litellm/llms/azure/completion/transformation.py

from typing import Optional, Union

from ...openai.completion.transformation import OpenAITextCompletionConfig


class AzureOpenAITextConfig(OpenAITextCompletionConfig):
    """
    Reference: https://platform.openai.com/docs/api-reference/chat/create

    The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. It inherits from `OpenAIConfig`. Below are the parameters::

    - `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition.

    - `function_call` (string or object): This optional parameter controls how the model calls functions.

    - `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs.

    - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion.

    - `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion.

    - `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message.

    - `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics.

    - `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens.

    - `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2.

    - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling.
    """

    def __init__(
        self,
        frequency_penalty: Optional[int] = None,
        logit_bias: Optional[dict] = None,
        max_tokens: Optional[int] = None,
        n: Optional[int] = None,
        presence_penalty: Optional[int] = None,
        stop: Optional[Union[str, list]] = None,
        temperature: Optional[int] = None,
        top_p: Optional[int] = None,
    ) -> None:
        super().__init__(
            frequency_penalty=frequency_penalty,
            logit_bias=logit_bias,
            max_tokens=max_tokens,
            n=n,
            presence_penalty=presence_penalty,
            stop=stop,
            temperature=temperature,
            top_p=top_p,
        )