Struct CreateResponse

pub struct CreateResponse {

    pub metadata: Option<Metadata>,
    pub temperature: Option<Number>,
    pub top_p: Option<Number>,
    pub user: Option<String>,
    pub service_tier: Option<ServiceTier>,
    pub top_logprobs: Option<i64>,
    pub previous_response_id: Option<String>,
    pub model: Option<ModelIdsResponses>,
    pub reasoning: Option<Reasoning>,
    pub background: Option<bool>,
    pub max_output_tokens: Option<i64>,
    pub max_tool_calls: Option<i64>,
    pub text: Option<ResponsePropertiesText>,
    pub tools: Option<Vec<Tool>>,
    pub tool_choice: Option<ResponsePropertiesToolChoice>,
    pub prompt: Option<Prompt>,
    pub truncation: Option<ResponsePropertiesTruncation>,
    pub input: Option<CreateResponseInput>,
    pub include: Option<Vec<Includable>>,
    pub parallel_tool_calls: Option<bool>,
    pub store: Option<bool>,
    pub instructions: Option<String>,
    pub stream: Option<bool>,
}

Fields

metadata: Option<Metadata>

temperature: Option<Number>

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.

top_p: Option<Number>

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.

user: Option<String>

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.

service_tier: Option<ServiceTier>

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.

previous_response_id: Option<String>

The unique ID of the previous response to the model. Use this to create multi-turn conversations. Learn more about conversation state.

model: Option<ModelIdsResponses>

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.

reasoning: Option<Reasoning>

background: Option<bool>

Whether to run the model response in the background. Learn more.

max_output_tokens: Option<i64>

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

max_tool_calls: Option<i64>

The maximum number of total calls to built-in tools that can be processed in a response. This maximum number applies across all built-in tool calls, not per individual tool. Any further attempts to call a tool by the model will be ignored.

text: Option<ResponsePropertiesText>

Configuration options for a text response from the model. Can be plain text or structured JSON data. Learn more:

• Text inputs and outputs
• Structured Outputs

tools: Option<Vec<Tool>>

An array of tools the model may call while generating a response. You can specify which tool to use by setting the tool_choice parameter.

The two categories of tools you can provide the model are:

• Built-in tools: Tools that are provided by OpenAI that extend the model’s capabilities, like web search or file search. Learn more about built-in tools.
• Function calls (custom tools): Functions that are defined by you, enabling the model to call your own code. Learn more about function calling.

tool_choice: Option<ResponsePropertiesToolChoice>

How the model should select which tool (or tools) to use when generating a response. See the tools parameter to see how to specify which tools the model can call.

prompt: Option<Prompt>

truncation: Option<ResponsePropertiesTruncation>

The truncation strategy to use for the model response.

• auto: If the context of this response and previous ones exceeds the model’s context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.
• disabled (default): If a model response will exceed the context window size for a model, the request will fail with a 400 error.

input: Option<CreateResponseInput>

Text, image, or file inputs to the model, used to generate a response.

Learn more:

• Text inputs and outputs
• Image inputs
• File inputs
• Conversation state
• Function calling

include: Option<Vec<Includable>>

Specify additional output data to include in the model response. Currently supported values are:

• code_interpreter_call.outputs: Includes the outputs of python code execution in code interpreter tool call items.
• computer_call_output.output.image_url: Include image urls from the computer call output.
• file_search_call.results: Include the search results of the file search tool call.
• message.input_image.image_url: Include image urls from the input message.
• message.output_text.logprobs: Include logprobs with assistant messages.
• reasoning.encrypted_content: Includes an encrypted version of reasoning tokens in reasoning item outputs. This enables reasoning items to be used in multi-turn conversations when using the Responses API statelessly (like when the store parameter is set to false, or when an organization is enrolled in the zero data retention program).

parallel_tool_calls: Option<bool>

Whether to allow the model to run tool calls in parallel.

store: Option<bool>

Whether to store the generated model response for later retrieval via API.

instructions: Option<String>

A system (or developer) message inserted into the model’s context.

When using along with previous_response_id, the instructions from a previous response will not be carried over to the next response. This makes it simple to swap out system (or developer) messages in new responses.

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.

Implementations

impl CreateResponse

pub fn builder() -> CreateResponseBuilder<((), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ())>

Create a builder for building CreateResponse. On the builder, call .metadata(...)(optional), .temperature(...)(optional), .top_p(...)(optional), .user(...)(optional), .service_tier(...)(optional), .top_logprobs(...)(optional), .previous_response_id(...)(optional), .model(...)(optional), .reasoning(...)(optional), .background(...)(optional), .max_output_tokens(...)(optional), .max_tool_calls(...)(optional), .text(...)(optional), .tools(...)(optional), .tool_choice(...)(optional), .prompt(...)(optional), .truncation(...)(optional), .input(...)(optional), .include(...)(optional), .parallel_tool_calls(...)(optional), .store(...)(optional), .instructions(...)(optional), .stream(...)(optional) to set the values of the fields. Finally, call .build() to create the instance of CreateResponse.

Trait Implementations

impl Clone for CreateResponse

fn clone(&self) -> CreateResponse

Returns a duplicate of the value.

const fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateResponse

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for CreateResponse

fn default() -> CreateResponse

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for CreateResponse

fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>

where D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for CreateResponse

fn eq(&self, other: &CreateResponse) -> bool

Tests for self and other values to be equal, and is used by ==.

const fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for CreateResponse

fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>

where S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for CreateResponse