Struct async_openai::types::CreateChatCompletionRequestArgs

pub struct CreateChatCompletionRequestArgs { /* private fields */ }

Builder for CreateChatCompletionRequest.

Implementations

impl CreateChatCompletionRequestArgs

pub fn model<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self

ID of the model to use. Currently, only gpt-3.5-turbo and gpt-3.5-turbo-0301 are supported.

pub fn messages<VALUE: Into<Vec<ChatCompletionRequestMessage>>>( &mut self, value: VALUE ) -> &mut Self

The messages to generate chat completions for, in the chat format.

pub fn temperature<VALUE: Into<f32>>(&mut self, value: VALUE) -> &mut Self

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.

pub fn top_p<VALUE: Into<f32>>(&mut self, value: VALUE) -> &mut Self

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.

pub fn n<VALUE: Into<u8>>(&mut self, value: VALUE) -> &mut Self

How many chat completion choices to generate for each input message.

pub fn stream<VALUE: Into<bool>>(&mut self, value: VALUE) -> &mut Self

If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.

pub fn stop<VALUE: Into<Stop>>(&mut self, value: VALUE) -> &mut Self

Up to 4 sequences where the API will stop generating further tokens.

pub fn presence_penalty<VALUE: Into<f32>>(&mut self, value: VALUE) -> &mut Self

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.

pub fn frequency_penalty<VALUE: Into<f32>>(&mut self, value: VALUE) -> &mut Self

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.

pub fn logit_bias<VALUE: Into<HashMap<String, Value>>>( &mut self, value: VALUE ) -> &mut Self

Modify the likelihood of specified tokens appearing in the completion.

Accepts a json 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 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.

pub fn build(&self) -> Result<CreateChatCompletionRequest, OpenAIError>

Builds a new CreateChatCompletionRequest.

Errors

If a required field has not been initialized.

Trait Implementations

impl Clone for CreateChatCompletionRequestArgs

fn clone(&self) -> CreateChatCompletionRequestArgs

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateChatCompletionRequestArgs

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

Formats the value using the given formatter.

impl Default for CreateChatCompletionRequestArgs

fn default() -> Self

Returns the “default value” for a type.