ferrous-llm

A unified cross-platform Rust library for interacting with multiple Large Language Model providers. Ferrous-LLM provides a modular, type-safe, and performant abstraction layer that allows developers to easily switch between different LLM providers while maintaining consistent APIs.

🚀 Features

• Multi-Provider Support: Unified interface for OpenAI, Anthropic, and Ollama providers
• Modular Architecture: Separate crates for core functionality and each provider
• Type Safety: Leverages Rust's type system for safe LLM interactions
• Streaming Support: Real-time streaming capabilities for chat completions
• Memory Management: Dedicated memory crate for conversation context handling
• Async/Await: Full async support with tokio runtime
• Comprehensive Examples: Working examples for all supported providers
• Extensible Design: Easy to add new providers and capabilities

📦 Installation

Add ferrous-llm to your Cargo.toml:

[dependencies]
ferrous-llm = "0.2.0"

Feature Flags

By default, no providers are enabled. Enable the providers you need:

[dependencies]
ferrous-llm = { version = "0.2.0", features = ["openai", "anthropic", "ollama"] }

Available features:

• openai - OpenAI provider support
• anthropic - Anthropic Claude provider support
• ollama - Ollama local model provider support
• full - All providers (equivalent to enabling all individual features)

🏗️ Architecture

Ferrous-LLM is organized as a workspace with the following crates:

• ferrous-llm-core - Core traits, types, and error handling
• ferrous-llm-openai - OpenAI provider implementation
• ferrous-llm-anthropic - Anthropic provider implementation
• ferrous-llm-ollama - Ollama provider implementation
• ferrous-llm-memory - Memory and context management utilities

🔧 Quick Start

Basic Chat Example

use ferrous_llm::{
    ChatProvider, ChatRequest, Message, MessageContent,
    Parameters, Role, Metadata,
    openai::{OpenAIConfig, OpenAIProvider},
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Load configuration from environment
    let config = OpenAIConfig::from_env()?;
    let provider = OpenAIProvider::new(config)?;

    // Create a chat request
    let request = ChatRequest {
        messages: vec![
            Message {
                role: Role::User,
                content: MessageContent::Text("Hello! Explain Rust in one sentence.".to_string()),
                name: None,
                tool_calls: None,
                tool_call_id: None,
                created_at: chrono::Utc::now(),
            }
        ],
        parameters: Parameters {
            temperature: Some(0.7),
            max_tokens: Some(100),
            ..Default::default()
        },
        metadata: Metadata::default(),
    };

    // Send the request
    let response = provider.chat(request).await?;
    println!("Response: {}", response.content());

    Ok(())
}

Streaming Chat Example

use ferrous_llm::{
    StreamingProvider, ChatRequest, Message, MessageContent, Role,
    anthropic::{AnthropicConfig, AnthropicProvider},
};
use futures::StreamExt;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = AnthropicConfig::from_env()?;
    let provider = AnthropicProvider::new(config)?;

    let request = ChatRequest {
        messages: vec![
            Message {
                role: Role::User,
                content: MessageContent::Text("Tell me a story".to_string()),
                name: None,
                tool_calls: None,
                tool_call_id: None,
                created_at: chrono::Utc::now(),
            }
        ],
        parameters: Default::default(),
        metadata: Default::default(),
    };

    let mut stream = provider.chat_stream(request).await?;

    while let Some(chunk) = stream.next().await {
        match chunk {
            Ok(data) => print!("{}", data.content()),
            Err(e) => eprintln!("Stream error: {}", e),
        }
    }

    Ok(())
}

🔌 Supported Providers

OpenAI

use ferrous_llm::openai::{OpenAIConfig, OpenAIProvider};

// From environment variables
let config = OpenAIConfig::from_env()?;

// Or configure manually
let config = OpenAIConfig {
    api_key: "your-api-key".to_string(),
    model: "gpt-4".to_string(),
    base_url: "https://api.openai.com/v1".to_string(),
    timeout: std::time::Duration::from_secs(30),
};

let provider = OpenAIProvider::new(config)?;

Environment Variables:

• OPENAI_API_KEY - Your OpenAI API key (required)
• OPENAI_MODEL - Model to use (default: "gpt-3.5-turbo")
• OPENAI_BASE_URL - API base URL (default: "https://api.openai.com/v1")

Anthropic

use ferrous_llm::anthropic::{AnthropicConfig, AnthropicProvider};

let config = AnthropicConfig::from_env()?;
let provider = AnthropicProvider::new(config)?;

Environment Variables:

• ANTHROPIC_API_KEY - Your Anthropic API key (required)
• ANTHROPIC_MODEL - Model to use (default: "claude-3-sonnet-20240229")
• ANTHROPIC_BASE_URL - API base URL (default: "https://api.anthropic.com")

Ollama

use ferrous_llm::ollama::{OllamaConfig, OllamaProvider};

let config = OllamaConfig::from_env()?;
let provider = OllamaProvider::new(config)?;

Environment Variables:

• OLLAMA_MODEL - Model to use (default: "llama2")
• OLLAMA_BASE_URL - Ollama server URL (default: "http://localhost:11434")

🎯 Core Traits

Ferrous-LLM follows the Interface Segregation Principle with focused traits:

ChatProvider

Core chat functionality that most LLM providers support.

#[async_trait]
pub trait ChatProvider: Send + Sync {
    type Config: ProviderConfig;
    type Response: ChatResponse;
    type Error: ProviderError;

    async fn chat(&self, request: ChatRequest) -> Result<Self::Response, Self::Error>;
}

StreamingProvider

Extends ChatProvider with streaming capabilities.

#[async_trait]
pub trait StreamingProvider: ChatProvider {
    type StreamItem: Send + 'static;
    type Stream: Stream<Item = Result<Self::StreamItem, Self::Error>> + Send + 'static;

    async fn chat_stream(&self, request: ChatRequest) -> Result<Self::Stream, Self::Error>;
}

Additional Traits

• CompletionProvider - Text completion (non-chat)
• ToolProvider - Function/tool calling
• EmbeddingProvider - Text embeddings
• ImageProvider - Image generation
• SpeechToTextProvider - Speech transcription
• TextToSpeechProvider - Speech synthesis

📚 Examples

The examples/ directory contains comprehensive examples:

• openai_chat.rs - Basic OpenAI chat
• openai_chat_streaming.rs - OpenAI streaming chat
• anthropic_chat.rs - Basic Anthropic chat
• anthropic_chat_streaming.rs - Anthropic streaming chat
• ollama_chat.rs - Basic Ollama chat
• ollama_chat_streaming.rs - Ollama streaming chat

Run examples with:

# Set up environment variables
export OPENAI_API_KEY="your-key"
export ANTHROPIC_API_KEY="your-key"

# Run specific examples
cargo run --example openai_chat --features openai
cargo run --example anthropic_chat_streaming --features anthropic
cargo run --example ollama_chat --features ollama

🧪 Testing

Run tests for all crates:

# Run all tests
cargo test --workspace

# Run tests for specific provider
cargo test -p ferrous-llm-openai
cargo test -p ferrous-llm-anthropic
cargo test -p ferrous-llm-ollama

# Run integration tests
cargo test --test integration_tests

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

1. Clone the repository:

   git clone https://github.com/your-username/ferrous-llm.git
   cd ferrous-llm
   

2. Install Rust (if not already installed):

   curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
   

3. Set up environment variables:

   cp .env.example .env
   # Edit .env with your API keys
   

4. Run tests:

   cargo test --workspace
   

Adding a New Provider

1. Create a new crate in crates/ferrous-llm-{provider}/
2. Implement the required traits from ferrous-llm-core
3. Add integration tests
4. Update the main crate's feature flags
5. Add examples demonstrating usage

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🔗 Links

• Documentation
• Crates.io
• Repository
• Issues

🙏 Acknowledgments

• The Rust community for excellent async and HTTP libraries
• OpenAI, Anthropic, and Ollama for their APIs and documentation
• Contributors and users of this library

Note: This library is in active development. APIs may change before 1.0 release.