Struct Response

pub struct Response {

    pub instructions: Option<String>,
    pub max_output_tokens: Option<i32>,
    pub model: Option<ModelIdsResponses>,
    pub previous_response_id: Option<String>,
    pub reasoning: Option<Reasoning>,
    pub text: Option<ResponsePropertiesText>,
    pub tool_choice: Option<Value>,
    pub tools: Option<Vec<Tool>>,
    pub truncation: Option<String>,
    pub metadata: Option<Metadata>,
    pub service_tier: Option<ServiceTier>,
    pub temperature: Option<f32>,
    pub top_p: Option<f32>,
    pub user: Option<String>,
    pub created_at: f32,
    pub error: ResponseError,
    pub id: String,
    pub incomplete_details: ResponseIncompleteDetails,
    pub object: String,
    pub output: Vec<OutputItem>,
    pub output_text: Option<String>,
    pub parallel_tool_calls: bool,
    pub status: Option<String>,
    pub usage: Option<ResponseUsage>,
}

Fields

instructions: Option<String>

Inserts a system (or developer) message as the first item in 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.

max_output_tokens: Option<i32>

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

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.

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.

reasoning: Option<Reasoning>

text: Option<ResponsePropertiesText>

tool_choice: Option<Value>

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.

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.

truncation: Option<String>

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.

metadata: Option<Metadata>

service_tier: Option<ServiceTier>

temperature: Option<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.

top_p: Option<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.

user: Option<String>

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

created_at: f32

Unix timestamp (in seconds) of when this Response was created.

error: ResponseError

id: String

Unique identifier for this Response.

incomplete_details: ResponseIncompleteDetails

object: String

The object type of this resource - always set to response.

output: Vec<OutputItem>

An array of content items generated by the model. - The length and order of items in the output array is dependent on the model’s response. - Rather than accessing the first item in the output array and assuming it’s an assistant message with the content generated by the model, you might consider using the output_text property where supported in SDKs.

output_text: Option<String>

SDK-only convenience property that contains the aggregated text output from all output_text items in the output array, if any are present. Supported in the Python and JavaScript SDKs.

parallel_tool_calls: bool

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

status: Option<String>

The status of the response generation. One of completed, failed, in_progress, or incomplete.

usage: Option<ResponseUsage>

Trait Implementations

impl Debug for Response

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for Response

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for Response

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.