Struct CreateChatCompletionRequest

pub struct CreateChatCompletionRequest {

    pub metadata: Option<Metadata>,
    pub temperature: Option<f64>,
    pub top_p: Option<f64>,
    pub user: Option<String>,
    pub service_tier: Option<ServiceTier>,
    pub messages: Vec<ChatCompletionRequestMessage>,
    pub model: ModelIdsShared,
    pub modalities: Option<ResponseModalities>,
    pub reasoning_effort: Option<ReasoningEffort>,
    pub max_completion_tokens: Option<i64>,
    pub frequency_penalty: Option<f64>,
    pub presence_penalty: Option<f64>,
    pub web_search_options: Option<CreateChatCompletionRequest_Variant2_WebSearchOptions>,
    pub top_logprobs: Option<i64>,
    pub response_format: Option<CreateChatCompletionRequest_Variant2_ResponseFormat>,
    pub audio: Option<CreateChatCompletionRequest_Variant2_Audio>,
    pub store: Option<bool>,
    pub stream: Option<bool>,
    pub stop: Option<StopConfiguration>,
    pub logit_bias: Option<CreateChatCompletionRequest_LogitBias>,
    pub logprobs: Option<bool>,
    pub max_tokens: Option<i64>,
    pub n: Option<i64>,
    pub prediction: Option<PredictionContent>,
    pub seed: Option<i64>,
    pub stream_options: Option<ChatCompletionStreamOptions>,
    pub tools: Option<Vec<ChatCompletionTool>>,
    pub tool_choice: Option<ChatCompletionToolChoiceOption>,
    pub parallel_tool_calls: Option<ParallelToolCalls>,
    pub function_call: Option<CreateChatCompletionRequest_Variant2_FunctionCall>,
    pub functions: Option<Vec<ChatCompletionFunctions>>,
}

Fields

metadata: Option<Metadata>

temperature: Option<f64>

What sampling temperature to use, between 0 and 2.

top_p: Option<f64>

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.

user: Option<String>

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

service_tier: Option<ServiceTier>

messages: Vec<ChatCompletionRequestMessage>

A list of messages comprising the conversation so far.

model: ModelIdsShared

Model ID used to generate the response, like gpt-4o or o3.

modalities: Option<ResponseModalities>

reasoning_effort: Option<ReasoningEffort>

max_completion_tokens: Option<i64>

An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.

frequency_penalty: Option<f64>

Number between -2.0 and 2.0.

presence_penalty: Option<f64>

Number between -2.0 and 2.0.

web_search_options: Option<CreateChatCompletionRequest_Variant2_WebSearchOptions>

top_logprobs: Option<i64>

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.

response_format: Option<CreateChatCompletionRequest_Variant2_ResponseFormat>

audio: Option<CreateChatCompletionRequest_Variant2_Audio>

store: Option<bool>

Whether or not to store the output of this chat completion request for use in our model distillation or evals products.

stream: Option<bool>

If set to true, the model response data will be streamed to the client as it is generated using server-sent events.

stop: Option<StopConfiguration>

logit_bias: Option<CreateChatCompletionRequest_LogitBias>

Modify the likelihood of specified tokens appearing in the completion.

logprobs: Option<bool>

Whether to return log probabilities of the output tokens or not.

max_tokens: Option<i64>

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

n: Option<i64>

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

prediction: Option<PredictionContent>

seed: Option<i64>

This feature is in Beta.

stream_options: Option<ChatCompletionStreamOptions>

tools: Option<Vec<ChatCompletionTool>>

A list of tools the model may call.

tool_choice: Option<ChatCompletionToolChoiceOption>

parallel_tool_calls: Option<ParallelToolCalls>

function_call: Option<CreateChatCompletionRequest_Variant2_FunctionCall>

functions: Option<Vec<ChatCompletionFunctions>>

Deprecated in favor of tools.

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.