RAGxplorer 🦙🦺

RAGxplorer is tool to build Retrieval Augmented Generation (RAG) visualisations.

Quick Start ⚡

Installation

pip install ragxplorer

Usage

from embedding_adapter import EmbeddingAdapter
client = RAGxplorer(embedding_model="thenlper/gte-large") # Choose any HuggingFace or OpenAI embedding model
client.load_pdf("presentation.pdf", verbose=True)
client.visualize_query("What are the top revenue drivers for Microsoft?")

Streamlit Demo (WIP) 🔎

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 file 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.