RandomDomainKnowledgeRowGenerator Operator Reference

RandomDomainKnowledgeRowGenerator does not read any input column values, but it still reads the input DataFrame itself. The operator builds generation_num prompts from domain_keys, calls llm_serving.generate_from_input(...), and assigns the returned list into dataframe[output_key].

See examples/good.md for a runnable example and examples/bad.md for common failure cases.

1. Import

from dataflow.operators.core_text import RandomDomainKnowledgeRowGenerator
from dataflow.prompts.general_text import SFTFromScratchGeneratorPrompt

2. Constructor

RandomDomainKnowledgeRowGenerator(
    llm_serving=llm,
    generation_num=200,
    domain_keys="machine learning, deep learning, neural networks",
    prompt_template=SFTFromScratchGeneratorPrompt(),
)

Important Constructor Notes

1. prompt_template=None is not a safe fallback. The code calls self.prompt_template.build_prompt(self.domain_keys) directly, so None raises AttributeError.
2. The default prompt class is SFTFromScratchGeneratorPrompt, and its build_prompt() method expects domain_keys: str.
3. The prompt asks the LLM to output a single-line JSON object containing fields such as instruction, input, output, and domain.

3. run() Signature

output_key = op.run(
    storage=self.storage.step(),
    output_key="generated_content",
)

Return Value

The method returns the string output_key.

4. Actual Runtime Logic

The source code behavior is:

1. Read the current DataFrame from storage.read("dataframe").
2. Ignore all column values in that DataFrame.
3. Build generation_num prompts by repeatedly calling prompt_template.build_prompt(domain_keys).
4. Call llm_serving.generate_from_input(llm_inputs).
5. Assign the returned list to dataframe[output_key].
6. Write the updated DataFrame back to storage and return output_key.

5. Critical Constraints

1. len(dataframe) should match generation_num. Otherwise dataframe[output_key] = generated_outputs can fail with a pandas length-mismatch error.
2. An empty seed file plus generation_num > 0 is not safe for the current implementation. The operator still writes back into the existing DataFrame instead of creating rows.

6. Usage Guidance

Use this operator when:

• You want one generated sample per existing seed row.
• The row contents are irrelevant, and you only need the row count plus an output column.
• You want the default SFTFromScratchGeneratorPrompt behavior for domain-focused SFT sample generation.

Do not use this operator when:

• You need prompts built from existing columns.
• You expect the operator to create rows from an empty DataFrame.
• You need per-row domain variation based on different input fields.