BestRAG

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BestRAG: A library for hybrid RAG, combining dense, sparse, and late interaction methods for efficient document storage and search.

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Introducing **BestRAG**! This Python library leverages a hybrid Retrieval-Augmented Generation (RAG) approach to efficiently store and retrieve embeddings. By combining dense, sparse, and late interaction embeddings, **BestRAG** offers a robust solution for managing large datasets.

## ✨ Features

🚀 **Hybrid RAG**: Utilizes dense, sparse, and late interaction embeddings for enhanced performance.  
🔌 **Easy Integration**: Simple API for storing and searching embeddings.  
📄 **PDF Support**: Directly store embeddings from PDF documents.

## 🚀 Installation

To install **BestRAG**, simply run:

```bash
pip install bestrag
```

## 📦 Usage

Here’s how you can use **BestRAG** in your projects:

```python
from bestrag import BestRAG

rag = BestRAG(
    url="https://YOUR_QDRANT_URL", 
    api_key="YOUR_API_KEY", 
    collection_name="YOUR_COLLECTION_NAME"
)

# Store embeddings from a PDF
rag.store_pdf_embeddings("your_pdf_file.pdf", "pdf_name")

# Search using a query
results = rag.search(query="your search query", limit=10)
print(results)

# Delete particular pdf embeddings
rag.delete_pdf_embeddings("home/notes.pdf")
```

> **Note**: Qdrant offers a free tier with 4GB of storage. To generate your API key and endpoint, visit [Qdrant](https://qdrant.tech/).

##  🤗 Try it on Hugging Face Spaces

You can explore and interact with **BestRAG** directly on Hugging Face Spaces: [BestRAG on Hugging Face](https://huggingface.co/spaces/samadpls/BestRAG)

## 🤝 Contributing

Feel free to contribute to **BestRAG**! Whether it’s reporting bugs, suggesting features, or submitting pull requests, your contributions are welcome.

## 📝 License

This project is licensed under the [MIT License](LICENSE).

---

Created by [samadpls](https://github.com/samadpls) 🎉

BestRAG

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