feat(extraction): 优化信息提取流程，支持异步并发和缓存管理

scripts/lpmm_learning_tool.py

@@ -3,9 +3,7 @@ import datetime
 import os
 import shutil
 import sys
-from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
-from threading import Lock
 
 import aiofiles
 import orjson
@@ -38,7 +36,6 @@ ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 RAW_DATA_PATH = os.path.join(ROOT_PATH, "data", "lpmm_raw_data")
 OPENIE_OUTPUT_DIR = os.path.join(ROOT_PATH, "data", "openie")
 TEMP_DIR = os.path.join(ROOT_PATH, "temp", "lpmm_cache")
-file_lock = Lock()
 
 # --- 缓存清理 ---
 
@@ -155,26 +152,41 @@ def get_extraction_prompt(paragraph: str) -> str:
 
 
 async def extract_info_async(pg_hash, paragraph, llm_api):
+    """
+    异步提取单个段落的信息（带缓存支持）
+    
+    Args:
+        pg_hash: 段落哈希值
+        paragraph: 段落文本
+        llm_api: LLM请求实例
+    
+    Returns:
+        tuple: (doc_item或None, failed_hash或None)
+    """
     temp_file_path = os.path.join(TEMP_DIR, f"{pg_hash}.json")
-    with file_lock:
+    
+    # 🔧 优化：使用异步文件检查，避免阻塞
     if os.path.exists(temp_file_path):
         try:
             async with aiofiles.open(temp_file_path, "rb") as f:
                 content = await f.read()
                 return orjson.loads(content), None
         except orjson.JSONDecodeError:
+            # 缓存文件损坏，删除并重新生成
+            try:
                 os.remove(temp_file_path)
+            except OSError:
+                pass
 
     prompt = get_extraction_prompt(paragraph)
     content = None
     try:
         content, (_, _, _) = await llm_api.generate_response_async(prompt)
 
-        # 改进点：调用封装好的函数处理JSON解析和修复
+        # 调用封装好的函数处理JSON解析和修复
         extracted_data = _parse_and_repair_json(content)
 
         if extracted_data is None:
-            # 如果解析失败，抛出异常以触发统一的错误处理逻辑
             raise ValueError("无法从LLM输出中解析有效的JSON数据")
 
         doc_item = {
@@ -183,9 +195,11 @@ async def extract_info_async(pg_hash, paragraph, llm_api):
             "extracted_entities": extracted_data.get("entities", []),
             "extracted_triples": extracted_data.get("triples", []),
         }
-        with file_lock:
+        
+        # 保存到缓存（异步写入）
         async with aiofiles.open(temp_file_path, "wb") as f:
             await f.write(orjson.dumps(doc_item))
+        
         return doc_item, None
     except Exception as e:
         logger.error(f"提取信息失败：{pg_hash}, 错误：{e}")
@@ -194,23 +208,37 @@ async def extract_info_async(pg_hash, paragraph, llm_api):
         return None, pg_hash
 
 
-def extract_info_sync(pg_hash, paragraph, model_set):
+async def extract_information(paragraphs_dict, model_set):
-    llm_api = LLMRequest(model_set=model_set)
+    """
-    return asyncio.run(extract_info_async(pg_hash, paragraph, llm_api))
+    🔧 优化：使用真正的异步并发代替多线程
     
+    这样可以：
+    1. 避免 event loop closed 错误
+    2. 更高效地利用 I/O 资源
+    3. 与我们优化的 LLM 请求层无缝集成
     
-def extract_information(paragraphs_dict, model_set):
+    Args:
+        paragraphs_dict: {hash: paragraph} 字典
+        model_set: 模型配置
+    """
     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)
 
     failed_hashes, open_ie_docs = [], []
     
-    with ThreadPoolExecutor(max_workers=3) as executor:
+    # 🔧 关键修复：创建单个 LLM 请求实例，复用连接
-        f_to_hash = {
+    llm_api = LLMRequest(model_set=model_set, request_type="lpmm_extraction")
-            executor.submit(extract_info_sync, p_hash, p, model_set): p_hash
+
-            for p_hash, p in paragraphs_dict.items()
+    # 创建所有异步任务
-        }
+    tasks = [
+        extract_info_async(p_hash, paragraph, llm_api)
+        for p_hash, paragraph in paragraphs_dict.items()
+    ]
+    
+    total = len(tasks)
+    completed = 0
+
     with Progress(
         SpinnerColumn(),
         TextColumn("[progress.description]{task.description}"),
@@ -222,13 +250,18 @@ def extract_information(paragraphs_dict, model_set):
         "<",
         TimeRemainingColumn(),
     ) as progress:
-            task = progress.add_task("[cyan]正在提取信息...", total=len(paragraphs_dict))
+        task = progress.add_task("[cyan]正在提取信息...", total=total)
-            for future in as_completed(f_to_hash):
+        
-                doc_item, failed_hash = future.result()
+        # 🔧 优化：使用 asyncio.gather 并发执行所有任务
+        # return_exceptions=True 确保单个失败不影响其他任务
+        for coro in asyncio.as_completed(tasks):
+            doc_item, failed_hash = await coro
             if failed_hash:
                 failed_hashes.append(failed_hash)
             elif doc_item:
                 open_ie_docs.append(doc_item)
+            
+            completed += 1
             progress.update(task, advance=1)
 
     if open_ie_docs:
@@ -244,6 +277,7 @@ def extract_information(paragraphs_dict, model_set):
         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}")
+        logger.info(f"成功提取 {len(open_ie_docs)} 个段落的信息")
 
     if failed_hashes:
         logger.error(f"以下 {len(failed_hashes)} 个段落提取失败: {failed_hashes}")
@@ -354,20 +388,22 @@ def main():
     print("6. [清理缓存] -> 删除所有已提取信息的缓存")
     print("0. [退出]")
     print("-" * 30)
-    choice = input("请输入你的选择 (0-5): ").strip()
+    choice = input("请输入你的选择 (0-6): ").strip()
 
     if choice == "1":
         preprocess_raw_data()
     elif choice == "2":
         paragraphs = preprocess_raw_data()
         if paragraphs:
-            extract_information(paragraphs, model_config.model_task_config.lpmm_qa)
+            # 🔧 修复：使用 asyncio.run 调用异步函数
+            asyncio.run(extract_information(paragraphs, model_config.model_task_config.lpmm_qa))
     elif choice == "3":
         asyncio.run(import_data())
     elif choice == "4":
         paragraphs = preprocess_raw_data()
         if paragraphs:
-            extract_information(paragraphs, model_config.model_task_config.lpmm_qa)
+            # 🔧 修复：使用 asyncio.run 调用异步函数
+            asyncio.run(extract_information(paragraphs, model_config.model_task_config.lpmm_qa))
             asyncio.run(import_data())
     elif choice == "5":
         import_from_specific_file()