https://github.com/tushar2704/phi-rag

https://github.com/tushar2704/phi-rag

Last synced: 11 days ago
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• Host: GitHub
• URL: https://github.com/tushar2704/phi-rag
• Owner: tushar2704
• Created: 2024-12-03T06:46:39.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-31T15:14:30.000Z (15 days ago)
• Last Synced: 2024-12-31T16:23:41.517Z (14 days ago)
• Size: 1000 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# Phi-RAG

A Retrieval-Augmented Generation (RAG) implementation using Phi models for enhanced question-answering capabilities.

## Description

Phi-RAG is a project that combines the power of Phi language models with RAG techniques to create more accurate and context-aware responses. It retrieves relevant information from a knowledge base before generating answers, leading to more informed and precise outputs.

## Features

- 🤖 Integration with Phi language models

- 📚 Retrieval-Augmented Generation implementation

- 🔍 Efficient document retrieval system

- 💡 Context-aware response generation

- 🎯 Improved answer accuracy

## Installation

```bash

# Clone the repository

git clone https://github.com/yourusername/phi-rag.git

# Navigate to the project directory

cd phi-rag

# Install dependencies

pip install -r requirements.txt

```

## Usage

```python

from phi_rag import PhiRAG

# Initialize the RAG system

rag = PhiRAG()

# Ask a question

response = rag.query("Your question here")

print(response)

```

## Requirements

- Python 3.8+

- PyTorch

- Transformers library

- Required Phi model dependencies

## Contributing

1. Fork the repository

2. Create your feature branch (`git checkout -b feature/amazing-feature`)

3. Commit your changes (`git commit -m 'Add some amazing feature'`)

4. Push to the branch (`git push origin feature/amazing-feature`)

5. Open a Pull Request

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Acknowledgments

- Phi model developers

- RAG methodology contributors

- Open-source AI community

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