rocketrag 0.1.5

Fast, efficient, minimal, extendible and elegant RAG system

🚀 RocketRAG

Fast, efficient, minimal, extendible and elegant RAG system

RocketRAG is a high-performance Retrieval-Augmented Generation (RAG) system designed with a focus on speed, simplicity, and extensibility. Built on top of state-of-the-art libraries, it provides both CLI and web server capabilities for seamless integration into any workflow.

https://github.com/user-attachments/assets/1bd7cc50-9eac-4402-80bd-42933ac35ca3

🎯 Mission

RocketRAG aims to be the fastest and most efficient RAG library while maintaining:

• Minimal footprint - Clean, lightweight codebase
• Maximum extensibility - Pluggable architecture for all components
• Peak performance - Leveraging the best-in-class libraries
• Ease of use - Simple CLI and API interfaces

⚡ Performance-First Architecture

RocketRAG is built on top of cutting-edge, performance-optimized libraries:

• Chonkie - Ultra-fast semantic chunking with model2vec
• Kreuzberg - Lightning-fast document loading and processing
• llama-cpp-python - Optimized LLM inference with GGUF support
• Milvus Lite - High-performance vector database
• Sentence Transformers - State-of-the-art embeddings

🚀 Quick Start

Installation

Using pip

pip install rocketrag

Using uvx (recommended for CLI usage)

# Run directly without installation
uvx rocketrag --help

# Or install globally
uvx install rocketrag

Basic Usage

from rocketrag import RocketRAG

rag = RocketRAG("./data") # Path do your data (supports PDF, TXT, MD, etc.)
rag.prepare() # Construct vector database

# Ask questions
answer, sources = rag.ask("What is the main topic of the documents?")
print(answer)

CLI Usage

# Prepare documents from a directory
rocketrag prepare --data-dir ./documents

# Ask questions via CLI
rocketrag ask "What are the key findings?"

# Start web server
rocketrag server --port 8000

Using uvx (no installation required)

# Same commands work with uvx
uvx rocketrag prepare --data-dir ./documents
uvx rocketrag ask "What are the key findings?"
uvx rocketrag server --port 8000

# Run as module
uvx --from rocketrag python -m rocketrag --help

🏗️ Architecture

RocketRAG follows a modular, plugin-based architecture:

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Document      │    │    Chunking     │    │   Vectorization │
│   Loaders       │───▶│   (Chonkie)     │───▶│ (SentenceTransf)│
│  (Kreuzberg)    │    │                 │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                        │
┌─────────────────┐    ┌─────────────────┐             │
│      LLM        │    │   Vector DB     │◀────────────┘
│ (llama-cpp-py)  │◀───│ (Milvus Lite)   │
│                 │    │                 │
└─────────────────┘    └─────────────────┘

Core Components

• BaseLoader: Pluggable document loading (PDF, TXT, MD, etc.)
• BaseChunker: Configurable chunking strategies (semantic, recursive, etc.)
• BaseVectorizer: Flexible embedding models
• BaseLLM: Swappable language models
• MilvusLiteDB: High-performance vector storage and retrieval

🔧 Configuration

Custom Components

from rocketrag import RocketRAG
from rocketrag.vectors import SentenceTransformersVectorizer
from rocketrag.chonk import ChonkieChunker
from rocketrag.llm import LLamaLLM
from rocketrag.loaders import KreuzbergLoader

# Configure high-performance components
vectorizer = SentenceTransformersVectorizer(
    model_name="minishlab/potion-multilingual-128M"  # Fast multilingual model
)

chunker = ChonkieChunker(
    method="semantic",  # Semantic chunking for better context
    embedding_model="minishlab/potion-multilingual-128M",
    chunk_size=512
)

llm = LLamaLLM(
    repo_id="unsloth/gemma-3n-E2B-it-GGUF",
    filename="*Q8_0.gguf"  # Quantized for speed
)

loader = KreuzbergLoader()  # Ultra-fast document processing

rag = RocketRAG(
    vectorizer=vectorizer,
    chunker=chunker,
    llm=llm,
    loader=loader
)

CLI Configuration

# Custom chunking strategy
rocketrag prepare \
  --chonker chonkie \
  --chonker-args '{"method": "semantic", "chunk_size": 512}' \
  --vectorizer-args '{"model_name": "all-MiniLM-L6-v2"}'

# Custom LLM for inference
rocketrag ask "Your question" \
  --repo-id "microsoft/DialoGPT-medium" \
  --filename "*.gguf"

🌐 Web Server

RocketRAG includes a FastAPI-based web server with OpenAI-compatible endpoints:

# Start server
rocketrag server --port 8000 --host 0.0.0.0

API Endpoints

• GET / - Interactive web interface
• POST /ask - Question answering
• POST /ask/stream - Streaming responses
• GET /chat - Chat interface
• GET /browse - Document browser
• GET /visualize - Vector visualization
• GET /health - Health check

Example API Usage

import requests

response = requests.post(
    "http://localhost:8000/ask",
    json={"question": "What are the main findings?"}
)

result = response.json()
print(result["answer"])
print(result["sources"])

🎨 Features

Core Features

• ⚡ Ultra-fast document processing with Kreuzberg
• 🧠 Semantic chunking with Chonkie and model2vec
• 🔍 High-performance vector search with Milvus Lite
• 🤖 Optimized LLM inference with llama-cpp-python
• 📊 Rich CLI interface with progress bars and formatting
• 🌐 Web server with interactive UI
• 🔌 Pluggable architecture for easy customization

Advanced Features

• 📈 Vector visualization for debugging and analysis
• 📚 Document browsing interface
• 💬 Streaming responses for real-time interaction
• 🔄 Batch processing for large document sets
• 📝 Metadata preservation throughout the pipeline
• 🎯 Context-aware chunking for better retrieval

🛠️ Development

Installation for Development

git clone https://github.com/yourusername/rocketrag.git
cd rocketrag
pip install -e ".[dev]"

Running Tests

pytest tests/

Code Quality

ruff check .
ruff format .

📊 Performance

RocketRAG is designed for speed:

• Document Loading: 10x faster with Kreuzberg's optimized parsers
• Chunking: Semantic chunking with model2vec for superior context preservation
• Vectorization: Optimized batch processing with sentence-transformers
• Retrieval: Sub-millisecond vector search with Milvus Lite
• Generation: GGUF quantization for 4x faster inference

🤝 Contributing

We welcome contributions! RocketRAG's modular architecture makes it easy to:

• Add new document loaders
• Implement custom chunking strategies
• Integrate different embedding models
• Support additional LLM backends
• Enhance the web interface

🙏 Acknowledgments

RocketRAG builds upon the excellent work of:

• Chonkie for semantic chunking
• Kreuzberg for document processing
• llama-cpp-python for LLM inference
• Milvus for vector storage
• Sentence Transformers for embeddings