RRAG - Enterprise Rust RAG Framework

RRAG (Rust RAG) is a high-performance, enterprise-ready framework for building Retrieval-Augmented Generation applications in Rust. Built from the ground up with safety, performance, and developer experience in mind.

🎯 Why RRAG?

🚀 Native Performance: Zero-cost abstractions with compile-time optimizations
🛡️ Memory Safety: Rust's ownership system prevents data races and memory leaks
⚡ Async First: Built on Tokio for maximum concurrency
🎯 Type Safety: Compile-time guarantees eliminate runtime errors
🔌 Modular Design: Pluggable architecture with swappable components
📊 Production Ready: Built-in observability, security, and monitoring

🏗️ Architecture Overview

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Documents     │───▶│   Processing    │───▶│   Vector Store  │
│   (Input)       │    │   Pipeline      │    │   (Storage)     │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                                ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Responses     │◀───│     Agent       │◀───│    Retriever    │
│   (Output)      │    │   (rsllm)       │    │   (Search)      │
└─────────────────┘    └─────────────────┘    └─────────────────┘

⚡ Quick Start

Add RRAG to your Cargo.toml:

[dependencies]
rrag = { version = "0.1", features = ["rsllm-client"] }
tokio = { version = "1.0", features = ["full"] }

Basic RAG Application

use rrag::prelude::*;

#[tokio::main]
async fn main() -> RragResult<()> {
    // Create a RAG system
    let rag = RragSystem::builder()
        .with_rsllm_client("http://localhost:8080")
        .with_vector_store(InMemoryStorage::new())
        .with_chunk_size(512)
        .build()
        .await?;
    
    // Add documents
    rag.ingest_documents(vec![
        Document::new("Rust is a systems programming language..."),
        Document::new("RAG combines retrieval with generation..."),
    ]).await?;
    
    // Query the system
    let response = rag.query("What is Rust?").await?;
    println!("Response: {}", response.text);
    
    Ok(())
}

🌟 Core Features

🔍 Advanced Retrieval

Hybrid Search: Combines semantic and keyword search with multiple fusion strategies
Graph-Based Retrieval: Knowledge graph construction with entity extraction
Multi-Modal Support: Process text, images, tables, charts, and documents
Smart Reranking: Cross-encoder models for precise result ranking

🧠 Intelligent Agents

Tool Integration: Built-in calculator, HTTP client, and custom tool support
Memory Management: Conversation buffers, token limits, and summarization
Streaming Responses: Real-time token streaming with async iterators

⚡ Performance & Scalability

Intelligent Caching: Multi-level caching with semantic similarity
Incremental Indexing: Efficient document updates without full rebuilds
Batch Processing: High-throughput document ingestion

📊 Production Features

Observability Dashboard: Real-time monitoring with web UI and metrics
Security & Rate Limiting: Authentication, authorization, and abuse prevention
Health Checks: Component monitoring and dependency tracking

📖 Documentation

Visit docs.rs/rrag for complete API documentation and examples.

🔧 Feature Flags

[dependencies.rrag]
version = "0.1"
features = [
    "rsllm-client",      # rsllm integration
    "http",              # HTTP tools and clients
    "concurrent",        # Concurrent data structures
    "multimodal",        # Multi-modal processing
    "observability",     # Monitoring and metrics
    "security",          # Authentication and rate limiting
]

📄 License

This project is licensed under the MIT License.

🤝 Contributing

Contributions welcome! Please see our contributing guidelines for details.

rrag 0.1.0-alpha.2