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: URL(string: "https://custom-api.example.com/v1/messages")!,
    headers: ["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", 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", 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", 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", 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>",
    headers: ["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", 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.

PDF Support (Beta)

let chat = LLMChatAnthropic(
    apiKey: "<YOUR_ANTHROPIC_API_KEY>",
    headers: ["anthropic-beta": "pdfs-2024-09-25"] // Required
)

let messages = [
    ChatMessage(role: .user, content: [.text("Explain this document"), .document(document)])
]

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

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

To learn more about PDF support, check out the Anthropic documentation.

Error Handling

LLMChatAnthropic provides structured error handling through the LLMChatAnthropicError enum. This enum contains three cases that represent different types of errors you might encounter:

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

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

    print(completion.content.first?.text ?? "No response")
} catch let error as LLMChatAnthropicError {
    switch error {
    case .serverError(let statusCode, let message):
        // Handle server-side errors (e.g., invalid API key, rate limits)
        print("Server Error [\(statusCode)]: \(message)")
    case .networkError(let error):
        // Handle network-related errors (e.g., no internet connection)
        print("Network Error: \(error.localizedDescription)")
    case .decodingError(let error):
        // Handle errors that occur when the response cannot be decoded
        print("Decoding Error: \(error.localizedDescription)")
    case .streamError:
        // Handle errors that occur when streaming responses
        print("Stream Error")
    case .cancelled:
        // Handle requests that are cancelled
        print("Request was cancelled")
    }
} catch {
    // Handle any other errors
    print("An unexpected error occurred: \(error)")
}

Related Packages

• swift-ai-model-retriever
• swift-json-schema
• swift-llm-chat-openai

Support

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.