📘 概述

CondorRefiner 是一个两阶段优化指令回复质量的算子。第一阶段调用大语言模型（LLM）生成对原始回复的评论（critique），第二阶段结合原始问题、原始回复以及生成的评论，再次调用LLM来改写并优化回复，从而提升指令对（instruction-response pair）的整体质量。

__init__函数

def __init__(self, llm_serving: LLMServingABC = None, prompt_template: Union[CondorRefinePrompt, DIYPromptABC] = None)

init参数说明

参数名	类型	默认值	说明
llm_serving	LLMServingABC	None	大语言模型服务实例，用于执行推理与生成。
prompt_template	Union[CondorRefinePrompt, DIYPromptABC]	None	prompt模板，支持自定义修改

Prompt模板说明

包括critique和refine部分

criqique

There is now a user’s question and a model’s response. You need to write a critique for this response, pointing out the
strengths and weaknesses of the model’s answer to help the model improve its response.

Your critique must strictly adhere to the following format:

[Critique Start]

[Strength Start]Strength[Strength End]

[Weakness Start]Weakness[Weakness End]

[Suggestion Start]Suggestion[Suggestion End]

[Critique End]

Here is the user’s question and the model’s response: {dialogue}

Now it’s your turn. Please provide your Critique as required:

refine

Now there is a user's question, a model's answer, and the user's feedback. Please help modify the model's answer based on the user's feedback to make it better.
Your improved answer must strictly adhere to the following format:

[Improved Answer Start]Your answer[Improved Answer End]

Below is the user's question, the model's answer, and the feedback:
[Question Start]{question}[Question End]
[Answer Start]{answer}[Answer End]
[Feedback Start]{critique}[Feedback End]

Now it's your turn, please provide your improved answer as required:

run函数

def run(self, storage: DataFlowStorage, input_instruction_key: str='instruction', input_output_key: str='output')

执行算子主逻辑，从存储中读取包含指令和回复的 DataFrame，经过“生成评论”和“优化回复”两个阶段，并将优化后的结果写回存储。

参数

名称	类型	默认值	说明
storage	DataFlowStorage	必需	数据流存储实例，负责读取与写入数据。
input_instruction_key	str	"instruction"	输入列名，对应指令字段。
input_output_key	str	"output"	输入列名，对应待优化的回复字段。优化后的回复将覆盖此列。