opendcai/dataflow-webui/prompted-generator

PromptedGenerator Operator Reference

PromptedGenerator is DataFlow's basic single-field LLM generation operator.

For each row in the current DataFrame, it reads the value from input_key. If that value is truthy, it builds one LLM input as:

user_prompt + str(row[input_key])

It then calls llm_serving.generate_from_input(...) in batch and writes the generated results into output_key.

1. Import

from dataflow.operators.core_text import PromptedGenerator

2. Constructor

PromptedGenerator(
    llm_serving,                               # required
    system_prompt="You are a helpful agent.",  # optional
    user_prompt="",                            # optional
    json_schema=None,                          # optional
)

3. run() Signature

op.run(
    storage=self.storage.step(),
    input_key="raw_content",
    output_key="generated_content",
)
# returns: output_key

4. Actual Execution Logic

The operator performs the following steps:

1. Read the current DataFrame from storage.
2. Iterate over the DataFrame row by row.
3. For each row, read raw_content = row.get(input_key, "").
4. Only if raw_content is truthy, append user_prompt + str(raw_content) to

the batch LLM input list.

1. Call:

llm_serving.generate_from_input(
    user_inputs=llm_inputs,
    system_prompt=system_prompt,
    json_schema=json_schema,
)

1. Write the generated list into dataframe[output_key].
2. Persist the updated DataFrame through storage.write(dataframe).
3. Return output_key.

5. Important Rules

1. input_key must exist in the current DataFrame.
2. user_prompt is a plain string prefix, not a template engine.
3. The implementation skips rows whose input_key value is empty, None, or

otherwise falsy when building LLM inputs.

6. Typical Usage

from dataflow.operators.core_text import PromptedGenerator
prompted_gen = PromptedGenerator(
    llm_serving=self.llm_serving,
    system_prompt="You are a helpful agent.",
    user_prompt="Summarize the following content in one sentence:\n",
)
prompted_gen.run(
    storage=self.storage.step(),
    input_key="raw_content",
    output_key="summary",
)

7. Return Value

return output_key

This is intended for downstream operator chaining.