参数优化针对问题做出了调整

backend/test1/ParamTuner.py

@@ -27,6 +27,13 @@ init_observability()
 # 读取文档
 documents = SimpleDirectoryReader("D:/LLM_model/text2sql/zjdataai-app-test/backend/data-test").load_data()
 
+# 参数字典
+param_dict = {
+    "chunk_size": [512, 1024],
+    "top_k": [1, 5],
+    "temperature": [0.1, 1.0]
+}
+
 # 辅助函数
 def _build_index(chunk_size, documents):
     # 构建索引
@@ -56,8 +63,18 @@ async def _evaluate_query_engine_async(query_engine, questions):
 
     return total_correct, len(results)
 
+
+
+# 生成问题
+question_generator = DatasetGenerator.from_documents(documents)
+eval_questions = question_generator.generate_questions_from_nodes(1)  # 假设生成10个问题
+
+# 打印生成的问题
+for i, q in enumerate(eval_questions, start=1):
+    print(f"问题 {i}: {q}")
+
 # 目标函数
-def objective_function(params_dict, documents, question_count):
+def objective_function(params_dict, documents, questions):
     chunk_size = params_dict["chunk_size"]
     top_k = params_dict["top_k"]
     temperature = params_dict["temperature"]
@@ -70,27 +87,25 @@ def objective_function(params_dict, documents, question_count):
         similarity_top_k=top_k, temperature=temperature
     )
 
-    # 生成问题
-    question_generator = DatasetGenerator.from_documents(documents)
-    eval_questions = question_generator.generate_questions_from_nodes(question_count)
-    
     # 评估查询引擎
-    correct, total = evaluate_query_engine(query_engine, eval_questions)
+    correct, total = 0, len(questions)
+    question_answers = []  # 添加列表来收集问题和答案
     
+    for question in questions:
+        response = query_engine.query(question)
+        if response is not None:
+            question_answers.append((question, response.response))
+            eval_result = FaithfulnessEvaluator().evaluate_response(response=response, query_str=question)
+            if eval_result.passing:
+                correct += 1
+
     # 计算分数
     score = correct / total if total > 0 else 0
-    return RunResult(score=score, params=params_dict)
+    return RunResult(score=score, params=params_dict, question_answers=question_answers)
-
-# 参数字典
-param_dict = {
-    "chunk_size": [512, 1024],
-    "top_k": [1, 5],
-    "temperature": [0.1, 1.0]
-}
 
 # 创建 ParamTuner 实例
 param_tuner = ParamTuner(
-    param_fn=lambda params_dict: objective_function(params_dict, documents, 1),
+    param_fn=lambda params_dict: objective_function(params_dict, documents, eval_questions),
     param_dict=param_dict,
     show_progress=True,
 )
@@ -101,7 +116,23 @@ best_result = results.best_run_result
 best_top_k = best_result.params["top_k"]
 best_chunk_size = best_result.params["chunk_size"]
 best_temperature = best_result.params["temperature"]
-print(f"Score: {best_result.score}")
+print(f"得分: {best_result.score}")
 print(f"Top-k: {best_top_k}")
-print(f"Chunk size: {best_chunk_size}")
+print(f"文本块大小: {best_chunk_size}")
-print(f"Temperature: {best_temperature}")
+print(f"温度: {best_temperature}")
+
+# 使用最佳参数再次运行查询引擎，并打印问题与答案
+best_vector_index = _build_index(best_chunk_size, documents)
+best_query_engine = best_vector_index.as_query_engine(
+    similarity_top_k=best_top_k, temperature=best_temperature
+)
+
+best_question_answers = []
+for question in eval_questions:
+    response = best_query_engine.query(question)
+    if response is not None:
+        best_question_answers.append((question, response.response))
+
+# 打印最佳参数下的问题与答案
+for i, (question, answer) in enumerate(best_question_answers, start=1):
+    print(f"最佳参数 - 问题 {i}: {question}\n答案: {answer}\n")