calibrate_qa_mapper

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基于参考文本校准问答对。当用户提到问答校准、校准QA、语言风格校准、问答对优化等需求时使用此skill。即使用户没有明确说出"校准"，只要任务涉及根据参考文本调整问答对使其更符合特定风格，就应该使用此skill。

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Calibrate QA Mapper

基于参考文本校准问答对，使问答对更详细、准确，并贴合参考文本的语言风格。

本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用同以下指令进行安装：

pip install py-data-juicer

核心参数

参数	类型	必填	默认值	说明
input_path	string	是	-	输入JSON文件路径
output_path	string	是	-	输出JSON文件路径
api_model	string	是	-	LLM模型名称，如 'qwen2.5-72b-instruct'
api_endpoint	string	否	-	API端点URL
response_path	string	否	choices.0.message.content	响应内容路径
text_key	string	否	text	参考文本字段名
query_key	string	否	query	问题字段名
response_key	string	否	response	回答字段名

使用方法

python scripts/run_calibrate_qa_mapper.py --input_path <input_path> --output_path <output_path> --api_model <model_name> [--api_endpoint <endpoint>] [--response_path <path>] [--text_key <key>] [--query_key <key>] [--response_key <key>]

实现原理

参照测试代码 test_calibrate_qa_mapper.py 中的 _run_op 函数：

# 1. 初始化算子（必须指定api_model）
op = CalibrateQAMapper(api_model='qwen2.5-72b-instruct')

# 2. 处理样本
samples = [{'text': reference, 'query': '...', 'response': '...'}]
result = op.process(samples)  # 使用process处理批次

输入输出格式

输入格式 (JSON数组)

[
  {
    "text": "参考文本，包含语言风格示例...",
    "query": "原始问题",
    "response": "原始回答"
  }
]

输出格式 (JSON数组)

[
  {
    "text": "参考文本，包含语言风格示例...",
    "query": "校准后的问题",
    "response": "校准后的回答"
  }
]

示例

示例1：基本用法

python scripts/run_calibrate_qa_mapper.py --input_path example_input.json --output_path output.json --api_model "qwen2.5-72b-instruct"

示例2：指定API端点

python scripts/run_calibrate_qa_mapper.py --input_path example_input.json --output_path output.json --api_model "qwen2.5-72b-instruct" --api_endpoint "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions"

注意事项

必须设置环境变量：使用前需设置 API key

export OPENAI_API_KEY=your_api_key
# 或
export DASHSCOPE_API_KEY=your_api_key

api_model 参数必须指定
输入数据需要包含 text（参考）、query（问题）、response（回答）三个字段
该算子调用 LLM API，可能需要较长时间