Struct openai_rust::completions::CompletionArguments

pub struct CompletionArguments {

    pub model: String,
    pub prompt: Option<String>,
    pub suffix: Option<String>,
    pub max_tokens: Option<u32>,
    pub temperature: Option<f32>,
    pub top_p: Option<f32>,
    pub n: Option<u32>,
    pub logprobs: Option<u8>,
    pub echo: Option<bool>,
    pub stop: Option<String>,
    pub presence_penalty: Option<f32>,
    pub frequency_penalty: Option<f32>,
    pub best_of: Option<u32>,
    pub user: Option<String>,
    /* private fields */
}

Request arguments for completions.

See https://platform.openai.com/docs/api-reference/completions/create.

let args = openai_rust::completions::CompletionArguments::new(
    "text-davinci-003",
    "The quick brown fox".to_owned()
);

Fields

model: String

ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

prompt: Option<String>

The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays.

Defaults to <|endoftext|>.

Note that <|endoftext|> is the document separator that the model sees during training, so if a prompt is not specified the model will generate as if from the beginning of a new document.

suffix: Option<String>

The suffix that comes after a completion of inserted text.

max_tokens: Option<u32>

The maximum number of tokens to generate in the chat completion.

The token count of your prompt plus max_tokens cannot exceed the model’s context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096).

temperature: Option<f32>

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.

We generally recommend altering this or top_p but not both.

top_p: Option<f32>

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: Option<u32>

How many completions to generate for each prompt.

Note: Because this parameter generates many completions,it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.

logprobs: Option<u8>

Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response.

The maximum value for logprobs is 5. If you need more than this, please contact us through our Help center and describe your use case.

echo: Option<bool>

Echo back the prompt in addition to the completion

stop: Option<String>

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

presence_penalty: Option<f32>

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.

See more information about frequency and presence penalties.

frequency_penalty: Option<f32>

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.

See more information about frequency and presence penalties.

best_of: Option<u32>

Generates best_of completions server-side and returns the “best” (the one with the highest log probability per token). Results cannot be streamed.

When used with n, best_of controls the number of candidate completions and n specifies how many to return – best_of must be greater than n.

Note: Because this parameter generates many completions,it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.

user: Option<String>

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Implementations

impl CompletionArguments

pub fn new(model: impl AsRef<str>, prompt: String) -> CompletionArguments

Trait Implementations

impl Clone for CompletionArguments

fn clone(&self) -> CompletionArguments

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CompletionArguments

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Serialize for CompletionArguments

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>where __S: Serializer,

Serialize this value into the given Serde serializer.