base on Open-source tool to visualise your RAG 🔮 # RAGxplorer 🦙🦺 [](https://pypi.org/project/ragxplorer/) [](https://ragxplorer.streamlit.app/) <img src="https://raw.githubusercontent.com/gabrielchua/RAGxplorer/main/images/logo.png" width="200"> RAGxplorer is a tool to build Retrieval Augmented Generation (RAG) visualisations. # Quick Start ⚡ **Installation** ```bash pip install ragxplorer ``` **Usage** ```python 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](https://github.com/gabrielchua/RAGxplorer-demo) # Contributing 👋 Contributions to RAGxplorer are welcome. Please read our [contributing guidelines (WIP)](.github/CONTRIBUTING.md) for details. # License 👀 This project is licensed under the MIT license - see the [LICENSE](LICENSE) for details. # Acknowledgments 💙 - DeepLearning.AI and Chroma for the inspiration and code labs in their [Advanced Retrival](https://www.deeplearning.ai/short-courses/advanced-retrieval-for-ai/) course. - The Streamlit community for the support and resources. ", Assign "at most 3 tags" to the expected json: {"id":"7148","tags":[]} "only from the tags list I provide: []" returns me the "expected json"