[add] batch chunk. qa_prompt set

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] = {}