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Creates a model response for the given chat conversation.
Inputs
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The model name for processing. Defaults to gpt-3.5-turbo.
default: gpt-3.5-turbo
An array of messages with role and content properties.
What sampling temperature to use, between 0 and 2.
Higher values like 0.8 will make the output more random,
while lower values like 0.2 will make it more focused and deterministic.
It is generally recommended to alter either the Temperature or Top-P, but not both.
default: 1
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. It is generally recommended to alter
either the Temperature or Top-P, but not both.
default: 1
The maximum number of tokens that can be generated in the chat completion.
The total length of input tokens and generated tokens is limited by the model's context length.
Number between -2.0 and 2.0. Positive values penalize new
tokens based on their existing frequency in the text so far, decreasing
the model's likelihood to repeat the same line verbatim.
Number between -2.0 and 2.0. Positive values penalize new
tokens based on whether they appear in the text so far, increasing the
model's likelihood to talk about new topics.
Modify the likelihood of specified tokens appearing in the completion.
Accepts an object that maps tokens (specified by their token ID in
the tokenizer) to an associated bias value from -100 to 100.
Mathematically, the bias is added to the logits generated by the mode
prior to sampling. The exact effect will vary per model, but values
between -1 and 1 should decrease or increase likelihood of selection;
values like -100 or 100 should result in a ban or exclusive selection
of the relevant token.
Outputs