OpenAI is a community-maintained repository containing Swift implementation over OpenAI public API.
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.
OpenAI is available with CocoaPods and Swift Package Manager.
CocoaPods is a dependency manager for Cocoa projects. For usage and installation instructions, visit their website. To integrate OpenAI into your Xcode project using CocoaPods, specify it in your Podfile:
pod 'OpenAIKit'The Swift Package Manager is a tool for automating the distribution of Swift code and is integrated into the swift compiler.
Once you have your Swift package set up, adding OpenAI as a dependency is as easy as adding it to the dependencies value of your Package.swift.
dependencies: [
.package(url: "https://github.com/FuturraGroup/OpenAI.git", .branch("main"))
]It is encouraged to use environment variables to inject the OpenAI API key, instead of hardcoding it in the source code. This is shown in our Example project.
let apiToken: String = "<Your OpenAI API Token here>"
let organizationName: String = "<Your OpenAI Organization name>"Initialize OpenAIKit with your API Token wherever convenient in your project. Organization name is optional.
import OpenAIKit
public let openAI = OpenAIKit(apiToken: apiToken, organization: organizationName)Create a call to the completions API, passing in a text prompt.
openAI.sendCompletion(prompt: "Hello!", model: .gptV3_5(.davinciText003), maxTokens: 2048) { [weak self] result in
switch result {
case .success(let aiResult):
DispatchQueue.main.async {
if let text = aiResult.choices.first?.text {
print("response text: \(text)") //"\n\nHello there, how may I assist you today?"
}
}
case .failure(let error):
DispatchQueue.main.async { [weak self] in
let alert = UIAlertController(title: "Error", message: error.localizedDescription, preferredStyle: .alert)
alert.addAction(UIAlertAction(title: "Ok", style: .default))
self?.present(alert, animated: true)
}
}
}Also supports async/await usage for all methods. Here is example.
let result = await openAI.sendCompletion(prompt: "Hello!", model: .gptV3_5(.davinciText003), maxTokens: 2048)
switch result {
case .success(let aiResult):
/// Hadle success response result
if let text = aiResult.choices.first?.text {
print("response text: \(text)") //"\n\nHello there, how may I assist you today?"
}
case .failure(let error):
/// Hadle error actions
print(error.localizedDescription)
}Chat completions almost the same as completions, there only few differences:
- It can understand context with pathing previous chat messages (read more).
- Response text located in message field of retrieved completion
- Supports ONLY gpt-3.5-turbo and gpt-3.5-turbo-0301 models (read more about models compatibility).
openAI.sendChatCompletion(newMessage: AIMessage(role: .user, content: prompt), previousMessages: [], model: .gptV3_5(.gptTurbo), maxTokens: 2048, n: 1, completion: { [weak self] result in
DispatchQueue.main.async { self?.stopLoading() }
switch result {
case .success(let aiResult):
// Handle result actions
if let text = aiResult.choices.first?.message?.content {
print(text)
}
case .failure(let error):
// Handle error actions
print(error.localizedDescription)
}
})You can retrieve response from OpenAI for Completions and Chat Completions in stream partial progress. Don't need to wait time when whole completion response will complete. You can handle and present results in live.
Example call on Chat Completions:
openAI.sendStreamChatCompletion(newMessage: AIMessage(role: .user, content: "Hello!"), model: .gptV3_5(.gptTurbo), maxTokens: 2048) { result in
switch result {
case .success(let streamResult):
/// Hadle success response result
if let streamMessage = streamResult.message?.choices.first?.message {
print("Stream message: \(streamMessage)") //"\n\nHello there, how may I assist you today?"
}
case .failure(let error):
// Handle error actions
print(error.localizedDescription)
}
}You can also stop stream manually like this:
openAI.sendStreamChatCompletion(newMessage: AIMessage(role: .user, content: "Hello!"), model: .gptV3_5(.gptTurbo), maxTokens: 2048) { result in
switch result {
case .success(let streamResult):
/// Hadle success response result
streamResult.stream.stopStream() /// Manually stop stream
case .failure(let error):
// Handle error actions
print(error.localizedDescription)
}
}DALL·E is a AI system that can create realistic images and art from a description in natural language. We currently support the ability, given a prommpt, to create a new image with a certain size, edit an existing image, or create variations of a user provided image.
The code below demonstrates how you can generate an image using DALL·E:
openAI.sendImagesRequest(prompt: "bird", size: .size512, n: 1) { [weak self] result in
switch result {
case .success(let aiResult):
DispatchQueue.main.async {
if let urlString = aiResult.data.first?.url {
print("generated image url: \(urlString)")
}
}
case .failure(let error):
DispatchQueue.main.async { [weak self] in
let alert = UIAlertController(title: "Error", message: error.localizedDescription, preferredStyle: .alert)
alert.addAction(UIAlertAction(title: "Ok", style: .default))
self?.present(alert, animated: true)
}
}
}Contributions for improvements are welcomed. Feel free to submit a pull request to help grow the library. If you have any questions, feature suggestions, or bug reports, please send them to Issues.
MIT License
Copyright (c) 2023 Futurra Group
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SOFTWARE.