Struct CreateChatCompletionRequestArgs 

pub struct CreateChatCompletionRequestArgs { /* private fields */ }

Builder for CreateChatCompletionRequest.

Implementations

impl CreateChatCompletionRequestArgs

pub fn messages<VALUE: Into<Vec<ChatCompletionRequestMessage>>>( &mut self, value: VALUE, ) -> &mut Self

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: Into<String>>(&mut self, value: VALUE) -> &mut Self

Model ID used to generate the response, like gpt-4o or o3. OpenAI offers a wide range of models with different capabilities, performance characteristics, and price points. Refer to the model guide to browse and compare available models.

pub fn modalities<VALUE: Into<Vec<ResponseModalities>>>( &mut self, value: VALUE, ) -> &mut Self

Output types that you would like the model to generate. Most models are capable of generating text, which is the default:

["text"] The gpt-4o-audio-preview model can also be used to generate audio. To request that this model generate both text and audio responses, you can use:

["text", "audio"]

pub fn verbosity<VALUE: Into<Verbosity>>(&mut self, value: VALUE) -> &mut Self

Constrains the verbosity of the model’s response. Lower values will result in more concise responses, while higher values will result in more verbose responses. Currently supported values are low, medium, and high.

pub fn reasoning_effort<VALUE: Into<ReasoningEffort>>( &mut self, value: VALUE, ) -> &mut Self

Constrains effort on reasoning for reasoning models. Currently supported values are minimal, low, medium, and high. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Note: The gpt-5-pro model defaults to (and only supports) high reasoning effort.

pub fn max_completion_tokens<VALUE: Into<u32>>( &mut self, value: VALUE, ) -> &mut Self

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

pub fn frequency_penalty<VALUE: Into<f32>>(&mut self, value: VALUE) -> &mut Self

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 presence_penalty<VALUE: Into<f32>>(&mut self, value: VALUE) -> &mut Self

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 web_search_options<VALUE: Into<WebSearchOptions>>( &mut self, value: VALUE, ) -> &mut Self

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

pub fn top_logprobs<VALUE: Into<u8>>(&mut self, value: VALUE) -> &mut Self

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 response_format<VALUE: Into<ResponseFormat>>( &mut self, value: VALUE, ) -> &mut Self

An object specifying the format that the model must output.

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

Setting to { "type": "json_object" } enables the older JSON mode, which ensures the message the model generates is valid JSON. Using json_schema is preferred for models that support it.

pub fn audio<VALUE: Into<ChatCompletionAudio>>( &mut self, value: VALUE, ) -> &mut Self

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

pub fn store<VALUE: Into<bool>>(&mut self, value: VALUE) -> &mut Self

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

Supports text and image inputs. Note: image inputs over 8MB will be dropped.

pub fn stream<VALUE: Into<bool>>(&mut self, value: VALUE) -> &mut Self

If set to true, the model response data will be streamed to the client as it is generated using server-sent events. See the Streaming section below for more information, along with the streaming responses guide for more information on how to handle the streaming events.

pub fn stop<VALUE: Into<StopConfiguration>>( &mut self, value: VALUE, ) -> &mut Self

Not supported with latest reasoning models o3 and o4-mini.

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

pub fn logit_bias<VALUE: Into<HashMap<String, i8>>>( &mut self, value: VALUE, ) -> &mut Self

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: Into<bool>>(&mut self, value: VALUE) -> &mut Self

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 max_tokens<VALUE: Into<u32>>(&mut self, value: VALUE) -> &mut Self

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 o-series models.

pub fn n<VALUE: Into<u8>>(&mut self, value: VALUE) -> &mut Self

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 prediction<VALUE: Into<PredictionContent>>( &mut self, value: VALUE, ) -> &mut Self

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 seed<VALUE: Into<i64>>(&mut self, value: VALUE) -> &mut Self

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 stream_options<VALUE: Into<ChatCompletionStreamOptions>>( &mut self, value: VALUE, ) -> &mut Self

pub fn service_tier<VALUE: Into<ServiceTier>>( &mut self, value: VALUE, ) -> &mut Self

Specifies the processing type used for serving the request.

• If set to ‘auto’, then the request will be processed with the service tier configured in the Project settings. Unless otherwise configured, the Project will use ‘default’.
• If set to ‘default’, then the request will be processed with the standard pricing and performance for the selected model.
• If set to ‘flex’ or ‘priority’, then the request will be processed with the corresponding service tier.
• When not set, the default behavior is ‘auto’.

When the service_tier parameter is set, the response body will include the service_tier value based on the processing mode actually used to serve the request. This response value may be different from the value set in the parameter.

pub fn temperature<VALUE: Into<f32>>(&mut self, value: VALUE) -> &mut Self

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: Into<f32>>(&mut self, value: VALUE) -> &mut Self

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: Into<Vec<ChatCompletionTools>>>( &mut self, value: VALUE, ) -> &mut Self

A list of tools the model may call. You can provide either custom tools or function tools.

pub fn tool_choice<VALUE: Into<ChatCompletionToolChoiceOption>>( &mut self, value: VALUE, ) -> &mut Self

Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. none is the default when no tools are present. auto is the default if tools are present.

pub fn parallel_tool_calls<VALUE: Into<bool>>( &mut self, value: VALUE, ) -> &mut Self

Whether to enable parallel function calling during tool use.

pub fn user<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self

This field is being replaced by safety_identifier and prompt_cache_key. Use prompt_cache_key instead to maintain caching optimizations. A stable identifier for your end-users. Used to boost cache hit rates by better bucketing similar requests and to help OpenAI detect and prevent abuse. Learn more.

pub fn safety_identifier<VALUE: Into<String>>( &mut self, value: VALUE, ) -> &mut Self

A stable identifier used to help detect users of your application that may be violating OpenAI’s usage policies.

The IDs should be a string that uniquely identifies each user. We recommend hashing their username or email address, in order to avoid sending us any identifying information. Learn more.

pub fn prompt_cache_key<VALUE: Into<String>>( &mut self, value: VALUE, ) -> &mut Self

Used by OpenAI to cache responses for similar requests to optimize your cache hit rates. Replaces the user field. Learn more.

pub fn function_call<VALUE: Into<ChatCompletionFunctionCall>>( &mut self, value: VALUE, ) -> &mut Self

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: Into<Vec<ChatCompletionFunctions>>>( &mut self, value: VALUE, ) -> &mut Self

Deprecated in favor of tools.

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

pub fn metadata<VALUE: Into<Metadata>>(&mut self, value: VALUE) -> &mut Self

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

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

Formats the value using the given formatter.

impl Default for CreateChatCompletionRequestArgs

fn default() -> Self

Returns the “default value” for a type.