ofalvai/anthropic-rs

Anthropic Rust SDK 🦀 with async support.

Anthropic unofficial Rust SDK 🦀

anthropic-rs is an unofficial Rust library to interact with Anthropic REST API, with async support.

Wanna play with Claude in Rust? This is the place to be!

Usage

#[tokio::main]
async fn main() -> Result<(), Box<dyn Error>> {
    // Load the environment variables from the .env file.
    dotenv().ok();

    // Build from configuration.
    let cfg = AnthropicConfig::new()?;
    let client = Client::try_from(cfg)?;

    let complete_request = CompleteRequestBuilder::default()
        .prompt(format!("{HUMAN_PROMPT}How many toes do dogs have?{AI_PROMPT}"))
        .model("claude-instant-1".to_string())
        .stream_response(false)
        .stop_sequences(vec![HUMAN_PROMPT.to_string()])
        .build()?;

    // Send a completion request.
    let complete_response = client.complete(complete_request).await?;

    println!("completion response: {complete_response:?}");

    Ok(())
}

You can find full working examples in the examples directory.

Configuration

anthropic-rs uses dotenv to automatically load environment variables from a .env file. You can also set these variables manually in your environment. Here is an example of the configuration variables used:

ANTHROPIC_API_KEY="..."
ANTHROPIC_DEFAULT_MODEL="claude-v1"

Replace the "..." with your actual tokens and preferences.

You can also set these variables manually when you crate a new Client instance, see more details in usage section.

Features

• Completion (/v1/complete)
• Manage stream mode

Contributing

Contributions to anthropic-rs are welcomed! Feel free to submit a pull request or create an issue.

License

anthropic-rs is licensed under the MIT License.

Acknowledgements

• Anthropic API reference for the clear and concise documentation.
• The architecture of the SDK is inspired by async-openai, an asynchronous Rust library developed for OpenAI. We extend our heartfelt gratitude to the creators for their invaluable work. We envisage significant benefits in developing a standardized interface for interaction with various AI GPT providers' APIs. As an example, it would facilitate the development of versatile wrappers that could seamlessly interface with different providers.