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| """ | |
| Support for gpt model family | |
| """ | |
| from typing import List, Optional, Union | |
| from litellm.llms.base_llm.completion.transformation import BaseTextCompletionConfig | |
| from litellm.types.llms.openai import AllMessageValues, OpenAITextCompletionUserMessage | |
| from litellm.types.utils import Choices, Message, ModelResponse, TextCompletionResponse | |
| from ..chat.gpt_transformation import OpenAIGPTConfig | |
| from .utils import _transform_prompt | |
| class OpenAITextCompletionConfig(BaseTextCompletionConfig, OpenAIGPTConfig): | |
| """ | |
| Reference: https://platform.openai.com/docs/api-reference/completions/create | |
| The class `OpenAITextCompletionConfig` provides configuration for the OpenAI's text completion API interface. Below are the parameters: | |
| - `best_of` (integer or null): This optional parameter generates server-side completions and returns the one with the highest log probability per token. | |
| - `echo` (boolean or null): This optional parameter will echo back the prompt in addition to the completion. | |
| - `frequency_penalty` (number or null): Defaults to 0. It is a numbers from -2.0 to 2.0, where positive values decrease the model's likelihood to repeat the same line. | |
| - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion. | |
| - `logprobs` (integer or null): This optional parameter includes the log probabilities on the most likely tokens as well as the chosen tokens. | |
| - `max_tokens` (integer or null): This optional parameter sets the maximum number of tokens to generate in the completion. | |
| - `n` (integer or null): This optional parameter sets how many completions to generate for each prompt. | |
| - `presence_penalty` (number or null): Defaults to 0 and can be between -2.0 and 2.0. Positive values increase 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. | |
| - `suffix` (string or null): Defines the suffix that comes after a completion of inserted text. | |
| - `temperature` (number or null): This optional parameter defines the sampling temperature to use. | |
| - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling. | |
| """ | |
| best_of: Optional[int] = None | |
| echo: Optional[bool] = None | |
| frequency_penalty: Optional[int] = None | |
| logit_bias: Optional[dict] = None | |
| logprobs: Optional[int] = None | |
| max_tokens: Optional[int] = None | |
| n: Optional[int] = None | |
| presence_penalty: Optional[int] = None | |
| stop: Optional[Union[str, list]] = None | |
| suffix: Optional[str] = None | |
| def __init__( | |
| self, | |
| best_of: Optional[int] = None, | |
| echo: Optional[bool] = None, | |
| frequency_penalty: Optional[int] = None, | |
| logit_bias: Optional[dict] = None, | |
| logprobs: Optional[int] = None, | |
| max_tokens: Optional[int] = None, | |
| n: Optional[int] = None, | |
| presence_penalty: Optional[int] = None, | |
| stop: Optional[Union[str, list]] = None, | |
| suffix: Optional[str] = None, | |
| temperature: Optional[float] = None, | |
| top_p: Optional[float] = None, | |
| ) -> None: | |
| locals_ = locals().copy() | |
| for key, value in locals_.items(): | |
| if key != "self" and value is not None: | |
| setattr(self.__class__, key, value) | |
| def get_config(cls): | |
| return super().get_config() | |
| def convert_to_chat_model_response_object( | |
| self, | |
| response_object: Optional[TextCompletionResponse] = None, | |
| model_response_object: Optional[ModelResponse] = None, | |
| ): | |
| try: | |
| ## RESPONSE OBJECT | |
| if response_object is None or model_response_object is None: | |
| raise ValueError("Error in response object format") | |
| choice_list = [] | |
| for idx, choice in enumerate(response_object["choices"]): | |
| message = Message( | |
| content=choice["text"], | |
| role="assistant", | |
| ) | |
| choice = Choices( | |
| finish_reason=choice["finish_reason"], | |
| index=idx, | |
| message=message, | |
| logprobs=choice.get("logprobs", None), | |
| ) | |
| choice_list.append(choice) | |
| model_response_object.choices = choice_list | |
| if "usage" in response_object: | |
| setattr(model_response_object, "usage", response_object["usage"]) | |
| if "id" in response_object: | |
| model_response_object.id = response_object["id"] | |
| if "model" in response_object: | |
| model_response_object.model = response_object["model"] | |
| model_response_object._hidden_params[ | |
| "original_response" | |
| ] = response_object # track original response, if users make a litellm.text_completion() request, we can return the original response | |
| return model_response_object | |
| except Exception as e: | |
| raise e | |
| def get_supported_openai_params(self, model: str) -> List: | |
| return [ | |
| "functions", | |
| "function_call", | |
| "temperature", | |
| "top_p", | |
| "n", | |
| "stream", | |
| "stream_options", | |
| "stop", | |
| "max_tokens", | |
| "presence_penalty", | |
| "frequency_penalty", | |
| "logit_bias", | |
| "user", | |
| "response_format", | |
| "seed", | |
| "tools", | |
| "tool_choice", | |
| "max_retries", | |
| "logprobs", | |
| "top_logprobs", | |
| "extra_headers", | |
| ] | |
| def transform_text_completion_request( | |
| self, | |
| model: str, | |
| messages: Union[List[AllMessageValues], List[OpenAITextCompletionUserMessage]], | |
| optional_params: dict, | |
| headers: dict, | |
| ) -> dict: | |
| prompt = _transform_prompt(messages) | |
| return { | |
| "model": model, | |
| "prompt": prompt, | |
| **optional_params, | |
| } | |