Rig Demo
use rig::providers::openai;
use rig::completion::Prompt;
#[tokio::main]
async fn main() -> Result<()> {
let client = openai::Client::from_env();
let gpt4 = client.agent("gpt-4").build();
let response = gpt4.prompt("Translate 'Hello, world!' to French.").await?;
println!("Translation: {}", response);
Ok(())
}Core Features of Rig
Unified LLM Interface
Consistent API across different LLM providers, simplifying integration and reducing vendor lock-in.
Rust-Powered Performance
Leverage Rust's zero-cost abstractions and memory safety for high-performance LLM operations.
Advanced AI Workflow Abstractions
Implement complex AI systems like RAG and multi-agent setups with pre-built, modular components.
Type-Safe LLM Interactions
Utilize Rust's strong type system to ensure compile-time correctness in LLM interactions.
Seamless Vector Store Integration
Built-in support for vector stores, enabling efficient similarity search and retrieval for AI applications.
Flexible Embedding Support
Easy-to-use APIs for working with embeddings, crucial for semantic search and content-based recommendations.
Why Developers Choose Rig for AI Development
Efficient Development
- Type-safe API reduces runtime errors
- Async-first design for optimal resource utilization
- Seamless integration with Rust's ecosystem (Tokio, Serde, etc.)
Production-Ready Architecture
- Modular design for easy customization and extension
- Comprehensive error handling with custom error types
- Built-in support for tracing and logging