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. Example Python code.

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

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

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.

See more information about frequency and presence penalties.

pub fn logit_bias<VALUE: Into<HashMap<String, Value>>>( &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 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 max_tokens<VALUE: Into<u32>>(&mut self, value: VALUE) -> &mut Self

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.

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 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.

See more information about frequency and presence penalties.

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

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

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

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

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

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

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

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

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

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