OliveSensorAPI/scripts/qa_generation/main.py

import os
import json
import time
from tqdm import tqdm
import concurrent.futures
from datetime import datetime

from config.config import result_dir, storage_interval, window_size, overlap_size, multi_process_num
from model.qwen import call_qwen_single_turn
from util.logger import get_logger
from util.data_loader import get_file_list, get_txt_content, capture_qa

logger = get_logger()

"""
生成 QA 对
model_name: 可调用的模型名称，暂时只实现了 qwen
interval: 存储间隔，即每隔多少条存一次文件，过密的间隔会增大 IO 开销
"""
def generate_qa(
    model_name: str = 'qwen',
    interval: int = 10,
):
    current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    
    if model_name == 'qwen':
        model_caller = call_qwen_single_turn
    else:
        logger.warning('This model is currently not supported and will call the default model - qwen.')
        model_caller = call_qwen_single_turn
        model_name = 'qwen'
    
    logger.info(f'The called model is: {model_name}.')
    logger.info(f'The storage interval is: {interval}.')

    file_list = get_file_list()
    storage_counter = 0
    storage_list = []
    for file_path in file_list:
        contents = get_txt_content(file_path, window_size=window_size, overlap_size=overlap_size)
        storage_list = []
        
        _, file_name = os.path.split(file_path)
        storage_jsonl_path = os.path.join(
            result_dir, f'{current_time}-{file_name}-{model_name}.jsonl')
        logger.info(f'The generated QA will be stored in {storage_jsonl_path}.')

        response_list = []
        logger.info("Begin generate QA data")
        with tqdm(total=len(contents)) as pbar:
            with concurrent.futures.ThreadPoolExecutor(max_workers=multi_process_num) as executor:
                # 创建一个Future列表，它们将对应每个worker_function的结果
                futures = {executor.submit(model_caller, content): content for content in contents}
            
                for future in concurrent.futures.as_completed(futures):
                    try:
                        response_list.append(future.result())
                        pbar.update(1)
                    except Exception as exc:
                        logger.error("Item generated an exception: %s" % (exc))

        logger.info("Begin capture LLM response")
        for response in tqdm(response_list):
            captured_qa = capture_qa(response)
            # print(captured_qa) # 打印QA对
            if captured_qa is None:
                continue

            storage_list.extend(captured_qa)
            storage_counter += 1
            if storage_counter % interval == 0:
                storage_counter = 0
                with open(storage_jsonl_path, 'a', encoding='utf-8') as f:
                    for item in storage_list:
                        f.write(json.dumps(item, ensure_ascii=False) + '\n')
                    storage_list = []
    
        # 如果有剩余，存入
        if storage_list:
            with open(storage_jsonl_path, 'a', encoding='utf-8') as f:
                for item in storage_list:
                    f.write(json.dumps(item, ensure_ascii=False) + '\n')
                storage_list = []


if __name__ == '__main__':

    # 创建generated文件夹
    os.makedirs('./data/generated', exist_ok=True)
    generate_qa(interval=storage_interval)