xpark.dataset.TextPredicateEval

class xpark.dataset.TextPredicateEval(predicate: str, /, *, base_url: str, model: str, api_key: str = 'NOT_SET', max_qps: int | None = None, max_retries: int = 0, fallback_response: bool | None = None, **kwargs: dict[str, Any])

TextPredicateEval processor evaluates whether input texts satisfy a given predicate condition.

This processor uses a Large Language Model (LLM) to determine if each text in a column matches the specified predicate, returning True or False for each input.

Parameters:

• predicate – The predicate to evaluate.

• base_url – The base URL of the LLM server.

• model – The request model name.

• api_key – The request API key.

• max_qps – The maximum number of requests per second.

• max_retries – The maximum number of retries per request in the event of failures. We retry with exponential backoff upto this specific maximum retries.

• fallback_response – The response value to return when the LLM request fails. If set to None, the exception will be raised instead.

• **kwargs – Keyword arguments to pass to the openai.AsyncClient.chat.completions.create API.

Examples

from xpark.dataset.expressions import col
from xpark.dataset import TextPredicateEval, from_items

ds = from_items(["The iconic tower in the capital of France is illuminated with lights."])
ds = ds.with_column(
    "eval",
    TextPredicateEval(
        predicate="The text describes Paris",
        model="deepseek-v3-0324",
        base_url=os.getenv("LLM_ENDPOINT"),
        api_key=os.getenv("LLM_API_KEY"),
    )
    .options(num_workers={"IO": 1}, batch_size=1)
    .with_column(col("item")),
)

print(ds.take_all())

Methods

__call__(texts)	Call self as a function.
options(**kwargs)
with_column(texts)

__call__(texts: pa.ChunkedArray) → pa.Array

Call self as a function.

options(**kwargs: Unpack[ExprUDFOptions]) → Self

with_column(texts: pa.ChunkedArray) → pa.Array