def common_prompt_prefix_validator()

in src/openai/lib/_validators.py [0:0]

• 26 lines of code
• 5 McCabe index (conditional complexity)

def common_prompt_prefix_validator(df: pd.DataFrame) -> Remediation:
    """
    This validator will suggest to remove a common prefix from the prompt if a long one exist.
    """
    MAX_PREFIX_LEN = 12

    immediate_msg = None
    optional_msg = None
    optional_fn = None  # type: ignore

    common_prefix = get_common_xfix(df.prompt, xfix="prefix")
    if common_prefix == "":
        return Remediation(name="common_prefix")

    def remove_common_prefix(x: Any, prefix: Any) -> Any:
        x["prompt"] = x["prompt"].str[len(prefix) :]
        return x

    if (df.prompt == common_prefix).all():
        # already handled by common_suffix_validator
        return Remediation(name="common_prefix")

    if common_prefix != "":
        immediate_msg = f"\n- All prompts start with prefix `{common_prefix}`"
        if MAX_PREFIX_LEN < len(common_prefix):
            immediate_msg += ". Fine-tuning doesn't require the instruction specifying the task, or a few-shot example scenario. Most of the time you should only add the input data into the prompt, and the desired output into the completion"
            optional_msg = f"Remove prefix `{common_prefix}` from all prompts"

            def optional_fn(x: Any) -> Any:
                return remove_common_prefix(x, common_prefix)

    return Remediation(
        name="common_prompt_prefix",
        immediate_msg=immediate_msg,
        optional_msg=optional_msg,
        optional_fn=optional_fn,
    )