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Diffstat (limited to '.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/transformation.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/litellm/llms/openai/completion/transformation.py | 158 |
1 files changed, 158 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/transformation.py new file mode 100644 index 00000000..1aef72d3 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/transformation.py @@ -0,0 +1,158 @@ +""" +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) + + @classmethod + 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, + } |