117 lines
4.2 KiB
Python
117 lines
4.2 KiB
Python
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# -*- coding: utf-8 -*-
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import json
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import os
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import re
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from tqdm import tqdm
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import SparkApi
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# 输入文件路径
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input_file = 'output/train_expanded.jsonl'
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# 输出文件路径
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output_file = 'output/train_expanded_2.jsonl'
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# 断点文件路径
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checkpoint_file = 'output/e2_progress_checkpoint.txt'
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# 调用API生成问答对
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def generate_qa_via_api(content):
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appid = "48d04aae"
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api_secret = "ZDE1ZGZmNTQ1YWYxZjcxYTI5Mjk0NGIz"
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api_key = "3ad87d03c4e3a4fb7d7b36a7dfa3be00"
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domain = "4.0Ultra"
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Spark_url = "wss://spark-api.xf-yun.com/v4.0/chat"
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prompt = (
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f"你是一位油橄榄栽培领域的专家，需要基于给定内容生成高质量的问答对。"
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f"生成的问答对用于油橄榄知识库微调，请确保问答的准确性和相关性。具体要求如下：\n"
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f"1. 根据给定内容生成**三个**相关的问题和回答。\n"
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f"2. 你可以简化问题、提取具体要素进行提问，或扩展内容生成额外的相关问题。\n"
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f"3. **问题必须简洁明了**，并涵盖内容中的关键信息。\n"
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f"4. 每个回答应该准确且**不超过50字**，同时**不少于20字**，以保证内容的简洁和有用性。\n"
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f"5. 仅围绕油橄榄栽培的相关内容生成问答对，忽略其他无关信息。\n\n"
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f"以下是给定内容：\n\n"
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f"内容：{content}\n\n"
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f"请按如下格式生成输出：\n"
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f"问题1：<生成第一个问题>\n"
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f"回答1：<生成第一个回答>\n"
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f"问题2：<生成第二个问题>\n"
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f"回答2：<生成第二个回答>\n"
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f"问题3：<生成第三个问题>\n"
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f"回答3：<生成第三个回答>\n\n"
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f"请确保每个问题和回答都保持与内容的紧密相关性，并保持专业性。"
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)
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question = [{"role": "user", "content": prompt}]
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SparkApi.answer = ""
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SparkApi.main(appid, api_key, api_secret, Spark_url, domain, question)
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return SparkApi.answer.strip()
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# 加载断点进度
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def load_checkpoint():
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if os.path.exists(checkpoint_file):
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with open(checkpoint_file, 'r') as f:
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return int(f.read().strip()) # 返回已处理的行索引
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return 0 # 没有断点则从0开始
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# 保存断点进度
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def save_checkpoint(index):
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with open(checkpoint_file, 'w') as f:
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f.write(str(index))
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# 解析返回的问答对，处理多个问答对的情况
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def parse_multiple_qa(answer_text):
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qa_pairs = []
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# 通过正则表达式找到所有的问答对
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pattern = re.compile(r"问题\d+：(.*?)回答\d+：(.*?)(问题|$)", re.S)
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matches = pattern.findall(answer_text)
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for match in matches:
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question = match[0].strip()
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answer = match[1].strip()
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qa_pairs.append({"input": question, "output": answer})
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return qa_pairs
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if __name__ == '__main__':
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# 加载原始数据集
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with open(input_file, 'r', encoding='utf-8') as f:
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text_data = [json.loads(line) for line in f]
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# 加载断点进度
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start_index = load_checkpoint()
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# 从断点开始继续生成问答对
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with open(output_file, 'a', encoding='utf-8') as f:
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for i in tqdm(range(start_index, len(text_data))):
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item = text_data[i]
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input_content = item['input']
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try:
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# 使用API生成新的问答对
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api_generated_qa = generate_qa_via_api(input_content)
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# 解析API生成的问答对并添加到数据集
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qa_pairs = parse_multiple_qa(api_generated_qa)
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expanded_data = [{"input": qa_pair['input'], "output": qa_pair['output']} for qa_pair in qa_pairs]
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# 保存生成的问答对
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for qa in expanded_data:
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json.dump(qa, f, ensure_ascii=False)
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f.write('\n')
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# 保存当前的进度索引
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save_checkpoint(i)
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except Exception as e:
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print(f"Error processing item {i}: {e}")
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# 跳过当前条目继续处理
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save_checkpoint(i)
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continue
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print(f"已生成 {output_file} 文件，包含扩展的问答对。")
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