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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Open-source tool to visualise your RAG 🔮
| Date | Stars |
|---|---|
| 2026-07-24 | 1221 |
| 2026-07-25 | 1221 |
| 2026-07-28 | 1221 |
| 2026-07-30 | 1221 |
| 2026-08-06 | 1221 |
Today
— stars today
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Momentum
0.0
growth rate 0.00%/day
# 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.
Excerpt of 1,942 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9e769e08a97191e3, topic:rag, readme:retrieval-augmented generation, readme:retrieval augmented
matched fp:9e769e08a97191e3, topic:llm
matched fp:9e769e08a97191e3, topic:streamlit
