Interact with Anthropic and Anthropic-compatible chat completion APIs in a simple and elegant way.
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.
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:
- Open your project in Xcode.
- Click on
File
->Swift Packages
->Add Package Dependency...
- Enter the repository URL:
https://github.com/kevinhermawan/swift-llm-chat-anthropic.git
- Choose the version you want to add. You probably want to add the latest version.
- Click
Add Package
.
You can find the documentation here: https://kevinhermawan.github.io/swift-llm-chat-anthropic/documentation/llmchatanthropic
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"]
)
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()
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()
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.
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.
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.
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.
Your support is greatly appreciated!
Contributions are welcome! Please open an issue or submit a pull request if you have any suggestions or improvements.
This repository is available under the Apache License 2.0.