prompted-refiner

name	prompted-refiner
description	Reference documentation for the PromptedRefiner operator. Use when: refining text with LLM, overwriting original column.
trigger_keywords	["PromptedRefiner","prompted-refiner","text refinement","LLM rewriting"]
version	1.0.0

PromptedRefiner Operator Reference

PromptedRefiner uses an LLM to refine/rewrite text in a column and overwrites the original column with refined results.

1. Import

from dataflow.operators.core_text import PromptedRefiner

2. Constructor

PromptedRefiner(
    llm_serving=llm_serving,
    system_prompt="You are a helpful agent.",
)

Parameter	Required	Default	Description
llm_serving	Yes	None	LLM service object
system_prompt	No	"You are a helpful agent."	Instruction for text refinement

3. run() Signature

op.run(
    storage=self.storage.step(),
    input_key="raw_content",
)
# returns: None

Parameter	Required	Default	Description
storage	Yes	None	Storage step object
input_key	No	"raw_content"	Column to refine (overwritten in place)

4. Usage Example

from dataflow.operators.core_text import PromptedRefiner
from dataflow.serving import APILLMServing_request
from dataflow.utils.storage import FileStorage

class MyPipeline:
    def __init__(self):
        self.storage = FileStorage(
            first_entry_file_name="./data/input.jsonl",
            cache_path="./cache",
            file_name_prefix="step",
            cache_type="jsonl"
        )

        self.llm_serving = APILLMServing_request(
            api_url="https://api.openai.com/v1/chat/completions",
            key_name_of_api_key="DF_API_KEY",
            model_name="gpt-4o",
            max_workers=10
        )

        self.refiner = PromptedRefiner(
            llm_serving=self.llm_serving,
            system_prompt="Refine the following text for clarity."
        )

    def forward(self):
        self.refiner.run(
            storage=self.storage.step(),
            input_key="content"
        )

if __name__ == "__main__":
    pipeline = MyPipeline()
    pipeline.forward()

5. Runtime Logic

1. Read DataFrame from storage.
2. Extract text from input_key column.
3. For each non-empty row, concatenate system_prompt + text as LLM input.
4. Call LLM to generate refined text.
5. Overwrite input_key column with refined results.
6. Empty/falsy rows are skipped (not sent to LLM).
7. Return None.

6. Important Notes

• Overwrites input_key column in place (original text is lost)
• To preserve original text, copy column first using PandasOperator
• Empty rows in input_key are skipped but remain in DataFrame
• No output_key parameter exists