Create Chat Completion

The name of the model to query.

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..

Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.

An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.

The maximum number of tokens to generate.

How many completions to generate for each prompt.

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.

Whether to stream the response back to the client. If true, stream tokens as Server-Sent Events as the model generates them instead of waiting for the full model response. If false, return a single JSON object containing the results.

A decimal number that determines the degree of randomness in the response. A value of 1 will always yield the same output. A temperature less than 1 favors more correctness and is appropriate for question answering or summarization. A value greater than 1 introduces more randomness in the output.

The top_p (nucleus) parameter is used to dynamically adjust the number of choices for each predicted token based on the cumulative probabilities. It specifies a probability threshold, below which all less likely tokens are filtered out. This technique helps to maintain diversity and generate more fluent and natural-sounding text.

A unique identifier representing end-user.