Struct OpenAiCompletionRequest

pub struct OpenAiCompletionRequest {
    pub model: String,
    pub messages: Vec<CompletionRequestMessage>,
    pub logit_bias: Option<HashMap<String, Value>>,
    pub logprobs: Option<bool>,
    pub top_logprobs: Option<u8>,
    pub max_tokens: Option<u64>,
    pub temperature: Option<f32>,
    pub frequency_penalty: Option<f32>,
    pub presence_penalty: Option<f32>,
    pub stop: Option<Stop>,
    pub top_p: Option<f32>,
}

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<CompletionRequestMessage>

A list of messages comprising the conversation so far. Example Python code.

logit_bias: Option<HashMap<String, 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.

logprobs: Option<bool>

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.

top_logprobs: Option<u8>

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.

max_tokens: Option<u64>

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

The total length of input tokens and generated tokens is limited by the model’s context length. Example Python code for counting tokens.

temperature: Option<f32>

min: 0.0, max: 2.0, default: None

frequency_penalty: Option<f32>

min: -2.0, max: 2.0, default: None

See more information about frequency and presence penalties.

presence_penalty: Option<f32>

min: -2.0, max: 2.0, default: None

See more information about frequency and presence penalties.

stop: Option<Stop>

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

top_p: Option<f32>

min: 0.0, max: 1.0, default: None

Implementations

impl OpenAiCompletionRequest

pub fn new(req: &CompletionRequest) -> Result<Self, CompletionError>

Trait Implementations

impl Clone for OpenAiCompletionRequest

fn clone(&self) -> OpenAiCompletionRequest

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for OpenAiCompletionRequest

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

Formats the value using the given formatter.

impl Default for OpenAiCompletionRequest

fn default() -> OpenAiCompletionRequest

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for OpenAiCompletionRequest

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for OpenAiCompletionRequest

fn eq(&self, other: &OpenAiCompletionRequest) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for OpenAiCompletionRequest

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for OpenAiCompletionRequest