[前端+RAG] 修复PDF文字重叠；上传异步化(LLM+向量化后台执行)；摘要关键词模型改为deepseek-v3

langchain-chat/server/knowledge_base/kb_doc_api.py

@@ -269,6 +269,62 @@ 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,
+):
+    """后台线程：执行 LLM 导读生成 + 向量化，不阻塞上传响应。"""
+    import time
+    start_time = time.time()
+
+    kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
+
+    # 1. 生成 LLM 导读（摘要、关键词、章节速览）
+    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()
+
+            # 将 LLM 结果写入缓存文件，供 Java 后端轮询读取
+            import json
+            cache_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "knowledge_base", knowledge_base_name)
+            os.makedirs(cache_dir, exist_ok=True)
+            cache_file = os.path.join(cache_dir, f"{filename}.llm_result.json")
+            with open(cache_file, 'w', encoding='utf-8') as f:
+                json.dump(llm_result, f, ensure_ascii=False)
+            logger.info(f"[后台] LLM 导读生成完成: {filename}")
+        except Exception as e:
+            logger.error(f"[后台] LLM 导读生成失败 {filename}: {e}")
+
+    # 2. 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()
+        logger.info(f"[后台] 向量化完成，总耗时: {time.time() - start_time:.2f}s")
+    except Exception as e:
+        logger.error(f"[后台] 向量化失败: {e}")
+
+
 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"]),
@@ -282,16 +338,15 @@ 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，并/或向量化
+    API接口：上传文件，先提取全文快速返回，LLM导读+向量化后台异步执行
     """
-    import time  # 添加计时模块
+    import time
     start_time = time.time()
     if not validate_kb_name(knowledge_base_name):
         return BaseResponse(code=403, msg="Don't attack me")
 
     kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
     if kb is None:
-        # 自动创建知识库
         kb = KBServiceFactory.get_service(knowledge_base_name, DEFAULT_VS_TYPE, EMBEDDING_MODEL)
         try:
             kb.create_kb()
@@ -303,68 +358,52 @@ def upload_docs_new(
 
     failed_files = {}
     file_names = list(docs.keys())
-
-    # 生成摘要、关键词、章节速览的结果存储
     llm_results = {}
 
-    # 先将上传的文件保存到磁盘
+    # 保存文件到磁盘 + 提取全文（快速操作）
     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)
 
-        # 针对成功上传的文件，生成摘要、关键词、章节速览
+        # 仅提取全文（快速），不调用 LLM
         try:
             knowledge_file = KnowledgeFile(filename=filename, knowledge_base_name=knowledge_base_name)
-            # 使用线程池运行异步函数，避免事件循环冲突
-            import concurrent.futures
-            def run_async_in_thread():
-                new_loop = asyncio.new_event_loop()
-                asyncio.set_event_loop(new_loop)
-                try:
-                    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()
+            full_text_data = knowledge_file.get_full_text()
+            import json as _json
+            try:
+                full_text = _json.loads(full_text_data).get("full_text", "")
+            except:
+                full_text = ""
             llm_results[filename] = {
-                "full_text": llm_result.get("full_text", "获取全文失败"),
-                "article_abstract": llm_result.get("article_abstract", "生成摘要失败"),
-                "article_keywords": llm_result.get("article_keywords", "生成关键词失败"),
-                "article_paragraph": llm_result.get("article_paragraph", "生成章节速览失败")
+                "full_text": full_text,
+                "article_abstract": "导读生成中...",
+                "article_keywords": "导读生成中...",
+                "article_paragraph": "导读生成中..."
             }
         except Exception as e:
-            logger.error(f"生成LLM结果时出错：{e}", exc_info=e if log_verbose else None)
+            logger.error(f"提取全文失败 {filename}: {e}")
             llm_results[filename] = {
-                "article_abstract": "生成摘要失败",
-                "article_keywords": "生成关键词失败",
-                "article_paragraph": "生成章节速览失败"
+                "full_text": "",
+                "article_abstract": "导读生成中...",
+                "article_keywords": "导读生成中...",
+                "article_paragraph": "导读生成中..."
             }
 
-    # 对保存的文件进行向量化
-    if to_vector_store:
-        update_st = time.time()
-        result = _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,
-        )
-        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={
+    # 后台异步执行 LLM 导读 + 向量化（不阻塞响应）
+    import threading
+    bg_thread = threading.Thread(
+        target=_background_llm_and_vectorize,
+        args=(knowledge_base_name, file_names, chunk_size, chunk_overlap,
+              zh_title_enhance, docs, not_refresh_vs_cache),
+        daemon=True
+    )
+    bg_thread.start()
+
+    logger.info(f"文件上传+全文提取用时: {time.time() - start_time:.2f}s，LLM+向量化已转后台")
+    return BaseResponse(code=200, msg="文件上传完成，导读生成中", data={
         "failed_files": failed_files,
         "llm_results": llm_results
     })

langchain-chat/server/knowledge_base/utils.py

@@ -390,7 +390,7 @@ class KnowledgeFile:
             llm_time = time.time()
             abstract_task = get_llm_model_response_async(
                 strategy_name="gen_abstract",
-                llm_model_name=LLM_MODELS[1],
+                llm_model_name=LLM_MODELS[0],
                 template_prompt_name="gen_abstract",
                 prompt_param_dict={"context": full_text},
                 temperature=0.7,
@@ -399,7 +399,7 @@ class KnowledgeFile:
 
             keywords_task = get_llm_model_response_async(
                 strategy_name="gen_keywords",
-                llm_model_name=LLM_MODELS[1],
+                llm_model_name=LLM_MODELS[0],
                 template_prompt_name="gen_keywords",
                 prompt_param_dict={"context": full_text},
                 temperature=0.7,