Struct CompletionRequest 

pub struct CompletionRequest {

    pub model: String,
    pub prompt: Prompt,
    pub best_of: Option<usize>,
    pub echo: Option<bool>,
    pub frequency_penalty: Option<f32>,
    pub logit_bias: Option<HashMap<String, isize>>,
    pub logprobs: Option<usize>,
    pub max_tokens: Option<usize>,
    pub n: Option<usize>,
    pub presence_penalty: Option<f32>,
    pub seed: Option<usize>,
    pub stop: Option<StopKeywords>,
    pub stream: bool,
    pub stream_options: Option<StreamOptions>,
    pub suffix: Option<String>,
    pub temperature: Option<f32>,
    pub top_p: Option<f32>,
    pub user: Option<String>,
    pub extra_body: Map<String, Value>,
}

Fields

model: String

ID of the model to use. Note that not all models are supported for completion.

prompt: Prompt

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

best_of: Option<usize>

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.

echo: Option<bool>

Echo back the prompt in addition to the completion

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.

more info about frequency/presence penalties

logit_bias: Option<HashMap<String, isize>>

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 tokenizer tool to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model 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.

As an example, you can pass {"50256": -100} to prevent the <|end-of-stream|> token from being generated.

logprobs: Option<usize>

Include the log probabilities on the logprobs most likely output 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.

max_tokens: Option<usize>

The maximum number of tokens that can be generated in the completion.

The token count of your prompt plus max_tokens cannot exceed the model’s context length. Example Python code for counting tokens.

n: Option<usize>

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.

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.

seed: Option<usize>

If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.

Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.

stop: Option<StopKeywords>

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

Note: Not supported with latest reasoning models o3 and o4-mini.

stream: bool

Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. Example Python code.

stream_options: Option<StreamOptions>

Options for streaming response. Only set this when you set stream: true.

suffix: Option<String>

The suffix that comes after a completion of inserted text.

This parameter is only supported for gpt-3.5-turbo-instruct.

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.

It is generally recommended to alter 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.

It is generally recommended to alter this or temperature but not both.

user: Option<String>

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

extra_body: Map<String, Value>

Add additional JSON properties to the request

Trait Implementations

impl Clone for CompletionRequest

fn clone(&self) -> CompletionRequest

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CompletionRequest

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

Formats the value using the given formatter.

impl Default for CompletionRequest

fn default() -> CompletionRequest

Returns the “default value” for a type.

impl Post for CompletionRequest

fn is_streaming(&self) -> bool

impl PostNoStream for CompletionRequest

type Response = Completion

fn get_response_string( &self, url: &str, key: &str, ) -> impl Future<Output = Result<String, OapiError>> + Send + Sync

Sends a POST request to the specified URL with the provided api-key.

fn get_response( &self, url: &str, key: &str, ) -> impl Future<Output = Result<Self::Response, OapiError>> + Send + Sync

impl PostStream for CompletionRequest

type Response = Completion

fn get_stream_response_string( &self, url: &str, api_key: &str, ) -> impl Future<Output = Result<BoxStream<'static, Result<String, OapiError>>, OapiError>> + Send + Sync

Sends a streaming POST request to the specified URL with the provided api-key.

fn get_stream_response( &self, url: &str, api_key: &str, ) -> impl Future<Output = Result<BoxStream<'static, Result<Self::Response, OapiError>>, OapiError>> + Send + Sync

impl Serialize for CompletionRequest

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

where __S: Serializer,

Serialize this value into the given Serde serializer.