mochi-neko/clust

An unofficial Rust client for the Anthropic/Claude API.

clust

An unofficial Rust client for the Anthropic/Claude API.

Installation

Run the following Cargo command in your project directory:

cargo add clust

or add the following line to your Cargo.toml:

[dependencies]
clust = "0.9.0"

Supported APIs

• Messages
  • Create a Message
  • Streaming Messages

Feature flags

• macros: Enable the clust::attributse::clust_tool attribute macro for generating clust::messages::Tool or clust::messages::AsyncTool from a Rust function.

Usages

API key and client

First you need to create a new API client: clust::Client with your Anthropic API key from environment variable: " ANTHROPIC_API_KEY"

use clust::Client;

let client = Client::from_env().unwrap();

or specify the API key directly:

use clust::Client;
use clust::ApiKey;

let client = Client::from_api_key(ApiKey::new("your-api-key"));

If you want to customize the client, you can use builder pattern by clust::ClientBuilder:

use clust::ClientBuilder;
use clust::ApiKey;
use clust::Version;

let client = ClientBuilder::new(ApiKey::new("your-api-key"))
    .version(Version::V2023_06_01)
    .client(reqwest::ClientBuilder::new().timeout(std::time::Duration::from_secs(10)).build().unwrap())
    .build();

Models and max tokens

You can specify the model by clust::messages::ClaudeModel.

use clust::messages::ClaudeModel;
use clust::messages::MessagesRequestBody;

let model = ClaudeModel::Claude3Sonnet20240229;

let request_body = MessagesRequestBody {
    model,
    ..Default::default ()
};

Because max number of tokens of text generation: clust::messages::MaxTokens depends on the model, you need to create clust::messages::MaxTokens with the model.

use clust::messages::ClaudeModel;
use clust::messages::MaxTokens;
use clust::messages::MessagesRequestBody;

let model = ClaudeModel::Claude3Sonnet20240229;
let max_tokens = MaxTokens::new(1024, model).unwrap();

let request_body = MessagesRequestBody {
    model,
    max_tokens,
    ..Default::default ()
};

Prompt

You can specify the system prompt by clust::messages::SystemPrompt and there is no "system" role in the message.

use clust::messages::SystemPrompt;
use clust::messages::MessagesRequestBody;

let system_prompt = SystemPrompt::new("You are an excellent AI assistant.");

let request_body = MessagesRequestBody {
    system: Some(system_prompt),
    ..Default::default ()
};

Messages and contents

Build messages by a vector of clust::messages::Message:

use clust::messages::Role;
use clust::messages::Content;

/// The message.
pub struct Message {
    /// The role of the message.
    pub role: Role,
    /// The content of the message.
    pub content: Content,
}

You can create each role message as follows:

use clust::messages::Message;

let message = Message::user("Hello, Claude!");
let message = Message::assistant("Hello, user!");

and a content: clust::messages::Content.

use clust::messages::ContentBlock;

/// The content of the message.
pub enum Content {
    /// The single text content.
    SingleText(String),
    /// The multiple content blocks.
    MultipleBlocks(Vec<ContentBlock>),
}

Multiple blocks is a vector of content block: clust::messages::ContentBlock:

use clust::messages::TextContentBlock;
use clust::messages::ImageContentBlock;

/// The content block of the message.
pub enum ContentBlock {
    /// The text content block.
    Text(TextContentBlock),
    /// The image content block.
    Image(ImageContentBlock),
}

You can create a content as follows:

use clust::messages::Content;
use clust::messages::ContentBlock;
use clust::messages::TextContentBlock;
use clust::messages::ImageContentBlock;
use clust::messages::ImageContentSource;
use clust::messages::ImageMediaType;

// Single text content
let content = Content::SingleText("Hello, Claude!".to_string());
// or use `From` trait
let content = Content::from("Hello, Claude!");

// Multiple content blocks
let content = Content::MultipleBlocks(vec![
    ContentBlock::Text(TextContentBlock::new("Hello, Claude!")),
    ContentBlock::Image(ImageContentBlock::new(ImageContentSource::base64(
        ImageMediaType::Png,
        "Base64 encoded image data",
    ))),
]);
// or use `From` trait for `String` or `ImageContentSource`
let content = Content::from(vec![
    ContentBlock::from("Hello, Claude!"),
    ContentBlock::from(ImageContentSource::base64(
        ImageMediaType::Png,
        "Base64 encoded image data",
    )),
]);

Request body

The request body is defined by clust::messages::MessagesRequestBody.

See also MessagesRequestBody for other options.

use clust::messages::MessagesRequestBody;
use clust::messages::ClaudeModel;
use clust::messages::Message;
use clust::messages::MaxTokens;
use clust::messages::SystemPrompt;

let request_body = MessagesRequestBody {
    model: ClaudeModel::Claude3Sonnet20240229,
    messages: vec![Message::user("Hello, Claude!")],
    max_tokens: MaxTokens::new(1024, ClaudeModel::Claude3Sonnet20240229).unwrap(),
    system: Some(SystemPrompt::new("You are an excellent AI assistant.")),
    ..Default::default ()
};

You can also use the builder pattern with clust::messages::MessagesRequestBuilder:

use clust::messages::MessagesRequestBuilder;
use clust::messages::ClaudeModel;
use clust::messages::Message;
use clust::messages::SystemPrompt;

let request_body = MessagesRequestBuilder::new_with_max_tokens(
    ClaudeModel::Claude3Sonnet20240229,
    1024,
).unwrap()
.messages(vec![Message::user("Hello, Claude!")])
.system(SystemPrompt::new("You are an excellent AI assistant."))
.build();

API calling

Call the API by clust::Client::create_a_message with the request body.

use clust::Client;
use clust::messages::MessagesRequestBody;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let client = Client::from_env()?;
    let request_body = MessagesRequestBody::default();

    // Call the async API.
    let response = client
        .create_a_message(request_body)
        .await?;

    // You can extract the text content from `clust::messages::MessagesResponseBody.content.flatten_into_text()`.
    println!("Content: {}", response.content.flatten_into_text()?);

    Ok(())
}

Streaming

When you want to stream the response incrementally, you can use clust::Client::create_a_message_stream with the stream option: StreamOption::ReturnStream.

use clust::Client;
use clust::messages::MessagesRequestBody;
use clust::messages::StreamOption;

use tokio_stream::StreamExt;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let client = Client::from_env()?;
    let request_body = MessagesRequestBody {
        stream: Some(StreamOption::ReturnStream),
        ..Default::default()
    };

    // Call the async API and get the stream.
    let mut stream = client
        .create_a_message_stream(request_body)
        .await?;

    // Poll the stream.
    while let Some(chunk) = stream.next().await {
         // Handle the chunk.
    }

    Ok(())
}

Tool use

Support tool use for two methods:

1. Use clust_tool attribute macro for Rust function

When you define a tool as Rust function with documentation comment like this:

/// Get the current weather in a given location
///
/// ## Arguments
/// - `location` - The city and state, e.g. San Francisco, CA
fn get_weather(location: String) -> String {
    "15 degrees".to_string() // Dummy response
}

you can use the clust::clust_macros::clust_tool attribute macro with macros feature flag to generate code:

/// Get the current weather in a given location
///
/// ## Arguments
/// - `location` - The city and state, e.g. San Francisco, CA
#[clust_tool] // <- Generate `clust::messages::Tool` for this function
fn get_weather(location: String) -> String {
    "15 degrees".to_string() // Dummy response
}

and create an instance of clust::messages::Tool that named by ClustTool_{function_name} from the function:

let tool = ClustTool_get_weather {};

Get the tool definition from clust::messages::Tool for API request:

let tool_definition = tool.definition();

and call the tool with tool use got from the API response:

let tool_result = tool.call(tool_use);

See also a tool use example and clust_tool for details.

2. Manually implement clust::messages::Tool or clust::messages::AsyncTool

You can manually implement clust::messages::Tool or clust::messages::AsyncTool for your tool.

Examples

Create a message

An example of creating a message with the API key loaded from the environment variable: ANTHROPIC_API_KEY

ANTHROPIC_API_KEY={your-api-key}

is as follows:

use clust::messages::ClaudeModel;
use clust::messages::MaxTokens;
use clust::messages::Message;
use clust::messages::MessagesRequestBody;
use clust::messages::SystemPrompt;
use clust::Client;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // 1. Create a new API client with the API key loaded from the environment variable: `ANTHROPIC_API_KEY`.
    let client = Client::from_env()?;

    // 2. Create a request body.
    let model = ClaudeModel::Claude3Sonnet20240229;
    let messages = vec![Message::user(
        "Where is the capital of France?",
    )];
    let max_tokens = MaxTokens::new(1024, model)?;
    let system_prompt = SystemPrompt::new("You are an excellent AI assistant.");
    let request_body = MessagesRequestBody {
        model,
        messages,
        max_tokens,
        system: Some(system_prompt),
        ..Default::default()
    };

    // 3. Call the API.
    let response = client
        .create_a_message(request_body)
        .await?;

    println!("Result:\n{}", response);

    Ok(())
}

Streaming messages with tokio backend

An example of creating a message stream with the API key loaded from the environment variable: ANTHROPIC_API_KEY

ANTHROPIC_API_KEY={your-api-key}

with tokio-stream is as follows:

use clust::messages::ClaudeModel;
use clust::messages::MaxTokens;
use clust::messages::Message;
use clust::messages::MessagesRequestBody;
use clust::messages::SystemPrompt;
use clust::messages::StreamOption;
use clust::messages::StreamChunk;
use clust::Client;

use tokio_stream::StreamExt;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // 1. Create a new API client with the API key loaded from the environment variable: `ANTHROPIC_API_KEY`.
    let client = Client::from_env()?;

    // 2. Create a request body with `stream` option.
    let model = ClaudeModel::Claude3Sonnet20240229;
    let messages = vec![Message::user(
        "Where is the capital of France?",
    )];
    let max_tokens = MaxTokens::new(1024, model)?;
    let system_prompt = SystemPrompt::new("You are an excellent AI assistant.");
    let request_body = MessagesRequestBody {
        model,
        messages,
        max_tokens,
        system: Some(system_prompt),
        stream: Some(StreamOption::ReturnStream),
        ..Default::default()
    };

    // 3. Call the streaming API.
    let mut stream = client
        .create_a_message_stream(request_body)
        .await?;

    let mut buffer = String::new();

    // 4. Poll the stream.
    // NOTE: The `tokio_stream::StreamExt` run on the `tokio` runtime.
    while let Some(chunk) = stream.next().await {
        match chunk {
            | Ok(chunk) => {
                println!("Chunk:\n{}", chunk);
                match chunk {
                    | StreamChunk::ContentBlockDelta(content_block_delta) => {
                        // Buffer message delta.
                        buffer.push_str(&content_block_delta.delta.text);
                    }
                    | _ => {}
                }
            }
            | Err(error) => {
                eprintln!("Chunk error:\n{:?}", error);
            }
        }
    }

    println!("Result:\n{}", buffer);

    Ok(())
}

Create a message with vision

See an example with vision.

Conversation

See a conversation example.

Tool use

See a tool use example.

Other examples

See also the examples directory for more examples.

Changelog

See CHANGELOG.

License

Licensed under either of the Apache License, Version 2.0 or the MIT license at your option.