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use chrono::{DateTime, Utc, serde::ts_seconds};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use crate::types::OutputContent;
use super::{InputItem, OutputItem, ReasoningConfig, TextConfig, Tool, ToolChoice, Truncation};
/// The Response object.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Response {
/// When this Response was created.
#[serde(with = "ts_seconds")]
pub created_at: DateTime<Utc>,
/// Unique identifier for this Response.
pub id: String,
/// Details about why the response is incomplete.
pub incomplete_details: Option<IncompleteDetails>,
/// 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 be not be carried over to the next response. This makes it simple to swap out system (or developer) messages in new responses.
pub instructions: Option<String>,
/// An upper bound for the number of tokens that can be generated for a response, including visible output tokens and [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
pub max_output_tokens: Option<u64>,
/// Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
/// Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
pub metadata: HashMap<String, String>,
/// Model ID used to generate the response, like gpt-4o or o1. OpenAI offers a wide range of models with different capabilities, performance characteristics, and price points.
/// Refer to the [model guide](https://platform.openai.com/docs/models) to browse and compare available models.
pub model: String,
/// 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` function.
pub output: Vec<OutputItem>,
/// Whether to allow the model to run tool calls in parallel.
pub parallel_tool_calls: bool,
/// The unique ID of the previous response to the model. Use this to create multi-turn conversations.
/// Learn more about [conversation state](https://platform.openai.com/docs/guides/conversation-state).
pub previous_response_id: Option<String>,
/// Configuration options for [reasoning models](https://platform.openai.com/docs/guides/reasoning).
/// Only available for o-series models.
pub reasoning: ReasoningConfig,
/// The status of the response generation.
pub status: ResponseStatus,
/// 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 temperature: f32,
/// Configuration options for a text response from the model. Can be plain text or structured JSON data. Learn more:
/// - [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
/// - [Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs)
pub text: TextConfig,
/// 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.
pub tool_choice: ToolChoice,
/// 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](https://platform.openai.com/docs/guides/tools-web-search) or [file search](https://platform.openai.com/docs/guides/tools-file-search). Learn more about [built-in tools](https://platform.openai.com/docs/guides/tools).
/// - **Function calls (custom tools)**: Functions that are defined by you, enabling the model to call your own code. Learn more about [function calling](https://platform.openai.com/docs/guides/function-calling).
pub tools: Vec<Tool>,
/// 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 top_p: f32,
/// The truncation strategy to use for the model response.
pub truncation: Truncation,
/// Represents token usage details including input tokens, output tokens, a breakdown of output tokens, and the total tokens used.
pub usage: Usage,
/// Whether the response was stored on OpenAI's server for later retrieval.
pub store: bool,
/// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids).
pub user: Option<String>,
}
impl Response {
#[must_use]
pub fn output_text(&self) -> String {
self.output
.iter()
.filter_map(|output| match output {
OutputItem::Message(message) => Some(message),
_ => None,
})
.flat_map(|message| &message.content)
.map(|content| match content {
OutputContent::Text { text, .. } => text.to_owned(),
OutputContent::Refusal { refusal } => refusal.to_owned(),
})
.collect::<String>()
}
}
/// Represents token usage details including input tokens, output tokens, a breakdown of output tokens, and the total tokens used.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Usage {
/// The number of input tokens.
pub input_tokens: u64,
/// A detailed breakdown of the input tokens.
pub input_tokens_details: InputTokensDetails,
/// The number of output tokens.
pub output_tokens: u64,
/// A detailed breakdown of the output tokens.
pub output_tokens_details: OutputTokensDetails,
/// The total number of tokens used.
pub total_tokens: u64,
}
/// A detailed breakdown of the input tokens.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InputTokensDetails {
/// The number of cached tokens.
pub cached_tokens: u64,
}
/// A detailed breakdown of the output tokens.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OutputTokensDetails {
/// The number of reasoning tokens.
pub reasoning_tokens: u64,
}
/// Details about why a response is incomplete.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IncompleteDetails {
/// The reason why the response is incomplete.
pub reason: String,
}
/// The status of the response generation.
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq)]
#[serde(rename_all = "snake_case")]
pub enum ResponseStatus {
Failed,
Completed,
Incomplete,
InProgress,
}
/// An error object returned when the model fails to generate a Response.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Error {
/// The type of error.
pub r#type: String,
/// A human-readable description of the error.
pub message: String,
/// The error code for the response.
pub code: Option<String>,
/// The parameter that caused the error.
pub param: Option<String>,
}
/// A list of items used to generate a model response.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InputItemList {
/// A list of items used to generate this response.
data: Vec<InputItem>,
/// The ID of the first item in the list.
first_id: String,
/// The ID of the last item in the list.
last_id: String,
/// Whether there are more items available.
has_more: bool,
}
#[allow(clippy::redundant_pub_crate)]
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(untagged)]
pub(crate) enum ResponseResult {
Ok(Box<Response>),
Err { error: Error },
}
impl From<ResponseResult> for Result<Response, Error> {
fn from(val: ResponseResult) -> Self {
match val {
ResponseResult::Err { error } => Err(error),
ResponseResult::Ok(response) => Ok(*response),
}
}
}