daft.functions.jev_prob#

def jev_prob(
    row: Expression,
    condition: str,
    *,
    model: str | None = None,
    api_key: str | None = None,
    base_url: str | None = None,
    timeout: float = 60.0,
) -> Expression:
    """Probability that each row satisfies a plain-English condition.

    Args:
        row (Expression): The value to judge. Pass `to_struct(...)` to judge several columns; field names are visible to the model.
        condition (str): A plain-English condition, e.g. "the customer is angry".
        model (str | None): The Jev model. Defaults to `TYPESAFE_DEFAULT_MODEL`, else `jev-latest`.
        api_key (str | None): TypeSafe API key. Defaults to `TYPESAFE_API_KEY`.
        base_url (str | None): API root, e.g. to route through a proxy. Defaults to `TYPESAFE_BASE_URL`, else `https://api.typesafe.ai`.
        timeout (float): Seconds allowed for each request, which carries up to 25 rows. Defaults to 60.

    Returns:
        Expression: A Float64 probability in [0, 1], or null where `row` is null.

    Examples:
        >>> import daft
        >>> from daft.functions import jev_prob
        >>> df = daft.from_pydict({"ticket": ["I was charged twice!!", "Thanks, all sorted."]})
        >>> df = df.with_column("p_angry", jev_prob(df["ticket"], "the customer is angry"))  # doctest: +SKIP
        >>> df.sort("p_angry", desc=True).show()  # doctest: +SKIP
    """
    return _client(api_key, model, base_url, timeout).noul(serialize(row, format="json"), condition)