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feat: adds Predicted Outputs (#17)
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‎Playground/Playground/Views/AppView.swift‎

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@@ -30,6 +30,10 @@ struct AppView: View {
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}
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if provider == .openai {
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NavigationLink("Predicted Outputs") {
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PredictedOutputsView(provider: provider)
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}
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NavigationLink("Response Format") {
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ResponseFormatView(provider: provider)
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}

‎Playground/Playground/Views/PredictedOutputsView.swift‎

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//
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// PredictedOutputsView.swift
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// Playground
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//
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// Created by Kevin Hermawan on 11/5/24.
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//
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import SwiftUI
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import LLMChatOpenAI
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struct PredictedOutputsView: View {
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let provider: ServiceProvider
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@Environment(AppViewModel.self) private var viewModel
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@State private var isPreferencesPresented: Bool = false
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@State private var prompt: String = "Replace the Username property with an Email property. Respond only with code, and with no markdown formatting."
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@State private var response: String = ""
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@State private var acceptedPredictionTokens: Int = 0
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@State private var rejectedPredictionTokens: Int = 0
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@State private var inputTokens: Int = 0
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@State private var outputTokens: Int = 0
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@State private var totalTokens: Int = 0
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private let prediction = """
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/// <summary>
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/// Represents a user with a first name, last name, and username.
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/// </summary>
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public class User
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{
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/// <summary>
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/// Gets or sets the user's first name.
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/// </summary>
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public string FirstName { get; set; }
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/// <summary>
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/// Gets or sets the user's last name.
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/// </summary>
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public string LastName { get; set; }
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/// <summary>
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/// Gets or sets the user's username.
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/// </summary>
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public string Username { get; set; }
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}
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"""
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var body: some View {
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@Bindable var viewModelBindable = viewModel
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VStack {
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Form {
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Section("Prompt") {
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TextField("Prompt", text: $prompt)
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}
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Section("Prediction") {
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Text(prediction)
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}
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Section("Response") {
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Text(response)
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}
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Section("Prediction Section") {
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Text("Accepted Prediction Tokens")
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.badge(acceptedPredictionTokens.formatted())
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Text("Rejected Prediction Tokens")
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.badge(rejectedPredictionTokens.formatted())
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}
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UsageSection(inputTokens: inputTokens, outputTokens: outputTokens, totalTokens: totalTokens)
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}
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VStack {
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SendButton(stream: viewModel.stream, onSend: onSend, onStream: onStream)
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}
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}
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.toolbar {
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ToolbarItem(placement: .principal) {
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NavigationTitle("Predicted Outputs")
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}
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ToolbarItem(placement: .primaryAction) {
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Button("Preferences", systemImage: "gearshape", action: { isPreferencesPresented.toggle() })
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}
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}
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.sheet(isPresented: $isPreferencesPresented) {
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PreferencesView()
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}
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.onAppear {
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viewModel.setup(for: provider)
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}
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.onDisappear {
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viewModel.selectedModel = ""
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}
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}
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private func onSend() {
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clear()
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let messages = [
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ChatMessage(role: .user, content: prompt),
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ChatMessage(role: .user, content: prediction)
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]
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let options = ChatOptions(
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prediction: .init(type: .content, content: [.init(type: "text", text: prediction)]),
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temperature: viewModel.temperature
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)
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Task {
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do {
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let completion = try await viewModel.chat.send(model: viewModel.selectedModel, messages: messages, options: options)
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if let content = completion.choices.first?.message.content {
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self.response = content
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}
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if let usage = completion.usage {
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if let completionTokensDetails = usage.completionTokensDetails {
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self.acceptedPredictionTokens = completionTokensDetails.acceptedPredictionTokens
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self.rejectedPredictionTokens = completionTokensDetails.rejectedPredictionTokens
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}
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self.inputTokens = usage.promptTokens
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self.outputTokens = usage.completionTokens
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self.totalTokens = usage.totalTokens
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}
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} catch {
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print(String(describing: error))
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}
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}
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}
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private func onStream() {
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clear()
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let messages = [
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ChatMessage(role: .user, content: prompt),
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ChatMessage(role: .user, content: prediction)
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]
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let options = ChatOptions(
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prediction: .init(type: .content, content: prediction),
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temperature: viewModel.temperature
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)
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Task {
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do {
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for try await chunk in viewModel.chat.stream(model: viewModel.selectedModel, messages: messages, options: options) {
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if let content = chunk.choices.first?.delta.content {
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self.response += content
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}
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if let usage = chunk.usage {
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if let completionTokensDetails = usage.completionTokensDetails {
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self.acceptedPredictionTokens = completionTokensDetails.acceptedPredictionTokens
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self.rejectedPredictionTokens = completionTokensDetails.rejectedPredictionTokens
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}
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self.inputTokens = usage.promptTokens ?? 0
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self.outputTokens = usage.completionTokens ?? 0
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self.totalTokens = usage.totalTokens ?? 0
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}
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}
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} catch {
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print(String(describing: error))
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}
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}
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}
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private func clear() {
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response = ""
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acceptedPredictionTokens = 0
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rejectedPredictionTokens = 0
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inputTokens = 0
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outputTokens = 0
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totalTokens = 0
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}
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}

‎README.md‎

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To learn more about function calling, check out the [OpenAI documentation](https://platform.openai.com/docs/guides/function-calling).
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#### Predicted Outputs
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```swift
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private let code = """
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/// <summary>
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/// Represents a user with a first name, last name, and username.
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/// </summary>
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public class User
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{
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/// <summary>
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/// Gets or sets the user's first name.
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/// </summary>
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public string FirstName { get; set; }
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/// <summary>
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/// Gets or sets the user's last name.
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/// </summary>
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public string LastName { get; set; }
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/// <summary>
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/// Gets or sets the user's username.
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/// </summary>
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public string Username { get; set; }
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}
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"""
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let messages = [
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ChatMessage(role: .user, content: "Replace the Username property with an Email property. Respond only with code, and with no markdown formatting."),
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ChatMessage(role: .user, content: code)
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]
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let options = ChatOptions(
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prediction: .init(type: .content, content: code)
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)
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Task {
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do {
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let completion = try await chat.send(model: "gpt-4o", messages: messages, options: options)
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print(completion.choices.first?.message.content ?? "")
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} catch {
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print(String(describing: error))
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}
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}
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```
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To learn more about predicted outputs, check out the [OpenAI documentation](https://platform.openai.com/docs/guides/latency-optimization#use-predicted-outputs).
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#### Structured Outputs
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```swift

‎Sources/LLMChatOpenAI/ChatCompletion.swift‎

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public let promptTokensDetails: PromptTokensDetails?
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public struct CompletionTokensDetails: Decodable, Sendable {
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/// When using Predicted Outputs, the number of tokens in the prediction that appeared in the completion.
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public let acceptedPredictionTokens: Int
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/// When using Predicted Outputs, the number of tokens in the prediction that did not appear in the completion.
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/// However, like reasoning tokens, these tokens are still counted in the total completion tokens for purposes of billing, output, and context window limits.
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public let rejectedPredictionTokens: Int
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/// Tokens generated by the model for reasoning.
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public let reasoningTokens: Int
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private enum CodingKeys: String, CodingKey {
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case acceptedPredictionTokens = "accepted_prediction_tokens"
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case reasoningTokens = "reasoning_tokens"
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case rejectedPredictionTokens = "rejected_prediction_tokens"
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}
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}
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‎Sources/LLMChatOpenAI/ChatCompletionChunk.swift‎

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public let promptTokensDetails: PromptTokensDetails?
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public struct CompletionTokensDetails: Decodable, Sendable {
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/// When using Predicted Outputs, the number of tokens in the prediction that appeared in the completion.
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public let acceptedPredictionTokens: Int
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/// When using Predicted Outputs, the number of tokens in the prediction that did not appear in the completion.
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/// However, like reasoning tokens, these tokens are still counted in the total completion tokens for purposes of billing, output, and context window limits.
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public let rejectedPredictionTokens: Int
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/// Tokens generated by the model for reasoning.
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public let reasoningTokens: Int
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private enum CodingKeys: String, CodingKey {
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case acceptedPredictionTokens = "accepted_prediction_tokens"
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case reasoningTokens = "reasoning_tokens"
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case rejectedPredictionTokens = "rejected_prediction_tokens"
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}
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}
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