[add] batch chunk. qa_prompt set

api/app/controllers/chunk_controller.py

@@ -285,6 +285,67 @@ 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.get("/{kb_id}/{document_id}/{doc_id}", response_model=ApiResponse)
 async def get_chunk(
         kb_id: uuid.UUID,

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"))
 

api/app/core/rag/prompts/generator.py

@@ -138,9 +138,19 @@ def question_proposal(chat_mdl, content, topn=3):
     return "\n".join([p["question"] for p in pairs])
 
 
-def qa_proposal(chat_mdl, content, topn=3):
-    """生成 QA 对，返回 [{"question": ..., "answer": ...}, ...]"""
-    template = PROMPT_JINJA_ENV.from_string(QUESTION_PROMPT_TEMPLATE)
+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)
+    else:
+        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: "}]

api/app/schemas/chunk_schema.py

@@ -77,3 +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")

api/app/tasks.py

@@ -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(
@@ -318,6 +319,9 @@ 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]] = []
@@ -335,15 +339,27 @@ def parse_document(file_key: str, document_id: uuid.UUID, file_name: str = ""):
                     cached = get_llm_cache(chat_model.model_name, content, "qa",
                                            {"topn": auto_questions_topn})
                     if not cached:
-                        pairs = qa_proposal(chat_model, content, auto_questions_topn)
-                        cached = pairs
-                        set_llm_cache(chat_model.model_name, content, cached, "qa",
+                        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, []
+                        # 缓存存 JSON 字符串
+                        set_llm_cache(chat_model.model_name, content, json.dumps(pairs, ensure_ascii=False), "qa",
                                       {"topn": auto_questions_topn})
-                    elif isinstance(cached, str):
-                        # 兼容旧缓存格式（纯文本问题）
+                        return global_idx, pairs
+                    # 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):
+                                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
-                        cached = parse_qa_pairs(cached) if cached else []
-                    return global_idx, cached
+                        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 QA 对
                 qa_map: dict[int, list] = {}