dependencies: [
    .package(url: "https://github.com/MacPaw/OpenAI.git", branch: "main")
]

let openAI = OpenAI(apiToken: "YOUR_TOKEN_HERE")

let configuration = OpenAI.Configuration(token: "YOUR_TOKEN_HERE", organizationIdentifier: "YOUR_ORGANIZATION_ID_HERE", timeoutInterval: 60.0)
let openAI = OpenAI(configuration: configuration)

struct ChatQuery: Codable {
    /// ID of the model to use.
    public let model: Model
    /// An object specifying the format that the model must output.
    public let responseFormat: ResponseFormat?
    /// The messages to generate chat completions for
    public let messages: [Message]
    /// 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.
    public let tools: [Tool]?
    /// Controls how the model responds to tool calls. "none" means the model does not call a function, and responds to the end-user. "auto" means the model can pick between and end-user 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.
    public let toolChoice: ToolChoice?
    /// 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  We generally recommend altering this or top_p but not both.
    public let temperature: Double?
    /// 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.
    public let topP: Double?
    /// How many chat completion choices to generate for each input message.
    public let n: Int?
    /// Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
    public let stop: [String]?
    /// The maximum number of tokens to generate in the completion.
    public let maxTokens: Int?
    /// 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.
    public let presencePenalty: Double?
    /// 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.
    public let frequencyPenalty: Double?
    /// Modify the likelihood of specified tokens appearing in the completion.
    public let logitBias: [String:Int]?
    /// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
    public let user: String?
}

struct ChatResult: Codable, Equatable {
    public struct Choice: Codable, Equatable {
        public let index: Int
        public let message: Chat
        public let finishReason: String
    }
    
    public struct Usage: Codable, Equatable {
        public let promptTokens: Int
        public let completionTokens: Int
        public let totalTokens: Int
    }
    
    public let id: String
    public let object: String
    public let created: TimeInterval
    public let model: Model
    public let choices: [Choice]
    public let usage: Usage
}

let query = ChatQuery(model: .gpt3_5Turbo, messages: [.init(role: .user, content: "who are you")])
let result = try await openAI.chats(query: query)

openAI.chatsStream(query: query) { partialResult in
    switch partialResult {
    case .success(let result):
        print(result.choices)
    case .failure(let error):
        //Handle chunk error here
    }
} completion: { error in
    //Handle streaming error here
}

openAI
    .chatsStream(query: query)
    .sink { completion in
        //Handle completion result here
    } receiveValue: { result in
        //Handle chunk here
    }.store(in: &cancellables)

for try await result in openAI.chatsStream(query: query) {
   //Handle result here
}

let openAI = OpenAI(apiToken: "...")
// Declare functions which GPT-3 might decide to call.
let functions = [
  FunctionDeclaration(
      name: "get_current_weather",
      description: "Get the current weather in a given location",
      parameters:
        JSONSchema(
          type: .object,
          properties: [
            "location": .init(type: .string, description: "The city and state, e.g. San Francisco, CA"),
            "unit": .init(type: .string, enumValues: ["celsius", "fahrenheit"])
          ],
          required: ["location"]
        )
  )
]
let query = ChatQuery(
  model: "gpt-3.5-turbo-0613",  // 0613 is the earliest version with function calls support.
  messages: [
      Chat(role: .user, content: "What's the weather like in Boston?")
  ],
  tools: functions.map { Tool.function($0) }
)
let result = try await openAI.chats(query: query)

struct MovieInfo: StructuredOutput {
    
    let title: String
    let director: String
    let release: Date
    let genres: [MovieGenre]
    let cast: [String]
    
    static let example: Self = { 
        .init(
            title: "Earth",
            director: "Alexander Dovzhenko",
            release: Calendar.current.date(from: DateComponents(year: 1930, month: 4, day: 1))!,
            genres: [.drama],
            cast: ["Stepan Shkurat", "Semyon Svashenko", "Yuliya Solntseva"]
        )
    }()
}

enum MovieGenre: String, Codable, StructuredOutputEnum {
    case action, drama, comedy, scifi
    
    var caseNames: [String] { Self.allCases.map { $0.rawValue } }
}

let query = ChatQuery(
    messages: [.system(.init(content: "Best Picture winner at the 2011 Oscars"))],
    model: .gpt4_o,
    responseFormat: .jsonSchema(name: "movie-info", type: MovieInfo.self)
)
let result = try await openAI.chats(query: query)

struct ImagesQuery: Codable {
    /// A text description of the desired image(s). The maximum length is 1000 characters.
    public let prompt: String
    /// The number of images to generate. Must be between 1 and 10.
    public let n: Int?
    /// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
    public let size: String?
}

struct ImagesResult: Codable, Equatable {
    public struct URLResult: Codable, Equatable {
        public let url: String
    }
    public let created: TimeInterval
    public let data: [URLResult]
}

let query = ImagesQuery(prompt: "White cat with heterochromia sitting on the kitchen table", n: 1, size: "1024x1024")
openAI.images(query: query) { result in
  //Handle result here
}
//or
let result = try await openAI.images(query: query)

public struct ImageEditsQuery: Codable {
    /// The image to edit. Must be a valid PNG file, less than 4MB, and square. If mask is not provided, image must have transparency, which will be used as the mask.
    public let image: Data
    public let fileName: String
    /// An additional image whose fully transparent areas (e.g. where alpha is zero) indicate where image should be edited. Must be a valid PNG file, less than 4MB, and have the same dimensions as image.
    public let mask: Data?
    public let maskFileName: String?
    /// A text description of the desired image(s). The maximum length is 1000 characters.
    public let prompt: String
    /// The number of images to generate. Must be between 1 and 10.
    public let n: Int?
    /// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
    public let size: String?
}

let data = image.pngData()
let query = ImageEditQuery(image: data, fileName: "whitecat.png", prompt: "White cat with heterochromia sitting on the kitchen table with a bowl of food", n: 1, size: "1024x1024")
openAI.imageEdits(query: query) { result in
  //Handle result here
}
//or
let result = try await openAI.imageEdits(query: query)

public struct ImageVariationsQuery: Codable {
    /// The image to edit. Must be a valid PNG file, less than 4MB, and square. If mask is not provided, image must have transparency, which will be used as the mask.
    public let image: Data
    public let fileName: String
    /// The number of images to generate. Must be between 1 and 10.
    public let n: Int?
    /// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
    public let size: String?
}

let data = image.pngData()
let query = ImageVariationQuery(image: data, fileName: "whitecat.png", n: 1, size: "1024x1024")
openAI.imageVariations(query: query) { result in
  //Handle result here
}
//or
let result = try await openAI.imageVariations(query: query)

public struct AudioSpeechQuery: Codable, Equatable {
    //...
    public let model: Model // tts-1 or tts-1-hd  
    public let input: String
    public let voice: AudioSpeechVoice
    public let responseFormat: AudioSpeechResponseFormat
    public let speed: String? // Initializes with Double?
    //...
}

/// Audio data for one of the following formats :`mp3`, `opus`, `aac`, `flac`, `pcm`
public let audioData: Data?

let query = AudioSpeechQuery(model: .tts_1, input: "Hello, world!", voice: .alloy, responseFormat: .mp3, speed: 1.0)

openAI.audioCreateSpeech(query: query) { result in
    // Handle response here
}
//or
let result = try await openAI.audioCreateSpeech(query: query)

public struct AudioTranscriptionQuery: Codable, Equatable {
    
    public let file: Data
    public let fileName: String
    public let model: Model
    
    public let prompt: String?
    public let temperature: Double?
    public let language: String?
}

public struct AudioTranscriptionResult: Codable, Equatable {
    
    public let text: String
}

let data = Data(contentsOfURL:...)
let query = AudioTranscriptionQuery(file: data, fileName: "audio.m4a", model: .whisper_1)        

openAI.audioTranscriptions(query: query) { result in
    //Handle result here
}
//or
let result = try await openAI.audioTranscriptions(query: query)

public struct AudioTranslationQuery: Codable, Equatable {
    
    public let file: Data
    public let fileName: String
    public let model: Model
    
    public let prompt: String?
    public let temperature: Double?
}    

public struct AudioTranslationResult: Codable, Equatable {
    
    public let text: String
}

let data = Data(contentsOfURL:...)
let query = AudioTranslationQuery(file: data, fileName: "audio.m4a", model: .whisper_1)  

openAI.audioTranslations(query: query) { result in
    //Handle result here
}
//or
let result = try await openAI.audioTranslations(query: query)

struct EmbeddingsQuery: Codable {
    /// ID of the model to use.
    public let model: Model
    /// Input text to get embeddings for
    public let input: String
}

struct EmbeddingsResult: Codable, Equatable {

    public struct Embedding: Codable, Equatable {

        public let object: String
        public let embedding: [Double]
        public let index: Int
    }
    public let data: [Embedding]
    public let usage: Usage
}

let query = EmbeddingsQuery(model: .textSearchBabbageDoc, input: "The food was delicious and the waiter...")
openAI.embeddings(query: query) { result in
  //Handle response here
}
//or
let result = try await openAI.embeddings(query: query)

public extension Model {
    static let gpt4_turbo_preview = "gpt-4-turbo-preview"
    static let gpt4_vision_preview = "gpt-4-vision-preview"
    static let gpt4_0125_preview = "gpt-4-0125-preview"
    static let gpt4_1106_preview = "gpt-4-1106-preview"
    static let gpt4 = "gpt-4"
    static let gpt4_0613 = "gpt-4-0613"
    static let gpt4_0314 = "gpt-4-0314"
    static let gpt4_32k = "gpt-4-32k"
    static let gpt4_32k_0613 = "gpt-4-32k-0613"
    static let gpt4_32k_0314 = "gpt-4-32k-0314"
    
    static let gpt3_5Turbo = "gpt-3.5-turbo"
    static let gpt3_5Turbo_0125 = "gpt-3.5-turbo-0125"
    static let gpt3_5Turbo_1106 = "gpt-3.5-turbo-1106"
    static let gpt3_5Turbo_0613 = "gpt-3.5-turbo-0613"
    static let gpt3_5Turbo_0301 = "gpt-3.5-turbo-0301"
    static let gpt3_5Turbo_16k = "gpt-3.5-turbo-16k"
    static let gpt3_5Turbo_16k_0613 = "gpt-3.5-turbo-16k-0613"
    
    static let textDavinci_003 = "text-davinci-003"
    static let textDavinci_002 = "text-davinci-002"
    static let textCurie = "text-curie-001"
    static let textBabbage = "text-babbage-001"
    static let textAda = "text-ada-001"
    
    static let textDavinci_001 = "text-davinci-001"
    static let codeDavinciEdit_001 = "code-davinci-edit-001"
    
    static let tts_1 = "tts-1"
    static let tts_1_hd = "tts-1-hd"
    
    static let whisper_1 = "whisper-1"

    static let dall_e_2 = "dall-e-2"
    static let dall_e_3 = "dall-e-3"
    
    static let davinci = "davinci"
    static let curie = "curie"
    static let babbage = "babbage"
    static let ada = "ada"
    
    static let textEmbeddingAda = "text-embedding-ada-002"
    static let textSearchAda = "text-search-ada-doc-001"
    static let textSearchBabbageDoc = "text-search-babbage-doc-001"
    static let textSearchBabbageQuery001 = "text-search-babbage-query-001"
    static let textEmbedding3 = "text-embedding-3-small"
    static let textEmbedding3Large = "text-embedding-3-large"
    
    static let textModerationStable = "text-moderation-stable"
    static let textModerationLatest = "text-moderation-latest"
    static let moderation = "text-moderation-007"
}

let query = ChatQuery(model: .gpt4_turbo_preview, messages: [
    .init(role: .system, content: "You are Librarian-GPT. You know everything about the books."),
    .init(role: .user, content: "Who wrote Harry Potter?")
])
let result = try await openAI.chats(query: query)
XCTAssertFalse(result.choices.isEmpty)

public struct ModelsResult: Codable, Equatable {
    
    public let data: [ModelResult]
    public let object: String
}

openAI.models() { result in
  //Handle result here
}
//or
let result = try await openAI.models()

public struct ModelQuery: Codable, Equatable {
    
    public let model: Model
}    

public struct ModelResult: Codable, Equatable {

    public let id: Model
    public let object: String
    public let ownedBy: String
}

let query = ModelQuery(model: .gpt4)
openAI.model(query: query) { result in
  //Handle result here
}
//or
let result = try await openAI.model(query: query)

public struct ModerationsQuery: Codable {
    
    public let input: String
    public let model: Model?
}    

public struct ModerationsResult: Codable, Equatable {

    public let id: String
    public let model: Model
    public let results: [CategoryResult]
}

let query = ModerationsQuery(input: "I want to kill them.")
openAI.moderations(query: query) { result in
  //Handle result here
}
//or
let result = try await openAI.moderations(query: query)

public struct Vector {

    /// Returns the similarity between two vectors
    ///
    /// - Parameters:
    ///     - a: The first vector
    ///     - b: The second vector
    public static func cosineSimilarity(a: [Double], b: [Double]) -> Double {
        return dot(a, b) / (mag(a) * mag(b))
    }

    /// Returns the difference between two vectors. Cosine distance is defined as `1 - cosineSimilarity(a, b)`
    ///
    /// - Parameters:
    ///     - a: The first vector
    ///     - b: The second vector
    public func cosineDifference(a: [Double], b: [Double]) -> Double {
        return 1 - Self.cosineSimilarity(a: a, b: b)
    }
}

let vector1 = [0.213123, 0.3214124, 0.421412, 0.3214521251, 0.412412, 0.3214124, 0.1414124, 0.3214521251, 0.213123, 0.3214124, 0.1414124, 0.4214214, 0.213123, 0.3214124, 0.1414124, 0.3214521251, 0.213123, 0.3214124, 0.1414124, 0.3214521251]
let vector2 = [0.213123, 0.3214124, 0.1414124, 0.3214521251, 0.213123, 0.3214124, 0.1414124, 0.3214521251, 0.213123, 0.511515, 0.1414124, 0.3214521251, 0.213123, 0.3214124, 0.1414124, 0.3214521251, 0.213123, 0.3214124, 0.1414124, 0.3213213]
let similarity = Vector.cosineSimilarity(a: vector1, b: vector2)
print(similarity) //0.9510201910206734

func images(query: ImagesQuery) -> AnyPublisher<ImagesResult, Error>
func embeddings(query: EmbeddingsQuery) -> AnyPublisher<EmbeddingsResult, Error>
func chats(query: ChatQuery) -> AnyPublisher<ChatResult, Error>
func model(query: ModelQuery) -> AnyPublisher<ModelResult, Error>
func models() -> AnyPublisher<ModelsResult, Error>
func moderations(query: ModerationsQuery) -> AnyPublisher<ModerationsResult, Error>
func audioTranscriptions(query: AudioTranscriptionQuery) -> AnyPublisher<AudioTranscriptionResult, Error>
func audioTranslations(query: AudioTranslationQuery) -> AnyPublisher<AudioTranslationResult, Error>

let query = AssistantsQuery(model: Model.gpt4_o_mini, name: name, description: description, instructions: instructions, tools: tools, toolResources: toolResources)
openAI.assistantCreate(query: query) { result in
   //Handle response here
}

let query = AssistantsQuery(model: Model.gpt4_o_mini, name: name, description: description, instructions: instructions, tools: tools, toolResources: toolResources)
openAI.assistantModify(query: query, assistantId: "asst_1234") { result in
    //Handle response here
}

openAI.assistants() { result in
   //Handle response here
}

let threadsQuery = ThreadsQuery(messages: [Chat(role: message.role, content: message.content)])
openAI.threads(query: threadsQuery) { result in
  //Handle response here
}

let threadsQuery = ThreadQuery(messages: [Chat(role: message.role, content: message.content)])
let threadRunQuery = ThreadRunQuery(assistantId: "asst_1234"  thread: threadsQuery)
openAI.threadRun(query: threadRunQuery) { result in
  //Handle response here
}

openAI.threadsMessages(threadId: currentThreadId) { result in
  //Handle response here
}

let query = MessageQuery(role: message.role.rawValue, content: message.content)
openAI.threadsAddMessage(threadId: currentThreadId, query: query) { result in
  //Handle response here
}

let runsQuery = RunsQuery(assistantId:  currentAssistantId)
openAI.runs(threadId: threadsResult.id, query: runsQuery) { result in
  //Handle response here
}

openAI.runRetrieve(threadId: currentThreadId, runId: currentRunId) { result in
  //Handle response here
}

openAI.runRetrieveSteps(threadId: currentThreadId, runId: currentRunId) { result in
  //Handle response here
}

let output = RunToolOutputsQuery.ToolOutput(toolCallId: "call123", output: "Success")
let query = RunToolOutputsQuery(toolOutputs: [output])
openAI.runSubmitToolOutputs(threadId: currentThreadId, runId: currentRunId, query: query) { result in
  //Handle response here
}

let query = FilesQuery(purpose: "assistants", file: fileData, fileName: url.lastPathComponent, contentType: "application/pdf")
openAI.files(query: query) { result in
  //Handle response here
}

let cancellableRequest = object.chats(query: query, completion: { _ in })
cancellableReques

let task = Task {
    do {
        let chatResult = try await openAIClient.chats(query: .init(messages: [], model: "asd"))
    } catch {
        // Handle cancellation or error
    }
}
            
task.cancel()

let subscription = openAIClient
    .images(query: query)
    .sink(receiveCompletion: { completion in }, receiveValue: { imagesResult in })
    
subscription.cancel()