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rag-colls - Implement recent advanced RAG techniques

Project description

rag-colls

rag-colls

rag-colls a.k.a RAG Collections.

Simple and easy to use, production-ready advanced RAG techniques.

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GitHub CI GitHub CI

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📑 Table of Contents

📖 Documentation

Please visit documentation to get latest update.

🔧 Installation

  • You can easily install it from pypi:
pip install -U rag-colls
  • Docker - 🐳:
# Clone the repository
git clone https://github.com/hienhayho/rag-colls.git
cd rag-colls/

# Choose python version and setup OPENAI_API_KEY
export PYTHON_VERSION="3.11"
export OPENAI_API_KEY="your-openai-api-key-here"

# Docker build
DOCKER_BUILDKIT=1 docker build \
                -f docker/Dockerfile \
                --build-arg OPENAI_API_KEY="$OPENAI_API_KEY" \
                --build-arg PYTHON_VERSION="$PYTHON_VERSION" \
                -t rag-colls:$PYTHON_VERSION .

docker run -it --name rag_colls --shm-size=2G rag-colls:$PYTHON_VERSION

📚 Notebooks

We have provided some notebooks for example usage.

RAG Tech Code Guide Tech Description
BasicRAG BasicRAG Colab Integrate with Chromadb
ContextualRAG ContextualRAG Colab Integrate with Chromadb and BM25s version
RAFT RAFT Colab Boost RAG with SFT

🚀 Upcoming

We are currently working on these projects and will be updated soon.

RAG Tech Link
Graph-RAG Blog, Paper
RAG-RL Paper

🎉 Quickstart

Please refer to example for more information.

💻 Develop Guidance

Please refer to DEVELOP.md for more information.

💎 Acknowledgement

This project is supported by UIT AIClub.

©️ LICENSE

rag-colls is under MIT LICENSE.

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