chore: initial snapshot for gitea/github upload
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"""
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Support for gpt model family
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"""
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from typing import List, Optional, Union
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from litellm.llms.base_llm.completion.transformation import BaseTextCompletionConfig
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from litellm.types.llms.openai import AllMessageValues, OpenAITextCompletionUserMessage
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from litellm.types.utils import Choices, Message, ModelResponse, TextCompletionResponse
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from ..chat.gpt_transformation import OpenAIGPTConfig
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from .utils import _transform_prompt
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class OpenAITextCompletionConfig(BaseTextCompletionConfig, OpenAIGPTConfig):
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"""
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Reference: https://platform.openai.com/docs/api-reference/completions/create
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The class `OpenAITextCompletionConfig` provides configuration for the OpenAI's text completion API interface. Below are the parameters:
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- `best_of` (integer or null): This optional parameter generates server-side completions and returns the one with the highest log probability per token.
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- `echo` (boolean or null): This optional parameter will echo back the prompt in addition to the completion.
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- `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.
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- `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion.
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- `logprobs` (integer or null): This optional parameter includes the log probabilities on the most likely tokens as well as the chosen tokens.
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- `max_tokens` (integer or null): This optional parameter sets the maximum number of tokens to generate in the completion.
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- `n` (integer or null): This optional parameter sets how many completions to generate for each prompt.
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- `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.
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- `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens.
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- `suffix` (string or null): Defines the suffix that comes after a completion of inserted text.
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- `temperature` (number or null): This optional parameter defines the sampling temperature to use.
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- `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling.
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"""
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best_of: Optional[int] = None
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echo: Optional[bool] = None
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frequency_penalty: Optional[int] = None
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logit_bias: Optional[dict] = None
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logprobs: Optional[int] = None
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max_tokens: Optional[int] = None
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n: Optional[int] = None
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presence_penalty: Optional[int] = None
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stop: Optional[Union[str, list]] = None
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suffix: Optional[str] = None
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def __init__(
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self,
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best_of: Optional[int] = None,
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echo: Optional[bool] = None,
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frequency_penalty: Optional[int] = None,
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logit_bias: Optional[dict] = None,
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logprobs: Optional[int] = None,
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max_tokens: Optional[int] = None,
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n: Optional[int] = None,
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presence_penalty: Optional[int] = None,
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stop: Optional[Union[str, list]] = None,
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suffix: Optional[str] = None,
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temperature: Optional[float] = None,
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top_p: Optional[float] = None,
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) -> None:
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locals_ = locals().copy()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return super().get_config()
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def convert_to_chat_model_response_object(
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self,
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response_object: Optional[TextCompletionResponse] = None,
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model_response_object: Optional[ModelResponse] = None,
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):
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try:
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## RESPONSE OBJECT
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if response_object is None or model_response_object is None:
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raise ValueError("Error in response object format")
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choice_list: List[Choices] = []
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for idx, choice in enumerate(response_object["choices"]):
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message = Message(
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content=choice["text"],
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role="assistant",
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)
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choice = Choices(
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finish_reason=choice["finish_reason"],
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index=idx,
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message=message,
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logprobs=choice.get("logprobs", None),
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)
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choice_list.append(choice)
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model_response_object.choices = choice_list # type: ignore
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if "usage" in response_object:
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setattr(model_response_object, "usage", response_object["usage"])
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if "id" in response_object:
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model_response_object.id = response_object["id"]
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if "model" in response_object:
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model_response_object.model = response_object["model"]
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model_response_object._hidden_params[
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"original_response"
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] = response_object # track original response, if users make a litellm.text_completion() request, we can return the original response
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return model_response_object
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except Exception as e:
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raise e
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def get_supported_openai_params(self, model: str) -> List:
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return [
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"functions",
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"function_call",
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"temperature",
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"top_p",
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"n",
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"stream",
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"stream_options",
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"stop",
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"max_tokens",
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"presence_penalty",
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"frequency_penalty",
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"logit_bias",
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"user",
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"response_format",
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"seed",
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"tools",
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"tool_choice",
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"max_retries",
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"logprobs",
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"top_logprobs",
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"extra_headers",
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]
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def transform_text_completion_request(
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self,
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model: str,
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messages: Union[List[AllMessageValues], List[OpenAITextCompletionUserMessage]],
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optional_params: dict,
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headers: dict,
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) -> dict:
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prompt = _transform_prompt(messages)
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return {
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"model": model,
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"prompt": prompt,
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**optional_params,
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}
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