prompted-filter

name	prompted-filter
description	Reference documentation for the PromptedFilter operator. Covers the constructor, actual scoring and filtering behavior, and pipeline usage notes. Use when: filtering rows based on LLM semantic quality judgment rather than simple rule-based conditions.
trigger_keywords	["PromptedFilter","prompted-filter","LLM filtering","quality filtering","semantic filtering"]
version	1.0.0

PromptedFilter Operator Reference

PromptedFilter internally uses PromptedEvaluator to score each row of text, writes the score into output_key, and keeps only rows with scores in [min_score, max_score].

1. Import

from dataflow.operators.core_text import PromptedFilter

2. Constructor

PromptedFilter(
    llm_serving,
    system_prompt="Please evaluate the quality of this data on a scale from 1 to 5.",
    min_score=1,
    max_score=5,
)

Parameter	Required	Default	Description
llm_serving	Yes	None	LLM service object implementing generate_from_input(...). Internally passed to PromptedEvaluator.
system_prompt	No	"Please evaluate the quality of this data on a scale from 1 to 5."	Defines the LLM's scoring criteria. The scale described should align with min_score/max_score.
min_score	No	1	Lower bound of the score range to keep (inclusive).
max_score	No	5	Upper bound of the score range to keep (inclusive).

Important Constructor Notes

1. The operator creates an internal PromptedEvaluator(llm_serving, system_prompt) instance.
2. Source code comment at line 35 incorrectly states min_score default is 5; actual default is 1.
3. Source code comment at line 36 incorrectly states max_score default is 5; actual default is 5 (correct).

3. run() Signature

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

Parameter	Required	Default	Description
storage	Yes	None	DataFlowStorage step object. The operator reads a DataFrame from here and writes the filtered DataFrame back.
input_key	No	"raw_content"	Text column to evaluate. This column must already exist.
output_key	No	"eval"	Column to write LLM scores into. Silently overwrites if already exists.

Return Value

Returns output_key string. Pipeline forward() methods should not return values.

4. Actual Runtime Logic

The source code behavior is:

1. Read the DataFrame from storage.read("dataframe").
2. Call self.prompted_evaluator.eval(dataframe, input_key) to get a list of numeric scores.
3. Write the scores into dataframe[output_key].
4. Filter the DataFrame: keep only rows where dataframe[output_key] >= self.min_score AND dataframe[output_key] <= self.max_score.
5. Write the filtered DataFrame back via storage.write(filtered_dataframe).
6. Return output_key.

Key Behavior Notes

1. The input_key column must exist in the current DataFrame.
2. Rows with scores outside [min_score, max_score] are permanently deleted.
3. The output DataFrame retains both the output_key score column and all original columns.
4. If output_key already exists, it is silently overwritten.

5. Pipeline Usage Pattern

from dataflow.operators.core_text import PromptedFilter
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.filter = PromptedFilter(
            llm_serving=self.llm_serving,
            system_prompt="Evaluate the quality of this text on a scale from 1 to 5.",
            min_score=4,
            max_score=5
        )

    def forward(self):
        self.filter.run(
            storage=self.storage.step(),
            input_key="content",
            output_key="quality_score"
        )

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

Note: forward() has no return value, following the standard pipeline pattern.