Struct async_openai::types::CreateChatCompletionRequest

pub struct CreateChatCompletionRequest {

    pub messages: Vec<ChatCompletionRequestMessage>,
    pub model: String,
    pub frequency_penalty: Option<f32>,
    pub logit_bias: Option<HashMap<String, Value>>,
    pub max_tokens: Option<u16>,
    pub n: Option<u8>,
    pub presence_penalty: Option<f32>,
    pub response_format: Option<ChatCompletionResponseFormat>,
    pub seed: Option<i64>,
    pub stop: Option<Stop>,
    pub stream: Option<bool>,
    pub temperature: Option<f32>,
    pub top_p: Option<f32>,
    pub tools: Option<Vec<ChatCompletionTool>>,
    pub tool_choice: Option<ChatCompletionToolChoiceOption>,
    pub user: Option<String>,
    pub function_call: Option<ChatCompletionFunctionCall>,
    pub functions: Option<Vec<ChatCompletionFunctions>>,
}

Fields

messages: Vec<ChatCompletionRequestMessage>

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

model: String

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

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<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.

max_tokens: Option<u16>

The maximum number of tokens to generate 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.

n: Option<u8>

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

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.

response_format: Option<ChatCompletionResponseFormat>

An object specifying the format that the model must output.

Setting to { "type": "json_object" } enables JSON mode, which guarantees the message the model generates is valid JSON.

Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in increased latency and appearance of a “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.

seed: Option<i64>

This feature is in Beta. 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<Stop>

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

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.

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.

tools: Option<Vec<ChatCompletionTool>>

A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for.

tool_choice: Option<ChatCompletionToolChoiceOption>

user: Option<String>

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

function_call: Option<ChatCompletionFunctionCall>

👎Deprecated

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.

functions: Option<Vec<ChatCompletionFunctions>>

👎Deprecated

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

Trait Implementations

impl Clone for CreateChatCompletionRequest

fn clone(&self) -> CreateChatCompletionRequest

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateChatCompletionRequest

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

Formats the value using the given formatter.

impl Default for CreateChatCompletionRequest

fn default() -> CreateChatCompletionRequest

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for CreateChatCompletionRequest

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for CreateChatCompletionRequest

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

This method tests for self and other values to be equal, and is used by ==.

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

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

impl Serialize for CreateChatCompletionRequest

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 CreateChatCompletionRequest