优化意图识别示例，更新文档相关性判断逻辑，增强Excel数据验证功能，改进日志记录，调整参数以提升代码可读性和灵活性。

rag2_0/demo/intent_recognition_example.py

@@ -28,7 +28,7 @@ from rag2_0.tool.ModelTool import OpenAiLLM
 load_dotenv()
 
 # 示例查询
-examples_query = """主网电力建设计价通软件, 35kV的软件   土质比例不能一起设置吗"""
+examples_query = """ PE2211PK0801是什么软件"""
 conversation_context=""
 chat_history=[
         {
@@ -100,27 +100,23 @@ class QueryRewriteProcessor:
                 "relevance_score": 0.0
             }
         
-        # 构建文档内容
+        doc_text_list = json.dumps(retrieved_doc, ensure_ascii=False, indent=2)
-        doc_contents = []
-        for i, doc in enumerate(retrieved_doc[:3]):  # 只取前3个文档进行判断
-            content = doc.get("content", "")
-            title = doc.get("title", "")
-            doc_contents.append(f"文档{i+1}标题: {title}\n文档{i+1}内容: {content}")
-        
-        doc_text = "\n\n".join(doc_contents)
         class TempModel(BaseModel):
-            is_relevant: bool = Field(description="是否与用户提问相关")
+            can_solve_problem: bool = Field(description="是否能解决用户问题")
             relevance_score: int = Field(description="相关性评分，0-100分")
-            explanation: str = Field(description="解释各个文档与提问的相关性或不相关性")
+            explanation: str = Field(description="解释文档是否能解决(回答)提问")
 
-        parser = PydanticOutputParser(pydantic_object=TempModel)
+        class most_relevant_document(BaseModel):
+            most_relevant_document: TempModel = Field(description="最相关的文档的判断结果")
+
+        parser = PydanticOutputParser(pydantic_object=most_relevant_document)
         # 构建提示词
-        prompt = f"""请判断以下检索文档是否与用户提问相关，并给出相关性评分（0-100分）。
+        prompt = f"""请判断以下检索文档列表中是否与用户提问相关,能够解决用户的问题，并给出相关性评分（0-100分）。输出最相关的文档的判断结果。
 
 用户提问: {query}
 
-检索文档:
+检索文档列表:
-{doc_text}
+{doc_text_list}
 
 请按照以下JSON格式返回结果:
 {parser.get_format_instructions()}
@@ -131,10 +127,10 @@ class QueryRewriteProcessor:
             llm = OpenAiLLM(api_key=self.api_key, base_url=self.base_url, model="deepseek-ai/DeepSeek-R1", response_format={"type": "json_object"})
             response = llm.invoke(prompt)
             
-            result = parser.parse(response.content)
+            result = parser.parse(response.content).most_relevant_document
                 
             return {
-                "is_relevant": result.is_relevant,
+                "is_relevant": result.can_solve_problem,
                 "relevance_score": result.relevance_score,
                 "explanation": result.explanation
             }
@@ -418,9 +414,6 @@ def main():
             # 在调试模式下使用完整的参数
             print(json.dumps(processor.process_query(
                 query, 
-                conversation_context=conversation_context, 
-                chat_history=chat_history, 
-                previous_slots=previous_slots,
                 enable_retrieval=enable_retrieval
             ), ensure_ascii=False, indent=2))
 

rag2_0/demo/merge_nouns_with_llm.py

@@ -121,7 +121,7 @@ class TermMerger:
             else:
                 return term_list[0]
         except Exception as e:
-            logging.error(f"处理词条 {name} 时出错: {e}")
+            logging.error(f"处理词条 {name} 时出错: {e}", exc_info=True)
             return term_list[0]
 
     def merge(self):

rag2_0/demo/validate_excel_data_batch.py

@@ -33,7 +33,7 @@ class ValidationResult(BaseModel):
 class ExcelDataValidator:
     """Excel数据验证类，用于批量验证Excel数据中的问题分类、问题拆解、检索关键词和问题改写"""
     
-    def __init__(self, input_file=None, output_file=None, workers=4, batch_size=10, debug=False):
+    def __init__(self, input_file=None, output_file=None, workers=4, debug=False):
         """
         初始化验证器
         
@@ -41,7 +41,6 @@ class ExcelDataValidator:
             input_file: 输入Excel文件路径
             output_file: 输出结果Excel文件路径
             workers: 并行工作线程数
-            batch_size: 每批处理的行数
             debug: 是否启用调试模式（串行处理）
         """
         # 加载环境变量
@@ -50,7 +49,6 @@ class ExcelDataValidator:
         self.input_file = input_file
         self.output_file = output_file
         self.workers = workers
-        self.batch_size = batch_size
         self.debug = debug
         self.df = None
         
@@ -86,7 +84,7 @@ class ExcelDataValidator:
             
         try:
             df = pd.read_excel(file_path)
-            required_columns = ["问题", "问题分类", "问题改写", "槽点信息"]
+            required_columns = ["问题", "问题分类", "问题改写", "槽位信息", "检索的内容"]
             for col in required_columns:
                 if col not in df.columns:
                     logging.error(f"缺少必要的列: {col}", exc_info=True)
@@ -320,7 +318,7 @@ class ExcelDataValidator:
         query = row["问题"]
         query_class = row.get("问题分类", "")
         rewrite = row.get("问题改写", "")
-        slot_info = row.get("槽点信息", "")
+        slot_info = row.get("槽位信息", "")
         retrieve_content = row.get("检索的内容", "")
         
         if self.debug:
@@ -359,15 +357,16 @@ class ExcelDataValidator:
                 if len(query_class_list) >= 2:
                     result = self.validate_classification(llm, rewrite, query_class_list[0], query_class_list[1])
                     if isinstance(result, tuple) and len(result) >= 3:
-                        is_correct, error_reason, confidence_score = result[:3]
+                        is_correct, error_reason, classification_confidence = result[:3]
+                        confidence_score = max(confidence_score, classification_confidence)
                         
                         if self.debug:
-                            logging.info(f"  问题分类验证结果: {'通过' if is_correct else '不通过'}, 置信度: {confidence_score:.2f}")
+                            logging.info(f"  问题分类验证结果: {'通过' if is_correct else '不通过'}, 置信度: {classification_confidence:.2f}")
                             if not is_correct:
                                 logging.info(f"  错误原因: {error_reason}")
                         
                         if not is_correct:
-                            return index, False, "问题分类", error_reason, confidence_score
+                            return index, False, "问题分类", error_reason, classification_confidence
             
 
             
@@ -416,13 +415,6 @@ class ExcelDataValidator:
             logging.error(error_msg, exc_info=True)
             return index, False, "处理错误", error_msg, 0.0
     
-    def process_batch(self, llm, batch_data):
-        """处理一批数据"""
-        results = []
-        for row_data in batch_data:
-            results.append(self.validate_row(llm, row_data))
-        return results
-    
     def create_llm_instances(self, count):
         """创建多个LLM实例"""
         api_key = os.getenv("OPENAI_API_KEY")
@@ -437,7 +429,7 @@ class ExcelDataValidator:
 
         return [OpenAiLLM(**llm_params) for _ in range(count)]
     
-    def validate(self, input_file=None, output_file=None, workers=None, batch_size=None, debug=None):
+    def validate(self, input_file=None, output_file=None, workers=None, debug=None):
         """
         执行验证过程
         
@@ -445,7 +437,7 @@ class ExcelDataValidator:
             input_file: 输入Excel文件路径
             output_file: 输出结果Excel文件路径
             workers: 并行工作线程数
-            batch_size: 每批处理的行数
+            batch_size: 每批处理的行数（已弃用，保留参数保持兼容）
             debug: 是否启用调试模式（串行处理）
             
         Returns:
@@ -454,7 +446,6 @@ class ExcelDataValidator:
         input_file = input_file or self.input_file
         output_file = output_file or self.output_file
         workers = workers or self.workers
-        batch_size = batch_size or self.batch_size
         debug = debug if debug is not None else self.debug
         
         # 读取数据
@@ -492,21 +483,20 @@ class ExcelDataValidator:
                 # 输出当前结果
                 logging.info(f"行 {index} 验证结果: {'通过' if is_correct else '不通过'}, 错误环节: {error_phase}, 错误原因: {error_reason}, 置信度: {confidence_score:.2f}")
         else:
-            # 正常模式：并行处理
+            # 正常模式：并行处理，每行单独处理
-            batches = [all_rows[i:i+batch_size] for i in range(0, len(all_rows), batch_size)]
+            llm_instances = self.create_llm_instances(min(workers, len(all_rows)))
-            llm_instances = self.create_llm_instances(min(workers, len(batches)))
             
             with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as executor:
-                # 为每个批次分配一个LLM实例
+                # 为每行分配一个LLM实例
-                future_to_batch = {
+                future_to_row = {
-                    executor.submit(self.process_batch, llm_instances[i % len(llm_instances)], batch): 
+                    executor.submit(self.validate_row, llm_instances[i % len(llm_instances)], row_data): 
-                    i for i, batch in enumerate(batches)
+                    i for i, row_data in enumerate(all_rows)
                 }
                 
                 # 使用tqdm显示进度条
-                for future in tqdm(concurrent.futures.as_completed(future_to_batch), total=len(batches), desc="批次处理进度"):
+                for future in tqdm(concurrent.futures.as_completed(future_to_row), total=len(all_rows), desc="处理进度"):
-                    batch_results = future.result()
+                    result = future.result()
-                    all_results.extend(batch_results)
+                    all_results.append(result)
         
         # 按行索引排序结果，确保与原始数据顺序一致
         all_results.sort(key=lambda x: x[0])
@@ -558,16 +548,14 @@ class ExcelDataValidator:
 def main():
     """主函数"""
     # 解析命令行参数
-    input_excel = os.path.join(os.path.dirname(__file__), "..", "..", "data", "excel", "1500条点踩软件问题测试_检索结果.xlsx")
+    input_excel = os.path.join(os.path.dirname(__file__), "..", "..", "data", "excel", "1500条点踩软件问题测试_意图分类.xlsx")
     output_excel = os.path.join(os.path.dirname(__file__), "..", "..", "data", "excel", "自动验证_问题分类重写结果.xlsx")
 
     parser = argparse.ArgumentParser(description="验证Excel数据中的问题分类、问题拆解、检索关键词和问题改写")
     parser.add_argument("--input", "-i", type=str, help="输入Excel文件路径", default=input_excel)
     parser.add_argument("--output", "-o", type=str, help="输出结果Excel文件路径", default=output_excel)
     parser.add_argument("--workers", "-w", type=int, default=20, help="并行工作线程数")
-    parser.add_argument("--batch-size", "-b", type=int, default=5, help="每批处理的行数")
+    logging.info(f"输入文件路径: {args.input}, 输出文件路径: {args.output}, 并行工作线程数: {args.workers}")
-    parser.add_argument("--debug", "-d", action="store_true", help="启用调试模式（串行处理）")
-
     args = parser.parse_args()
     is_debug = hasattr(sys, 'gettrace') and sys.gettrace() is not None
     
@@ -576,7 +564,6 @@ def main():
         input_file=args.input,
         output_file=args.output,
         workers=args.workers,
-        batch_size=args.batch_size,
         debug=is_debug
     )
     validator.validate()

rag2_0/tool/ModelTool.py

@@ -82,7 +82,7 @@ class SiliconFlowReRankerModel:
             results = response.json()
             return [{"document": item["document"]["text"], "score": item["relevance_score"], "index": item["index"]} for item in results["results"]]
         except requests.exceptions.RequestException as e:
-            logging.error(f"重排序请求失败: {str(e)}")
+            logging.error(f"重排序请求失败: {str(e)}", exc_info=True)
             return []
         
 class XinferenceReRankerModel: