random-domain-knowledge-row-generator

name	random-domain-knowledge-row-generator
description	Reference documentation for the RandomDomainKnowledgeRowGenerator operator. [Purpose] Calls an LLM repeatedly with the same domain-generation prompt and writes the generated results into one column of an existing DataFrame. [When to use] Use it when you already have a seed DataFrame with the exact target row count and want to fill one output column with domain-specific generated content. If you need to build prompts from existing row fields, use PromptedGenerator or FormatStrPromptedGenerator instead.
trigger_keywords	["RandomDomainKnowledgeRowGenerator","random-domain-knowledge","domain generation","synthetic sft rows"]
version	1.0.0

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.

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1. Import

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

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2. Constructor

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

Parameter	Required	Default	Description
llm_serving	Yes	None	LLM serving object. It must implement generate_from_input(user_inputs, ...). Examples in dataflow.serving include APILLMServing_request, LiteLLMServing, and LocalModelLLMServing_vllm.
generation_num	Yes	None	Number of prompts to build and number of outputs expected from the LLM call.
domain_keys	Yes	None	Domain description passed directly into SFTFromScratchGeneratorPrompt.build_prompt(domain_keys). The source annotation is str, so use a string such as "finance, accounting, tax".
prompt_template	No in signature, but effectively required	None	Prompt object used for every generation call. In practice you must pass an instantiated SFTFromScratchGeneratorPrompt() or another prompt allowed by @prompt_restrict(...). Leaving it as None will fail before generation starts.

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.

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3. run() Signature

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

Parameter	Required	Default	Description
storage	Yes	None	DataFlowStorage / FileStorage step object. The operator reads a DataFrame from here and writes the updated DataFrame back.
output_key	No	"generated_content"	Column name used to store generated results.

Return Value

The method returns the string output_key.

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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.

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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.

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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.