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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。

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ouyangyouzhang
2025-06-25 09:10:28 +08:00
parent 7142c7c43e
commit 33bc91f0fe
4 changed files with 35 additions and 55 deletions
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rag2_0/demo/intent_recognition_example.py
+13 -20
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@@ -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_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)
doc_text_list = json.dumps(retrieved_doc, ensure_ascii=False, indent=2)
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:
{doc_text}
检索文档列表:
{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
+1 -1
View File
@@ -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
+20 -33
View File
@@ -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
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(batches)))
# 正常模式：并行处理，每行单独处理
llm_instances = self.create_llm_instances(min(workers, len(all_rows)))
with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as executor:
# 为每个批次分配一个LLM实例
future_to_batch = {
executor.submit(self.process_batch, llm_instances[i % len(llm_instances)], batch):
i for i, batch in enumerate(batches)
# 为每行分配一个LLM实例
future_to_row = {
executor.submit(self.validate_row, llm_instances[i % len(llm_instances)], row_data):
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"):
batch_results = future.result()
all_results.extend(batch_results)
for future in tqdm(concurrent.futures.as_completed(future_to_row), total=len(all_rows), desc="处理进度"):
result = future.result()
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")
parser.add_argument("--debug", "-d", action="store_true", help="启用调试模式（串行处理）")
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}")
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
+1 -1
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@@ -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: