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.

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.