[RAG] 彻底改回同步上传(模型已换v3足够快)，删除异步后台线程代码

langchain-chat/server/knowledge_base/kb_doc_api.py

@@ -270,75 +270,6 @@ def upload_docs(
     return BaseResponse(code=200, msg="文件上传与向量化完成", data={"failed_files": failed_files})
 
 
-def _background_llm_and_vectorize(
-    knowledge_base_name: str,
-    file_names: List[str],
-    chunk_size: int,
-    chunk_overlap: int,
-    zh_title_enhance: bool,
-    docs: dict,
-    not_refresh_vs_cache: bool,
-    embedding_ids: dict,
-):
-    """后台线程：LLM 导读 + 向量化，完成后直连 MySQL 更新结果。"""
-    import time
-    import pymysql
-    start_time = time.time()
-
-    kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
-
-    for filename in file_names:
-        try:
-            knowledge_file = KnowledgeFile(filename=filename, knowledge_base_name=knowledge_base_name)
-            new_loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(new_loop)
-            try:
-                llm_result = new_loop.run_until_complete(knowledge_file.get_llm_result())
-            finally:
-                new_loop.close()
-
-            # 直连 MySQL 更新导读结果
-            embedding_id = embedding_ids.get(filename, filename)
-            try:
-                conn = pymysql.connect(**ck_mysql_config)
-                with conn.cursor() as cursor:
-                    cursor.execute(
-                        "UPDATE gpt_upload_file SET article_abstract=%s, article_keywords=%s, article_paragraph=%s WHERE embedding_id=%s",
-                        (
-                            str(llm_result.get("article_abstract", "生成摘要失败")),
-                            str(llm_result.get("article_keywords", "生成关键词失败")),
-                            str(llm_result.get("article_paragraph", "生成章节速览失败")),
-                            embedding_id
-                        )
-                    )
-                    conn.commit()
-                conn.close()
-                logger.info(f"[后台] LLM 导读已更新到数据库: {filename}")
-            except Exception as db_e:
-                logger.error(f"[后台] MySQL 更新失败 {filename}: {db_e}")
-
-        except Exception as e:
-            logger.error(f"[后台] LLM 导读生成失败 {filename}: {e}")
-
-    # 向量化
-    try:
-        _update_docs_impl(
-            knowledge_base_name=knowledge_base_name,
-            file_names=file_names,
-            override_custom_docs=True,
-            chunk_size=chunk_size,
-            chunk_overlap=chunk_overlap,
-            zh_title_enhance=zh_title_enhance,
-            docs=docs,
-            not_refresh_vs_cache=True,
-        )
-        if kb and not not_refresh_vs_cache:
-            kb.save_vector_store()
-    except Exception as e:
-        logger.error(f"[后台] 向量化失败: {e}")
-    logger.info(f"[后台] 总耗时: {time.time() - start_time:.2f}s")
-
-
 def upload_docs_new(
         files: List[UploadFile] = File(..., description="上传文件，支持多文件"),
         knowledge_base_name: str = Form(..., description="知识库名称", examples=["samples"]),
@@ -352,7 +283,7 @@ def upload_docs_new(
         not_refresh_vs_cache: bool = Form(False, description="暂不保存向量库（用于FAISS）"),
 ) -> BaseResponse:
     """
-    API接口：上传文件，提取全文后快速返回，LLM导读+向量化后台异步执行并直连MySQL更新结果
+    API接口：上传文件，同步生成导读（模型已优化为deepseek-v3），然后向量化
     """
     import time
     start_time = time.time()
@@ -373,51 +304,61 @@ def upload_docs_new(
     failed_files = {}
     file_names = list(docs.keys())
     llm_results = {}
-    embedding_ids = {}
 
-    # 保存文件到磁盘 + 提取全文（快速）
     for result in _save_files_in_thread(files, knowledge_base_name=knowledge_base_name, override=override):
         filename = result["data"]["file_name"]
         if result["code"] != 200:
             failed_files[filename] = result["msg"]
+
         if filename not in file_names:
             file_names.append(filename)
-        embedding_ids[filename] = filename
 
         try:
             knowledge_file = KnowledgeFile(filename=filename, knowledge_base_name=knowledge_base_name)
-            full_text_data = knowledge_file.get_full_text()
+            import concurrent.futures
-            import json as _json
+            def run_async_in_thread():
-            try:
+                new_loop = asyncio.new_event_loop()
-                full_text = _json.loads(full_text_data).get("full_text", "")
+                asyncio.set_event_loop(new_loop)
-            except:
+                try:
-                full_text = ""
+                    return new_loop.run_until_complete(knowledge_file.get_llm_result())
+                finally:
+                    new_loop.close()
+
+            with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+                future = executor.submit(run_async_in_thread)
+                llm_result = future.result()
             llm_results[filename] = {
-                "full_text": full_text,
+                "full_text": llm_result.get("full_text", "获取全文失败"),
-                "article_abstract": "导读生成中，请稍后刷新查看...",
+                "article_abstract": llm_result.get("article_abstract", "生成摘要失败"),
-                "article_keywords": "导读生成中，请稍后刷新查看...",
+                "article_keywords": llm_result.get("article_keywords", "生成关键词失败"),
-                "article_paragraph": "导读生成中，请稍后刷新查看..."
+                "article_paragraph": llm_result.get("article_paragraph", "生成章节速览失败")
             }
         except Exception as e:
-            logger.error(f"提取全文失败 {filename}: {e}")
+            logger.error(f"生成LLM结果时出错：{e}", exc_info=e if log_verbose else None)
             llm_results[filename] = {
-                "full_text": "",
+                "article_abstract": "生成摘要失败",
-                "article_abstract": "导读生成中，请稍后刷新查看...",
+                "article_keywords": "生成关键词失败",
-                "article_keywords": "导读生成中，请稍后刷新查看...",
+                "article_paragraph": "生成章节速览失败"
-                "article_paragraph": "导读生成中，请稍后刷新查看..."
             }
 
-    # 后台异步：LLM 导读 + 向量化，完成后直连 MySQL 更新
+    if to_vector_store:
-    import threading
+        update_st = time.time()
-    threading.Thread(
+        result = _update_docs_impl(
-        target=_background_llm_and_vectorize,
+            knowledge_base_name=knowledge_base_name,
-        args=(knowledge_base_name, file_names, chunk_size, chunk_overlap,
+            file_names=file_names,
-              zh_title_enhance, docs, not_refresh_vs_cache, embedding_ids),
+            override_custom_docs=True,
-        daemon=True
+            chunk_size=chunk_size,
-    ).start()
+            chunk_overlap=chunk_overlap,
-
+            zh_title_enhance=zh_title_enhance,
-    logger.info(f"文件上传+全文提取: {time.time() - start_time:.2f}s，LLM+向量化转后台")
+            docs=docs,
-    return BaseResponse(code=200, msg="文件上传完成，导读生成中", data={
+            not_refresh_vs_cache=True,
+        )
+        failed_files.update(result.data["failed_files"])
+        if not not_refresh_vs_cache:
+            kb.save_vector_store()
+        logger.info(f'向量化用时：{time.time() - update_st}')
+    logger.info(f"总执行时间: {time.time() - start_time:.2f}s")
+    return BaseResponse(code=200, msg="文件上传与向量化完成", data={
         "failed_files": failed_files,
         "llm_results": llm_results
     })