What is OpenAI
OpenAI is a non-profit artificial intelligence research organization founded in San Francisco, California in 2015. It was created with the purpose of advancing digital intelligence in ways that benefit humanity as a whole and promote societal progress. The organization strives to develop AI (Artificial Intelligence) programs and systems that can think, act and adapt quickly on their own – autonomously. OpenAI’s mission is to ensure safe and responsible use of AI for civic good, economic growth and other public benefits; this includes cutting-edge research into important topics such as general AI safety, natural language processing, applied reinforcement learning methods, machine vision algorithms etc.
The OpenAI API can be applied to virtually any task that involves understanding or generating natural language or code. We offer a spectrum of models with different levels of power suitable for different tasks, as well as the ability to fine-tune your own custom models. These models can be used for everything from content generation to semantic search and classification.
Completions Streaming
Completions streaming is available by using completionsStream function. Tokens will be sent one-by-one.
Closures
openAI.completionsStream(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
}
Combine
openAI
.completionsStream(query: query)
.sink { completion in
//Handle completion result here
} receiveValue: { result in
//Handle chunk here
}.store(in: &cancellables)
Structured concurrency
for try await result in openAI.completionsStream(query: query) {
//Handle result here
}
Review Completions Documentation for more info.
Images
Given a prompt and/or an input image, the model will generate a new image.
As Artificial Intelligence continues to develop, so too does the intriguing concept of Dall-E. Developed by OpenAI, a research lab for artificial intelligence purposes, Dall-E has been classified as an AI system that can generate images based on descriptions provided by humans. With its potential applications spanning from animation and illustration to design and engineering - not to mention the endless possibilities in between - it’s easy to see why there is such excitement over this new technology.
Create Image
Request
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?
}
Response
struct ImagesResult: Codable, Equatable {
public struct URLResult: Codable, Equatable {
public let url: String
}
public let created: TimeInterval
public let data: [URLResult]
}
Example
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)
(lldb) po result
▿ ImagesResult
- created : 1671453505.0
▿ data : 1 element
▿ 0 : URLResult
- url : "https://oaidalleapiprodscus.blob.core.windows.net/private/org-CWjU5cDIzgCcVjq10pp5yX5Q/user-GoBXgChvLBqLHdBiMJBUbPqF/img-WZVUK2dOD4HKbKwW1NeMJHBd.png?st=2022-12-19T11%3A38%3A25Z&se=2022-12-19T13%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2022-12-19T09%3A35%3A16Z&ske=2022-12-20T09%3A35%3A16Z&sks=b&skv=2021-08-06&sig=mh52rmtbQ8CXArv5bMaU6lhgZHFBZz/ePr4y%2BJwLKOc%3D"
Generated image
Create Image Edit
Creates an edited or extended image given an original image and a prompt.
Request
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?
}
Response
Uses the ImagesResult response similarly to ImagesQuery.
Example
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)
Create Image Variation
Creates a variation of a given image.
Request
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?
}
Response
Uses the ImagesResult response similarly to ImagesQuery.
Example
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)
Review Images Documentation for more info.
Audio Create Speech
This function sends an AudioSpeechQuery to the OpenAI API to create audio speech from text using a specific voice and format.
Learn more about voices.
Learn more about models.
Request:
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?
//...
}
Response:
/// Audio data for one of the following formats :`mp3`, `opus`, `aac`, `flac`
public let audioData: Data?
Example:
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)
OpenAI Create Speech – Documentation
Audio Transcriptions
Transcribes audio into the input language.
Request
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?
}
Response
public struct AudioTranscriptionResult: Codable, Equatable {
public let text: String
}
Example
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)
Audio Translations
Translates audio into into English.
Request
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?
}
Response
public struct AudioTranslationResult: Codable, Equatable {
public let text: String
}
Example
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)
Review Audio Documentation for more info.
List Models
Lists the currently available models.
Response
public struct ModelsResult: Codable, Equatable {
public let data: [ModelResult]
public let object: String
}
Example
openAI.models() { result in
//Handle result here
}
//or
let result = try await openAI.models()
Retrieve Model
Retrieves a model instance, providing ownership information.
Request
public struct ModelQuery: Codable, Equatable {
public let model: Model
}
Response
public struct ModelResult: Codable, Equatable {
public let id: Model
public let object: String
public let ownedBy: String
}
Example
let query = ModelQuery(model: .gpt4)
openAI.model(query: query) { result in
//Handle result here
}
//or
let result = try await openAI.model(query: query)
Review Models Documentation for more info.
Moderations
Given a input text, outputs if the model classifies it as violating OpenAI’s content policy.
Request
public struct ModerationsQuery: Codable {
public let input: String
public let model: Model?
}
Response
public struct ModerationsResult: Codable, Equatable {
public let id: String
public let model: Model
public let results: [CategoryResult]
}
Example
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)
Review Moderations Documentation for more info.
Utilities
The component comes with several handy utility functions to work with the vectors.
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)
}
}
Example
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
In data analysis, cosine similarity is a measure of similarity between two sequences of numbers.
Read more about Cosine Similarity here.
Combine Extensions
The library contains built-in Combine extensions.
func completions(query: CompletionsQuery) -> AnyPublisher<CompletionsResult, Error>
func images(query: ImagesQuery) -> AnyPublisher<ImagesResult, Error>
func embeddings(query: EmbeddingsQuery) -> AnyPublisher<EmbeddingsResult, Error>
func chats(query: ChatQuery) -> AnyPublisher<ChatResult, Error>
func edits(query: EditsQuery) -> AnyPublisher<EditsResult, 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>
Example Project
You can find example iOS application in Demo folder.
Contribution Guidelines
Make your Pull Requests clear and obvious to anyone viewing them.
Set main as your target branch.
Use Conventional Commits principles in naming PRs and branches:
Feat: ...for new features and new functionality implementations.Bug: ...for bug fixes.Fix: ...for minor issues fixing, like typos or inaccuracies in code.Chore: ...for boring stuff like code polishing, refactoring, deprecation fixing etc.
PR naming example: Feat: Add Threads API handling or Bug: Fix message result duplication
Branch naming example: feat/add-threads-API-handling or bug/fix-message-result-duplication
Write description to pull requests in following format:
- What
…
- Why
…
- Affected Areas
…
- More Info
…
We’ll appreciate you including tests to your code if it is needed and possible. ❤️