724 lines
33 KiB
Python
724 lines
33 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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QA生成器 - 简化版
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基于selected.json文件生成问答对
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只使用字段中文名、字段英文名、抽象中文名作为提问基础
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"""
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import json
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import os
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import random
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from typing import List, Dict, Any
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from config import QAConfig
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class QAGenerator:
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"""QA生成器 - 简化版"""
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def __init__(self, config: QAConfig = None):
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"""初始化生成器"""
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self.config = config or QAConfig()
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os.makedirs(self.config.OUTPUT_DIR, exist_ok=True)
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random.seed(self.config.RANDOM_SEED)
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# 问题模板前缀
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self.QUESTION_PREFIXES = [
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"请告诉我",
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"查询",
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"请问",
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"请解释",
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"请输出",
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"请列举",
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"请说明",
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"请查找",
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"请确认"
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]
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# 答句模板前缀
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self.ANSWER_PREFIXES = [
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"该字段的",
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"查询结果显示，",
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"经查询，该字段的",
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"根据记录显示，",
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"该数据的",
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"查询结果：",
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"经系统查询，",
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"根据记录，",
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"该值的"
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]
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# 答句模板后缀
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self.ANSWER_SUFFIXES = [
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"。",
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"。"
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]
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# 验证集专用模板（正式但有别于训练集）
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self.VERIFICATION_QUESTION_PREFIXES = [
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"请问",
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"想咨询一下",
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"请问您",
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"我想了解一下",
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"请教一下",
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"您好，",
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"能否告诉我",
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"请问如何",
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"我想咨询",
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"希望了解"
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]
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self.VERIFICATION_ANSWER_PREFIXES = [
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"根据查询，",
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"经查询，",
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"查询结果显示，",
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"根据记录，",
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"数据表明，",
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"经系统查询，",
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"根据数据，",
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"查询结果：",
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"经核实，",
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"数据显示，"
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]
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self.VERIFICATION_ANSWER_SUFFIXES = [
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"。",
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"。",
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"。",
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"。",
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"。",
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"。",
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"。",
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"。",
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"。",
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"。"
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]
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# 模型数据缓存
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self.model_data_cache = {
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"逻辑模型_逻辑模型中文名": {},
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"逻辑模型_逻辑模型英文名": {},
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"物理模型_物理模型中文名": {},
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"物理模型_物理模型英文名": {}
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}
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def get_random_element(self, elements: List[str]) -> str:
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"""从列表中随机获取一个元素"""
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return random.choice(elements) if elements else ""
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def load_json(self, file_path: str) -> List[Dict]:
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"""加载JSON文件"""
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with open(file_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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def generate_qa_for_item(self, item: Dict) -> List[Dict]:
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"""为单个数据项生成问答对
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基于字段中文名、字段英文名询问其他所有字段
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"""
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qa_pairs = []
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# 获取两个核心字段
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field_chinese_name = item.get('字段中文名', '')
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field_english_name = item.get('字段英文名', '')
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# 基于字段中文名提问
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if field_chinese_name:
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# 询问值类型
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if item.get('值类型'):
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question = f"字段中文名为'{field_chinese_name}'的值类型是什么？"
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answer = f"值类型为「{item['值类型']}」"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# 询问是否枚举
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if item.get('是否枚举'):
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question = f"字段中文名为'{field_chinese_name}'是否枚举？"
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answer = f"是否枚举为「{item['是否枚举']}」"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# 询问枚举数量
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if item.get('枚举数量') is not None:
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question = f"字段中文名为'{field_chinese_name}'的枚举数量是多少？"
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answer = f"枚举数量为{item['枚举数量']}"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# 询问总长度
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if item.get('总长度') is not None:
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question = f"字段中文名为'{field_chinese_name}'的总长度是多少？"
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answer = f"总长度为{item['总长度']}"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# 询问小数位
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if item.get('小数位') is not None:
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question = f"字段中文名为'{field_chinese_name}'的小数位是多少？"
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answer = f"小数位为{item['小数位']}"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# 询问字段英文名
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if field_english_name:
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question = f"字段中文名为'{field_chinese_name}'的字段英文名是什么？"
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answer = f"字段英文名为「{field_english_name}」"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# 基于字段英文名提问
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if field_english_name:
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# 询问值类型
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if item.get('值类型'):
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question = f"字段英文名为'{field_english_name}'的值类型是什么？"
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answer = f"值类型为「{item['值类型']}」"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# 询问是否枚举
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if item.get('是否枚举'):
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question = f"字段英文名为'{field_english_name}'是否枚举？"
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answer = f"是否枚举为「{item['是否枚举']}」"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# 询问总长度
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if item.get('总长度') is not None:
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question = f"字段英文名为'{field_english_name}'的总长度是多少？"
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answer = f"总长度为{item['总长度']}"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# 询问小数位
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if item.get('小数位') is not None:
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question = f"字段英文名为'{field_english_name}'的小数位是多少？"
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answer = f"小数位为{item['小数位']}"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# 询问字段中文名
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if field_chinese_name:
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question = f"字段英文名为'{field_english_name}'的字段中文名是什么？"
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answer = f"字段中文名为「{field_chinese_name}」"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# ==================== 新增：根据中文字段名询问完整定义 ====================
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if field_chinese_name:
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question = f"字段中文名为'{field_chinese_name}'的定义是什么？"
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# 构建完整的定义信息
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definition_parts = []
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for key, value in item.items():
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if key not in ['字段中文名'] and value is not None:
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definition_parts.append(f"{key}：{value}")
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elif key not in ['字段中文名'] and value is None:
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definition_parts.append(f"{key}：null")
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definition_text = "， ".join(definition_parts)
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answer = f"{field_chinese_name}的定义为：{definition_text}"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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# ==================== 新增：根据英文字段名询问完整定义 ====================
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if field_english_name:
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question = f"字段英文名为'{field_english_name}'的定义是什么？"
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# 构建完整的定义信息
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definition_parts = []
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for key, value in item.items():
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if key not in ['字段英文名'] and value is not None:
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definition_parts.append(f"{key}：{value}")
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elif key not in ['字段英文名'] and value is None:
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definition_parts.append(f"{key}：null")
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definition_text = "， ".join(definition_parts)
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answer = f"{field_english_name}的定义为：{definition_text}"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
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})
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return qa_pairs
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def generate_verification_qa_for_item(self, item: Dict) -> List[Dict]:
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"""为单个数据项生成验证集问答对（正式但有别于训练集的表达）"""
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qa_pairs = []
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# 获取两个核心字段
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field_chinese_name = item.get('字段中文名', '')
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field_english_name = item.get('字段英文名', '')
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# 基于字段中文名提问（正式但有变化）
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if field_chinese_name:
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# 询问值类型
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if item.get('值类型'):
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question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_chinese_name}'字段的数据类型是什么？"
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answer = f"数据类型是「{item['值类型']}」"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
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})
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# 询问是否枚举
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if item.get('是否枚举'):
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question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_chinese_name}'字段是否为枚举类型？"
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answer = f"枚举类型为「{item['是否枚举']}」"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
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})
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# 询问枚举数量
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if item.get('枚举数量') is not None:
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question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_chinese_name}'字段的枚举数量是多少？"
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answer = f"枚举数量为{item['枚举数量']}"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
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})
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# 询问总长度
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if item.get('总长度') is not None:
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question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_chinese_name}'字段的总长度是多少？"
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answer = f"总长度为{item['总长度']}"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
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})
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# 询问小数位
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if item.get('小数位') is not None:
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question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_chinese_name}'字段的小数位是多少？"
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answer = f"小数位为{item['小数位']}"
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qa_pairs.append({
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"instruct": question,
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"input": "",
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"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
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})
|
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# 询问字段英文名
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if field_english_name:
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question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_chinese_name}'字段对应的英文名是什么？"
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answer = f"英文名为「{field_english_name}」"
|
||
qa_pairs.append({
|
||
"instruct": question,
|
||
"input": "",
|
||
"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
|
||
})
|
||
|
||
# 基于字段英文名提问（正式但有变化）
|
||
if field_english_name:
|
||
# 询问值类型
|
||
if item.get('值类型'):
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_english_name}'字段的数据类型是什么？"
|
||
answer = f"数据类型是「{item['值类型']}」"
|
||
qa_pairs.append({
|
||
"instruct": question,
|
||
"input": "",
|
||
"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
|
||
})
|
||
|
||
# 询问是否枚举
|
||
if item.get('是否枚举'):
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_english_name}'字段是否为枚举类型？"
|
||
answer = f"枚举类型为「{item['是否枚举']}」"
|
||
qa_pairs.append({
|
||
"instruct": question,
|
||
"input": "",
|
||
"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
|
||
})
|
||
|
||
# 询问总长度
|
||
if item.get('总长度') is not None:
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_english_name}'字段的总长度是多少？"
|
||
answer = f"总长度为{item['总长度']}"
|
||
qa_pairs.append({
|
||
"instruct": question,
|
||
"input": "",
|
||
"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
|
||
})
|
||
|
||
# 询问小数位
|
||
if item.get('小数位') is not None:
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_english_name}'字段的小数位是多少？"
|
||
answer = f"小数位为{item['小数位']}"
|
||
qa_pairs.append({
|
||
"instruct": question,
|
||
"input": "",
|
||
"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
|
||
})
|
||
|
||
# 询问字段中文名
|
||
if field_chinese_name:
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_english_name}'字段对应的中文名是什么？"
|
||
answer = f"中文名为「{field_chinese_name}」"
|
||
qa_pairs.append({
|
||
"instruct": question,
|
||
"input": "",
|
||
"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
|
||
})
|
||
|
||
# ==================== 验证集：根据中文字段名询问完整定义 ====================
|
||
if field_chinese_name:
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_chinese_name}'字段的具体定义是什么？"
|
||
# 构建完整的定义信息
|
||
definition_parts = []
|
||
for key, value in item.items():
|
||
if key not in ['字段中文名'] and value is not None:
|
||
definition_parts.append(f"{key}：{value}")
|
||
elif key not in ['字段中文名'] and value is None:
|
||
definition_parts.append(f"{key}：null")
|
||
|
||
definition_text = "， ".join(definition_parts)
|
||
answer = f"{field_chinese_name}的定义为：{definition_text}"
|
||
qa_pairs.append({
|
||
"instruct": question,
|
||
"input": "",
|
||
"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
|
||
})
|
||
|
||
# ==================== 验证集：根据英文字段名询问完整定义 ====================
|
||
if field_english_name:
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}'{field_english_name}'字段的具体定义是什么？"
|
||
# 构建完整的定义信息
|
||
definition_parts = []
|
||
for key, value in item.items():
|
||
if key not in ['字段英文名'] and value is not None:
|
||
definition_parts.append(f"{key}：{value}")
|
||
elif key not in ['字段英文名'] and value is None:
|
||
definition_parts.append(f"{key}：null")
|
||
|
||
definition_text = "， ".join(definition_parts)
|
||
answer = f"{field_english_name}的定义为：{definition_text}"
|
||
qa_pairs.append({
|
||
"instruct": question,
|
||
"input": "",
|
||
"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
|
||
})
|
||
|
||
return qa_pairs
|
||
|
||
def generate_qa_for_data(self, data: List[Dict]) -> List[Dict]:
|
||
"""为所有数据生成QA"""
|
||
all_qa = []
|
||
|
||
# 首先收集模型数据
|
||
self.collect_model_data(data)
|
||
|
||
for item in data:
|
||
qa_pairs = self.generate_qa_for_item(item)
|
||
all_qa.extend(qa_pairs)
|
||
|
||
# 生成基于模型的问题
|
||
model_qa_pairs = self.generate_model_based_qa(data)
|
||
all_qa.extend(model_qa_pairs)
|
||
|
||
return all_qa
|
||
|
||
def generate_verification_qa_for_data(self, data: List[Dict]) -> List[Dict]:
|
||
"""为所有数据生成验证集QA（口语化、拟人化表达）"""
|
||
all_qa = []
|
||
|
||
# 首先收集模型数据
|
||
self.collect_model_data(data)
|
||
|
||
for item in data:
|
||
qa_pairs = self.generate_verification_qa_for_item(item)
|
||
all_qa.extend(qa_pairs)
|
||
|
||
# 生成基于模型的问题（验证集版）
|
||
model_qa_pairs = self.generate_verification_model_based_qa(data)
|
||
all_qa.extend(model_qa_pairs)
|
||
|
||
return all_qa
|
||
|
||
def collect_model_data(self, data: List[Dict]):
|
||
"""收集模型相关数据用于后续查询"""
|
||
for item in data:
|
||
# 收集逻辑模型数据
|
||
if "逻辑模型_逻辑模型中文名" in item and item["逻辑模型_逻辑模型中文名"]:
|
||
model_name = item["逻辑模型_逻辑模型中文名"]
|
||
if model_name not in self.model_data_cache["逻辑模型_逻辑模型中文名"]:
|
||
self.model_data_cache["逻辑模型_逻辑模型中文名"][model_name] = []
|
||
self.model_data_cache["逻辑模型_逻辑模型中文名"][model_name].append(item.get("字段中文名", ""))
|
||
|
||
if "逻辑模型_逻辑模型英文名" in item and item["逻辑模型_逻辑模型英文名"]:
|
||
model_name = item["逻辑模型_逻辑模型英文名"]
|
||
if model_name not in self.model_data_cache["逻辑模型_逻辑模型英文名"]:
|
||
self.model_data_cache["逻辑模型_逻辑模型英文名"][model_name] = []
|
||
self.model_data_cache["逻辑模型_逻辑模型英文名"][model_name].append(item.get("字段中文名", ""))
|
||
|
||
# 收集物理模型数据
|
||
if "物理模型_物理模型中文名" in item and item["物理模型_物理模型中文名"]:
|
||
model_name = item["物理模型_物理模型中文名"]
|
||
if model_name not in self.model_data_cache["物理模型_物理模型中文名"]:
|
||
self.model_data_cache["物理模型_物理模型中文名"][model_name] = []
|
||
self.model_data_cache["物理模型_物理模型中文名"][model_name].append(item.get("字段中文名", ""))
|
||
|
||
if "物理模型_物理模型英文名" in item and item["物理模型_物理模型英文名"]:
|
||
model_name = item["物理模型_物理模型英文名"]
|
||
if model_name not in self.model_data_cache["物理模型_物理模型英文名"]:
|
||
self.model_data_cache["物理模型_物理模型英文名"][model_name] = []
|
||
self.model_data_cache["物理模型_物理模型英文名"][model_name].append(item.get("字段中文名", ""))
|
||
|
||
def generate_model_based_qa(self, data: List[Dict]) -> List[Dict]:
|
||
"""生成基于模型的问题（优化版：只对有足够字段的模型生成问题）"""
|
||
qa_pairs = []
|
||
|
||
# 为每个模型类型生成问题
|
||
for model_type, model_dict in self.model_data_cache.items():
|
||
for model_name, field_names in model_dict.items():
|
||
# 去重字段名
|
||
unique_field_names = list(set(field_names))
|
||
# 过滤掉空值
|
||
unique_field_names = [name for name in unique_field_names if name and name.strip()]
|
||
|
||
# 优化：只对有3个或更多字段的模型生成问题，避免问题过多
|
||
if len(unique_field_names) < 3:
|
||
continue
|
||
|
||
# 根据模型类型生成不同的问题
|
||
if "逻辑模型" in model_type:
|
||
if "中文名" in model_type:
|
||
question = f"逻辑模型中文名为'{model_name}'的元素有哪些？"
|
||
answer_prefix = f"{model_name}对应的元素有:"
|
||
else:
|
||
question = f"逻辑模型英文名为'{model_name}'的元素有哪些？"
|
||
answer_prefix = f"逻辑模型'{model_name}'对应的元素有:"
|
||
else: # 物理模型
|
||
if "中文名" in model_type:
|
||
question = f"物理模型中文名为'{model_name}'的元素有哪些？"
|
||
answer_prefix = f"{model_name}对应的元素有:"
|
||
else:
|
||
question = f"物理模型英文名为'{model_name}'的元素有哪些？"
|
||
answer_prefix = f"物理模型'{model_name}'对应的元素有:"
|
||
|
||
# 构建答案
|
||
field_list = "、".join(unique_field_names[:10]) # 限制最多10个字段
|
||
if len(unique_field_names) > 10:
|
||
field_list += f"等{len(unique_field_names)}个字段"
|
||
|
||
answer = f"{answer_prefix}{field_list}"
|
||
|
||
qa_pairs.append({
|
||
"instruct": question,
|
||
"input": "",
|
||
"output": f"{self.get_random_element(self.ANSWER_PREFIXES)}{answer}{self.get_random_element(self.ANSWER_SUFFIXES)}"
|
||
})
|
||
|
||
return qa_pairs
|
||
|
||
def generate_verification_model_based_qa(self, data: List[Dict]) -> List[Dict]:
|
||
"""生成基于模型的问题（验证集版：正式但有别于训练集）"""
|
||
qa_pairs = []
|
||
|
||
# 为每个模型类型生成问题
|
||
for model_type, model_dict in self.model_data_cache.items():
|
||
for model_name, field_names in model_dict.items():
|
||
# 去重字段名
|
||
unique_field_names = list(set(field_names))
|
||
# 过滤掉空值
|
||
unique_field_names = [name for name in unique_field_names if name and name.strip()]
|
||
|
||
# 优化：只对有3个或更多字段的模型生成问题，避免问题过多
|
||
if len(unique_field_names) < 3:
|
||
continue
|
||
|
||
# 根据模型类型生成不同的问题（正式但有变化）
|
||
if "逻辑模型" in model_type:
|
||
if "中文名" in model_type:
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}逻辑模型'{model_name}'包含哪些字段？"
|
||
answer_prefix = f"{model_name}包含的字段有："
|
||
else:
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}逻辑模型'{model_name}'包含哪些字段？"
|
||
answer_prefix = f"逻辑模型'{model_name}'包含的字段有："
|
||
else: # 物理模型
|
||
if "中文名" in model_type:
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}物理模型'{model_name}'包含哪些字段？"
|
||
answer_prefix = f"{model_name}包含的字段有："
|
||
else:
|
||
question = f"{self.get_random_element(self.VERIFICATION_QUESTION_PREFIXES)}物理模型'{model_name}'包含哪些字段？"
|
||
answer_prefix = f"物理模型'{model_name}'包含的字段有："
|
||
|
||
# 构建答案
|
||
field_list = "、".join(unique_field_names[:10]) # 限制最多10个字段
|
||
if len(unique_field_names) > 10:
|
||
field_list += f"等{len(unique_field_names)}个字段"
|
||
|
||
answer = f"{answer_prefix}{field_list}"
|
||
|
||
qa_pairs.append({
|
||
"instruct": question,
|
||
"input": "",
|
||
"output": f"{self.get_random_element(self.VERIFICATION_ANSWER_PREFIXES)}{answer}{self.get_random_element(self.VERIFICATION_ANSWER_SUFFIXES)}"
|
||
})
|
||
|
||
return qa_pairs
|
||
|
||
def shuffle_qa_pairs(self, qa_pairs: List[Dict]) -> List[Dict]:
|
||
"""随机打乱问答对顺序"""
|
||
if self.config.SHUFFLE_OUTPUT:
|
||
random.shuffle(qa_pairs)
|
||
return qa_pairs
|
||
|
||
def save_qa(self, qa_pairs: List[Dict], filename: str):
|
||
"""保存QA到文件"""
|
||
output_path = os.path.join(self.config.OUTPUT_DIR, filename)
|
||
with open(output_path, 'w', encoding='utf-8') as f:
|
||
json.dump(qa_pairs, f, ensure_ascii=False, indent=2)
|
||
|
||
size_kb = os.path.getsize(output_path) / 1024
|
||
print(f"[OK] 已生成: {output_path}")
|
||
print(f" 共 {len(qa_pairs)} 条问答对, {size_kb:.1f} KB")
|
||
|
||
def generate_report(self, total_qa_count: int):
|
||
"""生成生成报告"""
|
||
report = {
|
||
"生成时间": "2025-12-31",
|
||
"版本": "简化版",
|
||
"输入文件": "selected.json",
|
||
"输出目录": self.config.OUTPUT_DIR,
|
||
"随机种子": self.config.RANDOM_SEED,
|
||
"总问答对数量": total_qa_count,
|
||
"说明": "基于字段中文名、字段英文名询问其他字段，新增：根据中文字段名/英文字段名询问完整定义，新增：根据逻辑模型/物理模型查询对应元素（仅对字段数>=3的模型生成）"
|
||
}
|
||
|
||
report_path = os.path.join(self.config.OUTPUT_DIR, "QA生成报告.json")
|
||
with open(report_path, 'w', encoding='utf-8') as f:
|
||
json.dump(report, f, ensure_ascii=False, indent=2)
|
||
|
||
print(f"[OK] 已生成: {report_path}")
|
||
|
||
def process_selected_json(self, generate_verification: bool = False):
|
||
"""处理selected.json文件"""
|
||
input_file = os.path.join(self.config.INPUT_DIR, "selected.json")
|
||
|
||
if not os.path.exists(input_file):
|
||
print(f"[ERROR] 文件不存在: {input_file}")
|
||
return
|
||
|
||
print("="*60)
|
||
if generate_verification:
|
||
print("QA生成器 - 验证集版（正式化表达但有别于训练集）")
|
||
else:
|
||
print("QA生成器 - 简化版")
|
||
print("="*60)
|
||
print(f"\n[INFO] 加载数据: {input_file}")
|
||
|
||
try:
|
||
data = self.load_json(input_file)
|
||
print(f" 数据记录: {len(data)} 条")
|
||
|
||
print(f"\n[INFO] 生成问答对...")
|
||
if generate_verification:
|
||
qa_pairs = self.generate_verification_qa_for_data(data)
|
||
output_filename = "selected_QA_Verification.json"
|
||
else:
|
||
qa_pairs = self.generate_qa_for_data(data)
|
||
output_filename = "selected_QA.json"
|
||
print(f" 生成数量: {len(qa_pairs)} 条")
|
||
|
||
print(f"\n[INFO] 打乱顺序...")
|
||
qa_pairs = self.shuffle_qa_pairs(qa_pairs)
|
||
|
||
print(f"\n[INFO] 保存文件...")
|
||
self.save_qa(qa_pairs, output_filename)
|
||
|
||
print(f"\n[INFO] 生成报告...")
|
||
if generate_verification:
|
||
self.generate_verification_report(len(qa_pairs))
|
||
else:
|
||
self.generate_report(len(qa_pairs))
|
||
|
||
print(f"\n[DONE] 处理完成!")
|
||
print(f"[OUT] 输出目录: {self.config.OUTPUT_DIR}")
|
||
print(f"[TOTAL] 总计生成: {len(qa_pairs)} 条问答对")
|
||
|
||
except Exception as e:
|
||
print(f"[ERROR] 处理文件时出错: {str(e)}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
def generate_verification_report(self, total_qa_count: int):
|
||
"""生成验证集生成报告"""
|
||
report = {
|
||
"生成时间": "2025-12-31",
|
||
"版本": "验证集版",
|
||
"输入文件": "selected.json",
|
||
"输出目录": self.config.OUTPUT_DIR,
|
||
"随机种子": self.config.RANDOM_SEED,
|
||
"总问答对数量": total_qa_count,
|
||
"说明": "验证集：基于字段中文名、字段英文名询问其他字段，正式化表达但有别于训练集，新增：根据中文字段名/英文字段名询问完整定义，新增：根据逻辑模型/物理模型查询对应元素（仅对字段数>=3的模型生成）"
|
||
}
|
||
|
||
report_path = os.path.join(self.config.OUTPUT_DIR, "QA生成报告_验证集.json")
|
||
with open(report_path, 'w', encoding='utf-8') as f:
|
||
json.dump(report, f, ensure_ascii=False, indent=2)
|
||
|
||
print(f"[OK] 已生成: {report_path}")
|
||
|
||
|
||
def main():
|
||
"""主函数"""
|
||
# 使用默认配置
|
||
config = QAConfig()
|
||
|
||
# 创建生成器
|
||
generator = QAGenerator(config)
|
||
|
||
# 生成训练集
|
||
print("\n" + "="*60)
|
||
print("开始生成训练集")
|
||
print("="*60)
|
||
generator.process_selected_json(generate_verification=False)
|
||
|
||
# 生成验证集
|
||
print("\n" + "="*60)
|
||
print("开始生成验证集")
|
||
print("="*60)
|
||
generator.process_selected_json(generate_verification=True)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|