LLMChatOpenAI

Interact with OpenAI and OpenAI-compatible chat completion APIs in a simple and elegant way.

Overview

LLMChatOpenAI is a simple yet powerful Swift package that elegantly encapsulates the complexity of interacting with OpenAI and OpenAI-compatible chat completion APIs. It offers a complete set of Swift-idiomatic methods for sending chat completion requests, streaming responses, and retrieving available models.

Installation

You can add LLMChatOpenAI as a dependency to your project using Swift Package Manager by adding it to the dependencies value of your Package.swift.

dependencies: [
    .package(url: "https://github.com/kevinhermawan/swift-llm-chat-openai.git", .upToNextMajor(from: "1.0.0"))
],
targets: [
    .target(
        /// ...
        dependencies: [.product(name: "LLMChatOpenAI", package: "swift-llm-chat-openai")])
]

Alternatively, in Xcode:

1. Open your project in Xcode.
2. Click on File -> Swift Packages -> Add Package Dependency...
3. Enter the repository URL: https://github.com/kevinhermawan/swift-llm-chat-openai.git
4. Choose the version you want to add. You probably want to add the latest version.
5. Click Add Package.

Documentation

You can find the documentation here: https://kevinhermawan.github.io/swift-llm-chat-openai/documentation/llmchatopenai

Usage

Initialization

import LLMChatOpenAI

// Basic initialization
let chat = LLMChatOpenAI(apiKey: "<YOUR_OPENAI_API_KEY>")

// Initialize with custom endpoints and headers
let chat = LLMChatOpenAI(
    apiKey: "<YOUR_API_KEY>",
    endpoint: "https://custom-api.example.com/v1/chat/completions",
    modelEndpoint: "https://custom-api.example.com/v1/models",
    customHeaders: ["Custom-Header": "Value"]
)

Sending Chat Completion

let messages = [
    ChatMessage(role: .system, content: "You are a helpful assistant."),
    ChatMessage(role: .user, content: "What is the capital of Indonesia?")
]

let task = Task {
    do {
        let completion = try await chat.send(model: "gpt-4o", messages: messages)

        print(completion.choices.first?.message.content ?? "No response")
    } catch {
        print(String(describing: error))
    }
}

// To cancel completion
task.cancel()

Streaming Chat Completion

let messages = [
    ChatMessage(role: .system, content: "You are a helpful assistant."),
    ChatMessage(role: .user, content: "What is the capital of Indonesia?")
]

let task = Task {
    do {
        for try await chunk in chat.stream(model: "gpt-4o", messages: messages) {
            if let content = chunk.choices.first?.delta.content {
                print(content, terminator: "")
            }
        }
    } catch {
        print(String(describing: error))
    }
}

// To cancel completion
task.cancel()

Retrieving Available Models

Task {
    do {
        let models = try await chat.models()

        for model in models.data {
            print("Model ID: \(model.id)")
        }
    } catch {
        print(String(describing: error))
    }
}

Advanced Usage

Multimodal

let messages = [
    ChatMessage(
        role: .user,
        content: [
            .image("https://images.pexels.com/photos/45201/kitty-cat-kitten-pet-45201.jpeg", detail: .high),
            .text("What is in this image?")
        ]
    )
]

Task {
    do {
        let completion = try await chat.send(model: "gpt-4o", messages: messages)

        print(completion.choices.first?.message.content ?? "")
    } catch {
        print(String(describing: error))
    }
}

Tool Calling

let messages = [
    ChatMessage(role: .user, content: "Recommend a book similar to '1984'")
]

let recommendBookTool = ChatOptions.Tool(
    type: "function",
    function: .init(
        name: "recommend_book",
        description: "Recommend a book based on a given book and genre",
        parameters: .object(
            properties: [
                "reference_book": .string(description: "The name of a book the user likes"),
                "genre": .enum(
                    description: "The preferred genre for the book recommendation",
                    values: [.string("fiction"), .string("non-fiction")]
                )
            ],
            required: ["reference_book", "genre"],
            additionalProperties: .boolean(false)
        ),
        strict: true
    )
)

let options = ChatOptions(tools: [recommendBookTool])

Task {
    do {
        let completion = try await chat.send(model: "gpt-4o", messages: messages, options: options)

        if let toolCalls = completion.choices.first?.message.toolCalls {
            print(toolCalls.first?.function.arguments ?? "")
        }
    } catch {
        print(String(describing: error))
    }
}

Response Format

let messages = [
   ChatMessage(role: .system, content: "You are a helpful assistant. Respond with a JSON object containing the book title and author."),
   ChatMessage(role: .user, content: "Can you recommend a philosophy book?")
]

let responseFormat = ChatOptions.ResponseFormat(
    type: .jsonSchema,
    jsonSchema: .init(
        name: "get_book_info",
        schema: .object(
            properties: [
                "title": .string(description: "The title of the book"),
                "author": .string(description: "The author of the book")
            ],
            required: ["title", "author"]
        )
    )
)

let options = ChatOptions(responseFormat: responseFormat)

Task {
   do {
       let completion = try await chat.send(model: "gpt-4o", messages: messages, options: options)

       print(completion.choices.first?.message.content ?? "")
   } catch {
       print(String(describing: error))
   }
}

Donations

If you find LLMChatOpenAI helpful and would like to support its development, consider making a donation. Your contribution helps maintain the project and develop new features.

• GitHub Sponsors
• Buy Me a Coffee

Your support is greatly appreciated!

Contributing

Contributions are welcome! Please open an issue or submit a pull request if you have any suggestions or improvements.

License

This repository is available under the Apache License 2.0.