pub struct CreateChatCompletionRequest {Show 27 fields
pub create_model_response_properties: CreateModelResponseProperties,
pub messages: Vec<ChatCompletionRequestMessage>,
pub model: ModelIdsShared,
pub modalities: Option<ResponseModalities>,
pub reasoning_effort: Option<ReasoningEffort>,
pub max_completion_tokens: Option<u64>,
pub frequency_penalty: Option<f64>,
pub presence_penalty: Option<f64>,
pub web_search_options: Option<CreateChatCompletionRequestWebSearchOptions>,
pub top_logprobs: Option<u64>,
pub response_format: Option<CreateChatCompletionRequestResponseFormat>,
pub audio: Option<CreateChatCompletionRequestAudio>,
pub store: Option<bool>,
pub stream: Option<bool>,
pub stop: Option<StopConfiguration>,
pub logit_bias: Option<HashMap<String, u64>>,
pub logprobs: Option<bool>,
pub max_tokens: Option<u64>,
pub n: Option<u64>,
pub prediction: Option<CreateChatCompletionRequestPrediction>,
pub seed: Option<u64>,
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<CreateChatCompletionRequestFunctionCall>,
pub functions: Option<Vec<ChatCompletionFunctions>>,
}Fields§
§create_model_response_properties: CreateModelResponseProperties§messages: 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.
model: ModelIdsSharedModel 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.
modalities: Option<ResponseModalities>§reasoning_effort: Option<ReasoningEffort>§max_completion_tokens: Option<u64>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. 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.
presence_penalty: Option<f64>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.
web_search_options: Option<CreateChatCompletionRequestWebSearchOptions>This tool searches the web for relevant results to use in a response. Learn more about the web search tool.
top_logprobs: Option<u64>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.
response_format: Option<CreateChatCompletionRequestResponseFormat>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.
audio: Option<CreateChatCompletionRequestAudio>Parameters for audio output. Required when audio output is requested with
modalities: ["audio"]. Learn more.
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. See the Streaming section below for more information, along with the streaming responses guide for more information on how to handle the streaming events.
stop: Option<StopConfiguration>§logit_bias: Option<HashMap<String, u64>>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.
logprobs: Option<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.
max_tokens: Option<u64>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.
n: Option<u64>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.
prediction: Option<CreateChatCompletionRequestPrediction>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.
seed: Option<u64>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.
stream_options: Option<ChatCompletionStreamOptions>§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. A max of 128 functions are supported.
tool_choice: Option<ChatCompletionToolChoiceOption>§parallel_tool_calls: Option<ParallelToolCalls>§function_call: Option<CreateChatCompletionRequestFunctionCall>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.
functions: Option<Vec<ChatCompletionFunctions>>Deprecated in favor of tools.
A list of functions the model may generate JSON inputs for.
Implementations§
Source§impl CreateChatCompletionRequest
impl CreateChatCompletionRequest
Sourcepub fn builder() -> CreateChatCompletionRequestBuilder<((), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ())>
pub fn builder() -> CreateChatCompletionRequestBuilder<((), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ())>
Create a builder for building CreateChatCompletionRequest.
On the builder, call .create_model_response_properties(...)(optional), .messages(...), .model(...), .modalities(...)(optional), .reasoning_effort(...)(optional), .max_completion_tokens(...)(optional), .frequency_penalty(...)(optional), .presence_penalty(...)(optional), .web_search_options(...)(optional), .top_logprobs(...)(optional), .response_format(...)(optional), .audio(...)(optional), .store(...)(optional), .stream(...)(optional), .stop(...)(optional), .logit_bias(...)(optional), .logprobs(...)(optional), .max_tokens(...)(optional), .n(...)(optional), .prediction(...)(optional), .seed(...)(optional), .stream_options(...)(optional), .tools(...)(optional), .tool_choice(...)(optional), .parallel_tool_calls(...)(optional), .function_call(...)(optional), .functions(...)(optional) to set the values of the fields.
Finally, call .build() to create the instance of CreateChatCompletionRequest.
Trait Implementations§
Source§impl Clone for CreateChatCompletionRequest
impl Clone for CreateChatCompletionRequest
Source§fn clone(&self) -> CreateChatCompletionRequest
fn clone(&self) -> CreateChatCompletionRequest
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source. Read more