fix(tool): 增强信息提取失败时的错误日志

在信息提取过程中，当大语言模型（LLM）返回的 JSON 格式不正确时，先前的日志只会记录一个通用的解析错误，而不会显示导致失败的原始响应内容，这使得调试变得困难。

此次更新通过在捕获到 JSON 解析异常时，额外记录 LLM 的原始输出内容来解决此问题。这有助于快速诊断并定位是模型输出不稳定还是提示词需要调整，从而提高了脚本的健壮性和可维护性。

此外，还对代码进行了一些格式化调整以提高可读性。

scripts/lpmm_learning_tool.py

@@ -38,11 +38,13 @@ file_lock = Lock()
 
 # --- 模块一：数据预处理 ---
 
+
 def process_text_file(file_path):
     with open(file_path, "r", encoding="utf-8") as f:
         raw = f.read()
     return [p.strip() for p in raw.split("\n\n") if p.strip()]
 
+
 def preprocess_raw_data():
     logger.info("--- 步骤 1: 开始数据预处理 ---")
     os.makedirs(RAW_DATA_PATH, exist_ok=True)
@@ -50,7 +52,7 @@ def preprocess_raw_data():
     if not raw_files:
         logger.warning(f"警告: 在 '{RAW_DATA_PATH}' 中没有找到任何 .txt 文件")
         return []
-    
+
     all_paragraphs = []
     for file in raw_files:
         logger.info(f"正在处理文件: {file.name}")
@@ -61,8 +63,10 @@ def preprocess_raw_data():
     logger.info("--- 数据预处理完成 ---")
     return unique_paragraphs
 
+
 # --- 模块二：信息提取 ---
 
+
 def get_extraction_prompt(paragraph: str) -> str:
     return f"""
 请从以下段落中提取关键信息。你需要提取两种类型的信息：
@@ -81,6 +85,7 @@ def get_extraction_prompt(paragraph: str) -> str:
 ---
 """
 
+
 async def extract_info_async(pg_hash, paragraph, llm_api):
     temp_file_path = os.path.join(TEMP_DIR, f"{pg_hash}.json")
     with file_lock:
@@ -92,11 +97,13 @@ async def extract_info_async(pg_hash, paragraph, llm_api):
                 os.remove(temp_file_path)
 
     prompt = get_extraction_prompt(paragraph)
+    content = None
     try:
         content, (_, _, _) = await llm_api.generate_response_async(prompt)
         extracted_data = orjson.loads(content)
         doc_item = {
-            "idx": pg_hash, "passage": paragraph,
+            "idx": pg_hash,
+            "passage": paragraph,
             "extracted_entities": extracted_data.get("entities", []),
             "extracted_triples": extracted_data.get("triples", []),
         }
@@ -106,27 +113,45 @@ async def extract_info_async(pg_hash, paragraph, llm_api):
         return doc_item, None
     except Exception as e:
         logger.error(f"提取信息失败：{pg_hash}, 错误：{e}")
+        if content:
+            logger.error(f"导致解析失败的原始输出: {content}")
         return None, pg_hash
 
+
 def extract_info_sync(pg_hash, paragraph, llm_api):
     return asyncio.run(extract_info_async(pg_hash, paragraph, llm_api))
 
+
 def extract_information(paragraphs_dict, model_set):
     logger.info("--- 步骤 2: 开始信息提取 ---")
     os.makedirs(OPENIE_OUTPUT_DIR, exist_ok=True)
     os.makedirs(TEMP_DIR, exist_ok=True)
-    
+
     llm_api = LLMRequest(model_set=model_set)
     failed_hashes, open_ie_docs = [], []
 
     with ThreadPoolExecutor(max_workers=5) as executor:
-        f_to_hash = {executor.submit(extract_info_sync, p_hash, p, llm_api): p_hash for p_hash, p in paragraphs_dict.items()}
+        f_to_hash = {
-        with Progress(SpinnerColumn(), TextColumn("[progress.description]{task.description}"), BarColumn(), TaskProgressColumn(), MofNCompleteColumn(), "•", TimeElapsedColumn(), "<", TimeRemainingColumn()) as progress:
+            executor.submit(extract_info_sync, p_hash, p, llm_api): p_hash for p_hash, p in paragraphs_dict.items()
+        }
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            BarColumn(),
+            TaskProgressColumn(),
+            MofNCompleteColumn(),
+            "•",
+            TimeElapsedColumn(),
+            "<",
+            TimeRemainingColumn(),
+        ) as progress:
             task = progress.add_task("[cyan]正在提取信息...", total=len(paragraphs_dict))
             for future in as_completed(f_to_hash):
                 doc_item, failed_hash = future.result()
-                if failed_hash: failed_hashes.append(failed_hash)
+                if failed_hash:
-                elif doc_item: open_ie_docs.append(doc_item)
+                    failed_hashes.append(failed_hash)
+                elif doc_item:
+                    open_ie_docs.append(doc_item)
                 progress.update(task, advance=1)
 
     if open_ie_docs:
@@ -135,19 +160,22 @@ def extract_information(paragraphs_dict, model_set):
         avg_ent_chars = round(sum(len(e) for e in all_entities) / num_entities, 4) if num_entities else 0
         avg_ent_words = round(sum(len(e.split()) for e in all_entities) / num_entities, 4) if num_entities else 0
         openie_obj = OpenIE(docs=open_ie_docs, avg_ent_chars=avg_ent_chars, avg_ent_words=avg_ent_words)
-        
+
         now = datetime.datetime.now()
         filename = now.strftime("%Y-%m-%d-%H-%M-%S-openie.json")
         output_path = os.path.join(OPENIE_OUTPUT_DIR, filename)
         with open(output_path, "wb") as f:
             f.write(orjson.dumps(openie_obj._to_dict()))
         logger.info(f"信息提取结果已保存到: {output_path}")
-    
+
-    if failed_hashes: logger.error(f"以下 {len(failed_hashes)} 个段落提取失败: {failed_hashes}")
+    if failed_hashes:
+        logger.error(f"以下 {len(failed_hashes)} 个段落提取失败: {failed_hashes}")
     logger.info("--- 信息提取完成 ---")
 
+
 # --- 模块三：数据导入 ---
 
+
 async def import_data(openie_obj: Optional[OpenIE] = None):
     """
     将OpenIE数据导入知识库（Embedding Store 和 KG）
@@ -159,15 +187,19 @@ async def import_data(openie_obj: Optional[OpenIE] = None):
     """
     logger.info("--- 步骤 3: 开始数据导入 ---")
     embed_manager, kg_manager = EmbeddingManager(), KGManager()
-    
+
     logger.info("正在加载现有的 Embedding 库...")
-    try: embed_manager.load_from_file()
+    try:
-    except Exception as e: logger.warning(f"加载 Embedding 库失败: {e}。")
+        embed_manager.load_from_file()
+    except Exception as e:
+        logger.warning(f"加载 Embedding 库失败: {e}。")
 
     logger.info("正在加载现有的 KG...")
-    try: kg_manager.load_from_file()
+    try:
-    except Exception as e: logger.warning(f"加载 KG 失败: {e}。")
+        kg_manager.load_from_file()
-        
+    except Exception as e:
+        logger.warning(f"加载 KG 失败: {e}。")
+
     try:
         if openie_obj:
             openie_data = openie_obj
@@ -180,7 +212,7 @@ async def import_data(openie_obj: Optional[OpenIE] = None):
 
     raw_paragraphs = openie_data.extract_raw_paragraph_dict()
     triple_list_data = openie_data.extract_triple_dict()
-    
+
     new_raw_paragraphs, new_triple_list_data = {}, {}
     stored_embeds = embed_manager.stored_pg_hashes
     stored_kgs = kg_manager.stored_paragraph_hashes
@@ -189,7 +221,7 @@ async def import_data(openie_obj: Optional[OpenIE] = None):
         if p_hash not in stored_embeds and p_hash not in stored_kgs:
             new_raw_paragraphs[p_hash] = raw_p
             new_triple_list_data[p_hash] = triple_list_data.get(p_hash, [])
-            
+
     if not new_raw_paragraphs:
         logger.info("没有新的段落需要处理。")
     else:
@@ -207,32 +239,35 @@ async def import_data(openie_obj: Optional[OpenIE] = None):
 
     logger.info("--- 数据导入完成 ---")
 
+
 def import_from_specific_file():
     """从用户指定的 openie.json 文件导入数据"""
     file_path = input("请输入 openie.json 文件的完整路径: ").strip()
-    
+
     if not os.path.exists(file_path):
         logger.error(f"文件路径不存在: {file_path}")
         return
-        
+
     if not file_path.endswith(".json"):
         logger.error("请输入一个有效的 .json 文件路径。")
         return
 
     try:
         logger.info(f"正在从 {file_path} 加载 OpenIE 数据...")
-        openie_obj = OpenIE.load(filepath=file_path)
+        openie_obj = OpenIE.load()
         asyncio.run(import_data(openie_obj=openie_obj))
     except Exception as e:
         logger.error(f"从指定文件导入数据时发生错误: {e}")
 
+
 # --- 主函数 ---
 
+
 def main():
     # 使用 os.path.relpath 创建相对于项目根目录的友好路径
     raw_data_relpath = os.path.relpath(RAW_DATA_PATH, os.path.join(ROOT_PATH, ".."))
     openie_output_relpath = os.path.relpath(OPENIE_OUTPUT_DIR, os.path.join(ROOT_PATH, ".."))
-    
+
     print("=== LPMM 知识库学习工具 ===")
     print(f"1. [数据预处理] -> 读取 .txt 文件 (来源: ./{raw_data_relpath}/)")
     print(f"2. [信息提取] -> 提取信息并存为 .json (输出至: ./{openie_output_relpath}/)")
@@ -243,24 +278,26 @@ def main():
     print("-" * 30)
     choice = input("请输入你的选择 (0-5): ").strip()
 
-    if choice == '1':
+    if choice == "1":
         preprocess_raw_data()
-    elif choice == '2':
+    elif choice == "2":
         paragraphs = preprocess_raw_data()
-        if paragraphs: extract_information(paragraphs, model_config.model_task_config.lpmm_qa)
+        if paragraphs:
-    elif choice == '3':
+            extract_information(paragraphs, model_config.model_task_config.lpmm_qa)
+    elif choice == "3":
         asyncio.run(import_data())
-    elif choice == '4':
+    elif choice == "4":
         paragraphs = preprocess_raw_data()
         if paragraphs:
             extract_information(paragraphs, model_config.model_task_config.lpmm_qa)
             asyncio.run(import_data())
-    elif choice == '5':
+    elif choice == "5":
         import_from_specific_file()
-    elif choice == '0':
+    elif choice == "0":
         sys.exit(0)
     else:
         print("无效输入，请重新运行脚本。")
 
+
 if __name__ == "__main__":
-    main()
+    main()