arXivRAG

Overview

arXivRAG is a comprehensive tool designed to enhance the retrieval and generation of academic content from the arXiv database. Leveraging advanced Retrieval-Augmented Generation (RAG) techniques, arXivRAG provides researchers, students, and enthusiasts with the ability to discover and generate summaries, insights, and analyses of arXiv papers efficiently.

🔍 Features

Core features

• Retrieval-Augmented Generation: Combines the power of retrieval systems with generative models to enhance the accuracy and relevance of responses.
• arXiv Integration: Directly queries the arXiv repository to fetch and summarize academic papers.
• User-Friendly Interface: Provides an easy-to-use interface for querying and obtaining summaries of scientific papers.
• Customizable: Allows users to customize the retrieval and generation parameters to suit their specific needs.

Advance features

• Enhanced Search: Advanced search capabilities to quickly find relevant papers.
• Summarization: Automatic generation of concise summaries for arXiv papers.
• Custom Queries: Tailored query support to retrieve specific information from academic papers.
• Real-Time Access: Seamless integration with the arXiv API for real-time data access.
• Citation and Trend Analysis: Analyze citation networks, visualize the impact of papers, and identify emerging research trends based on recent publications and citation patterns.

🚀 Installation

To get started with arXivRAG, follow these steps:

1. Clone the repository:

git clone https://github.com/phitrann/arXivRAG.git
cd arXivRAG

2. Create a virtual environment (we recommend using conda):

conda create -n arxiv-rag python=3.10
conda activate arxiv-rag

3. Install the required dependencies:

pip install -r requirements.txt

💻 Usage

To use arXivRAG, follow these steps:

1. Run the main script:

python main.py

3. Query the system:

• Enter your query related to a scientific paper.
• The system will retrieve relevant papers from arXiv and generate a summary.

Configuration

You can customize the behavior of arXivRAG by modifying the configuration file config.yaml. Key parameters include:

• retrieval_model: The model used for retrieving relevant papers.
• generation_model: The model used for generating summaries.
• num_retrievals: The number of papers to retrieve for each query.
• max_summary_length: The maximum length of the generated summary.

❤️ Contributing

We welcome contributions from the community! If you have ideas for new features or improvements, feel free to open an issue or submit a pull request.

In case you want to submit a pull request, please follow these steps:

1. Fork the repository.
2. Create a new branch:

git checkout -b feature/your-feature-name

4. Make your changes and commit them:

git commit -m "Add your commit message"

5. Push to the branch:

git push origin feature/your-feature-name

6. Create a pull request.

📜 License

This project is released under the Apache 2.0 license. See the LICENSE file for details.

Acknowledgements

• Thanks to the contributors of the arXivRAG project.
• Special thanks to the developers of the retrieval and generation models used in this project.