xpark.dataset.TextExtract#
- class xpark.dataset.TextExtract(labels: list[str], /, *, ensure_ascii: bool = False, base_url: str, model: str, api_key: str = 'NOT_SET', max_qps: int | None = None, max_retries: int = 0, fallback_response: str | None = '{}', **kwargs: dict[str, Any])[source]#
- TextExtract processor extracts structured information from text based on user-defined
labels using an LLM model, and returns the results as a JSON string.
- Parameters:
labels – The labels to extract from the text.
ensure_ascii – If True, the output JSON will escape all non-ASCII characters. If False (default), non-ASCII characters will be preserved in the output. This is useful when working with multilingual text to maintain readability.
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 TextExtract, from_items ds = from_items(["John Doe lives in New York and works for Acme Corp"]) ds = ds.with_column( "extracted", TextExtract( ["person", "location", "organization"], 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#