Awesome
RAGxplorer 🦙🦺
RAGxplorer is a tool to build Retrieval Augmented Generation (RAG) visualisations.
Quick Start âš¡
Installation
pip install ragxplorer
Usage
from ragxplorer import RAGxplorer
client = RAGxplorer(embedding_model="thenlper/gte-large")
client.load_pdf("presentation.pdf", verbose=True)
client.visualize_query("What are the top revenue drivers for Microsoft?")
A quickstart Jupyter notebook tutorial on how to use ragxplorer can be found at https://github.com/gabrielchua/RAGxplorer/blob/main/tutorials/quickstart.ipynb
Or as a Colab notebook:
<a target="_blank" href="https://colab.research.google.com/github/vince-lam/RAGxplorer/blob/issue29-create-tutorials/tutorials/quickstart.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>Streamlit Demo 🔎
The demo can be found here: https://ragxplorer.streamlit.app/
<img src="https://raw.githubusercontent.com/gabrielchua/RAGxplorer/main/images/example.png" width="650">View the project here
Contributing 👋
Contributions to RAGxplorer are welcome. Please read our contributing guidelines (WIP) for details.
License 👀
This project is licensed under the MIT license - see the LICENSE for details.
Acknowledgments 💙
- DeepLearning.AI and Chroma for the inspiration and code labs in their Advanced Retrival course.
- The Streamlit community for the support and resources.