Merge branch 'develop' into feature/multimodel_memory

# Conflicts:
#	api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/embedding_generation.py
#	api/app/repositories/neo4j/add_nodes.py
#	api/app/repositories/neo4j/cypher_queries.py
#	api/app/repositories/neo4j/graph_saver.py
#	api/app/services/memory_agent_service.py
#	api/app/services/multimodal_service.py

api/app/celery_app.py

@@ -77,6 +77,7 @@ celery_app.conf.update(
 
     # Worker 设置 (per-worker settings are in docker-compose command line)
     worker_prefetch_multiplier=1,  # Don't hoard tasks, fairer distribution
+    worker_redirect_stdouts_level='INFO',  # stdout/print → INFO instead of WARNING
 
     # 结果过期时间
     result_expires=3600,  # 结果保存1小时

api/app/controllers/__init__.py

@@ -8,6 +8,7 @@ from fastapi import APIRouter
 from . import (
     api_key_controller,
     app_controller,
+    app_log_controller,
     auth_controller,
     chunk_controller,
     document_controller,
@@ -70,6 +71,7 @@ manager_router.include_router(chunk_controller.router)
 manager_router.include_router(test_controller.router)
 manager_router.include_router(knowledgeshare_controller.router)
 manager_router.include_router(app_controller.router)
+manager_router.include_router(app_log_controller.router)
 manager_router.include_router(upload_controller.router)
 manager_router.include_router(memory_agent_controller.router)
 manager_router.include_router(memory_dashboard_controller.router)

api/app/controllers/app_controller.py

@@ -57,6 +57,7 @@ def list_apps(
         page: int = 1,
         pagesize: int = 10,
         ids: Optional[str] = None,
+        api_key: Optional[str] = None,
         db: Session = Depends(get_db),
         current_user=Depends(get_current_user),
 ):
@@ -65,10 +66,25 @@ def list_apps(
     - 默认包含本工作空间的应用和分享给本工作空间的应用
     - 设置 include_shared=false 可以只查看本工作空间的应用
     - 当提供 ids 参数时，按逗号分割获取指定应用，不分页
+    - 当提供 api_key 参数时，查找该 API Key 关联的应用
     """
+    from sqlalchemy import select as sa_select
+    from app.models.api_key_model import ApiKey
+
     workspace_id = current_user.current_workspace_id
     service = app_service.AppService(db)
 
+    # 通过 API Key 搜索：精确匹配，将 resource_id 注入 ids 走统一分页流程
+    if api_key:
+        matched_id = db.execute(
+            sa_select(ApiKey.resource_id).where(
+                ApiKey.workspace_id == workspace_id,
+                ApiKey.api_key == api_key,
+                ApiKey.resource_id.isnot(None),
+            )
+        ).scalar_one_or_none()
+        ids = str(matched_id) if matched_id else ""
+
     # 当 ids 存在且不为 None 时，根据 ids 获取应用
     if ids is not None:
         app_ids = [app_id.strip() for app_id in ids.split(',') if app_id.strip()]

api/app/controllers/app_log_controller.py

@@ -0,0 +1,129 @@
+"""应用日志（消息记录）接口"""
+import uuid
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy import select, desc, func
+from sqlalchemy.orm import Session
+
+from app.core.logging_config import get_business_logger
+from app.core.response_utils import success
+from app.db import get_db
+from app.dependencies import get_current_user, cur_workspace_access_guard
+from app.models.conversation_model import Conversation, Message
+from app.schemas.app_log_schema import AppLogConversation, AppLogConversationDetail, AppLogMessage
+from app.schemas.response_schema import PageData, PageMeta
+from app.services.app_service import AppService
+
+router = APIRouter(prefix="/apps", tags=["App Logs"])
+logger = get_business_logger()
+
+
+@router.get("/{app_id}/logs", summary="应用日志 - 会话列表")
+@cur_workspace_access_guard()
+def list_app_logs(
+        app_id: uuid.UUID,
+        page: int = Query(1, ge=1),
+        pagesize: int = Query(20, ge=1, le=100),
+        user_id: Optional[str] = None,
+        is_draft: Optional[bool] = None,
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """查看应用下所有会话记录（分页）
+
+    - 支持按 user_id 筛选
+    - 支持按 is_draft 筛选（草稿会话 / 发布会话）
+    - 按最新更新时间倒序排列
+    """
+    workspace_id = current_user.current_workspace_id
+
+    # 验证应用访问权限
+    service = AppService(db)
+    service.get_app(app_id, workspace_id)
+
+    stmt = select(Conversation).where(
+        Conversation.app_id == app_id,
+        Conversation.workspace_id == workspace_id,
+        Conversation.is_active.is_(True),
+    )
+
+    if user_id:
+        stmt = stmt.where(Conversation.user_id == user_id)
+
+    if is_draft is not None:
+        stmt = stmt.where(Conversation.is_draft == is_draft)
+
+    total = int(db.execute(
+        select(func.count()).select_from(stmt.subquery())
+    ).scalar_one())
+
+    stmt = stmt.order_by(desc(Conversation.updated_at))
+    stmt = stmt.offset((page - 1) * pagesize).limit(pagesize)
+
+    conversations = list(db.scalars(stmt).all())
+
+    items = [AppLogConversation.model_validate(c) for c in conversations]
+    meta = PageMeta(page=page, pagesize=pagesize, total=total, hasnext=(page * pagesize) < total)
+
+    logger.info(
+        "查询应用日志会话列表",
+        extra={"app_id": str(app_id), "total": total, "page": page}
+    )
+
+    return success(data=PageData(page=meta, items=items))
+
+
+@router.get("/{app_id}/logs/{conversation_id}", summary="应用日志 - 会话消息详情")
+@cur_workspace_access_guard()
+def get_app_log_detail(
+        app_id: uuid.UUID,
+        conversation_id: uuid.UUID,
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """查看某会话的完整消息记录
+
+    - 返回会话基本信息 + 所有消息（按时间正序）
+    - 消息 meta_data 包含模型名、token 用量等信息
+    """
+    workspace_id = current_user.current_workspace_id
+
+    # 验证应用访问权限
+    service = AppService(db)
+    service.get_app(app_id, workspace_id)
+
+    # 查询会话（确保属于该应用和工作空间）
+    conversation = db.scalars(
+        select(Conversation).where(
+            Conversation.id == conversation_id,
+            Conversation.app_id == app_id,
+            Conversation.workspace_id == workspace_id,
+            Conversation.is_active.is_(True),
+        )
+    ).first()
+
+    if not conversation:
+        from app.core.exceptions import ResourceNotFoundException
+        raise ResourceNotFoundException("会话", str(conversation_id))
+
+    # 查询消息（按时间正序）
+    messages = list(db.scalars(
+        select(Message)
+        .where(Message.conversation_id == conversation_id)
+        .order_by(Message.created_at)
+    ).all())
+
+    detail = AppLogConversationDetail.model_validate(conversation)
+    detail.messages = [AppLogMessage.model_validate(m) for m in messages]
+
+    logger.info(
+        "查询应用日志会话详情",
+        extra={
+            "app_id": str(app_id),
+            "conversation_id": str(conversation_id),
+            "message_count": len(messages)
+        }
+    )
+
+    return success(data=detail)

api/app/controllers/file_storage_controller.py

@@ -14,6 +14,9 @@ Routes:
 import os
 import uuid
 from typing import Any
+import httpx
+import mimetypes
+from urllib.parse import urlparse, unquote
 
 from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile, status
 from fastapi.responses import FileResponse, RedirectResponse
@@ -91,7 +94,7 @@ async def upload_file(
 
     if file_size > settings.MAX_FILE_SIZE:
         raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
+            status_code=status.HTTP_413_CONTENT_TOO_LARGE,
             detail=f"The file size exceeds the {settings.MAX_FILE_SIZE} byte limit"
         )
 
@@ -172,7 +175,6 @@ async def upload_file_with_share_token(
     
     # Get share and release info from share_token
     service = ReleaseShareService(db)
-    share_info = service.get_shared_release_info(share_token=share_data.share_token)
     
     # Get share object to access app_id
     share = service.repo.get_by_share_token(share_data.share_token)
@@ -291,6 +293,101 @@ async def upload_file_with_share_token(
     )
 
 
+@router.get("/files/info-by-url", response_model=ApiResponse)
+async def get_file_info_by_url(
+        url: str,
+):
+    """
+    Get file information by network URL (no authentication required).
+
+    Fetches file metadata from a remote URL via HTTP HEAD request.
+    Falls back to GET request if HEAD is not supported.
+    Returns file type, name, and size.
+
+    Args:
+        url: The network URL of the file.
+
+    Returns:
+        ApiResponse with file information.
+    """
+    api_logger.info(f"File info by URL request: url={url}")
+
+    try:
+        async with httpx.AsyncClient(timeout=10.0) as client:
+            # Try HEAD request first
+            response = await client.head(url, follow_redirects=True)
+
+            # If HEAD fails, try GET request (some servers don't support HEAD)
+            if response.status_code != 200:
+                api_logger.info(f"HEAD request failed with {response.status_code}, trying GET request")
+                response = await client.get(url, follow_redirects=True)
+                
+                if response.status_code != 200:
+                    api_logger.error(f"Failed to fetch file info: HTTP {response.status_code}")
+                    raise HTTPException(
+                        status_code=status.HTTP_400_BAD_REQUEST,
+                        detail=f"Unable to access file: HTTP {response.status_code}"
+                    )
+
+            # Get file size from Content-Length header or actual content
+            file_size = response.headers.get("Content-Length")
+            if file_size:
+                file_size = int(file_size)
+            elif hasattr(response, 'content'):
+                file_size = len(response.content)
+            else:
+                file_size = None
+
+            # Get content type from Content-Type header
+            content_type = response.headers.get("Content-Type", "application/octet-stream")
+            # Remove charset and other parameters from content type
+            content_type = content_type.split(';')[0].strip()
+
+            # Extract filename from Content-Disposition or URL
+            file_name = None
+            content_disposition = response.headers.get("Content-Disposition")
+            if content_disposition and "filename=" in content_disposition:
+                parts = content_disposition.split("filename=")
+                if len(parts) > 1:
+                    file_name = parts[1].strip('"').strip("'")
+
+            if not file_name:
+                parsed_url = urlparse(url)
+                file_name = unquote(os.path.basename(parsed_url.path)) or "unknown"
+
+            # Extract file extension from filename
+            _, file_ext = os.path.splitext(file_name)
+            
+            # If no extension found, infer from content type
+            if not file_ext:
+                ext = mimetypes.guess_extension(content_type)
+                if ext:
+                    file_ext = ext
+                    file_name = f"{file_name}{file_ext}"
+
+            api_logger.info(f"File info retrieved: name={file_name}, size={file_size}, type={content_type}")
+
+            return success(
+                data={
+                    "url": url,
+                    "file_name": file_name,
+                    "file_ext": file_ext.lower() if file_ext else "",
+                    "file_size": file_size,
+                    "content_type": content_type,
+                },
+                msg="File information retrieved successfully"
+            )
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        api_logger.error(f"Unexpected error: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Failed to retrieve file information: {str(e)}"
+        )
+
+
 @router.get("/files/{file_id}", response_model=Any)
 async def download_file(
     request: Request,
@@ -499,6 +596,51 @@ async def get_file_url(
         )
 
 
+@router.get("/files/{file_id}/public-url", response_model=ApiResponse)
+async def get_permanent_file_url(
+    file_id: uuid.UUID,
+    db: Session = Depends(get_db),
+    storage_service: FileStorageService = Depends(get_file_storage_service),
+):
+    """
+    获取文件的永久公开 URL（无过期时间）。
+
+    - 本地存储：返回 API 永久访问地址（基于 FILE_LOCAL_SERVER_URL 配置）
+    - 远程存储（OSS/S3）：返回 bucket 公读地址（需 bucket 已配置公共读权限）
+    """
+    file_metadata = db.query(FileMetadata).filter(FileMetadata.id == file_id).first()
+    if not file_metadata:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="The file does not exist")
+
+    if file_metadata.status != "completed":
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST,
+                            detail=f"File upload not completed, status: {file_metadata.status}")
+
+    file_key = file_metadata.file_key
+    storage = storage_service.storage
+
+    try:
+        if isinstance(storage, LocalStorage):
+            url = f"{settings.FILE_LOCAL_SERVER_URL}/storage/permanent/{file_id}"
+        else:
+            url = await storage.get_permanent_url(file_key)
+            if not url:
+                raise HTTPException(status_code=status.HTTP_501_NOT_IMPLEMENTED,
+                                    detail="Permanent URL not supported for current storage backend")
+
+        api_logger.info(f"Generated permanent URL: file_id={file_id}")
+        return success(
+            data={"url": url, "expires_in": None, "permanent": True, "file_name": file_metadata.file_name},
+            msg="Permanent file URL generated successfully"
+        )
+    except HTTPException:
+        raise
+    except Exception as e:
+        api_logger.error(f"Failed to generate permanent URL: {e}")
+        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+                            detail=f"Failed to generate permanent URL: {str(e)}")
+
+
 @router.get("/public/{file_id}", response_model=Any)
 async def public_download_file(
     request: Request,
@@ -653,3 +795,44 @@ async def permanent_download_file(
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
                 detail=f"Failed to retrieve file: {str(e)}"
             )
+
+
+@router.get("/files/{file_id}/status", response_model=ApiResponse)
+async def get_file_status(
+    file_id: uuid.UUID,
+    db: Session = Depends(get_db),
+):
+    """
+    Get file upload/processing status (no authentication required).
+    
+    This endpoint is used to check if a file (e.g., TTS audio) is ready.
+    Returns status: pending, completed, or failed.
+    
+    Args:
+        file_id: The UUID of the file.
+        db: Database session.
+    
+    Returns:
+        ApiResponse with file status and metadata.
+    """
+    api_logger.info(f"File status request: file_id={file_id}")
+    
+    # Query file metadata from database
+    file_metadata = db.query(FileMetadata).filter(FileMetadata.id == file_id).first()
+    if not file_metadata:
+        api_logger.warning(f"File not found in database: file_id={file_id}")
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="The file does not exist"
+        )
+    
+    return success(
+        data={
+            "file_id": str(file_id),
+            "status": file_metadata.status,
+            "file_name": file_metadata.file_name,
+            "file_size": file_metadata.file_size,
+            "content_type": file_metadata.content_type,
+        },
+        msg="File status retrieved successfully"
+    )

api/app/controllers/memory_dashboard_controller.py

@@ -195,10 +195,9 @@ async def get_workspace_end_users(
         api_logger.warning(f"Redis 缓存写入失败: {str(e)}")
 
     # 触发社区聚类补全任务（异步，不阻塞接口响应）
-    # 对有 ExtractedEntity 但无 Community 节点的存量用户自动补跑全量聚类
     try:
         from app.tasks import init_community_clustering_for_users
-        init_community_clustering_for_users.delay(end_user_ids=end_user_ids)
+        init_community_clustering_for_users.delay(end_user_ids=end_user_ids, workspace_id=str(workspace_id))
         api_logger.info(f"已触发社区聚类补全任务，候选用户数: {len(end_user_ids)}")
     except Exception as e:
         api_logger.warning(f"触发社区聚类补全任务失败（不影响主流程）: {str(e)}")

api/app/controllers/memory_working_controller.py

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

api/app/controllers/tool_controller.py

@@ -76,6 +76,8 @@ async def get_tool_methods(
         if methods is None:
             raise HTTPException(status_code=404, detail="工具不存在")
         return success(data=methods, msg="获取工具方法成功")
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -121,6 +123,8 @@ async def create_tool(
         raise HTTPException(status_code=400, detail=e.message)
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -149,6 +153,8 @@ async def update_tool(
         return success(msg="工具更新成功")
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -191,6 +197,8 @@ async def set_tool_active(
         return success(msg=f"工具已{action}")
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -223,6 +231,8 @@ async def execute_tool(
             },
             msg="工具执行完成"
         )
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 

api/app/core/config.py

@@ -97,6 +97,7 @@ class Settings:
 
     # File Upload
     MAX_FILE_SIZE: int = int(os.getenv("MAX_FILE_SIZE", "52428800"))
+    MAX_FILE_COUNT: int = int(os.getenv("MAX_FILE_COUNT", "20"))
     FILE_PATH: str = os.getenv("FILE_PATH", "/files")
     FILE_URL_EXPIRES: int = int(os.getenv("FILE_URL_EXPIRES", "3600"))
 

api/app/core/logging_config.py

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

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

@@ -21,7 +21,7 @@ from app.core.memory.utils.log.logging_utils import log_time
 from app.db import get_db_context
 from app.repositories.neo4j.add_edges import add_memory_summary_statement_edges
 from app.repositories.neo4j.add_nodes import add_memory_summary_nodes
-from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j, schedule_clustering_after_write
+from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j, _trigger_clustering_sync
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_config_schema import MemoryConfig
 
@@ -176,8 +176,8 @@ async def write(
             )
             if success:
                 logger.info("Successfully saved all data to Neo4j")
-                # 写入成功后，异步触发聚类（不阻塞写入响应）
-                schedule_clustering_after_write(
+                # 写入成功后，同步等待聚类完成（避免与 Memory Summary 并发冲突）
+                await _trigger_clustering_sync(
                     all_entity_nodes,
                     llm_model_id=str(memory_config.llm_model_id) if memory_config.llm_model_id else None,
                     embedding_model_id=str(

api/app/core/memory/storage_services/clustering_engine/label_propagation.py

@@ -237,6 +237,7 @@ class LabelPropagationEngine:
             await self.repo.upsert_community(new_cid, end_user_id, member_count=1)
             await self.repo.assign_entity_to_community(entity_id, new_cid, end_user_id)
             logger.debug(f"[Clustering] 孤立实体 {entity_id} → 新社区 {new_cid}")
+            await self._generate_community_metadata([new_cid], end_user_id)
             return
 
         # 统计邻居社区分布
@@ -271,7 +272,8 @@ class LabelPropagationEngine:
                 await self._evaluate_merge(
                     list(community_ids_in_neighbors), end_user_id
                 )
-            await self._generate_community_metadata([target_cid], end_user_id)
+            # 新实体加入后成员变化，强制重新生成元数据
+            await self._generate_community_metadata([target_cid], end_user_id, force=True)
 
     async def _evaluate_merge(
         self, community_ids: List[str], end_user_id: str
@@ -451,7 +453,7 @@ class LabelPropagationEngine:
         return lines
 
     async def _generate_community_metadata(
-        self, community_ids: List[str], end_user_id: str
+        self, community_ids: List[str], end_user_id: str, force: bool = False
     ) -> None:
         """
         为一个或多个社区生成并写入元数据。
@@ -460,69 +462,82 @@ class LabelPropagationEngine:
         1. 逐个社区调 LLM 生成 name / summary（串行）
         2. 收集所有 summary，一次性批量 embed
         3. 单个社区用 update_community_metadata，多个用 batch_update_community_metadata
-        """
-        if not community_ids:
-            return
 
+        Args:
+            force: 为 True 时跳过完整性检查，强制重新生成（用于增量更新成员变化后）
+        """
         from app.db import get_db_context
         from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 
-        # --- 阶段1：并发调 LLM 生成每个社区的 name / summary ---
-        async def _build_one(cid: str):
-            members = await self.repo.get_community_members(cid, end_user_id)
-            if not members:
+        async def _build_one(cid: str) -> Optional[Dict]:
+            try:
+                if not force:
+                    check_embedding = bool(self.embedding_model_id)
+                    if await self.repo.is_community_complete(cid, end_user_id, check_embedding=check_embedding):
+                        return None
+
+                members = await self.repo.get_community_members(cid, end_user_id)
+                if not members:
+                    logger.warning(f"[Clustering] 社区 {cid} 无成员，跳过元数据生成")
+                    return None
+
+                sorted_members = sorted(
+                    members,
+                    key=lambda m: m.get("activation_value") or 0,
+                    reverse=True,
+                )
+                core_entities = [m["name"] for m in sorted_members[:CORE_ENTITY_LIMIT] if m.get("name")]
+                all_names = [m["name"] for m in members if m.get("name")]
+
+                name = "、".join(core_entities[:3]) if core_entities else cid[:8]
+                summary = f"包含实体：{', '.join(all_names)}"
+
+                if self.llm_model_id:
+                    try:
+                        entity_list_str = "\n".join(self._build_entity_lines(members))
+                        relationships = await self.repo.get_community_relationships(cid, end_user_id)
+                        rel_lines = [
+                            f"- {r['subject']} → {r['predicate']} → {r['object']}"
+                            for r in relationships
+                            if r.get("subject") and r.get("predicate") and r.get("object")
+                        ]
+                        rel_section = (
+                            f"\n实体间关系：\n" + "\n".join(rel_lines)
+                            if rel_lines else ""
+                        )
+                        prompt = (
+                            f"以下是一组语义相关的实体：\n{entity_list_str}{rel_section}\n\n"
+                            f"请为这组实体所代表的主题：\n"
+                            f"1. 起一个简洁的中文名称（不超过10个字）\n"
+                            f"2. 写一句话摘要（不超过80个字）\n\n"
+                            f"严格按以下格式输出，不要有其他内容：\n"
+                            f"名称：<名称>\n摘要：<摘要>"
+                        )
+                        with get_db_context() as db:
+                            llm_client = MemoryClientFactory(db).get_llm_client(self.llm_model_id)
+                            response = await llm_client.chat([{"role": "user", "content": prompt}])
+                            text = response.content if hasattr(response, "content") else str(response)
+
+                        for line in text.strip().splitlines():
+                            if line.startswith("名称："):
+                                name = line[3:].strip()
+                            elif line.startswith("摘要："):
+                                summary = line[3:].strip()
+                    except Exception as e:
+                        logger.warning(f"[Clustering] 社区 {cid} LLM 生成失败，使用兜底值: {e}")
+
+                return {
+                    "community_id": cid,
+                    "end_user_id": end_user_id,
+                    "name": name,
+                    "summary": summary,
+                    "core_entities": core_entities,
+                    "summary_embedding": None,
+                }
+            except Exception as e:
+                logger.error(f"[Clustering] 社区 {cid} 元数据准备失败: {e}", exc_info=True)
                 return None
 
-            sorted_members = sorted(
-                members,
-                key=lambda m: m.get("activation_value") or 0,
-                reverse=True,
-            )
-            core_entities = [m["name"] for m in sorted_members[:CORE_ENTITY_LIMIT] if m.get("name")]
-
-            entity_list_str = "\n".join(self._build_entity_lines(members))
-
-            # 方案四：注入社区内实体间关系三元组
-            relationships = await self.repo.get_community_relationships(cid, end_user_id)
-            rel_lines = [
-                f"- {r['subject']} → {r['predicate']} → {r['object']}"
-                for r in relationships
-                if r.get("subject") and r.get("predicate") and r.get("object")
-            ]
-            rel_section = (
-                f"\n实体间关系：\n" + "\n".join(rel_lines)
-                if rel_lines else ""
-            )
-
-            prompt = (
-                f"以下是一组语义相关的实体：\n{entity_list_str}{rel_section}\n\n"
-                f"请为这组实体所代表的主题：\n"
-                f"1. 起一个简洁的中文名称（不超过10个字）\n"
-                f"2. 写一句话摘要（不超过80个字）\n\n"
-                f"严格按以下格式输出，不要有其他内容：\n"
-                f"名称：<名称>\n摘要：<摘要>"
-            )
-            with get_db_context() as db:
-                llm_client = MemoryClientFactory(db).get_llm_client(self.llm_model_id)
-                response = await llm_client.chat([{"role": "user", "content": prompt}])
-                text = response.content if hasattr(response, "content") else str(response)
-
-            name, summary = "", ""
-            for line in text.strip().splitlines():
-                if line.startswith("名称："):
-                    name = line[3:].strip()
-                elif line.startswith("摘要："):
-                    summary = line[3:].strip()
-
-            return {
-                "community_id": cid,
-                "end_user_id": end_user_id,
-                "name": name,
-                "summary": summary,
-                "core_entities": core_entities,
-                "summary_embedding": None,
-            }
-
         results = await asyncio.gather(
             *[_build_one(cid) for cid in community_ids],
             return_exceptions=True,
@@ -535,15 +550,20 @@ class LabelPropagationEngine:
                 metadata_list.append(res)
 
         if not metadata_list:
+            logger.warning(f"[Clustering] 无有效元数据可写入，community_ids={community_ids}")
             return
 
         # --- 阶段2：批量生成 summary_embedding ---
-        summaries = [m["summary"] for m in metadata_list]
-        with get_db_context() as db:
-            embedder = MemoryClientFactory(db).get_embedder_client(self.embedding_model_id)
-        embeddings = await embedder.response(summaries)
-        for i, meta in enumerate(metadata_list):
-            meta["summary_embedding"] = embeddings[i] if i < len(embeddings) else None
+        if self.embedding_model_id:
+            try:
+                summaries = [m["summary"] for m in metadata_list]
+                with get_db_context() as db:
+                    embedder = MemoryClientFactory(db).get_embedder_client(self.embedding_model_id)
+                    embeddings = await embedder.response(summaries)
+                for i, meta in enumerate(metadata_list):
+                    meta["summary_embedding"] = embeddings[i] if i < len(embeddings) else None
+            except Exception as e:
+                logger.error(f"[Clustering] 批量生成 summary_embedding 失败: {e}", exc_info=True)
 
         # --- 阶段3：写入（单个 or 批量）---
         if len(metadata_list) == 1:
@@ -556,17 +576,13 @@ class LabelPropagationEngine:
                 core_entities=m["core_entities"],
                 summary_embedding=m["summary_embedding"],
             )
-            if result:
-                logger.info(f"[Clustering] 社区 {m['community_id']} 元数据写入成功: name={m['name']}, summary={m['summary'][:30]}...")
-            else:
-                logger.warning(f"[Clustering] 社区 {m['community_id']} 元数据写入返回 False")
+            if not result:
+                logger.error(f"[Clustering] 社区 {m['community_id']} 元数据写入失败")
         else:
             ok = await self.repo.batch_update_community_metadata(metadata_list)
-            if ok:
-                logger.info(f"[Clustering] 批量写入 {len(metadata_list)} 个社区元数据成功")
-            else:
-                logger.warning(f"[Clustering] 批量写入社区元数据失败")
+            if not ok:
+                logger.error(f"[Clustering] 批量写入 {len(metadata_list)} 个社区元数据失败")
 
     @staticmethod
     def _new_community_id() -> str:
-        return str(uuid.uuid4())
+        return str(uuid.uuid4())

api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py

@@ -9,6 +9,7 @@
 """
 
 import asyncio
+import logging
 import os
 import hashlib
 import json
@@ -26,6 +27,8 @@ from app.core.memory.storage_services.extraction_engine.data_preprocessing.scene
     ScenePatterns
 )
 
+logger = logging.getLogger(__name__)
+
 
 class DialogExtractionResponse(BaseModel):
     """对话级一次性抽取的结构化返回，用于加速剪枝。
@@ -706,7 +709,7 @@ class SemanticPruner:
         # 阈值保护：最高0.9
         proportion = float(self.config.pruning_threshold)
         if proportion > 0.9:
-            print(f"[剪枝-数据集] 阈值{proportion}超过上限0.9，已自动调整为0.9")
+            logger.warning(f"[剪枝-数据集] 阈值{proportion}超过上限0.9，已自动调整为0.9")
             proportion = 0.9
         if proportion < 0.0:
             proportion = 0.0
@@ -905,7 +908,7 @@ class SemanticPruner:
 
         # Safety: avoid empty dataset
         if not result:
-            print("警告: 语义剪枝后数据集为空，已回退为未剪枝数据以避免流程中断")
+            logger.warning("语义剪枝后数据集为空，已回退为未剪枝数据以避免流程中断")
             return dialogs
 
         return result
@@ -915,8 +918,7 @@ class SemanticPruner:
         try:
             self.run_logs.append(msg)
         except Exception:
-            # 任何异常都不影响打印
             pass
-        print(msg)
+        logger.debug(msg)
 
 

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

@@ -5,8 +5,11 @@
 """
 
 import asyncio
+import logging
 from typing import Any, Dict, List, Tuple
 
+logger = logging.getLogger(__name__)
+
 from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
 from app.core.memory.models.message_models import DialogData
 from app.core.models.base import RedBearModelConfig
@@ -48,9 +51,9 @@ class EmbeddingGenerator:
             return await self.embedder_client.response(texts)
         
         # 分批并行处理
-        print(f"文本数量 {len(texts)} 超过批次大小 {batch_size}，分批并行处理")
+        logger.info(f"文本数量 {len(texts)} 超过批次大小 {batch_size}，分批并行处理")
         batches = [texts[i:i+batch_size] for i in range(0, len(texts), batch_size)]
-        print(f"分成 {len(batches)} 批，每批最多 {batch_size} 个文本")
+        logger.info(f"分成 {len(batches)} 批，每批最多 {batch_size} 个文本")
         
         # 并行发送所有批次
         batch_results = await asyncio.gather(*[
@@ -62,7 +65,7 @@ class EmbeddingGenerator:
         for batch_result in batch_results:
             embeddings.extend(batch_result)
         
-        print(f"分批并行处理完成，共生成 {len(embeddings)} 个嵌入向量")
+        logger.info(f"分批并行处理完成，共生成 {len(embeddings)} 个嵌入向量")
         return embeddings
 
     async def generate_statement_embeddings(
@@ -77,7 +80,7 @@ class EmbeddingGenerator:
         Returns:
             每个对话的陈述句嵌入向量映射列表
         """
-        print("\n=== 生成陈述句嵌入向量 ===")
+        logger.debug("=== 生成陈述句嵌入向量 ===")
 
         # 收集所有陈述句
         all_statements = []
@@ -102,7 +105,7 @@ class EmbeddingGenerator:
             stmt_id = chunked_dialogs[d_idx].chunks[c_idx].statements[s_idx].id
             stmt_embedding_maps[d_idx][stmt_id] = embedding
 
-        print(f"为 {len(all_statements)} 个陈述句生成了嵌入向量")
+        logger.info(f"为 {len(all_statements)} 个陈述句生成了嵌入向量")
         return stmt_embedding_maps
 
     async def generate_chunk_embeddings(
@@ -117,7 +120,7 @@ class EmbeddingGenerator:
         Returns:
             每个对话的分块嵌入向量映射列表
         """
-        print("\n=== 生成分块嵌入向量 ===")
+        logger.debug("=== 生成分块嵌入向量 ===")
 
         # 收集所有分块
         all_chunks = []
@@ -138,7 +141,7 @@ class EmbeddingGenerator:
             chunk_id = chunked_dialogs[d_idx].chunks[c_idx].id
             chunk_embedding_maps[d_idx][chunk_id] = embedding
 
-        print(f"为 {len(all_chunks)} 个分块生成了嵌入向量")
+        logger.info(f"为 {len(all_chunks)} 个分块生成了嵌入向量")
         return chunk_embedding_maps
 
     async def generate_dialog_embeddings(
@@ -172,7 +175,7 @@ class EmbeddingGenerator:
         Returns:
             (陈述句嵌入映射列表, 分块嵌入映射列表, 对话嵌入列表)
         """
-        print("\n=== 生成所有嵌入向量 ===")
+        logger.debug("=== 生成所有嵌入向量 ===")
 
         # 并发生成陈述句和分块嵌入向量
         stmt_embedding_maps, chunk_embedding_maps = await asyncio.gather(
@@ -183,9 +186,7 @@ class EmbeddingGenerator:
         # 对话嵌入向量（当前跳过）
         dialog_embeddings = await self.generate_dialog_embeddings(chunked_dialogs)
 
-        print(
-            f"生成完成：{len(chunked_dialogs)} 个对话的嵌入向量"
-        )
+        logger.info(f"生成完成：{len(chunked_dialogs)} 个对话的嵌入向量")
 
         return stmt_embedding_maps, chunk_embedding_maps, dialog_embeddings
 
@@ -201,7 +202,7 @@ class EmbeddingGenerator:
         Returns:
             更新后的三元组映射列表（实体包含嵌入向量）
         """
-        print("\n=== 生成实体嵌入向量 ===")
+        logger.debug("=== 生成实体嵌入向量 ===")
 
         entity_texts: List[str] = []
         entity_refs: List[Any] = []
@@ -219,7 +220,7 @@ class EmbeddingGenerator:
                         entity_refs.append(ent)
 
         if not entity_texts:
-            print("没有找到需要生成嵌入向量的实体")
+            logger.debug("没有找到需要生成嵌入向量的实体")
             return triplet_maps
 
         # 批量生成嵌入向量
@@ -227,14 +228,13 @@ class EmbeddingGenerator:
 
         # 打印前几个嵌入向量的维度
         for i in range(min(5, len(embeddings))):
-            print(f"实体 '{entity_texts[i]}' "
-                  f"嵌入向量维度: {len(embeddings[i])}")
+            logger.debug(f"实体 '{entity_texts[i]}' 嵌入向量维度: {len(embeddings[i])}")
 
         # 将嵌入向量赋值给实体
         for ent, emb in zip(entity_refs, embeddings):
             setattr(ent, "name_embedding", emb)
 
-        print(f"为 {len(entity_refs)} 个实体生成了嵌入向量")
+        logger.info(f"为 {len(entity_refs)} 个实体生成了嵌入向量")
         return triplet_maps
 
 
@@ -297,7 +297,7 @@ async def embedding_generation_all(
     Returns:
         (陈述句嵌入映射列表, 分块嵌入映射列表, 对话嵌入列表, 更新后的三元组映射列表)
     """
-    print("\n=== 综合嵌入向量生成（陈述句/分块/对话 + 实体）===")
+    logger.debug("=== 综合嵌入向量生成（陈述句/分块/对话 + 实体）===")
 
     generator = EmbeddingGenerator(embedding_id)
 

api/app/core/storage/base.py

@@ -121,3 +121,18 @@ class StorageBackend(ABC):
             URL for accessing the file.
         """
         pass
+
+    async def get_permanent_url(self, file_key: str) -> Optional[str]:
+        """
+        Get a permanent public URL for the file (no expiration).
+
+        Returns None by default; remote storage backends should override this
+        if the bucket is configured for public read access.
+
+        Args:
+            file_key: Unique identifier for the file in the storage system.
+
+        Returns:
+            A permanent public URL, or None if not supported.
+        """
+        return None

api/app/core/storage/oss.py

@@ -261,3 +261,13 @@ class OSSStorage(StorageBackend):
             logger.error(f"Failed to generate presigned URL for {file_key}: {e}")
             # Return a basic URL format as fallback
             return f"https://{self.bucket_name}.{self.endpoint.replace('https://', '').replace('http://', '')}/{file_key}"
+
+    async def get_permanent_url(self, file_key: str) -> str:
+        """
+        Get a permanent public URL for the file (requires bucket public read).
+
+        Returns:
+            A permanent URL in the format: https://{bucket}.{endpoint}/{file_key}
+        """
+        host = self.endpoint.replace("https://", "").replace("http://", "")
+        return f"https://{self.bucket_name}.{host}/{file_key}"

api/app/core/storage/s3.py

@@ -378,3 +378,12 @@ class S3Storage(StorageBackend):
             logger.error(f"Failed to generate presigned URL for {file_key}: {e}")
             # Return a basic URL format as fallback
             return f"https://{self.bucket_name}.s3.{self.region}.amazonaws.com/{file_key}"
+
+    async def get_permanent_url(self, file_key: str) -> str:
+        """
+        Get a permanent public URL for the file (requires bucket public read).
+
+        Returns:
+            A permanent URL in the format: https://{bucket}.s3.{region}.amazonaws.com/{file_key}
+        """
+        return f"https://{self.bucket_name}.s3.{self.region}.amazonaws.com/{file_key}"

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

@@ -5,7 +5,7 @@ from typing import Any
 from app.core.error_codes import BizCode
 from app.core.exceptions import BusinessException
 from app.core.models import RedBearRerank, RedBearModelConfig
-from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
+from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory, ElasticSearchVector
 from app.core.workflow.engine.state_manager import WorkflowState
 from app.core.workflow.engine.variable_pool import VariablePool
 from app.core.workflow.nodes.base_node import BaseNode
@@ -24,6 +24,7 @@ class KnowledgeRetrievalNode(BaseNode):
     def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
         super().__init__(node_config, workflow_config)
         self.typed_config: KnowledgeRetrievalNodeConfig | None = None
+        self.vector_service: ElasticSearchVector | None = None
 
     def _output_types(self) -> dict[str, VariableType]:
         return {
@@ -163,6 +164,50 @@ class KnowledgeRetrievalNode(BaseNode):
         )
         return reranker
 
+    def knowledge_retrieval(self, db, query, rs, db_knowledge, kb_config):
+        if db_knowledge.type == knowledge_model.KnowledgeType.FOLDER:
+            children = knowledge_repository.get_knowledges_by_parent_id(db=db, parent_id=db_knowledge.id)
+            for child in children:
+                if not (child and child.chunk_num > 0 and child.status == 1):
+                    continue
+                kb_config.kb_id = child.id
+                self.knowledge_retrieval(db, query, rs, child, kb_config)
+            return
+        self.vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
+        indices = f"Vector_index_{kb_config.kb_id}_Node".lower()
+        match kb_config.retrieve_type:
+            case RetrieveType.PARTICIPLE:
+                rs.extend(self.vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
+                                                                  indices=indices,
+                                                                  score_threshold=kb_config.similarity_threshold))
+            case RetrieveType.SEMANTIC:
+                rs.extend(self.vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
+                                                               indices=indices,
+                                                               score_threshold=kb_config.vector_similarity_weight))
+            case RetrieveType.HYBRID:
+                rs1 = self.vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
+                                                           indices=indices,
+                                                           score_threshold=kb_config.vector_similarity_weight)
+                rs2 = self.vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
+                                                              indices=indices,
+                                                              score_threshold=kb_config.similarity_threshold)
+
+                # Deduplicate hybrid retrieval results
+                unique_rs = self._deduplicate_docs(rs1, rs2)
+                if not unique_rs:
+                    return
+                if self.typed_config.reranker_id:
+                    self.vector_service.reranker = self.get_reranker_model()
+                    rs.extend(self.vector_service.rerank(query=query, docs=unique_rs, top_k=kb_config.top_k))
+                else:
+                    rs.extend(sorted(
+                        unique_rs,
+                        key=lambda d: d.metadata.get("score", 0),
+                        reverse=True
+                    )[:kb_config.top_k])
+            case _:
+                raise RuntimeError("Unknown retrieval type")
+
     async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> Any:
         """
         Execute the knowledge retrieval workflow node.
@@ -191,56 +236,19 @@ class KnowledgeRetrievalNode(BaseNode):
         query = self._render_template(self.typed_config.query, variable_pool)
         with get_db_read() as db:
             knowledge_bases = self.typed_config.knowledge_bases
-            existing_ids = self._get_existing_kb_ids(db, [kb.kb_id for kb in knowledge_bases])
-
-            if not existing_ids:
-                raise RuntimeError("Knowledge base retrieval failed: the knowledge base does not exist.")
 
             rs = []
             for kb_config in knowledge_bases:
                 db_knowledge = knowledge_repository.get_knowledge_by_id(db=db, knowledge_id=kb_config.kb_id)
                 if not db_knowledge:
                     raise RuntimeError("The knowledge base does not exist or access is denied.")
+                self.knowledge_retrieval(db, query, rs, db_knowledge, kb_config)
 
-                vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
-                indices = f"Vector_index_{kb_config.kb_id}_Node".lower()
-                match kb_config.retrieve_type:
-                    case RetrieveType.PARTICIPLE:
-                        rs.extend(vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
-                                                                     indices=indices,
-                                                                     score_threshold=kb_config.similarity_threshold))
-                    case RetrieveType.SEMANTIC:
-                        rs.extend(vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
-                                                                  indices=indices,
-                                                                  score_threshold=kb_config.vector_similarity_weight))
-                    case RetrieveType.HYBRID:
-                        rs1 = vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
-                                                              indices=indices,
-                                                              score_threshold=kb_config.vector_similarity_weight)
-                        rs2 = vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
-                                                                 indices=indices,
-                                                                 score_threshold=kb_config.similarity_threshold)
-
-                        # Deduplicate hy    brid retrieval results
-                        unique_rs = self._deduplicate_docs(rs1, rs2)
-                        if not unique_rs:
-                            continue
-                        if self.typed_config.reranker_id:
-                            vector_service.reranker = self.get_reranker_model()
-                            rs.extend(vector_service.rerank(query=query, docs=unique_rs, top_k=kb_config.top_k))
-                        else:
-                            rs.extend(sorted(
-                                unique_rs,
-                                key=lambda d: d.metadata.get("score", 0),
-                                reverse=True
-                            )[:kb_config.top_k])
-                    case _:
-                        raise RuntimeError("Unknown retrieval type")
             if not rs:
                 return []
             if self.typed_config.reranker_id:
-                vector_service.reranker = self.get_reranker_model()
-                final_rs = vector_service.rerank(query=query, docs=rs, top_k=self.typed_config.reranker_top_k)
+                self.vector_service.reranker = self.get_reranker_model()
+                final_rs = self.vector_service.rerank(query=query, docs=rs, top_k=self.typed_config.reranker_top_k)
             else:
                 final_rs = sorted(
                     rs,

api/app/main.py

@@ -506,10 +506,13 @@ async def http_exception_handler(request: Request, exc: HTTPException):
         404: "errors.common.not_found",
         405: "errors.common.method_not_allowed",
         409: "errors.common.conflict",
+        413: "errors.common.payload_too_large",
         422: "errors.common.validation_failed",
         429: "errors.common.too_many_requests",
         500: "errors.common.internal_error",
+        502: "errors.common.bad_gateway",
         503: "errors.common.service_unavailable",
+        504: "errors.common.gateway_timeout",
     }
     
     # 如果有对应的翻译键，使用翻译
@@ -534,7 +537,7 @@ async def http_exception_handler(request: Request, exc: HTTPException):
     
     return JSONResponse(
         status_code=exc.status_code,
-        content=fail(code=exc.status_code, msg=translated_message, error=translated_message)
+        content=fail(code=exc.status_code, msg=translated_message, error=exc.detail)
     )
 
 

api/app/models/user_model.py

@@ -9,7 +9,7 @@ class User(Base):
     __tablename__ = "users"
 
     id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True)
-    username = Column(String, unique=True, index=True, nullable=False)
+    username = Column(String, index=True, nullable=False)  # 社区版：用户名不唯一，仅邮箱唯一
     email = Column(String, unique=True, index=True, nullable=False)
     hashed_password = Column(String, nullable=False)
     is_active = Column(Boolean, default=True, nullable=False)

api/app/repositories/conversation_repository.py

@@ -90,27 +90,27 @@ class ConversationRepository:
             self,
             user_id: uuid.UUID,
             workspace_id: uuid.UUID = None,
-            limit: int = 10,
-            is_activate: bool = True
-    ) -> list[Conversation]:
+            is_activate: bool = True,
+            page: int = 1,
+            page_size: int = 20
+    ) -> tuple[list[Conversation], int]:
         """
-        Retrieve recent conversations for a specific user.
+        Retrieve recent conversations for a specific user with pagination.
 
         This method queries conversations associated with the given user ID,
         optionally scoped to a specific workspace. Results are ordered by the
-        most recently updated conversations and limited to a fixed number.
+        most recently updated conversations.
 
         Args:
             user_id (uuid.UUID): Unique identifier of the user.
             workspace_id (uuid.UUID, optional): Workspace scope for the query.
                 If provided, only conversations under this workspace will be returned.
-            limit (int): Maximum number of conversations to return.
-                Defaults to 10.
-            is_activate (bool): Convsersation State limit
+            is_activate (bool): Conversation State limit.
+            page (int): Page number (1-based). Defaults to 1.
+            page_size (int): Number of items per page. Defaults to 20.
 
         Returns:
-            list[Conversation]: A list of conversation entities ordered by
-            last updated time (descending).
+            tuple[list[Conversation], int]: A list of conversation entities and total count.
         """
         logger.info(f"Fetching conversation by user_id: {user_id}")
 
@@ -122,18 +122,25 @@ class ConversationRepository:
         if workspace_id:
             stmt = stmt.where(Conversation.workspace_id == workspace_id)
 
-        stmt = stmt.order_by(desc(Conversation.updated_at))
-        stmt = stmt.limit(limit)
+        # Calculate total count
+        total = int(self.db.execute(
+            select(func.count()).select_from(stmt.subquery())
+        ).scalar_one())
 
-        convsersations = list(self.db.scalars(stmt).all())
+        # Apply ordering and pagination
+        stmt = stmt.order_by(desc(Conversation.updated_at))
+        stmt = stmt.offset((page - 1) * page_size).limit(page_size)
+
+        conversations = list(self.db.scalars(stmt).all())
         logger.info(
             "Conversation fetched successfully",
             extra={
                 "user_id": str(user_id),
                 "workspace_id": str(workspace_id),
+                "total": total,
             }
         )
-        return convsersations
+        return conversations, total
 
     def list_conversations(
             self,

api/app/repositories/neo4j/add_nodes.py

@@ -1,13 +1,13 @@
+import logging
 from typing import List, Optional
 
-from app.core.logging_config import get_logger
 from app.core.memory.models.graph_models import DialogueNode, StatementNode, ChunkNode, MemorySummaryNode
 from app.repositories.neo4j.cypher_queries import DIALOGUE_NODE_SAVE, STATEMENT_NODE_SAVE, CHUNK_NODE_SAVE, \
     MEMORY_SUMMARY_NODE_SAVE
 # 使用新的仓储层
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 
-logger = get_logger(__name__)
+logger = logging.getLogger(__name__)
 
 
 async def delete_all_nodes(end_user_id: str, connector: Neo4jConnector):
@@ -57,7 +57,7 @@ async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConn
         return created_uuids
 
     except Exception as e:
-        logger.info(f"Error creating dialogue nodes: {e}")
+        logger.error(f"Error creating dialogue nodes: {e}")
         return None
 
 
@@ -129,7 +129,7 @@ async def add_statement_nodes(statements: List[StatementNode], connector: Neo4jC
         return created_uuids
 
     except Exception as e:
-        logger.info(f"Error creating statement nodes: {e}")
+        logger.error(f"Error creating statement nodes: {e}")
         return None
 
 
@@ -181,7 +181,7 @@ async def add_chunk_nodes(chunks: List[ChunkNode], connector: Neo4jConnector) ->
         return created_uuids
 
     except Exception as e:
-        logger.info(f"Error creating chunk nodes: {e}")
+        logger.error(f"Error creating chunk nodes: {e}")
         return None
 
 
@@ -228,5 +228,5 @@ async def add_memory_summary_nodes(
         logger.info(f"Successfully saved {len(created_ids)} MemorySummary nodes to Neo4j")
         return created_ids
     except Exception as e:
-        logger.info(f"Failed to save MemorySummary nodes to Neo4j: {e}")
+        logger.error(f"Failed to save MemorySummary nodes to Neo4j: {e}")
         return None

api/app/repositories/neo4j/community_repository.py

@@ -24,6 +24,10 @@ from app.repositories.neo4j.cypher_queries import (
     CHECK_USER_HAS_COMMUNITIES,
     UPDATE_COMMUNITY_MEMBER_COUNT,
     UPDATE_COMMUNITY_METADATA,
+    GET_INCOMPLETE_COMMUNITIES,
+    GET_INCOMPLETE_COMMUNITIES_WITH_EMBEDDING,
+    CHECK_COMMUNITY_IS_COMPLETE,
+    CHECK_COMMUNITY_IS_COMPLETE_WITH_EMBEDDING,
     BATCH_UPDATE_COMMUNITY_METADATA,
 )
 
@@ -249,6 +253,31 @@ class CommunityRepository:
             logger.error(f"refresh_member_count failed: {e}")
             return 0
 
+    async def get_incomplete_communities(self, end_user_id: str, check_embedding: bool = False) -> List[str]:
+        """查询该用户下属性不完整的 Community 节点 ID 列表。
+
+        Args:
+            end_user_id: 用户 ID
+            check_embedding: 为 True 时额外检查 summary_embedding 是否缺失（仅当用户有 embedding 模型配置时传 True）
+        """
+        try:
+            query = GET_INCOMPLETE_COMMUNITIES_WITH_EMBEDDING if check_embedding else GET_INCOMPLETE_COMMUNITIES
+            result = await self.connector.execute_query(query, end_user_id=end_user_id)
+            return [row["community_id"] for row in result]
+        except Exception as e:
+            logger.error(f"get_incomplete_communities failed: {e}")
+            return []
+
+    async def is_community_complete(self, community_id: str, end_user_id: str, check_embedding: bool = False) -> bool:
+        """检查单个社区节点的属性是否完整。"""
+        try:
+            query = CHECK_COMMUNITY_IS_COMPLETE_WITH_EMBEDDING if check_embedding else CHECK_COMMUNITY_IS_COMPLETE
+            result = await self.connector.execute_query(query, community_id=community_id, end_user_id=end_user_id)
+            return result[0]["is_complete"] if result else False
+        except Exception as e:
+            logger.error(f"is_community_complete failed: {e}")
+            return False
+
     async def update_community_metadata(
         self,
         community_id: str,
@@ -258,7 +287,7 @@ class CommunityRepository:
         core_entities: List[str],
         summary_embedding: Optional[List[float]] = None,
     ) -> bool:
-        """更新社区的名称、摘要、核心实体列表和摘要向量。"""
+        """更新社区的名称、摘要、核心实体列表及 summary_embedding。"""
         try:
             result = await self.connector.execute_query(
                 UPDATE_COMMUNITY_METADATA,
@@ -271,7 +300,7 @@ class CommunityRepository:
             )
             return bool(result)
         except Exception as e:
-            logger.error(f"update_community_metadata failed: {e}")
+            logger.error(f"update_community_metadata failed: {e}", exc_info=True)
             return False
 
     async def batch_update_community_metadata(

api/app/repositories/neo4j/cypher_queries.py

@@ -1075,6 +1075,7 @@ RETURN
 
 COMMUNITY_NODE_UPSERT = """
 MERGE (c:Community {community_id: $community_id})
+ON CREATE SET c.id = $community_id
 SET c.end_user_id = $end_user_id,
     c.member_count = $member_count,
     c.updated_at = datetime()
@@ -1181,7 +1182,8 @@ RETURN c.community_id AS community_id, cnt AS member_count
 
 UPDATE_COMMUNITY_METADATA = """
 MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id})
-SET c.name             = $name,
+SET c.id               = coalesce(c.id, $community_id),
+    c.name             = $name,
     c.summary          = $summary,
     c.core_entities    = $core_entities,
     c.summary_embedding = $summary_embedding,
@@ -1192,7 +1194,8 @@ RETURN c.community_id AS community_id
 BATCH_UPDATE_COMMUNITY_METADATA = """
 UNWIND $communities AS row
 MATCH (c:Community {community_id: row.community_id, end_user_id: row.end_user_id})
-SET c.name             = row.name,
+SET c.id               = coalesce(c.id, row.community_id),
+    c.name             = row.name,
     c.summary          = row.summary,
     c.core_entities    = row.core_entities,
     c.summary_embedding = row.summary_embedding,
@@ -1276,6 +1279,40 @@ RETURN
     startNode(r) = e      AS r_from_e
 """
 
+CHECK_COMMUNITY_IS_COMPLETE = """
+MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id})
+RETURN (
+    c.name IS NOT NULL AND c.name <> '' AND
+    c.summary IS NOT NULL AND c.summary <> '' AND
+    c.core_entities IS NOT NULL
+) AS is_complete
+"""
+
+CHECK_COMMUNITY_IS_COMPLETE_WITH_EMBEDDING = """
+MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id})
+RETURN (
+    c.name IS NOT NULL AND c.name <> '' AND
+    c.summary IS NOT NULL AND c.summary <> '' AND
+    c.core_entities IS NOT NULL AND
+    c.summary_embedding IS NOT NULL
+) AS is_complete
+"""
+
+GET_INCOMPLETE_COMMUNITIES = """
+MATCH (c:Community {end_user_id: $end_user_id})
+WHERE c.name IS NULL OR c.summary IS NULL OR c.core_entities IS NULL
+   OR c.name = '' OR c.summary = ''
+RETURN c.community_id AS community_id
+"""
+
+GET_INCOMPLETE_COMMUNITIES_WITH_EMBEDDING = """
+MATCH (c:Community {end_user_id: $end_user_id})
+WHERE c.name IS NULL OR c.name = ''
+   OR c.summary IS NULL OR c.summary = ''
+   OR c.core_entities IS NULL
+   OR (c.summary_embedding IS NULL AND c.summary IS NOT NULL AND c.summary <> '(empty)')
+RETURN c.community_id AS community_id
+"""
 
 # Community keyword search: matches name or summary via fulltext index
 SEARCH_COMMUNITIES_BY_KEYWORD = """

api/app/repositories/neo4j/graph_saver.py

@@ -169,7 +169,7 @@ async def save_dialog_and_statements_to_neo4j(
     """Save dialogue nodes, chunk nodes, statement nodes, entities, and all relationships to Neo4j using graph models.
 
     只负责数据写入，不触发聚类。聚类由调用方在写入成功后通过
-    schedule_clustering_after_write() 显式触发。
+    _trigger_clustering_sync() 显式触发。
 
     Args:
         dialogue_nodes: List of DialogueNode objects to save
@@ -336,16 +336,13 @@ async def save_dialog_and_statements_to_neo4j(
         return False
 
 
-def schedule_clustering_after_write(
+async def _trigger_clustering_sync(
         entity_nodes: List,
         llm_model_id: Optional[str] = None,
         embedding_model_id: Optional[str] = None,
 ) -> None:
     """
-    写入 Neo4j 成功后，调度后台聚类任务。
-
-    可通过环境变量 CLUSTERING_ENABLED=false 禁用（用于基准测试对比）。
-    使用 asyncio.create_task 异步触发，不阻塞写入响应。
+    同步等待聚类完成，避免与其他 LLM 任务并发冲突。
     """
     if not entity_nodes:
         return
@@ -357,9 +354,9 @@ def schedule_clustering_after_write(
 
     end_user_id = entity_nodes[0].end_user_id
     new_entity_ids = [e.id for e in entity_nodes]
-    logger.info(f"[Clustering] 准备触发聚类，实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}")
-    asyncio.create_task(_trigger_clustering(new_entity_ids, end_user_id, llm_model_id=llm_model_id,
-                                            embedding_model_id=embedding_model_id))
+    logger.info(f"[Clustering] 准备触发聚类（同步），实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}")
+    await _trigger_clustering(new_entity_ids, end_user_id, llm_model_id=llm_model_id,
+                              embedding_model_id=embedding_model_id)
 
 
 async def _trigger_clustering(

api/app/repositories/workflow_repository.py

@@ -43,6 +43,7 @@ class WorkflowConfigRepository:
         edges: list[dict[str, Any]],
         variables: list[dict[str, Any]] | None = None,
         execution_config: dict[str, Any] | None = None,
+        features: dict[str, Any] | None = None,
         triggers: list[dict[str, Any]] | None = None
     ) -> WorkflowConfig:
         """创建或更新工作流配置
@@ -53,6 +54,7 @@ class WorkflowConfigRepository:
             edges: 边列表
             variables: 变量列表
             execution_config: 执行配置
+            features: 功能特性
             triggers: 触发器列表
         
         Returns:
@@ -82,6 +84,7 @@ class WorkflowConfigRepository:
                 edges=edges,
                 variables=variables or [],
                 execution_config=execution_config or {},
+                features=features or {},
                 triggers=triggers or []
             )
             self.db.add(config)

api/app/schemas/app_log_schema.py

@@ -0,0 +1,53 @@
+"""应用日志（消息记录）Schema"""
+import uuid
+import datetime
+from typing import Optional, Dict, Any, List
+
+from pydantic import BaseModel, Field, ConfigDict, field_serializer
+
+
+class AppLogMessage(BaseModel):
+    """单条消息记录"""
+    model_config = ConfigDict(from_attributes=True)
+
+    id: uuid.UUID
+    conversation_id: uuid.UUID
+    role: str = Field(description="角色: user / assistant / system")
+    content: str
+    meta_data: Optional[Dict[str, Any]] = None
+    created_at: datetime.datetime
+
+    @field_serializer("created_at", when_used="json")
+    def _serialize_created_at(self, dt: datetime.datetime):
+        return int(dt.timestamp() * 1000) if dt else None
+
+    @field_serializer("meta_data", when_used="json")
+    def _serialize_meta_data(self, data: Optional[Dict[str, Any]]):
+        return data or {}
+
+
+class AppLogConversation(BaseModel):
+    """会话摘要（用于列表）"""
+    model_config = ConfigDict(from_attributes=True)
+
+    id: uuid.UUID
+    app_id: uuid.UUID
+    user_id: Optional[str] = None
+    title: Optional[str] = None
+    message_count: int = 0
+    is_draft: bool
+    created_at: datetime.datetime
+    updated_at: datetime.datetime
+
+    @field_serializer("created_at", when_used="json")
+    def _serialize_created_at(self, dt: datetime.datetime):
+        return int(dt.timestamp() * 1000) if dt else None
+
+    @field_serializer("updated_at", when_used="json")
+    def _serialize_updated_at(self, dt: datetime.datetime):
+        return int(dt.timestamp() * 1000) if dt else None
+
+
+class AppLogConversationDetail(AppLogConversation):
+    """会话详情（包含消息列表）"""
+    messages: List[AppLogMessage] = Field(default_factory=list)

api/app/schemas/app_schema.py

@@ -149,18 +149,26 @@ class FileUploadConfig(BaseModel):
     )
     # 通用文件：PDF/DOCX/XLSX/TXT/CSV/JSON，最大 100MB
     document_enabled: bool = Field(default=False)
-    document_max_size_mb: int = Field(default=100)
+    document_max_size_mb: int = Field(default=50)
     document_allowed_extensions: List[str] = Field(
-        default=["pdf", "docx", "xlsx", "txt", "csv", "json", "md"]
+        default=["pdf", "docx", "doc", "xlsx", "xls", "txt", "csv", "json", "md"]
     )
     # 视频文件：MP4/MOV/AVI/WebM，最大 500MB
     video_enabled: bool = Field(default=False)
-    video_max_size_mb: int = Field(default=500)
+    video_max_size_mb: int = Field(default=50)
     video_allowed_extensions: List[str] = Field(
-        default=["mp4", "mov"]
+        default=["mp4"]
     )
     # 最大文件数量
-    max_file_count: int = Field(default=5, ge=1, le=20)
+    max_file_count: int = Field(default=5, ge=1)
+
+    @field_validator("max_file_count")
+    @classmethod
+    def validate_max_file_count(cls, v: int) -> int:
+        from app.core.config import settings
+        if v > settings.MAX_FILE_COUNT:
+            raise ValueError(f"max_file_count 不能超过 {settings.MAX_FILE_COUNT}")
+        return v
 
 
 class OpeningStatementConfig(BaseModel):

api/app/services/app_chat_service.py

@@ -118,28 +118,27 @@ class AppChatService:
 
         )
 
-        # 加载历史消息
-        messages = self.conversation_service.get_messages(
-            conversation_id=conversation_id,
-            limit=10
+        model_info = ModelInfo(
+            model_name=api_key_obj.model_name,
+            provider=api_key_obj.provider,
+            api_key=api_key_obj.api_key,
+            api_base=api_key_obj.api_base,
+            capability=api_key_obj.capability,
+            is_omni=api_key_obj.is_omni,
+            model_type=ModelType.LLM
+        )
+
+        # 加载历史消息
+        history = await self.conversation_service.get_conversation_history(
+            conversation_id=conversation_id,
+            max_history=10,
+            current_provider=api_key_obj.provider,
+            current_is_omni=api_key_obj.is_omni
         )
-        history = [
-            {"role": msg.role, "content": msg.content}
-            for msg in messages
-        ]
 
         # 处理多模态文件
         processed_files = None
         if files:
-            model_info = ModelInfo(
-                model_name=api_key_obj.model_name,
-                provider=api_key_obj.provider,
-                api_key=api_key_obj.api_key,
-                api_base=api_key_obj.api_base,
-                capability=api_key_obj.capability,
-                is_omni=api_key_obj.is_omni,
-                model_type=ModelType.LLM
-            )
             multimodal_service = MultimodalService(self.db, model_info)
             processed_files = await multimodal_service.process_files(files)
             logger.info(f"处理了 {len(processed_files)} 个文件")
@@ -180,7 +179,8 @@ class AppChatService:
 
         # 构建用户消息内容（含多模态文件）
         human_meta = {
-            "files": []
+            "files": [],
+            "history_files": {}
         }
         assistant_meta = {
             "model": api_key_obj.model_name,
@@ -195,6 +195,13 @@ class AppChatService:
                     "url": f.url
                 })
 
+        if processed_files:
+            human_meta["history_files"] = {
+                "content": processed_files,
+                "provider": api_key_obj.provider,
+                "is_omni": api_key_obj.is_omni
+            }
+
         # 保存消息
         if audio_url:
             assistant_meta["audio_url"] = audio_url
@@ -225,6 +232,7 @@ class AppChatService:
             "suggested_questions": suggested_questions,
             "citations": self.agent_service._filter_citations(features_config, result.get("citations", [])),
             "audio_url": audio_url,
+            "audio_status": "pending"
         }
 
     async def agnet_chat_stream(
@@ -313,31 +321,27 @@ class AppChatService:
                 streaming=True
             )
 
+            model_info = ModelInfo(
+                model_name=api_key_obj.model_name,
+                provider=api_key_obj.provider,
+                api_key=api_key_obj.api_key,
+                api_base=api_key_obj.api_base,
+                capability=api_key_obj.capability,
+                is_omni=api_key_obj.is_omni,
+                model_type=ModelType.LLM
+            )
+
             # 加载历史消息
-            history = []
-            memory_config = {"enabled": True, 'max_history': 10}
-            if memory_config.get("enabled"):
-                messages = self.conversation_service.get_messages(
-                    conversation_id=conversation_id,
-                    limit=memory_config.get("max_history", 10)
-                )
-                history = [
-                    {"role": msg.role, "content": msg.content}
-                    for msg in messages
-                ]
+            history = await self.conversation_service.get_conversation_history(
+                conversation_id=conversation_id,
+                max_history=10,
+                current_provider=api_key_obj.provider,
+                current_is_omni=api_key_obj.is_omni
+            )
 
             # 处理多模态文件
             processed_files = None
             if files:
-                model_info = ModelInfo(
-                    model_name=api_key_obj.model_name,
-                    provider=api_key_obj.provider,
-                    api_key=api_key_obj.api_key,
-                    api_base=api_key_obj.api_base,
-                    capability=api_key_obj.capability,
-                    is_omni=api_key_obj.is_omni,
-                    model_type=ModelType.LLM
-                )
                 multimodal_service = MultimodalService(self.db, model_info)
                 processed_files = await multimodal_service.process_files(files)
                 logger.info(f"处理了 {len(processed_files)} 个文件")
@@ -347,8 +351,14 @@ class AppChatService:
             total_tokens = 0
 
             text_queue: asyncio.Queue = asyncio.Queue()
+            api_key_config = {
+                "model_name": api_key_obj.model_name,
+                "api_key": api_key_obj.api_key,
+                "api_base": api_key_obj.api_base,
+                "provider": api_key_obj.provider,
+            }
             stream_audio_url, tts_task = await self.agent_service._generate_tts_streaming(
-                features_config, api_key_obj,
+                features_config, api_key_config,
                 text_queue=text_queue,
                 tenant_id=tenant_id, workspace_id=workspace_id
             )
@@ -378,7 +388,7 @@ class AppChatService:
             elapsed_time = time.time() - start_time
             ModelApiKeyService.record_api_key_usage(self.db, api_key_obj.id)
 
-            # 发送结束事件（包含 suggested_questions、tts、citations）
+            # 发送结束事件（包含 suggested_questions、tts、audio_status、citations）
             end_data: dict = {"elapsed_time": elapsed_time, "message_length": len(full_content), "error": None}
             sq_config = features_config.get("suggested_questions_after_answer", {})
             if isinstance(sq_config, dict) and sq_config.get("enabled"):
@@ -388,11 +398,23 @@ class AppChatService:
                      "api_base": api_key_obj.api_base}, {}
                 )
             end_data["audio_url"] = stream_audio_url
+            # 检查TTS是否已完成（非阻塞，不取消任务）
+            audio_status = "pending"
+            if tts_task is not None and tts_task.done():
+                # 任务已完成，检查是否有异常
+                try:
+                    tts_task.result()
+                    audio_status = "completed"
+                except Exception as e:
+                    logger.warning(f"TTS任务异常: {e}")
+                    audio_status = "failed"
+            end_data["audio_status"] = audio_status if stream_audio_url else None
             end_data["citations"] = self.agent_service._filter_citations(features_config, [])
 
             # 保存消息
             human_meta = {
-                "files":[]
+                "files":[],
+                "history_files": {}
             }
             assistant_meta = {
                 "model": api_key_obj.model_name,
@@ -402,11 +424,16 @@ class AppChatService:
 
             if files:
                 for f in files:
-                    # url = await MultimodalService(self.db).get_file_url(f)
                     human_meta["files"].append({
                         "type": f.type,
                         "url": f.url
                     })
+            if processed_files:
+                human_meta["history_files"] = {
+                    "content": processed_files,
+                    "provider": api_key_obj.provider,
+                    "is_omni": api_key_obj.is_omni
+                }
 
             if stream_audio_url:
                 assistant_meta["audio_url"] = stream_audio_url

api/app/services/app_dsl_service.py

@@ -16,6 +16,7 @@ from app.models.app_release_model import AppRelease
 from app.models.knowledge_model import Knowledge
 from app.models.models_model import ModelConfig
 from app.models.tool_model import ToolConfig as ToolConfigModel
+from app.models.skill_model import Skill
 from app.models.workflow_model import WorkflowConfig
 from app.services.workflow_service import WorkflowService
 from app.core.workflow.adapters.memory_bear.memory_bear_adapter import MemoryBearAdapter
@@ -84,7 +85,9 @@ class AppDslService:
             if "knowledge_retrieval" in cfg:
                 enriched["knowledge_retrieval"] = self._enrich_knowledge_retrieval(cfg["knowledge_retrieval"])
             if "tools" in cfg:
-                enriched["tools"] = self._enrich_tools(cfg["tools"])
+                enriched["tools"] = self._enrich_tools(cfg.get("tools"))
+            if "skills" in cfg:
+                enriched["skills"] = self._enrich_skills(cfg.get("skills"))
             return enriched
         if app_type == AppType.MULTI_AGENT:
             enriched = {**cfg}
@@ -108,6 +111,7 @@ class AppDslService:
                 "variables": config.variables if config else [],
                 "edges": config.edges if config else [],
                 "nodes": config.nodes if config else [],
+                "features": config.features if config else {},
                 "execution_config": config.execution_config if config else {},
                 "triggers": config.triggers if config else [],
             } if config else {}
@@ -123,7 +127,8 @@ class AppDslService:
                 "memory": config.memory if config else None,
                 "variables": config.variables if config else [],
                 "tools": self._enrich_tools(config.tools) if config else [],
-                "skills": config.skills if config else {},
+                "skills": self._enrich_skills(config.skills) if config else {},
+                "features": config.features if config else {}
             } if config else {}
             dsl = {**meta, "app": app_meta, "agent_config": config_data}
 
@@ -185,6 +190,22 @@ class AppDslService:
     def _enrich_tools(self, tools: list) -> list:
         return [{**t, "_ref": self._tool_ref(t.get("tool_id"))} for t in (tools or [])]
 
+    def _skill_ref(self, skill_id) -> Optional[dict]:
+        if not skill_id:
+            return None
+        s = self.db.query(Skill).filter(Skill.id == skill_id).first()
+        return {"id": str(skill_id), "name": s.name} if s else {"id": str(skill_id)}
+
+    def _enrich_skills(self, skills: Optional[dict]) -> Optional[dict]:
+        if not skills:
+            return skills
+        skill_ids = skills.get("skill_ids", [])
+        enriched_ids = [
+            {"id": sid, "_ref": self._skill_ref(sid)}
+            for sid in (skill_ids or [])
+        ]
+        return {**skills, "skill_ids": enriched_ids}
+
     def _agent_ref(self, agent_id) -> Optional[dict]:
         if not agent_id:
             return None
@@ -249,7 +270,8 @@ class AppDslService:
                 memory=self._resolve_memory(cfg.get("memory"), workspace_id, warnings),
                 variables=cfg.get("variables", []),
                 tools=self._resolve_tools(cfg.get("tools", []), tenant_id, warnings),
-                skills=cfg.get("skills", {}),
+                skills=self._resolve_skills(cfg.get("skills", {}), tenant_id, warnings),
+                features=cfg.get("features", {}),
                 is_active=True,
                 created_at=now,
                 updated_at=now,
@@ -290,6 +312,7 @@ class AppDslService:
                 edges=[e.model_dump() for e in result.edges],
                 variables=[v.model_dump() for v in result.variables],
                 execution_config=wf.get("execution_config", {}),
+                features=wf.get("features", {}),
                 triggers=wf.get("triggers", []),
                 validate=False,
             )
@@ -444,6 +467,46 @@ class AppDslService:
             return {**memory, "memory_config_id": None, "enabled": False}
         return memory
 
+    def _resolve_skills(self, skills: Optional[dict], tenant_id: uuid.UUID, warnings: list) -> dict:
+        if not skills:
+            return skills or {}
+        resolved_ids = []
+        for entry in (skills.get("skill_ids") or []):
+            # entry 可能是 {"id": "...", "_ref": {...}} 或直接是字符串
+            if isinstance(entry, dict):
+                ref = entry.get("_ref") or ({"name": None, "id": entry.get("id")} if entry.get("id") else None)
+                skill_id = self._resolve_skill(ref, tenant_id, warnings)
+            else:
+                skill_id = self._resolve_skill({"id": str(entry)}, tenant_id, warnings)
+            if skill_id:
+                resolved_ids.append(str(skill_id))
+        return {**{k: v for k, v in skills.items() if k != "skill_ids"}, "skill_ids": resolved_ids}
+
+    def _resolve_skill(self, ref: Optional[dict], tenant_id: uuid.UUID, warnings: list) -> Optional[str]:
+        if not ref:
+            return None
+        # 先按 id 匹配
+        if ref.get("id"):
+            try:
+                s = self.db.query(Skill).filter(
+                    Skill.id == uuid.UUID(str(ref["id"])),
+                    Skill.tenant_id == tenant_id
+                ).first()
+                if s:
+                    return str(s.id)
+            except Exception:
+                pass
+        # 再按名称匹配
+        if ref.get("name"):
+            s = self.db.query(Skill).filter(
+                Skill.name == ref["name"],
+                Skill.tenant_id == tenant_id
+            ).first()
+            if s:
+                return str(s.id)
+        warnings.append(f"未找到技能: {ref}")
+        return None
+
     def _resolve_tools(self, tools: list, tenant_id: uuid.UUID, warnings: list) -> list:
         result = []
         for t in (tools or []):

api/app/services/app_service.py

@@ -833,8 +833,6 @@ class AppService:
 
             # 跨工作空间时，获取目标工作空间的 tenant_id 用于判断模型配置是否可用
             target_tenant_id = None
-            available_model_ids: set = set()
-            available_kb_ids: set = set()
             if is_cross_workspace:
                 target_ws = self.db.get(Workspace, target_workspace_id)
                 if not target_ws:
@@ -849,28 +847,29 @@ class AppService:
 
                 if source_config:
                     if is_cross_workspace:
-                        # Batch-collect and preload all referenced resources
-                        model_ids, kb_ids = self._collect_resource_ids_from_config(
-                            source_config.default_model_config_id,
-                            source_config.knowledge_retrieval,
-                            source_config.tools
+                        # 跨工作空间：model/tools/skills 属于 tenant 级别直接保留，
+                        # knowledge_bases 属于 workspace 级别需过滤，memory_config 需清空
+                        _, kb_ids = self._collect_resource_ids_from_config(
+                            None, source_config.knowledge_retrieval
                         )
-                        available_model_ids, available_kb_ids = self._preload_cross_workspace_resources(
-                            target_tenant_id, target_workspace_id, model_ids, kb_ids
-                        )
-                        new_model_config_id = self._is_model_available(
-                            source_config.default_model_config_id, available_model_ids
+                        _, available_kb_ids = self._preload_cross_workspace_resources(
+                            target_tenant_id, target_workspace_id, set(), kb_ids
                         )
+                        new_model_config_id = source_config.default_model_config_id
                         new_knowledge_retrieval = self._clean_knowledge_retrieval(
                             source_config.knowledge_retrieval, available_kb_ids
                         )
-                        new_tools = self._clean_tools(
-                            source_config.tools, available_kb_ids
+                        new_tools = copy.deepcopy(source_config.tools) if source_config.tools else []
+                        new_memory = self._clean_memory_cross_workspace(
+                            source_config.memory, target_workspace_id
                         )
+                        new_skills = copy.deepcopy(source_config.skills) if source_config.skills else {}
                     else:
                         new_model_config_id = source_config.default_model_config_id
                         new_knowledge_retrieval = copy.deepcopy(source_config.knowledge_retrieval) if source_config.knowledge_retrieval else None
                         new_tools = copy.deepcopy(source_config.tools) if source_config.tools else []
+                        new_memory = copy.deepcopy(source_config.memory) if source_config.memory else None
+                        new_skills = copy.deepcopy(source_config.skills) if source_config.skills else {}
 
                     new_config = AgentConfig(
                         id=uuid.uuid4(),
@@ -879,9 +878,11 @@ class AppService:
                         default_model_config_id=new_model_config_id,
                         model_parameters=copy.deepcopy(source_config.model_parameters) if source_config.model_parameters else None,
                         knowledge_retrieval=new_knowledge_retrieval,
-                        memory=copy.deepcopy(source_config.memory) if source_config.memory else None,
+                        memory=new_memory,
                         variables=copy.deepcopy(source_config.variables) if source_config.variables else [],
                         tools=new_tools,
+                        skills=new_skills,
+                        features=copy.deepcopy(source_config.features) if source_config.features else {},
                         is_active=True,
                         created_at=now,
                         updated_at=now,
@@ -894,28 +895,14 @@ class AppService:
                 ).first()
 
                 if source_config:
-                    if is_cross_workspace:
-                        model_ids, kb_ids = self._collect_resource_ids_from_workflow_nodes(
-                            source_config.nodes
-                        )
-                        available_model_ids, available_kb_ids = self._preload_cross_workspace_resources(
-                            target_tenant_id, target_workspace_id, model_ids, kb_ids
-                        )
-                        new_nodes = self._clean_workflow_nodes_for_cross_workspace(
-                            source_config.nodes or [],
-                            available_model_ids,
-                            available_kb_ids
-                        )
-                    else:
-                        new_nodes = copy.deepcopy(source_config.nodes) if source_config.nodes else []
-
                     new_config = WorkflowConfig(
                         id=uuid.uuid4(),
                         app_id=new_app.id,
-                        nodes=new_nodes,
+                        nodes=copy.deepcopy(source_config.nodes) if source_config.nodes else [],
                         edges=copy.deepcopy(source_config.edges) if source_config.edges else [],
                         variables=copy.deepcopy(source_config.variables) if source_config.variables else [],
                         execution_config=copy.deepcopy(source_config.execution_config) if source_config.execution_config else {},
+                        features=copy.deepcopy(source_config.features) if source_config.features else {},
                         triggers=copy.deepcopy(source_config.triggers) if source_config.triggers else [],
                         is_active=True,
                         created_at=now,
@@ -929,24 +916,15 @@ class AppService:
                 ).first()
 
                 if source_config:
-                    if is_cross_workspace:
-                        model_ids = {source_config.default_model_config_id} if source_config.default_model_config_id else set()
-                        available_model_ids, _ = self._preload_cross_workspace_resources(
-                            target_tenant_id, target_workspace_id, model_ids, set()
-                        )
-                        new_model_config_id = self._is_model_available(
-                            source_config.default_model_config_id, available_model_ids
-                        )
-                    else:
-                        new_model_config_id = source_config.default_model_config_id
-
+                    # multi_agent 的 model_config_id/sub_agents/routing_rules 均属于 tenant 级别直接保留
+                    # 跨空间时 master_agent_id（AppRelease）属于源空间，需清空
                     new_config = MultiAgentConfig(
                         id=uuid.uuid4(),
                         app_id=new_app.id,
                         master_agent_id=source_config.master_agent_id if not is_cross_workspace else None,
                         master_agent_name=source_config.master_agent_name,
-                        default_model_config_id=new_model_config_id,
-                        model_parameters=source_config.model_parameters,
+                        default_model_config_id=source_config.default_model_config_id,
+                        model_parameters=copy.deepcopy(source_config.model_parameters) if source_config.model_parameters else None,
                         orchestration_mode=source_config.orchestration_mode,
                         sub_agents=copy.deepcopy(source_config.sub_agents) if source_config.sub_agents else [],
                         routing_rules=copy.deepcopy(source_config.routing_rules) if source_config.routing_rules else None,
@@ -1037,8 +1015,7 @@ class AppService:
     @staticmethod
     def _collect_resource_ids_from_config(
             model_config_id: Optional[uuid.UUID],
-            knowledge_retrieval: Optional[dict],
-            tools: Optional[list]
+            knowledge_retrieval: Optional[dict]
     ) -> tuple:
         """Extract all model config IDs and knowledge base IDs from an app config."""
         model_ids: set = set()
@@ -1048,62 +1025,12 @@ class AppService:
             model_ids.add(model_config_id)
 
         if knowledge_retrieval and isinstance(knowledge_retrieval, dict):
-            if "kb_ids" in knowledge_retrieval:
-                for kid in knowledge_retrieval.get("kb_ids", []):
-                    if kid:
-                        kb_ids.add(str(kid))
-            if knowledge_retrieval.get("knowledge_id"):
-                kb_ids.add(str(knowledge_retrieval["knowledge_id"]))
-
-        if tools:
-            for tool in tools:
-                if isinstance(tool, dict):
-                    kid = tool.get("knowledge_id") or tool.get("kb_id")
-                    if kid:
-                        kb_ids.add(str(kid))
+            if "knowledge_bases" in knowledge_retrieval:
+                for kid in knowledge_retrieval.get("knowledge_bases", []):
+                    kb_ids.add(str(kid.get("kb_id")))
 
         return model_ids, kb_ids
 
-    @staticmethod
-    def _collect_resource_ids_from_workflow_nodes(nodes: list) -> tuple:
-        """Extract all model config IDs and knowledge base IDs from workflow nodes."""
-        model_ids: set = set()
-        kb_ids: set = set()
-
-        for node in (nodes or []):
-            if not isinstance(node, dict):
-                continue
-            data = node.get("data", {})
-            if not isinstance(data, dict):
-                continue
-            for key in ("model_config_id", "default_model_config_id"):
-                val = data.get(key)
-                if val:
-                    try:
-                        model_ids.add(uuid.UUID(str(val)))
-                    except (ValueError, AttributeError):
-                        pass
-            kr = data.get("knowledge_retrieval")
-            if isinstance(kr, dict):
-                for kid in kr.get("kb_ids", []):
-                    if kid:
-                        kb_ids.add(str(kid))
-                if kr.get("knowledge_id"):
-                    kb_ids.add(str(kr["knowledge_id"]))
-            if data.get("knowledge_id"):
-                kb_ids.add(str(data["knowledge_id"]))
-            for kid in data.get("kb_ids", []):
-                if kid:
-                    kb_ids.add(str(kid))
-
-        return model_ids, kb_ids
-
-    @staticmethod
-    def _is_model_available(model_config_id: Optional[uuid.UUID], available_model_ids: set) -> Optional[uuid.UUID]:
-        if not model_config_id:
-            return None
-        return model_config_id if model_config_id in available_model_ids else None
-
     @staticmethod
     def _is_kb_available(kb_id: Optional[str], available_kb_ids: set) -> Optional[str]:
         if not kb_id:
@@ -1124,95 +1051,53 @@ class AppService:
 
         cleaned = copy.deepcopy(knowledge_retrieval)
 
-        if "kb_ids" in cleaned and isinstance(cleaned["kb_ids"], list):
-            cleaned["kb_ids"] = [
-                kid for kid in cleaned["kb_ids"]
-                if self._is_kb_available(kid, available_kb_ids)
+        if "knowledge_bases" in cleaned and isinstance(cleaned["knowledge_bases"], list):
+            cleaned["knowledge_bases"] = [
+                kb for kb in cleaned["knowledge_bases"]
+                if self._is_kb_available(kb.get("kb_id"), available_kb_ids)
             ]
 
-        if "knowledge_id" in cleaned:
-            cleaned["knowledge_id"] = self._is_kb_available(
-                cleaned.get("knowledge_id"), available_kb_ids
-            )
-
         return cleaned
 
-    def _clean_tools(
+    def _clean_memory_cross_workspace(
             self,
-            tools: Optional[list],
-            available_kb_ids: set
-    ) -> list:
-        """Clean tools config, keeping built-in tools and tools with available KBs."""
-        if not tools:
-            return []
+            memory: Optional[dict],
+            target_workspace_id: uuid.UUID
+    ) -> Optional[dict]:
+        """Clear memory_config_id/memory_content if it doesn't belong to target workspace."""
+        if not memory:
+            return None
 
-        cleaned = []
-        for tool in tools:
-            if not isinstance(tool, dict):
-                cleaned.append(tool)
-                continue
+        from app.models.memory_config_model import MemoryConfig
 
-            tool_type = tool.get("type", "")
-            if tool_type in ("builtin", "built_in", "system"):
-                cleaned.append(copy.deepcopy(tool))
-                continue
+        cleaned = copy.deepcopy(memory)
+        # 兼容旧字段 memory_content 和新字段 memory_config_id
+        mid = cleaned.get("memory_config_id") or cleaned.get("memory_content")
+        if mid:
+            try:
+                mid_uuid = uuid.UUID(str(mid))
+            except (ValueError, AttributeError):
+                exists = self.db.query(MemoryConfig).filter(
+                    MemoryConfig.config_id_old == int(mid),
+                    MemoryConfig.workspace_id == target_workspace_id
+                ).first()
+                if not exists:
+                    cleaned["memory_config_id"] = None
+                    cleaned.pop("memory_content", None)
+                    cleaned["enabled"] = False
+                return cleaned
 
-            kb_id = tool.get("knowledge_id") or tool.get("kb_id")
-            if kb_id:
-                if self._is_kb_available(kb_id, available_kb_ids):
-                    cleaned.append(copy.deepcopy(tool))
-                continue
+            exists = self.db.query(
+                self.db.query(MemoryConfig).filter(
+                    MemoryConfig.config_id == mid_uuid,
+                    MemoryConfig.workspace_id == target_workspace_id
+                ).exists()
+            ).scalar()
+            if not exists:
+                cleaned["memory_config_id"] = None
+                cleaned.pop("memory_content", None)
+                cleaned["enabled"] = False
 
-            cleaned.append(copy.deepcopy(tool))
-
-        return cleaned
-
-    def _clean_workflow_nodes_for_cross_workspace(
-            self,
-            nodes: list,
-            available_model_ids: set,
-            available_kb_ids: set
-    ) -> list:
-        """Clean workflow nodes, using pre-loaded resource sets. Uses deepcopy to avoid mutating source."""
-        if not nodes:
-            return []
-
-        cleaned = []
-        for node in nodes:
-            if not isinstance(node, dict):
-                cleaned.append(node)
-                continue
-
-            node_copy = copy.deepcopy(node)
-            data = node_copy.get("data")
-            if not isinstance(data, dict):
-                cleaned.append(node_copy)
-                continue
-
-            for key in ("model_config_id", "default_model_config_id"):
-                if key in data and data[key]:
-                    try:
-                        mid = uuid.UUID(str(data[key]))
-                    except (ValueError, AttributeError):
-                        data[key] = None
-                        continue
-                    data[key] = str(mid) if mid in available_model_ids else None
-
-            if "knowledge_retrieval" in data and data["knowledge_retrieval"]:
-                data["knowledge_retrieval"] = self._clean_knowledge_retrieval(
-                    data["knowledge_retrieval"], available_kb_ids
-                )
-            if "knowledge_id" in data:
-                data["knowledge_id"] = self._is_kb_available(
-                    data.get("knowledge_id"), available_kb_ids
-                )
-            if "kb_ids" in data and isinstance(data["kb_ids"], list):
-                data["kb_ids"] = [
-                    kid for kid in data["kb_ids"]
-                    if self._is_kb_available(kid, available_kb_ids)
-                ]
-
-            cleaned.append(node_copy)
         return cleaned
 
     def list_apps(

api/app/services/conversation_service.py

@@ -21,6 +21,7 @@ from app.models.conversation_model import ConversationDetail
 from app.models.prompt_optimizer_model import RoleType
 from app.repositories.conversation_repository import ConversationRepository, MessageRepository
 from app.schemas.conversation_schema import ConversationOut
+from app.schemas.model_schema import ModelInfo
 from app.services import workspace_service
 from app.services.model_service import ModelConfigService
 
@@ -119,25 +120,27 @@ class ConversationService:
 
     def get_user_conversations(
             self,
-            user_id: uuid.UUID
-    ) -> list[Conversation]:
+            user_id: uuid.UUID,
+            page: int = 1,
+            page_size: int = 20
+    ) -> tuple[list[Conversation], int]:
         """
-        Retrieve recent conversations for a specific user
-
-        This method delegates persistence logic to the repository layer and
-        applies service-level defaults (e.g. recent conversation limit).
+        Retrieve recent conversations for a specific user with pagination.
 
         Args:
             user_id (uuid.UUID): Unique identifier of the user.
+            page (int): Page number (1-based). Defaults to 1.
+            page_size (int): Number of items per page. Defaults to 20.
 
         Returns:
-            list[Conversation]: A list of recent conversation entities.
+            tuple[list[Conversation], int]: A list of recent conversation entities and total count.
         """
-        conversations = self.conversation_repo.get_conversation_by_user_id(
+        conversations, total = self.conversation_repo.get_conversation_by_user_id(
             user_id,
-            limit=10
+            page=page,
+            page_size=page_size
         )
-        return conversations
+        return conversations, total
 
     def list_conversations(
             self,
@@ -267,10 +270,12 @@ class ConversationService:
 
         return messages
 
-    def get_conversation_history(
+    async def get_conversation_history(
             self,
             conversation_id: uuid.UUID,
-            max_history: Optional[int] = None
+            max_history: Optional[int] = None,
+            current_provider: Optional[str] = None,
+            current_is_omni: Optional[bool] = None
     ) -> List[dict]:
         """
         Retrieve historical conversation messages formatted as dictionaries.
@@ -278,6 +283,8 @@ class ConversationService:
         Args:
             conversation_id (uuid.UUID): Conversation UUID.
             max_history (Optional[int]): Maximum number of messages to retrieve.
+            current_provider (Optional[str]): Current provider for file handling.
+            current_is_omni (Optional[bool]): Current omni flag for file handling.
 
         Returns:
             List[dict]: List of message dictionaries with keys 'role' and 'content'.
@@ -287,14 +294,30 @@ class ConversationService:
             limit=max_history
         )
 
-        # 转换为字典格式
-        history = [
-            {
+        history = []
+        for msg in messages:
+            msg_dict = {
                 "role": msg.role,
-                "content": msg.content
+                "content": [{"type": "text", "text": msg.content}]
             }
-            for msg in messages
-        ]
+
+            # 处理用户消息中的多模态文件
+            if msg.role == "user" and msg.meta_data:
+                history_files = msg.meta_data.get("history_files", {})
+
+                if history_files and current_provider and current_is_omni is not None:
+                    # 检查是否需要重新处理文件
+                    stored_provider = history_files.get("provider")
+                    stored_is_omni = history_files.get("is_omni")
+
+                    # 如果provider或is_omni不匹配，需要重新处理
+                    if stored_provider != current_provider or stored_is_omni != current_is_omni:
+                        continue
+
+                    # provider和is_omni匹配，直接使用存储的内容
+                    msg_dict["content"].extend(history_files.get("content"))
+
+            history.append(msg_dict)
 
         return history
 
@@ -510,6 +533,7 @@ class ConversationService:
         provider = api_config.provider
         api_key = api_config.api_key
         api_base = api_config.api_base
+        is_omni = api_config.is_omni
         model_type = config.type
 
         llm = RedBearLLM(
@@ -517,14 +541,17 @@ class ConversationService:
                 model_name=model_name,
                 provider=provider,
                 api_key=api_key,
-                base_url=api_base
+                base_url=api_base,
+                is_omni=is_omni
             ),
             type=ModelType(model_type)
         )
 
-        conversation_messages = self.get_conversation_history(
+        conversation_messages = await self.get_conversation_history(
             conversation_id=conversation_id,
-            max_history=20
+            max_history=20,
+            current_provider=provider,
+            current_is_omni=is_omni
         )
         if len(conversation_messages) == 0:
             return ConversationOut(

api/app/services/draft_run_service.py

@@ -579,25 +579,28 @@ class AgentRunService:
                 user_id=user_id
             )
 
+            model_info = ModelInfo(
+                model_name=api_key_config["model_name"],
+                provider=api_key_config["provider"],
+                api_key=api_key_config["api_key"],
+                api_base=api_key_config["api_base"],
+                capability=api_key_config["capability"],
+                is_omni=api_key_config["is_omni"],
+                model_type=model_config.type
+            )
+
             # 6. 加载历史消息
             history = await self._load_conversation_history(
                 conversation_id=conversation_id,
-                max_history=10
+                max_history=10,
+                current_provider=api_key_config.get("provider"),
+                current_is_omni=api_key_config.get("is_omni", False)
             )
 
             # 6. 处理多模态文件
             processed_files = None
             if files:
                 # 获取 provider 信息
-                model_info = ModelInfo(
-                    model_name=api_key_config["model_name"],
-                    provider=api_key_config["provider"],
-                    api_key=api_key_config["api_key"],
-                    api_base=api_key_config["api_base"],
-                    capability=api_key_config["capability"],
-                    is_omni=api_key_config["is_omni"],
-                    model_type=ModelType.LLM
-                )
                 provider = api_key_config.get("provider", "openai")
                 multimodal_service = MultimodalService(self.db, model_info)
                 processed_files = await multimodal_service.process_files(files)
@@ -659,7 +662,10 @@ class AgentRunService:
                         })
                     },
                     files=files,
-                    audio_url=audio_url
+                    processed_files=processed_files,
+                    audio_url=audio_url,
+                    provider=api_key_config.get("provider"),
+                    is_omni=api_key_config.get("is_omni", False)
                 )
 
             response = {
@@ -676,6 +682,7 @@ class AgentRunService:
                 ) if not sub_agent else [],
                 "citations": self._filter_citations(features_config, result.get("citations", [])),
                 "audio_url": audio_url,
+                "audio_status": "pending"
             }
 
             logger.info(
@@ -815,25 +822,28 @@ class AgentRunService:
                 sub_agent=sub_agent
             )
 
+            model_info = ModelInfo(
+                model_name=api_key_config["model_name"],
+                provider=api_key_config["provider"],
+                api_key=api_key_config["api_key"],
+                api_base=api_key_config["api_base"],
+                capability=api_key_config["capability"],
+                is_omni=api_key_config["is_omni"],
+                model_type=model_config.type
+            )
+
             # 6. 加载历史消息
             history = await self._load_conversation_history(
                 conversation_id=conversation_id,
-                max_history=memory_config.get("max_history", 10)
+                max_history=memory_config.get("max_history", 10),
+                current_provider=api_key_config.get("provider"),
+                current_is_omni=api_key_config.get("is_omni", False)
             )
 
             # 6. 处理多模态文件
             processed_files = None
             if files:
                 # 获取 provider 信息
-                model_info = ModelInfo(
-                    model_name=api_key_config["model_name"],
-                    provider=api_key_config["provider"],
-                    api_key=api_key_config["api_key"],
-                    api_base=api_key_config["api_base"],
-                    capability=api_key_config["capability"],
-                    is_omni=api_key_config["is_omni"],
-                    model_type=ModelType.LLM
-                )
                 provider = api_key_config.get("provider", "openai")
                 multimodal_service = MultimodalService(self.db, model_info)
                 processed_files = await multimodal_service.process_files(files)
@@ -905,10 +915,13 @@ class AgentRunService:
                         "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens}
                     },
                     files=files,
-                    audio_url=stream_audio_url
+                    processed_files=processed_files,
+                    audio_url=stream_audio_url,
+                    provider=api_key_config.get("provider"),
+                    is_omni=api_key_config.get("is_omni", False)
                 )
 
-            # 12. 发送结束事件（包含 suggested_questions 和 tts）
+            # 12. 发送结束事件（包含 suggested_questions、audio_url 和 audio_status）
             end_data: Dict[str, Any] = {
                 "conversation_id": conversation_id,
                 "elapsed_time": elapsed_time,
@@ -919,6 +932,17 @@ class AgentRunService:
                     features_config, full_content, api_key_config, effective_params
                 )
                 end_data["audio_url"] = stream_audio_url
+                # 检查TTS是否已完成（非阻塞，不取消任务）
+                audio_status = "pending"
+                if tts_task is not None and tts_task.done():
+                    # 任务已完成，检查是否有异常
+                    try:
+                        tts_task.result()
+                        audio_status = "completed"
+                    except Exception as e:
+                        logger.warning(f"TTS任务异常: {e}")
+                        audio_status = "failed"
+                end_data["audio_status"] = audio_status if stream_audio_url else None
                 end_data["citations"] = self._filter_citations(features_config, [])
             yield self._format_sse_event("end", end_data)
 
@@ -1115,13 +1139,17 @@ class AgentRunService:
     async def _load_conversation_history(
             self,
             conversation_id: str,
-            max_history: int = 10
+            max_history: int = 10,
+            current_provider: Optional[str] = None,
+            current_is_omni: Optional[bool] = None
     ) -> List[Dict[str, str]]:
-        """加载会话历史消息
+        """加载会话历史消息，并根据当前模型配置处理多模态文件
 
         Args:
             conversation_id: 会话ID
             max_history: 最大历史消息数量
+            current_provider: 当前模型的provider
+            current_is_omni: 当前模型的is_omni
 
         Returns:
             List[Dict]: 历史消息列表
@@ -1129,9 +1157,12 @@ class AgentRunService:
         try:
 
             conversation_service = ConversationService(self.db)
-            history = conversation_service.get_conversation_history(
+            # 获取 API 配置用于多模态处理
+            history = await conversation_service.get_conversation_history(
                 conversation_id=uuid.UUID(conversation_id),
-                max_history=max_history
+                max_history=max_history,
+                current_provider=current_provider,
+                current_is_omni=current_is_omni
             )
 
             logger.debug(
@@ -1159,7 +1190,10 @@ class AgentRunService:
             app_id: Optional[uuid.UUID] = None,
             user_id: Optional[str] = None,
             files: Optional[List[FileInput]] = None,
-            audio_url: Optional[str] = None
+            processed_files: Optional[List[Dict[str, Any]]] = None,
+            audio_url: Optional[str] = None,
+            provider: Optional[str] = None,
+            is_omni: Optional[bool] = None
     ) -> None:
         """保存会话消息（会话已通过 _ensure_conversation 确保存在）
 
@@ -1170,6 +1204,11 @@ class AgentRunService:
             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
+            files: 原始文件输入
+            processed_files: 处理后的文件
+            audio_url: 音频URL
+            provider: 模型供应商
+            is_omni: 是否为全模态模型
         """
         try:
             from app.services.conversation_service import ConversationService
@@ -1179,15 +1218,24 @@ class AgentRunService:
 
             # 保存消息（会话已经存在）
             human_meta = {
-                "files": []
+                "files": [],
+                "history_files": {}
             }
             if files:
                 for f in files:
-                    # url = await MultimodalService(self.db).get_file_url(f)
                     human_meta["files"].append({
                         "type": f.type,
                         "url": f.url
                     })
+
+            # 保存 history_files，包含 provider 和 is_omni 信息
+            if processed_files:
+                human_meta["history_files"] = {
+                    "content": processed_files,
+                    "provider": provider,
+                    "is_omni": is_omni
+                }
+
             # 保存用户消息
             conversation_service.add_message(
                 conversation_id=conv_uuid,
@@ -1413,8 +1461,9 @@ class AgentRunService:
             workspace_id: Optional[uuid.UUID] = None,
     ) -> tuple[Optional[str], Optional[asyncio.Task]]:
         """文本流式输入并行合成音频。
-        返回 (audio_url, task)，audio_url 立即可用，task 完成后文件内容就绪。
+        返回 (audio_url, task)，audio_url 立即可用（pending状态），task 完成后文件内容就绪。
         调用方向 text_queue put 文本 chunk，结束时 put None。
+        前端可通过 GET /storage/files/{file_id}/status 轮询检查音频是否就绪。
         """
         tts_config = features_config.get("text_to_speech", {})
         if not isinstance(tts_config, dict) or not tts_config.get("enabled"):
@@ -1801,6 +1850,7 @@ class AgentRunService:
                     ),
                     "cost_estimate": self._estimate_cost(usage, model_info["model_config"]),
                     "audio_url": result.get("audio_url"),
+                    "audio_status": result.get("audio_status"),
                     "citations": result.get("citations", []),
                     "suggested_questions": result.get("suggested_questions", []),
                     "error": None
@@ -1878,6 +1928,7 @@ class AgentRunService:
             "results": [{
                 **r,
                 "audio_url": r.get("audio_url"),
+                "audio_status": r.get("audio_status"),
                 "citations": r.get("citations", []),
                 "suggested_questions": r.get("suggested_questions", []),
             } for r in results],
@@ -2009,6 +2060,7 @@ class AgentRunService:
                 full_content = ""
                 returned_conversation_id = model_conversation_id
                 audio_url = None
+                audio_status = None
                 citations = []
                 suggested_questions = []
 
@@ -2067,6 +2119,7 @@ class AgentRunService:
                             # 从 end 事件中提取 features 输出字段
                             if event_type == "end" and event_data:
                                 audio_url = event_data.get("audio_url")
+                                audio_status = event_data.get("audio_status")
                                 citations = event_data.get("citations", [])
                                 suggested_questions = event_data.get("suggested_questions", [])
 
@@ -2096,6 +2149,7 @@ class AgentRunService:
                     "message": full_content,
                     "elapsed_time": elapsed,
                     "audio_url": audio_url,
+                    "audio_status": audio_status,
                     "citations": citations,
                     "suggested_questions": suggested_questions,
                     "error": None
@@ -2110,6 +2164,7 @@ class AgentRunService:
                     "elapsed_time": elapsed,
                     "message_length": len(full_content),
                     "audio_url": audio_url,
+                    "audio_status": audio_status,
                     "citations": citations,
                     "suggested_questions": suggested_questions,
                     "timestamp": time.time()
@@ -2246,6 +2301,7 @@ class AgentRunService:
                 "message": r.get("message"),
                 "elapsed_time": r.get("elapsed_time", 0),
                 "audio_url": r.get("audio_url"),
+                "audio_status": r.get("audio_status"),
                 "citations": r.get("citations", []),
                 "suggested_questions": r.get("suggested_questions", []),
                 "error": r.get("error")

api/app/services/memory_forget_service.py

@@ -619,7 +619,7 @@ class MemoryForgetService:
                     recent_trends.append({
                         'date': date_str,
                         'merged_count': record.merged_count,
-                        'average_activation': record.average_activation_value,
+                        'average_activation': round(record.average_activation_value, 2) if record.average_activation_value is not None else None,
                         'total_nodes': record.total_nodes,
                         'execution_time': int(record.execution_time.timestamp() * 1000)
                     })

api/app/services/multimodal_service.py

@@ -12,10 +12,12 @@ import base64
 import csv
 import io
 import json
+import zipfile
 from abc import ABC, abstractmethod
 from typing import List, Dict, Any, Optional
 
 import PyPDF2
+import chardet
 import httpx
 import magic
 import openpyxl
@@ -39,12 +41,10 @@ PDF_MIME = ['application/pdf']
 DOC_MIME = [
     'application/msword',
     'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
-    'application/zip'
 ]
 XLSX_MIME = [
     'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
     'application/vnd.ms-excel',
-    'application/zip'
 ]
 CSV_MIME = ['text/csv', 'application/csv']
 JSON_MIME = ['application/json']
@@ -402,6 +402,71 @@ class MultimodalService:
         logger.info(f"成功处理 {len(result)}/{len(files)} 个文件，provider={self.provider}")
         return result
 
+    async def history_process_files(
+            self,
+            files: Optional[List[FileInput]],
+    ) -> List[Dict[str, Any]]:
+        """
+        处理文件列表，返回 LLM 可用的格式
+
+        Args:
+            files: 文件输入列表
+
+        Returns:
+            List[Dict]: LLM 可用的内容格式列表（根据 provider 返回不同格式）
+        """
+        if not files:
+            return []
+
+        # 获取对应的策略
+        # dashscope 的 omni 模型使用 OpenAI 兼容格式
+        if self.provider == "dashscope" and self.is_omni:
+            strategy_class = OpenAIFormatStrategy
+        else:
+            strategy_class = PROVIDER_STRATEGIES.get(self.provider)
+            if not strategy_class:
+                logger.warning(f"未找到 provider '{self.provider}' 的策略，使用默认策略")
+                strategy_class = DashScopeFormatStrategy
+
+        result = []
+        for idx, file in enumerate(files):
+            strategy = strategy_class(file)
+            if not file.url:
+                file.url = await self.get_file_url(file)
+            try:
+                if file.type == FileType.IMAGE and "vision" in self.capability:
+                    is_support, content = await self._process_image(file, strategy)
+                    result.append(content)
+                elif file.type == FileType.DOCUMENT:
+                    is_support, content = await self._process_document(file, strategy)
+                    result.append(content)
+                elif file.type == FileType.AUDIO and "audio" in self.capability:
+                    is_support, content = await self._process_audio(file, strategy)
+                    result.append(content)
+                elif file.type == FileType.VIDEO and "video" in self.capability:
+                    is_support, content = await self._process_video(file, strategy)
+                    result.append(content)
+                else:
+                    logger.warning(f"不支持的文件类型: {file.type}")
+            except Exception as e:
+                logger.error(
+                    f"处理文件失败",
+                    extra={
+                        "file_index": idx,
+                        "file_type": file.type,
+                        "error": str(e)
+                    },
+                    exc_info=True
+                )
+                # 继续处理其他文件，不中断整个流程
+                result.append({
+                    "type": "text",
+                    "text": f"[文件处理失败: {str(e)}]"
+                })
+
+        logger.info(f"成功处理 {len(result)}/{len(files)} 个文件，provider={self.provider}")
+        return result
+
     async def _process_image(self, file: FileInput, strategy) -> tuple[bool, Dict[str, Any]]:
         """
         处理图片文件
@@ -561,12 +626,12 @@ class MultimodalService:
                     file.set_content(file_content)
             file_mime_type = magic.from_buffer(file_content, mime=True)
             if file_mime_type in TEXT_MIME:
-                return file_content.decode("utf-8")
+                return self._decode_text_safe(file_content)
             elif file_mime_type in PDF_MIME:
                 return await self._extract_pdf_text(file_content)
-            elif file_mime_type in DOC_MIME and file.file_type.endswith(('docx', 'doc')):
+            elif self._is_word_file(file_content, file_mime_type):
                 return await self._extract_word_text(file_content)
-            elif file_mime_type in XLSX_MIME and file.file_type.endswith(("xlsx", "xls")):
+            elif self._is_excel_file(file_content, file_mime_type):
                 return await self._extract_xlsx_text(file_content)
             elif file_mime_type in CSV_MIME:
                 return await self._extract_csv_text(file_content)
@@ -595,52 +660,156 @@ class MultimodalService:
 
     @staticmethod
     async def _extract_word_text(file_content: bytes) -> str:
-        """提取 Word 文档文本"""
+        """提取 Word 文档文本（支持 .docx 和旧版 .doc）"""
+        # 先尝试 docx（ZIP 格式）
+        if file_content[:2] == b'PK':
+            try:
+                word_file = io.BytesIO(file_content)
+                doc = Document(word_file)
+                return '\n'.join(p.text for p in doc.paragraphs)
+            except Exception as e:
+                logger.error(f"提取 docx 文本失败: {e}")
+                return f"[docx 提取失败: {str(e)}]"
+
+        # 旧版 .doc（OLE2 格式）
         try:
-            word_file = io.BytesIO(file_content)
-            doc = Document(word_file)
-            text_parts = [paragraph.text for paragraph in doc.paragraphs]
-            return '\n'.join(text_parts)
+            import olefile
+            ole = olefile.OleFileIO(io.BytesIO(file_content))
+            if not ole.exists('WordDocument'):
+                return "[doc 提取失败: 未找到 WordDocument 流]"
+            # 读取 WordDocument 流，提取可见 ASCII/Unicode 文本
+            stream = ole.openstream('WordDocument').read()
+            # Word Binary Format: 文本在流中以 UTF-16-LE 编码存储
+            # 简单提取：过滤出可打印字符段
+            try:
+                text = stream.decode('utf-16-le', errors='ignore')
+            except Exception:
+                text = stream.decode('latin-1', errors='ignore')
+            # 过滤控制字符，保留可打印内容
+            import re
+            text = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f\x7f]', '', text)
+            text = re.sub(r' +', ' ', text).strip()
+            ole.close()
+            return text
         except Exception as e:
-            logger.error(f"提取 Word 文本失败: {e}")
-            return f"[Word 提取失败: {str(e)}]"
+            logger.error(f"提取 doc 文本失败: {e}")
+            return f"[doc 提取失败: {str(e)}]"
 
     @staticmethod
     async def _extract_xlsx_text(file_content: bytes) -> str:
-        """提取 Excel 文本"""
+        """提取 Excel 文本（支持 .xlsx 和旧版 .xls）"""
+        # xlsx（ZIP 格式）
+        if file_content[:2] == b'PK':
+            try:
+                wb = openpyxl.load_workbook(io.BytesIO(file_content), read_only=True, data_only=True)
+                parts = []
+                for sheet in wb.worksheets:
+                    parts.append(f"[Sheet: {sheet.title}]")
+                    for row in sheet.iter_rows(values_only=True):
+                        parts.append('\t'.join('' if v is None else str(v) for v in row))
+                return '\n'.join(parts)
+            except Exception as e:
+                logger.error(f"提取 xlsx 文本失败: {e}")
+                return f"[xlsx 提取失败: {str(e)}]"
+
+        # xls（OLE2/BIFF 格式）
         try:
-            wb = openpyxl.load_workbook(io.BytesIO(file_content), read_only=True, data_only=True)
+            import xlrd
+            wb = xlrd.open_workbook(file_contents=file_content)
             parts = []
-            for sheet in wb.worksheets:
-                parts.append(f"[Sheet: {sheet.title}]")
-                for row in sheet.iter_rows(values_only=True):
-                    parts.append('\t'.join('' if v is None else str(v) for v in row))
+            for sheet in wb.sheets():
+                parts.append(f"[Sheet: {sheet.name}]")
+                for row_idx in range(sheet.nrows):
+                    parts.append('\t'.join(str(sheet.cell_value(row_idx, col)) for col in range(sheet.ncols)))
             return '\n'.join(parts)
         except Exception as e:
-            logger.error(f"提取 Excel 文本失败: {e}")
-            return f"[Excel 提取失败: {str(e)}]"
+            logger.error(f"提取 xls 文本失败: {e}")
+            return f"[xls 提取失败: {str(e)}]"
 
-    @staticmethod
-    async def _extract_csv_text(file_content: bytes) -> str:
+    async def _extract_csv_text(self, file_content: bytes) -> str:
         """提取 CSV 文本"""
         try:
-            text = file_content.decode('utf-8-sig')
+            text = self._decode_text_safe(file_content)
             reader = csv.reader(io.StringIO(text))
             return '\n'.join('\t'.join(row) for row in reader)
         except Exception as e:
             logger.error(f"提取 CSV 文本失败: {e}")
             return f"[CSV 提取失败: {str(e)}]"
 
-    @staticmethod
-    async def _extract_json_text(file_content: bytes) -> str:
+    async def _extract_json_text(self, file_content: bytes) -> str:
         """提取 JSON 文本"""
         try:
-            data = json.loads(file_content.decode('utf-8'))
+            text = self._decode_text_safe(file_content)
+            data = json.loads(text)
             return json.dumps(data, ensure_ascii=False, indent=2)
         except Exception as e:
             logger.error(f"提取 JSON 文本失败: {e}")
             return f"[JSON 提取失败: {str(e)}]"
 
+    def _is_word_file(self, file_content: bytes, mime_type: str) -> bool:
+        """判断是不是 Word 文件（doc / docx），不依赖后缀"""
+        # 旧版 .doc
+        if mime_type == 'application/msword':
+            return True
+
+        # 新版 .docx（ZIP 内部包含 word/document.xml）
+        header = file_content[:4]
+        if header == b'PK\x03\x04':
+            try:
+                with zipfile.ZipFile(io.BytesIO(file_content)) as zf:
+                    return "word/document.xml" in zf.namelist()
+            except:
+                pass
+
+        return False
+
+    def _is_excel_file(self, file_content: bytes, mime_type: str) -> bool:
+        """判断是不是 Excel 文件（xls / xlsx），不依赖后缀"""
+        # 旧版 .xls
+        if mime_type == 'application/vnd.ms-excel':
+            return True
+
+        # 新版 .xlsx（ZIP 内部包含 xl/workbook.xml）
+        header = file_content[:4]
+        if header == b'PK\x03\x04':
+            try:
+                with zipfile.ZipFile(io.BytesIO(file_content)) as zf:
+                    return "xl/workbook.xml" in zf.namelist()
+            except:
+                pass
+
+        return False
+
+    @staticmethod
+    def _decode_text_safe(file_content: bytes) -> str:
+        """
+        【万能文本解码】
+        自动检测编码，支持 utf-8 / gbk / gb2312 / utf-8-sig / ascii 等
+        永远不报错，永远不乱码
+        """
+        if not file_content:
+            return ""
+
+        # 1. 自动检测文件编码
+        detect = chardet.detect(file_content)
+        encoding = detect.get("encoding") or "utf-8"
+        encoding = encoding.lower()
+
+        # 2. 兼容常见中文编码
+        compatible_encodings = ["utf-8", "gbk", "gb18030", "gb2312", "ascii", "latin-1"]
+
+        # 3. 按优先级尝试解码
+        for enc in [encoding] + compatible_encodings:
+            if not enc:
+                continue
+            try:
+                return file_content.decode(enc.strip())
+            except (UnicodeDecodeError, LookupError):
+                continue
+
+        # 终极兜底
+        return file_content.decode("utf-8", errors="replace")
+
 
 def get_multimodal_service(db: Session) -> MultimodalService:
     """获取多模态服务实例（依赖注入）"""

api/app/services/user_memory_service.py

@@ -1408,12 +1408,11 @@ async def analytics_memory_types(
     if end_user_id:
         try:
             conversation_repo = ConversationRepository(db)
-            conversations = conversation_repo.get_conversation_by_user_id(
+            conversations, total = conversation_repo.get_conversation_by_user_id(
                 user_id=uuid.UUID(end_user_id),
-                limit=100,  # 获取更多会话以准确统计
                 is_activate=True
             )
-            work_count = len(conversations)
+            work_count = total
             logger.debug(f"工作记忆数量（会话数）: {work_count} (end_user_id={end_user_id})")
         except Exception as e:
             logger.warning(f"获取会话数量失败，工作记忆数量设为0: {str(e)}")

api/app/services/user_service.py

@@ -78,18 +78,7 @@ def create_user(db: Session, user: UserCreate) -> User:
     business_logger.info(f"创建用户: {user.username}, email: {user.email}")
     
     try:
-        # 检查用户名是否已存在
-        business_logger.debug(f"检查用户名是否已存在: {user.username}")
-        db_user_by_username = user_repository.get_user_by_username(db, username=user.username)
-        if db_user_by_username:
-            business_logger.warning(f"用户名已存在: {user.username}")
-            raise BusinessException(
-                "用户名已存在", 
-                code=BizCode.DUPLICATE_NAME,
-                context={"username": user.username, "email": user.email}
-            )
-        
-        # 检查邮箱是否已注册
+        # 检查邮箱是否已注册（邮箱保持唯一）
         business_logger.debug(f"检查邮箱是否已注册: {user.email}")
         db_user_by_email = user_repository.get_user_by_email(db, email=user.email)
         if db_user_by_email:
@@ -164,22 +153,7 @@ def create_superuser(db: Session, user: UserCreate, current_user: User) -> User:
         )
     
     try:
-        # 检查用户名是否已存在
-        business_logger.debug(f"检查用户名是否已存在: {user.username}")
-        db_user_by_username = user_repository.get_user_by_username(db, username=user.username)
-        if db_user_by_username:
-            business_logger.warning(f"用户名已存在: {user.username}")
-            raise BusinessException(
-                "用户名已存在", 
-                code=BizCode.DUPLICATE_NAME,
-                context={
-                    "username": user.username,
-                    "email": user.email,
-                    "created_by": str(current_user.id)
-                }
-            )
-        
-        # 检查邮箱是否已注册
+        # 检查邮箱是否已注册（邮箱保持唯一）
         business_logger.debug(f"检查邮箱是否已注册: {user.email}")
         db_user_by_email = user_repository.get_user_by_email(db, email=user.email)
         if db_user_by_email:

api/app/services/workflow_service.py

@@ -57,6 +57,7 @@ class WorkflowService:
             edges: list[dict[str, Any]],
             variables: list[dict[str, Any]] | None = None,
             execution_config: dict[str, Any] | None = None,
+            features: dict[str, Any] | None = None,
             triggers: list[dict[str, Any]] | None = None,
             validate: bool = True
     ) -> WorkflowConfig:
@@ -68,6 +69,7 @@ class WorkflowService:
             edges: 边列表
             variables: 变量列表
             execution_config: 执行配置
+            features: 功能特性
             triggers: 触发器列表
             validate: 是否验证配置
 
@@ -83,6 +85,7 @@ class WorkflowService:
             "edges": edges,
             "variables": variables or [],
             "execution_config": execution_config or {},
+            "features": features or {},
             "triggers": triggers or []
         }
 
@@ -103,6 +106,7 @@ class WorkflowService:
             edges=edges,
             variables=variables,
             execution_config=execution_config,
+            features=features,
             triggers=triggers
         )
 

api/app/tasks.py

@@ -2625,13 +2625,15 @@ def init_interest_distribution_for_users(self, end_user_ids: List[str]) -> Dict[
     time_limit=7200,  # 2小时硬超时
     soft_time_limit=6900,
 )
-def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]:
+def init_community_clustering_for_users(self, end_user_ids: List[str], workspace_id: Optional[str] = None) -> Dict[str, Any]:
     """触发型任务：检查指定用户列表，对有 ExtractedEntity 但无 Community 节点的用户执行全量聚类。
 
     由 /dashboard/end_users 接口触发，已有社区节点的用户直接跳过。
+    任务完成且所有用户数据均完整时，写入 Redis 标记，避免下次重复投递。
 
     Args:
         end_user_ids: 需要检查的用户 ID 列表
+        workspace_id: 工作空间 ID，用于完成标记
 
     Returns:
         包含任务执行结果的字典
@@ -2657,6 +2659,7 @@ def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[s
 
             # 批量预取所有用户的配置（内置兜底：用户配置不可用时自动回退到工作空间默认配置）
             user_llm_map: Dict[str, Optional[str]] = {}
+            user_embedding_map: Dict[str, Optional[str]] = {}
             try:
                 with get_db_context() as db:
                     from app.services.memory_agent_service import get_end_users_connected_configs_batch
@@ -2668,21 +2671,54 @@ def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[s
                             try:
                                 cfg = MemoryConfigService(db).load_memory_config(config_id=config_id)
                                 user_llm_map[uid] = str(cfg.llm_model_id) if cfg.llm_model_id else None
+                                user_embedding_map[uid] = str(cfg.embedding_model_id) if cfg.embedding_model_id else None
                             except Exception as e:
-                                logger.warning(f"[CommunityCluster] 用户 {uid} 加载 LLM 配置失败，将使用 None: {e}")
+                                logger.warning(f"[CommunityCluster] 用户 {uid} 加载配置失败，将使用 None: {e}")
                                 user_llm_map[uid] = None
+                                user_embedding_map[uid] = None
                         else:
                             user_llm_map[uid] = None
+                            user_embedding_map[uid] = None
             except Exception as e:
-                logger.warning(f"[CommunityCluster] 批量获取 LLM 配置失败，所有用户将使用 None: {e}")
+                logger.warning(f"[CommunityCluster] 批量获取配置失败，所有用户将使用 None: {e}")
 
             for end_user_id in end_user_ids:
                 try:
-                    # 已有社区节点则跳过
+                    # 已有社区节点时，检查是否存在属性不完整的节点
                     has_communities = await repo.has_communities(end_user_id)
                     if has_communities:
-                        skipped += 1
-                        logger.debug(f"[CommunityCluster] 用户 {end_user_id} 已有社区节点，跳过")
+                        llm_model_id = user_llm_map.get(end_user_id)
+                        embedding_model_id = user_embedding_map.get(end_user_id)
+                        incomplete_ids = await repo.get_incomplete_communities(
+                            end_user_id, check_embedding=bool(embedding_model_id)
+                        )
+                        if not incomplete_ids:
+                            skipped += 1
+                            logger.debug(f"[CommunityCluster] 用户 {end_user_id} 社区节点均完整，跳过")
+                            continue
+
+                        # 对不完整的社区节点逐一补全元数据
+                        engine = LabelPropagationEngine(
+                            connector=connector,
+                            llm_model_id=llm_model_id,
+                            embedding_model_id=embedding_model_id,
+                        )
+                        logger.info(
+                            f"[CommunityCluster] 用户 {end_user_id} 发现 {len(incomplete_ids)} 个属性不完整的社区，开始补全"
+                        )
+                        patch_ok = 0
+                        patch_fail = 0
+                        for cid in incomplete_ids:
+                            try:
+                                await engine._generate_community_metadata([cid], end_user_id)
+                                patch_ok += 1
+                            except Exception as patch_err:
+                                patch_fail += 1
+                                logger.error(f"[CommunityCluster] 社区 {cid} 元数据补全失败: {patch_err}")
+                        logger.info(
+                            f"[CommunityCluster] 用户 {end_user_id} 社区补全完成: 成功={patch_ok}, 失败={patch_fail}"
+                        )
+                        initialized += 1
                         continue
 
                     # 检查是否有 ExtractedEntity 节点
@@ -2692,11 +2728,13 @@ def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[s
                         logger.debug(f"[CommunityCluster] 用户 {end_user_id} 无实体节点，跳过")
                         continue
 
-                    # 每个用户使用自己的 llm_model_id
+                    # 每个用户使用自己的 llm_model_id / embedding_model_id
                     llm_model_id = user_llm_map.get(end_user_id)
+                    embedding_model_id = user_embedding_map.get(end_user_id)
                     engine = LabelPropagationEngine(
                         connector=connector,
                         llm_model_id=llm_model_id,
+                        embedding_model_id=embedding_model_id,
                     )
 
                     logger.info(

api/app/version_info.json

@@ -1,4 +1,38 @@
 {
+  "v0.2.8": {
+    "introduction": {
+      "codeName": "景玉",
+      "releaseDate": "2026-3-20",
+      "upgradePosition": "🐻 MemoryBear v0.2.8 社区版全面升级应用共享、多模态交互与平台基础设施，引入语音交互、感知记忆和云端存储，打造更强大的开放 AI 记忆平台",
+      "coreUpgrades": [
+        "1. 应用共享与发布<br>* 应用共享（Agent、工作流、Agent 集群）：全类型应用共享至其他空间<br>* 分享应用默认开启记忆功能：发布分享后记忆默认开启，关闭时提醒<br>* 工作流记忆分享规则：按记忆配置自动控制分享页记忆开关<br>* 分享会话联网搜索修复：恢复分享应用的联网搜索能力",
+        "2. 多模态与交互 💬<br>* 语音输入：模型接口和应用支持语音输入<br>* 语音回复：应用支持语音回复模态<br>* 多模态感知记忆：记忆系统支持视觉、音频、图片和文件的感知记忆<br>* 对话框文件展示：试运行和体验分享中正确展示上传文件",
+        "3. 平台与基础设施 ⚙️<br>* i18n 国际化：全面多语言多地区支持<br>* 云端文件存储（OSS + S3）：支持阿里云 OSS 和 S3 云端上传<br>* Flower 容器监控：Celery 异步任务监控与管理",
+        "4. EndUser 身份迁移 🔐<br>* EndUser 从 app_id 迁移至 workspace_id：身份从应用级迁移至工作空间级",
+        "5. 情景记忆 🧠<br>* 情景记忆聚类算法：基于社区图谱的聚类算法，支持老用户图谱生成",
+        "6. 稳健性与缺陷修复 🔧<br>* MCP 服务删除后工具 404：修复删除 MCP 服务后接口报错<br>* 应用导出配置不一致：导出已保存配置而非画布状态<br>* 工作流节点 ID 重复：修复复制节点后 ID 冲突<br>* 条件分支连线错误：修复保存刷新后连线错乱<br>* 回复节点内容丢失：修复点击画布后内容消失<br>* 连接桩规则优化：禁止非法连接方向<br>* 知识库状态列宽度：锁定或自适应宽度<br>* 等待中文档预览：支持未完成解析文档预览<br>* 知识库关联修复：统一修复关联问题<br>* 多模态对话连续性：修复多模态内容后无法继续对话<br>* 时区统一：环境变量统一控制存储和任务时区<br>* 遗忘强度精度：修复小数显示过长",
+        "<br>",
+        "v0.2.8 社区版在应用共享和多模态交互方面实现重大升级，感知记忆扩展了平台的认知维度。后续将深化多智能体协作、情景记忆聚类，并持续优化平台稳定性与开放生态。",
+        "MemoryBear —— 让 AI 拥有记忆 🐻✨"
+      ]
+    },
+    "introduction_en": {
+      "codeName": "JingYu",
+      "releaseDate": "2026-3-20",
+      "upgradePosition": "🐻 MemoryBear v0.2.8 Community delivers multimodal interaction, perceptual memory, cloud storage, and workspace-level identity for a more capable open AI memory platform",
+      "coreUpgrades": [
+        "1. Application Sharing & Publishing<br>* Application Sharing (Agent, Workflow, Agent Cluster): Full sharing across all app types<br>* Memory Enabled by Default: Memory auto-enabled on shared apps with disable reminder<br>* Workflow Memory Sharing Rules: Auto-controlled based on memory configuration<br>* Shared Session Web Search Fix: Restored web search for shared apps",
+        "2. Multimodal & Interaction 💬<br>* Voice Input: Model interfaces and apps support voice input<br>* Voice Reply: Apps support voice reply modality<br>* Multimodal Perceptual Memory: Memory system supports visual, audio, image, and file perception<br>* File Display in Chat: Uploaded files display correctly in dry-run and sharing",
+        "3. Platform & Infrastructure ⚙️<br>* i18n Internationalization: Full multi-language multi-region support<br>* Cloud File Storage (OSS + S3): Alibaba Cloud OSS and S3 cloud uploads<br>* Flower Container Monitoring: Celery async task monitoring and management",
+        "4. EndUser Identity Migration 🔐<br>* EndUser Migration from app_id to workspace_id: Identity migrated to workspace level",
+        "5. Episodic Memory 🧠<br>* Episodic Memory Clustering: Community-graph-based clustering with legacy user support",
+        "6. Robustness & Bug Fixes 🔧<br>* MCP Service Deletion 404: Fixed tool endpoint error after MCP removal<br>* App Export Config Mismatch: Exports saved config instead of canvas state<br>* Workflow Duplicate Node ID: Fixed ID conflict on node duplication<br>* Conditional Branch Wiring: Fixed wiring reset after save/refresh<br>* Reply Node Content Loss: Fixed content disappearing on canvas click<br>* Port Connection Rules: Prohibited invalid connection directions<br>* Knowledge Base Status Width: Locked or adaptive column width<br>* Pending Document Preview: Preview support for unparsed documents<br>* Knowledge Base Association Fixes: Consolidated association fixes<br>* Multimodal Conversation Continuity: Fixed single-round limit after multimodal input<br>* Timezone Unification: Env-var controlled unified timezone<br>* Forgetting Strength Precision: Fixed excessive decimal display",
+        "<br>",
+        "v0.2.8 Community delivers major upgrades in application sharing and multimodal interaction, with perceptual memory expanding the platform's cognitive dimensions. Multi-agent collaboration, episodic clustering, and continued platform stability improvements are ahead.",
+        "MemoryBear — Give AI Memory 🐻✨"
+      ]
+    }
+  },
   "v0.2.7": {
     "introduction": {
       "codeName": "武陵",

api/migrations/versions/05a681a6ca93_202603231611.py

@@ -0,0 +1,32 @@
+"""202603231611
+
+Revision ID: 05a681a6ca93
+Revises: 74b51dfece29
+Create Date: 2026-03-23 16:12:44.110292
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '05a681a6ca93'
+down_revision: Union[str, None] = '74b51dfece29'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index(op.f('ix_users_username'), table_name='users')
+    op.create_index(op.f('ix_users_username'), 'users', ['username'], unique=False)
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index(op.f('ix_users_username'), table_name='users')
+    op.create_index(op.f('ix_users_username'), 'users', ['username'], unique=True)
+    # ### end Alembic commands ###

web/src/api/knowledgeBase.ts

@@ -52,6 +52,10 @@ export const getKnowledgeBaseTypeList = async (): Promise<string[]> => {
   // 如果不是数组，返回空数组
   return [];
 };
+// 获取文件地址
+export const getFileUrl = (fileId: string) => {
+  return `${apiPrefix}/files/${fileId}`;
+};
 // 知识库文档解析类型
 export const getKnowledgeBaseDocumentParseTypeList = async () => {
   const response = await request.get(`${apiPrefix}/knowledges/parsertype`);

web/src/api/memory.ts

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-02-03 14:00:06 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-13 10:48:41
+ * @Last Modified time: 2026-03-19 18:35:10
  */
 import { request } from '@/utils/request'
 import type { AxiosRequestConfig } from 'axios'
@@ -218,8 +218,8 @@ export const getExplicitMemory = (end_user_id: string) => {
 export const getExplicitMemoryDetails = (data: { end_user_id: string, memory_id: string; }) => {
   return request.post(`/memory/explicit-memory/details`, data)
 }
-export const getConversations = (end_user_id: string) => {
-  return request.get(`/memory/work/${end_user_id}/conversations`)
+export const getConversations = (end_user_id: string, page = 1, pagesize = 20) => {
+  return request.get(`/memory/work/${end_user_id}/conversations`, { page, pagesize })
 }
 export const getConversationMessages = (end_user_id: string, conversation_id: string) => {
   return request.get(`/memory/work/${end_user_id}/messages`, { conversation_id })

web/src/components/Chat/ChatContent.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2025-12-10 16:46:17 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 13:38:20
+ * @Last Modified time: 2026-03-19 19:45:40
  */
 import { type FC, useRef, useEffect, useState } from 'react'
 import clsx from 'clsx'
@@ -143,15 +143,20 @@ const ChatContent: FC<ChatContentProps> = ({
                     }
                     return (
                       <div key={file.url || file.uid} className="rb:relative rb:rounded-lg rb:bg-[#F0F3F8] rb:p-1! rb:cursor-pointer" onClick={() => handleDownload(file)}>
-                        {(file.type.includes('doc') || file.type.includes('docx') || file.type.includes('word') || file.type.includes('wordprocessingml.document')) && <div
-                          className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/word.svg')]"
-                        ></div>}
-                        {(file.type.includes('pdf')) && <div
-                          className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/pdf.svg')]"
-                        ></div>}
-                        {(file.type.includes('excel') || file.type.includes('spreadsheetml.sheet') || file.type.includes('csv')) && <div
-                          className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/excel.svg')]"
-                        ></div>}
+                        {(file.type.includes('excel') || file.type.includes('spreadsheetml.sheet') || file.type.includes('csv'))
+                          ? <div
+                            className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/excel.svg')]"
+                          ></div>
+                          :(file.type.includes('pdf'))
+                          ? <div
+                            className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/pdf.svg')]"
+                          ></div>
+                          : (file.type.includes('doc') || file.type.includes('docx') || file.type.includes('word') || file.type.includes('wordprocessingml.document'))
+                          ? <div
+                            className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/word.svg')]"
+                          ></div>
+                          : null
+                        }
                       </div>
                     )
                   })}

web/src/components/Chat/ChatInput.tsx

@@ -2,10 +2,11 @@
  * @Author: ZhaoYing 
  * @Date: 2025-12-10 16:46:14 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 16:05:56
+ * @Last Modified time: 2026-03-19 18:44:51
  */
 import { type FC, useEffect, useMemo } from 'react'
-import { Flex, Input, Form } from 'antd'
+import { Flex, Input, Form, Spin } from 'antd'
+import clsx from 'clsx'
 
 import SendIcon from '@/assets/images/conversation/send.svg'
 import SendDisabledIcon from '@/assets/images/conversation/sendDisabled.svg'
@@ -69,6 +70,8 @@ const ChatInput: FC<ChatInputProps> = ({
     onSend(values.message)
   }
 
+  console.log('previewFileList', previewFileList)
+
   return (
     <div className={`rb:absolute rb:bottom-3 rb:left-0 rb:right-0 rb:w-full ${className}`}>
       <Flex vertical justify="space-between" className="rb:border rb:border-[#DFE4ED] rb:rounded-xl rb:min-h-30">
@@ -76,62 +79,78 @@ const ChatInput: FC<ChatInputProps> = ({
           {previewFileList.map((file) => {
             if (file.type.includes('image')) {
               return (
-                <div key={file.url || file.uid} className="rb:inline-block rb:group rb:relative rb:rounded-lg">
-                  <img src={file.url} alt={file.name} className="rb:size-12! rb:rounded-lg rb:object-cover rb:cursor-pointer" />
-                  <div
-                    className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
-                    onClick={() => handleDelete(file)}
-                  ></div>
-                </div>
+                <Spin key={`${file.url || file.uid}_${file.status}`} spinning={file.status === 'uploading'}>
+                  <div key={file.url || file.uid} className={clsx("rb:inline-block rb:group rb:relative rb:rounded-lg", {
+                    'rb:border rb:border-[#FF5D34]': file.status === 'error'
+                  })}>
+                    <img src={file.url} alt={file.name} className="rb:size-12! rb:rounded-lg rb:object-cover rb:cursor-pointer" />
+                    <div
+                      className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
+                      onClick={() => handleDelete(file)}
+                    ></div>
+                  </div>
+                </Spin>
               )
             }
             if (file.type.includes('video')) {
               return (
-                <div key={file.url || file.uid} className="rb:w-45 rb:h-16 rb:inline-block rb:group rb:relative rb:rounded-lg">
-                  <video src={file.url} controls className="rb:w-45 rb:h-16 rb:rounded-lg rb:object-cover rb:cursor-pointer" />
-                  <div
-                    className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
-                    onClick={() => handleDelete(file)}
-                  ></div>
-                </div>
+                <Spin key={`${file.url || file.uid}_${file.status}`} spinning={file.status === 'uploading'}>
+                  <div key={file.url || file.uid} className={clsx("rb:w-45 rb:h-16 rb:inline-block rb:group rb:relative rb:rounded-lg", {
+                    'rb:border rb:border-[#FF5D34]': file.status === 'error'
+                  })}>
+                    <video src={file.url} controls className="rb:w-45 rb:h-15.5 rb:rounded-lg rb:object-cover rb:cursor-pointer" />
+                    <div
+                      className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
+                      onClick={() => handleDelete(file)}
+                    ></div>
+                  </div>
+                </Spin>
               )
             }
             if (file.type.includes('audio')) {
               return (
-                <div key={file.url || file.uid} className="rb:w-45 rb:h-16 rb:inline-flex rb:items-center rb:group rb:relative rb:rounded-lg rb:bg-[#F0F3F8] rb:py-2 rb:px-2.5 rb:gap-2">
-                  <audio src={file.url} controls className="rb:w-45 rb:h-16" />
+                <Spin key={`${file.url || file.uid}_${file.status}`} spinning={file.status === 'uploading'}>
+                  <div key={file.url || file.uid} className={clsx("rb:w-45 rb:h-16 rb:inline-flex rb:items-center rb:group rb:relative rb:rounded-lg rb:bg-[#F0F3F8] rb:py-2 rb:px-2.5 rb:gap-2", {
+                    'rb:border rb:border-[#FF5D34]': file.status === 'error'
+                  })}>
+                    <audio src={file.url} controls className="rb:w-45 rb:h-15.5" />
+                    <div
+                      className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
+                      onClick={() => handleDelete(file)}
+                    ></div>
+                  </div>
+                </Spin>
+              )
+            }
+            return (
+              <Spin key={`${file.url || file.uid}_${file.status}`} spinning={file.status === 'uploading'}>
+                <div key={file.url || file.uid} className={clsx("rb:w-45 rb:text-[12px] rb:gap-2.5 rb:flex rb:items-center rb:group rb:relative rb:rounded-lg rb:bg-[#F0F3F8] rb:py-2 rb:px-2.5", {
+                  'rb:border rb:border-[#FF5D34]': file.status === 'error'
+                })}>
+                  {file.type.includes('pdf')
+                    ? <div
+                      className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/pdf_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/pdf.svg')]"
+                    ></div>
+                    : (file.type.includes('excel') || file.type.includes('spreadsheetml.sheet') || file.type.includes('csv'))
+                    ? <div
+                      className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/excel_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/excel.svg')]"
+                    ></div>
+                    : (file.type.includes('doc') || file.type.includes('docx') || file.type.includes('word') || file.type.includes('wordprocessingml.document'))
+                    ? <div
+                      className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/word_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/word.svg')]"
+                    ></div>
+                    : null
+                  }
+                  <div className="rb:flex-1 rb:w-32.5">
+                    <div className="rb:leading-4 rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap">{file.name}</div>
+                    <div className="rb:leading-3.5 rb:mt-0.5 rb:text-[#5B6167] rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap">{file.type} · {file.size}</div>
+                  </div>
                   <div
                     className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
                     onClick={() => handleDelete(file)}
                   ></div>
-                </div>
-              )
-            }
-            return (
-              <div key={file.url || file.uid} className="rb:w-45 rb:text-[12px] rb:gap-2.5 rb:flex rb:items-center rb:group rb:relative rb:rounded-lg rb:bg-[#F0F3F8] rb:py-2 rb:px-2.5">
-                {file.type.includes('pdf')
-                  ? <div
-                    className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/pdf_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/pdf.svg')]"
-                  ></div>
-                  : (file.type.includes('excel') || file.type.includes('spreadsheetml.sheet') || file.type.includes('csv'))
-                  ? <div
-                    className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/excel_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/excel.svg')]"
-                  ></div>
-                  : (file.type.includes('doc') || file.type.includes('docx') || file.type.includes('word') || file.type.includes('wordprocessingml.document'))
-                  ? <div
-                    className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/word_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/word.svg')]"
-                  ></div>
-                  : null
-                }
-                <div className="rb:flex-1 rb:w-32.5">
-                  <div className="rb:leading-4 rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap">{file.name}</div>
-                  <div className="rb:leading-3.5 rb:mt-0.5 rb:text-[#5B6167] rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap">{file.type} · {file.size}</div>
-                </div>
-                <div
-                  className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
-                  onClick={() => handleDelete(file)}
-                ></div>
-              </div>
+                  </div>
+              </Spin>
             )
           })}
         </Flex></div>}

web/src/components/Chat/ChatToolbar.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-03-17 14:22:25 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-18 15:55:13
+ * @Last Modified time: 2026-03-19 18:59:37
  */
 // Toolbar component for chat input area, supporting file upload, audio recording, and variable configuration
 import { useRef, forwardRef, useImperativeHandle, type ReactNode, useEffect } from 'react'
@@ -49,6 +49,7 @@ interface FormValues {
   memory?: boolean;
 }
 
+const max_file_count = 1;
 const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
   features,
   extra,
@@ -85,10 +86,18 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
 
   // Append newly uploaded file to the file list when upload is complete
   const fileChange = (file?: any) => {
-    if (file?.status !== 'done') return
-    const files = [...(queryValues?.files || []), file]
-    form.setFieldValue('files', files)
-    onFilesChange?.(files)
+    console.log('file', file)
+    const lastFiles = form.getFieldValue('files') || [];
+    const index = lastFiles.findIndex((item: any) => item.uid === file.uid)
+    if (index > -1) {
+      lastFiles[index] = file
+    } else {
+      lastFiles.push(file)
+    }
+    form.setFieldValue('files', [...lastFiles])
+    onFilesChange?.([...lastFiles])
+
+    console.log('lastFiles', lastFiles)
   }
 
   // Append recorded audio file to the file list and notify parent
@@ -128,8 +137,8 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
       key: 'url',
       label: t('memoryConversation.addRemoteFile'),
       onClick: () => {
-        if ((queryValues?.files?.length || 0) >= file_upload.max_file_count) {
-          messageApi.warning(t('common.fileNumTip', { num: file_upload.max_file_count }))
+        if ((queryValues?.files?.length || 0) >= max_file_count) {
+          messageApi.warning(t('common.fileNumTip', { num: max_file_count }))
           return
         }
         uploadFileListModalRef.current?.handleOpen()
@@ -145,7 +154,7 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
           onChange={fileChange}
           requestConfig={uploadRequestConfig}
           featureConfig={file_upload}
-          disabled={(queryValues?.files?.length || 0) >= file_upload.max_file_count}
+          disabled={(queryValues?.files?.length || 0) >= max_file_count}
         />
       )
     })
@@ -177,7 +186,7 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
         {file_upload?.audio_enabled && file_upload?.allowed_transfer_methods?.includes('local_file') && (
           <Flex align="center">
             <AudioRecorder
-              disabled={(queryValues?.files?.length || 0) >= file_upload.max_file_count}
+              disabled={(queryValues?.files?.length || 0) >= max_file_count}
               action={uploadAction}
               requestConfig={uploadRequestConfig}
               onRecordingComplete={handleRecordingComplete}

web/src/components/DocumentPreview/index.tsx

@@ -4,7 +4,7 @@
  * @Author: yujiangping
  * @Date: 2026-03-16 19:01:12
  * @LastEditors: yujiangping
- * @LastEditTime: 2026-03-18 18:35:53
+ * @LastEditTime: 2026-03-20 12:12:20
  */
 import { useState, useEffect, useRef, useCallback, type FC } from 'react';
 import { Spin, Alert, Button, Table, InputNumber, Image } from 'antd';
@@ -309,23 +309,64 @@ const DocumentPreview: FC<DocumentPreviewProps> = ({
     }
   };
 
+  const [csvTruncated, setCsvTruncated] = useState(false);
+
   const isCsvFile = () => getFileExtension() === '.csv';
 
+  // CSV 预览大小限制：1MB
+  const CSV_PREVIEW_SIZE = 1 * 1024 * 1024;
+  // 最大预览行数
+  const MAX_PREVIEW_ROWS = 500;
+
+  const fetchFileBufferWithLimit = async (url: string, maxBytes?: number): Promise<ArrayBuffer> => {
+    const requestUrl = getRequestUrl(url);
+    const headers: Record<string, string> = {
+      'Authorization': `Bearer ${cookieUtils.get('authToken') || ''}`,
+    };
+    if (maxBytes) {
+      headers['Range'] = `bytes=0-${maxBytes - 1}`;
+    }
+    const response = await fetch(requestUrl, {
+      credentials: 'include',
+      headers,
+    });
+    if (!response.ok && response.status !== 206) {
+      throw new Error(`HTTP ${response.status}: ${response.statusText}`);
+    }
+    return response.arrayBuffer();
+  };
+
   const loadExcelFile = async () => {
     setLoading(true);
     setError(false);
     setErrorMessage('');
+    setCsvTruncated(false);
     try {
-      const arrayBuffer = await fetchFileBuffer(fileUrl);
-
-      // CSV 文件需要处理编码问题（可能是 GBK/GB2312）
+      // CSV 文件需要处理编码问题（可能是 GBK/GB2312），且大文件只取前 1MB
       if (isCsvFile()) {
+        let arrayBuffer: ArrayBuffer;
+        let truncated = false;
+        try {
+          // 先尝试 Range 请求只取前 1MB
+          arrayBuffer = await fetchFileBufferWithLimit(fileUrl, CSV_PREVIEW_SIZE);
+          // 如果返回的数据刚好等于限制大小，说明可能被截断了
+          if (arrayBuffer.byteLength >= CSV_PREVIEW_SIZE) {
+            truncated = true;
+          }
+        } catch {
+          // Range 请求不支持时，全量获取后截断
+          const fullBuffer = await fetchFileBuffer(fileUrl);
+          if (fullBuffer.byteLength > CSV_PREVIEW_SIZE) {
+            arrayBuffer = fullBuffer.slice(0, CSV_PREVIEW_SIZE);
+            truncated = true;
+          } else {
+            arrayBuffer = fullBuffer;
+          }
+        }
+
         let csvText: string;
-        // 先尝试 UTF-8 解码
         const utf8Text = new TextDecoder('utf-8').decode(arrayBuffer);
-        // 检测是否有乱码特征（常见的 GBK 被错误解析为 UTF-8 的替换字符）
         if (utf8Text.includes('\uFFFD') || /[\x80-\xff]/.test(utf8Text.slice(0, 200))) {
-          // 尝试 GBK 解码
           try {
             csvText = new TextDecoder('gbk').decode(arrayBuffer);
           } catch {
@@ -334,19 +375,35 @@ const DocumentPreview: FC<DocumentPreviewProps> = ({
         } else {
           csvText = utf8Text;
         }
+
+        // 如果被截断，去掉最后一行不完整的数据
+        if (truncated) {
+          const lastNewline = csvText.lastIndexOf('\n');
+          if (lastNewline > 0) {
+            csvText = csvText.substring(0, lastNewline);
+          }
+        }
+
         const workbook = XLSX.read(csvText, { type: 'string' });
         const sheets = workbook.SheetNames.map(sheetName => {
           const worksheet = workbook.Sheets[sheetName];
-          const data = XLSX.utils.sheet_to_json(worksheet, { header: 1 }) as any[][];
+          let data = XLSX.utils.sheet_to_json(worksheet, { header: 1 }) as any[][];
+          // 限制最大行数
+          if (data.length > MAX_PREVIEW_ROWS + 1) {
+            data = data.slice(0, MAX_PREVIEW_ROWS + 1); // +1 保留表头
+            truncated = true;
+          }
           return { sheetName, data };
         });
+        setCsvTruncated(truncated);
         setExcelData(sheets);
         setLoading(false);
         return;
       }
 
+      const arrayBuffer = await fetchFileBuffer(fileUrl);
       const workbook = XLSX.read(arrayBuffer, { type: 'array' });
-      const sheets = workbook.SheetNames.map(sheetName => {
+      const sheets = workbook.SheetNames.map((sheetName: string) => {
         const worksheet = workbook.Sheets[sheetName];
         const data = XLSX.utils.sheet_to_json(worksheet, { header: 1 }) as any[][];
         return { sheetName, data };
@@ -522,9 +579,14 @@ const DocumentPreview: FC<DocumentPreviewProps> = ({
         )
       )}
 
-      {/* Excel 预览 */}
+      {/* Excel/CSV 预览 */}
       {isExcelFile() && !error && !loading && (
         <div className="rb:w-full rb:flex-1 rb:overflow-auto rb:bg-white rb:p-4 rb:rounded rb:border rb:border-gray-200">
+          {csvTruncated && (
+            <div className="rb:mb-3 rb:px-3 rb:py-2 rb:bg-yellow-50 rb:border rb:border-yellow-200 rb:rounded rb:text-sm rb:text-yellow-700">
+              文件较大，仅预览前 {MAX_PREVIEW_ROWS} 行数据
+            </div>
+          )}
           {excelData.map((sheet, index) => (
             <div key={index} className="rb:mb-6">
               <h3 className="rb:text-lg rb:font-semibold rb:mb-3">{sheet.sheetName}</h3>
@@ -541,6 +603,7 @@ const DocumentPreview: FC<DocumentPreviewProps> = ({
                   scroll={{ x: 'max-content' }}
                   size="small"
                   bordered
+                  virtual
                 />
               )}
             </div>

web/src/i18n/en.ts

@@ -460,6 +460,7 @@ export const en = {
       nameInvalid: 'Name cannot start or end with a space',
       notAllSpaces: 'Cannot be all spaces',
       view: 'View',
+      callbackUrlInvalid: 'Please enter a valid URL',
     },
     model: {
       searchPlaceholder: 'search model…',

web/src/i18n/zh.ts

@@ -1093,6 +1093,7 @@ export const zh = {
       nameInvalid: '不能是空格开头或结尾',
       notAllSpaces: '不能是纯空格',
       view: '查看',
+      callbackUrlInvalid: '请输入有效的 URL',
     },
     model: {
       searchPlaceholder: '搜索模型…',

web/src/views/ApplicationConfig/TestChat/index.tsx

@@ -183,7 +183,7 @@ const TestChat: FC<TestChatProps> = ({
 
   const handleSend = () => {
     if (loading || !application || !message || !message?.trim()) return
-    const files = toolbarRef.current?.getFiles() || []
+    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
     const variables = toolbarRef.current?.getVariables() || []
     const { isCanSend, params } = buildVariableParams(variables)
     if (!isCanSend) return
@@ -235,7 +235,7 @@ const TestChat: FC<TestChatProps> = ({
 
   const handleWorkflowSend = () => {
     if (loading || !application || !message || !message?.trim()) return
-    const files = toolbarRef.current?.getFiles() || []
+    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
     const variables = toolbarRef.current?.getVariables() || []
     const { isCanSend, params } = buildVariableParams(variables)
     if (!isCanSend) return

web/src/views/ApplicationConfig/components/Chat.tsx

@@ -191,7 +191,7 @@ const Chat: FC<ChatProps> = ({
       .then(() => {
         const message = msg
         if (!message?.trim()) return
-        const files = toolbarRef.current?.getFiles() || []
+        const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
         // Validate required variables before sending
         let isCanSend = true
         const params: Record<string, any> = {}
@@ -352,7 +352,7 @@ const Chat: FC<ChatProps> = ({
       .then(() => {
         const message = msg
         if (!message || message.trim() === '') return
-        const files = toolbarRef.current?.getFiles() || []
+        const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
         addUserMessage(message, files)
         setMessage(undefined)
         toolbarRef.current?.setFiles([])

web/src/views/ApplicationConfig/components/FeaturesConfig/FeaturesConfigModal.tsx

@@ -24,7 +24,7 @@ interface FeaturesConfigModalProps {
   refresh: (value: FeaturesConfigForm) => void;
   source?: Application['type'];
 }
-
+const max_file_count = 1;
 /**
  * Modal for copying applications
  */
@@ -133,7 +133,7 @@ const FeaturesConfigModal = forwardRef<FeaturesConfigModalRef, FeaturesConfigMod
                     </div>
                     <div>
                       <div className="rb:text-[12px] rb:text-[#5B6167] rb:py-1">{t('application.maxCount')}</div>
-                      {fu.max_file_count} {t('application.unix')}
+                      {max_file_count} {t('application.unix')}
                     </div>
                   </Flex>
                   <Button block onClick={handleOpenSettings}>{t('application.setting')}</Button>

web/src/views/ApplicationConfig/components/FeaturesConfig/FileUploadSettingModal.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-03-05 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 15:18:20
+ * @Last Modified time: 2026-03-19 20:19:14
  */
 import { forwardRef, useImperativeHandle, useState } from 'react';
 import { Form, InputNumber, Flex, Switch, Row, Col, Radio } from 'antd';
@@ -82,28 +82,27 @@ const defaultValues: FileUpload = {
     "mp3",
     "wav",
     "m4a",
-    "ogg",
-    "flac"
   ],
   document_enabled: false,
   document_max_size_mb: 100,
   document_allowed_extensions: [
     "pdf",
     "docx",
+    "doc",
     "xlsx",
+    "xls",
     "txt",
     "csv",
-    "json"
+    "json",
+    "md",
   ],
   video_enabled: false,
   video_max_size_mb: 100,
   video_allowed_extensions: [
     "mp4",
     "mov",
-    "avi",
-    "webm"
   ],
-  max_file_count: 5,
+  max_file_count: 1,
   allowed_transfer_methods: 'both'
 }
 
@@ -168,8 +167,8 @@ const FileUploadSettingModal = forwardRef<FileUploadSettingModalRef, FileUploadS
           </Radio.Group>
         </Form.Item>
 
-        <div className="rb:text-[12px] rb:text-[#5B6167] rb:mb-1">{t('application.maxCount')}</div>
-        <Form.Item label={t('application.maxCount')} name="max_file_count">
+        {/* <div className="rb:text-[12px] rb:text-[#5B6167] rb:mb-1">{t('application.maxCount')}</div> */}
+        <Form.Item label={t('application.maxCount')} name="max_file_count" hidden>
           <InputNumber min={1} max={20} precision={0} className="rb:w-full!" placeholder={t('common.pleaseEnter')} />
         </Form.Item>
 

web/src/views/Conversation/components/FileUpload.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-02-06 21:09:42 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-18 20:32:54
+ * @Last Modified time: 2026-03-19 18:38:41
  */
 /**
  * File Upload Component
@@ -23,7 +23,7 @@
 import { useState, useEffect, forwardRef, useImperativeHandle, useMemo } from 'react';
 import { Upload, Progress, App } from 'antd';
 import type { UploadProps, UploadFile } from 'antd';
-import type { UploadProps as RcUploadProps } from 'antd/es/upload/interface';
+import type { UploadProps as RcUploadProps, RcFile, UploadFileStatus } from 'antd/es/upload/interface';
 import { useTranslation } from 'react-i18next';
 
 import { request } from '@/utils/request'
@@ -221,17 +221,29 @@ const UploadFiles = forwardRef<UploadFilesRef, UploadFilesProps>(({
    */
   const handleCustomRequest: RcUploadProps['customRequest'] = async (options) => {
     const { file, onSuccess, onError } = options;
-    
-    try {
-      const formData = new FormData();
-      formData.append('file', file);
-      
-      const response = await request.uploadFile(action, formData, requestConfig);
-
-      onSuccess?.({data: response});
-    } catch (error) {
-      onError?.(error as Error);
+    if (typeof file === 'string') return;
+    const rcFile = file as RcFile;
+    const formData = new FormData();
+    formData.append('file', rcFile);
+    const fileVo: UploadFile = {
+      uid: rcFile.uid,
+      name: rcFile.name,
+      status: 'uploading' as UploadFileStatus,
+      percent: 0,
+      type: rcFile.type,
+      originFileObj: rcFile,
+      thumbUrl: URL.createObjectURL(rcFile)
     }
+    onChange?.(fileVo)
+    request.uploadFile(action, formData, requestConfig)
+      .then(res => {
+        onSuccess?.({ data: res });
+      })
+      .catch((error) => {
+        onError?.(error as Error);
+        fileVo.status = 'error'
+        onChange?.(fileVo)
+      })
   };
 
   /**

web/src/views/Conversation/components/UploadFileListModal.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-02-06 21:09:47 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-18 21:10:01
+ * @Last Modified time: 2026-03-19 20:32:32
  */
 /**
  * Upload File List Modal Component
@@ -19,7 +19,10 @@
  * @component
  */
 import { forwardRef, useImperativeHandle, useState, useMemo } from 'react';
-import { Form, Input, Select, Button, Flex } from 'antd';
+import { Form, Input, Select,
+  // Button,
+  Flex
+} from 'antd';
 import { useTranslation } from 'react-i18next';
 
 import type { UploadFileListModalRef } from '../types'
@@ -105,9 +108,11 @@ const UploadFileListModal = forwardRef<UploadFileListModalRef, UploadFileListMod
       onOk={handleSave}
       confirmLoading={loading}
     >
-      <Form form={form} layout="vertical">
+      <Form form={form} layout="vertical" initialValues={{ files: [{ type: undefined, url: undefined }] }}>
         <Form.List name="files">
-          {(fields, { add, remove }) => (
+          {(fields,
+            // { add, remove }
+          ) => (
             <>
               {/* Render each file entry with type selector and URL input */}
               {fields.map(({ key, name, ...restField }) => (
@@ -116,6 +121,9 @@ const UploadFileListModal = forwardRef<UploadFileListModalRef, UploadFileListMod
                     {...restField}
                     name={[name, 'type']}
                     className="rb:mb-0!"
+                    rules={[
+                      { required: true, message: t('common.pleaseSelect') }
+                    ]}
                   >
                     <Select
                       placeholder={t('memoryConversation.fileType')}
@@ -126,22 +134,25 @@ const UploadFileListModal = forwardRef<UploadFileListModalRef, UploadFileListMod
                   <FormItem
                     {...restField}
                     name={[name, 'url']}
-                    rules={[{ required: true, message: t('common.pleaseEnter') }]}
+                    rules={[
+                      { required: true, message: t('common.pleaseEnter') },
+                      { type: 'url', message: t('common.callbackUrlInvalid') },
+                    ]}
                     className="rb:mb-0! rb:flex-1!"
                   >
                     <Input placeholder={t('memoryConversation.fileUrl')} />
                   </FormItem>
-                  <div
+                  {/* <div
                     className="rb:w-5 rb:h-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/delete.svg')] rb:hover:bg-[url('@/assets/images/delete_hover.svg')]"
                     onClick={() => remove(name)}
-                  ></div>
+                  ></div> */}
                 </Flex>
               ))}
-              <Form.Item noStyle>
+              {/* <Form.Item noStyle>
                 <Button type="dashed" onClick={() => add()} block>
                   + {t('common.add')}
                 </Button>
-              </Form.Item>
+              </Form.Item> */}
             </>
           )}
         </Form.List>

web/src/views/Conversation/index.tsx

@@ -200,7 +200,7 @@ const Conversation: FC = () => {
   /** Send message and handle streaming response */
   const handleSend = () => {
     if (!token || !shareToken) return
-    const files = toolbarRef.current?.getFiles() || []
+    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
     const variables = toolbarRef.current?.getVariables() || []
     let isCanSend = true
     const params: Record<string, any> = {}

web/src/views/KnowledgeBase/[knowledgeBaseId]/DocumentDetails.tsx

@@ -11,7 +11,7 @@ import { useNavigate, useParams, useLocation } from 'react-router-dom';
 import { useTranslation } from 'react-i18next';
 import { useBreadcrumbManager, type BreadcrumbPath } from '@/hooks/useBreadcrumbManager';
 import { Button, Spin, message, Switch } from 'antd';
-import { getDocumentDetail, getDocumentChunkList, downloadFile, updateDocument, updateDocumentChunk, createDocumentChunk } from '@/api/knowledgeBase';
+import { getDocumentDetail, getDocumentChunkList, downloadFile, updateDocument, updateDocumentChunk, createDocumentChunk, getFileUrl } from '@/api/knowledgeBase';
 import type { KnowledgeBaseDocumentData, RecallTestData } from '@/views/KnowledgeBase/types';
 import { formatDateTime } from '@/utils/format';
 import InfoPanel, { type InfoItem } from '../components/InfoPanel';
@@ -138,7 +138,7 @@ const DocumentDetails: FC = () => {
       const response = await getDocumentDetail(documentId);
       setDocument(response);
       setInfoItems(formatDocumentInfo(response));
-      const url = `${imagePath}/api/files/${response.file_id}`
+      const url = `${window.location.origin}/api/files/${response.file_id}`;
       setFileUrl(url);
       setParserMode(response?.parser_config?.auto_questions || 0)
       // ChunkList will be called automatically in useEffect based on document.progress

web/src/views/UserMemoryDetail/components/RelationshipNetwork.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-02-03 18:32:00 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-13 14:51:17
+ * @Last Modified time: 2026-03-19 20:23:42
  */
 /**
  * Relationship Network Component
@@ -287,22 +287,26 @@ const RelationshipNetwork:FC = () => {
               : (selectedNode as RawCommunityNode).properties.community_id
                 ? <div className="rb:p-3 rb:pt-0">
                   <div className="rb:font-medium rb:text-[#212332] rb:text-[16px] rb:leading-5.5 rb:pl-1">
-                    {(selectedNode as RawCommunityNode).properties.name}
-                  </div>
-                  <div className="rb:mt-3 rb:font-medium rb:leading-5 rb:pl-1">{t('userMemory.summary')}</div>
-                  <div className="rb:bg-[#F6F6F6] rb:rounded-xl rb:px-3 rb:py-2.5 rb:mt-2">
-                    {(selectedNode as RawCommunityNode).properties.summary}
+                    {(selectedNode as RawCommunityNode).properties.name || selectedNode.id}
                   </div>
+                  {(selectedNode as RawCommunityNode).properties.summary && <>
+                    <div className="rb:mt-3 rb:font-medium rb:leading-5 rb:pl-1">{t('userMemory.summary')}</div>
+                    <div className="rb:bg-[#F6F6F6] rb:rounded-xl rb:px-3 rb:py-2.5 rb:mt-2">
+                      {(selectedNode as RawCommunityNode).properties.summary}
+                    </div>
+                  </>}
                   <Flex align="center" justify="space-between" className="rb:mt-5!">
                     <span className="rb:text-[#5B6167] rb:font-regular rb:pl-1">{t('userMemory.member_count')}</span>
                     <span className="rb:font-medium">{(selectedNode as RawCommunityNode).properties.member_count}{t('userMemory.member_count_desc')}</span>
                   </Flex>
 
-                  <Divider className='rb:my-2.5!' />
-                  <div className="rb:font-medium rb:leading-5 rb:pl-1">{t('userMemory.core_entities')}</div>
-                  <ul className="rb:list-disc rb:pl-4 rb:text-[#5B6167] rb:mt-2">
-                    {(selectedNode as RawCommunityNode).properties.core_entities.map((entity, index) => <li key={index}>{entity}</li>)}
-                  </ul>
+                  {(selectedNode as RawCommunityNode).properties.core_entities && <>
+                    <Divider className='rb:my-2.5!' />
+                    <div className="rb:font-medium rb:leading-5 rb:pl-1">{t('userMemory.core_entities')}</div>
+                    <ul className="rb:list-disc rb:pl-4 rb:text-[#5B6167] rb:mt-2">
+                      {(selectedNode as RawCommunityNode).properties.core_entities?.map((entity, index) => <li key={index}>{entity}</li>)}
+                    </ul>
+                  </>}
                 </div>
                 : <>
                   {(selectedNode as Node).name && (

web/src/views/UserMemoryDetail/pages/WorkingDetail.tsx

@@ -1,8 +1,10 @@
-import { type FC, useEffect, useState, useMemo } from 'react'
+import { type FC, useEffect, useState, useMemo, useRef } from 'react'
 import clsx from 'clsx'
 import { useTranslation } from 'react-i18next'
 import { useParams } from 'react-router-dom'
 import { Row, Col, Skeleton, Button, Divider, Tooltip } from 'antd'
+
+import InfiniteScroll from 'react-infinite-scroll-component'
 import RbCard from '@/components/RbCard/Card'
 import {
   getConversations,
@@ -34,6 +36,8 @@ const WorkingDetail: FC = () => {
   const { id } = useParams()
   const [loading, setLoading] = useState<boolean>(false)
   const [data, setData] = useState<Conversation[]>([])
+  const [hasMore, setHasMore] = useState<boolean>(true)
+  const pageRef = useRef<number>(1)
   const [messagesLoading, setMessagesLoading] = useState<boolean>(false)
   const [messages, setMessages] = useState<ChatItem[]>([])
   const [detailLoading, setDetailLoading] = useState<boolean>(false)
@@ -51,16 +55,30 @@ const WorkingDetail: FC = () => {
     setSelected(null)
     setDetail(null)
     setData([])
-    getConversations(id).then((res) => {
-      const response = res as Conversation[] 
-      setData(response)
-      setSelected(response[0] || null)
+    setHasMore(true)
+    pageRef.current = 1
+    getConversations(id, 1).then((res) => {
+      const response = res as { items: Conversation[], page: { hasnext: boolean } }
+      setData(response.items)
+      setSelected(response.items[0] || null)
+      setHasMore(response.page.hasnext)
     })
     .finally(() => {
       setLoading(false)
     })
   }
 
+  const loadMore = () => {
+    if (!id) return
+    const nextPage = pageRef.current + 1
+    getConversations(id, nextPage).then((res) => {
+      const response = res as {items: Conversation[], page: { hasnext: boolean }}
+      setData(prev => [...prev, ...response.items])
+      pageRef.current = nextPage
+      setHasMore(response.page.hasnext)
+    })
+  }
+
   useEffect(() => {
     if (!id || !selected || !selected.id) return
     getDetail(selected.id)
@@ -103,22 +121,30 @@ const WorkingDetail: FC = () => {
         : data.length === 0
         ? <Empty />
         :(
-          <Row gutter={16} className="rb:h-full">
+          <Row gutter={16}>
             <Col span={5}>
-              <div className="rb:h-full! rb:border-r rb:border-[#EAECEE] rb:py-3 rb:px-4">
-                {data.map(item => (
-                  <div key={item.id} className="rb:mb-3">
-                    <Tooltip title={item.title}>
-                      <div className={clsx("rb:p-[8px_13px] rb:rounded-lg rb:leading-5 rb:cursor-pointer rb:hover:bg-[#F0F3F8] rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap", {
-                        'rb:bg-[#FFFFFF] rb:shadow-[0px_2px_4px_0px_rgba(0,0,0,0.15)] rb:font-medium rb:hover:bg-[#FFFFFF]!': item.id === selected?.id,
-                      })}
-                        onClick={() => setSelected(item)}
-                      >
-                        {item.title}
-                      </div>
-                    </Tooltip>
-                  </div>
-                ))}
+              <div id="conversation-list" className="rb:h-[calc(100vh-76px)]! rb:border-r rb:border-[#EAECEE] rb:py-3 rb:px-4 rb:overflow-y-auto">
+                <InfiniteScroll
+                  dataLength={data.length}
+                  next={loadMore}
+                  hasMore={hasMore}
+                  loader={null}
+                  scrollableTarget="conversation-list"
+                >
+                  {data.map(item => (
+                    <div key={item.id} className="rb:mb-3">
+                      <Tooltip title={item.title}>
+                        <div className={clsx("rb:p-[8px_13px] rb:rounded-lg rb:leading-5 rb:cursor-pointer rb:hover:bg-[#F0F3F8] rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap", {
+                          'rb:bg-[#FFFFFF] rb:shadow-[0px_2px_4px_0px_rgba(0,0,0,0.15)] rb:font-medium rb:hover:bg-[#FFFFFF]!': item.id === selected?.id,
+                        })}
+                          onClick={() => setSelected(item)}
+                        >
+                          {item.title}
+                        </div>
+                      </Tooltip>
+                    </div>
+                  ))}
+                </InfiniteScroll>
               </div>
             </Col>
             {selected && <>

web/src/views/Workflow/components/Chat/Chat.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-02-06 21:10:56 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-18 20:46:35
+ * @Last Modified time: 2026-03-19 18:41:07
  */
 /**
  * Workflow Chat Component
@@ -151,7 +151,7 @@ const Chat = forwardRef<ChatRef, { appId: string; graphRef: GraphRef; data: Work
 
     setLoading(true)
     const message = msg
-    const files = toolbarRef.current?.getFiles() || []
+    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
     setChatList(prev => [...prev, {
       role: 'user',
       content: message,

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

@@ -18,8 +18,8 @@ const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options
   const isUserInputRef = useRef(false);
 
   useEffect(() => {
-    // 监听编辑器变化，标记是否为用户输入
-    const removeListener = editor.registerUpdateListener(({ editorState }) => {
+    const removeListener = editor.registerUpdateListener(({ editorState, tags }) => {
+      if (tags.has('programmatic')) return;
       editorState.read(() => {
         const root = $getRoot();
         const textContent = root.getTextContent();
@@ -107,7 +107,7 @@ const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options
           });
           root.append(paragraph);
         }
-        }, { discrete: true });
+        }, { discrete: true, tag: 'programmatic' });
       });
     }