Merge branch 'release/v0.2.3' into develop

api/app/celery_app.py

@@ -7,6 +7,10 @@ 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）
@@ -64,6 +68,11 @@ 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'},

api/app/controllers/ontology_controller.py

@@ -116,14 +116,6 @@ def _get_ontology_service(
                 detail=f"找不到指定的LLM模型: {llm_id}"
             )
         
-        # 检查是否为组合模型
-        if hasattr(model_config, 'is_composite') and model_config.is_composite:
-            logger.error(f"Model {llm_id} is a composite model, which is not supported for ontology extraction")
-            raise HTTPException(
-                status_code=400,
-                detail="本体提取不支持使用组合模型，请选择单个模型"
-            )
-        
         # 验证模型配置了API密钥
         if not model_config.api_keys:
             logger.error(f"Model {llm_id} has no API key configuration")

api/app/core/agent/langchain_agent.py

@@ -291,8 +291,10 @@ class LangChainAgent:
 
         return messages
 
+    # TODO: 移到memory module
     async def term_memory_save(self,long_term_messages,actual_config_id,end_user_id,type):
         db = next(get_db())
+        #TODO: 魔法数字
         scope=6
 
         try:
@@ -302,6 +304,12 @@ class LangChainAgent:
 
             from app.core.memory.agent.utils.redis_tool import write_store
             result = write_store.get_session_by_userid(end_user_id)
+            
+            # Handle case where no session exists in Redis (returns False)
+            if not result or result is False:
+                logger.debug(f"No existing session in Redis for user {end_user_id}, skipping short-term memory update")
+                return
+                
             if type=="chunk" or type=="aggregate":
                 data = await format_parsing(result, "dict")
                 chunk_data = data[:scope]
@@ -309,7 +317,14 @@ class LangChainAgent:
                     repo.upsert(end_user_id, chunk_data)
                     logger.info(f'写入短长期：')
             else:
+                # TODO: This branch handles type="time" strategy, currently unused.
+                # Will be activated when time-based long-term storage is implemented.
+                # TODO: 魔法数字 - extract 5 to a constant
                 long_time_data = write_store.find_user_recent_sessions(end_user_id, 5)
+                # Handle case where no session exists in Redis (returns False or empty)
+                if not long_time_data or long_time_data is False:
+                    logger.debug(f"No recent sessions in Redis for user {end_user_id}")
+                    return
                 long_messages = await messages_parse(long_time_data)
                 repo.upsert(end_user_id, long_messages)
                 logger.info(f'写入短长期：')
@@ -509,9 +524,12 @@ class LangChainAgent:
             elapsed_time = time.time() - start_time
             if memory_flag:
                 long_term_messages=await agent_chat_messages(message_chat,content)
-                # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
+                # TODO: DUPLICATE WRITE - Remove this immediate write once batched write (term_memory_save) is verified stable.
+                # This writes to Neo4j immediately via Celery task, but term_memory_save also writes to Neo4j
+                # when the window buffer reaches scope (6 messages). This causes duplicate entities in the graph.
+                # Recommended: Keep only term_memory_save for batched efficiency, or only self.write for real-time.
                 await self.write(storage_type, actual_end_user_id, message_chat, content, user_rag_memory_id, actual_end_user_id, actual_config_id)
-                '''长期'''
+                # Batched long-term memory storage (Redis buffer + Neo4j when window full)
                 await self.term_memory_save(long_term_messages,actual_config_id,end_user_id,"chunk")
             response = {
                 "content": content,
@@ -695,9 +713,13 @@ class LangChainAgent:
                         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）
+                    # TODO: DUPLICATE WRITE - Remove this immediate write once batched write (term_memory_save) is verified stable.
+                    # This writes to Neo4j immediately via Celery task, but term_memory_save also writes to Neo4j
+                    # when the window buffer reaches scope (6 messages). This causes duplicate entities in the graph.
+                    # Recommended: Keep only term_memory_save for batched efficiency, or only self.write for real-time.
                     long_term_messages = await agent_chat_messages(message_chat, full_content)
                     await self.write(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id, end_user_id, actual_config_id)
+                    # Batched long-term memory storage (Redis buffer + Neo4j when window full)
                     await self.term_memory_save(long_term_messages, actual_config_id, end_user_id, "chunk")
                 
             except Exception as e:

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

@@ -43,6 +43,7 @@ async def write_messages(end_user_id,langchain_messages,memory_config):
                 for node_name, node_data in update_event.items():
                     if 'save_neo4j' == node_name:
                         massages = node_data
+            # TODO：删除
             massagesstatus = massages.get('write_result')['status']
             contents = massages.get('write_result')
             print(contents)
@@ -60,6 +61,7 @@ async def window_dialogue(end_user_id,langchain_messages,memory_config,scope):
         scope：窗口大小
     '''
     scope=scope
+    redis_messages = []
     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]
@@ -91,6 +93,9 @@ async def memory_long_term_storage(end_user_id,memory_config,time):
         memory_config: 内存配置对象
     '''
     long_time_data = write_store.find_user_recent_sessions(end_user_id, time)
+    # Handle case where no session exists in Redis (returns False or empty)
+    if not long_time_data or long_time_data is False:
+        return
     format_messages = await chat_data_format(long_time_data)
     if format_messages!=[]:
         await write_messages(end_user_id, format_messages, memory_config)
@@ -108,8 +113,9 @@ async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config
     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:
+        # Handle case where no session exists in Redis (returns False or empty)
+        if not result or result is False:
             history = []
         else:
             history = await format_parsing(result)

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

@@ -1,18 +1,14 @@
 
 import asyncio
-import json
 import sys
 import warnings
 from contextlib import asynccontextmanager
 from langgraph.constants import END, START
 from langgraph.graph import StateGraph
 
-from app.core.memory.agent.langgraph_graph.tools.write_tool import format_parsing, chat_data_format, messages_parse
-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.services.memory_config_service import MemoryConfigService
 
 warnings.filterwarnings("ignore", category=RuntimeWarning)
 logger = get_agent_logger(__name__)
@@ -40,27 +36,55 @@ 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):
-    from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue,aggregate_judgment
+    """Dispatch long-term memory storage to Celery background tasks.
-    from app.core.memory.agent.langgraph_graph.tools.write_tool import chat_data_format
-    from app.core.memory.agent.utils.redis_tool import write_store
-    write_store.save_session_write(end_user_id, await chat_data_format(langchain_messages))
-    # 获取数据库会话
-    db_session = next(get_db())
-    config_service = MemoryConfigService(db_session)
-    memory_config = config_service.load_memory_config(
-        config_id=memory_config,  # 改为整数
-        service_name="MemoryAgentService"
-    )
-    if long_term_type=='chunk':
-        '''方案一:对话窗口6轮对话'''
-        await window_dialogue(end_user_id,langchain_messages,memory_config,scope)
-    if long_term_type=='time':
-        """时间"""
-        await memory_long_term_storage(end_user_id, memory_config,5)
-    if  long_term_type=='aggregate':
     
-        """方案三：聚合判断"""
+    Args:
-        await aggregate_judgment(end_user_id, langchain_messages, memory_config)
+        long_term_type: Storage strategy - 'chunk' (window), 'time', or 'aggregate'
+        langchain_messages: List of messages to store
+        memory_config: Memory configuration ID (string)
+        end_user_id: End user identifier
+        scope: Window size for 'chunk' strategy (default: 6)
+    """
+    from app.tasks import (
+        long_term_storage_window_task,
+        # TODO: Uncomment when implemented
+        # long_term_storage_time_task,
+        # long_term_storage_aggregate_task,
+    )
+    from app.core.logging_config import get_logger
+    
+    logger = get_logger(__name__)
+    
+    # Convert config to string if needed
+    config_id = str(memory_config) if memory_config else ''
+    
+    if long_term_type == 'chunk':
+        # Strategy 1: Window-based batching (6 rounds of dialogue)
+        logger.info(f"[LONG_TERM] Dispatching window task - end_user_id={end_user_id}, scope={scope}")
+        long_term_storage_window_task.delay(
+            end_user_id=end_user_id,
+            langchain_messages=langchain_messages,
+            config_id=config_id,
+            scope=scope
+        )
+    # TODO: Uncomment when time-based strategy is fully implemented
+    # elif long_term_type == 'time':
+    #     # Strategy 2: Time-based retrieval
+    #     logger.info(f"[LONG_TERM] Dispatching time task - end_user_id={end_user_id}")
+    #     long_term_storage_time_task.delay(
+    #         end_user_id=end_user_id,
+    #         config_id=config_id,
+    #         time_window=5
+    #     )
+    # TODO: Uncomment when aggregate strategy is fully implemented
+    # elif long_term_type == 'aggregate':
+    #     # Strategy 3: Aggregate judgment (deduplication)
+    #     logger.info(f"[LONG_TERM] Dispatching aggregate task - end_user_id={end_user_id}")
+    #     long_term_storage_aggregate_task.delay(
+    #         end_user_id=end_user_id,
+    #         langchain_messages=langchain_messages,
+    #         config_id=config_id
+    #     )
 
 
 # async def main():

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

api/app/repositories/memory_config_repository.py

@@ -235,6 +235,8 @@ class MemoryConfigRepository:
                 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

api/app/schemas/memory_storage_schema.py

@@ -236,6 +236,8 @@ class ConfigParamsCreate(BaseModel):  # 创建配置参数模型（仅 body，
     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）

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_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}
@@ -203,6 +209,7 @@ 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": config.scene_id,
                 "llm_id": config.llm_id,
                 "embedding_id": config.embedding_id,
                 "rerank_id": config.rerank_id,

api/app/tasks.py

@@ -1069,6 +1069,7 @@ def workspace_reflection_task(self) -> Dict[str, Any]:
                             f"工作空间 {workspace_id} 反思处理完成，处理了 {len(workspace_reflection_results)} 个任务")
 
                     except Exception as e:
+                        db.rollback()  # Rollback failed transaction to allow next query
                         api_logger.error(f"处理工作空间 {workspace_id} 反思失败: {str(e)}")
                         all_reflection_results.append({
                             "workspace_id": str(workspace_id),
@@ -1207,3 +1208,290 @@ def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Di
         return result
     finally:
         loop.close()
+
+
+# =============================================================================
+# Long-term Memory Storage Tasks (Batched Write Strategies)
+# =============================================================================
+
+@celery_app.task(name="app.core.memory.agent.long_term_storage.window", bind=True)
+def long_term_storage_window_task(
+    self,
+    end_user_id: str,
+    langchain_messages: List[Dict[str, Any]],
+    config_id: str,
+    scope: int = 6
+) -> Dict[str, Any]:
+    """Celery task for window-based long-term memory storage.
+    
+    Accumulates messages in Redis buffer until window size (scope) is reached,
+    then writes batched messages to Neo4j.
+    
+    Args:
+        end_user_id: End user identifier
+        langchain_messages: List of messages [{"role": "user/assistant", "content": "..."}]
+        config_id: Memory configuration ID
+        scope: Window size (number of messages before triggering write)
+        
+    Returns:
+        Dict containing task status and metadata
+    """
+    from app.core.logging_config import get_logger
+    logger = get_logger(__name__)
+    
+    logger.info(f"[LONG_TERM_WINDOW] Starting task - end_user_id={end_user_id}, scope={scope}")
+    start_time = time.time()
+    
+    async def _run() -> Dict[str, Any]:
+        from app.core.memory.agent.langgraph_graph.routing.write_router import window_dialogue
+        from app.core.memory.agent.langgraph_graph.tools.write_tool import chat_data_format
+        from app.core.memory.agent.utils.redis_tool import write_store
+        from app.services.memory_config_service import MemoryConfigService
+        
+        db = next(get_db())
+        try:
+            # Save to Redis buffer first
+            write_store.save_session_write(end_user_id, await chat_data_format(langchain_messages))
+            
+            # Load memory config
+            config_service = MemoryConfigService(db)
+            memory_config = config_service.load_memory_config(
+                config_id=config_id,
+                service_name="LongTermStorageTask"
+            )
+            
+            # Execute window-based dialogue storage
+            await window_dialogue(end_user_id, langchain_messages, memory_config, scope)
+            
+            return {"status": "SUCCESS", "strategy": "window", "scope": scope}
+        finally:
+            db.close()
+    
+    try:
+        import nest_asyncio
+        nest_asyncio.apply()
+    except ImportError:
+        pass
+    
+    try:
+        loop = asyncio.get_event_loop()
+        if loop.is_closed():
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+    except RuntimeError:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+    
+    try:
+        result = loop.run_until_complete(_run())
+        elapsed_time = time.time() - start_time
+        
+        logger.info(f"[LONG_TERM_WINDOW] Task completed - elapsed_time={elapsed_time:.2f}s")
+        
+        return {
+            **result,
+            "end_user_id": end_user_id,
+            "config_id": config_id,
+            "elapsed_time": elapsed_time,
+            "task_id": self.request.id
+        }
+    except Exception as e:
+        elapsed_time = time.time() - start_time
+        logger.error(f"[LONG_TERM_WINDOW] Task failed - error={str(e)}", exc_info=True)
+        
+        return {
+            "status": "FAILURE",
+            "strategy": "window",
+            "error": str(e),
+            "end_user_id": end_user_id,
+            "config_id": config_id,
+            "elapsed_time": elapsed_time,
+            "task_id": self.request.id
+        }
+
+
+# @celery_app.task(name="app.core.memory.agent.long_term_storage.time", bind=True)
+# def long_term_storage_time_task(
+#     self,
+#     end_user_id: str,
+#     config_id: str,
+#     time_window: int = 5
+# ) -> Dict[str, Any]:
+#     """Celery task for time-based long-term memory storage.
+    
+#     Retrieves recent sessions from Redis within time window and writes to Neo4j.
+    
+#     Args:
+#         end_user_id: End user identifier
+#         config_id: Memory configuration ID
+#         time_window: Time window in minutes for retrieving recent sessions
+        
+#     Returns:
+#         Dict containing task status and metadata
+#     """
+#     from app.core.logging_config import get_logger
+#     logger = get_logger(__name__)
+    
+#     logger.info(f"[LONG_TERM_TIME] Starting task - end_user_id={end_user_id}, time_window={time_window}")
+#     start_time = time.time()
+    
+#     async def _run() -> Dict[str, Any]:
+#         from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage
+#         from app.services.memory_config_service import MemoryConfigService
+        
+#         db = next(get_db())
+#         try:
+#             # Load memory config
+#             config_service = MemoryConfigService(db)
+#             memory_config = config_service.load_memory_config(
+#                 config_id=config_id,
+#                 service_name="LongTermStorageTask"
+#             )
+            
+#             # Execute time-based storage
+#             await memory_long_term_storage(end_user_id, memory_config, time_window)
+            
+#             return {"status": "SUCCESS", "strategy": "time", "time_window": time_window}
+#         finally:
+#             db.close()
+    
+#     try:
+#         import nest_asyncio
+#         nest_asyncio.apply()
+#     except ImportError:
+#         pass
+    
+#     try:
+#         loop = asyncio.get_event_loop()
+#         if loop.is_closed():
+#             loop = asyncio.new_event_loop()
+#             asyncio.set_event_loop(loop)
+#     except RuntimeError:
+#         loop = asyncio.new_event_loop()
+#         asyncio.set_event_loop(loop)
+    
+#     try:
+#         result = loop.run_until_complete(_run())
+#         elapsed_time = time.time() - start_time
+        
+#         logger.info(f"[LONG_TERM_TIME] Task completed - elapsed_time={elapsed_time:.2f}s")
+        
+#         return {
+#             **result,
+#             "end_user_id": end_user_id,
+#             "config_id": config_id,
+#             "elapsed_time": elapsed_time,
+#             "task_id": self.request.id
+#         }
+#     except Exception as e:
+#         elapsed_time = time.time() - start_time
+#         logger.error(f"[LONG_TERM_TIME] Task failed - error={str(e)}", exc_info=True)
+        
+#         return {
+#             "status": "FAILURE",
+#             "strategy": "time",
+#             "error": str(e),
+#             "end_user_id": end_user_id,
+#             "config_id": config_id,
+#             "elapsed_time": elapsed_time,
+#             "task_id": self.request.id
+#         }
+
+
+# @celery_app.task(name="app.core.memory.agent.long_term_storage.aggregate", bind=True)
+# def long_term_storage_aggregate_task(
+#     self,
+#     end_user_id: str,
+#     langchain_messages: List[Dict[str, Any]],
+#     config_id: str
+# ) -> Dict[str, Any]:
+#     """Celery task for aggregate-based long-term memory storage.
+    
+#     Uses LLM to determine if new messages describe the same event as history.
+#     Only writes to Neo4j if messages represent new information (not duplicates).
+    
+#     Args:
+#         end_user_id: End user identifier
+#         langchain_messages: List of messages [{"role": "user/assistant", "content": "..."}]
+#         config_id: Memory configuration ID
+        
+#     Returns:
+#         Dict containing task status, is_same_event flag, and metadata
+#     """
+#     from app.core.logging_config import get_logger
+#     logger = get_logger(__name__)
+    
+#     logger.info(f"[LONG_TERM_AGGREGATE] Starting task - end_user_id={end_user_id}")
+#     start_time = time.time()
+    
+#     async def _run() -> Dict[str, Any]:
+#         from app.core.memory.agent.langgraph_graph.routing.write_router import aggregate_judgment
+#         from app.core.memory.agent.langgraph_graph.tools.write_tool import chat_data_format
+#         from app.core.memory.agent.utils.redis_tool import write_store
+#         from app.services.memory_config_service import MemoryConfigService
+        
+#         db = next(get_db())
+#         try:
+#             # Save to Redis buffer first
+#             write_store.save_session_write(end_user_id, await chat_data_format(langchain_messages))
+            
+#             # Load memory config
+#             config_service = MemoryConfigService(db)
+#             memory_config = config_service.load_memory_config(
+#                 config_id=config_id,
+#                 service_name="LongTermStorageTask"
+#             )
+            
+#             # Execute aggregate judgment
+#             result = await aggregate_judgment(end_user_id, langchain_messages, memory_config)
+            
+#             return {
+#                 "status": "SUCCESS",
+#                 "strategy": "aggregate",
+#                 "is_same_event": result.get("is_same_event", False),
+#                 "wrote_to_neo4j": not result.get("is_same_event", False)
+#             }
+#         finally:
+#             db.close()
+    
+#     try:
+#         import nest_asyncio
+#         nest_asyncio.apply()
+#     except ImportError:
+#         pass
+    
+#     try:
+#         loop = asyncio.get_event_loop()
+#         if loop.is_closed():
+#             loop = asyncio.new_event_loop()
+#             asyncio.set_event_loop(loop)
+#     except RuntimeError:
+#         loop = asyncio.new_event_loop()
+#         asyncio.set_event_loop(loop)
+    
+#     try:
+#         result = loop.run_until_complete(_run())
+#         elapsed_time = time.time() - start_time
+        
+#         logger.info(f"[LONG_TERM_AGGREGATE] Task completed - is_same_event={result.get('is_same_event')}, elapsed_time={elapsed_time:.2f}s")
+        
+#         return {
+#             **result,
+#             "end_user_id": end_user_id,
+#             "config_id": config_id,
+#             "elapsed_time": elapsed_time,
+#             "task_id": self.request.id
+#         }
+#     except Exception as e:
+#         elapsed_time = time.time() - start_time
+#         logger.error(f"[LONG_TERM_AGGREGATE] Task failed - error={str(e)}", exc_info=True)
+        
+#         return {
+#             "status": "FAILURE",
+#             "strategy": "aggregate",
+#             "error": str(e),
+#             "end_user_id": end_user_id,
+#             "config_id": config_id,
+#             "elapsed_time": elapsed_time,
+#             "task_id": self.request.id
+#         }

web/src/components/PageScrollList/index.tsx

@@ -142,7 +142,7 @@ const PageScrollList = forwardRef(<T, Q = Record<string, unknown>>({
           dataLength={data.length}
           next={loadMoreData}
           hasMore={hasMore}
-          loader={needLoading ? <PageLoading /> : undefined}
+          loader={loading && needLoading ? <PageLoading /> : false}
           // endMessage={<Divider plain>It is all, nothing more 🤐</Divider>}
           scrollableTarget="scrollableDiv"
           className='rb:h-full!'

web/src/styles/index.css

@@ -181,6 +181,3 @@ body {
   min-height: 100%;
   max-height: 100%;
 }
-#scrollableDiv .infinite-scroll-component__outerdiv {
-  height: 100%;
-}

web/src/views/ApplicationConfig/components/Editor/index.tsx

@@ -21,6 +21,7 @@ import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext
 import InitialValuePlugin from './plugin/InitialValuePlugin'
 import LineBreakPlugin from './plugin/LineBreakPlugin';
 import InsertTextPlugin from './plugin/InsertTextPlugin';
+import EditablePlugin from './plugin/EditablePlugin';
 
 /**
  * Editor ref methods exposed to parent components
@@ -50,6 +51,7 @@ interface LexicalEditorProps {
   onChange?: (value: string) => void;
   /** Editor height in pixels */
   height?: number;
+  disabled?: boolean;
 }
 
 /**
@@ -71,6 +73,7 @@ const EditorContent = forwardRef<EditorRef, LexicalEditorProps>(({
   value,
   placeholder = "Please enter content...",
   onChange,
+  disabled
 }, ref) => {
   const [editor] = useLexicalComposerContext();
   
@@ -132,7 +135,11 @@ const EditorContent = forwardRef<EditorRef, LexicalEditorProps>(({
       <RichTextPlugin
         contentEditable={
           <ContentEditable
-            className={clsx("rb:outline-none rb:resize-none rb:text-[14px] rb:leading-5 rb:px-4 rb:py-5 rb:bg-[#FBFDFF] rb:border rb:border-[#DFE4ED] rb:rounded-lg rb:overflow-auto", className)}
+            className={clsx(
+              "rb:outline-none rb:resize-none rb:text-[14px] rb:leading-5 rb:px-4 rb:py-5 rb:bg-[#FBFDFF] rb:border rb:border-[#DFE4ED] rb:rounded-lg rb:overflow-auto",
+              disabled && "rb:cursor-not-allowed rb:bg-[#F6F8FC] rb:text-[#5B6167]",
+              className
+            )}
           />
         }
         placeholder={
@@ -145,6 +152,7 @@ const EditorContent = forwardRef<EditorRef, LexicalEditorProps>(({
       <LineBreakPlugin onChange={onChange} />
       <InitialValuePlugin value={value} />
       <InsertTextPlugin />
+      <EditablePlugin disabled={disabled} />
     </div>
   );
 });
@@ -158,6 +166,7 @@ const Editor = forwardRef<EditorRef, LexicalEditorProps>((props, ref) => {
     namespace: 'Editor',
     theme,
     nodes: [],
+    editable: !props.disabled,
     onError: (error: Error) => {
       console.error(error);
     },

web/src/views/ApplicationConfig/components/Editor/plugin/EditablePlugin.tsx

@@ -0,0 +1,48 @@
+/*
+ * @Author: ZhaoYing 
+ * @Date: 2026-02-04 11:20:49 
+ * @Last Modified by:   ZhaoYing 
+ * @Last Modified time: 2026-02-04 11:20:49 
+ */
+import { useEffect } from 'react';
+import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
+
+/**
+ * Props for the EditablePlugin component
+ */
+interface EditablePluginProps {
+  /** Whether the editor should be disabled (read-only mode) */
+  disabled?: boolean;
+}
+
+/**
+ * EditablePlugin - A Lexical editor plugin that controls the editable state of the editor
+ * 
+ * This plugin allows you to dynamically toggle between editable and read-only modes.
+ * When disabled is true, the editor becomes read-only and users cannot modify content.
+ * When disabled is false or undefined, the editor is fully editable.
+ * 
+ * @param {EditablePluginProps} props - Component props
+ * @param {boolean} [props.disabled] - Controls whether the editor is in read-only mode
+ * @returns {null} This plugin doesn't render any UI elements
+ * 
+ * @example
+ * ```tsx
+ * <LexicalComposer>
+ *   <EditablePlugin disabled={isReadOnly} />
+ * </LexicalComposer>
+ * ```
+ */
+export default function EditablePlugin({ disabled }: EditablePluginProps) {
+  // Get the editor instance from Lexical composer context
+  const [editor] = useLexicalComposerContext();
+
+  // Update editor's editable state whenever the disabled prop changes
+  useEffect(() => {
+    // Set editor to editable when disabled is false, read-only when disabled is true
+    editor.setEditable(!disabled);
+  }, [editor, disabled]);
+
+  // This plugin doesn't render any UI, it only manages editor state
+  return null;
+}

web/src/views/Prompt/Prompt.tsx

@@ -156,9 +156,9 @@ const Prompt: FC<{ editVo: HistoryItem | null; refresh: () => void; }> = ({ edit
     currentPromptValueRef.current = undefined;
     setChatList([])
     refresh()
+    updateSession()
   }
 
-  console.log(values)
   return (
     <>
       <Form form={form}>
@@ -217,12 +217,13 @@ const Prompt: FC<{ editVo: HistoryItem | null; refresh: () => void; }> = ({ edit
                 ref={editorRef}
                 placeholder={t('prompt.promptPlaceholder')}
                 className="rb:h-[calc(100vh-260px)]"
+                disabled={loading}
                 // onChange={(value) => form.setFieldValue('current_prompt', value)}
               />
             </Form.Item>
             <div className="rb:grid rb:grid-cols-2 rb:gap-4 rb:mt-6">
-              <Button type="primary" block disabled={!values?.current_prompt} onClick={handleSave}>{t('common.save')}</Button>
+              <Button type="primary" block disabled={!values?.current_prompt || loading} onClick={handleSave}>{t('common.save')}</Button>
-              <Button block disabled={!values?.current_prompt} onClick={handleCopy}>{t('common.copy')}</Button>
+              <Button block disabled={!values?.current_prompt || loading} onClick={handleCopy}>{t('common.copy')}</Button>
             </div>
           </div>
         </div>

web/src/views/SpaceManagement/components/SpaceModal.tsx

@@ -103,6 +103,8 @@ const SpaceModal = forwardRef<SpaceModalRef, SpaceModalProps>(({
             }).catch(() => {
               handleUpdate(formData)
             })
+          } else {
+            handleUpdate(formData)
           }
         }
       })
@@ -158,6 +160,7 @@ const SpaceModal = forwardRef<SpaceModalRef, SpaceModalProps>(({
           label={t('space.spaceIcon')}
           valuePropName="fileList"
           hidden={currentStep === 1}
+          rules={[{ required: true, message: t('common.selectPlaceholder', { title: t('space.spaceIcon') }) }]}
         >
           <UploadImages />
         </Form.Item>

web/src/views/Workflow/components/Editor/index.tsx

@@ -242,7 +242,7 @@ const Editor: FC<LexicalEditorProps> =({
         {enableLineNumbers && <LineNumberPlugin />}
         <AutocompletePlugin options={options} enableJinja2={enableJinja2} />
         <CharacterCountPlugin setCount={(count) => { setCount(count) }} onChange={onChange} />
-        <InitialValuePlugin value={value} options={options} enableJinja2={enableJinja2} />
+        <InitialValuePlugin key={language} value={value} options={options} enableLineNumbers={enableLineNumbers} />
         {enableLineNumbers && <BlurPlugin />}
       </div>
     </LexicalComposer>

web/src/views/Workflow/components/Editor/plugin/BlurPlugin.tsx

@@ -16,6 +16,12 @@ export default function BlurPlugin() {
             return;
           }
           
+          // 检查是否是粘贴操作导致的焦点变化
+          const relatedTarget = e.relatedTarget as HTMLElement;
+          if (!relatedTarget || relatedTarget === document.body) {
+            return;
+          }
+          
           editor.update(() => {
             $setSelection(null);
           });

web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx

@@ -8,12 +8,13 @@ import { type Suggestion } from '../plugin/AutocompletePlugin'
 interface InitialValuePluginProps {
   value: string;
   options?: Suggestion[];
-  enableJinja2?: boolean;
+  enableLineNumbers?: boolean;
 }
 
-const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options = [], enableJinja2 = false }) => {
+const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options = [], enableLineNumbers = false }) => {
   const [editor] = useLexicalComposerContext();
   const prevValueRef = useRef<string>('');
+  const prevEnableLineNumbersRef = useRef<boolean>(enableLineNumbers);
   const isUserInputRef = useRef(false);
 
   useEffect(() => {
@@ -32,7 +33,7 @@ const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options
   }, [editor]);
 
   useEffect(() => {
-    if (value !== prevValueRef.current && !isUserInputRef.current) {
+    if ((value !== prevValueRef.current || enableLineNumbers !== prevEnableLineNumbersRef.current) && !isUserInputRef.current) {
       queueMicrotask(() => {
         editor.update(() => {
         const root = $getRoot();
@@ -40,7 +41,7 @@ const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options
 
         const parts = value.split(/(\{\{[^}]+\}\})/);
 
-        if (enableJinja2) {
+        if (enableLineNumbers) {
           // Handle newlines properly in Jinja2 mode
           const lines = value.split('\n');
           lines.forEach((line) => {
@@ -104,8 +105,9 @@ const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options
     }
     
     prevValueRef.current = value;
+    prevEnableLineNumbersRef.current = enableLineNumbers;
     isUserInputRef.current = false;
-  }, [value, options, editor, enableJinja2]);
+  }, [value, options, editor, enableLineNumbers]);
 
   return null;
 };

web/src/views/Workflow/components/Editor/plugin/JavaScriptHighlightPlugin.tsx

@@ -1,6 +1,6 @@
-import { useEffect } from 'react';
+import { useEffect, useRef } from 'react';
 import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
-import { TextNode, $createTextNode, $getSelection, $isRangeSelection } from 'lexical';
+import { TextNode, $createTextNode, $getSelection, $isRangeSelection, COMMAND_PRIORITY_LOW, PASTE_COMMAND } from 'lexical';
 
 const JS_KEYWORDS = new Set([
   'async', 'await', 'break', 'case', 'catch', 'class', 'const', 'continue', 'debugger', 'default',
@@ -11,13 +11,31 @@ const JS_KEYWORDS = new Set([
 
 const JavaScriptHighlightPlugin = () => {
   const [editor] = useLexicalComposerContext();
+  const isPastingRef = useRef(false);
+
+  useEffect(() => {
+    return editor.registerCommand(
+      PASTE_COMMAND,
+      () => {
+        isPastingRef.current = true;
+        setTimeout(() => {
+          isPastingRef.current = false;
+        }, 100);
+        return false;
+      },
+      COMMAND_PRIORITY_LOW
+    );
+  }, [editor]);
 
   useEffect(() => {
     return editor.registerNodeTransform(TextNode, (textNode: TextNode) => {
+      if (isPastingRef.current) return;
+      
       const text = textNode.getTextContent();
       
       if (textNode.hasFormat('code')) return;
       if (!needsHighlight(text)) return;
+      if (textNode.getStyle()) return;
       
       const parent = textNode.getParent();
       if (!parent) return;

web/src/views/Workflow/components/Editor/plugin/Python3HighlightPlugin.tsx

@@ -1,6 +1,6 @@
-import { useEffect } from 'react';
+import { useEffect, useRef } from 'react';
 import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
-import { TextNode, $createTextNode, $getSelection, $isRangeSelection } from 'lexical';
+import { TextNode, $createTextNode, $getSelection, $isRangeSelection, COMMAND_PRIORITY_LOW, PASTE_COMMAND } from 'lexical';
 
 const PYTHON_KEYWORDS = new Set([
   'False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break', 'class', 'continue',
@@ -11,12 +11,30 @@ const PYTHON_KEYWORDS = new Set([
 
 const Python3HighlightPlugin = () => {
   const [editor] = useLexicalComposerContext();
+  const isPastingRef = useRef(false);
+
+  useEffect(() => {
+    return editor.registerCommand(
+      PASTE_COMMAND,
+      () => {
+        isPastingRef.current = true;
+        setTimeout(() => {
+          isPastingRef.current = false;
+        }, 100);
+        return false;
+      },
+      COMMAND_PRIORITY_LOW
+    );
+  }, [editor]);
 
   useEffect(() => {
     return editor.registerNodeTransform(TextNode, (textNode: TextNode) => {
+      if (isPastingRef.current) return;
+      
       const text = textNode.getTextContent();
       
       if (textNode.hasFormat('code')) return;
+      if (textNode.getStyle()) return;
       if (!needsHighlight(text)) return;
       
       const parent = textNode.getParent();

web/src/views/Workflow/components/Properties/CodeExecution/index.tsx

@@ -33,7 +33,6 @@ const codeTemplate = {
 const CodeExecution: FC<CodeExecutionProps> = ({ options }) => {
   const { t } = useTranslation()
   const form = Form.useFormInstance()
-  const values = Form.useWatch([], form) || {}
 
   const handleRefresh = () => {
     const code = form.getFieldValue('code') || ''
@@ -66,7 +65,6 @@ const CodeExecution: FC<CodeExecutionProps> = ({ options }) => {
     form.setFieldValue('code', newTemplate)
   }
   const handleChangeLanguage = (value: string) => {
-    form.setFieldValue('code', codeTemplate[value as keyof typeof codeTemplate])
     form.setFieldsValue({
       input_variables: [{ name: 'arg1' }, { name: 'arg2' }],
       code: codeTemplate[value as keyof typeof codeTemplate]
@@ -109,8 +107,12 @@ const CodeExecution: FC<CodeExecutionProps> = ({ options }) => {
             </Form.Item>
           </Col>
         </Row>
-        <Form.Item name="code" noStyle>
+        <Form.Item noStyle shouldUpdate={(prev, curr) => prev.language !== curr.language}>
-          <Editor size="small" language={values.language} />
+          {() => (
+            <Form.Item name="code" noStyle>
+              <Editor size="small" language={form.getFieldValue('language')} />
+            </Form.Item>
+          )}
         </Form.Item>
       </Space>
       

web/src/views/Workflow/hooks/useWorkflowGraph.ts

@@ -159,7 +159,7 @@ export const useWorkflowGraph = ({
               nodeLibraryConfig.config[key].defaultValue = Object.entries(config[key]).map(([name, value]) => ({ name, value }))
             } else if (type === 'code' && key === 'code' && config[key] && nodeLibraryConfig.config && nodeLibraryConfig.config[key]) {
               try {
-                nodeLibraryConfig.config[key].defaultValue = atob(config[key] as string)
+                nodeLibraryConfig.config[key].defaultValue = decodeURIComponent(atob(config[key] as string))
               } catch {
                 nodeLibraryConfig.config[key].defaultValue = config[key]
               }
@@ -943,7 +943,7 @@ export const useWorkflowGraph = ({
                 const code = data.config[key].defaultValue || ''
                 itemConfig = {
                   ...itemConfig,
-                  code: btoa(code || '')
+                  code: btoa(encodeURIComponent(code || ''))
                 }
               } else if (key === 'memory' && data.config[key] && 'defaultValue' in data.config[key]) {
                 const { messages, ...rest } = data.config[key].defaultValue