Add new files and update existing files

backend/test1/test_parameters.py

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+import os
+from ctypes import cast
+import sys
+
+from llama_index.core import VectorStoreIndex, SQLDatabase
+from llama_index.core.indices.struct_store import SQLTableRetrieverQueryEngine
+from llama_index.core.objects import SQLTableNodeMapping, ObjectIndex
+from llama_index.readers.database import DatabaseReader
+from sqlalchemy import create_engine
+
+from app.api.routers.chat import generate_filters
+from app.engine import get_index, makeDescriptionByEngine
+from app.engine.loaders.db import CustomDatabaseReader
+from app.engine.vectordb import get_vector_store
+from app.observability import init_observability
+from app.settings import init_settings
+from dotenv import load_dotenv
+
+load_dotenv()
+
+def read_questions(file_path):
+    questions = []
+    with open(file_path, 'r', encoding='utf-8') as file:
+        for line in file:
+            if "question" in line:
+                question_part = line.split("：")[1].strip()  # 提取 "question" 后的内容
+                questions.append(question_part)
+    return questions
+
+def save_results_to_file(question, result, file_path):
+    with open(file_path, 'a', encoding='utf-8') as file:
+        file.write(f"问题: {question}\n")
+        file.write(f"结果: {result}\n\n")
+
+def main():
+    # 从命令行读取questions_file_path
+    if len(sys.argv) < 2:
+        print("请提供questions.txt文件的路径")
+        sys.exit(1)
+    questions_file_path = sys.argv[1]
+    # 更新环境变量
+    os.environ['TOP_K'] = str(5)  # 向量的TOP_K值
+    os.environ['LLM_TEMPERATURE'] = str(0.1)  # 温度值
+    os.environ['similarity_top_k'] = str(5)  # SQL的TOP_K值
+
+    init_settings()
+    init_observability()
+
+    index = get_index()
+
+    top_k = int(os.getenv("TOP_K"))  # 向量的TOP_K值
+    temperature = float(os.getenv("LLM_TEMPERATURE"))  # 温度值
+    similarity_top_k = float(os.getenv("similarity_top_k"))  # SQL的TOP_K值
+    filters = generate_filters([])
+
+    engine = create_engine(os.getenv("SQL_DATABASE_URL", ""))
+    sql_database = SQLDatabase(engine)
+    table_schema_objs = makeDescriptionByEngine(sql_database)
+    table_node_mapping = SQLTableNodeMapping(sql_database)
+    # 创建SQL查询工具
+    sql_obj_index = ObjectIndex.from_objects(
+        table_schema_objs,
+        table_node_mapping,
+        index_cls=VectorStoreIndex,
+    )
+    sql_query_engine = SQLTableRetrieverQueryEngine(sql_database,
+                                                    sql_obj_index.as_retriever(similarity_top_k=similarity_top_k))
+
+    questions = read_questions(questions_file_path)
+
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+
+    results_file_path = os.path.join(script_dir, "query_results.txt")
+    
+    # 如果文件为空，则写入参数值
+    if os.path.getsize(results_file_path) == 0:
+        with open(results_file_path, 'w', encoding='utf-8') as file:
+            file.write(f"TOP_K: {top_k}\n")
+            file.write(f"LLM_TEMPERATURE: {temperature}\n")
+            file.write(f"similarity_top_k: {similarity_top_k}\n\n")
+
+    # 循环执行查询
+    for i, question in enumerate(questions):
+        print(f"Executing query {i+1}: {question}")
+        # query_engine = index.as_query_engine(
+        #     similarity_top_k=top_k, filters=filters
+        # )
+        # query_result = query_engine.query(question)
+
+        # print(f"向量查询结果: {query_result}\n")
+        # save_results_to_file(question, f"向量查询结果: {query_result}", results_file_path)
+
+        sql_query_result = sql_query_engine.query(question)
+        print(f"SQL查询结果: {sql_query_result}\n")
+        save_results_to_file(question, f"SQL查询结果: {sql_query_result}", results_file_path)
+
+if __name__ == "__main__":
+    from phoenix.trace import using_project
+
+    with using_project("ly_zjapp_test") as obj:
+        main()