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Deleted the DocString, as it's present in docs

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Zakhar Kogan 2023-08-02 20:50:44 +03:00
parent 05ca788fb3
commit 8f785d704b
1 changed files with 0 additions and 25 deletions
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litellm/main.py
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@ -65,31 +65,6 @@ def completion(
# Optional liteLLM function params
*, force_timeout=60, azure=False, logger_fn=None, verbose=False
):
# Docstring
'''
Parameters:
Required:
model (str): The model name to use for completion.
messages (list): A list of messages to feed into the completion engine.
Optional:
functions (list): A list of functions to call.
function_call (str): A string that calls the functions passed in the functions parameter.
temperature (float): What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or top_p but not both.
top_p (float): An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
n (int): How many completions to generate for each prompt.
stream (bool): Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as available, with the stream terminated by a data: [DONE] message. Otherwise, tokens will be returned as a standard JSON response.
stop (list): One or more sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
max_tokens (int): How many tokens to complete to. Can return fewer if a stop sequence is hit. In text-generation tasks, the API may return fewer than the max length.
presence_penalty (float): What penalty to apply if a token is already present at all. Bigger values mean the model will be less likely to repeat itself.
frequency_penalty (float): What penalty to apply if a token is already present in the text so far. Bigger values mean the model will be less likely to repeat itself.
logit_bias (dict): Modify the likelihood of specified tokens appearing in the completion. Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this parameter to bias the completion.
user (str): A unique identifier representing your end-user.
Returns:
response (dict): A dictionary containing the completion response.
Most parameters are taken from OpenAI API Reference: https://platform.openai.com/docs/api-reference/chat/create
'''
try:
# check if user passed in any of the OpenAI optional params
optional_params = get_optional_params(