LLMChatAnthropic

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

Overview

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

Installation

You can add LLMChatAnthropic 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-anthropic.git", .upToNextMajor(from: "1.0.0"))
],
targets: [
    .target(
        /// ...
        dependencies: [.product(name: "LLMChatAnthropic", package: "swift-llm-chat-anthropic")])
]

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-anthropic.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-anthropic/documentation/llmchatanthropic

Usage

Initialization

import LLMChatAnthropic

// Basic initialization
let chat = LLMChatAnthropic(apiKey: "<YOUR_ANTHROPIC_API_KEY>")

// Initialize with custom endpoint and headers
let chat = LLMChatAnthropic(
    apiKey: "<YOUR_API_KEY>",
    endpoint: "https://custom-api.example.com/v1/chat/completions",
    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: "claude-3-5-sonnet-20240620", messages: messages)

        print(completion.content.first?.text ?? "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: "claude-3-5-sonnet-20240620", messages: messages) {
            if let text = chunk.delta?.text {
                print(text, terminator: "")
            }
        }
    } catch {
        print(String(describing: error))
    }
}

// To cancel completion
task.cancel()

Advanced Usage

Vision

let messages = [
    ChatMessage(
        role: .user,
        content: [
            .image("https://images.pexels.com/photos/45201/kitty-cat-kitten-pet-45201.jpeg"), // Also supports base64 strings
            .text("What is in this image?")
        ]
    )
]

Task {
    do {
        let completion = try await chat.send(model: "claude-3-5-sonnet-20240620", messages: messages)

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

To learn more about vision, check out the Anthropic documentation.

Tool Use

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

let recommendBookTool = ChatOptions.Tool(
    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)
    )
)

let options = ChatOptions(tools: [recommendBookTool])

Task {
    do {
        let completion = try await chat.send(model: "claude-3-5-sonnet-20240620", messages: messages, options: options)

        if let toolInput = completion.content.first(where: { $0.type == "tool_use" })?.toolInput {
            print(toolInput)
       }
    } catch {
        print(String(describing: error))
    }
}

To learn more about tool use, check out the Anthropic documentation.

Prompt Caching (Beta)

let chat = LLMChatAnthropic(
    apiKey: "<YOUR_ANTHROPIC_API_KEY>",
    customHeaders: ["anthropic-beta": "prompt-caching-2024-07-31"] // Required
)

let messages = [
    ChatMessage(role: .system, content: "<YOUR_LONG_PROMPT>", cacheControl: .init(type: .ephemeral)),
    ChatMessage(role: .user, content: "What is the capital of Indonesia?")
]

let task = Task {
    do {
        let completion = try await chat.send(model: "claude-3-5-sonnet-20240620", messages: messages)

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

To learn more about prompt caching, check out the Anthropic documentation.

Donations

If you find LLMChatAnthropic 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.