Merge branch 'feature/rag2' into develop

* feature/rag2:
  [add] batch add chunk for v1
  [fix] index_not_found_exception
  [fix] delete chunk refresh index
  [fix] es vector
  [fix] file upload
  no message
  [add] import qa chunks
  [add] task log
  [fix] qa cache
  [add] batch chunk.  qa_prompt set
  [modify] rag qa chunk

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

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)