Struct ChatParameters

pub struct ChatParameters {

    pub model: String,
    pub messages: Vec<Message>,
    pub functions: Option<Vec<Function>>,
    pub function_call: Option<Value>,
    pub temperature: Option<f32>,
    pub top_p: Option<f32>,
    pub n: Option<i32>,
    pub stream: Option<bool>,
    pub stop: Option<Vec<String>>,
    pub max_tokens: Option<i32>,
    pub presence_penalty: Option<f32>,
    pub frequency_penalty: Option<f32>,
    pub logit_bias: Option<Value>,
    pub user: Option<String>,
}

Fields

model: String

ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.

messages: Vec<Message>

A list of messages comprising the conversation so far.

functions: Option<Vec<Function>>

A list of functions the model may generate JSON inputs for.

function_call: Option<Value>

Controls how the model responds to function calls. “none” means the model does not call a function, and responds to the end-user. “auto” means the model can pick between an end-user or calling a function. Specifying a particular function via {"name":\ "my_function"} forces the model to call that function. “none” is the default when no functions are present. “auto” is the default if functions are present.

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<i32>

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

stream: Option<bool>

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. Example Python code.

stop: Option<Vec<String>>

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

max_tokens: Option<i32>

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

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.

logit_bias: Option<Value>

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.

user: Option<String>

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

Implementations

impl ChatParameters

pub fn new(model: String, messages: Vec<Message>) -> Self

pub fn functions(self, functions: Vec<Function>) -> Self

pub fn function_call(self, function_call: Value) -> Self

pub fn temperature(self, temperature: f32) -> Self

pub fn top_p(self, top_p: f32) -> Self

pub fn n(self, n: i32) -> Self

pub fn stream(self, stream: bool) -> Self

pub fn stop(self, stop: Vec<String>) -> Self

pub fn max_tokens(self, max_tokens: i32) -> Self

pub fn presence_penalty(self, presence_penalty: f32) -> Self

pub fn frequency_penalty(self, frequency_penalty: f32) -> Self

pub fn logit_bias(self, logit_bias: Value) -> Self

pub fn user(self, user: String) -> Self

pub fn build(self) -> ChatParameters

Trait Implementations

impl Debug for ChatParameters

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for ChatParameters

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for ChatParameters

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

where __S: Serializer,

Serialize this value into the given Serde serializer.