import os
import json
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 sqlalchemy import create_engine

from app.api.routers.chat import generate_filters
from app.engine import get_index, makeDescriptionByEngine
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):
    with open(file_path, 'r', encoding='utf-8') as file:
        data = json.load(file)
    questions = [item["question"] for item in data]
    return questions

def save_results_to_file(question, result, file_path):
    result_data = {
        "question": question,
        "result": result
    }
    with open(file_path, 'a', encoding='utf-8') as file:
        json.dump(result_data, file, ensure_ascii=False)
        file.write('\n')

def main(questions_file):
    # 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))

    script_dir = os.path.dirname(os.path.abspath(__file__))
    questions_file_path = os.path.join(script_dir, questions_file)
    results_file_path = os.path.join(script_dir, "parameters_results.json")

    questions = read_questions(questions_file_path)
    
    # 如果文件为空，则写入参数值
    if not os.path.isfile(results_file_path):
        with open(results_file_path, 'w', encoding='utf-8') as file:
            json.dump({
                "TOP_K": top_k,
                "LLM_TEMPERATURE": temperature,
                "similarity_top_k": similarity_top_k
            }, file, ensure_ascii=False)
            file.write('\n')

    # 循环执行查询
    for i, question in enumerate(questions):
        print(f"Executing query {i+1}: {question}")
        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__":
    if len(sys.argv) < 2:
        print("请提供questions.json文件的路径")
        sys.exit(1)
    questions_file = sys.argv[1]

    from phoenix.trace import using_project

    with using_project(questions_file) as obj:
        main(questions_file)