增加了问题生成脚本

backend/unit_test/corr_test.py

@@ -1,5 +1,20 @@
-import json
 from dotenv import load_dotenv
+load_dotenv()
+
+from llama_index.core.evaluation import CorrectnessEvaluator
+from app.engine import get_chat_engine  
+from app.engine.index import get_index
+from app.observability import init_observability
+from app.settings import init_settings
+
+init_settings()
+init_observability()
+
+index = get_index()
+
+
+import os
+import json
 import asyncio
 import nest_asyncio
 nest_asyncio.apply()
@@ -55,31 +70,14 @@ DEFAULT_EVAL_TEMPLATE = ChatPromptTemplate(
     ]
 )
 
-from app.api.routers.models import ChatData, Message
-from llama_index.core.chat_engine.types import BaseChatEngine, NodeWithScore
-from llama_index.core.vector_stores.types import MetadataFilter, MetadataFilters
-from llama_index.core.evaluation import CorrectnessEvaluator
-from app.engine import get_chat_engine  
-from app.api.routers.chat import generate_filters
-from app.engine.index import get_index
-from app.observability import init_observability
-from app.settings import init_settings
-
-
-load_dotenv()
-
-
-init_settings()
-init_observability()
-
-index = get_index()
 
 # 初始化聊天引擎和评估器
 chat_engine = get_chat_engine()
 corr_evaluator_qwen = CorrectnessEvaluator()
 
 # 加载本地问题回答文件
-file_path = 'D:/LLM_model/text2sql/zjdataai-app-test/backend/unit_test/test.json'
+script_dir = os.path.dirname(os.path.abspath(__file__))
+file_path = os.path.join(script_dir, 'questions_and_answers.json')
 output_file_path = file_path.replace('.json', '_test.json')
 
 with open(file_path, 'r', encoding='utf-8') as f:
@@ -88,8 +86,13 @@ with open(file_path, 'r', encoding='utf-8') as f:
 # 异步函数用于评估查询
 async def evaluate_query(question, answer, index, output_file):
     response = await chat_engine.astream_chat(question)
-    content_str = str(response.sources[0])
-    
+
+    # 检查sources是否为空
+    if response.sources:
+        content_str = str(response.sources[0])
+    else:
+        content_str = "<无回答>"
+
     result = corr_evaluator_qwen.evaluate(
         query=question,
         response=content_str, 
@@ -101,13 +104,13 @@ async def evaluate_query(question, answer, index, output_file):
         "问题": question,
         "答案": answer,
         "回答": result.response,
-        "得分(0~5)": result.score,
+        "得分(1~5)": result.score,
         "评价": result.feedback
     }
     
     with open(output_file, 'a', encoding='utf-8') as f:
         f.write(json.dumps(result_dict, ensure_ascii=False, indent=4))
-        f.write(',')
+        f.write(',\n')
 
 # 主异步函数
 async def main():

backend/unit_test/question.py

@@ -0,0 +1,58 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+from app.observability import init_observability
+from app.settings import init_settings
+
+import nest_asyncio
+nest_asyncio.apply()
+
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core import SimpleDirectoryReader
+
+from llama_index.core.evaluation import DatasetGenerator
+
+import json
+
+init_settings()
+init_observability()
+
+documents = SimpleDirectoryReader("backend\data-test").load_data()
+
+splitter = SentenceSplitter(chunk_size=512)
+
+# question_generator = DatasetGenerator.from_documents(documents)
+quest_prompt = (
+    "你是一个电力造价工程相关的项目经理，现在给你一些上下文信息，"
+    "你需要根据现有的上下文信息，来生成{num_questions_per_chunk}个电力造价工程相关的问题和对应的回答，"
+    "问题的实例应该是这种类型的：'人工费的费率是多少?，费率是100','前期工作管理费用的金额是多少?，金额是0',"
+    "这种类似的问题和答案，生成的问题和答案必须一一对应，要符合文件里的内容，不要生成一些无关的问题，不要生成一些重复的问题，"
+    "不要生成一些过于简单的问题，不要生成一些过于复杂的问题。"
+)
+
+question_generator = DatasetGenerator.from_documents(
+    documents=documents,
+    question_gen_query=quest_prompt,
+    num_questions_per_chunk=5  #生成的问题数
+)
+
+eval_questions = question_generator.generate_questions_from_nodes(5)
+
+# print(eval_questions)
+
+# 处理生成的问题和答案，转换为JSON格式
+qa_pairs = []
+for qa in eval_questions:
+    # 处理可能没有 '，' 的情况
+    if '？' in qa:
+        question, answer = qa.split("？", 1)
+        qa_pairs.append({
+            "question": question.strip(),
+            "answer": answer.strip()
+        })
+    else:
+        print(f"无法处理的问题和答案: {qa}")
+
+# 保存为JSON文件
+with open("backend/unit_test/questions_and_answers.json", "w", encoding="utf-8") as f:
+    json.dump(qa_pairs, f, ensure_ascii=False, indent=4)