Struct CreateChatCompletionRequestArgs 

pub struct CreateChatCompletionRequestArgs { /* private fields */ }

Builder for CreateChatCompletionRequest.

Implementations

impl CreateChatCompletionRequestArgs

pub fn messages<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<Vec<ChatCompletionRequestMessage>>,

A list of messages comprising the conversation so far. Depending on the model you use, different message types (modalities) are supported, like text, images, and audio.

pub fn model<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<String>,

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

pub fn store<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<bool>,

Whether or not to store the output of this chat completion request

for use in our model distillation or evals products.

pub fn reasoning_effort<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<ReasoningEffort>,

o1 models only

Constrains effort on reasoning for reasoning models.

Currently supported values are low, medium, and high. Reducing

reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

pub fn metadata<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<Value>,

Developer-defined tags and values used for filtering completions in the dashboard.

pub fn frequency_penalty<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<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.

pub fn logit_bias<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<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.

pub fn logprobs<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<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.

pub fn top_logprobs<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<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.

pub fn max_tokens<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<u32>,

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

This value can be used to control costs for text generated via API. This value is now deprecated in favor of max_completion_tokens, and is not compatible with o1 series models.

pub fn max_completion_tokens<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<u32>,

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

pub fn n<VALUE>(&mut self, value: VALUE) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<u8>,

How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.

pub fn modalities<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<Vec<ChatCompletionModalities>>,

pub fn prediction<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<PredictionContent>,

Configuration for a Predicted Output,which can greatly improve response times when large parts of the model response are known ahead of time. This is most common when you are regenerating a file with only minor changes to most of the content.

pub fn audio<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<ChatCompletionAudio>,

Parameters for audio output. Required when audio output is requested with modalities: ["audio"]. Learn more.

pub fn presence_penalty<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<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.

pub fn response_format<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<ResponseFormat>,

An object specifying the format that the model must output. Compatible with GPT-4o, GPT-4o mini, GPT-4 Turbo and all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.

Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which guarantees the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.

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 a long-running and seemingly “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.

pub fn seed<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<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.

pub fn service_tier<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<ServiceTier>,

Specifies the latency tier to use for processing the request. This parameter is relevant for customers subscribed to the scale tier service:

• If set to ‘auto’, the system will utilize scale tier credits until they are exhausted.
• If set to ‘default’, the request will be processed using the default service tier with a lower uptime SLA and no latency guarentee.
• When not set, the default behavior is ‘auto’.

When this parameter is set, the response body will include the service_tier utilized.

pub fn stop<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<Stop>,

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

pub fn stream<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<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.

pub fn stream_options<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<ChatCompletionStreamOptions>,

pub fn temperature<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<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.

pub fn top_p<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<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.

pub fn tools<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<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. A max of 128 functions are supported.

pub fn tool_choice<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<ChatCompletionToolChoiceOption>,

pub fn parallel_tool_calls<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<bool>,

Whether to enable parallel function calling during tool use.

pub fn user<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<String>,

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

pub fn web_search_options<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<WebSearchOptions>,

This tool searches the web for relevant results to use in a response. Learn more about the web search tool.

pub fn function_call<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<ChatCompletionFunctionCall>,

Deprecated in favor of tool_choice.

Controls which (if any) function is called by the model. none means the model will not call a function and instead generates a message. auto means the model can pick between generating a message 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.

pub fn functions<VALUE>( &mut self, value: VALUE, ) -> &mut CreateChatCompletionRequestArgs

where VALUE: Into<Vec<ChatCompletionFunctions>>,

Deprecated in favor of tools.

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

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 duplicate 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<(), Error>

Formats the value using the given formatter.

impl Default for CreateChatCompletionRequestArgs

fn default() -> CreateChatCompletionRequestArgs

Returns the “default value” for a type.