CondorGenerator is an operator that generates Synthetic Fine-Tuning (SFT) data from scratch based on predefined knowledge tree tags. It operates in a two-stage process: first generating questions of varying difficulty levels, and then generating corresponding answers for each question.

__init__

def __init__(self, llm_serving: LLMServingABC = None, num_samples=15, use_task_diversity=True)

Parameter	Type	Default	Description
llm_serving	LLMServingABC	None	The Large Language Model serving instance, used to execute inference and generation.
num_samples	int	15	Total number of samples to generate. It is recommended to keep this value below 5000 unless more tags are added.
use_task_diversity	bool	True	Whether to use task scenario diversity enhancement to enrich the generated questions.

Prompt Template Descriptions

Prompt Template Name	Primary Use	Applicable Scenarios	Feature Description

run

def run(self, storage: DataFlowStorage)

Parameter	Type	Default	Description
storage	DataFlowStorage	Required	The DataFlowStorage instance, responsible for writing the generated data.

🧠 Example Usage

🧾 Output Format

Field	Type	Description
difficulty	str	Difficulty level of the question (e.g., "Easy", "Medium", "Hard").
instruction	str	The generated question text.
output	str	The generated answer for the corresponding instruction.