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https://github.com/gabrielchua/ragxplorer
Open-source tool to visualise your RAG 🔮
https://github.com/gabrielchua/ragxplorer
interactive llm python rag streamlit visualization
Last synced: 4 days ago
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Open-source tool to visualise your RAG 🔮
- Host: GitHub
- URL: https://github.com/gabrielchua/ragxplorer
- Owner: gabrielchua
- License: mit
- Created: 2024-01-11T15:31:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-03T15:10:06.000Z (about 2 months ago)
- Last Synced: 2025-02-15T00:54:35.501Z (4 days ago)
- Topics: interactive, llm, python, rag, streamlit, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 1.07 MB
- Stars: 1,106
- Watchers: 11
- Forks: 100
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
# RAGxplorer 🦙🦺
[](https://pypi.org/project/ragxplorer/)
[](https://ragxplorer.streamlit.app/)
RAGxplorer is a tool to build Retrieval Augmented Generation (RAG) visualisations.
# Quick Start âš¡
**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
Or as a Colab notebook:
# Streamlit Demo 🔎
The demo can be found here:
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.