
+        return int(dt.timestamp() * 1000) if dt else None
+    
+    @field_serializer("updated_at", when_used="json")
+    def _serialize_updated_at(self, dt: datetime.datetime):
+        """将更新时间序列化为毫秒时间戳"""
+        return int(dt.timestamp() * 1000) if dt else None
+    
+    model_config = ConfigDict(from_attributes=True)
+
+
+class ClassBatchCreateResponse(BaseModel):
+    """批量创建类型响应模型
+    
+    用于返回批量创建的结果统计和详情。
+    
+    Attributes:
+        total: 总共尝试创建的数量
+        success_count: 成功创建的数量
+        failed_count: 失败的数量
+        items: 成功创建的类型列表
+        errors: 失败的错误信息列表（可选）
+    
+    Examples:
+        >>> response = ClassBatchCreateResponse(
+        ...     total=3,
+        ...     success_count=2,
+        ...     failed_count=1,
+        ...     items=[class1, class2],
+        ...     errors=["创建类型 '药品' 失败: 类型名称已存在"]
+        ... )
+    """
+    total: int = Field(..., description="总共尝试创建的数量")
+    success_count: int = Field(..., description="成功创建的数量")
+    failed_count: int = Field(0, description="失败的数量")
+    items: List[ClassResponse] = Field(..., description="成功创建的类型列表")
+    errors: Optional[List[str]] = Field(None, description="失败的错误信息列表")
+
+
+class ClassListResponse(BaseModel):
+    """类型列表响应模型
+    
+    用于返回本体类型列表。
+    
+    Attributes:
+        total: 总数量
+        scene_id: 所属场景ID
+        scene_name: 场景名称
+        scene_description: 场景描述
+        items: 类型列表
+    
+    Examples:
+        >>> response = ClassListResponse(
+        ...     total=3,
+        ...     scene_id=uuid.uuid4(),
+        ...     scene_name="医疗场景",
+        ...     scene_description="用于医疗领域的本体建模",
+        ...     items=[class1, class2, class3]
+        ... )
+    """
+    total: int = Field(..., description="总数量")
+    scene_id: UUID = Field(..., description="所属场景ID")
+    scene_name: str = Field(..., description="场景名称")
+    scene_description: Optional[str] = Field(None, description="场景描述")
+    items: List[ClassResponse] = Field(..., description="类型列表")

api/app/schemas/prompt_optimizer_schema.py

@@ -22,6 +22,23 @@ class PromptOptMessage(BaseModel):
     )
 
 
+class PromptSaveRequest(BaseModel):
+    session_id: UUID = Field(
+        ...,
+        description="Session ID"
+    )
+
+    title: str = Field(
+        ...,
+        description="Prompt Title"
+    )
+
+    prompt: str = Field(
+        ...,
+        description="Optimized prompt content"
+    )
+
+
 class PromptOptModelSet(BaseModel):
     id: UUID | None = Field(
         default=None,

api/app/services/app_chat_service.py

@@ -171,7 +171,14 @@ class AppChatService:
         self.conversation_service.save_conversation_messages(
             conversation_id=conversation_id,
             user_message=message,
-            assistant_message=result["content"]
+            assistant_message=result["content"],
+            meta_data={
+                "usage": result.get("usage", {
+                    "prompt_tokens": 0,
+                    "completion_tokens": 0,
+                    "total_tokens": 0
+                })
+            }
         )
 
         elapsed_time = time.time() - start_time
@@ -310,6 +317,7 @@ class AppChatService:
 
             # 流式调用 Agent
             full_content = ""
+            total_tokens = 0
             async for chunk in agent.chat_stream(
                     message=message,
                     history=history,
@@ -320,9 +328,12 @@ class AppChatService:
                     config_id=config_id,
                     memory_flag=memory_flag
             ):
-                full_content += chunk
+                if isinstance(chunk, int):
-                # 发送消息块事件
+                    total_tokens = chunk
-                yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n"
+                else:
+                    full_content += chunk
+                    # 发送消息块事件
+                    yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n"
 
             elapsed_time = time.time() - start_time
 
@@ -339,7 +350,7 @@ class AppChatService:
                 content=full_content,
                 meta_data={
                     "model": api_key_obj.model_name,
-                    "usage": {}
+                    "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens}
                 }
             )
 
@@ -416,7 +427,11 @@ class AppChatService:
             meta_data={
                 "mode": result.get("mode"),
                 "elapsed_time": result.get("elapsed_time"),
-                "sub_results": result.get("sub_results")
+                "usage": result.get("usage", {
+                            "prompt_tokens": 0,
+                            "completion_tokens": 0,
+                            "total_tokens": 0
+                        })
             }
         )
 
@@ -458,6 +473,7 @@ class AppChatService:
             yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id)}, ensure_ascii=False)}\n\n"
 
             full_content = ""
+            total_tokens = 0
 
             # 2. 创建编排器
             orchestrator = MultiAgentOrchestrator(self.db, config)
@@ -474,16 +490,26 @@ class AppChatService:
                     storage_type=storage_type,
                     user_rag_memory_id=user_rag_memory_id
             ):
-                yield event
+                if "sub_usage" in event:
-                # 尝试提取内容（用于保存）
+                    if "data:" in event:
-                if "data:" in event:
+                        try:
-                    try:
+                            data_line = event.split("data: ", 1)[1].strip()
-                        data_line = event.split("data: ", 1)[1].strip()
+                            data = json.loads(data_line)
-                        data = json.loads(data_line)
+                            if "total_tokens" in data:
-                        if "content" in data:
+                                total_tokens += data["total_tokens"]
-                            full_content += data["content"]
+                        except:
-                    except:
+                            pass
-                        pass
+                else:
+                    yield event
+                    # 尝试提取内容（用于保存）
+                    if "data:" in event:
+                        try:
+                            data_line = event.split("data: ", 1)[1].strip()
+                            data = json.loads(data_line)
+                            if "content" in data:
+                                full_content += data["content"]
+                        except:
+                            pass
 
             elapsed_time = time.time() - start_time
 
@@ -499,7 +525,12 @@ class AppChatService:
                 role="assistant",
                 content=full_content,
                 meta_data={
-                    "elapsed_time": elapsed_time
+                    "elapsed_time": elapsed_time,
+                    "usage": {
+                        "prompt_tokens": 0,
+                        "completion_tokens": 0,
+                        "total_tokens": total_tokens
+                    }
                 }
             )
 

api/app/services/app_statistics_service.py

@@ -187,7 +187,7 @@ class AppStatisticsService:
                 daily_tokens[date_str] = 0
             daily_tokens[date_str] += int(tokens)
         
-        daily_data = [{"date": date, "tokens": tokens} for date, tokens in sorted(daily_tokens.items()) if tokens != 0]
+        daily_data = [{"date": date, "count": tokens} for date, tokens in sorted(daily_tokens.items()) if tokens != 0]
-        total = sum(row["tokens"] for row in daily_data)
+        total = sum(row["count"] for row in daily_data)
         
         return {"daily": daily_data, "total": total}

api/app/services/conversation_service.py

@@ -1,4 +1,5 @@
 """会话服务"""
+import os
 import uuid
 from datetime import datetime, timedelta
 from typing import Annotated
@@ -298,7 +299,8 @@ class ConversationService:
             self,
             conversation_id: uuid.UUID,
             user_message: str,
-            assistant_message: str
+            assistant_message: str,
+            meta_data: Optional[dict] = None
     ):
         """
         Save a pair of user and assistant messages to the conversation.
@@ -307,6 +309,7 @@ class ConversationService:
             conversation_id (uuid.UUID): Conversation UUID.
             user_message (str): User's message content.
             assistant_message (str): Assistant's response content.
+            meta_data (Optional[dict]): Optional metadata for the messages.
         """
         self.add_message(
             conversation_id=conversation_id,
@@ -317,7 +320,8 @@ class ConversationService:
         self.add_message(
             conversation_id=conversation_id,
             role="assistant",
-            content=assistant_message
+            content=assistant_message,
+            meta_data=meta_data
         )
 
         logger.debug(
@@ -526,12 +530,12 @@ class ConversationService:
                 takeaways=[],
                 info_score=0,
             )
-
+        prompt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prompt')
-        with open('app/services/prompt/conversation_summary_system.jinja2', 'r', encoding='utf-8') as f:
+        with open(os.path.join(prompt_path, 'conversation_summary_system.jinja2'), 'r', encoding='utf-8') as f:
             system_prompt = f.read()
         rendered_system_message = Template(system_prompt).render()
 
-        with open('app/services/prompt/conversation_summary_user.jinja2', 'r', encoding='utf-8') as f:
+        with open(os.path.join(prompt_path, 'conversation_summary_user.jinja2'), 'r', encoding='utf-8') as f:
             user_prompt = f.read()
         rendered_user_message = Template(user_prompt).render(
             language=language,

api/app/services/draft_run_service.py

@@ -110,6 +110,8 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str
                 result = task_service.get_task_memory_read_result(task.id)
                 status = result.get("status")
                 logger.info(f"读取任务状态：{status}")
+                if memory_content:
+                    memory_content = memory_content['answer']
 
             finally:
                 db.close()
@@ -123,7 +125,6 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str
                     "content_length": len(str(memory_content))
                 }
             )
-
             return f"检索到以下历史记忆：\n\n{memory_content}"
         except Exception as e:
             logger.error("长期记忆检索失败", extra={"error": str(e), "error_type": type(e).__name__})
@@ -442,7 +443,14 @@ class DraftRunService:
                     user_message=message,
                     assistant_message=result["content"],
                     app_id=agent_config.app_id,
-                    user_id=user_id
+                    user_id=user_id,
+                    meta_data={
+                        "usage": result.get("usage", {
+                            "prompt_tokens": 0,
+                            "completion_tokens": 0,
+                            "total_tokens": 0
+                        })
+                    }
                 )
 
             response = {
@@ -649,6 +657,7 @@ class DraftRunService:
 
             # 9. 流式调用 Agent
             full_content = ""
+            total_tokens = 0
             async for chunk in agent.chat_stream(
                 message=message,
                 history=history,
@@ -659,14 +668,22 @@ class DraftRunService:
                 user_rag_memory_id=user_rag_memory_id,
                 memory_flag=memory_flag
             ):
-                full_content += chunk
+                if isinstance(chunk, int):
-                # 发送消息块事件
+                    total_tokens = chunk
-                yield self._format_sse_event("message", {
+                else:
-                    "content": chunk
+                    full_content += chunk
-                })
+                    # 发送消息块事件
+                    yield self._format_sse_event("message", {
+                        "content": chunk
+                    })
 
             elapsed_time = time.time() - start_time
 
+            if sub_agent:
+                yield self._format_sse_event("sub_usage", {
+                        "total_tokens": total_tokens
+                    })
+
             # 10. 保存会话消息
             if not sub_agent and agent_config.memory and agent_config.memory.get("enabled"):
                 await self._save_conversation_message(
@@ -674,7 +691,10 @@ class DraftRunService:
                     user_message=message,
                     assistant_message=full_content,
                     app_id=agent_config.app_id,
-                    user_id=user_id
+                    user_id=user_id,
+                    meta_data={
+                        "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens}
+                    }
                 )
 
             # 11. 发送结束事件
@@ -898,6 +918,7 @@ class DraftRunService:
         conversation_id: str,
         user_message: str,
         assistant_message: str,
+        meta_data: dict,
         app_id: Optional[uuid.UUID] = None,
         user_id: Optional[str] = None
     ) -> None:
@@ -909,6 +930,7 @@ class DraftRunService:
             assistant_message: AI 回复消息
             app_id: 应用ID（未使用，保留用于兼容性）
             user_id: 用户ID（未使用，保留用于兼容性）
+            meta_data: token消耗
         """
         try:
             from app.services.conversation_service import ConversationService
@@ -927,7 +949,8 @@ class DraftRunService:
             conversation_service.add_message(
                 conversation_id=conv_uuid,
                 role="assistant",
-                content=assistant_message
+                content=assistant_message,
+                meta_data=meta_data
             )
 
             logger.debug(

api/app/services/emotion_analytics_service.py

@@ -17,12 +17,15 @@ from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from pydantic import BaseModel, Field
 from sqlalchemy.orm import Session
 
+from app.utils.config_utils import resolve_config_id
+
 logger = get_business_logger()
 
 
 class EmotionSuggestion(BaseModel):
     """情绪建议模型"""
-    type: str = Field(..., description="建议类型：emotion_balance/activity_recommendation/social_connection/stress_management")
+    type: str = Field(...,
+                      description="建议类型：emotion_balance/activity_recommendation/social_connection/stress_management")
     title: str = Field(..., description="建议标题")
     content: str = Field(..., description="建议内容")
     priority: str = Field(..., description="优先级：high/medium/low")
@@ -37,33 +40,33 @@ class EmotionSuggestionsResponse(BaseModel):
 
 class EmotionAnalyticsService:
     """情绪分析服务
-    
+
     提供情绪数据的分析和统计功能，包括：
     - 情绪标签统计
     - 情绪词云数据
     - 情绪健康指数计算
     - 个性化情绪建议生成
-    
+
     Attributes:
         emotion_repo: 情绪数据仓储实例
     """
-    
+
     def __init__(self):
         """初始化情绪分析服务"""
         connector = Neo4jConnector()
         self.emotion_repo = EmotionRepository(connector)
         logger.info("情绪分析服务初始化完成")
-    
+
     async def get_emotion_tags(
-        self,
+            self,
-        end_user_id: str,
+            end_user_id: str,
-        emotion_type: Optional[str] = None,
+            emotion_type: Optional[str] = None,
-        start_date: Optional[str] = None,
+            start_date: Optional[str] = None,
-        end_date: Optional[str] = None,
+            end_date: Optional[str] = None,
-        limit: int = 10
+            limit: int = 10
     ) -> Dict[str, Any]:
         """获取情绪标签统计
-        
+
         查询指定用户的情绪类型分布，包括计数、百分比和平均强度。
         确保返回所有6个情绪维度（joy、sadness、anger、fear、surprise、neutral），
         即使某些维度没有数据也会返回count=0的记录。
@@ -71,8 +74,8 @@ class EmotionAnalyticsService:
         """
         try:
             logger.info(f"获取情绪标签统计: user={end_user_id}, type={emotion_type}, "
-                       f"start={start_date}, end={end_date}, limit={limit}")
+                        f"start={start_date}, end={end_date}, limit={limit}")
-            
+
             # 调用仓储层查询
             tags = await self.emotion_repo.get_emotion_tags(
                 end_user_id=end_user_id,
@@ -81,13 +84,13 @@ class EmotionAnalyticsService:
                 end_date=end_date,
                 limit=limit
             )
-            
+
             # 定义所有6个情绪维度
             all_emotion_types = ['joy', 'sadness', 'anger', 'fear', 'surprise', 'neutral']
-            
+
             # 将查询结果转换为字典，方便查找
             tags_dict = {tag["emotion_type"]: tag for tag in tags}
-            
+
             # 补全缺失的情绪维度
             complete_tags = []
             for emotion in all_emotion_types:
@@ -101,52 +104,52 @@ class EmotionAnalyticsService:
                         "percentage": 0.0,
                         "avg_intensity": 0.0
                     })
-            
+
             # 计算总数
             total_count = sum(tag["count"] for tag in complete_tags)
-            
+
             # 如果有数据，重新计算百分比（因为补全了0值项）
             if total_count > 0:
                 for tag in complete_tags:
                     if tag["count"] > 0:
                         tag["percentage"] = round((tag["count"] / total_count) * 100, 2)
-            
+
             # 构建时间范围信息
             time_range = {}
             if start_date:
                 time_range["start_date"] = start_date
             if end_date:
                 time_range["end_date"] = end_date
-            
+
             # 格式化响应
             response = {
                 "tags": complete_tags,
                 "total_count": total_count,
                 "time_range": time_range if time_range else None
             }
-            
+
             logger.info(f"情绪标签统计完成: total_count={total_count}, tags_count={len(complete_tags)}")
             return response
-            
+
         except Exception as e:
             logger.error(f"获取情绪标签统计失败: {str(e)}", exc_info=True)
             raise
-    
+
     async def get_emotion_wordcloud(
-        self,
+            self,
-        end_user_id: str,
+            end_user_id: str,
-        emotion_type: Optional[str] = None,
+            emotion_type: Optional[str] = None,
-        limit: int = 50
+            limit: int = 50
     ) -> Dict[str, Any]:
         """获取情绪词云数据
-        
+
         查询情绪关键词及其频率，用于生成词云可视化。
-        
+
         Args:
             end_user_id: 宿主ID（用户组ID）
             emotion_type: 可选的情绪类型过滤
             limit: 返回关键词的最大数量
-            
+
         Returns:
             Dict: 包含情绪词云数据的响应：
                 - keywords: 关键词列表
@@ -154,39 +157,39 @@ class EmotionAnalyticsService:
         """
         try:
             logger.info(f"获取情绪词云数据: user={end_user_id}, type={emotion_type}, limit={limit}")
-            
+
             # 调用仓储层查询
             keywords = await self.emotion_repo.get_emotion_wordcloud(
                 end_user_id=end_user_id,
                 emotion_type=emotion_type,
                 limit=limit
             )
-            
+
             # 计算总关键词数量
             total_keywords = len(keywords)
-            
+
             # 格式化响应
             response = {
                 "keywords": keywords,
                 "total_keywords": total_keywords
             }
-            
+
             logger.info(f"情绪词云数据获取完成: total_keywords={total_keywords}")
             return response
-            
+
         except Exception as e:
             logger.error(f"获取情绪词云数据失败: {str(e)}", exc_info=True)
             raise
-    
+
     def _calculate_positivity_rate(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]:
         """计算积极率
-        
+
         根据情绪类型分类正面、负面和中性情绪，计算积极率。
         公式：(正面数 / (正面数 + 负面数)) * 100
-        
+
         Args:
             emotions: 情绪数据列表，每个包含 emotion_type 字段
-            
+
         Returns:
             Dict: 包含积极率计算结果：
                 - score: 积极率分数（0-100）
@@ -197,38 +200,38 @@ class EmotionAnalyticsService:
         # 定义情绪分类
         positive_emotions = {'joy', 'surprise'}
         negative_emotions = {'sadness', 'anger', 'fear'}
-        
+
         # 统计各类情绪数量
         positive_count = sum(1 for e in emotions if e.get('emotion_type') in positive_emotions)
         negative_count = sum(1 for e in emotions if e.get('emotion_type') in negative_emotions)
         neutral_count = sum(1 for e in emotions if e.get('emotion_type') == 'neutral')
-        
+
         # 计算积极率
         total_non_neutral = positive_count + negative_count
         if total_non_neutral > 0:
             score = (positive_count / total_non_neutral) * 100
         else:
             score = 50.0  # 如果没有非中性情绪，默认为50
-        
+
         logger.debug(f"积极率计算: positive={positive_count}, negative={negative_count}, "
-                    f"neutral={neutral_count}, score={score:.2f}")
+                     f"neutral={neutral_count}, score={score:.2f}")
-        
+
         return {
             "score": round(score, 2),
             "positive_count": positive_count,
             "negative_count": negative_count,
             "neutral_count": neutral_count
         }
-    
+
     def _calculate_stability(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]:
         """计算稳定性
-        
+
         基于情绪强度的标准差计算情绪稳定性。
         公式：(1 - min(std_deviation, 1.0)) * 100
-        
+
         Args:
             emotions: 情绪数据列表，每个包含 emotion_intensity 字段
-            
+
         Returns:
             Dict: 包含稳定性计算结果：
                 - score: 稳定性分数（0-100）
@@ -236,7 +239,7 @@ class EmotionAnalyticsService:
         """
         # 提取所有情绪强度
         intensities = [e.get('emotion_intensity', 0.0) for e in emotions if e.get('emotion_intensity') is not None]
-        
+
         # 计算标准差
         if len(intensities) >= 2:
             std_deviation = statistics.stdev(intensities)
@@ -244,29 +247,29 @@ class EmotionAnalyticsService:
             std_deviation = 0.0  # 只有一个数据点，标准差为0
         else:
             std_deviation = 0.0  # 没有数据，标准差为0
-        
+
         # 计算稳定性分数
         # 标准差越小，稳定性越高
         score = (1 - min(std_deviation, 1.0)) * 100
-        
+
         logger.debug(f"稳定性计算: intensities_count={len(intensities)}, "
-                    f"std_deviation={std_deviation:.3f}, score={score:.2f}")
+                     f"std_deviation={std_deviation:.3f}, score={score:.2f}")
-        
+
         return {
             "score": round(score, 2),
             "std_deviation": round(std_deviation, 3)
         }
-    
+
     def _calculate_resilience(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]:
         """计算恢复力
-        
+
         分析情绪转换模式，统计从负面情绪恢复到正面情绪的能力。
         公式：(负面到正面转换次数 / 总负面情绪数) * 100
-        
+
         Args:
             emotions: 情绪数据列表，每个包含 emotion_type 和 created_at 字段
                      应该按时间顺序排列
-            
+
         Returns:
             Dict: 包含恢复力计算结果：
                 - score: 恢复力分数（0-100）
@@ -275,24 +278,24 @@ class EmotionAnalyticsService:
         # 定义情绪分类
         positive_emotions = {'joy', 'surprise'}
         negative_emotions = {'sadness', 'anger', 'fear'}
-        
+
         # 统计负面到正面的转换次数
         recovery_count = 0
         negative_count = 0
-        
+
         for i in range(len(emotions)):
             current_emotion = emotions[i].get('emotion_type')
-            
+
             # 统计负面情绪总数
             if current_emotion in negative_emotions:
                 negative_count += 1
-                
+
                 # 检查下一个情绪是否为正面
                 if i + 1 < len(emotions):
                     next_emotion = emotions[i + 1].get('emotion_type')
                     if next_emotion in positive_emotions:
                         recovery_count += 1
-        
+
         # 计算恢复力分数
         if negative_count > 0:
             recovery_rate = recovery_count / negative_count
@@ -301,28 +304,28 @@ class EmotionAnalyticsService:
             # 如果没有负面情绪，恢复力设为100（最佳状态）
             recovery_rate = 1.0
             score = 100.0
-        
+
         logger.debug(f"恢复力计算: negative_count={negative_count}, "
-                    f"recovery_count={recovery_count}, score={score:.2f}")
+                     f"recovery_count={recovery_count}, score={score:.2f}")
-        
+
         return {
             "score": round(score, 2),
             "recovery_rate": round(recovery_rate, 3)
         }
-    
+
     async def calculate_emotion_health_index(
-        self,
+            self,
-        end_user_id: str,
+            end_user_id: str,
-        time_range: str = "30d"
+            time_range: str = "30d"
     ) -> Dict[str, Any]:
         """计算情绪健康指数
-        
+
         综合积极率、稳定性和恢复力计算情绪健康指数。
-        
+
         Args:
             end_user_id: 宿主ID（用户组ID）
             time_range: 时间范围（7d/30d/90d）
-            
+
         Returns:
             Dict: 包含情绪健康指数的完整响应：
                 - health_score: 综合健康分数（0-100）
@@ -336,13 +339,13 @@ class EmotionAnalyticsService:
         """
         try:
             logger.info(f"计算情绪健康指数: user={end_user_id}, time_range={time_range}")
-            
+
             # 获取时间范围内的情绪数据
             emotions = await self.emotion_repo.get_emotions_in_range(
                 end_user_id=end_user_id,
                 time_range=time_range
             )
-            
+
             # 如果没有数据，返回默认值
             if not emotions:
                 logger.warning(f"用户 {end_user_id} 在时间范围 {time_range} 内没有情绪数据")
@@ -357,20 +360,20 @@ class EmotionAnalyticsService:
                     "emotion_distribution": {},
                     "time_range": time_range
                 }
-            
+
             # 计算各维度指标
             positivity_rate = self._calculate_positivity_rate(emotions)
             stability = self._calculate_stability(emotions)
             resilience = self._calculate_resilience(emotions)
-            
+
             # 计算综合健康分数
             # 公式：positivity_rate * 0.4 + stability * 0.3 + resilience * 0.3
             health_score = (
-                positivity_rate["score"] * 0.4 +
+                    positivity_rate["score"] * 0.4 +
-                stability["score"] * 0.3 +
+                    stability["score"] * 0.3 +
-                resilience["score"] * 0.3
+                    resilience["score"] * 0.3
             )
-            
+
             # 确定健康等级
             if health_score >= 80:
                 level = "优秀"
@@ -380,13 +383,13 @@ class EmotionAnalyticsService:
                 level = "一般"
             else:
                 level = "较差"
-            
+
             # 统计情绪分布
             emotion_distribution = {}
             for emotion_type in ['joy', 'sadness', 'anger', 'fear', 'surprise', 'neutral']:
                 count = sum(1 for e in emotions if e.get('emotion_type') == emotion_type)
                 emotion_distribution[emotion_type] = count
-            
+
             # 格式化响应
             response = {
                 "health_score": round(health_score, 2),
@@ -399,22 +402,22 @@ class EmotionAnalyticsService:
                 "emotion_distribution": emotion_distribution,
                 "time_range": time_range
             }
-            
+
             logger.info(f"情绪健康指数计算完成: score={health_score:.2f}, level={level}")
             return response
-            
+
         except Exception as e:
             logger.error(f"计算情绪健康指数失败: {str(e)}", exc_info=True)
             raise
-    
+
     def _analyze_emotion_patterns(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]:
         """分析情绪模式
-        
+
         识别主要负面情绪、情绪触发因素和波动时段。
-        
+
         Args:
             emotions: 情绪数据列表，每个包含 emotion_type、emotion_intensity、created_at 字段
-            
+
         Returns:
             Dict: 包含情绪模式分析结果：
                 - dominant_negative_emotion: 主要负面情绪类型
@@ -422,19 +425,19 @@ class EmotionAnalyticsService:
                 - emotion_volatility: 情绪波动性（高/中/低）
         """
         negative_emotions = {'sadness', 'anger', 'fear'}
-        
+
         # 统计负面情绪分布
         negative_emotion_counts = {}
         for emotion in emotions:
             emotion_type = emotion.get('emotion_type')
             if emotion_type in negative_emotions:
                 negative_emotion_counts[emotion_type] = negative_emotion_counts.get(emotion_type, 0) + 1
-        
+
         # 识别主要负面情绪
         dominant_negative_emotion = None
         if negative_emotion_counts:
             dominant_negative_emotion = max(negative_emotion_counts, key=negative_emotion_counts.get)
-        
+
         # 识别高强度情绪（强度 >= 0.7）
         high_intensity_emotions = [
             {
@@ -445,7 +448,7 @@ class EmotionAnalyticsService:
             for e in emotions
             if e.get('emotion_intensity', 0) >= 0.7
         ]
-        
+
         # 评估情绪波动性
         intensities = [e.get('emotion_intensity', 0.0) for e in emotions if e.get('emotion_intensity') is not None]
         if len(intensities) >= 2:
@@ -458,29 +461,29 @@ class EmotionAnalyticsService:
                 volatility = "低"
         else:
             volatility = "未知"
-        
+
         logger.debug(f"情绪模式分析: dominant_negative={dominant_negative_emotion}, "
-                    f"high_intensity_count={len(high_intensity_emotions)}, volatility={volatility}")
+                     f"high_intensity_count={len(high_intensity_emotions)}, volatility={volatility}")
-        
+
         return {
             "dominant_negative_emotion": dominant_negative_emotion,
             "high_intensity_emotions": high_intensity_emotions[:5],  # 最多返回5个
             "emotion_volatility": volatility
         }
-    
+
     async def generate_emotion_suggestions(
-        self,
+            self,
-        end_user_id: str,
+            end_user_id: str,
-        db: Session,
+            db: Session,
     ) -> Dict[str, Any]:
         """生成个性化情绪建议
-        
+
         基于情绪健康数据和用户画像生成个性化建议。
-        
+
         Args:
             end_user_id: 宿主ID（用户组ID）
             db: 数据库会话
-            
+
         Returns:
             Dict: 包含个性化建议的响应：
                 - health_summary: 健康状态摘要
@@ -488,17 +491,17 @@ class EmotionAnalyticsService:
         """
         try:
             logger.info(f"生成个性化情绪建议: user={end_user_id}")
-            
+
             # 1. 从 end_user_id 获取关联的 memory_config_id
             llm_client = None
             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 not None:
                     from app.services.memory_config_service import (
                         MemoryConfigService,
@@ -513,35 +516,35 @@ class EmotionAnalyticsService:
                     llm_client = factory.get_llm_client(str(memory_config.llm_model_id))
             except Exception as e:
                 logger.warning(f"无法获取 end_user {end_user_id} 的配置，将使用默认配置: {e}")
-            
+
             # 2. 获取情绪健康数据
             health_data = await self.calculate_emotion_health_index(end_user_id, time_range="30d")
-            
+
             # 3. 获取情绪数据用于模式分析
             emotions = await self.emotion_repo.get_emotions_in_range(
                 end_user_id=end_user_id,
                 time_range="30d"
             )
-            
+
             # 4. 分析情绪模式
             patterns = self._analyze_emotion_patterns(emotions)
-            
+
             # 5. 获取用户画像数据（简化版，直接从Neo4j获取）
             user_profile = await self._get_simple_user_profile(end_user_id)
-            
+
             # 6. 构建LLM prompt
             prompt = await self._build_suggestion_prompt(health_data, patterns, user_profile)
-            
+
             # 7. 调用LLM生成建议（使用配置中的LLM）
             if llm_client is None:
                 # 无法获取配置时，抛出错误而不是使用默认配置
                 raise ValueError("无法获取LLM配置，请确保end_user关联了有效的memory_config")
-            
+
             # 将 prompt 转换为 messages 格式
             messages = [
                 {"role": "user", "content": prompt}
             ]
-            
+
             # 8. 使用结构化输出直接获取 Pydantic 模型
             try:
                 suggestions_response = await llm_client.response_structured(
@@ -552,7 +555,7 @@ class EmotionAnalyticsService:
                 logger.error(f"LLM 结构化输出失败: {str(e)}")
                 # 返回默认建议
                 suggestions_response = self._get_default_suggestions(health_data)
-            
+
             # 8. 验证建议数量（3-5条）
             if len(suggestions_response.suggestions) < 3:
                 logger.warning(f"建议数量不足: {len(suggestions_response.suggestions)}")
@@ -560,7 +563,7 @@ class EmotionAnalyticsService:
             elif len(suggestions_response.suggestions) > 5:
                 logger.warning(f"建议数量过多: {len(suggestions_response.suggestions)}")
                 suggestions_response.suggestions = suggestions_response.suggestions[:5]
-            
+
             # 9. 格式化响应
             response = {
                 "health_summary": suggestions_response.health_summary,
@@ -575,26 +578,26 @@ class EmotionAnalyticsService:
                     for s in suggestions_response.suggestions
                 ]
             }
-            
+
             logger.info(f"个性化建议生成完成: suggestions_count={len(response['suggestions'])}")
             return response
-            
+
         except Exception as e:
             logger.error(f"生成个性化建议失败: {str(e)}", exc_info=True)
             raise
-    
+
     async def _get_simple_user_profile(self, end_user_id: str) -> Dict[str, Any]:
         """获取简化的用户画像数据
-        
+
         Args:
             end_user_id: 用户ID
-            
+
         Returns:
             Dict: 用户画像数据
         """
         try:
             connector = Neo4jConnector()
-            
+
             # 查询用户的实体和标签
             query = """
             MATCH (e:Entity)
@@ -603,59 +606,59 @@ class EmotionAnalyticsService:
             ORDER BY e.created_at DESC
             LIMIT 20
             """
-            
+
             entities = await connector.execute_query(query, end_user_id=end_user_id)
-            
+
             # 提取兴趣标签
             interests = [e["name"] for e in entities if e.get("type") in ["INTEREST", "HOBBY"]][:5]
             # 后期会引入用户的习惯。。
             return {
                 "interests": interests if interests else ["未知"]
             }
-            
+
         except Exception as e:
             logger.error(f"获取用户画像失败: {str(e)}")
             return {"interests": ["未知"]}
-    
+
     async def _build_suggestion_prompt(
-        self,
+            self,
-        health_data: Dict[str, Any],
+            health_data: Dict[str, Any],
-        patterns: Dict[str, Any],
+            patterns: Dict[str, Any],
-        user_profile: Dict[str, Any]
+            user_profile: Dict[str, Any]
     ) -> str:
         """构建情绪建议生成的prompt
-        
+
         Args:
             health_data: 情绪健康数据
             patterns: 情绪模式分析结果
             user_profile: 用户画像数据
-            
+
         Returns:
             str: LLM prompt
         """
         from app.core.memory.utils.prompt.prompt_utils import (
             render_emotion_suggestions_prompt,
         )
-        
+
         prompt = await render_emotion_suggestions_prompt(
             health_data=health_data,
             patterns=patterns,
             user_profile=user_profile
         )
-        
+
         return prompt
-    
+
     def _get_default_suggestions(self, health_data: Dict[str, Any]) -> EmotionSuggestionsResponse:
         """获取默认建议（当LLM调用失败时使用）
-        
+
         Args:
             health_data: 情绪健康数据
-            
+
         Returns:
             EmotionSuggestionsResponse: 默认建议
         """
         health_score = health_data.get('health_score', 0)
-        
+
         if health_score >= 80:
             summary = "您的情绪健康状况优秀，请继续保持积极的生活态度。"
         elif health_score >= 60:
@@ -664,7 +667,7 @@ class EmotionAnalyticsService:
             summary = "您的情绪健康需要关注，建议采取一些改善措施。"
         else:
             summary = "您的情绪健康需要重点关注，建议寻求专业帮助。"
-        
+
         suggestions = [
             EmotionSuggestion(
                 type="emotion_balance",
@@ -700,54 +703,54 @@ class EmotionAnalyticsService:
                 ]
             )
         ]
-        
+
         return EmotionSuggestionsResponse(
             health_summary=summary,
             suggestions=suggestions
         )
-    
+
     async def get_cached_suggestions(
-        self,
+            self,
-        end_user_id: str,
+            end_user_id: str,
-        db: Session,
+            db: Session,
     ) -> Optional[Dict[str, Any]]:
         """从 Redis 缓存获取个性化情绪建议
-        
+
         Args:
             end_user_id: 宿主ID（用户组ID）
             db: 数据库会话（保留参数以保持接口兼容性）
-            
+
         Returns:
             Dict: 缓存的建议数据，如果不存在或已过期返回 None
         """
         try:
             from app.cache.memory.emotion_memory import EmotionMemoryCache
-            
+
             logger.info(f"尝试从 Redis 缓存获取情绪建议: user={end_user_id}")
-            
+
             # 从 Redis 获取缓存
             cached_data = await EmotionMemoryCache.get_emotion_suggestions(end_user_id)
-            
+
             if cached_data is None:
                 logger.info(f"用户 {end_user_id} 的建议缓存不存在或已过期")
                 return None
-            
+
             logger.info(f"成功从 Redis 缓存获取建议: user={end_user_id}")
             return cached_data
-            
+
         except Exception as e:
             logger.error(f"从 Redis 缓存获取建议失败: {str(e)}", exc_info=True)
             return None
-    
+
     async def save_suggestions_cache(
-        self,
+            self,
-        end_user_id: str,
+            end_user_id: str,
-        suggestions_data: Dict[str, Any],
+            suggestions_data: Dict[str, Any],
-        db: Session,
+            db: Session,
-        expires_hours: int = 24
+            expires_hours: int = 24
     ) -> None:
         """保存建议到 Redis 缓存
-        
+
         Args:
             end_user_id: 宿主ID（用户组ID）
             suggestions_data: 建议数据
@@ -756,24 +759,24 @@ class EmotionAnalyticsService:
         """
         try:
             from app.cache.memory.emotion_memory import EmotionMemoryCache
-            
+
             logger.info(f"保存建议到 Redis 缓存: user={end_user_id}, expires={expires_hours}小时")
-            
+
             # 计算过期时间（秒）
             expire_seconds = expires_hours * 3600
-            
+
             # 保存到 Redis
             success = await EmotionMemoryCache.set_emotion_suggestions(
                 user_id=end_user_id,
                 suggestions_data=suggestions_data,
                 expire=expire_seconds
             )
-            
+
             if success:
                 logger.info(f"建议缓存保存成功: user={end_user_id}")
             else:
                 logger.warning(f"建议缓存保存失败: user={end_user_id}")
-            
+
         except Exception as e:
             logger.error(f"保存建议缓存失败: {str(e)}", exc_info=True)
             # 不抛出异常，缓存失败不应影响主流程

api/app/services/handoffs_service.py

@@ -4,7 +4,7 @@ import uuid
 from typing import List, Dict, Any, Optional, AsyncGenerator, Annotated
 from typing_extensions import TypedDict
 
-from langchain_core.messages import HumanMessage, AIMessage, BaseMessage
+from langchain_core.messages import HumanMessage, AIMessage, BaseMessage, AIMessageChunk
 from langgraph.graph import StateGraph, START, END
 from langgraph.types import Command
 from langgraph.checkpoint.memory import MemorySaver
@@ -727,9 +727,12 @@ class HandoffsService:
         
         # 提取响应
         response_content = ""
+        total_tokens = 0
         for msg in result.get("messages", []):
             if isinstance(msg, AIMessage):
                 response_content = msg.content
+                response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
+                total_tokens = response_meta.get("token_usage", {}).get("total_tokens", 0) if response_meta else 0
                 break
         
         return {
@@ -737,7 +740,12 @@ class HandoffsService:
             "active_agent": result.get("active_agent"),
             "response": response_content,
             "message_count": len(result.get("messages", [])),
-            "handoff_count": result.get("handoff_count", 0)
+            "handoff_count": result.get("handoff_count", 0),
+            "usage": {
+                    "prompt_tokens": 0,
+                    "completion_tokens": 0,
+                    "total_tokens": total_tokens
+                }
         }
     
     async def chat_stream(
@@ -830,6 +838,12 @@ class HandoffsService:
                 
                 # 捕获 LLM 结束事件，输出收集到的工具调用
                 elif kind == "on_chat_model_end":
+                    output_message = event.get("data", {}).get("output", {})
+                    if isinstance(output_message, AIMessageChunk):
+                        response_meta = output_message.response_metadata if hasattr(output_message, 'response_metadata') else None
+                        total_tokens = response_meta.get("token_usage", {}).get("total_tokens",
+                                                                                0) if response_meta else 0
+                        yield f"event: sub_usage\ndata: {json.dumps({"total_tokens": total_tokens}, ensure_ascii=False)}\n\n"
                     if collected_tool_calls:
                         # 找到参数最完整的 transfer 工具调用
                         best_tc = None

api/app/services/memory_agent_service.py

@@ -334,7 +334,9 @@ class MemoryAgentService:
                             langchain_messages.append(HumanMessage(content=msg['content']))
                         elif msg['role'] == 'assistant':
                             langchain_messages.append(AIMessage(content=msg['content']))
-
+                    print(100*'-')
+                    print(langchain_messages)
+                    print(100*'-')
                     # 初始状态 - 包含所有必要字段
                     initial_state = {
                         "messages": langchain_messages,

api/app/services/memory_dashboard_service.py

@@ -53,7 +53,10 @@ def get_workspace_end_users(
     workspace_id: uuid.UUID, 
     current_user: User
 ) -> List[EndUser]:
-    """获取工作空间的所有宿主（优化版本：减少数据库查询次数）"""
+    """获取工作空间的所有宿主（优化版本：减少数据库查询次数）
+    
+    返回结果按 updated_at 从新到旧排序（NULL 值排在最后）
+    """
     business_logger.info(f"获取工作空间宿主列表: workspace_id={workspace_id}, 操作者: {current_user.username}")
     
     try:        
@@ -68,9 +71,14 @@ def get_workspace_end_users(
         app_ids = [app.id for app in apps_orm]
         
         # 批量查询所有 end_users（一次查询而非循环查询）
+        # 按 updated_at 降序排序，NULL 值排在最后；id 作为次级排序键保证确定性
         from app.models.end_user_model import EndUser as EndUserModel
+        from sqlalchemy import desc, nullslast
         end_users_orm = db.query(EndUserModel).filter(
             EndUserModel.app_id.in_(app_ids)
+        ).order_by(
+            nullslast(desc(EndUserModel.updated_at)),
+            desc(EndUserModel.id)
         ).all()
         
         # 转换为 Pydantic 模型（只在需要时转换）

api/app/services/memory_reflection_service.py

@@ -18,6 +18,7 @@ from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.models.app_model import App
 from app.models.app_release_model import AppRelease
 from app.models.end_user_model import EndUser
+from app.utils.config_utils import resolve_config_id
 
 api_logger = get_api_logger()
 
@@ -88,51 +89,44 @@ class WorkspaceAppService:
         
         for release in app_releases:
             memory_content = self._extract_memory_content(release.config)
-            
-
             if memory_content and memory_content in processed_configs:
                 continue
-            
+
             release_info = {
                 "app_id": str(release.app_id),
                 "config": memory_content
             }
-            
+
 
             if memory_content:
                 processed_configs.add(memory_content)
                 memory_config_info = self._get_memory_config(memory_content)
-                
                 if memory_config_info:
                     if not any(dc["config_id"] == memory_config_info["config_id"] for dc in app_info["memory_configs"]):
                         app_info["memory_configs"].append(memory_config_info)
-            
+
             app_info["releases"].append(release_info)
-    
+
     def _extract_memory_content(self, config: Any) -> str:
         """Extract memory_comtent from config"""
         if not config or not isinstance(config, dict):
             return None
-        
+
         memory_obj = config.get('memory')
         if memory_obj and isinstance(memory_obj, dict):
             return memory_obj.get('memory_content')
-        
+
         return None
-    
+
     def _get_memory_config(self, memory_content: str) -> Dict[str, Any]:
         """Retrieve memory_config information based on memory_content"""
         try:
-            memory_config_result = MemoryConfigRepository.query_reflection_config_by_id(self.db, int(memory_content))
+            memory_content = resolve_config_id(memory_content, self.db)
+            memory_config_result = MemoryConfigRepository.query_reflection_config_by_id(self.db, (memory_content))
 
-            # memory_config_query, memory_config_params = MemoryConfigRepository.build_select_reflection(memory_content)
-            # memory_config_result = self.db.execute(text(memory_config_query), memory_config_params).fetchone()
-            # if memory_config_result is None:
-            #     return None
-            
             if memory_config_result:
                 return {
-                    "config_id": memory_config_result.config_id,
+                    "config_id": memory_content,
                     "enable_self_reflexion": memory_config_result.enable_self_reflexion,
                     "iteration_period": memory_config_result.iteration_period,
                     "reflexion_range": memory_config_result.reflexion_range,
@@ -144,20 +138,22 @@ class WorkspaceAppService:
                 }
         except Exception as e:
             api_logger.warning(f"查询memory_config失败，memory_content: {memory_content}, 错误: {str(e)}")
-        
+
         return None
-    
+
     def _process_end_users(self, app: App, app_info: Dict[str, Any]) -> None:
         """Processing end-user information for applications"""
         end_users = self.db.query(EndUser).filter(EndUser.app_id == app.id).all()
-        
+
         for end_user in end_users:
             end_user_info = {
                 "id": str(end_user.id),
                 "app_id": str(end_user.app_id)
             }
             app_info["end_users"].append(end_user_info)
-    
+        print(100*'-')
+        print(app_info)
+
     def get_end_user_reflection_time(self, end_user_id: str) -> Optional[Any]:
         """
         Read the reflection time of end users
@@ -176,7 +172,7 @@ class WorkspaceAppService:
         except Exception as e:
             api_logger.error(f"读取用户反思时间失败，end_user_id: {end_user_id}, 错误: {str(e)}")
             return None
-    
+
     def update_end_user_reflection_time(self, end_user_id: str) -> bool:
         """
         Update the reflection time of end users to the current time
@@ -189,7 +185,7 @@ class WorkspaceAppService:
         """
         try:
             from datetime import datetime
-            
+
             end_user = self.db.query(EndUser).filter(EndUser.id == end_user_id).first()
             if end_user:
                 end_user.reflection_time = datetime.now()
@@ -207,7 +203,7 @@ class WorkspaceAppService:
 
 class MemoryReflectionService:
     """Memory reflection service category"""
-    
+
     def __init__(self,db: Session = Depends(get_db)):
         self.db=db
 
@@ -252,22 +248,22 @@ class MemoryReflectionService:
                 "end_user_id": end_user_id,
                 "config_data": config_data
             }
-    
+
     async def start_reflection_from_data(self, config_data: Dict[str, Any], end_user_id: str) -> Dict[str, Any]:
         """
         Starting Reflection from Configuration Data
-        
+
         Args:
             config_data: Configure data dictionary, including reflective configuration information
             end_user_id: end_user_id
-            
+
         Returns:
             Reflect on the execution results
         """
         try:
             config_id = config_data.get("config_id")
             api_logger.info(f"从配置数据启动反思，config_id: {config_id}, end_user_id: {end_user_id}")
-            
+
 
             if not config_data.get("enable_self_reflexion", False):
                 return {
@@ -277,7 +273,7 @@ class MemoryReflectionService:
                     "end_user_id": end_user_id,
                     "config_data": config_data
                 }
-            
+
 
             config_data_id=config_data['config_id']
             reflection_config=WorkspaceAppService(self.db)._get_memory_config(config_data_id)
@@ -290,7 +286,7 @@ class MemoryReflectionService:
                 # 检查是否需要执行反思
                 should_execute = False
                 hours_diff = 0
-                
+
                 if current_reflection_time is None:
                     # 首次执行反思
                     should_execute = True
@@ -302,11 +298,11 @@ class MemoryReflectionService:
                             reflection_time = datetime.fromisoformat(current_reflection_time)
                         else:
                             reflection_time = current_reflection_time
-                        
+
                         current_time = datetime.now()
                         time_diff = current_time - reflection_time
                         hours_diff = int(time_diff.total_seconds() / 3600)
-                        
+
                         # 检查是否达到反思周期
                         if hours_diff >= iteration_period:
                             should_execute = True
@@ -316,7 +312,7 @@ class MemoryReflectionService:
                     except (ValueError, TypeError) as e:
                         api_logger.warning(f"解析反思时间失败: {e}，将执行反思")
                         should_execute = True
-                
+
                 if should_execute:
                     api_logger.info(f"与上次的反思时间间隔为: {hours_diff} 小时")
                     # 3. 执行反思引擎
@@ -349,7 +345,7 @@ class MemoryReflectionService:
                         "next_reflection_in_hours": iteration_period - hours_diff
                     }
 
-            
+
         except Exception as e:
             config_id = config_data.get("config_id", "unknown")
             api_logger.error(f"启动反思失败，config_id: {config_id}, end_user_id: {end_user_id}, 错误: {str(e)}")
@@ -360,7 +356,7 @@ class MemoryReflectionService:
                 "end_user_id": end_user_id,
                 "config_data": config_data
             }
-    
+
     def _create_reflection_config_from_data(self, config_data: Dict[str, Any]) -> ReflectionConfig:
         """Create reflective configuration objects from configuration data"""
 
@@ -368,12 +364,12 @@ class MemoryReflectionService:
         if reflexion_range_value is None or reflexion_range_value == "":
             reflexion_range_value = "partial"
         reflexion_range = ReflectionRange(reflexion_range_value)
-        
+
         baseline_value = config_data.get("baseline")
         if baseline_value is None or baseline_value == "":
             baseline_value = "TIME"
         baseline = ReflectionBaseline(baseline_value)
-        
+
         # iteration_period =
         iteration_period = config_data.get("iteration_period", 24)
         if isinstance(iteration_period, str):
@@ -381,7 +377,6 @@ class MemoryReflectionService:
                 iteration_period = int(iteration_period)
             except (ValueError, TypeError):
                 iteration_period = 24  # 默认24小时
-        
         return ReflectionConfig(
             enabled=config_data.get("enable_self_reflexion", False),
             iteration_period=str(iteration_period),  # ReflectionConfig期望字符串

api/app/services/memory_storage_service.py

@@ -129,6 +129,12 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
             if not params.rerank_id:
                 params.rerank_id = configs.get('rerank')
 
+        # reflection_model_id 和 emotion_model_id 默认与 llm_id 一致
+        if not params.reflection_model_id:
+            params.reflection_model_id = params.llm_id
+        if not params.emotion_model_id:
+            params.emotion_model_id = params.llm_id
+
         config = MemoryConfigRepository.create(self.db, params)
         self.db.commit()
         return {"affected": 1, "config_id": config.config_id}
@@ -177,11 +183,11 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
 
     # --- Read All ---
     def get_all(self, workspace_id = None) -> List[Dict[str, Any]]: # 获取所有配置参数
-        configs = MemoryConfigRepository.get_all(self.db, workspace_id)
+        results = MemoryConfigRepository.get_all(self.db, workspace_id)
 
         # 将 ORM 对象转换为字典列表
         data_list = []
-        for config in configs:
+        for config, scene_name in results:
             # 安全地转换 user_id 为 int
             config_id_old = None
             if config.config_id_old:
@@ -203,6 +209,8 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
                 "end_user_id": config.end_user_id,
                 "config_id_old": config_id_old,
                 "apply_id": config.apply_id,
+                "scene_id": str(config.scene_id) if config.scene_id else None,
+                "scene_name": scene_name,  # 新增：场景名称
                 "llm_id": config.llm_id,
                 "embedding_id": config.embedding_id,
                 "rerank_id": config.rerank_id,
@@ -628,10 +636,9 @@ async def analytics_recent_activity_stats() -> Dict[str, Any]:
                 if m < 1:
                     latest_relative = "刚刚"
                 elif m < 60:
-                    latest_relative = f"{m}分钟前"
+                    latest_relative = "一会前"
                 else:
-                    h = int(m // 60)
+                    latest_relative = "较早前"
-                    latest_relative = f"{h}小时前" if h < 24 else f"{int(h // 24)}天前"
     except Exception:
         pass
 

api/app/services/model_service.py

@@ -347,7 +347,9 @@ class ModelConfigService:
             "is_public": model_data.is_public,
             "is_composite": True
         }
-        
+        if "load_balance_strategy" in model_data.model_fields_set:
+            model_config_data["load_balance_strategy"] = model_data.load_balance_strategy
+
         model = ModelConfigRepository.create(db, model_config_data)
         db.flush()
         
@@ -380,7 +382,7 @@ class ModelConfigService:
             for model_config in api_key.model_configs:
                 compatible_types = {ModelType.LLM, ModelType.CHAT}
                 config_type = model_config.type
-                request_type = model_data.type
+                request_type = existing_model.type
                 
                 if not (config_type == request_type or 
                         (config_type in compatible_types and request_type in compatible_types)):
@@ -391,12 +393,14 @@ class ModelConfigService:
         
         # 更新基本信息
         existing_model.name = model_data.name
-        existing_model.type = model_data.type
+        # existing_model.type = model_data.type
         existing_model.logo = model_data.logo
         existing_model.description = model_data.description
         existing_model.config = model_data.config
         existing_model.is_active = model_data.is_active
         existing_model.is_public = model_data.is_public
+        if "load_balance_strategy" in model_data.model_fields_set:
+            existing_model.load_balance_strategy = model_data.load_balance_strategy
         
         # 更新 API Keys 关联
         existing_model.api_keys.clear()
@@ -453,9 +457,11 @@ class ModelApiKeyService:
         return ModelApiKeyRepository.get_by_model_config(db, model_config_id, is_active)
 
     @staticmethod
-    async def create_api_key_by_provider(db: Session, data: model_schema.ModelApiKeyCreateByProvider) -> List[ModelApiKey]:
+    async def create_api_key_by_provider(db: Session, data: model_schema.ModelApiKeyCreateByProvider) -> tuple[
+        list[Any], list[Any]]:
         """根据provider为多个ModelConfig创建API Key"""
         created_keys = []
+        failed_models = []  # 记录验证失败的模型
         
         for model_config_id in data.model_config_ids:
             model_config = ModelConfigRepository.get_by_id(db, model_config_id)
@@ -501,10 +507,9 @@ class ModelApiKeyService:
                 test_message="Hello"
             )
             if not validation_result["valid"]:
-                raise BusinessException(
+                # 记录验证失败的模型，但不抛出异常
-                    f"模型配置验证失败: {validation_result['error']}",
+                failed_models.append(model_name)
-                    BizCode.INVALID_PARAMETER
+                continue
-                )
             
             # 创建API Key
             api_key_data = ModelApiKeyCreate(
@@ -526,7 +531,7 @@ class ModelApiKeyService:
             for key in created_keys:
                 db.refresh(key)
         
-        return created_keys
+        return created_keys, failed_models
 
     @staticmethod
     async def create_api_key(db: Session, api_key_data: ModelApiKeyCreate) -> ModelApiKey:
@@ -684,6 +689,9 @@ class ModelBaseService:
 
     @staticmethod
     def create_model_base(db: Session, data: model_schema.ModelBaseCreate):
+        existing = ModelBaseRepository.get_by_name_and_provider(db, data.name, data.provider)
+        if existing:
+            raise BusinessException("模型已存在", BizCode.DUPLICATE_NAME)
         model_base = ModelBaseRepository.create(db, data.model_dump())
         db.commit()
         db.refresh(model_base)

api/app/services/multi_agent_orchestrator.py

@@ -280,14 +280,22 @@ class MultiAgentOrchestrator:
 
             # 4. 提取子 Agent 的 conversation_id（用于多轮对话）
             sub_conversation_id = None
+            total_tokens = 0
+            
             if isinstance(results, dict):
                 sub_conversation_id = results.get("conversation_id") or results.get("result", {}).get("conversation_id")
+                # 提取 token 信息
+                usage = results.get("usage", {}) or results.get("result", {}).get("usage", {})
+                total_tokens += usage.get("total_tokens", 0)
             elif isinstance(results, list) and results:
                 for item in results:
                     if "result" in item:
                         sub_conversation_id = item["result"].get("conversation_id")
                         if sub_conversation_id:
                             break
+                    # 累加每个子 Agent 的 token
+                    usage = item.get("usage", {}) or item.get("result", {}).get("usage", {})
+                    total_tokens += usage.get("total_tokens", 0)
 
             logger.info(
                 "多 Agent 任务完成",
@@ -301,9 +309,15 @@ class MultiAgentOrchestrator:
             return {
                 "message": final_result,
                 "conversation_id": sub_conversation_id,
+                "mode": OrchestrationMode.SUPERVISOR,
                 "elapsed_time": elapsed_time,
                 "strategy": routing_decision.get("collaboration_strategy", "single"),
-                "sub_results": results
+                "sub_results": results,
+                "usage": {
+                    "prompt_tokens": 0,
+                    "completion_tokens": 0,
+                    "total_tokens": total_tokens
+                }
             }
 
         except Exception as e:
@@ -1552,10 +1566,12 @@ class MultiAgentOrchestrator:
             return {
                 "message": result.get("response", ""),
                 "conversation_id": result.get("conversation_id"),
+                "mode": OrchestrationMode.COLLABORATION,
                 "elapsed_time": elapsed_time,
                 "strategy": "collaboration",
                 "active_agent": result.get("active_agent"),
-                "sub_results": result
+                "sub_results": result,
+                "usage": result.get("usage")
             }
 
         except Exception as e:

api/app/services/multi_agent_service.py

@@ -1,5 +1,6 @@
 """多 Agent 配置管理服务"""
 import uuid
+import json
 from typing import Optional, List, Tuple, Any, Annotated
 
 from fastapi import Depends
@@ -427,6 +428,23 @@ class MultiAgentService:
             memory=getattr(request, 'memory', True)  # 记忆功能参数
         )
 
+        await self._save_conversation_message(
+            conversation_id=request.conversation_id,
+            user_message=request.message,
+            assistant_message=result.get("message", ""),
+            app_id=app_id,
+            user_id=request.user_id,
+            meta_data={
+                "mode": result.get("mode"),
+                "elapsed_time": result.get("elapsed_time"),
+                "usage": result.get("usage", {
+                    "prompt_tokens": 0,
+                    "completion_tokens": 0,
+                    "total_tokens": 0
+                })
+            }
+        )
+
         return result
 
     async def run_stream(
@@ -451,11 +469,14 @@ class MultiAgentService:
             raise ResourceNotFoundException("多 Agent 配置", str(app_id))
 
         if not config.is_active:
-            raise BusinessException("多 Agent 配置已禁用", BizCode.RESOURCE_DISABLED)
+            raise BusinessException("多 Agent 配置已禁用", BizCode.NOT_FOUND)
 
         # 2. 创建编排器
         orchestrator = MultiAgentOrchestrator(self.db, config)
 
+        full_content = ""
+        total_tokens = 0
+
         # 3. 流式执行任务
         async for event in orchestrator.execute_stream(
             message=request.message,
@@ -468,7 +489,88 @@ class MultiAgentService:
             storage_type=storage_type,
             user_rag_memory_id=user_rag_memory_id
         ):
-            yield event
+            if "sub_usage" in event:
+                if "data:" in event:
+                    try:
+                        data_line = event.split("data: ", 1)[1].strip()
+                        data = json.loads(data_line)
+                        if "total_tokens" in data:
+                            total_tokens += data["total_tokens"]
+                    except:
+                        pass
+            else:
+                yield event
+                if "data:" in event:
+                    try:
+                        data_line = event.split("data: ", 1)[1].strip()
+                        data = json.loads(data_line)
+                        if "content" in data:
+                            full_content += data["content"]
+                    except:
+                        pass
+
+        await self._save_conversation_message(
+            conversation_id=request.conversation_id,
+            user_message=request.message,
+            assistant_message=full_content,
+            app_id=app_id,
+            user_id=request.user_id,
+            meta_data={
+                "usage": {
+                    "prompt_tokens": 0,
+                    "completion_tokens": 0,
+                    "total_tokens": total_tokens
+                }
+            }
+        )
+
+    async def _save_conversation_message(
+        self,
+        conversation_id: uuid.UUID,
+        user_message: str,
+        assistant_message: str,
+        meta_data: dict,
+        app_id: Optional[uuid.UUID] = None,
+        user_id: Optional[str] = None
+    ) -> None:
+        """保存会话消息
+
+        Args:
+            conversation_id: 会话ID
+            user_message: 用户消息
+            assistant_message: AI 回复消息
+            meta_data: 元数据（包括 token 消耗）
+            app_id: 应用ID
+            user_id: 用户ID
+        """
+        try:
+            from app.services.conversation_service import ConversationService
+
+            conversation_service = ConversationService(self.db)
+
+            conversation_service.add_message(
+                conversation_id=conversation_id,
+                role="user",
+                content=user_message
+            )
+            conversation_service.add_message(
+                conversation_id=conversation_id,
+                role="assistant",
+                content=assistant_message,
+                meta_data=meta_data
+            )
+
+            logger.debug(
+                "保存多 Agent 会话消息",
+                extra={
+                    "conversation_id": conversation_id,
+                    "user_message_length": len(user_message),
+                    "assistant_message_length": len(assistant_message)
+                }
+            )
+
+        except Exception as e:
+            logger.warning("保存会话消息失败", extra={"error": str(e)})
 
     # def add_sub_agent(
     #     self,

api/app/services/prompt_optimizer_service.py

@@ -1,3 +1,4 @@
+import os
 import re
 import uuid
 from typing import Any, AsyncGenerator
@@ -16,9 +17,10 @@ from app.models.prompt_optimizer_model import (
     PromptOptimizerSession,
     RoleType
 )
-from app.repositories.model_repository import ModelConfigRepository
+from app.repositories.model_repository import ModelConfigRepository, ModelApiKeyRepository
 from app.repositories.prompt_optimizer_repository import (
-    PromptOptimizerSessionRepository
+    PromptOptimizerSessionRepository,
+    PromptReleaseRepository
 )
 from app.schemas.prompt_optimizer_schema import OptimizePromptResult
 
@@ -28,6 +30,8 @@ logger = get_business_logger()
 class PromptOptimizerService:
     def __init__(self, db: Session):
         self.db = db
+        self.optim_repo = PromptOptimizerSessionRepository(self.db)
+        self.release_repo = PromptReleaseRepository(self.db)
 
     def get_model_config(
             self,
@@ -78,10 +82,12 @@ class PromptOptimizerService:
         Returns:
             PromptOptimzerSession: The newly created prompt optimization session.
         """
-        session = PromptOptimizerSessionRepository(self.db).create_session(
+        session = self.optim_repo.create_session(
             tenant_id=tenant_id,
             user_id=user_id
         )
+        self.db.commit()
+        self.db.refresh(session)
         return session
 
     def get_session_message_history(
@@ -106,7 +112,7 @@ class PromptOptimizerService:
                 - role (str): The role of the message sender, e.g., 'system', 'user', or 'assistant'.
                 - content (str): The content of the message.
         """
-        history = PromptOptimizerSessionRepository(self.db).get_session_history(
+        history = self.optim_repo.get_session_history(
             session_id=session_id,
             user_id=user_id
         )
@@ -168,7 +174,8 @@ class PromptOptimizerService:
         logger.info(f"Prompt optimization started, user_id={user_id}, session_id={session_id}")
 
         # Create LLM instance
-        api_config: ModelApiKey = model_config.api_keys[0]
+        api_keys = ModelApiKeyRepository.get_by_model_config(self.db, model_config.id)
+        api_config: ModelApiKey = api_keys[0] if api_keys else None
         llm = RedBearLLM(RedBearModelConfig(
             model_name=api_config.model_name,
             provider=api_config.provider,
@@ -176,11 +183,12 @@ class PromptOptimizerService:
             base_url=api_config.api_base
         ), type=ModelType(model_config.type))
         try:
-            with open('app/services/prompt/prompt_optimizer_system.jinja2', 'r', encoding='utf-8') as f:
+            prompt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prompt')
+            with open(os.path.join(prompt_path, 'prompt_optimizer_system.jinja2'), 'r', encoding='utf-8') as f:
                 opt_system_prompt = f.read()
             rendered_system_message = Template(opt_system_prompt).render()
 
-            with open('app/services/prompt/prompt_optimizer_user.jinja2', 'r', encoding='utf-8') as f:
+            with open(os.path.join(prompt_path, 'prompt_optimizer_user.jinja2'), 'r', encoding='utf-8') as f:
                 opt_user_prompt = f.read()
         except FileNotFoundError:
             raise BusinessException(message="System prompt template not found", code=BizCode.NOT_FOUND)
@@ -295,4 +303,165 @@ class PromptOptimizerService:
             role=role,
             content=content
         )
+        self.db.commit()
+        self.db.refresh(message)
         return message
+
+    def save_prompt(
+            self,
+            tenant_id: uuid.UUID,
+            session_id: uuid.UUID,
+            title: str,
+            prompt: str
+    ) -> dict:
+        """
+        Create and save a new prompt release for a given session.
+
+        Args:
+            tenant_id (uuid.UUID): The ID of the tenant owning the prompt.
+            session_id (uuid.UUID): The ID of the session to associate with this prompt.
+            title (str): The title of the prompt release.
+            prompt (str): The content of the prompt.
+
+        Returns:
+            dict: A dictionary containing:
+                - id (UUID): The unique ID of the created prompt release.
+                - session_id (UUID): The session ID linked to the release.
+                - title (str): The title of the prompt.
+                - prompt (str): The prompt content.
+                - created_at (int): Timestamp (in milliseconds) of when the prompt was created.
+
+        Raises:
+            BusinessException: If a prompt release already exists for the given session.
+        """
+        session = self.optim_repo.get_session_by_id(session_id)
+        if session is None or session.tenant_id != tenant_id:
+            raise BusinessException(
+                "Session does not exist or the current user has no access",
+                BizCode.BAD_REQUEST
+            )
+
+        if self.release_repo.get_prompt_by_session_id(session_id):
+            raise BusinessException(
+                "A release already exists for the current session",
+                BizCode.BAD_REQUEST
+            )
+
+        prompt_obj = self.release_repo.create_prompt_release(
+            tenant_id=tenant_id,
+            title=title,
+            session_id=session_id,
+            prompt=prompt
+        )
+        self.db.commit()
+        self.db.refresh(prompt_obj)
+        return {
+            "id": prompt_obj.id,
+            "session_id": prompt_obj.session_id,
+            "title": prompt_obj.title,
+            "prompt": prompt_obj.prompt,
+            "created_at": int(prompt_obj.created_at.timestamp() * 1000)
+        }
+
+    def delete_prompt(
+            self,
+            tenant_id: uuid.UUID,
+            prompt_id: uuid.UUID
+    ) -> None:
+        """
+        Soft delete a prompt release by prompt_id.
+
+        Args:
+            tenant_id (uuid.UUID): Tenant identifier.
+            prompt_id (uuid.UUID): Prompt identifier.
+
+        Raises:
+            BusinessException: If the prompt does not exist or already deleted.
+        """
+        prompt_obj = self.release_repo.get_prompt_by_id(prompt_id)
+        if not prompt_obj or prompt_obj.is_delete:
+            raise BusinessException(
+                "Prompt does not exist or has already been deleted",
+                BizCode.NOT_FOUND
+            )
+
+        if prompt_obj.tenant_id != tenant_id:
+            raise BusinessException(
+                "No permission to delete this prompt",
+                BizCode.FORBIDDEN
+            )
+
+        self.release_repo.soft_delete_prompt(prompt_obj)
+        self.db.commit()
+        logger.info(f"Prompt soft deleted, prompt_id={prompt_id}, tenant_id={tenant_id}")
+
+    def get_release_list(
+            self,
+            tenant_id: uuid.UUID,
+            page: int,
+            page_size: int,
+            filter_keyword: str | None = None
+    ) -> dict[str, int | list[Any]]:
+        """
+        Get paginated list of prompt releases with optional filter.
+
+        Args:
+            tenant_id (uuid.UUID): Tenant identifier.
+            page (int): Page number (starting from 1).
+            page_size (int): Number of items per page.
+            filter_keyword (str | None): Optional keyword to filter by title.
+
+        Returns:
+            dict: Contains total count, pagination info, and list of releases.
+        """
+        offset = (page - 1) * page_size
+
+        # Get total count and releases based on filter
+        if filter_keyword:
+            total = self.release_repo.count_prompts_by_keyword(tenant_id, filter_keyword)
+            releases = self.release_repo.search_prompts_paginated(
+                tenant_id=tenant_id,
+                keyword=filter_keyword,
+                offset=offset,
+                limit=page_size
+            )
+        else:
+            total = self.release_repo.count_prompts(tenant_id)
+            releases = self.release_repo.get_prompts_paginated(
+                tenant_id=tenant_id,
+                offset=offset,
+                limit=page_size
+            )
+
+        items = []
+        for release in releases:
+            # Get first user message from session
+            first_message = self.optim_repo.get_first_user_message(
+                session_id=release.session_id
+            )
+
+            items.append({
+                "id": release.id,
+                "title": release.title,
+                "prompt": release.prompt,
+                "created_at": int(release.created_at.timestamp() * 1000),
+                "first_message": first_message
+            })
+
+        log_msg = f"Retrieved {len(items)} prompt releases, page={page}, tenant_id={tenant_id}"
+        if filter_keyword:
+            log_msg += f", filter='{filter_keyword}'"
+        logger.info(log_msg)
+
+        result = {
+            "page": {
+                "total": total,
+                "page": page,
+                "page_size": page_size,
+                "hasnext": page * page_size < total
+            },
+            "keyword": filter_keyword,
+            "items": items
+        }
+
+        return result