A Swift-based tool used to interact with the OpenAI HTTP APIs.
To integrate the library, Swift Package Manager is your tool of choice. Add the following dependency to the Package.swift file:
.Package(url: "https://github.com/tywysocki/OpenAI.git", from: "1.0.0")Import the library:
import OpenAICreate an API key here, and add it to your configuration.
let openAI = OpenAI(authToken:"API_KEY")With this library, you can take advantage of Swift concurrency. The code snippets provided below demonstrate both async/await and completion handler implementations.
Use the openAI.sendCompletion method to send a request to the completions endpoint of the API. All you need to do is provide the desired text prompt as input, and the model will generate a text completion that is intended to match the context or pattern you provided. Keep in mind that the model's success generally depends on the complexity of the task and quality of your prompt.
openAI.sendCompletion(with: "How are you doing today") { result in // Result<OpenAIModel, OpenAIError>
switch result {
case .success(let success):
print(success.choices.first?.text ?? "")
case .failure(let failure):
print(failure.localizedDescription)
}
}async/await:
do {
let result = try await openAI.sendCompletion(
with: "What is today's date?",
model: .gpt3(.davinci), // optional `OpenAIModelType`
maxTokens: 16, // optional `Int?`
temperature: 1 // optional `Double?`
)
// use result
} catch {
// ...
}For a list of supported models, refer to OpenAIModelType.swift. For detailed information about these models, refer to the OpenAI API Docs.
Access ChatGPT (aka GPT-3.5) and GPT-4 (beta) using the openAI.sendChat method. The chat models require a series of messages as input, and produce a model-generated message as output. Each element in the chat array is a ChatMessage object, containing a role (either "system", "user", or "assistant") and content (the message content). Typically, conversations begin with a system message (which establishes the behavior of the assistant) followed by alternating user and assistant messages.
Example API call:
do {
let chat: [ChatMessage] = [
ChatMessage(role: .system, content: "You are a helpful assistant."),
ChatMessage(role: .user, content: "Who played in the 30th Super Bowl?"),
ChatMessage(role: .assistant, content: "The 30th Super Bowl was played between the Dallas Cowboys and the Pittsburgh Steelers."),
ChatMessage(role: .user, content: "Who won the game?")
]
let result = try await openAI.sendChat(with: chat)
// use result
} catch {
// ...
}This library supports all API parameters, except for streaming message content that has not been completed yet.
do {
let chat: [ChatMessage] = [...]
let result = try await openAI.sendChat(
with: chat,
model: .chat(.chatgpt), // optional `OpenAIModelType`
user: nil, // optional `String?`
temperature: 1, // optional `Double?`
topProbabilityMass: 1, // optional `Double?`
choices: 1, // optional `Int?`
stop: nil, // optional `[String]?`
maxTokens: nil, // optional `Int?`
presencePenalty: nil, // optional `Double?`
frequencyPenalty: nil, // optional `Double?`
logitBias: nil // optional `[Int: Double]?`
)
// use result
} catch {
// ...
}To generate an original image based on a text prompt, use the openAI.sendImages method. For better results, provide a detailed text prompt.
openAI.sendImages(with: "Hand drawn sketch of a Porsche 911.", numImages: 1, size: .size1024) { result in // Result<OpenAIModel, OpenAIError>
switch result {
case .success(let success):
print(success.data.first?.url ?? "")
case .failure(let failure):
print(failure.localizedDescription)
}
}To edit text based on a prompt and a modification instruction, use openAI.sendEdits. The method takes a prompt and a modification instruction as input and returns an edited version of the prompt as output.
do {
let result = try await openAI.sendEdits(
with: "Fix the spelling mistake.",
model: .feature(.davinci), // optional `OpenAIModelType`
input: "Me name is Ty"
)
// use result
} catch {
// ...
}To obtain a vector representation of a text string, make a request to the embeddings endpoint with openAI.sendEmbeddings. The method returns a vector representation of the text string that can be easily consumed by machine learning models and algorithms. For more information and use cases, refer to the API documentation.
do {
let result = try await openAI.sendEmbeddings(
with: "The service was great and the clerk..."
)
// use result
} catch {
// ...
}The moderation endpoint is a tool you can use to check whether content complies with OpenAI's usage policies. To obtain a classification for a piece of text, send a request to the moderation endpoint with the openAI.sendModeration method.
do {
let result = try await openAI.sendModeration(
with: "Unmoderated text",
model: .moderation(.latest) // optional `OpenAIModelType`
)
// use result
} catch {
// ...
}The MIT License (MIT)
Copyright (c) 2022 - 2023 Tyler Wysocki
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
