[fix] system prompt fit error

[modify] QA pair
[add] batch add chunk for v1
2026-05-07 19:37:34 +08:00 · 2026-05-07 19:04:19 +08:00 · 2026-05-07 18:45:36 +08:00 · 2026-05-06 18:34:07 +08:00 · 2026-05-06 15:19:46 +08:00 · 2026-04-29 15:24:25 +08:00
31 changed files with 805 additions and 556 deletions
--- a/api/app/controllers/chunk_controller.py
+++ b/api/app/controllers/chunk_controller.py
@@ -1,8 +1,10 @@
 import os
+import csv
+import io
 from typing import Any, Optional
 import uuid

-from fastapi import APIRouter, Depends, HTTPException, status, Query
+from fastapi import APIRouter, Depends, HTTPException, status, Query, UploadFile, File
 from fastapi.encoders import jsonable_encoder
 from sqlalchemy.orm import Session

@@ -23,6 +25,7 @@ from app.models.user_model import User
 from app.schemas import chunk_schema
 from app.schemas.response_schema import ApiResponse
 from app.services import knowledge_service, document_service, file_service, knowledgeshare_service
+from app.services.file_storage_service import FileStorageService, get_file_storage_service, generate_kb_file_key
 from app.services.model_service import ModelApiKeyService

 # Obtain a dedicated API logger
@@ -271,6 +274,9 @@ async def create_chunk(
        "sort_id": sort_id,
        "status": 1,
    }
+    # QA chunk: 注入 chunk_type/question/answer 到 metadata
+    if create_data.is_qa:
+        metadata.update(create_data.qa_metadata)
    chunk = DocumentChunk(page_content=content, metadata=metadata)
    # 3. Segmented vector storage
    vector_service.add_chunks([chunk])
@@ -282,6 +288,187 @@ async def create_chunk(
    return success(data=jsonable_encoder(chunk), msg="Document chunk creation successful")


+@router.post("/{kb_id}/{document_id}/chunk/batch", response_model=ApiResponse)
+async def create_chunks_batch(
+        kb_id: uuid.UUID,
+        document_id: uuid.UUID,
+        batch_data: chunk_schema.ChunkBatchCreate,
+        db: Session = Depends(get_db),
+        current_user: User = Depends(get_current_user)
+):
+    """
+    Batch create chunks (max 8)
+    """
+    api_logger.info(f"Batch create chunks: kb_id={kb_id}, document_id={document_id}, count={len(batch_data.items)}, username: {current_user.username}")
+
+    if len(batch_data.items) > settings.MAX_CHUNK_BATCH_SIZE:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"Batch size exceeds limit: max {settings.MAX_CHUNK_BATCH_SIZE}, got {len(batch_data.items)}"
+        )
+
+    db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
+    if not db_knowledge:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="The knowledge base does not exist or access is denied")
+
+    db_document = db.query(Document).filter(Document.id == document_id).first()
+    if not db_document:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="The document does not exist or you do not have permission to access it")
+
+    vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
+
+    # Get current max sort_id
+    sort_id = 0
+    total, items = vector_service.search_by_segment(document_id=str(document_id), pagesize=1, page=1, asc=False)
+    if items:
+        sort_id = items[0].metadata["sort_id"]
+
+    chunks = []
+    for create_data in batch_data.items:
+        sort_id += 1
+        doc_id = uuid.uuid4().hex
+        metadata = {
+            "doc_id": doc_id,
+            "file_id": str(db_document.file_id),
+            "file_name": db_document.file_name,
+            "file_created_at": int(db_document.created_at.timestamp() * 1000),
+            "document_id": str(document_id),
+            "knowledge_id": str(kb_id),
+            "sort_id": sort_id,
+            "status": 1,
+        }
+        if create_data.is_qa:
+            metadata.update(create_data.qa_metadata)
+        chunks.append(DocumentChunk(page_content=create_data.chunk_content, metadata=metadata))
+
+    vector_service.add_chunks(chunks)
+
+    db_document.chunk_num += len(chunks)
+    db.commit()
+
+    return success(data=jsonable_encoder(chunks), msg=f"Batch created {len(chunks)} chunks successfully")
+
+
+@router.post("/{kb_id}/import_qa", response_model=ApiResponse)
+async def import_qa_new_doc(
+        kb_id: uuid.UUID,
+        file: UploadFile = File(..., description="CSV 或 Excel 文件（第一行标题跳过，第一列问题，第二列答案）"),
+        db: Session = Depends(get_db),
+        current_user: User = Depends(get_current_user),
+        storage_service: FileStorageService = Depends(get_file_storage_service),
+):
+    """
+    导入 QA 问答对并新建文档（CSV/Excel），异步处理
+    """
+    from app.schemas import file_schema, document_schema
+
+    api_logger.info(f"Import QA (new doc): kb_id={kb_id}, file={file.filename}, username: {current_user.username}")
+
+    # 1. 校验文件格式
+    filename = file.filename or ""
+    if not (filename.endswith(".csv") or filename.endswith(".xlsx") or filename.endswith(".xls")):
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="仅支持 CSV (.csv) 或 Excel (.xlsx) 格式")
+
+    # 2. 校验知识库
+    db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
+    if not db_knowledge:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="知识库不存在或无权访问")
+
+    # 3. 读取文件
+    contents = await file.read()
+    file_size = len(contents)
+    if file_size == 0:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="文件为空")
+
+    _, file_extension = os.path.splitext(filename)
+    file_ext = file_extension.lower()
+
+    # 4. 创建 File 记录
+    file_data = file_schema.FileCreate(
+        kb_id=kb_id, created_by=current_user.id,
+        parent_id=uuid.UUID("00000000-0000-0000-0000-000000000000"),
+        file_name=filename, file_ext=file_ext, file_size=file_size,
+    )
+    db_file = file_service.create_file(db=db, file=file_data, current_user=current_user)
+
+    # 5. 上传文件到存储后端
+    file_key = generate_kb_file_key(kb_id=kb_id, file_id=db_file.id, file_ext=file_ext)
+    try:
+        await storage_service.storage.upload(file_key=file_key, content=contents, content_type=file.content_type)
+    except Exception as e:
+        api_logger.error(f"Storage upload failed: {e}")
+        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"文件存储失败: {str(e)}")
+
+    db_file.file_key = file_key
+    db.commit()
+    db.refresh(db_file)
+
+    # 6. 创建 Document 记录（标记为 QA 类型）
+    doc_data = document_schema.DocumentCreate(
+        kb_id=kb_id, created_by=current_user.id, file_id=db_file.id,
+        file_name=filename, file_ext=file_ext, file_size=file_size,
+        file_meta={}, parser_id="qa",
+        parser_config={"doc_type": "qa", "auto_questions": 0}
+    )
+    db_document = document_service.create_document(db=db, document=doc_data, current_user=current_user)
+
+    api_logger.info(f"Created doc for QA import: file_id={db_file.id}, document_id={db_document.id}, file_key={file_key}")
+
+    # 7. 派发异步任务
+    from app.celery_app import celery_app
+    task = celery_app.send_task(
+        "app.core.rag.tasks.import_qa_chunks",
+        args=[str(kb_id), str(db_document.id), filename, contents],
+        queue="qa_import"
+    )
+
+    return success(data={
+        "task_id": task.id,
+        "document_id": str(db_document.id),
+        "file_id": str(db_file.id),
+    }, msg="QA 导入任务已提交，后台处理中")
+
+
+@router.post("/{kb_id}/{document_id}/import_qa", response_model=ApiResponse)
+async def import_qa_chunks(
+        kb_id: uuid.UUID,
+        document_id: uuid.UUID,
+        file: UploadFile = File(..., description="CSV 或 Excel 文件（第一行标题跳过，第一列问题，第二列答案）"),
+        db: Session = Depends(get_db),
+        current_user: User = Depends(get_current_user)
+):
+    """
+    导入 QA 问答对（CSV/Excel），异步处理
+    """
+    api_logger.info(f"Import QA chunks: kb_id={kb_id}, document_id={document_id}, file={file.filename}, username: {current_user.username}")
+
+    # 1. 校验文件格式
+    filename = file.filename or ""
+    if not (filename.endswith(".csv") or filename.endswith(".xlsx") or filename.endswith(".xls")):
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="仅支持 CSV (.csv) 或 Excel (.xlsx) 格式")
+
+    # 2. 校验知识库和文档
+    db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
+    if not db_knowledge:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="知识库不存在或无权访问")
+
+    db_document = db.query(Document).filter(Document.id == document_id).first()
+    if not db_document:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="文档不存在或无权访问")
+
+    # 3. 读取文件内容，派发异步任务
+    contents = await file.read()
+
+    from app.celery_app import celery_app
+    task = celery_app.send_task(
+        "app.core.rag.tasks.import_qa_chunks",
+        args=[str(kb_id), str(document_id), filename, contents],
+        queue="qa_import"
+    )
+
+    return success(data={"task_id": task.id}, msg="QA 导入任务已提交，后台处理中")
+
+
@router.get("/{kb_id}/{document_id}/{doc_id}", response_model=ApiResponse)
 async def get_chunk(
        kb_id: uuid.UUID,
@@ -342,6 +529,9 @@ async def update_chunk(
    if total:
        chunk = items[0]
        chunk.page_content = content
+        # QA chunk: 更新 metadata 中的 question/answer
+        if update_data.is_qa:
+            chunk.metadata.update(update_data.qa_metadata)
        vector_service.update_by_segment(chunk)
        return success(data=jsonable_encoder(chunk), msg="The document chunk has been successfully updated")
    else:
@@ -356,6 +546,7 @@ async def delete_chunk(
        kb_id: uuid.UUID,
        document_id: uuid.UUID,
        doc_id: str,
+        force_refresh: bool = Query(False, description="Force Elasticsearch refresh after deletion"),
        db: Session = Depends(get_db),
        current_user: User = Depends(get_current_user)
 ):
@@ -373,7 +564,7 @@ async def delete_chunk(

    vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
    if vector_service.text_exists(doc_id):
-        vector_service.delete_by_ids([doc_id])
+        vector_service.delete_by_ids([doc_id], refresh=force_refresh)
        # 更新 chunk_num
        db_document = db.query(Document).filter(Document.id == document_id).first()
        db_document.chunk_num -= 1
--- a/api/app/controllers/memory_dashboard_controller.py
+++ b/api/app/controllers/memory_dashboard_controller.py
@@ -1,4 +1,4 @@
-
+import asyncio
 import uuid
 from fastapi import APIRouter, Depends, HTTPException, status, Query
 from pydantic import BaseModel, Field
@@ -10,7 +10,7 @@ from app.dependencies import get_current_user
 from app.models.user_model import User
 from app.schemas.response_schema import ApiResponse

-from app.services import memory_dashboard_service, workspace_service
+from app.services import memory_dashboard_service, memory_storage_service, workspace_service
 from app.services.memory_agent_service import get_end_users_connected_configs_batch
 from app.services.app_statistics_service import AppStatisticsService
 from app.core.logging_config import get_api_logger
@@ -48,7 +48,7 @@ def get_workspace_total_end_users(


@router.get("/end_users", response_model=ApiResponse)
-def get_workspace_end_users(
+async def get_workspace_end_users(
    workspace_id: Optional[uuid.UUID] = Query(None, description="工作空间ID（可选，默认当前用户工作空间）"),
    keyword: Optional[str] = Query(None, description="搜索关键词（同时模糊匹配 other_name 和 id）"),
    page: int = Query(1, ge=1, description="页码，从1开始"),
@@ -58,15 +58,6 @@ def get_workspace_end_users(
 ):
    """
    获取工作空间的宿主列表（分页查询，支持模糊搜索）
-    
-    新增：记忆数量过滤：
-        Neo4j 模式：
-        - 使用 end_users.memory_count 过滤 memory_count > 0 的宿主
-        - memory_num.total 直接取 end_user.memory_count
-
-        RAG 模式：
-        - 使用 documents.chunk_num 聚合过滤 chunk 总数 > 0 的宿主
-        - memory_num.total 取聚合后的 chunk 总数

    返回工作空间下的宿主列表，支持分页查询和模糊搜索。
    通过 keyword 参数同时模糊匹配 other_name 和 id 字段。
@@ -89,29 +80,17 @@ def get_workspace_end_users(
    current_workspace_type = memory_dashboard_service.get_current_workspace_type(db, workspace_id, current_user)
    api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {workspace_id} 的宿主列表, 类型: {current_workspace_type}")

-    if current_workspace_type == "rag":
-        end_users_result = memory_dashboard_service.get_workspace_end_users_paginated_rag(
-            db=db,
-            workspace_id=workspace_id,
-            current_user=current_user,
-            page=page,
-            pagesize=pagesize,
-            keyword=keyword,
-        )
-        raw_items = end_users_result.get("items", [])
-        end_users = [item["end_user"] for item in raw_items]
-    else:
-        end_users_result = memory_dashboard_service.get_workspace_end_users_paginated(
-            db=db,
-            workspace_id=workspace_id,
-            current_user=current_user,
-            page=page,
-            pagesize=pagesize,
-            keyword=keyword,
-        )
-        raw_items = end_users_result.get("items", [])
-        end_users = raw_items
+    # 获取分页的 end_users
+    end_users_result = memory_dashboard_service.get_workspace_end_users_paginated(
+        db=db,
+        workspace_id=workspace_id,
+        current_user=current_user,
+        page=page,
+        pagesize=pagesize,
+        keyword=keyword
+    )

+    end_users = end_users_result.get("items", [])
    total = end_users_result.get("total", 0)

    if not end_users:
@@ -122,19 +101,50 @@ def get_workspace_end_users(
                "page": page,
                "pagesize": pagesize,
                "total": total,
-                "hasnext": (page * pagesize) < total,
-            },
+                "hasnext": (page * pagesize) < total
+            }
        }, msg="宿主列表获取成功")

    end_user_ids = [str(user.id) for user in end_users]

-    try:
-        memory_configs_map = get_end_users_connected_configs_batch(end_user_ids, db)
-    except Exception as e:
-        api_logger.error(f"批量获取记忆配置失败: {str(e)}")
-        memory_configs_map = {}
+    # 并发执行两个独立的查询任务
+    async def get_memory_configs():
+        """获取记忆配置（在线程池中执行同步查询）"""
+        try:
+            return await asyncio.to_thread(
+                get_end_users_connected_configs_batch,
+                end_user_ids, db
+            )
+        except Exception as e:
+            api_logger.error(f"批量获取记忆配置失败: {str(e)}")
+            return {}

-    # 触发按需初始化：为 implicit_emotions_storage / interest_distribution 中没有记录的用户异步生成数据
+    async def get_memory_nums():
+        """获取记忆数量"""
+        if current_workspace_type == "rag":
+            # RAG 模式：批量查询
+            try:
+                chunk_map = await asyncio.to_thread(
+                    memory_dashboard_service.get_users_total_chunk_batch,
+                    end_user_ids, db, current_user
+                )
+                return {uid: {"total": count} for uid, count in chunk_map.items()}
+            except Exception as e:
+                api_logger.error(f"批量获取 RAG chunk 数量失败: {str(e)}")
+                return {uid: {"total": 0} for uid in end_user_ids}
+
+        elif current_workspace_type == "neo4j":
+            # Neo4j 模式：批量查询（简化版本，只返回total）
+            try:
+                batch_result = await memory_storage_service.search_all_batch(end_user_ids)
+                return {uid: {"total": count} for uid, count in batch_result.items()}
+            except Exception as e:
+                api_logger.error(f"批量获取 Neo4j 记忆数量失败: {str(e)}")
+                return {uid: {"total": 0} for uid in end_user_ids}
+
+        return {uid: {"total": 0} for uid in end_user_ids}
+
+    # 触发按需初始化：为 implicit_emotions_storage 中没有记录的用户异步生成数据
    try:
        from app.celery_app import celery_app as _celery_app
        _celery_app.send_task(
@@ -149,26 +159,27 @@ def get_workspace_end_users(
    except Exception as e:
        api_logger.warning(f"触发按需初始化任务失败（不影响主流程）: {e}")

+    # 并发执行配置查询和记忆数量查询
+    memory_configs_map, memory_nums_map = await asyncio.gather(
+        get_memory_configs(),
+        get_memory_nums()
+    )
+
+    # 构建结果列表
    items = []
-    for index, end_user in enumerate(end_users):
+    for end_user in end_users:
        user_id = str(end_user.id)
        config_info = memory_configs_map.get(user_id, {})
-
-        if current_workspace_type == "rag":
-            memory_total = int(raw_items[index].get("memory_count", 0) or 0)
-        else:
-            memory_total = int(getattr(end_user, "memory_count", 0) or 0)
-
        items.append({
-            "end_user": {
-                "id": user_id,
-                "other_name": end_user.other_name,
+            'end_user': {
+                'id': user_id,
+                'other_name': end_user.other_name
            },
-            "memory_num": {"total": memory_total},
-            "memory_config": {
+            'memory_num': memory_nums_map.get(user_id, {"total": 0}),
+            'memory_config': {
                "memory_config_id": config_info.get("memory_config_id"),
-                "memory_config_name": config_info.get("memory_config_name"),
-            },
+                "memory_config_name": config_info.get("memory_config_name")
+            }
        })

    # 触发社区聚类补全任务（异步，不阻塞接口响应）
@@ -396,7 +407,6 @@ def get_current_user_rag_total_num(
    total_chunk = memory_dashboard_service.get_current_user_total_chunk(end_user_id, db, current_user)
    return success(data=total_chunk, msg="宿主RAG知识数据获取成功")

-
@router.get("/rag_content", response_model=ApiResponse)
 def get_rag_content(
    end_user_id: str = Query(..., description="宿主ID"),
--- a/api/app/controllers/service/rag_api_chunk_controller.py
+++ b/api/app/controllers/service/rag_api_chunk_controller.py
@@ -113,6 +113,33 @@ async def create_chunk(
                                               current_user=current_user)


+@router.post("/{kb_id}/{document_id}/chunk/batch", response_model=ApiResponse)
+@require_api_key(scopes=["rag"])
+async def create_chunks_batch(
+    kb_id: uuid.UUID,
+    document_id: uuid.UUID,
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    items: list = Body(..., description="chunk items list"),
+):
+    """
+    Batch create chunks (max 8)
+    """
+    body = await request.json()
+    batch_data = chunk_schema.ChunkBatchCreate(**body)
+    # 0. Obtain the creator of the api key
+    api_key = api_key_service.ApiKeyService.get_api_key(db, api_key_auth.api_key_id, api_key_auth.workspace_id)
+    current_user = api_key.creator
+    current_user.current_workspace_id = api_key_auth.workspace_id
+
+    return await chunk_controller.create_chunks_batch(kb_id=kb_id,
+                                                      document_id=document_id,
+                                                      batch_data=batch_data,
+                                                      db=db,
+                                                      current_user=current_user)
+
+
@router.get("/{kb_id}/{document_id}/{doc_id}", response_model=ApiResponse)
@require_api_key(scopes=["rag"])
 async def get_chunk(
@@ -176,6 +203,7 @@ async def delete_chunk(
    request: Request,
    api_key_auth: ApiKeyAuth = None,
    db: Session = Depends(get_db),
+    force_refresh: bool = Query(False, description="Force Elasticsearch refresh after deletion"),
 ):
    """
    delete document chunk
@@ -188,6 +216,7 @@ async def delete_chunk(
    return await chunk_controller.delete_chunk(kb_id=kb_id,
                                               document_id=document_id,
                                               doc_id=doc_id,
+                                               force_refresh=force_refresh,
                                               db=db,
                                               current_user=current_user)

--- a/api/app/core/config.py
+++ b/api/app/core/config.py
@@ -98,6 +98,7 @@ 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"))
+    MAX_CHUNK_BATCH_SIZE: int = int(os.getenv("MAX_CHUNK_BATCH_SIZE", "8"))
    FILE_PATH: str = os.getenv("FILE_PATH", "/files")
    FILE_URL_EXPIRES: int = int(os.getenv("FILE_URL_EXPIRES", "3600"))

--- a/api/app/core/memory/agent/utils/write_tools.py
+++ b/api/app/core/memory/agent/utils/write_tools.py
@@ -20,7 +20,6 @@ from app.core.memory.storage_services.extraction_engine.knowledge_extraction.mem
    memory_summary_generation
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.core.memory.utils.log.logging_utils import log_time
-from app.core.memory.utils.memory_count_utils import sync_end_user_memory_count_from_neo4j
 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
@@ -314,28 +313,6 @@ async def write(
    except Exception as cache_err:
        logger.warning(f"[WRITE] 写入活动统计缓存失败（不影响主流程）: {cache_err}", exc_info=True)

-    # 同步 Neo4j 记忆节点总数到 PostgreSQL end_users.memory_count
-    if end_user_id:
-        try:
-            memory_count_connector = Neo4jConnector()
-            try:
-                node_count = await sync_end_user_memory_count_from_neo4j(
-                    end_user_id,
-                    memory_count_connector,
-                )
-            finally:
-                await memory_count_connector.close()
-
-            logger.info(
-                f"[MemoryCount] 写入后同步 memory_count: "
-                f"end_user_id={end_user_id}, count={node_count}"
-            )
-        except Exception as e:
-            logger.warning(
-                f"[MemoryCount] 写入后同步 memory_count 失败（不影响主流程）: {e}",
-                exc_info=True,
-            )
-    
    # Close LLM/Embedder underlying httpx clients to prevent
    # 'RuntimeError: Event loop is closed' during garbage collection
    for client_obj in (llm_client, embedder_client):
@@ -354,4 +331,3 @@ async def write(

    logger.info("=== Pipeline Complete ===")
    logger.info(f"Total execution time: {total_time:.2f} seconds")
-
--- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py
+++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py
@@ -20,7 +20,6 @@ from uuid import UUID
 from datetime import datetime

 from app.core.memory.storage_services.forgetting_engine.forgetting_strategy import ForgettingStrategy
-from app.core.memory.utils.memory_count_utils import sync_end_user_memory_count_from_neo4j
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector


@@ -146,22 +145,7 @@ class ForgettingScheduler:
                }
                
                logger.info("没有可遗忘的节点对，遗忘周期结束")
-                # 同步 Neo4j 记忆节点总数到 PostgreSQL 的 end_users.memory_count
-                if end_user_id:
-                    try:
-                        node_count = await sync_end_user_memory_count_from_neo4j(
-                            end_user_id,
-                            self.connector,
-                        )
-                        logger.info(
-                            f"[MemoryCount] 遗忘后同步 memory_count: "
-                            f"end_user_id={end_user_id}, count={node_count}"
-                        )
-                    except Exception as e:
-                        logger.warning(
-                            f"[MemoryCount] 遗忘后同步 memory_count 失败（不影响主流程）: {e}",
-                            exc_info=True,
-                        )
+                
                return report
            
            # 步骤3：按激活值排序（激活值最低的优先）
@@ -318,22 +302,7 @@ class ForgettingScheduler:
                f"({reduction_rate:.2%}), "
                f"耗时 {duration:.2f} 秒"
            )
-            # 同步 Neo4j 记忆节点总数到 PostgreSQL 的 end_users.memory_count
-            if end_user_id:
-                try:
-                    node_count = await sync_end_user_memory_count_from_neo4j(
-                        end_user_id,
-                        self.connector,
-                    )
-                    logger.info(
-                        f"[MemoryCount] 遗忘后同步 memory_count: "
-                        f"end_user_id={end_user_id}, count={node_count}"
-                    )
-                except Exception as e:
-                    logger.warning(
-                        f"[MemoryCount] 遗忘后同步 memory_count 失败（不影响主流程）: {e}",
-                        exc_info=True,
-                    )
+            
            return report
        
        except Exception as e:
--- a/api/app/core/memory/utils/memory_count_utils.py
+++ b/api/app/core/memory/utils/memory_count_utils.py
@@ -1,36 +0,0 @@
-from uuid import UUID
-
-from app.db import get_db_context
-from app.models.end_user_model import EndUser
-from app.repositories.memory_config_repository import MemoryConfigRepository
-from app.repositories.neo4j.neo4j_connector import Neo4jConnector
-
-
-async def sync_end_user_memory_count_from_neo4j(
-    end_user_id: str,
-    connector: Neo4jConnector,
-) -> int:
-    """
-    Sync one end user's Neo4j memory node count to PostgreSQL.
-
-    The caller owns the Neo4j connector lifecycle.
-    """
-    if not end_user_id:
-        return 0
-
-    result = await connector.execute_query(
-        MemoryConfigRepository.SEARCH_FOR_ALL_BATCH,
-        end_user_ids=[end_user_id],
-    )
-    node_count = int(result[0]["total"]) if result else 0
-
-    with get_db_context() as db:
-        db.query(EndUser).filter(
-            EndUser.id == UUID(end_user_id)
-        ).update(
-            {"memory_count": node_count},
-            synchronize_session=False,
-        )
-        db.commit()
-
-    return node_count
--- a/api/app/core/rag/graphrag/general/index.py
+++ b/api/app/core/rag/graphrag/general/index.py
@@ -46,7 +46,10 @@ async def run_graphrag(
    start = trio.current_time()
    workspace_id, kb_id, document_id = row["workspace_id"], str(row["kb_id"]), row["document_id"]
    chunks = []
-    for d in settings.retriever.chunk_list(document_id, workspace_id, [kb_id], fields=["page_content", "document_id"], sort_by_position=True):
+    for d in settings.retriever.chunk_list(document_id, workspace_id, [kb_id], fields=["page_content", "document_id", "chunk_type"], sort_by_position=True):
+        # 跳过 QA chunks，只用原文 chunks 构建图谱
+        if d.get("chunk_type") == "qa":
+            continue
        chunks.append(d["page_content"])

    with trio.fail_after(max(120, len(chunks) * 60 * 10) if enable_timeout_assertion else 10000000000):
@@ -150,6 +153,9 @@ async def run_graphrag_for_kb(

        total, items = vector_service.search_by_segment(document_id=str(document_id), query=None, pagesize=9999, page=1, asc=True)
        for doc in items:
+            # 跳过 QA chunks，只用原文 chunks 构建图谱
+            if (doc.metadata or {}).get("chunk_type") == "qa":
+                continue
            content = doc.page_content
            if num_tokens_from_string(current_chunk + content) < 1024:
                current_chunk += content
--- a/api/app/core/rag/prompts/generator.py
+++ b/api/app/core/rag/prompts/generator.py
@@ -131,18 +131,52 @@ def keyword_extraction(chat_mdl, content, topn=3):


 def question_proposal(chat_mdl, content, topn=3):
-    template = PROMPT_JINJA_ENV.from_string(QUESTION_PROMPT_TEMPLATE)
-    rendered_prompt = template.render(content=content, topn=topn)
-
-    msg = [{"role": "system", "content": rendered_prompt}, {"role": "user", "content": "Output: "}]
-    _, msg = message_fit_in(msg, getattr(chat_mdl, 'max_length', 8096))
-    kwd = chat_mdl.chat(rendered_prompt, msg[1:], {"temperature": 0.2})
-    if isinstance(kwd, tuple):
-        kwd = kwd[0]
-    kwd = re.sub(r"^.*</think>", "", kwd, flags=re.DOTALL)
-    if kwd.find("**ERROR**") >= 0:
+    """生成问题（向后兼容，返回纯文本问题列表）"""
+    pairs = qa_proposal(chat_mdl, content, topn)
+    if not pairs:
        return ""
-    return kwd
+    return "\n".join([p["question"] for p in pairs])
+
+
+def qa_proposal(chat_mdl, content, topn=3, custom_prompt=None):
+    """生成 QA 对，返回 [{"question": ..., "answer": ...}, ...]
+    
+    Args:
+        chat_mdl: LLM 模型
+        content: 文本内容
+        topn: 生成 QA 对数量
+        custom_prompt: 自定义 prompt 模板（支持 Jinja2，可用变量: content, topn）
+    """
+    if custom_prompt:
+        template = PROMPT_JINJA_ENV.from_string(custom_prompt)
+        sys_prompt = template.render(topn=topn)
+    else:
+        sys_prompt = QUESTION_PROMPT_TEMPLATE
+    msg = [{"role": "system", "content": sys_prompt}, {"role": "user", "content": content}]
+    _, msg = message_fit_in(msg, getattr(chat_mdl, 'max_length', 8096))
+    raw = chat_mdl.chat(sys_prompt, msg[1:], {"temperature": 0.2})
+    if isinstance(raw, tuple):
+        raw = raw[0]
+    raw = re.sub(r"^.*</think>", "", raw, flags=re.DOTALL)
+    if raw.find("**ERROR**") >= 0:
+        return []
+    return parse_qa_pairs(raw)
+
+
+def parse_qa_pairs(text: str) -> list:
+    """解析 LLM 返回的 QA 对文本，格式: Q: xxx A: xxx"""
+    pairs = []
+    for line in text.strip().split("\n"):
+        line = line.strip()
+        if not line:
+            continue
+        # 匹配 Q: ... A: ... 格式
+        match = re.match(r'^Q:\s*(.+?)\s+A:\s*(.+)$', line, re.IGNORECASE)
+        if match:
+            q, a = match.group(1).strip(), match.group(2).strip()
+            if q and a:
+                pairs.append({"question": q, "answer": a})
+    return pairs


 def graph_entity_types(chat_mdl, scenario):
--- a/api/app/core/rag/prompts/question_prompt.md
+++ b/api/app/core/rag/prompts/question_prompt.md
@@ -1,19 +1,20 @@
 ## Role
-You are a text analyzer.
+You are a text analyzer and knowledge extraction expert.

 ## Task
-Propose {{ topn }} questions about a given piece of text content.
+Generate question-answer pairs from the given text content.

 ## Requirements
- Understand and summarize the text content, and propose the top {{ topn }} important questions.
+- Understand and summarize the text content, then generate up to {{ topn }} important question-answer pairs.
+- Each question-answer pair MUST be on a single line, formatted as: Q: <question> A: <answer>
 - The questions SHOULD NOT have overlapping meanings.
 - The questions SHOULD cover the main content of the text as much as possible.
- The questions MUST be in the same language as the given piece of text content.
- One question per line.
- Output questions ONLY.
-
---
-
-## Text Content
-{{ content }}
+- The answers MUST be concise, accurate, and directly derived from the text content.
+- The answers SHOULD be self-contained and understandable without additional context.
+- Both questions and answers MUST be in the same language as the given text content.
+- If the text is too short or lacks substantive content, generate fewer pairs rather than padding.
+- Output question-answer pairs ONLY, no extra explanation or commentary.

+## Example Output
+Q: What is the capital of France? A: The capital of France is Paris.
+Q: When was the Eiffel Tower built? A: The Eiffel Tower was built in 1889.
--- a/api/app/core/rag/vdb/elasticsearch/elasticsearch_vector.py
+++ b/api/app/core/rag/vdb/elasticsearch/elasticsearch_vector.py
@@ -5,7 +5,7 @@ from typing import Any
 from urllib.parse import urlparse

 import requests
-from elasticsearch import Elasticsearch, helpers
+from elasticsearch import Elasticsearch, helpers, NotFoundError
 from elasticsearch.helpers import BulkIndexError
 from packaging.version import parse as parse_version
 # langchain-community
@@ -53,13 +53,30 @@ class ElasticSearchVector(BaseVector):
        return "elasticsearch"

    def add_chunks(self, chunks: list[DocumentChunk], **kwargs):
-        # 实现 Elasticsearch 保存向量
-        texts = [chunk.page_content for chunk in chunks]
+        # QA chunks: embedding 只对 question 字段做；source chunks: 不做 embedding
+        texts_for_embedding = []
+        for chunk in chunks:
+            chunk_type = (chunk.metadata or {}).get("chunk_type", "chunk")
+            if chunk_type == "source":
+                # source chunk 不需要向量索引
+                texts_for_embedding.append("")
+            elif chunk_type == "qa":
+                # QA chunk: 用 question 字段做 embedding
+                texts_for_embedding.append((chunk.metadata or {}).get("question", chunk.page_content))
+            else:
+                # 普通 chunk: 用 page_content 做 embedding
+                texts_for_embedding.append(chunk.page_content)
+
        if self.is_multimodal_embedding:
-            # 火山引擎多模态 Embedding
-            embeddings = self.embeddings.embed_batch(texts)
+            embeddings = self.embeddings.embed_batch(texts_for_embedding)
        else:
-            embeddings = self.embeddings.embed_documents(list(texts))
+            embeddings = self.embeddings.embed_documents(texts_for_embedding)
+
+        # source chunk 的向量置空
+        for i, chunk in enumerate(chunks):
+            if (chunk.metadata or {}).get("chunk_type") == "source":
+                embeddings[i] = None
+
        self.create(chunks, embeddings, **kwargs)

    def create(self, chunks: list[DocumentChunk], embeddings: list[list[float]], **kwargs):
@@ -72,13 +89,25 @@ class ElasticSearchVector(BaseVector):
        uuids = self._get_uuids(chunks)
        actions = []
        for i, chunk in enumerate(chunks):
+            source = {
+                Field.CONTENT_KEY.value: chunk.page_content,
+                Field.METADATA_KEY.value: chunk.metadata or {},
+                Field.VECTOR.value: embeddings[i] or None
+            }
+            # 写入 QA 相关字段
+            meta = chunk.metadata or {}
+            if meta.get("chunk_type"):
+                source[Field.CHUNK_TYPE.value] = meta["chunk_type"]
+            if meta.get("question"):
+                source[Field.QUESTION.value] = meta["question"]
+            if meta.get("answer"):
+                source[Field.ANSWER.value] = meta["answer"]
+            if meta.get("source_chunk_id"):
+                source[Field.SOURCE_CHUNK_ID.value] = meta["source_chunk_id"]
+
            action = {
                "_index": self._collection_name,
-                "_source": {
-                    Field.CONTENT_KEY.value: chunk.page_content,
-                    Field.METADATA_KEY.value: chunk.metadata or {},
-                    Field.VECTOR.value: embeddings[i] or None
-                }
+                "_source": source
            }
            actions.append(action)
        # using bulk mode
@@ -113,7 +142,7 @@ class ElasticSearchVector(BaseVector):

        return True

-    def delete_by_ids(self, ids: list[str]):
+    def delete_by_ids(self, ids: list[str], *, refresh: bool = False):
        if not ids:
            return
        if not self._client.indices.exists(index=self._collection_name):
@@ -134,6 +163,8 @@ class ElasticSearchVector(BaseVector):
            actions = [{"_op_type": "delete", "_index": self._collection_name, "_id": es_id} for es_id in actual_ids]
            try:
                helpers.bulk(self._client, actions)
+                if refresh:
+                    self._client.indices.refresh(index=self._collection_name)
            except BulkIndexError as e:
                for error in e.errors:
                    delete_error = error.get('delete', {})
@@ -153,7 +184,7 @@ class ElasticSearchVector(BaseVector):
        else:
            return None

-    def delete_by_metadata_field(self, key: str, value: str):
+    def delete_by_metadata_field(self, key: str, value: str, *, refresh: bool = False):
        if not self._client.indices.exists(index=self._collection_name):
            return False
        actual_ids = self.get_ids_by_metadata_field(key, value)
@@ -162,6 +193,8 @@ class ElasticSearchVector(BaseVector):
            actions = [{"_op_type": "delete", "_index": self._collection_name, "_id": es_id} for es_id in actual_ids]
            try:
                helpers.bulk(self._client, actions)
+                if refresh:
+                    self._client.indices.refresh(index=self._collection_name)
            except BulkIndexError as e:
                for error in e.errors:
                    delete_error = error.get('delete', {})
@@ -192,6 +225,8 @@ class ElasticSearchVector(BaseVector):
            List of DocumentChunk objects that match the query.
        """
        indices = kwargs.get("indices", self._collection_name)  # Default single index, multiple indexes are also supported, such as "index1, index2, index3"
+        if not self._client.indices.exists(index=indices):
+            return 0, []

        # Calculate the start position for the current page
        from_ = pagesize * (page-1)
@@ -226,12 +261,15 @@ class ElasticSearchVector(BaseVector):
            })

        # For simplicity, we use from/size here which has a limit (usually up to 10,000).
-        result = self._client.search(
-            index=indices,
-            from_=from_,  # Only use from_ for the first page (simplified)
-            size=pagesize,
-            body=query_str,
-        )
+        try:
+            result = self._client.search(
+                index=indices,
+                from_=from_,  # Only use from_ for the first page (simplified)
+                size=pagesize,
+                body=query_str,
+            )
+        except NotFoundError:
+            return 0, []

        if "errors" in result:
            raise ValueError(f"Error during query: {result['errors']}")
@@ -241,10 +279,19 @@ class ElasticSearchVector(BaseVector):
        for res in result["hits"]["hits"]:
            source = res["_source"]
            page_content = source.get(Field.CONTENT_KEY.value)
-            # vector = source.get(Field.VECTOR.value)
            vector = None
            metadata = source.get(Field.METADATA_KEY.value, {})
+            chunk_type = source.get(Field.CHUNK_TYPE.value)
            score = res["_score"]
+
+            # 将 QA 字段注入 metadata 供前端展示
+            if chunk_type:
+                metadata["chunk_type"] = chunk_type
+            if chunk_type == "qa":
+                metadata["question"] = source.get(Field.QUESTION.value, "")
+                metadata["answer"] = source.get(Field.ANSWER.value, "")
+                page_content = f"Q: {metadata['question']}\nA: {metadata['answer']}"
+
            docs_and_scores.append((DocumentChunk(page_content=page_content, vector=vector, metadata=metadata), score))

        docs = []
@@ -267,13 +314,18 @@ class ElasticSearchVector(BaseVector):
            List of DocumentChunk objects that match the query.
        """
        indices = kwargs.get("indices", self._collection_name)  # Default single index, multi-index available，etc "index1,index2,index3"
+        if not self._client.indices.exists(index=indices):
+            return 0, []
        query_str = {"query": {"term": {f"{Field.DOC_ID.value}": doc_id}}}
-        result = self._client.search(
-            index=indices,
-            from_=0,  # Only use from_ for the first page (simplified)
-            size=1,
-            body=query_str,
-        )
+        try:
+            result = self._client.search(
+                index=indices,
+                from_=0,  # Only use from_ for the first page (simplified)
+                size=1,
+                body=query_str,
+            )
+        except NotFoundError:
+            return 0, []
        # print(result)
        if "errors" in result:
            raise ValueError(f"Error during query: {result['errors']}")
@@ -308,27 +360,43 @@ class ElasticSearchVector(BaseVector):
        Returns:
            updated count.
        """
-        indices = kwargs.get("indices", self._collection_name)  # Default single index, multi-index available，etc "index1,index2,index3"
-        if self.is_multimodal_embedding:
-            # 火山引擎多模态 Embedding
-            chunk.vector = self.embeddings.embed_text(chunk.page_content)
+        indices = kwargs.get("indices", self._collection_name)
+        chunk_type = (chunk.metadata or {}).get("chunk_type")
+
+        # QA chunk: embedding 基于 question；source chunk: 不更新向量
+        if chunk_type == "source":
+            embed_text = ""
+        elif chunk_type == "qa":
+            embed_text = (chunk.metadata or {}).get("question", chunk.page_content)
        else:
-            chunk.vector = self.embeddings.embed_query(chunk.page_content)
+            embed_text = chunk.page_content
+
+        if chunk_type != "source":
+            if self.is_multimodal_embedding:
+                chunk.vector = self.embeddings.embed_text(embed_text)
+            else:
+                chunk.vector = self.embeddings.embed_query(embed_text)
+
+        script_source = "ctx._source.page_content = params.new_content; ctx._source.vector = params.new_vector;"
+        params = {
+            "new_content": chunk.page_content,
+            "new_vector": chunk.vector if chunk_type != "source" else None
+        }
+
+        # QA chunk: 同时更新 question/answer 字段
+        if chunk_type == "qa":
+            script_source += " ctx._source.question = params.new_question; ctx._source.answer = params.new_answer;"
+            params["new_question"] = (chunk.metadata or {}).get("question", "")
+            params["new_answer"] = (chunk.metadata or {}).get("answer", "")

        body = {
            "script": {
-                "source": """
-                        ctx._source.page_content = params.new_content;
-                        ctx._source.vector = params.new_vector;
-                    """,
-                "params": {
-                    "new_content": chunk.page_content,
-                    "new_vector": chunk.vector
-                }
+                "source": script_source,
+                "params": params
            },
            "query": {
                "term": {
-                    Field.DOC_ID.value: chunk.metadata["doc_id"]  # exact match doc_id
+                    Field.DOC_ID.value: chunk.metadata["doc_id"]
                }
            }
        }
@@ -336,9 +404,6 @@ class ElasticSearchVector(BaseVector):
            index=indices,
            body=body,
        )
-        # Remove debug printing and use logging instead
-        # print(result)
-        # print(f"Update successful, number of affected documents: {result['updated']}")
        return result['updated']

    def change_status_by_document_id(self, document_id: str, status: int, **kwargs) -> str:
@@ -397,11 +462,11 @@ class ElasticSearchVector(BaseVector):
                            }
                        }
                    },
-                    "filter": {  # Add the filter condition of status=1
-                        "term": {
-                            "metadata.status": 1
-                        }
-                    }
+                    "filter": [
+                        {"term": {"metadata.status": 1}},
+                        # 排除 source chunk（仅供 GraphRAG 使用，不参与检索）
+                        {"bool": {"must_not": {"term": {Field.CHUNK_TYPE.value: "source"}}}}
+                    ]
                }
            }
        # If file_names_filter is passed in, merge the filtering conditions
@@ -415,22 +480,14 @@ class ElasticSearchVector(BaseVector):
                            },
                            "script": {
                                "source": f"cosineSimilarity(params.query_vector, '{Field.VECTOR.value}') + 1.0",
-                                # The script_score query calculates the cosine similarity between the embedding field of each document and the query vector. The addition of +1.0 is to ensure that the scores returned by the script are non-negative, as the range of cosine similarity is [-1, 1]
                                "params": {"query_vector": query_vector}
                            }
                        }
                    },
                    "filter": [
-                        {
-                            "term": {
-                                "metadata.status": 1
-                            }
-                        },
-                        {
-                            "terms": {
-                                "metadata.file_name": file_names_filter  # Additional file_name filtering
-                            }
-                        }
+                        {"term": {"metadata.status": 1}},
+                        {"terms": {"metadata.file_name": file_names_filter}},
+                        {"bool": {"must_not": {"term": {Field.CHUNK_TYPE.value: "source"}}}}
                    ],
                }
            }
@@ -451,8 +508,19 @@ class ElasticSearchVector(BaseVector):
            source = res["_source"]
            page_content = source.get(Field.CONTENT_KEY.value)
            metadata = source.get(Field.METADATA_KEY.value, {})
+            chunk_type = source.get(Field.CHUNK_TYPE.value)
            score = res["_score"]
            score = score / 2  # Normalized [0-1]
+
+            # QA chunk: 返回 Q+A 拼接作为上下文
+            if chunk_type == "qa":
+                question = source.get(Field.QUESTION.value, "")
+                answer = source.get(Field.ANSWER.value, "")
+                page_content = f"Q: {question}\nA: {answer}"
+                metadata["chunk_type"] = "qa"
+                metadata["question"] = question
+                metadata["answer"] = answer
+
            docs_and_scores.append((DocumentChunk(page_content=page_content, metadata=metadata), score))

        docs = []
@@ -491,11 +559,10 @@ class ElasticSearchVector(BaseVector):
                        }
                    }
                },
-                "filter": {  # Add the filter condition of status=1
-                    "term": {
-                        "metadata.status": 1
-                    }
-                }
+                "filter": [
+                    {"term": {"metadata.status": 1}},
+                    {"bool": {"must_not": {"term": {Field.CHUNK_TYPE.value: "source"}}}}
+                ]
            }
        }

@@ -512,16 +579,9 @@ class ElasticSearchVector(BaseVector):
                        }
                    },
                    "filter": [
-                        {
-                            "term": {
-                                "metadata.status": 1
-                            }
-                        },
-                        {
-                            "terms": {
-                                "metadata.file_name": file_names_filter  # Additional file_name filtering
-                            }
-                        }
+                        {"term": {"metadata.status": 1}},
+                        {"terms": {"metadata.file_name": file_names_filter}},
+                        {"bool": {"must_not": {"term": {Field.CHUNK_TYPE.value: "source"}}}}
                    ],
                }
            }
@@ -543,6 +603,17 @@ class ElasticSearchVector(BaseVector):
            source = res["_source"]
            page_content = source.get(Field.CONTENT_KEY.value)
            metadata = source.get(Field.METADATA_KEY.value, {})
+            chunk_type = source.get(Field.CHUNK_TYPE.value)
+
+            # QA chunk: 返回 Q+A 拼接作为上下文
+            if chunk_type == "qa":
+                question = source.get(Field.QUESTION.value, "")
+                answer = source.get(Field.ANSWER.value, "")
+                page_content = f"Q: {question}\nA: {answer}"
+                metadata["chunk_type"] = "qa"
+                metadata["question"] = question
+                metadata["answer"] = answer
+
            # Normalize the score to the [0,1] interval
            normalized_score = res["_score"] / max_score
            docs_and_scores.append((DocumentChunk(page_content=page_content, metadata=metadata), normalized_score))
@@ -652,7 +723,7 @@ class ElasticSearchVector(BaseVector):
                        },
                        Field.VECTOR.value: {
                            "type": "dense_vector",
-                            "dims": len(embeddings[0]),  # Make sure the dimension is correct here,The dimension size of the vector. When index is true, it cannot exceed 1024; when index is false or not specified, it cannot exceed 2048, which can improve retrieval efficiency
+                            "dims": len(next((e for e in embeddings if e is not None), [0]*768)),  # 跳过 None 获取向量维度，fallback 768
                            "index": True,
                            "similarity": "cosine"
                        }
--- a/api/app/core/rag/vdb/field.py
+++ b/api/app/core/rag/vdb/field.py
@@ -14,3 +14,8 @@ class Field(StrEnum):
    DOCUMENT_ID = "metadata.document_id"
    KNOWLEDGE_ID = "metadata.knowledge_id"
    SORT_ID = "metadata.sort_id"
+    # QA fields
+    CHUNK_TYPE = "chunk_type"  # "chunk" | "source" | "qa"
+    QUESTION = "question"
+    ANSWER = "answer"
+    SOURCE_CHUNK_ID = "source_chunk_id"
--- a/api/app/core/rag/vdb/vector_base.py
+++ b/api/app/core/rag/vdb/vector_base.py
@@ -27,14 +27,14 @@ class BaseVector(ABC):
        raise NotImplementedError

    @abstractmethod
-    def delete_by_ids(self, ids: list[str]):
+    def delete_by_ids(self, ids: list[str], *, refresh: bool = False):
        raise NotImplementedError

    def get_ids_by_metadata_field(self, key: str, value: str):
        raise NotImplementedError

    @abstractmethod
-    def delete_by_metadata_field(self, key: str, value: str):
+    def delete_by_metadata_field(self, key: str, value: str, *, refresh: bool = False):
        raise NotImplementedError

    @abstractmethod
--- a/api/app/models/end_user_model.py
+++ b/api/app/models/end_user_model.py
@@ -1,7 +1,7 @@
 import datetime
 import uuid

-from sqlalchemy import Column, DateTime, ForeignKey, Integer, String, Text
+from sqlalchemy import Column, DateTime, ForeignKey, String, Text
 from sqlalchemy.dialects.postgresql import UUID
 from sqlalchemy.orm import relationship

@@ -38,15 +38,6 @@ class EndUser(Base):
        comment="关联的记忆配置ID"
    )
    
-    memory_count = Column(
-        Integer,
-        nullable=False,
-        default=0,
-        server_default="0",
-        index=True,
-        comment="记忆节点总数",
-    )
-
    # 用户摘要四个维度 - User Summary Four Dimensions
    user_summary = Column(Text, nullable=True, comment="缓存的用户摘要（基本介绍）")
    personality_traits = Column(Text, nullable=True, comment="性格特点")
--- a/api/app/schemas/chunk_schema.py
+++ b/api/app/schemas/chunk_schema.py
@@ -20,13 +20,26 @@ class ChunkCreate(BaseModel):

    @property
    def chunk_content(self) -> str:
-        """
-        Get the actual content string regardless of input type
-        """
+        """Get the actual content string regardless of input type"""
        if isinstance(self.content, QAChunk):
-            return f"question: {self.content.question} answer: {self.content.answer}"
+            return self.content.question  # QA 模式下 page_content 存 question
        return self.content

+    @property
+    def is_qa(self) -> bool:
+        return isinstance(self.content, QAChunk)
+
+    @property
+    def qa_metadata(self) -> dict:
+        """返回 QA 相关的 metadata 字段"""
+        if isinstance(self.content, QAChunk):
+            return {
+                "chunk_type": "qa",
+                "question": self.content.question,
+                "answer": self.content.answer,
+            }
+        return {}
+

 class ChunkUpdate(BaseModel):
    content: Union[str, QAChunk] = Field(
@@ -35,13 +48,26 @@ class ChunkUpdate(BaseModel):

    @property
    def chunk_content(self) -> str:
-        """
-        Get the actual content string regardless of input type
-        """
+        """Get the actual content string regardless of input type"""
        if isinstance(self.content, QAChunk):
-            return f"question: {self.content.question} answer: {self.content.answer}"
+            return self.content.question  # QA 模式下 page_content 存 question
        return self.content

+    @property
+    def is_qa(self) -> bool:
+        return isinstance(self.content, QAChunk)
+
+    @property
+    def qa_metadata(self) -> dict:
+        """返回 QA 相关的 metadata 字段"""
+        if isinstance(self.content, QAChunk):
+            return {
+                "chunk_type": "qa",
+                "question": self.content.question,
+                "answer": self.content.answer,
+            }
+        return {}
+

 class ChunkRetrieve(BaseModel):
    query: str
@@ -51,3 +77,8 @@ class ChunkRetrieve(BaseModel):
    vector_similarity_weight: float | None = Field(None)
    top_k: int | None = Field(None)
    retrieve_type: RetrieveType | None = Field(None)
+
+
+class ChunkBatchCreate(BaseModel):
+    """批量创建 chunk"""
+    items: list[ChunkCreate] = Field(..., min_length=1, description="chunk 列表")
--- a/api/app/schemas/end_user_schema.py
+++ b/api/app/schemas/end_user_schema.py
@@ -19,6 +19,4 @@ class EndUser(BaseModel):
    
    # 用户摘要和洞察更新时间
    user_summary_updated_at: Optional[datetime.datetime] = Field(description="用户摘要最后更新时间", default=None)
-    memory_insight_updated_at: Optional[datetime.datetime] = Field(description="洞察报告最后更新时间", default=None)
-    #用户记忆节点总数（Neo4j模式）
-    memory_count: int = Field(description="记忆节点总数", default=0)
+    memory_insight_updated_at: Optional[datetime.datetime] = Field(description="洞察报告最后更新时间", default=None)
--- a/api/app/services/memory_dashboard_service.py
+++ b/api/app/services/memory_dashboard_service.py
@@ -1,5 +1,5 @@
 from sqlalchemy.orm import Session
-from sqlalchemy import desc, nullslast, or_, cast, String, func
+from sqlalchemy import desc, nullslast, or_, and_, cast, String
 from typing import List, Optional, Dict, Any
 import uuid
 from fastapi import HTTPException
@@ -102,7 +102,6 @@ def get_workspace_end_users_paginated(
    """获取工作空间的宿主列表（分页版本，支持模糊搜索）

    返回结果按 created_at 从新到旧排序（NULL 值排在最后）
-    固定过滤 memory_count > 0 的宿主，保证分页基于“有记忆宿主”集合计算。
    支持通过 keyword 参数同时模糊搜索 other_name 和 id 字段

    Args:
@@ -121,8 +120,7 @@ def get_workspace_end_users_paginated(
    try:
        # 构建基础查询
        base_query = db.query(EndUserModel).filter(
-            EndUserModel.workspace_id == workspace_id,
-            EndUserModel.memory_count > 0 , # 只查询有记忆的宿主
+            EndUserModel.workspace_id == workspace_id
        )

        # 构建搜索条件（过滤空字符串和None）
@@ -130,13 +128,20 @@ def get_workspace_end_users_paginated(

        if keyword:
            keyword_pattern = f"%{keyword}%"
+            # other_name 匹配始终生效；id 匹配仅对 other_name 为空的记录生效
            base_query = base_query.filter(
                or_(
                    EndUserModel.other_name.ilike(keyword_pattern),
-                    cast(EndUserModel.id, String).ilike(keyword_pattern),
+                    and_(
+                        or_(
+                            EndUserModel.other_name.is_(None),
+                            EndUserModel.other_name == "",
+                        ),
+                        cast(EndUserModel.id, String).ilike(keyword_pattern),
+                    ),
                )
            )
-            business_logger.info(f"应用模糊搜索: keyword={keyword}（匹配 other_name 或 id）")
+            business_logger.info(f"应用模糊搜索: keyword={keyword}（匹配 other_name；other_name 为空时匹配 id）")

        # 获取总记录数
        total = base_query.count()
@@ -164,98 +169,6 @@ def get_workspace_end_users_paginated(
        business_logger.error(f"获取工作空间宿主列表（分页）失败: workspace_id={workspace_id} - {str(e)}")
        raise

-def get_workspace_end_users_paginated_rag(
-    db: Session,
-    workspace_id: uuid.UUID,
-    current_user: User,
-    page: int,
-    pagesize: int,
-    keyword: Optional[str] = None,
-) -> Dict[str, Any]:
-    """RAG 模式宿主列表分页。
-
-    RAG 记忆数量以 documents.chunk_num 为准：
-    - file_name = end_user_id + ".txt"
-    - 只统计当前 workspace 下 permission_id="Memory" 的用户记忆知识库
-    - 在 SQL 层过滤 chunk 总数为 0 的宿主，保证分页准确
-    """
-    business_logger.info(
-        f"获取 RAG 宿主列表（分页）: workspace_id={workspace_id}, "
-        f"keyword={keyword}, page={page}, pagesize={pagesize}, 操作者: {current_user.username}"
-    )
-
-    try:
-        from app.models.document_model import Document
-        from app.models.knowledge_model import Knowledge
-
-        chunk_subquery = (
-            db.query(
-                Document.file_name.label("file_name"),
-                func.coalesce(func.sum(Document.chunk_num), 0).label("memory_count"),
-            )
-            .join(Knowledge, Document.kb_id == Knowledge.id)
-            .filter(
-                Knowledge.workspace_id == workspace_id,
-                Knowledge.status == 1,
-                Knowledge.permission_id == "Memory",
-                Document.status == 1,
-            )
-            .group_by(Document.file_name)
-            .subquery()
-        )
-
-        base_query = (
-            db.query(
-                EndUserModel,
-                chunk_subquery.c.memory_count.label("memory_count"),
-            )
-            .join(
-                chunk_subquery,
-                chunk_subquery.c.file_name == func.concat(cast(EndUserModel.id, String), ".txt"),
-            )
-            .filter(
-                EndUserModel.workspace_id == workspace_id,
-                chunk_subquery.c.memory_count > 0,
-            )
-        )
-
-        keyword = keyword.strip() if keyword else None
-        if keyword:
-            keyword_pattern = f"%{keyword}%"
-            base_query = base_query.filter(
-                or_(
-                    EndUserModel.other_name.ilike(keyword_pattern),
-                    cast(EndUserModel.id, String).ilike(keyword_pattern),
-                )
-            )
-
-        total = base_query.count()
-        if total == 0:
-            business_logger.info("RAG 模式下没有符合条件的宿主")
-            return {"items": [], "total": 0}
-
-        rows = base_query.order_by(
-            nullslast(desc(EndUserModel.created_at)),
-            desc(EndUserModel.id),
-        ).offset((page - 1) * pagesize).limit(pagesize).all()
-
-        items = []
-        for end_user_orm, memory_count in rows:
-            items.append({
-                "end_user": EndUserSchema.model_validate(end_user_orm),
-                "memory_count": int(memory_count or 0),
-            })
-
-        business_logger.info(f"成功获取 RAG 宿主记录 {len(items)} 条，总计 {total} 条")
-        return {"items": items, "total": total}
-
-    except HTTPException:
-        raise
-    except Exception as e:
-        business_logger.error(
-            f"获取 RAG 宿主列表（分页）失败: workspace_id={workspace_id} - {str(e)}"
-        )
-        raise

 def get_workspace_memory_increment(
    db: Session, 
--- a/api/app/tasks.py
+++ b/api/app/tasks.py
@@ -30,7 +30,7 @@ from app.core.rag.llm.cv_model import QWenCV
 from app.core.rag.llm.embedding_model import OpenAIEmbed
 from app.core.rag.llm.sequence2txt_model import QWenSeq2txt
 from app.core.rag.models.chunk import DocumentChunk
-from app.core.rag.prompts.generator import question_proposal
+from app.core.rag.prompts.generator import question_proposal, qa_proposal
 from app.core.rag.vdb.elasticsearch.elasticsearch_vector import (
    ElasticSearchVectorFactory,
 )
@@ -311,6 +311,7 @@ def parse_document(file_key: str, document_id: uuid.UUID, file_name: str = ""):
            vector_service.delete_by_metadata_field(key="document_id", value=str(document_id))
            # 2.2 Vectorize and import batch documents
            auto_questions_topn = db_document.parser_config.get("auto_questions", 0)
+            qa_prompt = db_document.parser_config.get("qa_prompt", None)
            chat_model = None
            if auto_questions_topn:
                chat_model = Base(
@@ -318,62 +319,123 @@ def parse_document(file_key: str, document_id: uuid.UUID, file_name: str = ""):
                    model_name=db_knowledge.llm.api_keys[0].model_name,
                    base_url=db_knowledge.llm.api_keys[0].api_base,
                )
+                logger.info(f"[QA] LLM model: {db_knowledge.llm.api_keys[0].model_name}, base_url: {db_knowledge.llm.api_keys[0].api_base}")
+                if qa_prompt:
+                    logger.info(f"[QA] Using custom prompt ({len(qa_prompt)} chars)")

            # 预先构建所有 batch 的 chunks，保证 sort_id 全局有序
            all_batch_chunks: list[list[DocumentChunk]] = []

            if auto_questions_topn:
-                # auto_questions 开启：先并发生成所有 chunk 的问题，再按 batch 分组
-                # 构建 (global_idx, item) 列表
+                # QA 模式（FastGPT 方案）：
+                # 1. 原 chunk 标记为 source（保留供 GraphRAG 使用，不参与检索）
+                # 2. LLM 生成 QA 对，每个 QA 对独立存储为 qa chunk
                indexed_items = list(enumerate(res))

-                def _generate_question(idx_item: tuple[int, dict]) -> tuple[int, str]:
-                    """为单个 chunk 生成问题（带缓存），返回 (global_idx, question_text)"""
+                def _generate_qa(idx_item: tuple[int, dict]) -> tuple[int, list]:
+                    """为单个 chunk 生成 QA 对（带缓存），返回 (global_idx, qa_pairs)"""
                    global_idx, item = idx_item
                    content = item["content_with_weight"]
-                    cached = get_llm_cache(chat_model.model_name, content, "question",
-                                           {"topn": auto_questions_topn})
+                    cache_params = {"topn": auto_questions_topn}
+                    if qa_prompt:
+                        import hashlib
+                        cache_params["prompt_hash"] = hashlib.md5(qa_prompt.encode()).hexdigest()[:8]
+                    cached = get_llm_cache(chat_model.model_name, content, "qa", cache_params)
                    if not cached:
-                        cached = question_proposal(chat_model, content, auto_questions_topn)
-                        set_llm_cache(chat_model.model_name, content, cached, "question",
-                                      {"topn": auto_questions_topn})
-                    return global_idx, cached
+                        logger.info(f"[QA] Cache miss for chunk {global_idx}, calling LLM. cache_params={cache_params}")
+                        try:
+                            pairs = qa_proposal(chat_model, content, auto_questions_topn, custom_prompt=qa_prompt)
+                        except Exception as e:
+                            logger.error(f"[QA] LLM call failed: model={chat_model.model_name}, base_url={getattr(chat_model, 'base_url', 'N/A')}, error={e}")
+                            return global_idx, []
+                        logger.info(f"[QA] Chunk {global_idx} generated {len(pairs)} QA pairs")
+                        # 缓存存 JSON 字符串
+                        set_llm_cache(chat_model.model_name, content, json.dumps(pairs, ensure_ascii=False), "qa",
+                                      cache_params)
+                        return global_idx, pairs
+                    logger.info(f"[QA] Cache hit for chunk {global_idx}, cache_params={cache_params}, cached_type={type(cached).__name__}")
+                    # 从缓存读取：可能是 JSON 字符串或旧格式纯文本
+                    if isinstance(cached, str):
+                        try:
+                            parsed = json.loads(cached)
+                            if isinstance(parsed, list):
+                                logger.info(f"[QA] Chunk {global_idx} loaded {len(parsed)} QA pairs from cache")
+                                return global_idx, parsed
+                        except (json.JSONDecodeError, TypeError):
+                            pass
+                        # 旧缓存格式（纯文本问题），尝试解析
+                        from app.core.rag.prompts.generator import parse_qa_pairs
+                        return global_idx, parse_qa_pairs(cached) if cached else []
+                    return global_idx, cached if isinstance(cached, list) else []

-                # 并发调用 LLM 生成问题
-                question_map: dict[int, str] = {}
+                # 并发调用 LLM 生成 QA 对
+                qa_map: dict[int, list] = {}
                with ThreadPoolExecutor(max_workers=AUTO_QUESTIONS_MAX_WORKERS) as q_executor:
-                    futures = {q_executor.submit(_generate_question, item): item[0]
+                    futures = {q_executor.submit(_generate_qa, item): item[0]
                               for item in indexed_items}
                    for future in futures:
-                        global_idx, cached = future.result()
-                        question_map[global_idx] = cached
+                        global_idx, pairs = future.result()
+                        qa_map[global_idx] = pairs

                progress_lines.append(
-                    f"{datetime.now().strftime('%H:%M:%S')} Auto questions generated for {total_chunks} chunks "
+                    f"{datetime.now().strftime('%H:%M:%S')} QA pairs generated for {total_chunks} chunks "
                    f"(workers={AUTO_QUESTIONS_MAX_WORKERS}).")

-                # 按 batch 分组组装 DocumentChunk
-                for batch_start in range(0, total_chunks, EMBEDDING_BATCH_SIZE):
-                    batch_end = min(batch_start + EMBEDDING_BATCH_SIZE, total_chunks)
-                    chunks = []
-                    for global_idx in range(batch_start, batch_end):
-                        item = res[global_idx]
-                        metadata = {
+                # 组装 chunks：source chunks + qa chunks
+                source_chunks = []
+                qa_chunks = []
+                qa_sort_id = 0
+
+                for global_idx in range(total_chunks):
+                    item = res[global_idx]
+                    source_chunk_id = uuid.uuid4().hex
+
+                    # source chunk：保留原文，供 GraphRAG 使用，不参与向量检索
+                    source_meta = {
+                        "doc_id": source_chunk_id,
+                        "file_id": str(db_document.file_id),
+                        "file_name": db_document.file_name,
+                        "file_created_at": int(db_document.created_at.timestamp() * 1000),
+                        "document_id": str(db_document.id),
+                        "knowledge_id": str(db_document.kb_id),
+                        "sort_id": global_idx,
+                        "status": 1,
+                        "chunk_type": "source",
+                    }
+                    source_chunks.append(
+                        DocumentChunk(page_content=item["content_with_weight"], metadata=source_meta))
+
+                    # qa chunks：每个 QA 对独立存储
+                    pairs = qa_map.get(global_idx, [])
+                    for pair in pairs:
+                        qa_meta = {
                            "doc_id": uuid.uuid4().hex,
                            "file_id": str(db_document.file_id),
                            "file_name": db_document.file_name,
                            "file_created_at": int(db_document.created_at.timestamp() * 1000),
                            "document_id": str(db_document.id),
                            "knowledge_id": str(db_document.kb_id),
-                            "sort_id": global_idx,
+                            "sort_id": qa_sort_id,
                            "status": 1,
+                            "chunk_type": "qa",
+                            "question": pair["question"],
+                            "answer": pair["answer"],
+                            "source_chunk_id": source_chunk_id,
                        }
-                        cached = question_map[global_idx]
-                        chunks.append(
-                            DocumentChunk(
-                                page_content=f"question: {cached} answer: {item['content_with_weight']}",
-                                metadata=metadata))
-                    all_batch_chunks.append(chunks)
+                        # page_content 存 question，用于向量索引
+                        qa_chunks.append(
+                            DocumentChunk(page_content=pair["question"], metadata=qa_meta))
+                        qa_sort_id += 1
+
+                # 按 batch 分组（source + qa 一起）
+                all_chunks = source_chunks + qa_chunks
+                for batch_start in range(0, len(all_chunks), EMBEDDING_BATCH_SIZE):
+                    batch_end = min(batch_start + EMBEDDING_BATCH_SIZE, len(all_chunks))
+                    all_batch_chunks.append(all_chunks[batch_start:batch_end])
+
+                progress_lines.append(
+                    f"{datetime.now().strftime('%H:%M:%S')} QA mode: {len(source_chunks)} source chunks + "
+                    f"{len(qa_chunks)} QA chunks prepared.")
            else:
                # 无 auto_questions：直接构建 chunks
                for batch_start in range(0, total_chunks, EMBEDDING_BATCH_SIZE):
@@ -635,6 +697,136 @@ def build_graphrag_for_document(document_id: str, knowledge_id: str):
            return f"build_graphrag_for_document '{document_id}' failed: {e}"


+@celery_app.task(name="app.core.rag.tasks.import_qa_chunks", queue="qa_import")
+def import_qa_chunks(kb_id: str, document_id: str, filename: str, contents: bytes):
+    """
+    异步导入 QA 问答对（CSV/Excel）
+    
+    文件格式：第一行标题（跳过），第一列问题，第二列答案
+    """
+    import csv as csv_module
+    import io
+
+    db = None
+    try:
+        from app.db import get_db_context
+        with get_db_context() as db:
+            db_document = db.query(Document).filter(Document.id == uuid.UUID(document_id)).first()
+            db_knowledge = db.query(Knowledge).filter(Knowledge.id == uuid.UUID(kb_id)).first()
+            if not db_document or not db_knowledge:
+                logger.error(f"[ImportQA] document={document_id} or knowledge={kb_id} not found")
+                return {"error": "document or knowledge not found", "imported": 0}
+
+            # 1. 解析文件
+            qa_pairs = []
+            failed_rows = []
+
+            if filename.endswith(".csv"):
+                try:
+                    text = contents.decode("utf-8-sig")
+                except UnicodeDecodeError:
+                    text = contents.decode("gbk", errors="ignore")
+
+                sniffer = csv_module.Sniffer()
+                try:
+                    dialect = sniffer.sniff(text[:2048])
+                    delimiter = dialect.delimiter
+                except csv_module.Error:
+                    delimiter = "," if "," in text[:500] else "\t"
+
+                reader = csv_module.reader(io.StringIO(text), delimiter=delimiter)
+                for i, row in enumerate(reader):
+                    if i == 0:
+                        continue
+                    if len(row) >= 2 and row[0].strip() and row[1].strip():
+                        qa_pairs.append({"question": row[0].strip(), "answer": row[1].strip()})
+                    elif len(row) >= 1 and row[0].strip():
+                        failed_rows.append(i + 1)
+
+            elif filename.endswith(".xlsx") or filename.endswith(".xls"):
+                try:
+                    import openpyxl
+                    wb = openpyxl.load_workbook(io.BytesIO(contents), read_only=True)
+                    for sheet in wb.worksheets:
+                        for i, row in enumerate(sheet.iter_rows(values_only=True)):
+                            if i == 0:
+                                continue
+                            if len(row) >= 2 and row[0] and row[1]:
+                                q = str(row[0]).strip()
+                                a = str(row[1]).strip()
+                                if q and a:
+                                    qa_pairs.append({"question": q, "answer": a})
+                            elif len(row) >= 1 and row[0]:
+                                failed_rows.append(i + 1)
+                    wb.close()
+                except Exception as e:
+                    logger.error(f"[ImportQA] Excel parse failed: {e}")
+                    return {"error": f"Excel parse failed: {e}", "imported": 0}
+
+            if not qa_pairs:
+                logger.warning(f"[ImportQA] No valid QA pairs found in {filename}")
+                return {"error": "No valid QA pairs found", "imported": 0}
+
+            logger.info(f"[ImportQA] Parsed {len(qa_pairs)} QA pairs from {filename}, failed_rows={failed_rows}")
+
+            # 2. 写入 ES
+            vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
+
+            sort_id = 0
+            total, items = vector_service.search_by_segment(document_id=document_id, pagesize=1, page=1, asc=False)
+            if items:
+                sort_id = items[0].metadata["sort_id"]
+
+            chunks = []
+            for pair in qa_pairs:
+                sort_id += 1
+                doc_id = uuid.uuid4().hex
+                metadata = {
+                    "doc_id": doc_id,
+                    "file_id": str(db_document.file_id),
+                    "file_name": db_document.file_name,
+                    "file_created_at": int(db_document.created_at.timestamp() * 1000),
+                    "document_id": document_id,
+                    "knowledge_id": kb_id,
+                    "sort_id": sort_id,
+                    "status": 1,
+                    "chunk_type": "qa",
+                    "question": pair["question"],
+                    "answer": pair["answer"],
+                }
+                chunks.append(DocumentChunk(page_content=pair["question"], metadata=metadata))
+
+            batch_size = 50
+            for i in range(0, len(chunks), batch_size):
+                batch = chunks[i:i + batch_size]
+                vector_service.add_chunks(batch)
+
+            # 3. 更新 chunk_num 和 progress
+            db_document.chunk_num += len(chunks)
+            db_document.progress = 1.0
+            db_document.progress_msg = f"QA 导入完成: {len(chunks)} 条"
+            db.commit()
+
+            result = {"imported": len(chunks), "failed_rows": failed_rows}
+            logger.info(f"[ImportQA] Done: imported={len(chunks)}, failed={len(failed_rows)}")
+            return result
+
+    except Exception as e:
+        logger.error(f"[ImportQA] Failed: {e}", exc_info=True)
+        # 尝试更新文档状态为失败
+        try:
+            from app.db import get_db_context
+            with get_db_context() as err_db:
+                doc = err_db.query(Document).filter(Document.id == uuid.UUID(document_id)).first()
+                if doc:
+                    doc.progress = -1.0
+                    doc.progress_msg = f"QA 导入失败: {str(e)[:200]}"
+                    err_db.commit()
+        except Exception:
+            pass
+        return {"error": str(e), "imported": 0}
+
+
@celery_app.task(name="app.core.rag.tasks.sync_knowledge_for_kb")
 def sync_knowledge_for_kb(kb_id: uuid.UUID):
    """
--- a/api/migrations/versions/1f85dce125e5_202604271530.py
+++ b/api/migrations/versions/1f85dce125e5_202604271530.py
@@ -1,47 +0,0 @@
-"""202604271530
-
-Revision ID: 1f85dce125e5
-Revises: 4e89970f9e7c
-Create Date: 2026-04-27 15:30:35.614679
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision: str = '1f85dce125e5'
-down_revision: Union[str, None] = '4e89970f9e7c'
-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.add_column('files', sa.Column('file_key', sa.String(length=512), nullable=True, comment='storage file key for FileStorageService'))
-    op.create_index(op.f('ix_files_file_key'), 'files', ['file_key'], unique=False)
-    op.alter_column('model_configs', 'capability',
-               existing_type=postgresql.ARRAY(sa.VARCHAR()),
-               comment="模型能力列表（如['vision', 'audio', 'video', 'thinking']）",
-               existing_comment="模型能力列表（如['vision', 'audio', 'video']）",
-               existing_nullable=False)
-    # ### end Alembic commands ###
-    op.execute("""
-        UPDATE files
-        SET file_key = 'kb/' || kb_id::text || '/' || parent_id::text || '/' || id::text || file_ext
-        WHERE file_ext != 'folder' AND file_key IS NULL
-    """)
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.alter_column('model_configs', 'capability',
-               existing_type=postgresql.ARRAY(sa.VARCHAR()),
-               comment="模型能力列表（如['vision', 'audio', 'video']）",
-               existing_comment="模型能力列表（如['vision', 'audio', 'video', 'thinking']）",
-               existing_nullable=False)
-    op.drop_index(op.f('ix_files_file_key'), table_name='files')
-    op.drop_column('files', 'file_key')
-    # ### end Alembic commands ###
--- a/api/migrations/versions/e2d60c6d1a1a_202604281230.py
+++ b/api/migrations/versions/e2d60c6d1a1a_202604281230.py
@@ -1,34 +0,0 @@
-"""202604281230
-
-Revision ID: e2d60c6d1a1a
-Revises: 1f85dce125e5
-Create Date: 2026-04-28 12:32:01.643954
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision: str = 'e2d60c6d1a1a'
-down_revision: Union[str, None] = '1f85dce125e5'
-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_column('tenants', 'api_ops_rate_limit')
-    op.drop_column('tenants', 'plan')
-    op.drop_column('tenants', 'plan_expired_at')
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.add_column('tenants', sa.Column('plan_expired_at', postgresql.TIMESTAMP(), autoincrement=False, nullable=True))
-    op.add_column('tenants', sa.Column('plan', sa.VARCHAR(length=50), autoincrement=False, nullable=True))
-    op.add_column('tenants', sa.Column('api_ops_rate_limit', sa.VARCHAR(length=100), autoincrement=False, nullable=True))
-    # ### end Alembic commands ###
--- a/web/src/assets/images/index/index_bg.png
+++ b/web/src/assets/images/index/index_bg.png
--- a/web/src/assets/images/index/index_bg@2x.png
+++ b/web/src/assets/images/index/index_bg@2x.png
--- a/web/src/assets/images/login/bg.mp4
+++ b/web/src/assets/images/login/bg.mp4
--- a/web/src/assets/images/login/check.png
+++ b/web/src/assets/images/login/check.png
--- a/web/src/assets/images/login/check.svg
+++ b/web/src/assets/images/login/check.svg
@@ -1,13 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<svg width="16px" height="16px" viewBox="0 0 16 16" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
-    <title>勾选</title>
-    <g id="空间外层页面优化" stroke="none" stroke-width="1" fill="none" fill-rule="evenodd">
-        <g id="登录页面" transform="translate(-64, -611)" fill="#FFFFFF" fill-rule="nonzero">
-            <g id="编组-8" transform="translate(64, 608)">
-                <g id="勾选" transform="translate(0, 3)">
-                    <path d="M12,0 C14.209139,0 16,1.790861 16,4 L16,12 C16,14.209139 14.209139,16 12,16 L4,16 C1.790861,16 0,14.209139 0,12 L0,4 C0,1.790861 1.790861,4.4408921e-16 4,0 L12,0 Z M11.9182266,4.80024782 C11.7273831,4.80024782 11.5444062,4.87629473 11.4097812,5.0115625 L6.552,9.86932813 L4.4284375,7.74489063 C4.29381317,7.60962766 4.11083967,7.53358379 3.92,7.53358379 C3.72916033,7.53358379 3.54618683,7.60962766 3.4115625,7.74489063 C3.27602096,7.87955071 3.19979999,8.06271883 3.19979999,8.25378125 C3.19979999,8.44484367 3.27602096,8.62801179 3.4115625,8.76267188 L6.0453125,11.3946719 C6.17993745,11.5299396 6.3629143,11.6059866 6.55375781,11.6059866 C6.74460132,11.6059866 6.92757818,11.5299396 7.06220312,11.3946719 L12.4311094,6.02667188 C12.5659036,5.89187668 12.6412595,5.70881589 12.6404302,5.51818919 C12.639587,5.3275625 12.5626279,5.14516989 12.4266562,5.0115625 C12.2920469,4.87629473 12.1090701,4.80024782 11.9182266,4.80024782 Z" id="形状结合"></path>
-                </g>
-            </g>
-        </g>
-    </g>
-</svg>
--- a/web/src/assets/images/login/title_en.png
+++ b/web/src/assets/images/login/title_en.png
--- a/web/src/assets/images/login/title_zh.png
+++ b/web/src/assets/images/login/title_zh.png
--- a/web/src/styles/index.css
+++ b/web/src/styles/index.css
@@ -467,29 +467,4 @@ input:-webkit-autofill:active {
  animation-name: onAutoFillStart;
  animation-duration: 1ms;
 }
-@keyframes onAutoFillStart { from {} to {} }
-/* Login input placeholder */
-.login-input input::placeholder {
-  color: #A8A9AA !important;
-}
-
-.login-input {
-  border-color: #A8A9AA;
-}
-
-/* Login input hover/focus border */
-.login-input:hover,
-.login-input:focus-within {
-  border-color: #FFFFFF !important;
-  box-shadow: none !important;
-}
-
-/* Override browser autofill styles */
-.login-input input:-webkit-autofill,
-.login-input input:-webkit-autofill:hover,
-.login-input input:-webkit-autofill:focus,
-.login-input input:-webkit-autofill:active {
-  -webkit-box-shadow: 0 0 0px 1000px #0A0A0A inset !important;
-  -webkit-text-fill-color: #FFFFFF !important;
-  transition: background-color 5000s ease-in-out 0s !important;
-}
+@keyframes onAutoFillStart { from {} to {} }
--- a/web/src/views/Index/index.tsx
+++ b/web/src/views/Index/index.tsx
@@ -102,7 +102,7 @@ const Index = () => {
    <Flex gap={12} wrap="nowrap" className="rb:w-full! rb:h-full! rb:overflow-y-auto">
      <div className="rb:flex-1 rb:min-w-0">
        <Flex vertical>
-          <div className='rb:w-full rb:h-26 rb:p-4 rb:bg-cover rb:bg-[url("@/assets/images/index/index_bg.png")] rb:rounded-xl rb:overflow-hidden'>
+          <div className='rb:w-full rb:h-26 rb:p-4 rb:bg-cover rb:bg-[url("@/assets/images/index/index_bg@2x.png")] rb:rounded-xl rb:overflow-hidden'>
            <div className="rb:font-[MiSans-Bold] rb:font-bold rb:text-white rb:text-[18px] rb:leading-7">
              {t('index.spaceTitle')}
            </div>
--- a/web/src/views/Login/index.tsx
+++ b/web/src/views/Login/index.tsx
@@ -14,33 +14,27 @@ import React, { useState, useEffect } from 'react';
 import { useTranslation } from 'react-i18next';
 import { Button, Input, Form, App } from 'antd';
 import type { FormProps } from 'antd';
-import clsx from 'clsx';

 import { useUser, type LoginInfo } from '@/store/user';
 import { login } from '@/api/user'
-import loginBg from '@/assets/images/login/bg.mp4'
-import check from '@/assets/images/login/check.svg'
+import loginBg from '@/assets/images/login/loginBg.png'
+import check from '@/assets/images/login/check.png'
 import email from '@/assets/images/login/email.svg'
 import lock from '@/assets/images/login/lock.svg'
 import type { LoginForm } from './types';
-import { useI18n } from '@/store/locale'

 /**
 * Input field styling
 */
-const inputClassName = "login-input rb:rounded-[8px]! rb:p-[12px]! rb:h-[44px]! rb:bg-transparent! rb:text-[#FFFFFF]! [&_input]:rb:text-[#FFFFFF]! [&_input]:rb:caret-[#FFFFFF]!"
+const inputClassName = "rb:rounded-[8px]! rb:p-[12px]! rb:h-[44px]!"

 /**
 * Login page component
 */const LoginPage: React.FC = () => {
  const { t } = useTranslation();
  const { clearUserInfo, updateLoginInfo, getUserInfo } = useUser();
-  const { language } = useI18n()
  const [loading, setLoading] = useState(false);
  const [form] = Form.useForm<LoginForm>();
-  const emailVal = Form.useWatch('email', form);
-  const passwordVal = Form.useWatch('password', form);
-  const canLogin = !!(emailVal && passwordVal);
  const { message } = App.useApp();

  useEffect(() => {
@@ -49,7 +43,6 @@ const inputClassName = "login-input rb:rounded-[8px]! rb:p-[12px]! rb:h-[44px]!

  /** Handle login form submission */
  const handleLogin: FormProps<LoginForm>['onFinish'] = async (values) => {
-    if (!canLogin) return;
    if (!values.email) {
      message.warning(t('login.emailPlaceholder'));
      return;
@@ -71,45 +64,42 @@ const inputClassName = "login-input rb:rounded-[8px]! rb:p-[12px]! rb:h-[44px]!


  return (
-    <div className="rb:min-h-screen rb:flex rb:h-screen rb:bg-[#0A0A0A] rb:text-[#FFFFFF]">
+    <div className="rb:min-h-screen rb:flex rb:h-screen">
      <div className="rb:relative rb:w-1/2 rb:h-screen rb:overflow-hidden">
-        <video src={loginBg} loop autoPlay playsInline muted className="rb:w-full rb:h-full rb:object-cover"></video>
-        <div className="rb:absolute rb:top-10 rb:left-12">
-          <div className={clsx("rb:h-8.25 rb:bg-cover", {
-            "rb:w-89 rb:bg-[url('@/assets/images/login/title_en.png')]": language !== 'zh',
-            "rb:w-42 rb:bg-[url('@/assets/images/login/title_zh.png')]": language === 'zh'
-          })}></div>
-          <div className="rb:text-[18px] rb:text-[rgba(255,255,255,0.7)] rb:leading-6.25 rb:font-regular rb:mt-3">{t('login.subTitle')}</div>
+        <img src={loginBg} alt="loginBg" className="rb:w-full rb:h-full rb:object-cover rb:absolute rb:top-1/2 rb:-translate-y-1/2 rb:left-0" />
+        <div className="rb:absolute rb:top-14 rb:left-16">
+          <div className="rb:text-[28px] rb:leading-8.25 rb:font-bold rb:font-[AlimamaShuHeiTi,AlimamaShuHeiTi] rb:mb-4">{t('login.title')}</div>
+          <div className="rb:text-[18px] rb:leading-6.25 rb:font-regular">{t('login.subTitle')}</div>
        </div>

-        <div className="rb:absolute rb:bottom-14 rb:left-12 rb:right-12 rb:grid rb:grid-cols-2 rb:gap-x-30 rb:gap-y-10.75">
-          {['intelligentMemory', 'instantRecall', 'knowledgeAssociation'].map((key, index) => (
-            <div key={key} className={`rb:flex${index === 0 ? ' rb:col-span-2' : ''}`}>
+        <div className="rb:absolute rb:bottom-20.25 rb:left-16 rb:grid rb:grid-cols-2 rb:gap-x-30 rb:gap-y-10.75">
+          {['intelligentMemory', 'instantRecall', 'knowledgeAssociation'].map(key => (
+            <div key={key} className="rb:flex">
              <img src={check} className="rb:w-4 rb:h-4 rb:mr-2 rb:mt-0.75" />
              <div className="rb:text-[16px] rb:leading-5.5">
                <div className="rb:font-medium">{t(`login.${key}`)}</div>
-                <div className="rb:text-[14px] rb:text-[rgba(255,255,255,0.7)] rb:leading-5 rb:font-regular! rb:mt-2">{t(`login.${key}Desc`)}</div>
+                <div className="rb:text-[#5B6167] rb:text-[14px] rb:leading-5 rb:font-regular! rb:mt-2">{t(`login.${key}Desc`)}</div>
              </div>
            </div>
          ))}
        </div>
      </div>

-      <div className="rb:flex rb:items-center rb:justify-center rb:flex-[1_1_auto]">
-        <div className="rb:w-110 rb:mx-auto">
-          <div className="rb:text-center rb:text-[24px] rb:font-[MiSans-Bold] rb:font-bold rb:leading-8 rb:mb-12">{t('login.welcome')}</div>
+      <div className="rb:bg-[#FFFFFF] rb:flex rb:items-center rb:justify-center rb:flex-[1_1_auto]">
+        <div className="rb:w-100 rb:mx-auto">
+          <div className="rb:text-center rb:text-[28px] rb:font-semibold rb:leading-8 rb:mb-12">{t('login.welcome')}</div>
          <Form
            form={form}
            onFinish={handleLogin}
          >
-            <Form.Item name="email" className="rb:mb-6!">
+            <Form.Item name="email" className="rb:mb-5!">
              <Input
                prefix={<img src={email} className="rb:w-5 rb:h-5 rb:mr-2" />}
                placeholder={t('login.emailPlaceholder')}
                className={inputClassName}
              />
            </Form.Item>
-            <Form.Item name="password" className="rb:mb-0!">
+            <Form.Item name="password">
              <Input.Password
                prefix={<img src={lock} className="rb:w-5 rb:h-5 rb:mr-2" />}
                placeholder={t('login.passwordPlaceholder')}
@@ -121,11 +111,7 @@ const inputClassName = "login-input rb:rounded-[8px]! rb:p-[12px]! rb:h-[44px]!
              block
              loading={loading}
              htmlType="submit"
-              disabled={!canLogin}
-              className={clsx("rb:h-11.5! rb:rounded-lg! rb:mt-12", {
-                'rb:hover:bg-[#2d6ef1]! rb:bg-[#155EEF]! rb:border-[#155EEF]!': canLogin,
-                'rb:bg-[#171719]! rb:border-[#171719]!': !canLogin
-              })}
+              className="rb:h-10! rb:rounded-lg! rb:mt-4"
            >
              {t('login.loginIn')}
            </Button>
--- a/web/src/views/ToolManagement/Market.tsx
+++ b/web/src/views/ToolManagement/Market.tsx
@@ -361,7 +361,7 @@ const Market: React.FC<{ getStatusTag?: (status: string) => ReactNode }> = () =>
              )}
            </Flex>
            <div>
-              <div className="rb:font-[MiSans-Bold] rb:font-bold rb:text-[16px] rb:leading-5.5">{source.name}</div>
+              <div className="rb:font-[MiSans Bold] rb:font-bold rb:text-[16px] rb:leading-5.5">{source.name}</div>
              <div className="rb:text-[#5B6167] rb:text-[12px] rb:leading-4.5">{t('tool.availableMcp')} ({mcpTotal})</div>
            </div>
          </Flex>
Author	SHA1	Message	Date
Mark	f8d1ed51a7	[fix] system prompt fit error	2026-05-07 19:37:34 +08:00
Mark	9fa83ed01e	[modify] QA pair	2026-05-07 19:04:19 +08:00
Mark	e222490bce	[add] batch add chunk for v1	2026-05-07 18:45:36 +08:00
Mark	ad2e885f72	[fix] index_not_found_exception	2026-05-06 18:34:07 +08:00
Mark	70c6d161c8	[fix] delete chunk refresh index	2026-05-06 15:19:46 +08:00
Mark	f85c0594c9	[fix] es vector	2026-04-29 15:24:25 +08:00
Mark	5fceba54b4	[fix] file upload	2026-04-29 13:41:14 +08:00
Mark	6e89302cb2	no message	2026-04-29 11:44:03 +08:00
Mark	90aa4cef21	[add] import qa chunks	2026-04-28 16:38:14 +08:00
Mark	6c47bb77ab	[add] task log	2026-04-28 16:13:26 +08:00
Mark	f667936664	[fix] qa cache	2026-04-28 15:53:07 +08:00
Mark	64e640d882	[add] batch chunk. qa_prompt set	2026-04-28 15:33:44 +08:00
Mark	140311048a	[modify] rag qa chunk	2026-04-28 14:04:36 +08:00